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DEPARTMENT OF ECONOMICS AND BUSINESS
AARHUS UNIVERSITY DENMARK
Empirical Essays on Heterogeneous Firms and
International Trade
Kaleb Girma Abreha
A PhD thesis submitted to
School of Business and Social Sciences, Aarhus University,
in partial fulfillment of the requirements of
the PhD degree in
Economics and Business
May 2015
Preface
This thesis was written between February 2012 and 2015 during my enrollment as a PhD student
at the Department of Economics and Business, Aarhus University. I am grateful to the department
for giving me the opportunity to join the graduate school and the financial support, without which
this thesis would not have been remotely possible. I am also grateful to the Tuborg Research
Centre for Globalization and Firms for the financial support and excellent research environment.
My sincere gratitude goes to my supervisor and coauthor Valérie Smeets for her guidance from
the very beginning. Her suggestions have extensively contributed to the content and style of this
thesis. Further, her attention to details has inspired me to be extra careful with my findings and
writings. I also thank my co-supervisor Philipp Schröder, who is the best leader in my experience.
I have benefited from his feedbacks and learnt quite a lot from his leadership style.
I have had a privilege to work with Frédéric Warzynski in a joint project. During the course of
this project, his questions and suggestions have helped me to be objective with what I am trying
to accomplish. I have improved the content of this thesis by incorporating many of his insightful
comments. I also truly appreciate his willingness to provide me with a helping hand anytime.
Between August and December 2014, I was visiting the Department of Economics at the Pennsylvania State University. I thank Mark Roberts for hosting me during my visit. I have benefited
tremendously from his lectures and his valuable comments on early drafts of my papers.
I thank the assessment committee members Tor Erikson (Aarhus University), Mark Roberts
(Pennsylvania State University) and Johannes Van Biesebroeck (KU Leuven) for carefully reading
the thesis manuscript and providing me with invaluable comments and suggestions. I have included
some of them in this version, and I look forward to incorporating the rest in my future work.
Over the past three years, I came to know many wonderful people. I thank you all, especially
those in the Tuborg Centre, for a number of interesting conversations and fun times we had. I
would also like to thank Susan Stilling, Susanne Christensen and Ann-Marie Gabel for their timely
assistance with a range of practical issues. Their help saved me a lot of time and effort.
I am also highly indebted to my family and friends for their patience, assistance and more
importantly encouragement. Thank you very much!
Kaleb Girma Abreha, Aarhus 2015
i
Contents
Introduction
iii
Dansk resumé
vii
1 Coping with the Crisis and Export Diversification
1
2 A Dynamic Model of Firm Activities: Evidence from Danish Manufacturing
34
3 Importing and Firm Productivity in Ethiopian Manufacturing
80
4 Imported Inputs and Firm Absorptive Capacity in Ethiopian Manufacturing 117
ii
Introduction
Research in international trade over the last two decades has witnessed a proliferation of studies
which use microdata sets. A pioneering paper by Bernard and Jensen (1995) and several subsequent studies have found that firms which participate in international trade constitute only a small
fraction. At the same time, these firms are more productive and outperform their domestic counterparts in a variety of performance measures. In the literature, these performance differentials
are mainly attributed to substantial entry costs in export markets (see e.g. Roberts and Tybout
1997; Bernard and Jensen 1999). The presence of these costs indicates that exporting firms are
inherently more productive prior to their export market participation. Also, there are expected
productivity gains by participating in international trade, albeit mixed findings in the literature
(see e.g. Bernard and Jensen 1999; Clerides et al. 1998). On the other hand, the accumulation of
these empirical findings, particularly firm heterogeneity, has led to the development of new models
of international trade with a prominent focus on firms instead of countries and industries (see e.g.
Melitz 2003; Bernard et al. 2003).
In the light of this vast and still growing literature, this thesis explores the behavior of firms
in international trade in two different economies; Denmark and Ethiopia. Denmark is a small
open economy with a heavy reliance on the world market as a source and an outlet for domestic
economic activities. In this respect, this thesis analyzes the reactions of exporting firms which
have faced a massive shock during the recent economic crisis while accounting for the product
and the destination dimensions of their exporting activities. Additionally, there is a prevalence of
simultaneous exporting and importing within the Danish manufacturing firms. Hence, this thesis
examines the determinants of a firm’s entry decision into exporting and importing and the resulting
impact of this decision on its activity scope and productivity growth.
On the contrary, Ethiopia is a low-income economy, with a small industrial base and a macroeconomic environment characterized by capacity constraints and market distortions. The main
questions addressed in this thesis include whether or not firms operating under different economic
and policy environments behave similarly as their counterparts in the developed and developing
economies. Further, the question of whether or not there are benefits for firms from participating
in international trade is considered. As a typical case of the least developed countries, this examination of firm internationalization in the Ethiopian manufacturing sector is insightful given that
firms in these economies have not been subjected to the same degree of scrutiny as those in the
developed and developing countries.
iii
This thesis is related to several studies, both theoretical contributions and empirical findings,
which have looked into the behavior of firms in international trade. It adds to the existing literature
in three main directions. First, it assesses how exporting firms have managed to cope with a large
shock associated with the recent economic crisis and the role of export diversification in that
regard. Second, it analyzes the complementarity between exporting and importing in determining
a firm’s trade participation decision in a unified empirical framework which accommodates multiple
dimensions of firm heterogeneity. Third, inspired by the empirical fact that firms in the least
developed countries heavily rely on imported inputs, it examines whether or not such a heavy
reliance is translated into any productivity gain. Further, it explores the determinants of the
distribution of the productivity gain, if there is any, across importing firms.
This thesis contains four chapters. Each chapter is a separate self-contained essay. The first two
chapters asses the characteristics of firm trade participation in the Danish economy. The last two
chapters study the extent of firm internationalization as well as the significance and determinants
of trade-driven knowledge spillovers in the Ethiopian economy. In line with the standard models of
heterogeneous firms and international trade, the focus is primarily on firms in the manufacturing
sector undertaking their core economic activities within the national boundaries of the countries
under study.
Chapter 1—Coping with the Crisis and Export Diversification—establishes a set of stylized facts
which summarizes salient features of Danish international production over the last decade. We
document a significant variation in the export participation of firms across sectors and industries,
a positive correlation between the scope and the scale of firms’ exporting activities, the presence of
carry-along trade, and a highly uneven distribution of sales not only across firms but also within
the export basket of firms.
Focusing on the recent economic crisis, we decompose aggregate export growth rates into firm,
product and destination components. In line with previous findings, we show that most of the
decline is attributed to the intensive margin; that is, incumbent firms reducing their export shipment. We follow a recent literature (Amador and Opromolla 2013; Gopinath and Neiman 2014)
by further decomposing the intensive margin into sub-intensive (the contribution to total export
change of continuing products) and sub-extensive margins (the contribution coming from product
churning). We find that the sub-intensive margin played the most important role during the crisis,
so that firms continued to ship the same products to the same destinations but in lower quantities.
We also find a significant effect of the sub-extensive margin, particularly during the recovery, as
firms started shipping new products to new destinations. More importantly, we also find that
the economic conditions of markets where firms ship matter in that export diversification into
fast-growing economies like China or more generally the BRICS was associated with better export
performance, so that trade reorientation helped firms to cope with the crisis.
Chapter 2—A Dynamic Model of Firm Activities: Evidence from Danish Manufacturing—
explores the nature of complementarity between exporting and importing within firms. Considering
iv
firms in the manufacturing sector over the period 2000-2007, a simple description of the data
uncovers a widespread occurrence of simultaneous exporting and importing within firms. The
data also reveals a significant export and import activity premia which follow a ranking where
two-way trading firms are the best performers followed by import-only, export-only and lastly
domestic firms. It is also found that there is a high persistence in the activity scope of firms.
Motivated by these empirical facts, I specify a dynamic discrete choice model of exporting and
importing following a modeling approach by Aw et al. (2011). In the model, firms are defined to
be heterogeneous in terms of their size (capital holding), factor payment (wage), and productivity.
The model provides a framework to analyze the determinants of a firm’s decision to export and
import while allowing for this decision to affect its future productivity trajectory. It also enables
the analysis of how large of a role market costs play in a firm’s entry decisions into exporting and
importing.
The parameter estimates exhibit a marked difference in the intensity of competition firms
face and their pricing strategies in the domestic and export markets where export markets are
characterized by a more elastic demand and a lower markup. The greater sensitivity of demand in
export markets accords with the fact that these markets host a larger number of firms and product
varieties. On the cost side, the estimates show that firms with a large capital holding and paying
higher wages are cost-efficient even after controlling for their productivity.
By extending the Levinsohn and Petrin (2003) algorithm to account for endogenous evolution
of firm productivity, I find that there is a learning-by-doing effect from exporting and importing,
which is especially greater from importing. As in Das et al. (2007), the start-up and running
costs of operations in the export and import markets are estimated using a Bayesian Markov
Chain Monte Carlo. In line with the self-selection hypothesis, the estimated sunk and fixed costs
of exporting and importing are substantial. And, these costs are greater for large firms, which
are more likely to operate in several markets simultaneously each involving non-negligible costs.
The learning effects in addition to the market entry costs further drive the selection of firms into
exporting and importing highlighting the complementarity between these trading activities.
Chapter 3—Importing and Firm Productivity in Ethiopian Manufacturing—analyzes the causal
relationship between importing and firm productivity. The motivation for this study comes from
the fact that the vast majority of the literature on firms in international trade has been restricted
to manufacturing firms in advanced economies and a few developing countries in Asia and Latin
America. Hence, African manufacturing firms have been greatly neglected. Even among a handful
of existing studies, utmost focus has been on exporting (see e.g. Bigsten et al. 2004; Bigsten
and Gebreeyesus 2009; Mengistae and Pattillo 2004; Van Biesebroeck 2005). In this respect, the
literature on African manufacturing firms remains largely incomplete, especially in view of high
import-to-GDP ratios and import shares of manufacture goods in these economies.
Using a panel of firms in Ethiopian manufacturing over the period 1996-2011, it is shown that
more productive firms self-select into import markets implying that importing involves irreversible
v
and periodic costs which only the most productive firms are able to absorb. To examine the
causal effect of importing on a firm’s productivity, I follow Kasahara and Rodrigue (2008) in
the specification of a structural model in which the static and dynamic effects of importing are
separately estimated. The estimation results provide evidence of learning-by-importing albeit an
initial, temporary productivity decline. Further, the results reveal that an intensive use of imported
inputs is associated with greater productivity improvement among importing firms.
Chapter 4—Imported Inputs and Firm Absorptive Capacity in Ethiopian Manufacturing—is
basically an extension of chapter 3, and it emphasizes the role of absorptive capacity regarding
productivity impact of imported inputs. This chapter is motivated by the fact that the productivity
gains from importing are relatively small in Ethiopian manufacturing sector as compared to findings
from studies in other countries. To this end, I estimate a standard production function in which
import and absorptive capacity (measured by the share of skilled employees in a firm’s workforce)
are included as additional variables. The estimates show that imported inputs are beneficial if a
firm has the necessary skill composition to absorb the embodied knowledge in those inputs. This
implies that imported inputs have no special purpose if a firm has no absorptive capacity at all.
Alternatively, I adopt a threshold regression and use a sample splitting technique developed
by Hansen (2000). The technique splits the entire sample into different regimes based on firms’
absorptive capacities. The estimates show that the effect of imported inputs is greater for firms
with a sufficiently high absorptive capacity. At the same time, the threshold estimate indicates
that most firms have absorptive capacity below the threshold requirement. Consequently, despite
the widespread use of imported inputs in the sector, the benefits of enhanced access to foreign
technology are confined to only few firms. These results on the importance of absorptive capacity provide firm-level support to the prevailing macroeconomic evidences which identify limited
absorptive capacity as an impediment to knowledge spillovers to the least developed countries.
vi
Dansk resumé
Forskningen i international handel har gennem de sidste 2 årtier oplevet en opblomstring af undersøgelser, som bruger mikrodatasæt. En banebrydende artikel af Bernard and Jensen (1995) samt
adskillige efterfølgende artikler har fundet, at virksomheder, som deltager i international handel,
kun udgør en lille del. Samtidig er disse virksomheder mere produktive og klarer sig bedre end
deres hjemlige konkurrenter på mange forskellige parametre. I litteraturen tilskrives disse forskellige præstationer hovedsagelig omkostningerne ved indtrædelse på eksportmarkeder (se fx Roberts
and Tybout 1997; Bernard and Jensen 1999). Tilstedeværelsen af disse omkostninger angiver, at
eksporterende virksomheder i sig selv er mere produktive, før de indtræder på eksportmarkedet.
På trods af blandede resultater i litteraturen, forventes der at våre produktivitetsgevinster ved at
deltage i international handel (se fx Bernard and Jensen 1999; Clerides et al. 1998). På den anden
side har akkumuleringen af disse empiriske resultater, især angående virksomheders heterogenitet,
ført til udvikling af nye modeller i international handel, som har markant fokus på virksomheder
i stedet for lande og brancher (se fx Melitz 2003; Bernard et al. 2003).
I lyset af denne omfattende og stadigt voksende litteratur undersøger denne afhandling adfærden hos virksomheder i international handel i to forskellige økonomier: Danmark og Etiopien.
Danmark er en lille åben økonomi med stor afhængighed af verdensmarkedet som kilde og som
afsætningsmulighed for hjemlige, økonomiske aktiviteter. I den henseende analyserer denne afhandling reaktionerne hos eksporterende virksomheder, som har oplevet et massivt chok under den
seneste, økonomiske krise, samtidig med at de har skullet stå til regnskab for omfanget af deres
eksportaktiviteter. Desuden er der en udbredt forekomst af samtidig eksport og import i de danske
fremstillingsvirksomheder. Således undersøger denne afhandling, hvilke faktorer der er afgørende
for en virksomheds beslutning om at eksportere og importere, samt denne beslutnings indflydelse
på virksomhedens aktivitetsomfang og produktivitetsvækst.
Etiopien er derimod en lavindkomstøkonomi med et lille industrielt grundlag og et makroøkonomisk
miljø, som er kendetegnet ved kapacitetsbegrænsninger og skævvridning af markedet. De vigtigste problemstillinger i denne afhandling handler om, hvorvidt virksomheder, som arbejder under
forskellige økonomiske og politiske forudsætninger, agerer på samme måde som de tilsvarende virksomheder i de udviklede økonomier og udviklingsøkonomierne eller ej. Desuden overvejes det, om
det er fordelagtigt for virksomheder at deltage i international handel eller ej. Denne undersøgelse
af virksomheders internationalisering i Etiopiens fremstillingssektor er velegnet som en typisk case
fra de mindst udviklede lande, forudsat at virksomheder i disse økonomier ikke har været udsat for
vii
den samme grad af pres som virksomheder i de udviklede økonomier og udviklingsøkonomierne.
Denne afhandling relaterer sig til adskillige undersøgelser, både teoretiske input og empiriske
resultater, som har kigget nærmere på virksomheders ageren i international handel. Afhandlingen bidrager til den eksisterende litteratur på hovedsagelig tre punkter. For det første vurderer
den, hvordan eksporterende virksomheder har håndteret chokket i forbindelse med den seneste,
økonomiske krise samt betydningen af omstilling af eksporten i den forbindelse. For det andet analyserer den komplementariteten mellem eksport og import, når man skal vurdere en virksomheds
beslutning om at træde ind på markedet på et samlet empirisk grundlag, som imødekommer
adskillige dimensioner af virksomhedsheterogenitet. For det tredje og inspireret af det empiriske
faktum, at virksomheder i de mindst udviklede lande er dybt afhængige af importerede produkter,
undersøger den, om en sådan dyb afhængighed omsættes til nogen form for produktivitetsgevinst.
Desuden udforsker den de bestemmende faktorer for fordelingen af produktivitetsgevinsten–hvis
der er nogen—på tværs af importerende virksomheder.
Afhandlingen indeholder fire kapitler. Hvert kapitel er en separat, uafhængig artikel. De første
to kapitler vurderer karakteristikaene for virksomheders handelsdeltagelse i den danske økonomi.
De sidste to kapitler undersøger omfanget af virksomheders internationalisering samt betydningen af og bestemmende faktorer for handelsrelateret videnoverførsel i den etiopiske økonomi. I
overensstemmelse med standard-modellerne for heterogene virksomheder og international handel
er fokus primært rettet mod virksomheder i fremstillingssektoren, som har deres kerneaktiviteter
inden for de nationale grænser i de observerede lande.
Kapitel 1—Coping with the Crisis and Export Diversification—påviser et sæt af stiliserede
fakta, som opsummerer hovedtrækkene i dansk international produktion i det seneste årti. Vi
dokumenterer en betydelig variation i virksomheders eksportdeltagelse på¡, tværs af sektorer og
brancher, en positiv sammenhæng mellem rækkevidden og omfanget af virksomheders eksportaktiviteter, tilstedeværelsen af handel i halvfabrikata samt en højst ulige fordeling af salg ikke kun
påtværs af virksomheder, men også inden for gruppen af eksportvirksomheder.
Med fokus på den seneste økonomiske krise opløser vi de samlede eksportvækstrater i virksomheds, produkt-og destinationskomponenter. I overensstemmelse med tidligere resultater viser vi, at det
meste af nedgangen kan tilskrives den intensive margin; dvs. nuværende virksomheder som reducerer deres eksportleverancer. Vi følger den nyere litteratur (Amador and Opromolla 2013;
Gopinath and Neiman 2014) ved yderligere at opløse den intensive margin i produktintensiv margin (bidraget til den totale eksportændring af vedvarende produkter) og produktekstensiv margin
(bidraget fra den så kaldte product churning). Vi finder, at den produktintensive margin spillede
den vigtigste rolle under krisen, idet virksomheder fortsatte med at sende de samme produkter
til de samme destinationer, blot i mindre mængder. Vi finder også en signifikant effekt af den
produktekstensive margin, specielt under det begyndende konjunkturopsving, idet virksomheder
begyndte at sende nye produkter til nye destinationer. Og hvad er mere væsentligt, såfinder vi
også at de økonomiske betingelser på de markeder, som virksomheder sender til, har betydning,
viii
idet eksportspredning til hurtigt voksende økonomier som Kina eller mere generelt BRIKS var
forbundet med bedre eksportresultater, således at handelsomlægning hjalp virksomhederne til at
klare krisen.
Kapitel 2—A Dynamic Model of Firm Activities: Evidence from Danish Manufacturing—
undersøger beskaffenheden af komplementariteten mellem eksport og import i virksomheder. Kigger man på virksomheder i fremstillingssektoren i perioden 2000-2007, afslører en simpel beskrivelse
af data en udbredt forekomst af samtidig eksport og import i virksomheder. Data afslører også en
betydelig eksport- og importaktivitet, som følger en rangorden, hvor virksomheder med tovejshandel klarer sig bedst efterfulgt af kun-importerende, kun-eksporterende og til sidst indenlandske
virksomheder. Der blev også fundet en stor udholdenhed i virksomheders aktivitetsomfang.
Motiveret af disse empiriske kendsgerninger specificerer jeg en dynamisk discrete-choice-model
for eksport og import og følger en modelleringsmetode af Aw et al. (2011). I denne model er virksomheder defineret til at være heterogene målt i størrelse (kapitalbeholdning), faktorbetaling (løn)
og produktivitet. Modellen tilvejebringer et grundlag til at analysere de bestemmende faktorer
for en virksomheds beslutning om at eksportere og importere og at lade denne beslutning påvirke
virksomhedens fremtidige produktivitetsbane. Den muliggør også en analyse af, hvor stor en rolle
omkostningerne spiller for en virksomheds beslutning om at eksportere og importere.
Parameterestimaterne viser en markant forskel i den konkurrenceintensitet, virksomhederne
oplever, og deres prisstrategier på hjemme-og eksportmarkederne, hvor eksportmarkeder er karakteriseret ved en mere elastisk efterspørgsel og en lavere avance. Den større efterspørgselsfølsomhed
på eksportmarkeder er i overensstemmelse med det faktum, at disse markeder indeholder et større
antal virksomheder og et større produktudvalg. På omkostningssiden viser estimaterne, at virksomheder med en stor kapitalbeholdning, og som betaler højere lønninger, er rentable, selv efter
der er taget højde for deres produktivitet.
Ved at udvide Levinsohn and Petrin (2003) algoritme til at forklare endogen udvikling af
virksomhedsproduktivitet finder jeg, at der er en learning-by-doing-effekt ved at eksportere og importere, som er specielt stor ved import. Som i Das et al. (2007) estimeres opstartsomkostninger og
løbende driftsomkostninger på eksport-og importmarkederne ved at anvende en Bayesian Markov
Chain Monte Carlo. I tråd med selvselektionshypotesen er de estimerede produktionsomkostninger og faste omkostninger ved at eksportere og importere væsentlige. Og disse omkostninger
er større for store virksomheder, som højst sandsynligt vil operere samtidigt påflere markeder,
og det indebærer ikke ubetydelige omkostninger. Sammen med etableringsomkostningerne driver
læringseffekterne virksomhederne til at vælge at eksportere og importere, idet de fremhæver komplementariteten mellem disse handelsaktiviteter.
Kapitel 3—Importing and Firm Productivity in Ethiopian Manufacturing—analysererårsagsforholdet
mellem import og virksomhedsproduktivitet. Motivationen til at lave denne undersøgelse er det
faktum, at det meste af litteraturen om virksomheder i international handel er begrænset til
fremstillings-virksomheder i udviklede økonomier og nogle få udviklingslande i Asien og Latiix
namerika. Således er afrikanske fremstillingsvirksomheder blevet overset. Selv i den lille håndfuld
af eksisterende undersøgelser har fokus været påeksport (se fx Bigsten et al. 2004; Bigsten and Gebreeyesus 2009; Mengistae and Pattillo 2004; Van Biesebroeck 2005). Som det ses, er litteraturen
om afrikanske fremstillingsvirksomheder ukomplet, især i lyset af høje importavancer i forhold til
BNP og importandele af fremstillingsvarer i disse økonomier.
Ved at anvende et panel af etiopiske fremstillingsvirksomheder i perioden 1996-2011 påvises
det, at mere produktive virksomheder selvselekterer ind på eksportmarkeder, hvilket antyder, at
import indebærer uigenkaldelige og periodiske omkostninger, som kun de mest produktive virksomheder kan absorbere. For at undersøge årsagseffekten af import for en virksomheds produktivitet, følger jeg Kasahara and Rodrigue (2008) i specifikationen af en strukturel model, hvor
de statiske og dynamiske effekter af import bliver estimeret hver for sig. Resultaterne tilvejebringer belæg for learning-by-importing påtrods af en første, forbigående produktivitetsnedgang.
Resultaterne afslører endvidere, at intensiv brug af importerede input er forbundet med større
produktivitetsforbedringer blandt importerende virksomheder.
Kapitel 4—Imported Inputs and Firm Absorptive Capacity in Ethiopian Manufacturing—er
dybest set en forlængelse af kapitel 3, og kapitlet understreger betydningen af absorberende kapacitet med hensyn til produktivitetsvirkningen af importerede input. Dette kapitel er motiveret
af, at produktivitetsfordelene ved at importere er relativt små i den etiopiske fremstillingssektor
sammenlignet med resultater fra undersøgelser i andre lande. Med det formål estimerer jeg en standard produktionsfunktion, hvor import og absorberende kapacitet (målt ved andelen af faglærte
medarbejdere i en virksomheds arbejdsstyrke) er medtaget som ekstra variabler. Estimaterne viser,
at importerede input er fordelagtige, hvis virksomheden har den nødvendige, uddannede medarbejdersammensætning til at kunne absorbere den indeholdte viden i disse input. Dette betyder, at
importerede input ikke har noget specielt formål, hvis virksomheden ingen absorberende kapacitet
har.
Alternativt vælger jeg en tærskelregression og bruger en teknik til opsplitning af stikprøver
udviklet af Hansen (2000). Teknikken opdeler hele prøven i forskellige ordninger baseret på virksomheders absorberende kapacitet. Estimaterne viser, at effekten af importerede input er større for
virksomheder med en tilstrækkelig høj absorberede kapacitet. Samtidig indikerer tærskelestimatet,
at de fleste virksomheder har en absorberende kapacitet under tærskelkravet. På trods af den udbredte anvendelse af importerede input i sektoren er fordelene ved forøget adgang til udenlandsk
teknologi derfor begrænset til ganske fåvirksomheder. Disse resultater vedrørende vigtigheden af
absorberende kapacitet understøtter på virksomhedsniveau de gældende makroøkonomiske holdepunkter, som identificerer begrænset absorberende kapacitet som en hindring for videnspredning
til de mindst udviklede lande.
x
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Clerides, S. K., Lach, S., & Tybout, J. R. (1998). Is Learning by Exporting Important? Microdynamic Evidence from Colombia, Mexico, and Morocco. Quarterly Journal of Economics,
903–947.
Das, S., Roberts, M. J., & Tybout, J. R. (2007). Market Entry Costs, Producer Heterogeneity,
and Export Dynamics. Econometrica, 75 (3), 837–873.
Gopinath, G., & Neiman, B. (2014). Trade Adjustment and Productivity in Large Crises. American
Economic Review , 104 (3), 793–831.
Hansen, B. E. (2000). Sample Splitting and Threshold Estimation. Econometrica, 68 (3), 575–603.
Kasahara, H., & Rodrigue, J. (2008). Does the Use of Imported Intermediates Increase Productivity? Plant-level Evidence. Journal of Development Economics, 87 (1), 106–118.
Levinsohn, J., & Petrin, A. (2003). Estimating Production Functions Using Inputs to Control for
Unobservables. Review of Economic Studies, 70 (2), 317–341.
Melitz, M. J. (2003). The Impact of Trade on Intra-industry Reallocations and Aggregate Industry
Productivity. Econometrica, 71 (6), 1695–1725.
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Firms. Journal of International Economics, 67 (2), 373–391.
xi
Chapter 1
Coping with the Crisis and Export
Diversification
1
Coping with the Crisis and Export Diversification∗
Kaleb Girma Abreha†
Valérie Smeets‡
Frédéric Warzynski§
Abstract
Using a highly disaggregated firm-product-destination level data, we document salient
features of Danish international production over the last decade. These include a significant
variation in the export participation of firms across industries, a positive correlation between
the scope (number of products exported and markets served) and the scale of exporting
activities, the existence of carry-along trade, and a considerable dominance of few firms and
few core products as evidenced by highly uneven distribution of sales not only across firms but
also within the export basket of firms. Further, we analyze how Danish exporters responded
to the global recession and the recovery that followed. We find that firms reacted mainly by
adjusting their scale of export shipments, and by extending their portfolio outside their core
product. More importantly, we also find that the economic conditions of markets where firms
sell matter: export diversification into fast-growing economies like China or more generally
the BRICS was associated with better export performance, so that trade reorientation helped
firms to cope with the crisis.
JEL Codes: F14, F6, L60
Keyword: Trade collapse, intensive and extensive margins, export diversification, Denmark
∗
We are grateful to Mark Roberts for a useful dissucssion on the early draft of the paper. We thank the Tuborg
Foundation and the Danish Council for Independent Research in Social Sciences (FSE) for generous financial
support. The usual disclaimer applies.
†
Department of Economics and Business, Aarhus University, Denmark, E-mail: [email protected]
‡
Department of Economics and Business, Aarhus University, Denmark, E-mail: [email protected]
§
Department of Economics and Business, Aarhus University, Denmark, E-mail: [email protected]
2
1
Introduction
Increased availability of micro-level datasets has over the last twenty years shifted the focus of
research in international trade from countries and industries to firms and products. This new line
of research extends a previous wave of empirical papers starting in the mid-nineties that challenged
the existing theories of international trade, which entirely focused on comparative advantage, increasing returns to scale and consumer love for variety. As summarized by Bernard et al. (2007),
these theories failed to capture important empirical regularities, most notably firm heterogeneity.
Numerous recent studies have shown that firms are rather different in several dimensions even
in narrowly defined industries. The pioneering paper in the field, Bernard and Jensen (1995),
shows that the fraction of firms active in export markets is rather small in the US manufacturing.
Additionally, these firms are systematically different from domestic firms in terms of size, productivity and input mix. That is, they are larger, more productive, more skill- and capital-intensive.1
This inspired new trade theories, starting with Bernard et al. (2003) and Melitz (2003), which
revolutionized this field of research.
More recently, researchers have documented additional stylized facts about exporters: domestic
and international productions are dominated by a few firms (see e.g. Bernard et al. 2009; Mayer
and Ottaviano 2008; Eriksson et al. 2009), and export sales are concentrated in a few products
within firms (Arkolakis and Muendler 2010). These empirical findings led to the development of
new models of multi-product firms whose production activity can further be categorized into the
production of core and peripheral products (see e.g. Bernard et al. 2010; Bernard et al. 2011; Eckel
and Neary 2010; Mayer et al. 2014).
In this paper, we use a rich transaction level data from Denmark to document new stylized
facts on the behavior of firms facing a large shock. Descriptive summaries of the data confirm a
considerable variation in the export participation of firms across industries. Despite changes in the
degree of firm and product participation in the export sector over time, the correlation between
the scope (number of exported products and markets served) and the scale of exporting activities
has remained positive, as shown by Arkolakis and Muendler (2010). Also, the export sector
is characterized by the overall dominance and growing importance of multi-product and multidestination firms whose activities involve carry-along trade, in line with Bernard et al. (2012).
Additionally, there is evidence of a highly skewed distribution of export sales between few core
and several peripheral products within the export basket of firms.
In line with previous studies, during the periods of large economic shock, we show that most of
the decline can be attributed to the intensive margin; that is, incumbent firms reducing their level of
1
Interestingly, these characteristics are not restricted to the US only. Studies from other countries such as Muûls
and Pisu (2009) for Belgium, Alvarez and López (2005) and Kasahara and Lapham (2013) for Chile, Isgut (2001)
for Colombia, Eriksson et al. (2009) for Denmark, Verardi and Wagner (2012) for Germany, Amiti and Davis (2012)
for Indonesia, Ruane and Sutherland (2005) for Ireland, Castellani et al. (2010) for Italy, De Loecker (2007) for
Slovenia, Máñez-Castillejo et al. (2010) for Spain, Andersson et al. (2008) for Sweden, and Van Biesebroeck (2005)
for Sub-Saharan African countries document similar characteristics of firms with international trade participation.
3
activity in export markets. We follow a recent literature (Amador and Opromolla 2013; Gopinath
and Neiman 2014) by further decomposing the intensive margin into sub-intensive (the contribution
to total export change of continuing products) and sub-extensive margins (the contribution coming
from product churning). We find that the sub-intensive margin played the most important role
during the 2008-2009 trade collapse, so that Danish firms continued to ship the same products to the
same countries but in lower quantities. We also find a significant effect of the sub-extensive margin,
particularly during the recovery, as firms started shipping new products to new destinations.
The main contribution of this paper is to analyze the role of export diversification. We perform
a set of empirical tests to better understand the choice of the product-destination portfolio of
firms and how it can be related to the export performance during a crisis. We first analyze what
determines the concentration of export sales in the core product on the various markets where
firms operate. In line with recent theoretical contributions (e.g. Mayer et al. 2014), we find that
distance, toughness of competition and wealth matter. Firms have a higher share of their core
products on more distant and larger markets, while the opposite is true on richer markets.
In the next step, we examine how firms’ export performance was affected by their presence in
fast-growing markets and their reliance on a core product. We find that firms which export to
China and the BRICS enjoy a significantly higher export growth, so that trade reorientation to
these markets helped them to cope with the crisis.2 On the other hand, export growth was lower
for firms’ core products, suggesting that a too heavy reliance on one product might have a negative
impact on growth.
Several papers have previously studied how firms reacted to large trade shocks. Bernard et
al. (2009) study the reaction of the US firms to the Asian crisis of 1997 and show the eminent
role of the intensive margin of trade. More recently, Gopinath and Neiman (2014) investigate the
behavior of firms in response to the Argentine 2001-2002 crisis and document the considerable
importance of the intensive margin as a trade adjustment mechanism, even if the sub-extensive
margin played a non-negligible role in the import trade of Argentina. Bricongne et al. (2012) show
that large French firms responded to the current economic crisis mainly by lowering their export
shipment whereas small firms exit export markets or reduce the number of products exported
and destinations served. Using Belgian data, Behrens et al. (2013) find that the trade collapse
mainly resulted from a decline in quantities and prices of existing export and import firm-product
transactions rather than from entry and exit of firms, products and trading partners. These studies
establish that trade adjustment mechanisms at the microeconomic level are keys to understanding
the aggregate trade collapse. Unlike these previous papers, our approach is focused on export
diversification as a way to cope with a crisis.
The rest of the paper is organized as follows. Section 2 describes the datasets that we use.
Section 3 presents some stylized facts about Danish exporters during our sample period. Section
4 discusses our export growth decomposition exercises. Section 5 looks at the link between export
2
BRICS countries comprise Brazil, China, India, Russia, and South Africa.
4
growth and diversification. Section 6 concludes.
2
Data description
The datasets used in this paper are all provided by Statistics Denmark. The focus of our analysis
is the time period 2000-2010. We combine three different datasets and merge them relatively
easily, as firms are identified by a common identification number. Our main source of information
provides detailed records on export and import transactions by all trading firms in the Danish
economy. It contains the value, weight and quantity of export and import transactions for each
firm and destination/source market at 8-digit CN.3
The second dataset contains information about the product portfolio of firms and describes
which products firms make domestically. It is based on a survey of all firms in the manufacturing
sector with at least 10 employees and therefore covers fewer firms. Like the trade statistics, we
have information on the value, weight and quantity of production at 8-digit CN for a panel of
manufacturing firms.
These two datasets do not contain information about overall sales revenue, employment size,
material input usage or capital structure of firms. We therefore use a third dataset that contains
accounting information for the universe of firms in the economy. This dataset covers more than
200,000 firms per year. Merging the other two datasets with this accounting data ensures that
we only consider firms with real economic activity (see Tables A.1–A.6 in the Appendix on the
evolution of the number of firms over our period of analysis). After dropping classified products
and trading partners and only considering trading firms with at least one employee, we end up
with 4,253,959 export transactions at 6-digit HS.
The industry where firms operate is based on the Statistical Classification of Economic Activities in the European Community (NACE). One practical difficulty is that this industrial classification was revised twice during our period of analysis; in 2003 and 2007. Unlike the first revision
which was relatively minor, the second one was substantial. The dataset is structured in such
a way that firms are redefined according to the new classification. In our analysis, we use the
latest Danish industrial classification which is comparable to NACE Rev.2. Finally, we deflate all
nominal variables by the consumer price index (CPI) using 1995 as a base year.
3
Stylized facts about Danish exporters
3.1
Cross-industry export participation rate
Table 1 shows substantial heterogeneity in terms of firm distribution as well as export participation
rate across industries. We see that the majority of firms are engaged in industries which are
3
Combined Nomenclature (CN) is a Harmonized system (HS) of product classification with further subdivisions
used in EU member countries.
5
inherently non–tradable such as agriculture, forestry and fishing, and service-sector industries
such as construction, wholesale and retail trade, health and social work, and transport, storage and
communication. In terms of international trade, exceedingly small fractions of these firms export,
below 1%. In contrast, industries in the manufacturing sector have a considerably higher export
market participation rate. A high percentage of firms engaged in the manufacturing of chemicals,
furniture, rubber and plastics, machinery and equipment, electrical and optical equipment, and
transport equipment are active in export markets. In these industries the average participation rate
is around 50% over the period under study. On the contrary, firms engaged in production of basic
and fabricated metals, food, beverage and tobacco, non-metallic mineral products, printing and
publishing, and wood products have a relatively lower export participation rate. More generally,
it is shown that there are significant differences in the distribution as well as in the export market
participation of firms not only across agriculture, manufacturing and service sectors but also across
industries within sectors, as demonstrated especially for the manufacturing sector.4
3.2
Scope and scale of firm export
Table 2 reports that around 13,000 firms export more than 4,500 products to more than 200
countries. This represents around 100,000 product-destination combinations. While the number
of exporting firms has slightly declined over time, the average value of export transaction has
increased as shown in Table 3. Similarly, average export shipment per product and destination
increased despite an increase in the overall number of products exported and destination markets
served. This indicates a greater role of the intensive margin of export trade. We also notice that
these three figures declined during the crisis. Further, a few superstar firms are reaching more
than 100 countries, exporting more than a thousand different types of products, and therefore
have thousands of product-destination exporting relations.
Figure 1 depicts more clearly the dynamics of the number and scale of exporting firms and
export transactions (defined at product-destination level) over the last decade. For comparison
purpose, we include the import equivalents of these firms and transactions. The figure shows that
importing firms outnumber their exporting counterparts for the entire time period.5 The average
export value at the firm level is much larger and increasing relative to imports. In contrast, the
number of export transactions is much higher, and the average value of an export transaction is
typically lower compared to an import transaction.
From Figure 1, it can clearly be seen that the number of trading firms as well as trade transactions declined during the economic recession in 2009. Unlike the 2009 recession, the number of
trading firms as well as export and import transactions have not been affected by the less dramatic
4
Table A.7 in the Appendix depicts lower cross-industry variations in firm distribution and export market
participation rate, and so is the pattern within industries over time. It also reveals a greater export market
participation rate for each of the industries. This shows a systematic variation in export market participation by
size in which larger firms are more likely to be exporters.
5
The opposite is true among firms in the manufacturing sector where importing is rarer than exporting.
6
trade collapse of 2003. However, firms greatly adjusted downward the volume of their export and
import shipments in both recessions.
Combining product and destination dimensions of exporting indicates that firms exporting more
products and reaching more markets saw their relative number and contribution increase between
2000 and 2010. Table 4 shows that, among exporters, those exporting only one product to only one
market destination constitute the largest group, 24.06% and 22.01% in 2000 and 2010 respectively,
albeit the median firm is multi-product and multi-destination. However, the contribution of these
firms to overall export is marginal (below 1%), while firms reaching more than five markets account
for more than 90% of the export value (up from 87% in 2000).
The unique nature of our dataset allows us to trace the sources of firm exports as own-produced
products or not. Table 5 shows the nature of firm production activities in terms of traded versus
non-traded products. While constructing the table, we restrict our sample to firms in the manufacturing sector because we observe the production portfolio for these firms only. We count the
number of products each firm produces and whether they are traded, and take the mean (and
median) for all the firms in a given year. The results reveal that greater fractions of products
firms produce are traded, and these products have become more prevalent over time. In contrast,
the fraction of products that are not traded at all has declined. This indicates a rising integration
of the manufacturing sector to the global economy. Even though the contribution of non-traded
products to the value of production rose, especially in the advent of the 2008-2009 crisis, their
share remained significantly small.
Coming to the trading element of firm activities, we see that firms export products that they
do not actually produce. Both the mean and median number of non-produced export products
are greater than their produced counterparts, highlighting the presence of carry-along trade in the
Danish export trade. This is in line with findings by Bernard et al. (2012) for Belgium and Amador
and Opromolla (2013) for Portugal on the existence of carry-along trade. Over time, we see that
the number of products the average exporting firm exports increased, and more so in the case of
non-produced export products. Despite the pervasiveness of carry-along trade, its contribution to
the overall export is very small, below 5%. It is not surprising that firms mainly import products
they do not produce, albeit there are times firms import goods that they produce themselves.6
3.3
Export concentration within firms
Another interesting aspect of international trade is the extent of concentration of exporting activity
within firms. To measure this, we first calculate the share of each product in the total export of
firms. Then, we rank products in descending order and calculate the cumulative shares of each
product in the export basket of each firm. As a final step, we take the mean of the cumulative
shares across firms with the same product scope.
6
This can partly be explained by the level of aggregation used to define products. At an even more disaggregated
level such as 10-digit HS, the incidence of importing products that firms produce themselves will be lower.
7
Table 6 portrays a highly skewed distribution of firm export sales across products within multiproduct firms. Irrespective of product scope, the first ranked product constitutes more than 50%
and 45% of firm export sales in 2000 and 2010, respectively. Further, two thirds of firm export
sales only come from the top three products. We also observe a decline in the unevenness of the
distribution of export sales over the ten years window, i.e. firms tend to export a more diversified portfolio. This unevenness of export sales distribution is consistent with previous findings of
Bernard et al. (2011) for the US, Arkolakis and Muendler (2010) for Brazil, and Amador and Opromolla (2013) for Portugal. The unevenness among Danish exporters is, however, lower compared
to their Brazilian counterparts as shown by Arkolakis and Muendler (2010) for the year 2000.7
Then, we examine how differently firms rely on their core products depending on the nature of
their production and export activities as well as the characteristics of the export destinations that
they serve. To establish this point, we regress a destination-specific share of the core product in
the export bundle of a firm on several destination market characteristics such as distance, market
size and income level.8
log(Export share)i,j,t =β0 + β1 log(Distance)j,t + β2 log(GDP )j,t + β3 log(GDP P C)j,t
Own−product
RT A
+ β4 log(#P roducts)i,j,t + β5 Di,j,t
+ β6 Dj,t
+ i,j,t
(1)
where Export sharei,j,t is the share of the core export product of firm i in destination j in time
period t, Distance is the geographical distance between Denmark and destination j which is timeinvariant and common for all firms exporting to that particular market, GDP refers to the GDP
which is used as a proxy for the intensity of competition, GDP P C is per capita GDP which
measures the level of income and size of demand in that market, #P roducts is the number of
products a firm exports to a given destination, DOwn−product is a dichotomous variable showing
whether a firm actually produces the core product it exports,9 DRT A denotes a dummy variable
showing whether there is a trade agreement between Denmark and destination country j, and is
an error term.
The estimation results in columns (1) and (2) from Table 7 show that firms serve larger and
more distant markets primarily with their core product. Following Mayer et al. (2014), this can be
explained by the fact that firms serve markets only with the products in which they are productive
enough to absorb production and trade costs, and firms are more efficient in the production of their
core products. Since more distant countries involve larger costs, firms are more likely to export
their core products to these markets. Similarly, larger markets represent tougher competition
and firms serve such markets primarily through products of their respective core competences.
7
Bernard et al. (2011) use a highly disaggregated 10–digit HS product classification for a single cross section
year 2002 whereas Amador and Opromolla (2013) define a product at 4-digit HS and report the average over the
time period 1996-2005. This makes comparison with these studies inappropriate.
8
Data on gravity variables such as bilateral distance, GDP, population, and regional trade agreements are
obtained from the CEPII database.
9
We construct this variable for a restricted sample of exporting firms in the manufacturing sector because it
requires information on the production portfolio, which is only available for these firms.
8
Relatedly, high purchasing power in destination markets provides an opportunity to export more
products. Not surprisingly, the estimates show that the export share of the core product declines if
a firm exports more products to a particular market destination. Further, trade-enhancing policy
measures such as regional trade agreements could be pro-competitive, therefore forcing firms to
focus on their core competences. These results are consistent with the findings of Mayer et al.
(2014) for France and Carballo et al. (2013) for Costa Rica, Ecuador and Uruguay.
Given the importance of carry-along trade in the economy, we run a similar regression while
controlling for whether a firm actually produces the core product it exports. The estimation results
are reported in columns (3) and (4). They show that distance, market size, income level and firm
export scope have the same effects as above, except the insignificance of the trade agreement
variable. We also see that whether or not firms actually produce their core export product matters
when it comes to how large of a role it plays in their export portfolio.
4
Export growth decomposition
We first decompose aggregate growth into intensive and extensive margins. As usual, we define
the intensive margin as changes in export coming from incumbent exporters, and the extensive
margin as changes in export resulting from entry of new exporters and exit of previously exporting
firms, so that the growth rate of exports can be disaggregated as follows:
X
X
X
∆Et =
(Ei,t − Ei,t−1 ) +
Ei,t −
Ei,t−1
(2)
i∈Ct
i∈Nt
i∈Xt
where Ei,t refers to export of firm i at time period t, and Ct , Nt and Xt denote a group of
incumbent, entering and exiting firms, respectively. The first term in equation (2) denotes the
intensive margin, the second term shows an increase in export due to new entrants into exporting,
and the last term sums the decline in export coming from firms exiting export markets. Extension
of the decomposition to product and product-destination combinations is straightforward.
Table 8 shows the dominant role of the intensive margin in the export trade. These features
indicate that the main drivers of trade are incumbent firms, products and product-destination
relationships. Additionally, our results show a positive and significant recovery of exports (7.42%),
mostly driven by the intensive margin.
Note that the two margins of trade do not always reinforce each other. For instance, unlike the
2008-2009 economic downturn where the entry and exit of firms, products and product-destination
relationships reinforced the contraction in the scale of existing trade relations, the extensive margin
helped to attenuate the negative export shock during 2002-2003. During the 2009-2010 period, the
introduction of new products and product-destinations helped the recovery of exports. However,
the recovery process was hindered by the exit of trading firms. This suggests that shocks have
lasting effects on the economy in terms of firm exits and closures.
9
A small role of entry and exit of firms or products at the country level is consistent with previous
studies which show that new exporters are small and therefore constitute a small share of exports.
What is new to a particular firm is less likely to be new for the economy, and even if it is new,
its share is mostly small. Given the multi-product and multi-destination nature of firm trading
activities, firms’ decisions to adjust their product and market mix are important mechanisms to
understand how they adapted to the crisis.
Therefore, we extend our decomposition exercise and consider the different margins of trade
adjustment at the firm level. This decomposition exercise here is equivalent to equation (2) except
that only incumbent firms and their scale and scope decisions in terms of product and productdestination dimensions are considered. Following Gopinath and Neiman (2014), we define the subintensive margin as the growth rate due to changes in value of existing firm-product relationships
whereas the sub-extensive margin refers to the growth rate due to continuing firms’ decisions to
terminate existing product relationships or introduce new ones.
X
X
X
∆Et =
Ei,t − Ei,t−1 +
Ei,t −
Ei,t−1
(3)
i∈Ct
i∈Nt
i∈Xt
where Ei,t refers to firm-product export relationships i at time period t; Ct , Nt and Xt denote a set
of maintained, newly added or dropped export products by incumbent firms, respectively. Again,
extension to firm-product-destination combinations is straightforward.
Table 9 shows that the sub-intensive margin still constitutes the most important export adjustment mechanism. However, unlike in the case of economy-wide churning of products and
product-destination combinations, adjusting product and product-destinations mix at the firm
level played a non-negligible role in overall export growth. In addition to attenuating the negative effects of the trade shocks during 2002-2003 and 2008-2009 recessions, export growth coming
from newly added products, and especially newly added product-destinations, was particularly
important in stimulating the recovery in the aftermath of the crises.
In another exercise, we decompose export growth into components coming from changes in scale
of export by continuing exporters, export starters and export quitters as in equation (2). Having
established the prominent role of continuing exporters as primary driving forces, we then decompose export changes due to continued, newly added or dropped market destination for incumbent
exporting firms as in Amador and Opromolla (2013).
X
X
X
∆Et =
(4)
∆Ei,t +
∆Ei,t +
∆Ei,t−1
i∈Ct
i∈Nt
i∈Xt
where the additional decompositions are given by:
"
#
X
X X
X
X
∆Ei,t =
∆Ej,i,t +
∆Ej,i,t +
∆Ej,i,t
i∈Ct
i∈Ct
j∈ADt
j∈DDt
"
X
j∈CDt
∆Ej,i,t =
(5)
j∈CDt
#
X
X
j∈CDt
k∈APt
∆Ek,j,i,t +
X
k∈DPt
10
∆Ek,j,i,t +
X
k∈CPt
∆Ek,j,i,t
(6)
Equations (4)-(6) summarize the steps involved in the decomposition in which E refers to
export flow depending on whether firm i at time t is entering Nt , exiting Xt or incumbent Ct ; then
exports by incumbent firms to different destinations depend on whether each destination j is newly
added ADt , dropped DDt or continued CDt , and finally export changes to continued destinations
depend on whether the exported product k is newly added APt , dropped DPt or continued CPt ,
respectively.
Results of this decomposition exercise are shown in Table 10. As discussed previously, we see
that continuing firms are more important than entering and exiting firms in the export trade.
Among incumbent exporting firms, continued export destinations are more important than newly
served ones highlighting the minimal effect of destination switching on export growth. For continued destinations, most of the growth comes from the changes in the export value of previously
exported products, suggesting the existence of core and peripheral export products. This suggests
that firms’ adjustment in their product-market portfolio might have helped them to mitigate the
export collapse in continued market destinations during the 2002-2003 and 2008-2009 recessions.
5
Export diversification and growth
In this section, we want to investigate how firms react to changes in the economic environment
by adjusting their product mix and export destinations conditional on survival. Such export
diversification decisions at the firm level usually affect the trajectory of firm export sales over
time.
To better understand these decisions, we start by plotting the export shares of destinations
against destination-specific export growth as depicted in Figure 2. We see a negative relationship
implying that the main export market destinations (mostly neighboring countries like Germany,
Sweden and Norway which are affected by similar shocks) experienced slower export growth rates.
This suggests that diversification into other markets could potentially improve export sales over
time. We focus our attention on specific high-growth nontraditional markets namely China and
more generally the BRICS. Interestingly, despite a stable or even declining number of exporting
firms in the economy, a growing number of Danish firms are indeed exporting to these markets as
shown in Figure 3.
As a next step, we run a simple least squares regression of export growth in which nontraditional markets—China and the BRICS—are explicitly considered:
M ulti−product
Destination
∆Ei,t =β0 + β1 Di,t−1
+ β2 Di,t−1
+ β3 log(#Employees)i,t−1
(7)
+ β4 log(#Destinations)i,t−1 + i,t
M ulti−product
where ∆Ei,t = log(Ei,t ) − log(Ei,t−1 ) refers to firm i’s export growth at time period t, Di,t−1
Destination
is a dummy if the firm is multi-product, Di,t−1
if the firm exports to particular markets of
interest such as China and the BRICS, Employees and Destinations refer to employment size
11
and export market scope of the firm, respectively.10 We run the regression for three different time
periods: period of trade collapse—2008-2009, period of trade collapse and recovery—2008-2010,
and entire sample period—2000-2010.11
Table 11 shows that in the midst of the economic crisis, firms that were serving the Chinese market had higher export growth, after controlling for firm size, multi-product and multi-destination
aspects of their export activity. The effect is not significant when we instead consider the BRICS
as a whole.
We also find that larger firms apparently sustained the shock better. After controlling for size,
more diversified firms in terms of product and markets destinations had lower export growth. This
can be seen as a mechanical effect, as firms involved in more destinations or selling more products
are more likely to be exporting to markets which were also affected by the crisis. This implies that
individual markets where firms are selling matter.
During our extended sample periods 2008-2010 and 2000-2010, our main finding remains valid:
export growth rates were larger for those firms exporting to China and the result extends to the
BRICS. The estimates on multi-product, number of market destinations, and employment size are
also very similar.
We then run a similar regression at the firm-product level in which products are now indexed
by j:
M ulti−product
Destination
+ β2 Di,j,t−1
∆Ei,j,t =β0 + β1 Di,t−1
+ β3 log(#Employees)i,t−1
(8)
Core−product
+ β4 log(#Destinations)i,j,t−1 + β5 Di,j,t−1
+ i,j,t
Table 12 shows that export presence in China and the BRICS had a positive effect on growth
over the whole period. It does not appear to have played an important role during the crisis, but
both the China and BRICS effects are positive and significant over the 2008-2010 period. Another
interesting finding is that firms’ core product experienced a negative shock both in the short and
medium run. This suggests that firms that were too dependent on one product had lower export
growth.
Controlling for these factors, we also observe a significant and negative correlation between
product export growth and export diversification in terms of markets served and number of products exported both in the short and medium run. We see these variables as important controls,
and the effect can be seen as mechanical as discussed before. Similarly, larger firms are more likely
to have faster product export growth over the whole period, although size did not help during the
crisis and the recovery.
10
We find similar results in all of the regressions when mid-point growth rates are used.
In this paper, we are mostly interested in the correlations between export growth and market presence. We do
not explicatively consider the selection process in high growth markets.
11
12
6
Conclusion
This paper uses a rich dataset from Denmark to examine the microeconomic foundations of international trade, and especially how firms adjust their behavior during a period of large shock. We
find that there are significant cross-sector and cross-industry variations in firm export participation
rates. Interestingly, broadening of the export activities is accompanied by deepening in that we
find a positive correlation between the scope and the scale of firm export activity. Additionally, we
document the dominance and increasing importance of a few superstar firms with broader export
scope. Highly uneven distribution of export sales within firms provides evidence of the existence of
core and peripheral products in the export portfolio of firms. This unevenness systematically varies
with destination market characteristics such as bilateral distances, economic size and wealth. We
also show that firms export products that they do not actually produce indicating the existence
of carry-along trade. Despite the pervasiveness of carry-along trade, the export share of this trade
is very small, albeit increasing over the last decade.
In view of the current economic crisis, Danish firms reacted differently. Some firms quit exporting. Others changed the mix of products they export and the market destinations they serve, and
this somehow helped them to mitigate the negative shocks of the crisis. However, the most important adjustment to cope with the crisis came from a few important firms reducing the shipment
of their core products to major market destinations. Our findings show a positive and significant
recovery of exports in the aftermath of the crisis, mostly through the intensive margin, further
establishing the fact that trade performance is driven by a few superstar firms, their core products,
and economic conditions of their main trading partners.
We also analyze how firms reacted to a large shock by diversifying their product-destination
portfolio. We stress the important role played by the intensive margin of trade. We show that
export diversification into fast-growing economies like China or the BRICS was associated with
better export performance. We also document that too much reliance on one core product was
harmful to growth.
Our results mostly display interesting cross-sectional correlations between export diversification
and growth. In future work, we would like to better understand how some firms managed to
successfully enter these high-growth markets; that is, introducing more dynamics and addressing
the endogeneity of the diversification process.
13
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15
16
1,115
6,997
Furniture
Health & social work
546
Transport equipment
54,345
192,508
Others
Total
670
1,011
Textiles & wearing apparel
Wood & wood products
614
Rubber & plastic products
42,323
2,556
Paper, publishing & printing
Wholesale & retail trade
663
Non-metallic mineral products
12,323
168
Mining & quarrying
Transport, storage & communication
2,066
Machinery & equipment
80
1,712
Food, beverage & tobacco products
Leather & leather products
1,618
Electrical & optical equipment
299
Chemicals & chemical products
23,983
3,579
Basic & fabricated metals
Construction
35,840
# Firms
Agriculture, forestry & fishing
Industry
6.74%
1.67%
26.0%
15.9%
2.77%
32.6%
35.2%
52.4%
14.3%
24.9%
17.9%
40.9%
37.5%
0.10%
32.3%
16.4%
36.9%
0.89%
59.9%
18.0%
0.66%
% Exporters
2000
225,516
70,015
552
40,485
21,774
260
734
519
1,101
537
162
1,491
60
14,298
353
1,496
844
31,260
253
3,161
35,161
# Firms
5.75%
1.77%
25.5%
17.0%
2.79%
47.3%
30.8%
59.2%
23.0%
24.0%
20.4%
51.2%
31.7%
0.10%
58.9%
19.4%
52.6%
0.64%
63.2%
20.1%
0.67%
% Exporters
2008
220,915
75,052
523
38,547
21,029
254
657
493
1,028
506
156
1,438
56
14,375
359
1,447
822
28,616
249
2,939
32,369
# Firms
5.65%
1.59%
24.9%
17.3%
2.67%
47.6%
32.1%
59.0%
22.6%
25.7%
20.5%
51.3%
32.1%
0.06%
54.9%
20.1%
55.2%
0.69%
63.1%
20.7%
0.78%
% Exporters
2009
Table 1: Industry-wise export participation rate of firms with at least one employee
22,951
78,076
487
38,577
21,688
249
618
483
975
485
152
1,442
57
15,078
355
1,423
836
27,341
242
2,859
31,528
# Firms
5.73%
1.63%
25.9%
17.7%
2.78%
49.0%
30.3%
59.6%
22.7%
24.3%
22.4%
51.9%
24.6%
0.11%
52.4%
21.0%
55.1%
0.66%
61.6%
22.0%
0.92%
% Exporters
2010
Table 2: Summary on exporting firms, exported products and export destinations
#
#
#
#
#
#
#
#
#
#
Exporting Firms
Destinations: Economy-wide
Destinations: Per firm, median
Destinations: Per firm, maximum
Exported products: Economy-wide
Exported products: Per firm, median
Exported products: Per firm, maximum
Exported product-destinations: Economy-wide
Exported product-destinations: Per firm, median
Exported product-destinations: Per firm, maximum
2000
2008
2009
2010
12,973
217
2
129
4,435
3
472
78,150
4
2,257
12,919
225
2
133
4,579
4
1,432
106,215
5
8,274
12,490
225
2
143
4,547
4
1,277
104,175
5
7,813
12,782
227
2
151
4,525
4
1,438
108,978
5
9,381
Table 3: Summary on export value in million DKK
2000
2008
2009
2010
Export value per firm
Mean
Median
Maximum
19.70
0.45
13,302.34
24.94
0.42
14,006.64
21.43
0.41
12,369.76
22.50
0.43
12,072.40
Export value per product
Mean
Median
Maximum
57.62
3.01
7,268.63
70.35
3.53
13,814.81
58.87
3.02
7,932.45
63.55
3.32
9,179.54
Export value per destination
Mean
Median
Maximum
1,177.65
31.11
49,355.19
1,431.76
26.41
53,429.89
1,189.73
24.65
45,777.98
1,266.71
30.31
47,330.82
17
Table 4: Product and destination scope of exporting firms
# Products
1
2
Share of exporting firms
Value share of exporting firms
# Destinations
# Destinations
3
4
5
5+
All
1
2
3
4
5
5+
All
0.39
0.28
0.18
0.08
0.06
0.20
1.17
0.23
0.28
0.14
0.14
0.06
0.39
1.23
0.27
0.19
0.25
0.12
0.05
0.58
1.46
1.36
0.10
0.15
0.15
0.05
1.45
3.26
0.05
0.09
0.14
0.07
0.14
0.65
1.15
0.82
1.77
1.65
1.75
1.91
83.82
91.72
3.13
2.71
2.50
2.30
2.28
87.08
100
0.39
0.12
0.17
0.05
0.05
0.48
1.25
0.22
0.33
0.20
0.13
0.07
0.49
1.43
0.11
0.41
0.08
0.18
0.09
1.04
1.91
0.13
0.12
0.09
0.13
0.16
1.22
1.85
0.43
0.03
0.09
0.23
0.06
0.75
1.59
0.35
1.21
1.04
1.04
1.25
86.82
91.97
1.63
2.22
1.67
1.97
1.72
90.79
100
Panel a. Year 2000
1
2
3
4
5
5+
All
24.06
5.63
2.61
1.15
0.58
1.66
35.67
2.77
5.85
2.93
1.61
0.90
2.30
16.36
1.13
1.73
2.13
1.38
0.82
2.56
9.74
0.49
0.59
0.79
0.91
0.56
2.36
5.71
0.31
0.42
0.49
0.42
0.51
2.10
4.25
0.88
1.39
1.68
1.77
1.78
20.76
28.26
29.64
15.61
10.62
7.24
5.15
31.74
100
Panel b. Year 2010
1
2
3
4
5
5+
All
22.01
6.17
2.92
1.46
0.92
2.29
35.78
1.78
5.02
3.00
1.85
1.24
3.66
16.55
0.47
1.18
1.62
1.47
1.08
3.74
9.56
0.26
0.44
0.55
0.78
0.66
3.38
6.07
0.12
0.19
0.29
0.38
0.34
2.18
3.49
0.44
0.78
0.98
1.13
1.31
23.90
28.55
25.07
13.79
9.36
7.07
5.55
39.16
100
18
19
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Year
1.15
1.14
1.10
1.03
1.04
1.06
1.04
1.02
1.01
0.97
0.98
Mean
1.68
1.99
0.72
0.59
0.56
0.64
2.53
2.38
5.50
2.76
2.25
1
1
1
1
1
1
1
1
1
1
1
Median
Non-traded
1.72
1.78
1.87
1.92
1.91
1.92
2.01
1.93
1.85
1.89
1.91
Median
Mean
Median
Produced
6.26
6.63
8.49
9.08
8.58
8.25
9.92
10.26
9.54
9.93
10.11
2
2
3
3
3
3
4
4
3
3
4
1.54
1.60
1.67
1.72
1.70
1.71
1.77
1.70
1.62
1.67
1.67
1
1
1
1
1
1
1
1
1
1
1
Panel a. Number of products (HS 6-digits)
Mean
Non-produced
Export
98.32
98.01
99.28
99.41
99.44
99.36
97.47
97.62
94.50
97.24
97.75
3.19
3.14
2.71
3.66
3.38
4.28
3.86
3.76
4.83
3.83
5.20
96.81
96.86
97.29
96.34
96.12
95.72
96.14
96.24
95.17
96.17
94.80
Panel b. Share in value of production, export and import
1
1
1
1
1
1
1
1
1
1
1
Median
Traded
Mean.
Production
10.66
11.23
14.14
14.35
14.81
14.85
15.98
16.72
16.14
14.56
15.95
2
3
5
5
5
5
6
6
5
4
4
1.05
1.14
1.24
1.28
1.28
1.29
1.38
1.33
1.27
1.26
1.27
79.32
80.25
82.71
81.91
80.53
81.52
83.01
83.30
82.55
85.23
82.99
0
0
0
0
0
0
0
0
0
0
0
Median
Produced
Mean
Import
Median
20.68
19.75
17.29
18.09
19.47
18.48
16.99
16.70
17.45
14.77
17.01
Mean
Non-produced
Table 5: Number of products produced and traded by firms in manufacturing sector
Table 6: Within firm distribution of export sales
2000
2010
Scope
Top 1
Top 2
Top 3
Top 4
Top 5
Top 1
Top 2
Top 3
Top 4
Top 5
1
2
3
4
5
6
7
8
9
10
10+
100
78.51
71.05
67.21
65.17
63.07
61.61
62.03
58.52
58.22
42.90
–
100
91.41
86.81
83.96
81.64
80.21
79.57
77.68
76.54
58.84
–
–
100
95.64
92.83
90.36
88.89
87.94
86.96
85.79
67.71
–
–
–
100
97.49
95.26
93.79
92.73
91.95
91.06
73.57
–
–
–
–
100
98.24
96.89
95.85
94.98
94.50
77.76
100
78.40
70.78
66.56
63.02
61.86
60.07
61.15
58.35
58.35
39.18
–
100
91.71
86.48
83.24
81.30
79.22
78.60
76.85
75.53
54.08
–
–
100
95.78
92.79
90.44
88.44
87.60
85.80
84.51
62.56
–
–
–
100
97.67
95.55
93.72
92.65
91.30
89.98
68.22
–
–
–
–
100
98.47
96.90
95.81
94.78
93.51
72.27
Mean
52.42
67.45
74.80
79.13
82.07
45.98
60.57
68.14
72.82
75.95
20
Table 7: Core product export share and gravity variables
Share of the core product
(1)
(2)
(3)
(4)
Distance
0.013∗∗∗
(34.68)
0.016∗∗∗
(27.76)
0.004∗∗∗
(8.00)
0.016∗∗∗
(27.71)
GDP
0.003∗∗∗
(16.91)
0.004∗∗∗
(17.45)
0.001∗
(2.18)
0.004∗∗∗
(17.36)
GDP per capita
-0.021∗∗∗
(-53.09)
-0.022∗∗∗
(-53.61)
-0.017∗∗∗
(-32.96)
-0.022∗∗∗
(-53.85)
# Exported products
-0.018∗∗∗
(-76.58)
-0.018∗∗∗
(-76.58)
-0.022∗∗∗
(-55.61)
-0.018∗∗∗
(-76.61)
DOwn−product
-
-
0.049∗∗∗
(52.69)
0.049∗∗∗
(52.71)
RTA
-
0.007∗∗∗
(6.06)
-
-0.001
(-1.02)
Year FE
Yes
Yes
Yes
Yes
Adj. R2
0.325
0.325
0.294
0.294
N
880,976
471,912
t statistics in parentheses, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
21
Table 8: Economy-wide decomposition of export growth
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
–
–
–
–
–
–
–
–
–
–
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Firm
Growth
3.50
3.99
-1.34
3.19
4.83
5.13
4.23
0.15
-16.90
7.42
Product
Product-destination
Intensive
Extensive
Intensive
Extensive
Intensive
Extensive
5.85
4.39
-2.77
3.54
7.95
7.56
2.85
2.75
-13.35
7.92
-2.35
-0.40
1.43
-0.35
-3.12
-2.44
1.38
-2.60
-3.55
-0.51
3.12
4.23
-1.44
3.18
4.64
5.09
5.61
0.12
-16.67
6.57
0.38
-0.23
0.10
0.01
0.18
0.04
-1.38
0.03
-0.24
0.85
3.13
3.43
-2.78
2.53
4.35
4.06
4.67
-0.80
-15.94
6.19
0.37
0.57
1.44
0.67
0.47
1.07
-0.44
0.95
-0.96
1.23
Table 9: Firm-level decomposition of export growth
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
–
–
–
–
–
–
–
–
–
–
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Firm-product
Growth
3.50
3.99
-1.34
3.19
4.83
5.13
4.23
0.15
-16.90
7.42
Firm-product-destination
Sub-intensive
Sub-extensive
Extensive
Sub-intensive
Sub-extensive
Extensive
5.20
3.33
-3.66
3.10
7.12
6.47
3.07
4.02
-14.11
5.06
0.65
1.06
0.89
0.43
0.83
1.10
-0.21
-1.27
0.76
2.86
-2.35
-0.40
1.43
-0.35
-3.12
-2.44
1.38
-2.60
-3.55
-0.51
3.49
2.29
-4.67
1.40
6.46
5.30
2.21
1.05
-12.21
3.47
2.36
2.10
1.90
2.14
1.49
2.26
0.64
1.70
-1.14
4.46
-2.35
-0.40
1.43
-0.35
-3.12
-2.44
1.38
-2.60
-3.55
-0.51
22
Table 10: Export growth decomposition: destination and product margins
Firms
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
–
–
–
–
–
–
–
–
–
–
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Destinations
Products
Growth
3.50
3.99
-1.34
3.19
4.83
5.13
4.23
0.15
-16.90
7.42
Continuing
Entering
Exiting
5.85
4.39
-2.77
3.54
7.95
7.56
2.85
2.75
-13.35
7.92
3.51
4.16
4.89
2.64
2.52
2.16
3.58
1.13
1.31
1.95
5.86
4.56
3.46
2.99
5.64
4.60
2.21
3.72
4.86
2.46
Continued Added
dest.
dest.
5.86
4.39
-2.7
3.56
7.92
7.59
2.85
2.75
-13.35
7.92
23
0.00
0.00
0.00
0.07
0.03
0.00
0.00
0.00
0.00
0.00
Dropped
dest.
0.01
0.00
0.00
0.08
0.00
0.02
0.00
0.00
0.00
0.00
Continued Added
prod.
prod.
5.54
4.67
-2.81
3.55
7.73
7.54
4.09
2.64
-13.12
7.07
0.40
4.76
0.11
0.03
0.21
0.06
5.69
0.15
0.02
1.26
Dropped
prod.
0.08
5.04
0.07
0.02
0.02
0.01
6.93
0.04
0.25
0.40
Table 11: Export growth: firm-level
2008-2009
2008-2010
2000-2010
Dep. Variable
(1)
(2)
(3)
(4)
(5)
(6)
DM ulti−product
-0.582***
(-15.12)
-0.583***
(-15.12)
-0.583***
(-21.07)
-0.581***
(-20.96)
-0.517***
(-35.58)
-0.513***
(-35.30)
log(# Destinations)
-0.104***
(-6.91)
-0.106***
(-6.54)
-0.097***
(-8.90)
-0.110***
(-9.34)
-0.094***
(-20.39)
-0.109***
(-21.12)
log(# Employees)
0.056***
(5.44)
0.058***
(5.64)
0.059***
(7.72)
0.060***
(8.00)
0.064***
(18.10)
0.066***
(18.45)
DChina
0.100***
(2.32)
-
0.093***
(2.97)
-
0.084***
(6.74)
-
DBRICS
-
0.069
(1.84)
-
0.112***
(4.12)
-
0.115***
(10.00)
Industry FE
Year FE
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Adj. R2
0.04
0.04
0.05
0.05
0.05
0.04
Obs.
10,245
20,497
t statistics in parentheses, * p < 0.05, ** p < 0.01, *** p < 0.001
24
108,016
Table 12: Export growth: firm-product-level
2008-2009
2008-2010
2000-2010
Dep. Variable
(1)
(2)
(3)
(4)
(5)
(6)
DM ulti−product
-0.192∗∗
(-3.10)
-0.191∗∗∗
(-3.09)
-0.249∗∗∗
(-5.86)
-0.248∗∗∗
(-5.84)
-0.166∗∗∗
(-11.51)
-0.166∗∗∗
(-11.51)
log(# Destinations)
-0.228∗∗∗
(-35.27)
-0.228∗∗∗
(-33.22)
-0.207∗∗∗
(-45.17)
-0.210∗∗∗
(-43.69)
-0.201∗∗∗
(-98.19)
-0.208∗∗∗
(-94.37)
log(# Employees)
-0.002
(-0.43)
-0.001
(-0.36)
-0.000
(-0.03)
0.000
(0.06)
0.013∗∗∗
(10.08)
0.013∗∗∗
(10.09)
DCore−product
-0.331∗∗∗
(-14.52)
-0.329∗∗∗
(-14.46)
-0.319∗∗∗
(19.86)
-0.318∗∗∗
(-19.84)
-0.277∗∗∗
(-48.60)
-0.277∗∗∗
(-48.72)
DChina
0.046
(1.49)
-
0.073∗∗∗
(3.48)
-
0.107∗∗∗
(10.13)
-
DBRICS
-
0.013
(0.62)
-
0.057∗∗∗
(3.79)
-
0.104∗∗∗
(14.71)
Industry FE
Year FE
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Adj. R2
0.02
0.02
0.02
0.02
0.02
0.02
Obs.
t statistics in parentheses,
76,192
∗
153,232
p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
25
698,860
16.4
2000
2002
2004
Year
2006
2008
2010
2000
2002
2004
Year
2006
2008
Number of exporting firms
Average export value (log scale)
Number of importing firms
Average import value (log scale)
2010
14.8
60,000
14.9
70,000
15
80,000
15.1
90,000
15.2
100,000
15.3
110,000
12,000
14,000
16.6
16,000
18,000
16.8
20,000
17
22,000
Figure 1: Scope and scale of exporting and importing activities
2000
2002
2004
Year
2006
2008
2010
2000
2002
2004
Year
2006
2008
Number of export transactions
Average export value (log scale)
Number of import transactions
Average import value (log scale)
Note: A transaction refers to the number of products (HS−6) exported to/imported from a destination/source country.
26
2010
Figure 2: Export share of trade partners and export growth
−6
−4
−4
−2
Export growth (log scale)
−2
0
Export growth (log scale)
0
2
2
4
2010
4
2000
−15
−10
−5
−20
0
Export share (log scale)
Actual values
−15
−10
−5
Export share (log scale)
Fitted values
27
0
600
3,000
800
3,500
1,000
4,000
1,200
4,500
1,400
1,600
5,000
Figure 3: Number of firms exporting to China and BRICS
2000
2002
2004
2006
2008
Year
China (Left scale)
BRICS (Right scale)
28
2010
Appendix: Tables
Table A.1: Economy-wide evolution of active firms in production
Producers
Entering
Exiting
Net entry
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
# Firms
# Firms
% Sh. firms
# Firms
% Sh. firms
# Firms
% Sh. firms
192,508
193,039
194,152
186,749
222,698
231,270
236,112
239,685
224,516
220,915
222,951
–
27,283
27,452
23,201
57,264
36,728
36,879
35,717
31,550
34,282
34,951
–
14.13%
14.14%
12.42%
25.71%
15.88%
15.62%
14.90%
14.05%
15.52%
15.68%
–
26,752
26,339
30,604
21,315
28,156
32,037
32,144
46,719
37,883
32,915
–
13.86%
13.57%
16.39%
9.57%
12.17%
13.57%
13.41%
20.81%
17.15%
14.76%
–
531
1,113
-7,403
35,949
8,572
4,842
3,573
-15,169
-3,601
2,036
–
0.28%
0.57%
-3.96%
16.14%
3.71%
2.05%
1.49%
-6.76%
-1.63%
0.91%
Table A.2: Economy-wide evolution of exporting firms
Exporters
Entering
Exiting
Net entry
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
# Firms
# Firms
% Sh. firms
# Firms
% Sh. firms
# Firms
% Sh. firms
12,973
13,133
13,843
14,147
13,839
13,556
13,830
13,478
13,919
12,490
12,782
–
2,714
3,064
2,862
2,558
2,510
2,804
2,441
2,273
2,245
2,530
–
20.67%
22.13%
20.23%
18.48%
18.52%
20.27%
18.11%
17.59%
17.97%
19.79%
–
2,554
2,354
2,558
2,866
2,793
2,530
2,793
2,832
2,674
2,238
–
19.45%
17.00%
18.08%
20.71%
20.60%
18.29%
20.72%
21.92%
21.41%
17.51%
–
160
710
304
-308
-283
274
-352
-559
-429
292
–
1.22%
5.13%
2.15%
-2.23%
-2.09%
1.98%
-2.61%
-4.33%
-3.43%
2.28%
29
Table A.3: Evolution of active firms in production in manufacturing sector
Producers
Entering
Exiting
Net entry
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
# Firms
# Firms
% Sh. firms
# Firms
% Sh. firms
# Firms
% Sh. firms
4,186
4,105
4,049
3,956
3,795
3,667
3,580
3,340
3,514
3,331
3,127
–
368
328
297
258
238
229
284
383
228
196
–
8.96%
8.10%
7.51%
6.80%
6.49%
6.40%
8.50%
10.90%
6.84%
6.27%
–
449
384
390
419
366
316
524
209
411
400
–
10.94%
9.48%
9.86%
11.04%
9.98%
8.83%
15.69%
5.95%
12.34%
12.79%
–
-81
-56
-93
-161
-128
-87
-240
174
-183
-204
–
-1.97%
-1.38%
-2.35%
-4.24%
-3.49%
-2.43%
-7.19%
4.951%
-5.49%
-6.52%
Table A.4: Evolution of exporting firms in manufacturing sector
Exporters
Entering
Exiting
Net entry
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
# Firms
# Firms
% Sh. firms
# Firms
% Sh. firms
# Firms
% Sh. firms
3,014
2,986
3,042
2,989
2,847
2,737
2,738
2,498
2,569
2,494
2,404
–
363
393
316
252
243
283
244
304
224
237
–
12.16%
12.92%
10.57%
8.85%
8.88%
10.34%
9.77%
11.83%
8.98%
9.86%
–
391
337
369
394
353
282
484
233
299
327
–
13.09%
11.08%
12.35%
13.84%
12.90%
10.30%
19.38%
9.07%
11.99%
13.60%
–
-28
56
-53
-142
-110
1
-240
71
-75
-90
–
-0.94%
1.84%
-1.77%
-4.99%
-4.02%
0.04%
-9.61%
2.76%
-3.01%
-3.74%
30
Table A.5: Economy-wide evolution of importing firms
Importers
Entering
Exiting
Net entry
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
# Firms
# Firms
% Sh. Firms
# Firms
% Sh. firms
# Firms
% Sh. firms
15,205
15,153
17,333
17,996
18,962
19,346
19,766
20,481
20,090
18,330
18,886
–
2,987
4,608
4,117
4,513
4,476
4,288
4,662
4,270
3,582
4,515
–
19.71%
26.59%
22.88%
23.80%
23.14%
21.69%
22.76%
21.25%
19.54%
23.91%
–
3,039
2,428
3,454
3,547
4,092
3,868
3,947
4,661
5,342
3,959
–
20.06%
14.01%
19.19%
18.71%
21.15%
19.57%
19.27%
23.20%
29.14%
20.96%
–
-52
2,180
663
966
384
420
715
-391
-1,760
556
–
-0.34%
12.58%
3.68%
5.09%
1.98%
2.12%
3.49%
-1.95%
-9.60%
2.94%
Table A.6: Evolution of importing firms in manufacturing sector
Importers
Entering
Exiting
Net entry
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
# Firms
# Firms
% Sh. firms
# Firms
% Sh. firms
# Firms
% Sh. firms
2,766
2,768
2,922
2,873
2,756
2,647
2,634
2,457
2,548
2,376
2,243
–
383
468
328
286
271
275
293
326
211
233
–
13.84%
16.02%
11.42%
10.38%
10.24%
10.44%
11.93%
12.79%
8.88%
10.39%
–
381
314
377
403
380
288
470
235
383
366
–
13.76%
10.75%
13.12%
14.62%
14.36%
10.93%
19.13%
9.22%
16.12%
16.32%
–
2
154
-49
-117
-109
-13
-177
91
-172
-133
–
0.07%
5.27%
-1.71%
-4.25%
-4.12%
-0.49%
-7.20%
3.57%
-7.24%
-5.93%
31
32
534
1,059
407
2,571
***
903
47
186
722
305
281
194
2,103
8,472
276
Electrical & optical equipment
Food, beverage & tobacco products
Furniture
Health & social work
Leather & leather products
Machinery & equipment
Mining & quarrying
Non-metallic mineral products
Paper, publishing & printing
Rubber & plastic products
Textiles & wearing apparel
Transport equipment
Transport, storage & communication
Wholesale & retail trade
Wood & wood products
20.2%
4.30%
47.5%
36.1%
10.7%
62.4%
79.0%
77.0%
36.7%
53.2%
51.1%
71.3%
94.1%
0.58%
68.8%
22.7%
71.7%
3.14%
87.2%
43.3%
8.51%
% Exporters
2000
33,775
11,670
192
7,692
2,933
120
137
267
292
172
47
669
***
2,221
221
914
358
4,066
125
977
697
# Firms
20.1%
5.25%
53.1%
37.4%
13.6%
78.3%
82.5%
85.0%
62.3%
51.2%
57.4%
84.3%
100%
0.27%
73.8%
27.5%
83.2%
3.10%
92.8%
48.4%
10.2%
% Exporters
2008
Note: *** indicates the number is intentionally unreported to keep the identity of firms anonymous.
36,116
4,205
Construction
Total
148
Chemicals & chemical products
11,499
1,129
Basic & fabricated metals
Others
1,058
# Firms
Agriculture, forestry & fishing
Industry
32,091
11,822
167
7,105
2,699
100
113
233
262
144
41
612
***
2,516
197
855
319
3,328
122
812
638
# Firms
19.6%
4.79%
51.5%
37.7%
13.4%
82.0%
87.6%
89.3%
63.4%
56.9%
61.0%
86.3%
83.3%
0.28%
75.6%
28.3%
85.6%
3.46%
91.8%
53.1%
10.0%
% Exporters
2009
Table A.7: Industry-wise export participation rate of firms with at least ten employees
31,007
11,653
142
6,926
2,654
94
112
223
234
132
37
599
***
2,301
175
820
312
3,079
113
770
628
# Firms
20.2%
5.19%
54.9%
38.7%
14.1%
81.9%
84.8%
90.6%
66.2%
58.3%
59.5%
87.1%
100%
0.48%
78.9%
29.6%
87.5%
3.38%
92.0%
56.5%
11.0%
% Exporters
2010
33
Chapter 2
A Dynamic Model of Firm Activities:
Evidence from Danish Manufacturing
34
A Dynamic Model of Firm Activities: Evidence from Danish
Manufacturing∗
Kaleb Girma Abreha†
Abstract
In this paper, I structurally estimate a dynamic discrete choice model of exporting and
importing. The model provides a framework to analyze the determinants of a firm’s decision
to export and import while allowing for its current decision to affect its future productivity
trajectory. Considering a panel of Danish manufacturing firms over the period 2000-2007, a
simple description of the data reveals considerable firm heterogeneity, significant export and
import activity premia, frequent incidence of simultaneous exporting and importing and high
persistence in the scope of firm trading activities. The parameter estimates of the model show
a marked difference in the demand elasticities in which export markets are characterized by
more elastic demand, tougher competition and lower markup than the domestic market. The
estimates also show that firms with larger capital holding and paying higher wages are costefficient even after controlling for their productivity. In line with the self-selection hypothesis,
I find substantial sunk and fixed costs of exporting and importing. I also find a positive
correlation between the size of these costs and the scale of firm operation. In addition,
exporting and more importantly importing improve firm productivity, and therefore these
learning effects further drive the selection of firms into trading activities.
JEL Codes: F14, L60
Keyword: Firm heterogeneity, exporting, importing, dynamic discrete choice model, MCMC
∗
I am grateful to Valérié Smeets, Frédéric Warzynski, Mark Roberts, Chad Syverson, Philipp Schröder, Federico
Clementi and participants at the Danish International Economics Workshop 2014, IAB-Aarhus Workshop 2014,
Aarhus-Kiel Wokshop 2013, DGPE Workshop 2013 and Tuborg Research Centre Seminars for insightful comments
and discussions. I acknowledge the Tuborg Foundation for financial support. The usual disclaimer applies.
†
Department of Economics and Business, Aarhus University, Denmark, E-mail: [email protected]
35
1
Introduction
Inspired by the pioneering work of Bernard and Jensen (1995), numerous studies on firms in
international trade have documented strong evidence of firm heterogeneity even in narrowly defined
industries. The findings of these studies show that only a small fraction of firms export, and these
firms are larger, more productive, more skill- and capital-intensive, and pay higher wages compared
to non-exporting firms. Further, increased availability of microdata sets has triggered studies
to explore the product and destination dimensions of firm exporting activities in an attempt to
explain the highly uneven distribution of export sales not only across firms but also across products
within the product portfolio of exporting firms. These explorations have led to the development
of new models of international production where the export scope is endogenous and firms have
core competency only in some of their products (see e.g. Eckel and Neary 2010; Arkolakis and
Muendler 2010; Bernard et al. 2011; Mayer et al. 2014).
At the same time, studies have started looking into possible explanations for the observed performance difference between exporting and non-exporting firms. Some studies point out the role
of market entry costs where firms are required to make substantial and irreversible investments
in export markets, and therefore it is only the productive firms which succeed in becoming exporters. Other studies resort to post-entry productivity improvements due to exposure to foreign
competition and technology associated with exporting.1
It is also a possibility that part of the observed performance differential is incorrectly attributed
to exporting. In this regard, Bernard et al. (2007) uncover that observed differences in performance
between exporting and non-exporting firms are partly driven by firms that simultaneously export
and import in US manufacturing. Similarly, Altomonte and Békés (2009) for Hungary and Vogel
and Wagner (2010) for Germany document that failure to control for import status of firms results
in the overestimation of the export premia.
On the other hand, the rapid growth rate of world trade in response to a small reduction in
tariff rates has instigated research on trade in intermediate inputs which are used in the production
of goods for export; vertical specialization.2 In this respect, Hummels et al. (2001) illustrate that
this trade accounted for 21% of the exports and experienced a growth rate of 30% between 1970
and 1990 for a group of OECD and emerging economies. This central role of vertical specialization
led to the development of new models of international fragmentation of production (see e.g. Antràs
2003; Antràs and Helpman 2004; Grossman and Rossi-Hansberg 2008; Yi 2003).
Having shown the role of vertical specialization in global trade and the importance of accounting
for simultaneous exporting and importing within firms, this paper structurally estimates a dynamic
discrete choice model of exporting and importing in which firms are heterogeneous in terms of
1
Bernard et al. (2012), Hayakawa et al. (2012), Redding (2010), Wagner (2007) and Wagner (2012) provide a
survey of notable theoretical and empirical contributions in this literature.
2
Yi (2003) argues that the elasticity of world trade to reductions in tariff rates is too high compared to the
predictions from standard trade models.
36
size (capital holding), factor price payment (wages) and productivity. With this formulation, it is
possible to characterize the intensity of competition (in terms of demand elasticity) and the reaction
of firms (in terms of markups) to different intensities of market competition in domestic and export
markets. It is also possible to test for the self–selection and learning–by–doing hypotheses, and
identify the mechanisms (such as market entry costs) which possibly explain the export and import
productivity premia.
In the context of the existing literature, the main contribution of this paper comes from using
an integrated approach that combines the role of market entry costs and learning opportunities
from trade participation to shed light on the productivity premia and ranking observed in the data.
Unlike most previous studies which only indicate the presence of markets costs of exporting and
importing, this study first examines their presence and then proceeds to estimate their magnitude.
It also examines the existence of any form of complementarity between exporting and importing
activities and the resulting impact on firm productivity and trade participation over time.
Considering a panel of Danish manufacturing firms over the period 2000-2007, a simple description of the data uncovers that most firms have active trade participation mainly through exporting.
Also, these trading firms outperform their non-trading counterparts in a variety of performance
measures. Further, there is a clear performance ranking in which two-way trading firms are ranked
first followed by import-only and export-only firms, respectively, in line with recent findings in the
literature (e.g. Altomonte and Békés 2009; Castellani et al. 2010; Muûls and Pisu 2009).3
Turning to the estimation results, the demand elasticities reveal that firms face intense competition in export markets, and they respond to this intense competition by charging a lower markup.
On the cost side, the parameter estimates show that firms with larger capital holding and paying
higher wages to their employees are more efficient even after controlling for their productivity.
In support of the self-selection hypothesis, the estimates show that exporting and importing
involve significant sunk and fixed costs which are, on average, greater for importing. These estimates also show that the size of market costs are positively correlated with the scale of firm
operations in both export and import markets. Additionally, the non-negligible magnitude of
these markets costs is manifested in terms of high state dependence in the scope of firm trading
activities, which is consistent with a series of studies that establish the presence of sizable market
entry costs (e.g. Bernard and Jensen 2004; Muûls and Pisu 2009; Roberts and Tybout 1997).
Further, the difference in sunk and fixed costs in export and import markets partly explains the
prevalence of exporting vis-à-vis other activities in the Danish manufacturing in that high sunk
and fixed costs deter firms from starting or continuing importing. This difference also partially
explains the productivity ranking of firms in which the import premia is the largest.
Parameter estimates of the productivity evolution equation provide support to learning-bydoing in exporting and importing, which is especially greater in the case of importing. These
3
Vogel and Wagner (2010) find similar results except that export-only firms are more productive than importonly firms.
37
learning effects further reinforce the selection of firms into exporting and importing. This result
on the complementarity between the learning effect and the selection process is consistent with
findings of studies which examine the impact of trade in intermediate inputs on the export behavior
of firms. For instance, Bas (2012), for Argentina, shows that input-tariff liberalization is associated
with increased likelihood of entry into exporting. Similarly, Aristei et al. (2013), for Eastern
European and Central Asian countries, find that exporting activity does not improve the chances
of sourcing inputs from abroad. But, importing has a positive, significant effect on export sales
mainly through its impact on productivity and product innovation. Relatedly, Bas and StraussKahn (2014), using French data, illustrate that increased access to imported inputs raises export
sales and scope by enhancing firm productivity through accessibility of better technology and
availability of cheaper production inputs. Also, Kasahara and Lapham (2013) demonstrate that
import protection may have a detrimental impact on exporting capability in the case of Chilean
manufacturing.
This paper is related to several studies that aimed at investigating the relationship between
exporting and importing within the framework of firm heterogeneity. The closest study to this
paper is Kasahara and Lapham (2013) who investigate the export and import decisions of Chilean
firms in a Melitz (2003) setting. Unlike their study, which allows for productivity gain only
from increasing returns in varieties of intermediate inputs, this paper considers an endogenous
evolution of firm productivity due to both exporting and importing. This paper also prominently
focuses on the dynamic productivity effects of exporting and importing. Further, it explores
additional dimensions of heterogeneity in the form of size and wage payments besides productivity.
Methodologically, this study follows a recent contribution by Aw et al. (2011) in which export and
R&D investment decisions are jointly examined. This paper is also related to Das et al. (2007) in
its estimation of sunk and fixed costs using a Bayesian approach.
The reminder of the paper is organized as follows. Section 2 describes the data source and
the construction of the variables used in the empirical analysis. Section 3 summarizes a set of
facts on exporting and importing activities. Section 4 develops a dynamic discrete choice model of
exporting and importing. Section 5 presents the empirical strategy. Section 6 reports and discusses
the estimation results. Section 7 concludes.
2
Data description
The data used in this paper are provided by Statistics Denmark. The scope of the study is confined
to the time period 2000-2007 and firms in the manufacturing sector, which covers industries 10-33
at the 2-digit according to NACE Rev.2 classification. I combine three datasets for the purpose at
hand. The first dataset contains detailed information about the production portfolio of firms and
describes which products firms make domestically. It covers all firms in the manufacturing sector
with at least 10 employees. After constructing firm-level production data, the dataset comprises
38
39,515 firm-year observations. The second dataset has accounting information about more than
160,000 firms annually and contains 1,322,736 firm-year observations. The third dataset provides
records of export and import transactions for the universe of firms in the economy. This dataset
contains information on the value, weight and quantity of export and import transactions for each
firm and destination/source market at the product level. After aggregation at the firm level, there
are 120,761 and 164,106 firm-year observations in the export and import datasets, respectively.
In the construction of the export and import variables, there are a few data issues. For exporting, the pervasiveness of carry-along trade may be a source of concern given the underlying
assumptions in standard trade models where firms export products they actually produce. Despite its prevalence, carry-along trade as a source of export sales is negligibly small in the Danish
manufacturing (see Abreha et al. 2014). In the case of importing, I do not distinguish between
trade in primary, consumer, intermediate and capital goods. Here the data concern comes from the
fact that the focus of this study primarily is on imports of capital and intermediate goods which
are used in production. However, this data concern is not problematic as it seems given that
international trade is exceedingly dominated by intermediate goods. According to the UNCTAD
(2014) report, capital and intermediate goods constitute the most important flow of world trade
especially for developed countries. Further, the focus on manufacturing sector and exclusion of
firms primarily engaged in wholesale and retail activities mitigate the above data concerns.
In order to make sure that firms with real economic activities are included, only those with
a reported level of physical production in Denmark are considered. I delimit the time period to
2000-2007 mainly because of a series of revisions of the industry classification that pose a challenge
on tracing firms and their core economic activities over time.4 Additionally, the years after 2007
are characterized by exceptionally large economic shocks which may unduly affect the outcomes of
the empirical analysis. I also abstract from modeling entry and exit decisions in the local market.
As a result, the final dataset comprises a balanced panel of 2,106 firms and 16,848 firm-year
observations.
3
Basic facts in the Danish data
This section summarizes salient features of firms in export and import markets. These summaries
corroborate previous findings on the behavior of firms in international trade and provide a rationale
for a joint consideration of exporting and importing activities.
4
Bernard et al. (2014) raise the issue of industry classification as it pertains to the Danish data and point out
that the revisions in 1993 and 2007 were major whereas the 2003 one was minor.
39
3.1
Firm heterogeneity
Table 1 reports the actual asset holdings of firms, their wage payment and productivity.5 The fact
that there is a big difference in the capital holdings of the average and the median firm reveals the
coexistence of a few large firms together with a significant number of small firms. This feature is
further illustrated by the sizable standard deviations in each time period.
In addition, firms vary in wage payment to their workers as evidenced by a non-negligible
difference in the average and the median wages and moderate standard deviations. However,
the wage differentials across firms are relatively small possibly due to labor mobility within and
across industries and sectors. It is no surprise that firms also exhibit productivity heterogeneity
particularly shown by large standard deviations as compared to the mean and the median values.
Over the years, we see that firms have become larger in their capital holding, raised their wage
payment and become more productive. Also, we notice that these dimensions of heterogeneities
have all increased.
Relatedly, Table 2 provides the size distribution of firms with different forms of trade participation. Size is defined by real capital holdings of a firm. Large firms are those with fixed assets
above the median capital holding in a given year, and the converse holds for small firms. We see
that small firms constitute a significant proportion of non-trading firms. On average, this group
comprises 75% of the firms that exclusively delimit their production activity to the domestic market. This disproportionate representation of small firms is consistent with findings which document
that larger firms are more likely to be trading. The size distribution also exhibits a discernible
pattern among trading firms, albeit less uneven. On balance, large firms constitute around 58%
of those engaged in exporting and importing over the entire sample period. We also observe a
slightly higher prevalence of large firms in importing than in exporting.
3.2
Incidence of exporting and importing
To examine the distribution of firms based on their scope of trading activities, I divide firms into
four mutually exclusive categories—domestic, export-only, import-only and two-way. Domestic
firms entirely restrict their activities to the local market. In contrast, export-only and import-only
firms extend their operation besides the local market where the former serve export markets and
the latter source inputs from the world market. There are also two-way trading firms which are
exporting and importing besides their operation in the local market.
Table 3 shows that exporting is the most common activity where around 74% of the firms
export. Despite the relative rarity of importing (70%), it is shown that most of the exporting
firms also import.6 We observe the high incidence of simultaneous exporting and importing in
5
Wages are defined at the firm level and refer to the total labor payments per employee in a given time period.
These high rates of trade participation demonstrate an open manufacturing sector, which is typical of a small
open economy. See Eriksson et al. (2009) for a comparison of trade participation of firms in Denmark vis-à-vis the
US. In another note, Bernard et al. (2007) also find that importing is less common in the US manufacturing sector.
6
40
which 63% of the firms engage in a two-way trading. Further, the sector has experienced increased
integration to the global economy as evident from the declining share of domestic firms, albeit
constituting the smallest group, and the rising share of exporting and importing firms. These
patterns are suggestive of strong complementarity between exporting and importing activities.
3.3
Export and import activity premia
To compare the performance of firms with a different scope of trade participation, I estimate the
trading activity premia from the regression:
yi,t = β0 + β1 Expi,t + β2 Impi,t + β3 Bothi,t + Controlsi,t + δt + τ + i,t
where yi,t is in logarithmic scale and refers to value added, sales, capital, material, energy, wage,
and employment size. Expi,t , Impi,t and Bothi,t are dummy variables taking a value of 1 if a firm
is export-only, import-only or two-way, respectively. Controlsi,t includes year and industry effects
and employment size (except for the last performance indicator). And, i,t is an iid error term.
Table 4 reports percentage differences in a given performance indicator of export-only, importonly and two-way trading firms relative to the domestic ones. We see that exporting and importing
are associated with significant premia. The estimates illustrate that trading firms are more productive, have greater sales per worker, employ more capital, material and energy per worker, and
hire more employees than non-trading firms.
The estimates reveal a systematic variation in the size of the premia among trading firms. The
premia estimates are systematically larger in size for two-way firms. Further, the import premia
are larger among firms which are exporting or importing but not both. From the premia estimates,
there are two results worth noting. First, we see that production operations of import-only firms
are not necessarily more capital-intensive compared to domestic firms. Second, the wage premia
is the smallest vis-à-vis the other premia estimates highlighting the crucial role of labor mobility
to yield comparability of labor compensation across firms.
To further establish the above performance ranking of firms with a different scope of trade
participation, I run the Kolmogorov-Smirnov test for first order stochastic dominance of the productivity distributions.7 As pointed out by Arnold and Hussinger (2010), the use of panel data may
violate the independence assumption necessary for the test. Consequently, the test is implemented
for separate time periods. Table 5 shows that the two-tailed tests reject the null hypothesis that
the productivity distributions of export-only, import-only and two-way firms are identical. On the
other hand, the one-tailed test reveals that the productivity distribution of two-way trading firms
stochastically dominates that of export-only and import-only firms implying that they are the
most productive. Similarly, the productivity distribution of import-only firms is dominant which
indicates that they are more productive than export-only firms.
7
See Delgado et al. (2002) for the details of the test procedure.
41
3.4
Persistence of exporting and importing
Table 6 presents the transition probabilities of firms changing the scope of their trade participation
over time. There is a strong persistence in the trading status of firms as shown by a high probability
of firms maintaining their current activity status. These probabilities are 80.55%, 55.03%, 57.76%
and 93.82% for domestic, import-only, export-only and two-way trading firms, respectively. We
also see that the probability of retaining current status is considerably high for domestic firms
indicating the difficulty of penetrating into export and import markets. It also indicates the limited
capacity of domestic firms to extend their operation beyond the local market. In contrast, firms
partially engaged in trading are more likely to add exporting (23.46%) or importing (26.66%) as
an additional activity compared to firms engaged in neither of these activities; 11.37% and 10.47%
to start exporting and importing, respectively. Relatedly, two-way firms are less likely to give
up exporting (2.82%) and importing (3.82%) compared to export-only (15.58%) and import-only
(21.51%) firms. The average of a cross-sectional distribution of trade participation shows that
around 80% of the firms are globally active via exporting or importing highlighting a high firm
trade participation in the sector.
The above empirical regularities uncover that it is crucial to accommodate additional dimensions of heterogeneity in size and wage payment besides the commonly known productivity. In
addition, exporting and importing activities are highly interrelated which underscores the usefulness of their joint examination for a better understanding of the behavior of firms in international
trade. In the next section, I describe a behavioral framework of firm profit maximization and
market entry decision which lays the foundation and provides intuition to the econometric model
in the empirical section.
4
A theoretical framework
The behavioral model of firm entry and exit into exporting and importing involves combining the
demand and the supply components which define consumer preference, costs, revenues, profits and
evolution of state variables. The theoretical framework considered here is similar to several models
of market entry and exit in the literature.8 In this paper, I closely follow the modeling approach
by Aw et al. (2011) to examine a firm’s choice problem of exporting and importing.
Suppose a production technology of firm i at time period t is represented by its short-run
marginal cost function:
ci,t = β0 + βk ki,t + βw wi,t + βx xt − ωi,t
(1)
where ki,t refers to capital holding, wi,t firm-specific wage which varies over time, xt vector of
variable input prices common to all firms in the market and ωi,t firm productivity, which is observed
8
Models of some of the key contributions in the literature include: Aw et al. (2011), Bernard and Jensen (2004),
Clerides et al. (1998), Das et al. (2007) and Roberts and Tybout (1997).
42
only by the firm. Notationally, unless stated otherwise, lower case letters refer to variables in
logarithmic scale. Under this formulation of the marginal cost function, firms have inherently
different cost structures attributable to their capital holding, wages and productivity. We see that
marginal cost is independent of output level which implies that shocks affecting output decision
in the domestic market does not have any effect on a similar decision in export markets, and vice
versa.
As common in the standard trade literature, I consider consumers with CES preferences. The
P d −ξd
Itd
d −ξd
d
and
= Φdt Pi,t
demand a firm faces takes a Dixit-Stigliz form given by Qi,t = P d Pi,td
t
t
−ξ
x
x
P
Ix
x −ξx zi,t
Qxi,t = Ptx Pi,tx
ezi,t = Φxt Pi,t
e in the domestic and export markets, respectively. In
t
t
both demand functions It refers to market size, Pt aggregate price index, Pi,t firm price index and
ξd , ξx > 1 demand elasticities in the respective markets.
Export demand is defined slightly differently in that an exporting firm faces a shock zi,t only
inherent in export markets. zi,t can be considered broadly to accommodate factors affecting demand
in export markets such as product attributes in addition to those included in the marginal cost
function. zi,t is assumed to be firm-specific, time-variant and known to the firm but unobservable
to the econometrician.
The respective domestic and export revenue and profit functions are given by
ξd
+ lnΦdt + (1 − ξd ) (β0 + βk ki,t + βw wi,t + βx xt − ωi,t )
= (1 − ξd ) ln
ξd − 1
ξx
= (1 − ξx ) ln
+ lnΦxt + (1 − ξx ) (β0 + βk ki,t + βw wi,t + βx xt − ωi,t ) + zi,t
ξx − 1
d
ri,t
x
ri,t
1 d
d
πi,t
Φdt , ki,t , ωi,t
Φdt , ki,t , ωi,t = Ri,t
ξd
1
x
x
(Φxt , ki,t , ωi,t , zi,t ) = Ri,t
(Φxt , ki,t , ωi,t , zi,t )
πi,t
ξx
(2)
(3)
In terms of market entry and exit, I abstract from entry and exit in the domestic market. It
is implicitly assumed that firms do not export or import without any production for the local
market. Under this setting, a firm makes entry and exit decisions with respect to exporting and
importing after comparing market entry costs with expected discounted payoffs associated with
each decision.
These market entry costs are sunk and fixed in their nature. It is now established that exporters
must acquire information about foreign markets, hire managers to look after overseas operations,
and undertake complementary investments in processing, marketing, distribution and related activities. As in the case of exporting, importers incur costs related to information acquisition about
foreign suppliers, controlling the quality of imported inputs and making import-related investment
activities such as storage. The characteristics of these costs indicate a high degree of similarity
on what constitutes sunk and fixed costs in the export and import markets. In this respect, al43
though exporting is a decision about final goods and importing about production inputs, it is not
problematic to consider these decisions as symmetrically in the model.
Given the dynamic nature of the choice problem, it is necessary to be explicit about the timing
assumptions of the model. A firm observes its state variables, maximizes static profits in domestic
and export markets, draws its sunk and fixed costs of exporting from a known cost distribution
and decides whether or not to export. Afterwards, the firm observes the market costs of importing
from its sunk and fixed cost distribution and chooses whether or not to source inputs from abroad.
More specifically, at the beginning of each time period a firm observes its state vector si,t ≡
ki,t , ωi,t , ei,t−1 , mi,t−1 , Φdt , Φxt , zi,t . I assume that current investment ii,t becomes productive in the
next period, and capital is deterministic and evolves according to ki,t = (1 − δ)ki,t−1 + ii,t−1 , where
δ is the rate of depreciation. Productivity follows a controlled Markov process which is given by:
ωi,t = E [ωi,t | ωi,t−1 , ei,t−1 , mi,t−1 ] + ζi,t
(4)
= α0 + α1 ωi,t−1 + α2 (ωi,t−1 )2 + α3 (ωi,t−1 )3 + α4 ei,t−1 + α5 mi,t−1 + α6 ei,t−1 mi,t−1 + ζi,t
where ζi,t is an error term assumed to possess a normal distribution N 0, σζ2 . By definition, ζi,t
is uncorrelated with ωi,t−1 , ei,t−1 and mi,t−1 . In this specification, we see that firm productivity
depends on its prior participation in export and import markets. The economic motivation for this
is that both exporting and importing represent some form of exposure to foreign technology which
can potentially improve a firm’s productivity, although exports represent outlets of final goods and
imports inputs of production. Specifically, there may be gains from exporting which comes in the
form of knowledge transfers from international buyers about product designs, quality standards,
production techniques and management practices. It can also result from intense competition in
the international market where firms can only survive by removing their existing inefficiencies. In
the case of importing, similar channels of knowledge transfer from foreign suppliers and competition
in input markets can be considered. Consequently, in the specification exporting and importing
are treated symmetrically.
Further, the specified productivity equation only captures dynamic effects of exporting and
importing. All static effects–which are associated with immediate effects on output of imported
inputs due to more variety and/or better quality of intermediates—are ruled out. Here, I rather
focus on whether or not firms benefit from their trade participation in the form of productivity
improvement even if they are not currently taking part in exporting, importing or both.9 This
formulation of firm productivity is different from Halpern et al. (2009) who separately estimate the
variety and quality effects of importing and Kasahara and Lapham (2013) which focus only on the
variety effect of importing. These effects are conditional on imperfect substitutability between local
and imported varieties and whether or not firms have used more varieties and/or better quality
intermediates, say, following trade liberalization as in Amiti and Konings (2007) and Goldberg et
9
A similar specification to this paper is also used in Zhang (2015).
44
al. (2010). Another feature of such models is the absence of any productivity gain when the firm
stops importing.
The state vector also contains a firm’s previous period export and import participation ei,t−1 and
mi,t−1 . It also includes the aggregate variables Φt ≡ (Φdt , Φxt ) which follow an exogenous first order
Markov process. The aggregate variable related to the domestic market Φdt is captured by time
dummy variables in the estimation of the revenue function and computation of firm productivity.
On the other hand, the export market size indicator Φxt is estimated as an element of the parameter
vector that summarizes the dynamic aspect of the entry and exit decisions. The export market
shock follows an AR(1) process
zi,t = ρzi,t−1 + νi,t ; νi,t ∼ N (0, σν2 )
(5)
where ρ is the autocorrelation coefficient, and νi,t is a noise term.
The value function of a firm with a state vector si,t choosing whether to export and import is
given by
Z n
o
xf
d
x
xs
E
D
V (si,t ) =
πi,t + max πi,t − ei,t−1 γi,t − (1 − ei,t−1 )γi,t + Vi,t (si,t ) , Vi,t (si,t )
dFγ (6)
ei,t
x
−
The firm decides whether to export after comparing the expected return from exporting πi,t
xf
xs
E
D
ei,t−1 γi,t − (1 − ei,t−1 )γi,t + Vi,t (si,t ) with the option value of not exporting Vi,t (si,t ). The sunk γ xs
and fixed γ xf costs are firm-specific and time-variant, and they are drawn from the cost distribution
dFγ (.). Assuming a fixed discount factor λ, the option value of exporting is
E
Vi,t
Z
(si,t ) =
−
n
max λEt Vi,t+1 (si,t+1 |ei,t =1, mi,t =1)
mi,t
mf
mi,t−1 γi,t
− (1 −
ms
mi,t−1 )γi,t
, λEt Vi,t+1
(7)
o
(si,t+1 |ei,t =1, mi,t =0) dFγ
and that of non-exporting
D
Vi,t
Z
(si,t ) =
−
n
max λEt Vi,t+1 (si,t+1 |ei,t =0, mi,t =1)
mi,t
mf
mi,t−1 γi,t
− (1 −
ms
, λEt Vi,t+1
mi,t−1 )γi,t
(8)
o
(si,t+1 |ei,t =0, mi,t =0) dFγ
We see that both option values involve a choice problem of whether to import which requires
integrating each of the value functions over the import cost distributions. Here the expected value,
which is integrated over the state vector, is given by
Z Z Z
0
0
0 Et Vi,t+1 (si,t+1 |ei,t , mi,t ) =
Vi,t+1 (si,t+1 ) dFω ω |ωi,t , ei,t , mi,t dFz z |zi,t dFΦ Φ |Φt
Φ0
z0
ω0
where dFω (.), dFz (.) and dFΦ (.) represent transition densities of the stochastic state variables.
To summarize, the parameter vector comprises demand parameters characterizing consumer
behavior in the domestic and export markets ξ = (ξd , ξx ), marginal cost parameters β = (βk , βw ),
45
productivity transition parameters α = (α0 , α1 , α2 , α3 , α4 , α5 , α6 , σζ ), and parameters defining the
dynamic aspect of the choice problem θ = (γ xs , γ xf , γ ms , γ mf , δx , ρ, σν ). δx approximates the export
market size Φxt and captures the constant and the time dummy variables in the econometric
specification of export revenue in equation (2).10
5
Estimation procedure
In this section, I present the empirical strategy to estimate the parameter vector and describe the
steps followed in some detail.
Demand, marginal cost and productivity parameters
Domestic and export market demand elasticities are estimated by exploiting a well defined relationship between total variable costs and domestic and export revenue functions. Under CES demand
preferences and from the profit maximization condition, total variable costs can be expressed as
the elasticity weighted sum of revenue from domestic and foreign sales as
T V Ci,t =
d
Ri,t
1
1−
ξd
+
x
Ri,t
1
1−
ξx
+ εi,t
(9)
where εi,t is appended as an iid error term capturing measurement errors in variable cost and
revenue. I estimate equation (9) by ordinary least squares and easily recover the domestic and
foreign demand elasticities.
As in Aw et al. (2011), I use the domestic revenue function, which is available for all firms
in the sample, to jointly estimate the marginal cost and productivity parameters. An iid error
term νi,t is added to equation (2) to account for measurement errors in revenue from the domestic
market. This yields the following estimating equation
d
ri,t
= (1 − ξd ) ln
ξd
ξd − 1
+ lnΦdt + (1 − ξd ) (β0 + βk ki,t + βw wi,t + βx xt − ωi,t ) + νi,t
(10)
As −(1 − ξd )ωi,t + νi,t is unknown, I use a semi-parametric approach by Levinsohn and Petrin
(2003) to back out the unobserved firm productivity ωi,t . For this purpose, I use electricity usage
eli,t as a proxy variable that contains information about productivity; eli,t = ft (ki,t , ωi,t ). Electricity
consumption is highly and instantly (that is, it is almost costless to adjust) responsive to shocks
in capital and productivity, and therefore ft (.) can be viewed as monotonically increasing in ki,t
and ωi,t . This monotonicity property allows inversion of f (.) and expression of ωi,t in terms of the
observable variables ki,t and eli,t : ωi,t = ft−1 (ki,t , eli,t ) = gt (ki,t , eli,t ). Under the proxy variable eli,t ,
10
Estimation of δx allows a counterfactual analysis of a policy change which affects the size of export markets
as shown in subsection 6.5.
46
equation (10) becomes:
d
ri,t
= δ0 +
X
δt Dt + (1 − ξd ) βw wit + gt (ki,t , eli,t ) + νi,t
(11)
t
d
) + β0 ), Dt captures time-varying domestic market aggregate
where δ0 denotes (1 − ξd )(ln( ξdξ−1
variable lnΦdt and common market-level factor prices (1 − ξd )βx xt , and gt (.) represents a non-linear
relationship between capital, productivity and domestic revenue.
Assuming gt (.) is a cubic function in ki,t and eli,t , I estimate equation (11) by ordinary least
squares with industry fixed effects. From this estimation, the wage parameter βw , and the fitted
values φ̂i,t = (1 − ξd )(βk ki,t − ωi,t ) are obtained. These fitted values are used to express a firm’s
productivity series as: ωi,t = ξd1−1 φ̂i,t + βk ki,t .
Substituting φ̂i,t into the productivity transition function in equation (4) and rearranging the
terms yields:
2
φ̂i,t = β̃k ki,t − α̃0 + α̃1 φ̂i,t−1 − β̃k ki,t−1 − α̃2 φ̂i,t−1 − β̃k ki,t−1
3
+ α̃3 φ̂i,t−1 − β̃k ki,t−1 − α̃4 ei,t−1 − α̃5 mi,t−1 − α̃6 ei,t−1 mi,t−1 − ζ̃i,t
(12)
where β̃k = (1 − ξd )βk , α̃0 = (1 − ξd )α0 , α̃1 = α1 , α̃2 = (1 − ξd )−1 α2 , α̃3 = (1 − ξd )−2 α3 ,
α̃4 = (1 − ξd )α4 , α̃5 = (1 − ξd )α5 , α̃6 = (1 − ξd )α6 and ζ̃it = (1 − ξd )ζi,t . Applying nonlinear
least squares to equation (12) provides estimates of the cost coefficient βk and the productivity
parameter vector α. Afterwards, the fitted values of a firm’s productivity are constructed as
follows: ω̂i,t = ξ̂ 1−1 φ̂i,t + β̂k ki,t where the “hat” denotes an estimated value.
d
Market cost and export revenue parameters
Over the entire time period, for firm i define the state vector sTi,0 = (si,0 , si,1 , ..., si,T ) and the
x,T
x,T
T
) where eTi,0 = (ei,0 , ei,1 , ..., ei,T ), mTi,0 = (mi,0 , mi,1 , ..., mi,T ) and Ri,0
=
data Di,0
= (eTi,0 , mTi,0 , Ri,0
x
x
x
(Ri,0
, Ri,1
, ..., Ri,T
) refer to the history of export and import participation and export revenue,
respectively.
The contribution of firm i to the likelihood can be formulated as:
x,T
T
T
T
T
T
T
T
|ωi,0
, ki,0
, ΦT0 ) = P (eTi,0 , mTi,0 |ωi,0
, ki,0
, ΦT0 , zi,0
)h(zi,0
)
| sTi,0 ; θ ≡ P (eTi,0 , mTi,0 , Ri,0
L Di,0
(13)
where h(.) is the marginal density function of zi,t which is constructed as in Das et al. (2007).11
Assume that the sunk and fixed costs are iid draws from independent exponential distribution
1
functions Fγ (γi,t ) = 1 − γ1 e− γ γi,t where γ refers to the mean value of the sunk and the fixed
11
x
T
The export demand shock zi,t is defined as zi,t = zi,t : Ri,t
> 0 with a marginal probability density h(zi,0
)=
N (0, Σνν ) where Σνν is an m × n square matrix whose diagonal elements are
σν2
|m−n|
,
1−ρ2 ρ
∀ m 6= n.
47
σν2
1−ρ2
and the off diagonal elements
cost distributions. This independence assumption allows the construction of the joint likelihood
function as a product of individual probabilities of exporting and importing.
The likelihood function of exporting for a firm with a state vector si,t over the entire period
can be expressed as
h
iei,0 h
i1−ei,0
P eTi,0 | sTi,0 = Φ (η0 + η1 ki,0 + η2 ωi,0 + η3 zi,0 )
× Φ (η0 + η1 ki,0 + η2 ωi,0 + η3 zi,0 )
×
T h
Y
iei,t
xf
x
E
D
xs
Eγ I(πi,t
+ Vi,t
(si,t ) − Vi,t
(si,t ) > ei,t−1 γi,t
− (1 − ei,t−1 )γi,t
)
(14)
t=1
h
i1−ei,t
xf
x
E
D
xs
× Eγ I(πi,t
+ Vi,t
(si,t ) − Vi,t
(si,t ) 6 ei,t−1 γi,t
− (1 − ei,t−1 )γi,t
)
iei,0
h
i1−ei,0
h
where Φ (η0 + η1 ki,0 + η2 ωi,0 + η3 zi,0 )
× Φ (η0 + η1 ki,0 + η2 ωi,0 + η3 zi,0 )
denotes the first period
likelihood function after correcting for the initial conditions problem by using Heckman’s approach,
and η 0 = (η0 , η1 , η2 , η3 )0 is vector of unconstrained parameters.12 In constructing the likelihood,
the operator Eγ (.) takes the expectation over dFγ (.) if I(.) is true.
Define ∆Et Vi,t+1 (si,t+1 |ei,t , mi,t ) = Et Vi,t+1 (si,t+1 |ei,t , mi,t =1)−Et Vi,t+1 (si,t+1 |ei,t , mi,t =0). The
likelihood function for an importing firm over the sample period is given by:
h
imi,0 h
i1−mi,0
P mTi,0 | sTi,0 = Φ (µ0 + µ1 ki,0 + µ2 ωi,0 + µ3 zi,0 )
× Φ (µ0 + µ1 ki,0 + µ2 ωi,0 + µ3 zi,0 )
×
T h
imi,t
Y
mf
ms
Eγ I(λ∆Et Vi,t+1 (si,t+1 |ei,t , mi,t ) > mi,t−1 γi,t
− (1 − mi,t−1 )γi,t
(15)
t=1
h
i1−mi,t
mf
ms
× Eγ I(λ∆Et Vi,t+1 (si,t+1 |ei,t , mi,t ) 6 mi,t−1 γi,t
− (1 − mi,t−1 )γi,t
h
imi,0
i1−mi,0
h
refers to the prob× Φ (µ0 + µ1 ki,0 + µ2 ωi,0 + µ3 zi,0 )
where Φ (µ0 + µ1 ki,0 + µ2 ωi,0 + µ3 zi,0 )
ability of importing in the first period after correcting for the initial conditions problem, and
µ0 = (µ0 , µ1 , µ2 , µ3 )0 is a vector of unconstrained parameters as in the exporting case.
There are no closed form solutions to these choice probabilities which lead to a practical difficulty in constructing the likelihood function. I obtain these probabilities by evaluating the value
functions iteratively. See the steps followed in Appendix A.
As pointed out by Das et al. (2007), the likelihood function may not necessarily be globally
concave in the parameter vector, and thereby it is computationally difficult to find a parameter vector that maximizes the likelihood function. As a result, instead of trying to maximize
the likelihood function, the posterior distribution of the parameter vector is estimated using a
Bayesian Markov Chain Monte Carlo (MCMC) method. Implementation of this method requires
specification of a prior distribution Π(θ) and a likelihood function of the data L(D|s; θ), where
T
T
T
, D2,0
, ..., Dn,0
) and s = (sT1,0 , sT2,0 , ..., sTn,0 ) denote the observed data and state variables
D = (D1,0
for n firms in the sample. Under a standard Bayesian inference approach, the posterior distribution
12
For a discussion on this issue, see Wooldridge 2002, pp. 493–495.
48
of the parameter vector is given by:
Π (θ) L (D | s; θ)
∝ Π (θ) L (D | s; θ)
P (θ | D, s) = R
Π (θ) L (D | s; θ)
(16)
A single component Metropolis-Hastings algorithm is used to draw parameter values from the
posterior distribution P (θ|D, s) to obtain the market cost and export revenue parameters.13 For
this purpose, the parameter vector θ is divided into five blocks in which a block is updated one
at a time. The first two blocks contain the fixed and the sunk cost parameters of exporting and
importing. The third block consists of the export demand shock and market size parameters.
And, the last two blocks comprise parameters included to deal with initial conditions problem
with respect to export and import participation, respectively.
The algorithm involves the following basic steps:
1. At iteration r = 0, define θ0 = γ xs,0 , γ xf,0 , γ ms,0 , γ mf,0 , δx0 , ρ0 , σν0 , η 0 , µ0 as starting values
for each parameter θj in the vector θ.14
r
)
2. Draw a candidate parameter vector θ̃jr = θjr + κνjr from a proposal distribution q(θ̃jr |θjr , θ−j
r
where νj is a random draw from a multivariate standard normal distribution. θ−j is defined
to be a parameter vector θr with the exception of the j th element, where the first j − 1
elements are updated and the rest still to be updated. I use κ to scale the random draws
such that the candidate parameter vector lies within appropriate support.
3. Evaluate the acceptance probability of the candidate parameter vector at iteration r as :
λrj
= min
The j
th
r
r
r
π (θ̃jr |θ−j
)Lr (D|s;θ̃jr ,θ−j
)q(θjr |θ̃jr ,θ−j
)
r
r
r
π (θjr |θ−j
)Lr (D|s;θjr ,θ−j
)q(θ̃jr |θjr ,θ−j
)
,1
block will be updated as follows:
θjr
=

θ̃r
j
with probability λrj
θ r
with probability 1 − λrj
block is updated is given by:
j
The parameter
vector at iteration r after the j th

(θ̃r , θr ) with probability λr
j
−j
j
θr =
(θr , θr ) with probability 1 − λr
j
−j
j
4. Repeat steps 2 and 3 until every block in each iteration is updated, and the maximum number
of iteration r = rmax is reached.
The mean and standard deviations of the posterior distribution
the parameter vector conq of
P
P
N
r
r
0
ditional on the data are obtained as follows θ̄ = N1 r=1 θr and N1 N
r=1 (θ − θ̄)(θ − θ̄) where
N = rmax − m after discarding the first m elements of the Markov chain as burn-ins.
13
For a discussion on this algorithm, see Gilks et al. 1996, pp. 9–16.
For simplicity of computation in terms of defining a proposal distribution and drawing candidate parameter
values, I assume that σν has a lognormal distribution as in Aw et al. (2011) and Das et al. (2007).
14
49
6
6.1
Estimation results
Demand, marginal cost and productivity
Table 7 presents estimates of the demand, marginal cost and productivity parameters. The demand
parameter 1 − 1/ξ is estimated to be 0.6451 and 0.8644 for the domestic and export markets,
respectively. We see that the difference in the demand parameters leads to a substantial difference
in the implied values of demand elasticities in the domestic and export markets, which are 2.817
and 7.373, respectively.15 Because export markets host a larger number of firms and a wider
variety of products than the local market, demand is highly sensitive and competition is more
intense. Exporting firms respond to this market condition by charging a lower markup. We see
that the markup rate in export markets (16%) is much lower as compared to the markup rate in
the domestic market (55%).16 The size difference in demand elasticities as well as markup rates
in the domestic and export markets are consistent with the predictions of theoretical models such
as Melitz and Ottaviano (2008) and Mayer et al. (2014), which connect markups to the size of a
market and its extent of trade integration.
Estimates of the cost function show that a firm with a large capital holding is more costefficient. Also, the wage rate a firm pays has a negative and significant effect on the marginal cost
after controlling for its capital size, productivity and prices of all other inputs used in production.
This result implies that a firm can elicit increased effort by paying its workers higher wages. It
also indicates that high wages are somehow proxies for a high quality workforce which contributes
to the efficiency of firms.
From the parameter estimates of the productivity equation, we observe a significant, nonlinear relationship between current and previous period levels of productivity, which highlights the
persistence in the evolution of firm productivity over time. The nonlinearity of this relationship
also holds when export and import are treated as continuous variables. In terms of the impact
of previous period exporting and importing on a firm’s current productivity, we see that the estimates on α4 and α5 are positive and significant implying learning-by-doing associated with trade
participation.
The magnitudes of these coefficients reveal that the expected productivity gain from importing
(1.42%) exceeds that of the gain from exporting (1.03%). Further, a firm simultaneously exporting
and importing enjoys greater productivity improvement (2.45%) than others that are only partially
engaged in international trade. However, the insignificance of α6 demonstrates that there are no
greater gains from importing for an exporting firm, and so is the case for an importing firm from
exporting. This result reveals that a firm that exports, imports or undertakes both enjoys a
15
By construction, demand elasticity in each market is common for all firms.
Unlike the estimates in this paper, Aw et al. (2011) find almost identical demand elasticities in the two markets.
They report demand elasticity estimates 6.38 and 6.10 and markups 18.6% and 19.6% in the domestic and export
markets, respectively.
16
50
discernible productivity improvement from such activities which enables it to maintain its status
or add another trading activity. It also indicates that the complementarity between exporting and
importing is mainly through productivity.
Similarly, the estimates of α4 and α5 , when export and import are defined as intensities among
exporting and importing firms, demonstrate that a firm shipping a significant portion of its output
abroad or hiring imported inputs more intensively experiences a greater productivity improvement.
This delineates that both the scope and the scale of a firm’s trading activities determine the size
of expected productivity gain from trade participation.
6.2
Market costs and export revenue
To examine if there is any correlation between a firm’s size, its productivity and trade participation,
I estimate a probit model of exporting and importing. Table 8 shows reduced form estimates of
the variables that determine firm activity in export and import markets. The estimates show that
a firm’s size and its productivity are positively and significantly correlated with the likelihood
of its participation in exporting and importing. The significance of the coefficients on lagged
period export and import dummy variables suggests the presence of substantial market costs
firms must bear, and these costs decline with prior experience in international trade. Further,
the complementarity between exporting and importing can be seen from the significance of the
coefficients on mi,t and ei,t in the probit regressions of exporting and importing, respectively. In the
same way, the bivariate probit estimates show that a firm with a large capital holding, which is more
productive, and with a previous history of trade participation is more likely to be exporting and
importing. We also see that there is a non-negligible correlation between the unobserved factors
which determine a firm’s involvement in export and import markets; the estimated correlation
coefficient ρ is 0.2813.
As an extension, Table 9 reports reduced form estimates of the export revenue equation under
the assumption that the previous history of trade participation does not affect export revenues
conditional on a firm exporting. The results show that the revenue from export markets is positively
and significantly associated with a firm’s productivity and its capital size. And, this result holds
with and without controlling for firm fixed effects. We also see that firm fixed effects explain a
significant portion of the variation in the level of export sales which is not attributed to a firm’s
capital holding and its productivity, as shown by high ρ = 0.897. For comparison, I run the same
regression for import expenditure and find similar results. In this case, the percentage of the
variance of the error term attributed to firm fixed effects is 0.882.
The above reduced-form estimates emphasize the role of market costs in a firm’s trade participation decisions. As a next step, these costs are estimated. Table 10 presents the market cost
estimates with and without size heterogeneity. We see that firms incur substantial sunk and fixed
costs when they export their output and source inputs from abroad. For small firms, exporting
51
requires about 2,550 (426) thousand DKK (USD) for entrants and around 99 (17) thousand DKK
(USD) for incumbents.17 For large firms, the estimated sunk and fixed costs are around 5,910
(986) and 394 (66) thousand DKK (USD), respectively. With no size heterogeneity, the magnitude
of these costs is estimated to be 3,008 (502) thousand DKK (USD) for new exporters and 159 (27)
thousand DKK (USD) for incumbent exporting firms.18
Importing is not a costless undertaking either. Small firms incur a sunk cost of 2,429 (405),
and a fixed cost of 130 (22) thousand DKK (USD). For large firms, these costs are around 6,152
(1,027) and 476 (79) thousand DKK (USD), respectively. Without size heterogeneity, the sunk
and fixed costs are estimated to be 3,280 (548) and 155 (26) thousand DKK (USD), respectively.
The implication of the sizes of these costs contradicts the findings by Smeets and Warzynski (2013)
who report selection effects in exporting but not in importing for Danish manufacturing firms.
We observe that the market costs of exporting and importing are positively correlated with
a scale of firms’ operations. This indicates that large exporting firms ship multiple products to
several destinations, each involving individual product-destination-specific sunk and fixed costs.
This relationship between market costs and scale of operation is consistent with the predictions
of theoretical models such as Arkolakis and Muendler (2010), and as empirically shown in Aw
et al. (2011) for Taiwanese data. In contrast, Das et al. (2007), for Colombian manufacturing,
find a lower sunk cost for large exporting firms compared to their small counterparts.19 Large
importing firms also face a similar pattern of distribution of market costs. As normally expected,
irrespective of firm size, the fixed costs of exporting and importing are substantially lower than
the corresponding sunk costs. Further, the sizable difference in the estimated market costs for
small and large firms highlights the importance of explicitly accounting for size heterogeneity in
the empirical analyses of firm trade participation.
We also see that importing generally involves greater sunk costs than exporting, which is
different from the cost estimates of Kasahara and Lapham (2013) who find sunk costs of exporting
to be larger for almost all industries they consider. This result partly explains the productivity
ranking of firms shown in Table 5 in that firms must be more productive to absorb greater market
costs associated with importing. This is also part of the reason why exporting is the most common
activity in the Danish manufacturing sector. Very high sunk and fixed costs deter firms from
becoming or maintaining their status as importers.20
Table 10 also reports estimates of the export revenue function which includes the intercept δx ,
17
I use the World Development Indicators database as a source for data on exchange rate. Using 2005 as a
reference year, the official exchange rate is 1 USD = 5.9911 DKK.
18
Due to differences in country currencies and choices of reference periods, comparison of the absolute magnitudes
of the sunk and fixed cost estimates across studies is inappropriate. To this end, the discussion here focuses on
relative sizes and distribution patterns of these costs.
19
Das et al. (2007) argue that this feature can be attributed to differences in nature of the products they export,
market conditions of the destinations they serve, existing networks they possess and the like.
20
High sunk costs particularly for large importing firms may be due to the high prevalence of intrafirm trade
which requires setting up production units in foreign affiliates (see e.g. Antràs 2003; Antràs and Helpman 2004;
Nunn and Trefler 2008).
52
the serial correlation parameter ρ, and the standard deviation σν of the export demand shock.
Importantly, we see that the estimate on ρ, which ranges between 0.644 and 0.693, is positive and
significant. This implies that export demand shocks are strongly serially correlated highlighting
that the effects of shocks on export participation and revenue carry over from one period to the
next. These export revenue coefficients are robust to choice of burn-ins and whether firms are
differentiated in terms of size.
6.3
In-sample model performance
Here the performance of the model in fitting the actual data is examined. In this exercise, I
take the initial period data for all firms and then simulate for the rest of the periods. In these
simulations, I use the demand, marginal cost and productivity parameters estimates from a model
where exporting and importing are treated as discrete variables. Additionally, the market cost and
export revenue estimates with size heterogeneity are used. I repeat the simulations 1000 times and
report the averages across these simulations.
Table 11 presents the actual and predicted rates of trade participation. The model correctly
predicts that two-way trading firms are the largest group followed by export-only and importonly firms. However, we observe that it overpredicts the number of export-only firms whereas it
underpredicts the other groups especially when comparison is made for individual time periods.
When considering all the time periods together, the model predictions fit the data reasonably well.
Relatedly, Table 12 shows the actual and predicted probabilities of the empirical transition
matrix. It reports two sets of predictions (Predicted 1 and 2), which are obtained after discarding
the first 33% and 67% of the MCMC estimates as burn-ins, respectively. Despite small differences in
some of the transition probabilities (especially with respect to export-only firms quitting exporting
or maintaining their exporting status), the predicted values highly resemble the empirical transition
probabilities in size and order. Besides, comparing the two predictions, we see that Predicted
2 fits the actual transition matrix much better. This is in line with the expectation that the
Metropolis-Hastings algorithm, after constructing relatively long Markov chains, eventually reaches
the stationary distribution of the parameter vector. See the trace plots and the running means in
Figures C.1–C.6 in Appendix C.
6.4
Robustness
The above parameter estimates may be sensitive to changes in functional forms and simplifying
assumptions used in the model and the empirical strategy. This subsection checks for the sensitivity
of the baseline estimates with respect to productivity transition, wage parameter and prior choices.
53
Linear Markov process
Estimates of the productivity evolution function reported in Table 7 show that there is a significant,
non-linear relationship between previous and current period productivity levels. However, it is very
difficult to provide economic intuition to those coefficients. To this end, I redefine the productivity
transition in equation (4) to have a linear controlled Markov process as ωi,t = θ0 + θ1 ωi,t−1 +
θ2 ei,t−1 + θ3 mi,t−1 + θ4 ei,t−1 mi,t−1 + ζi,t . Under this formulation, the long-run effects of exporting on
θ2
2 +θ4
productivity are 1−θ
for a non-importing firm and θ1−θ
for an importing firm. And, the long-run
1
1
θ3
3 +θ4
and θ1−θ
for a non-exporting and an
productivity improvements from importing becomes 1−θ
1
1
exporting firm, respectively.
From columns (1)-(4) of Table 13, we see that there is a strong state dependence in the evolution of a firm’s productivity over time, as shown by a large and highly significant estimate on
ωi,t−1 . From column (1), the long-run productivity effect of exporting is about 5.45% and 17.93%
for a non-importing and an importing firm, respectively. The corresponding effect from importing
is around 7.46% and 19.94% for a non-exporting and an exporting firm. These long-run effects
demonstrate substantial differences in the expected productivity gains between two-way trading
firms and the rest. Because the estimated coefficients used to infer the long-run effects are statistically insignificant, I run another regression with the interaction term omitted. The estimates are
reported in column (3). The coefficients on ei,t−1 and mi,t−1 are now larger and highly significant
while the coefficient on ωi,t−1 remains unchanged. The implied long-run productivity effects of
exporting and importing are 11.54% and 15.30%, respectively.
The same exercise is repeated in columns (2) and (4) except that the effects of differences in
the intensities of export and import participation among trading firms are now considered. The
results reveal that if the share of exports in the total sales of an exporter doubles, this will be
associated with 3.38% and 3.54% increase in productivity for a non-importing and an importing
firm, respectively. The effects of doubling of a firm’s import intensity are 4.66% and 4.82% for a
non-exporting and an exporting firm. Without taking into account the possibility of varying effects
of exporting and importing across firms with different trading scope, the expected productivity
gains from increased intensities of exporting and importing are 2.64% and 4.13%, respectively.
In general, these results establish that trading firms enjoy a sizable productivity gain in the
long run. The observed differences in the productivity trajectories are substantial not only between trading and non-trading firms but also among exporting and importing firms with varying
intensities of their export and import involvement.
Wage parameter
I now consider a scenario in which a firm’s wage is monotonically increasing in its size and productivity. This implies that wages contain information which can be used to back out unobserved firm
productivity. If the wage a firm pays depends on its size and productivity, wi,t will be correlated
54
with ki,t and ωi,t in equation (10). In this case, it is not possible to identify βw in the first stage,
and therefore βw is estimated along with βk in the second stage of the algorithm. The estimating
equation in the first stage is now given by:
d
ri,t
= δd +
X
δt Dt + ht (ki,t , wi,t , eli,t ) + νi,t
(17)
t
where ht (.) is non-linear in ki,t , wi,t and eli,t and is approximated by a third-order polynomial
function. From this regression, the fitted values φ̂i,t = (1 − ξd )(βw wi,t + βk ki,t − ωi,t ) are recovered,
and the productivity series ωi,t = ξd1−1 φ̂i,t + βw wi,t + βk ki,t are constructed. In the second stage, the
fitted values are inserted in the productivity transition equation which gives rise to the estimating
equation
φ̂i,t = β̃k ki,t + β̃w wi,t − α̃0 + α̃1 φ̂i,t−1 − β̃k ki,t−1 − β̃w wi,t−1 − α̃2 φ̂i,t−1 − β̃k ki,t−1 − β̃w wi,t−1
3
+ α̃3 φ̂i,t−1 − β̃k ki,t−1 − β̃w wi,t−1 − α̃4 ei,t−1 − α̃5 mi,t−1 − α̃6 ei,t−1 mi,t−1 − ζ̃i,t
2
(18)
where β̃w = (1 − ξd )β̃w , and the other coefficients are defined in the same way as in equation (12).
Equation (18) is estimated by running a nonlinear least squares program. The predicted values of
a firm’s productivity are obtained from the equation ω̂i,t = ξ̂ 1−1 φ̂i,t + β̂w wi,t + β̂k ki,t .
d
Columns (5) and (6) of Table 13 show that the estimated wage parameter is negative and statistically significant, and its size is comparable under the discrete and continuous cases. However,
as compared to βw reported in Table 7, βw obtained at the second stage is substantially lower in
magnitude, albeit identical sign and significance level. This indicates that failure to account for the
endogeneity of wage in the cost function results in upward bias in the estimated wage coefficient,
which indicates a positive correlation between a firm’s wage and its productivity. However, the
coefficients on ki,t and productivity equation are exceedingly similar both in size and significance
to the original estimates in Table 7.
Diffuse priors
The market cost and export revenue parameters reported above are obtained by implementing the
MCMC algorithm with fairly diffuse priors. It is a standard practice to undertake a prior sensitivity
analysis, especially in view of the possibility that the variance of the prior distribution can have
an effect on the posterior distribution. For this purpose, I double the standard deviations of all
the prior distributions with the exception of the priors on the export revenue function parameters.
Table 14 reports the mean and the standard deviation of the dynamic parameter estimates
from the new priors along with estimates from Table 10. Comparison of the new to the baseline
estimates shows that both sets of estimates are of the same order. Noticeable differences may be
observed for parameters on fixed costs for small exporting firms and large importing firms, export
55
market size and autocorrelation. In addition to choices of priors, sampling error partly explains
these differences in the estimates.
In general, the static and dynamic parameter estimates of the model are shown to be robust to
choice of functional forms and estimating assumptions. Also, the empirical model performs well
in terms of fitting the actual data. In the next section, I use the estimated empirical model to do
a counterfactual analysis.
6.5
Counterfactual experiment
Table 15 shows the predicted effects of sunk and fixed cost reductions and export market enlargement on the transition probabilities of firms entering and exiting export and import markets. The
parameter estimates used in these analyses are those obtained under size heterogeneity and discrete
trade participation decisions of entering or exiting export and import markets. The first column
shows the fitted values of probabilities of entry and exit in export and import markets before any
change in the policy environment, which is constructed from Table 12 (Predicted 2).
In the first scenario, I consider a hypothetical situation where sunk and fixed costs of exporting
decline by 50% while everything else remains unchanged. We see that the predicted probabilities
of entry into exporting and importing are greater than their baseline equivalents. Not surprisingly,
the size of the increase in these probabilities is greater for exporting. These probabilities are also
larger for firms with some form of prior involvement in international trade. Furthermore, we see
that the predicted exit probabilities from both export and import markets are smaller as compared
to the exit rates before the policy change.
Similarly, I now examine the effect of a 50% decline in sunk and fixed costs of importing on
entry and exit rates of trade participation. As in the previous case, import market cost reduction
increases the likelihood of firms entering into exporting and importing and decreases exit rates from
both activities. And, we observe that these effects are more pronounced for importing activities.
Finally, I consider a policy environment where there is an enlargement of the export market
by 50% while market entry costs remain unchanged. This market enlargement increases firm
profitability from exporting and therefore induces firm export participation. At the same time,
this reduces the rate of firm exit in both exporting and importing activities. The only exception
here is that two-way trading firms are now more likely to quit importing.
7
Conclusion
Using a panel of firms in the Danish manufacturing sector, I summarize interesting stylized facts
on firms in international trade. The descriptive summaries reveal substantial firm heterogeneity in
terms of size and wage payment besides productivity. In the data, we see that exporting is the most
common trading activity, and there is a high frequency of simultaneous exporting and importing
56
within firms. In addition, there is a high persistence in the trading status of firms indicating a
prominent role of start-up and running costs that international trade entails.
In light of these empirical regularities, I structurally estimate a dynamic discrete choice model of
exporting and importing. The model provides a framework to examine trade participation decisions
while allowing for a firm’s current choice to affect its future productivity. The estimation results
reveal that export markets are characterized by a more elastic demand, which indicates tougher
competition essentially due to the presence of more firms and product varieties. In response to this
tough competition, firms charge a lower markup in export markets as compared to their markup
in the local market. Parameter estimates of the marginal cost function show that by providing
employees with more capital to work with, firms can effectively postpone the diminishing returns
to labor and enhance efficiency. Also, firms can further lower their costs of production by eliciting
more effort from their employees by paying higher wages. It may also be the case that firms attract
a high quality workforce and improve efficiency by paying higher wages.
In terms of productivity growth over time, we see that trading firms experience post-entry
productivity improvements, which are particularly larger for importing firms. Further, it is shown
that exporting and importing involve substantial sunk and fixed costs of operation. And, these
costs increase with the scale of operations, consistent with the expectation that large firms are
likely to have numerous market-product exporting and importing relations which are costly to start
up and maintain. The magnitude of these costs of trading leads to a sorting pattern where firms
which are sufficiently productive succeed in becoming exporters and importers. The learning effects
firms experience from trade participation further magnify the selection process into exporting and
importing.
The results from the counterfactual analyses illustrate that a policy change which influences
the distribution of market costs of exporting and importing or profitability abroad has a significant
effect on firm trade participation. Whether or not the policy change is directed towards exporting
or importing, it will have direct and indirect effects on all firms due to the complementarity of
these activities.
For future research endeavor, it is interesting to explore these entry and exit decisions while
explicitly accounting for firm product scope. This is useful given the highly skewed distribution
of these activities across products within firms. It will also be insightful to examine the entry and
exit decisions at the individual market level.
57
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60
Table 1: Summary statistics on capital size, wage and productivity
Capital
Year
2000
2001
2002
2003
2004
2005
2006
2007
Wage
Productivity
Mean
Median
Std.
Mean
Median
Std.
Mean
Median
Std.
24.47
24.93
25.48
25.42
25.93
25.11
25.23
25.69
6.08
6.18
6.21
6.38
6.47
6.32
6.52
6.46
84.23
82.63
92.12
99.58
108.72
110.45
105.81
100.82
260.34
267.03
272.13
277.03
283.26
282.69
289.67
298.36
254.09
261.21
264.89
270.83
278.08
278.41
283.89
290.54
50.43
52.38
53.95
54.65
54.58
51.57
51.05
86.14
0.288
0.294
0.297
0.301
0.296
0.302
0.307
0.308
0.266
0.273
0.275
0.280
0.274
0.281
0.289
0.286
1.409
1.414
1.414
1.411
1.418
1.431
1.433
1.450
Note: Capital is in millions and wage in thousands of Danish Kroner. Productivity is estimated TFP using domestic
revenue. All nominal values are deflated by the CPI index using 1995 as a base year.
Table 2: Firm distribution by capital size
Year
2000
2001
2002
2003
2004
2005
2006
2007
2000-2007
Domestic
Export
Import
% Small
% Large
% Small
% Large
% Small
% Large
70.86
75.00
76.89
77.30
78.18
75.38
74.12
73.44
75.15
29.14
25.00
23.11
22.70
21.82
24.62
25.88
26.56
24.85
41.89
41.24
42.42
42.57
41.95
42.35
43.09
42.89
42.30
58.11
58.76
57.58
57.43
58.05
57.65
56.91
57.11
57.70
39.96
39.58
41.71
41.81
41.63
41.70
41.96
42.70
41.38
60.04
60.42
58.29
58.19
58.37
58.30
58.04
57.30
58.62
Note: Small (large) firms are those with a capital size below (above) the median value of capital holding.
61
Table 3: Sample summary on firm trade participation
Year
Domestic
Import-only
Export-only
Two-way
# Firms
% Firms
# Firms
% Firms
# Firms
% Firms
# Firms
% Firms
2000
2001
2002
2003
2004
2005
2006
2007
501
476
411
370
385
390
371
369
23.79
22.60
19.52
17.57
18.28
18.52
17.62
17.52
144
134
139
148
155
153
150
168
6.84
6.36
6.60
7.03
7.36
7.26
7.12
7.98
241
238
187
198
198
210
217
210
11.44
11.30
8.88
9.40
9.40
9.97
10.30
9.97
1,220
1,258
1,369
1,390
1,368
1,353
1,368
1,359
57.93
59.73
65.00
66.00
64.96
64.25
64.96
64.53
2000-2007
–
19.43
–
7.07
–
10.08
–
63.42
Table 4: Export and import participation premia
Value added per worker
Sales per worker
Capital per worker
Material per worker
Energy per worker
Wage
Employment size
Import-only
Export-only
Two-way
7.736∗∗∗
(0.012)
14.807∗∗∗
(0.014)
3.637
(0.036)
35.126∗∗∗
(0.026)
11.485∗∗∗
(0.027)
1.791∗∗∗
(0.006)
41.626∗∗∗
(0.030)
2.662∗∗
(0.010)
8.132∗∗∗
(0.012)
14.152∗∗∗
(0.032)
25.864∗∗∗
(0.023)
5.867∗∗
(0.024)
1.423∗∗∗
(0.005)
15.257∗∗∗
(0.027)
11.131∗∗∗
(0.008)
29.022∗∗∗
(0.009)
22.760∗∗∗
(0.024)
65.418∗∗∗
(0.018)
29.701∗∗∗
(0.019)
3.641∗∗∗
(0.004)
170.764∗∗∗
(0.0191)
Standard errors in parentheses, ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Industry and time
fixed effects are included in these regressions.
62
Table 5: Kolmogorov-Smirnov test for equality of productivity distributions
Export-only
vs
Import-only
2000
2001
2002
2003
2004
2005
2006
2007
∗
H0 :Ω1 (x)-Ω0 (x) ≤ 0
H0 :Ω1 (x)-Ω0 (x)= 0
Year
0.2091∗∗∗
0.1802∗∗∗
0.1662∗∗∗
0.1066
0.1024
0.1543∗∗∗
0.1303
-0.1274
Two-way
vs
Import-only
Two-way
vs
Export-only
Export-only
vs
Import-only
Two-way
vs
Import-only
0.2262∗∗∗
0.2320∗∗∗
0.1598∗∗
0.2965∗∗∗
0.2503∗∗∗
0.2870∗∗∗
0.2661∗∗∗
0.2003∗∗∗
0.3657∗∗∗
0.3757∗∗∗
0.2854∗∗∗
0.3189∗∗∗
0.3164∗∗∗
0.3710∗∗∗
0.3380∗∗∗
0.2908∗∗∗
-0.2091∗∗∗
-0.1802∗∗
-0.1662∗∗
-0.1066
-0.1024
-0.1543∗∗
-0.1303
-0.1274
0.0000
-0.0016
-0.0059
-0.0043
-0.0022
0.0000
-0.0015
-0.0051
Two-way
vs
Export-only
-0.0033
0.0000
-0.0015
-0.0029
-0.0066
-0.0015
0.0000
-0.0045
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Table 6: Transition probabilities of firms
Status t+1
Import-only Export-only
Domestic
Status t
Two-way
Domestic
Import-only
Export-only
Two-way
80.85
18.28
13.03
0.46
7.78
55.03
2.55
2.36
8.68
3.23
57.76
3.36
2.69
23.46
26.66
93.82
Cross-sectional
Average
18.80
7.10
9.89
64.20
63
Table 7: Demand, marginal cost and productivity estimation
Demand
1–1/ξ
σ
P/MC
Cost
Domestic
Export
0.6451∗∗∗
(0.0672)
0.8644∗∗∗
(0.0246)
2.8179
7.3731
1.55
1.16
-0.0946∗∗∗
(0.0018)
-0.5495∗∗∗
(0.0357)
βk
βw
Productivity
α0
α1
α2
α3
α4
α5
α6
Discrete
(e, m)
Continuous
(e, m)
-0.0115∗
(0.0066)
1.0268∗∗∗
(0.0162)
-0.1014∗∗∗
(0.0210)
-0.0887∗∗∗
(0.0074)
0.0102∗∗
(0.0042)
0.0141∗∗∗
(0.0048)
0.0065
(0.0061)
0.1293
0.0177
(0.0153)
0.8800∗∗∗
(0.0443)
-0.2476∗∗∗
(0.0384)
-0.1076∗∗∗
(0.0099)
0.0030∗∗∗
(0.0011)
0.0043∗∗∗
(0.0010)
0.0003
(0.0003)
0.1209
σζ
Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Industry and time fixed effects are included
in the first stage of the estimation algorithm.
Table 8: Reduced-form estimation of export and import participation
Probit
Bivariate Probit
P r(ei,t = 1)
P r(ei,t = 1)
P r(ei,t = 1)
P r(mi,t = 1)
P r(mi,t = 1)
P r(mi,t = 1)
P r(ei,t = 1)
P r(mi,t = 1)
eit−1
0.185∗∗∗
(0.0109)
1.297∗∗∗
(0.0625)
–
0.117∗∗∗
(0.0135)
0.672∗∗∗
(0.0794)
2.451∗∗∗
(0.0353)
0.192∗∗∗
(0.0110)
1.844∗∗∗
(0.0643)
–
0.120∗∗∗
(0.0136)
1.036∗∗∗
(0.0757)
–
mit−1
–
0.100∗∗∗
(0.0138)
0.444∗∗∗
(0.0804)
2.251∗∗∗
(0.0376)
0.606∗∗∗
(0.0394)
Yes
Yes
0.61
–
2.226∗∗∗
(0.038)
Yes
Yes
0.57
0.105∗∗∗
(0.0141)
0.943∗∗∗
(0.0772)
0.699∗∗∗
(0.0379)
2.000∗∗∗
(0.0360)
Yes
Yes
0.59
0.100∗∗∗
(0.0138)
0.452∗∗∗
(0.0805)
2.249∗∗∗
(0.0375)
0.607∗∗∗
(0.0392)
Yes
Yes
–
0.106∗∗∗
(0.141)
0.936∗∗∗
(0.0771)
0.701∗∗∗
(0.377)
1.999∗∗∗
(0.0360)
Yes
Yes
–
kit
ωit
Year FE
Industry FE
Pseudo R2
Obs.
Yes
Yes
0.22
Yes
Yes
0.60
Yes
Yes
0.26
14,282
14,282
14,282
ρ = 0.2813
Standard errors in parentheses, ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
64
Table 9: Reduced-form estimation of export revenue and import expenditure
Export revenue
Import expenditure
(1)
(2)
(3)
(4)
ki,t
0.428∗∗∗
(0.019)
0.149∗∗∗
(0.195)
0.300∗∗∗
(0.023)
0.135∗∗∗
(0.024)
ωi,t
3.120∗∗∗
(0.088)
Yes
Yes
No
0.590∗∗∗
(0.091)
Yes
Yes
Yes
3.523∗∗∗
(0.104)
Yes
Yes
No
0.590∗∗∗
(0.113)
Yes
Yes
Yes
0.45
0.03
0.38
0.04
–
0.897
–
0.882
Year FE
Industry FE
Firm FE
R2
ρ
Obs.
10,722
10,323
Standard errors in parentheses, ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
65
Table 10: Market cost and export revenue parameters
Firm size
Firm size
Priors
Small
Large
All
Small
Large
All
γ xs
2471.749
(105.789)
5915.596
(37.977)
3013.676
(19.773)
2549.697
(63.635)
5909.658
(33.329)
3008.301
(19.930)
γ xs ∼ N (0, 1000)
γ xf
99.601
(11.441)
395.151
(8.887)
157.509
(6.714)
99.439
(13.841)
394.023
(10.853)
158.838
(7.095)
γ xf ∼ N (0, 1000)
γ ms
2390.672
(66.847)
6100.426
(71.824)
3262.507
(30.199)
2429.102
(68.356)
6152.378
(37.116)
3280.487
(27.549)
γ ms ∼ N (0, 1000)
γ mf
133.903
(10.041)
448.210
(32.328)
158.233
(8.742)
129.753
(11.602)
475.610
(9.168)
154.663
(5.098)
γ mf ∼ N (0, 1000)
δx
4.323
(0.196)
2.683
(0.392)
4.171
(0.124)
2.353
(0.228)
δx ∼ N (0, 100)
ρ
0.648
(0.045)
0.693
(0.041)
0.644
(0.047)
0.673
(0.043)
ρ ∼ U (−1, 1)
log σν
-0.217
(0.004)
-0.212
(0.004)
-0.219
(0.002)
-0.211
(0.004)
log σν ∼ N (0, 10)
Initial conditions
η0
-4.778
(0.237)
-3.955
(0.318)
-4.733
(0.147)
-3.778
(0.157)
η0 ∼ N (0, 100)
η1
1.953
(0.378)
2.604
(0.252)
2.281
(0.209)
2.678
(0.258)
η1 ∼ N (0, 100)
η2
1.397
(0.141)
1.711
(0.246)
1.437
(0.073)
1.670
(0.261)
η2 ∼ N (0, 100)
η3
-0.476
(0.246)
-0.750
(0.235)
-0.290
(0.196)
-0.895
(0.146)
η3 ∼ N (0, 100)
µ0
-4.654
(0.266)
-5.863
(0.208)
-4.450
(0.141)
-5.933
(0.243)
µ0 ∼ N (0, 100)
µ1
1.250
(0.131)
0.686
(0.173)
1.287
(0.148)
0.694
(0.182)
µ1 ∼ N (0, 100)
µ2
1.836
(0.317)
2.358
(0.194)
2.103
(0.156)
2.488
(0.133)
µ2 ∼ N (0, 100)
µ3
0.129
(0.059)
0.094
(0.044)
0.089
(0.034)
0.090
(0.039)
µ3 ∼ N (0, 100)
Iteration
30,000
30,000
Burn-in
33 %
67%
Note: Standard errors in parentheses. The market cost estimates are in thousands of Danish Kroner (DKK). # Capital grids=10,
# Productivity grids=100, # Export demand shock =100 and λ=0.95.
66
Table 11: In-sample model performance: trade participation rate
Year
Domestic
Import-only
Export-only
Two-way
Actual
Predicted
Actual
Predicted
Actual
Predicted
Actual
Predicted
2000
2001
2002
2003
2004
2005
2006
2007
23.79
22.60
19.52
17.57
18.28
18.52
17.62
17.52
22.86
16.93
14.34
13.05
10.62
9.76
8.89
8.36
6.84
6.36
6.60
7.03
7.36
7.26
7.12
7.98
6.31
6.90
6.42
5.07
5.55
5.18
4.31
3.88
11.44
11.30
8.88
9.40
9.40
9.97
10.30
9.97
11.16
16.28
19.46
21.02
22.26
22.48
22.70
23.45
57.93
59.73
65.00
66.00
64.96
64.25
64.96
64.53
59.68
59.89
59.78
60.86
61.56
62.59
64.10
64.31
2000-2007
19.43
13.10
7.07
5.45
10.08
19.85
63.42
61.60
Table 12: In-sample model performance: transition matrix
Domestic
Import-only
Status t
Export-only
Two-way
Domestic
Status t+1
Import-only
Export-only
Both
Actual
Predicted 1
Predicted 2
80.85
69.84
79.92
7.78
13.13
8.84
8.68
12.58
8.38
2.69
4.45
2.86
Actual
Predicted 1
Predicted 2
18.28
7.58
14.25
55.03
71.35
67.17
3.23
0.87
0.95
23.46
20.20
17.63
Actual
Predicted 1
Predicted 2
13.03
1.13
2.08
2.55
0.10
0.15
57.76
86.95
82.64
26.66
11.82
15.13
Actual
Predicted 1
Predicted 2
0.46
0.13
0.20
2.36
0.39
0.42
3.36
15.41
9.84
93.82
84.07
89.54
Note: Predicted 1 and 2 are simulated using estimates in columns (1)-(2) and (4)-(5) of Table 10, respectively.
The predicted probabilities are obtained by averaging over 1000 simulations.
67
Table 13: Robustness: Linear Markov process and wag parameter
Discrete
(e, m)
Continuous
(e, m)
Discrete
(e, m)
Continuous
(e, m)
Discrete
(e, m)
Continuous
(e, m)
-0.0889∗∗∗
(0.0060)
–
-0.0716∗∗∗
(0.0066)
–
-0.0896∗∗∗
(0.0059)
–
-0.0723∗∗∗
(0.0065)
–
2
ωi,t−1
0.9303∗∗∗
(0.0034)
–
0.9378∗∗∗
(0.0040)
–
0.9307∗∗∗
(0.0034)
–
0.9381∗∗∗
(0.0039)
–
3
ωi,t−1
–
–
–
–
ei,t−1
0.0038
(0.0041)
0.0052
(0.0047)
0.0087
(0.0060)
0.1272
0.0021∗
(0.0011)
0.0029∗∗∗
(0.0010)
0.0001
(0.003)
0.1198
0.0080∗∗∗
(0.0030)
0.0106∗∗∗
(0.0030)
–
0.0016∗∗
(0.0007)
0.0025∗∗∗
(0.0006)
–
0.1272
0.1198
-0.1581∗∗∗
(0.0603)
-0.0758∗∗∗
(0.0084)
0.5882∗∗∗
(0.1061)
-0.3578∗∗∗
(0.0506)
-0.0894∗∗∗
(0.0074)
0.0102∗∗
(0.0042)
0.0138∗∗∗
(0.0048)
0.0059
(0.0061)
0.1289
-0.3485∗∗∗
(0.1357)
-0.0690∗∗∗
(0.0103)
0.2046
(0.1913)
-0.5281∗∗∗
(0.0767)
-0.1075∗∗∗
(0.0100)
0.0028∗∗
(0.0011)
0.0042∗∗∗
(0.0010)
0.0002
(0.0003)
0.1206
Constant
wi,t
ωi,t−1
mi,t−1
ei,t−1 × mi,t−1
σζ
Standard errors in parentheses, ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Table 14: Market cost and export revenue parameters: more diffuse priors
Firm size
Firm size
Priors
Small
Large
All
γ xs
2549.697
(63.635)
5909.658
(33.329)
3008.301
(19.930)
γ xf
99.439
(13.841)
394.023
(10.853)
γ ms
2429.102
(68.356)
γ mf
129.753
(11.602)
Priors
Small
Large
All
γ xs ∼ N (0, 1000)
2105.818
(58.823)
5928.713
(32.487)
2923.326
(57.493)
γ xs ∼ N (0, 2000)
158.838
(7.095)
γ xf ∼ N (0, 1000)
166.478
(12.177)
327.058
(18.122)
132.502
(11.683)
γ xf ∼ N (0, 2000)
6152.378
(37.116)
3280.487
(27.549)
γ ms ∼ N (0, 1000)
2131.701
(52.929)
5734.498
(33.536)
3183.016
(36.280)
γ ms ∼ N (0, 2000)
475.610
(9.168)
154.663
(5.098)
γ mf ∼ N (0, 1000)
165.490
(6.228)
342.958
(7.320)
167.340
(14.115)
γ mf ∼ N (0, 2000)
δx
4.171
(0.124)
2.353
(0.228)
δx ∼ N (0, 100)
4.276
(0.383)
3.889
(0.152)
δx ∼ N (0, 100)
ρ
0.644
(0.047)
0.673
(0.043)
ρ ∼ U (−1, 1)
0.775
(0.059)
0.887
(0.049)
ρ ∼ U (−1, 1)
log σν
-0.219
(0.002)
-0.211
(0.004)
log σν ∼ N (0, 10)
-0.215
(0.003)
-0.212
(0.002)
log σν ∼ N (0, 10)
Note: Standard errors in parentheses. The market cost estimates are in thousands of Danish Kroner. Iteration=30,000, Burn-in=67%,
# Capital grids=10, # Productivity grids=100, # Export demand shock grids=100 and λ=0.95.
68
Table 15: Counterfactual analysis
0
Initial
γ xs =
0
γ xf
=
1 xs
γ
2
1 xf
γ
2
0
γ ms =
0
γ mf
=
1 ms
γ
2
1 mf
γ
2
0
δx =
3
δ
2 x
Prob. of entry into export
Domestic
Import only
11.24
18.58
27.59
36.64
15.03
17.52
25.00
29.82
Domestic
Export only
11.70
15.28
19.41
20.22
28.60
29.26
19.25
11.39
Export only
Both
2.23
0.62
1.49
0.29
2.38
0.73
0.73
0.30
Import only
Both
15.20
10.04
12.71
7.97
3.73
3.48
9.15
15.28
Prob. of entry into import
Prob. of exit from export
Prob. of exit from import
69
Appendix A: Computation of value functions
Below are the steps followed in the computation of the value functions:
1. Discretize the continuous state variables into finite set sc ∈ (sc1 , sc2 , ..., scM ), where sc =
(k, ω, z).
2. Initialize the algorithm V 0 (s) and draw values of γ xs , γ xf , γ ms and γ mf from the parameter
space.
0
c
3. Compute EV (s ) =
1
M
M
X
c
c
|s )
V 0 (scm ) PMf (smf (s
, where f (scm ) is transition density of sc .
c |sc )
m=1
m
m=1
4. Depending on previous period import status m−1 , the option values of exporting V E0 (m−1 )
and and non-exporting V D0 (m−1 ) are obtained as follows:
V E0 (m−1 ) = P λEV 0 (e = 1, m = 1) − λEV 0 (e = 1, m = 0) > m−1 γ mf + (1 − m−1 )γ ms
× EV 0 (e = 1, m = 1) − m−1 γ mf − (1 − m−1 )γ ms
+ P λEV 0 (e = 1, m = 1) − λEV 0 (e = 1, m = 0) ≤ m−1 γ mf + (1 − m−1 )γ ms
× EV 0 (e = 1, m = 0)
V
D0
0
0
(m−1 ) = P λEV (e = 0, m = 1) − λEV (e = 0, m = 0) > m−1 γ
mf
+ (1 − m−1 )γ
ms
× EV 0 (e = 0, m = 1) − m−1 γ mf − (1 − m−1 )γ ms
0
0
mf
ms
+ P λEV (e = 0, m = 1) − λEV (e = 0, m = 0) ≤ m−1 γ + (1 − m−1 )γ
× EV 0 (e = 0, m = 0)
5. Based on previous period export status e−1 , the final step is obtaining V 1 (s)
V 1 (s) = π d + P π x + V E0 (m−1 ) − V D0 (m−1 ) > e−1 γ xf + (1 − e−1 )γ xs
x
E0
xf
xs
× π + V (m−1 ) − e−1 γ − (1 − e−1 )γ
+ P π x + V E0 (m−1 ) − V D0 (m−1 ) ≤ e−1 γ xf + (1 − e−1 )γ xs × V D0 (m−1 )
6. Repeat steps 3-5 until |V r+1 − V r |< ε, which constitutes the convergence criterion.
70
Appendix B: Demand, cost and productivity: industry-level analysis
The main analysis does not explicitly allow for variations across industries. In this appendix, I
explore some industry disaggregation in the first stages of the estimation procedure, which estimate
the parameters of the demand, marginal cost and productivity functions. Table B.1 reports the
results of this exercise. And, it displays non-negligible cross-industry variations for the selected
industries under study. For instance, the demand elasticities in the domestic market range from
2.53 to 8.01, and the implied values of the markups lie within the interval 14-65%. In the export
market, the values of demand elasticities and markups are within the range 2.0-9.28 and 12-100%.
We note that demand elasticities are not necessarily more elastic and markups lower in export
markets as compared to the domestic market.
In terms of productivity gain, the results show that firms in the basic and fabricated metals
benefit from both exporting (1%) and importing (1.4%). Similarly, firms in the furniture industry
gain from their past importing activity (10%). The results also show cross-industry differences in
productivity change coming from how intensively firms undertake exporting and importing instead
of switching their trading status.
In this paper, when the market costs and export revenue parameters are estimated, I assume
a cost distribution common for all firms. This assumption may not be too restrictive given that
in the model firms face individual, time-varying sunk and fixed costs of exporting and importing.
With regard to industry-level analysis, it is essential to mention some concerns for estimation
within the current setting. The fact that some industries are populated by few firms makes the
estimation task harder. Further, the small share of firms switching their trading status over
the sample period prevents the exploitation of within firm variation for precise estimation of the
parameters. Further, the use of a balanced panel in a sector with a relatively high firm turnover rate
(15-20%) and longer time horizon (8 years) magnifies the problem, albeit it substantially reduces
the practical burden of estimating the market costs and export revenue parameters. For future
research purpose, adopting an alternative approach based on unbalanced panel data and which
accounts for entry and exit decisions in production, without necessarily recovering the market costs
of domestic operation, is necessary.
71
Table B.1: Demand, marginal cost and productivity by industry
(13+14)
Printing and
publishing
(17+18)
Rubber and
plastic
(22)
Basic and
fabricated metals
(24+25)
Machinery and
equipment
(26-30)
163
1,304
26.38
67
536
13.43
163
1,304
60.74
127
1,016
33.86
384
3,072
52.60
449
3,592
29.62
119
952
47.06
Exporters (%)
Importers (%)
80.67
80.60
92.72
93.66
71.01
53.30
91.04
89.27
59.21
53.13
87.86
84.21
77.63
69.33
1 − 1/ξ d
0.612∗∗∗
(0.290)
0.752∗∗∗
(0.031)
0.723∗∗∗
(0.020)
0.702∗∗∗
(0.029)
0.710∗∗∗
(0.080)
0.875∗∗∗
(0.047)
0.622∗∗∗
(0.042)
1 − 1/ξ x
0.892∗∗∗
(0.013)
0.689∗∗∗
(0.029)
0.750∗∗∗
(0.024)
0.654∗∗∗
(0.0288)
0.849∗∗∗
(0.018)
0.508∗∗∗
(0.052)
0.601∗∗∗
(0.061)
σd
σx
2.533
9.275
4.026
3.213
3.054
4.003
3.360
2.888
3.437
6.624
8.005
2.033
2.647
2.503
(P/M C)d
(P/M C)x
1.65
1.12
1.33
1.45
1.49
1.33
1.42
1.53
1.41
1.18
1.14
2.00
1.61
1.67
βk
-0.112∗∗∗
(0.009)
-0.054∗∗
(0.006)
-0.118∗∗∗
(0.006)
-0.079∗∗∗
(0.007)
-0.078∗∗∗
(0.003)
-0.022∗∗∗
(0.001)
-0.146∗∗∗
(0.010)
βw
-0.620∗∗∗
(0.139)
-0.092∗∗∗
(0.150)
-0.225∗∗∗
(0.081)
-0.237∗∗∗
(0.559)
-0.420∗∗∗
(0.0839)
-0.137∗∗∗
(0.090)
-0.591∗∗∗
(0.251)
α0
26.090∗∗∗
(9.385)
279.590
(357.437)
-66.245∗∗∗
(14.120)
-675.947
(271.326)
17.294∗∗∗
(2.773)
46.986∗∗∗
(11.256)
-534.979
(434.736)
α1
-8.748∗∗∗
(4.341)
53.725∗∗∗
(2.127)
-37.519∗∗∗
(7.706)
-119.194∗∗∗
(1.058)
-12.960∗∗∗
(2.385)
-48.936∗∗∗
(12.307)
-56.011∗∗∗
(1.359)
α2
1.209∗∗∗
(0.321)
3.194∗∗∗
(0.310)
-7.461∗∗∗
(1.405)
-7.114∗∗∗
(0.121)
3.732∗∗∗
(0.684)
17.659∗∗∗
(4.484)
-1.993∗∗∗
(0.083)
α3
-0.050∗∗∗
(0.014)
0.061∗∗∗
(0.011)
-0.481∗∗∗
(0.086)
-0.140∗∗∗
(0.003)
-0.331∗∗∗
(0.065)
-2.079∗∗∗
(0.544)
-0.023∗∗∗
(0.001)
α4
0.011
(0.020)
0.035
(0.035)
-0.007
(0.012)
0.003
(0.023)
0.010∗∗∗
(0.005)
0.002
(0.003)
0.030
(0.021)
α5
-0.005
(0.020)
0.025
(0.032)
0.011
(0.018)
0.029
(0.024)
0.014∗∗∗
(0.007)
0.000
(0.005)
0.102∗∗∗
(0.029)
α6
0.001
(0.026)
0.014
(0.043)
0.027
(0.021)
-0.002
(0.027)
0.001
(0.008)
0.0010∗∗
(0.005)
-0.044
(0.033)
σζ
0.1449
0.0875
0.1323
0.1000
0.0822
0.0314
0.1581
α0
42.972∗∗∗
(6.391)
269.103
(374.058)
-54.379∗∗∗
(16.866)
-715.611∗∗∗
(276.469)
14.591∗∗∗
(3.401)
30.248∗∗∗
(9.987)
368.520
(250.986)
α1
-18.616∗∗∗
(2.927)
49.863∗∗∗
(2.493)
-32.387∗∗∗
(9.618)
-127.164∗∗∗
(1.160)
-12.182∗∗∗
(3.250)
-30.954∗∗∗
(11.041)
25.941∗∗∗
(1.930)
α2
2.971∗∗∗
(0.460)
2.808∗∗∗
(0.361)
-6.822∗∗∗
(1.834)
-7.639∗∗∗
(0.134)
3.957∗∗∗
(1.041)
11.232∗∗∗
(4.066)
0.487∗∗∗
(0.117)
α3
-0.149∗∗∗
(0.025)
0.049∗∗∗
(0.013)
-0.464∗∗∗
(0.117)
-0.152∗∗∗
(0.004)
-0.395∗∗∗
(0.111)
-1.314∗∗∗
(0.499)
0.002
(0.002)
α4
-0.003
(0.004)
0.006
(0.005)
0.001
(0.004)
0.002∗∗∗
(0.003)
0.003
(0.002)
0.002
(0.001)
0.004
(0.008)
α5
-0.001
(0.005)
0.016∗∗∗
(0.008)
0.004
(0.005)
0.005
(0.003)
0.005∗∗∗
(0.002)
0.001∗∗
(0.001)
0.011∗
(0.006)
α6
-0.001
(0.001)
0.001
(0.002)
0.000
(0.001)
-0.000
(0.001)
0.001
(0.000)
0.000
(0.000)
0.001
(0.002)
σζ
0.1347
0.0846
0.1379
0.1018
0.0747
0.0307
0.1578
NACE-Rev.2
# Firms
# Firm-year
Firms switching status (%)
Food and
beverages
(10+11)
Textiles
Furniture
(31)
Discrete (e, m)
Continuous (e, m)
Standard errors in parentheses , ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
72
Appendix C: Figures
Figure C.1: Trace plot: sunk export cost
2650
2600
2550
2500
2450
2400
2350
2300
2250
γ xs (Small firms)
6100
6050
6000
5950
5900
5850
5800
γ xs (Large firms)
3200
3150
3100
3050
3000
2950
γ xs (All firms)
73
Figure C.2: Trace plot: fixed export cost
160
150
140
130
120
110
100
90
80
γ xf (Small firms)
420
415
410
405
400
395
390
385
380
375
γ xf (Large firms)
180
170
160
150
140
130
120
γ xf (All firms)
74
Figure C.3: Trace plot: sunk import cost
2550
2500
2450
2400
2350
2300
2250
γ ms (Small firms)
6250
6200
6150
6100
6050
6000
5950
5900
γ ms (Large firms)
3350
3300
3250
3200
3150
3100
γ ms (All firms)
75
Figure C.4: Trace plot: fixed import cost
170
160
150
140
130
120
110
100
90
γ mf (Small firms)
500
480
460
440
420
400
380
γ mf (Large firms)
180
170
160
150
140
130
120
110
γ mf (All firms)
76
Figure C.5: Trace plot: export revenue parameters with size heterogeneity
5
4.8
4.6
4.4
4.2
4
3.8
δx
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
0.5
ρ
−0.206
−0.208
−0.21
−0.212
−0.214
−0.216
−0.218
−0.22
−0.222
−0.224
−0.226
log σν
77
Figure C.6: Trace plot: export revenue parameters without size heterogeneity
4.5
4
3.5
3
2.5
2
1.5
δx
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
ρ
−0.202
−0.204
−0.206
−0.208
−0.21
−0.212
−0.214
−0.216
−0.218
−0.22
−0.222
log σν
78
79
Chapter 3
Importing and Firm Productivity in
Ethiopian Manufacturing
80
Importing and Firm Productivity in Ethiopian Manufacturing∗
Kaleb Girma Abreha†
Abstract
In this paper, I investigate the causal relationship between importing and firm productivity. Using a rich dataset from Ethiopian manufacturing over the period 1996-2011, I find that
most firms source capital and intermediate goods from the world market. These firms are better performing as shown by significant, economically large import premia. I also find strong
evidence of self-selection of more productive firms into importing, highlighting substantial
import market entry costs. To examine the causal effect of importing on firm productivity,
I use a model in which the static and dynamic effects of importing are separately estimated.
The estimation results provide evidence of learning-by-importing. However, the small sizes
of the productivity gains suggest the limited absorptive capacity of firms in the economy.
JEL Codes: F14, L60
Keyword: Imported inputs, self-selection, learning-by-importing, Ethiopia
∗
I thank Valérie Smeets, Frédéric Warzynki and Mark Roberts for helpful comments. I also thank the Tuborg
Foundation for generous financial support. The usual disclaimer applies.
†
Department of Economics and Business, Aarhus University, Denmark, E-mail: [email protected]
81
1
Introduction
International knowledge flows are considered fundamental components of globalization. Several
studies identify foreign direct investment, trade, migration and others as important channels of
international linkages and knowledge spillovers across countries. A pioneering contribution by Coe
and Helpman (1995) on trade-driven international R&D spillovers documents significant R&D
knowledge transfer across OECD countries. In a follow-up paper, Coe et al. (1997) show that the
knowledge spillovers are not limited to developed countries in that developing countries substantially benefit from R&D investments elsewhere.
Subsequent studies have investigated the importance of different channels of international linkages and knowledge spillovers mostly using aggregate data and adopting cross-country regressions.
Recently, Acharya and Keller (2009) find that the contribution of international R&D spillovers to
productivity normally exceeds that of domestic R&D, and the technology transfers are asymmetric
across countries. They identify the asymmetry to emanate from differences in geographical distance between trading partners and the nature of goods traded. Other studies point out physical
and human capital, policy and institutional quality, and relative backwardness from the technological frontier as important determinants of the pace and size of technology diffusion across
countries. Acemoglu and Zilibotti (2001) emphasize that technology and skill mismatches lead to
productivity differences across countries even when they have equal access to technology. Coe et
al. (2009) demonstrate the importance of institutional factors on the degree of R&D spillovers.
No (2009) shows that the scope and magnitude of international R&D spillovers depend on both
production structure, pattern of international trade (in terms of volume and trading partners),
national innovative and absorptive capacities of countries.
However, restricting these investigations only to countries and industries masks varying roles of
different technology transfer channels given that firms are characterized by marked heterogeneity
in terms of global orientation, productivity, size, and factor intensity and payment even in narrowly
defined industries. With increasing recognition of firm heterogeneity, the scope of research on globalization, mainly international trade, has expanded to include firms and products besides countries
and industries. Consequently, the trend in the empirical trade literature has been characterized
by a surge in using microeconomic data.
Despite such a surge, prominent focus has been on the export side of international trade. It is
only recently that studies started looking into importing and its relationship with other firm activities such as exporting. A common feature of these studies is that there is a positive, statistically
significant and quantitatively large correlation between firm productivity and importing. However,
the evidence on the causal relationship is mixed. For instance, Kasahara and Lapham (2013) for
Chile, Vogel and Wagner (2010) for Germany, and Serti et al. (2010) and Castellani et al. (2010) for
Italy find evidence of self-selection of more productive firms into importing whereas Forlani (2010)
for Ireland and Smeets and Warzynski (2013) for Denmark find no supportive evidence. Evidence
82
on learning-by-importing is also inconclusive. Kasahara and Rodrigue (2008) for Chile, Smeets
and Warzynski (2013) for Denmark, Halpern et al. (2009) for Hungary, Forlani (2010) for Ireland,
Dovis and Milgram-Baleix (2009) and Augier et al. (2013) for Spain, and Lööf and Andersson
(2010) for Sweden find evidence of productivity gain from importing. On the contrary, Muendler
(2004) for Brazil, Vogel and Wagner (2010) for Germany, Van Biesebroeck (2008) for Colombia
and Zimbabwe find weak evidence. These mixed results are partly attributable to methodological choices or inherent differences in the nature of the import-productivity nexus across countries
and over time. Therefore, a complete understanding of the causal relationship calls for further
accumulation of empirical evidence from different countries.
Partly due to data unavailability, with the exception of few studies, the new empirical trade
literature is also characterized by neglect of firms in low-income countries. Mengistae and Pattillo (2004) find export productivity premia for Ethiopia, Ghana and Kenya. Bigsten et al.
(2004) provide weak evidence of self-selection into the export market but strong evidence of
learning-by-exporting for firms in Cameroon, Kenya, Ghana and Zimbabwe. Similarly, a study
by Van Biesebroeck (2005) on Sub-Saharan manufacturing firms from Burundi, Cameroon, Côte
d’Ivoire, Ethiopia, Ghana, Kenya, Tanzania, Zambia, and Zimbabwe shows that exporters are
better performing, and there is a productivity gain from exporting. Relatedly, Bigsten and Gebreeyesus (2009) document evidence of selection of more productive firms into exporting as well
as post-entry productivity improvement in the Ethiopian manufacturing. Foster-McGregor et al.
(2014) analyze firm productivity differences in 19 Sub-Saharan African countries and show that
those simultaneously exporting and importing are the most productive whereas their domestic
counterparts are the least productive.
The aforementioned studies on African manufacturing firms exclusively focus on the relationship
between firm productivity and exporting.1 For instance, even though the Ethiopian manufacturing
sector has been a subject of several empirical investigations, the importing behavior of firms in the
sector has been ignored. This is puzzling given the dominant role of imports in the foreign trade
of the country. Imports of goods and services form around 28% of GDP over the period 19962011 whereas the corresponding figure for exports is 13%. During the same period, manufacture
imports and exports constitute around 70% and 9% of the total merchandise imports and exports,
respectively. This provides a rationale for analyzing the import behavior of firms as an essential
step towards a complete understanding of the nature of and gains from international trade at the
firm and aggregate level in the context of a least developed country.
In this paper, I investigate the causal link between importing and productivity using firms in
Ethiopian manufacturing. I use a rich dataset over the period 1996-2011. Descriptive summaries
of the data display that the majority of the firms are globally active, and importing is the most
1
An exception is a study by Foster-McGregor et al. (2014) which investigates productivity differences of firms
importing besides exporting. One caveat of this study is its use of cross-sectional data, and therefore it neglects
the dynamics over time.
83
common form of global activity. Besides, I find a positive, statistically significant and economically
large import premia confirming previous findings which establish positive relationship between
importing and productivity and other firm performance measures.
In determining the direction of causality in the relationship between importing and productivity,
I test for commonly known hypotheses—productive firms become importers, and importing makes
firms productive. The main element of the first hypothesis is that there significant sunk and fixed
costs of operation in import markets, and it is only firms which are sufficiently productive enough
that succeed in souring inputs from abroad. To empirically test this, I consider a subsample of firms
which were not importing in the past, and estimate the pre-entry import productivity premium
between future importing and non-importing firms. The results of this exercises reveal a positive
and significant premium, and therefore provide evidence of self-selection of more productive firms
into importing.
To examine whether or not there is productivity gain from becoming an importer, I distinguish
between local and imported varieties of material inputs in the production function. Also, I treat
import participation as an additional state variable which determines the evolution of a firm’s productivity as well as its exit and investment decisions. As an identification strategy, I exploit within
firm changes in importing status as a source of variation and structurally estimate parameters of the
production function and productivity evolution equation. This approach enables not only proper
estimation of importing effects but also decomposition of the static and dynamic components. The
estimation results show that there are dynamic productivity gains (1.1-1.2%) in the period after
participating entering import market albeit a momentary adverse effect at the beginning. In the
long-run, these effects amount to a firm productivity improvement of 3.5-4.9%. In addition, if the
expenditure share of imported inputs doubles firm productivity by 2.1% immediately, 0.7% in the
period after and 4-22% in the long-run.
This paper is related to several contributions on firm international trade and economic growth
literature. It is related to the large and growing literature on firm heterogeneity and international
trade pioneered by Bernard and Jensen (1995). It is also related to trade-driven international
knowledge spillovers literature pioneered by Coe and Helpman (1995) and Coe et al. (1997).
Methodologically, I follow Kasahara and Rodrigue (2008) in the specification of the structural
model. Unlike their study, possibilities of firm-level technology spillovers via imported inputs are
studied in the context of a least developed country with a different macroeconomic environment,
factor and product markets and at a different stage of economic development. By focusing on a
typical least developed country, I accommodate not only the potentials of technology transfer but
also the absorptive capacity of a country in determining the size and pace of trade-related international knowledge spillovers. In this respect, this paper is linked to a strand of literature in economic
growth such as Acemoglu and Zilibotti (2001) who show a technology and worker skill mismatch
leading to productivity differences even when countries have same access to technology. It is also
related to Los and Timmer (2005) studying the pace of assimilation of spillovers of appropriate of
84
technology as an explanation for productivity differences across countries. At the microeconomic
level, this study is connected to recent contributions by Yasar and Morrison Paul (2007) and
Yasar and Paul (2008) investigating different channels of technology transfer, and Yasar (2013)
and Augier et al. (2013) examining firm absorptive capacity and productivity effects of imported
inputs. Unlike these studies, I consider the latest decade where the world experienced a surge in
trade in intermediate goods.2 I also exploit the panel structure and longer time dimension of the
data, which is rarely available for African manufacturing. To the best of my knowledge, related
studies on African manufacturing firms are missing, and this study provides the first evidence.
The rest of the paper is organized as follows. Section 2 provides background information
on sectoral composition and international trade structure of the Ethiopian economy. Section 3
presents the data source and establishes a set of stylized facts. Section 4 provides evidence on
selection of firms into importing. Section 5 develops a theoretical framework and an empirical
strategy to examine any productivity gain from importing. It also discusses the estimation results.
Section 6 concludes.
2
Overview of Ethiopian economy
Ethiopia is one of the least developed countries according to the World Bank economic classification
of countries. Typical of a least developed country, the economy has experienced a highly fluctuating
growth pattern ranging from -3.46% in 1998 to 13.57% in 2004. However, the economy enjoyed
nearly decades of rapid economic growth especially after 2003, albeit starting from a rather low
level. On average, it expanded by 7.62% annually over the period 1996–2011. Below is a brief
presentation of the salient features of the economy in terms of distribution of economic activities
and reallocations across sectors.
2.1
Sectoral composition
Ethiopian economy is highly agriculture-based. Table 1 shows that 48.02% of the value added in
the economy come from agriculture. However, the sector experienced a decline in its contribution
to the aggregate output from 55.35% in 1996 to 45.57% in 2011. This decline is due to a relatively
slower sectoral growth rate—5.74% in agriculture, 6.60% in manufacturing and 10.30% in the
service sector over the years 1996-2011. Owing to a heavy reliance on rain-fed agriculture, the
growth rate in the sector has been characterized by extreme fluctuations ranging from -10.48% in
2003 to 16.96% in 1996. Regarding the manufacturing sector, Ethiopia has a very narrow industrial
base. The share of the sector is very small, below 10%, for all the periods under the study. Despite
its strong growth performance, the contribution of the sector to the value added in the economy
2
According to the UNCTAD (2014) report, trade in intermediate goods totaled USD 7 trillion and accounted
for 40% of the world trade in 2011.
85
has declined recently. On the other hand, the service sector experienced a consistently high growth
rate and saw its contribution rising over time.
Table 2 summarizes the characteristics of the trade sector. We see that exports and imports
constitute, on average, 12.99% and 28.01% of total output in the economy. These sectors registered
rapid, yet fluctuating, growth rates; 12.13% and 12.77% respectively. These growth rates signify
the increase in the share of exports and imports from 9.35% and 16.43% in 1996 to 17% and
32.14% in 2011, respectively, showing increasing openness and growing integration to the global
economy. It is also important to note that the integration is dominantly through imports. We also
see the widening of the country’s trade deficit over the years despite comparable export and import
growth rates. In terms of traded items, manufacture exports constitute a very small portion of the
overall merchandise export, 9.08%, whereas manufacture imports constitute a significantly higher
proportion, 70.18%. While the share of manufacture exports remains more or less stable, the share
of manufacture imports declined significantly from 84.49% in 1996 to 67.13% in 2011.
2.2
Geographic orientation
Figure 1 shows the regional distribution of Ethiopian foreign trade. Trade is mainly concentrated in
high-income countries in Europe and North America. The country trades 69.10% of the exports and
55.54% of the imports with these high-income economies. Given that advanced economies account
for the largest share of R&D in the world, the concentration of trade with these countries makes
trade a likely conduit for international knowledge spillover. Middle Eastern and Northern African,
East Asian and Pacific, and Central and South Asian countries are the next important destinations
for exports constituting 14.03%, 6.07%, and 3.60%, respectively. The respective figures for imports
are 4.88%, 12.36%, and 8.40%. Trade with countries in Latin America and the Caribbean, and
Sub-Saharan Africa is very small and expanded only incrementally over time. At the same time,
we observe the declining importance of traditional markets namely high-income economies and the
growing importance of trade partners in Asia especially on the import trade.
To sum up, it is shown that the fundamental aspects of Ethiopian economy have not undergone
major structural changes. However, there have been sizable changes in terms of sectoral output
compositions and geographical orientations of international trade. Increasing overall openness of
the economy along with the dominance and increasing importance of imports in the economy is
particularly observed. These changes cause firms to adapt their behavior under different domestic
and global economic circumstances. To this end, rigorous analyses of firm behavior in terms
of market entry and exit decisions as well as the subsequent effects are required for a complete
understanding of the nature, determinants and effects of international trade.
86
3
Basic facts from Ethiopian manufacturing
In this section, I describe the data source and the variables defined. I also summarize the nature
of firm international trade primarily focusing on importing activity.
3.1
Data description
The dataset used in this paper comes from the Central Statistical Agency of Ethiopia. The agency
conducts annual large and medium-scale surveys of firms engaged in manufacturing activities.
Classification of economic activities as manufacturing is based on ISIC-Rev.3 classification and
includes industries 15-37 at 2-digit ISIC. The survey covers all firms with at least 10 employees
and which use power-driven machinery during the period 1996-2011. The dataset provides detailed
information on the level of production, local and export sales, input usage, employee composition,
and asset structure of firms. I define gross output as revenue generated from local and export sales.
I construct the capital variable by exploiting the information on initial stock, investment, value sold
and depreciated using the perpetual inventory method. Information on local and imported material
inputs as well as energy is also available. Using information on employees, I differentiate between
skilled workers: unpaid working proprietors; active partners and family workers, and administrative
and technical employees, and unskilled workers: apprentice and production workers. Because the
number of seasonal and temporary workers is infrequently reported, the measure of labor input is
confined to the number of working proprietors; apprentices, and permanent employees. Finally, I
deflate all nominal values using the consumer price index extracted from the World Development
Indicators database.
To have enough within industry variation, I regroup those industries with very few firms as
other manufacturing. This group comprises firms in Tobacco, paper, basic metals, machinery and
equipment, office equipment, electrical machinery, and motor vehicles industries. I exclude firms
with zero or unreported value of production, capital stock, material input, energy expense, number
of employees, and other inconsistencies to consider only firms with real economic activity. I also
exclude firms appearing only once during the period under study. In the final dataset, there are
2,350 firms and 12,510 firm-year observations.
3.2
Stylized facts
Here, I provide a simple description of the data that characterizes the international trade activities
of Ethiopian manufacturing firms.
Fact 1 There is a substantial variation in firm trade participation across industries.
Table 3 shows large differences in firm export and import participation rates across industries.
In 1996, textiles (20.83%), leather product (21.43%), wood products (7.69%), and wearing apparel
(6.67%) were industries with relatively high export market participation rates. In contrast, food
87
and beverage (2.34%), non-metallic products (1.96%), and furniture (1.85%) had very low export
participation rates. In the extreme case, there were no exporters in the printing and publishing,
chemicals, rubber and plastic, and fabricated metals industries. In 2011, there has been a dramatic
increase in the export participation rate of firms in each of the industries except the furniture
industry. For instance, the participation rate increased to 20.20% in food and beverage, 50.00%
in textile, 45.90% in leather products, and 21.05% in chemical industries.
There are also substantial heterogeneities in firm import participation across industries. In
1996, chemicals (100%), rubber and plastic (100%), fabricated metals (89.47%), and textiles
(83.33%) comprised industries with very high import participation rates. On the contrary, the
participation rate was as low as 21.57% in the case of non-metallic products. Over the 15-year
window, food and beverage, leather products, chemicals, rubber and plastic, and fabricated metals
saw their import participation rates decline while the remaining industries experienced a rise.
Comparing export and import participation rates, we observe that importing is the most common activity in which 69.74% of firms import while the corresponding figure for export is 5.86%
over the period 1996-2011. These characteristics are against findings from manufacturing sectors
of developed countries where the incidence of exporting is more common and importing is rarer.3
In terms of dynamics over time, we see that firms have become more globally engaged through
exporting while a slightly lower fraction of firms import. Export participation rose from 4.72% in
1996 to 14.77% in 2011, and import participation declined from 67.87% in 1996 to 64.89% in 2011.
In general, there are clear indications of increasing presence of firms in the world market mainly
because more firms have started serving export markets.
Fact 2 There are significant trade activity premia.
I divide firms into four mutually exclusive groups based on their exporting and importing
activities: domestic (neither exporting nor importing); export-only (serving domestic and export
markets but not importing); import-only (serving domestic market, and importing), and two-way
(serving domestic and export markets, and importing). To estimate the activity premium, I run
the following regression equation:
yi,t = β0 + β1 Expi,t + β2 Impi,t + β3 Bothi,t + Controlsi,t + δt + τ + i,t
where yi,t refers to performance indicators TFP, output, capital, material, energy, employment
size, and share of skilled workers. These performance measures are all in logarithmic scale. Expi,t ,
Impi,t , and Bothi,t are dummy variables taking a value of 1 if the firm is export-only, import-only
or two-way, respectively. In the regression, I control for year δt and industry τ fixed effects as
well as employment size (except for the last two indicators). All the estimates measure average
3
Findings by Bernard et al. (2007) for a large economy (the US) and Eriksson et al. (2009) for a small open
economy (Denmark) are typical cases of greater export participation rate in the manufacturing sectors of developed
countries.
88
percentage differences relative to domestic firms. Table 4 shows that two-way trading firms are the
most productive whose production activity is characterized by intensive use of capital, material,
energy and skilled workers. These firms are also the largest in size. Among firms partially engaged
in trade, we see that export-only firms are more productive, capital-intensive and larger in size
compared to import-only firms. The estimates indicate that export-only firms not necessarily use
more energy per worker and hire more skilled workers. And, import-only firms are less capitalintensive, and they do not necessarily use more material per worker as compared to domestic
firms.
Fact 3 There is high persistence in firm trade status.
Table 5 presents transition probabilities highlighting the dynamics of firm activities in terms
of scope. There is a high state dependence of firms engaged in the domestic market (67.19%),
import-only (84.30%) and two-way (76.95%). There is an exceptionally low incidence of state
dependence among export-only firms; 33.75%. Firms engaged in either exporting or importing are
more likely to add importing (38.75%) but less likely to add exporting (1.71%) as an additional
activity compared to firms engaged in neither of the activities; 31.40% and 2.10% to start importing
and exporting respectively. We here note the unexpectedly low probability of adding exporting
as an additional firm activity. Furthermore, firms doing both are less likely to abandon any of
these activities compared to those engaged in only one of them. These firms abandon exporting,
importing or both with a probability of 16.43%, 10.62%, and 4.01%, respectively. In contrast, firms
only exporting (importing) abandon exporting (importing) with a probability of 13.75% (13.76%).
Finally, the average of the cross-sectional distribution of trade participation shows a high incidence
of firm involvement in international trade; 71.41% of the firms are active in the world market via
exporting, importing or both.
To further highlight the extent of state dependence in firm activities, I run a probit regression
of a firm’s current period import/export status Di,t on its previous period productivity tf pi,t−1 ,
capital stock ki,t−1 , size of employment li,t , prior import Mi,t−1 and export Xi,t−1 participation
variables, and year δt and industry τ fixed effects.
P r(Di,t = 1) = Φ (β0 + β1 tf pi,t−1 + β2 ki,t−1 + β3 li,t−1 + β4 Mi,t−1 + β5 Xi,t−1 + δt + τ + i,t )
Table 6 reports the marginal effects from the probit regressions. We see that productivity, capital,
employment size and prior export market experiences are positively and significantly correlated
with the likelihood of current period participation in export market. Comparable results are also
found in the case of importing except now that large capital holding tend to lower the probability of
importing. Statistical significance of the previous period trade status of firms shows the presence
of market entry costs and complementarity between exporting and importing. The fact that
the current period import status is significantly correlated with past export market participation
strongly suggests that firms with export market experience are able to absorb at least part of the
89
import market entry costs, and therefore they are likely to become importers. Another plausible
explanation is significant productivity gains from exporting further driving the self-selection of
firms into importing.4 However, the complementary effect of importing in terms of increasing the
likelihood of exporting is weak as can be seen from the insignificance of the marginal effect of
previous importing in the export participation regression.
From Tables (5) and (6), it seems that the high persistence of firms’ importing status makes the
task of estimating the causal relationship between productivity and import market participation
harder. In other words, without sufficient within firm variation in importing status, it is not
possible to properly estimate such a relationship. However, Table 7 reports that 1,413 (60.13%)
firms do not change their importing status; they either import or never use imported inputs
throughout the sample period. In contrast, 937 (39.87%) firms change their importing status at
least once during the same period. And 512 (27.78%) firms switch their import participation more
than once. that there are enough variation in the data which can be used for precise estimation of
the parameters of interest. Such within firm changes in importing provide the necessary variation
required for identification of the effect of importing.
4
Selection into importing
The empirical facts in the previous section establish that, on average, importing firms are more
productive. They also indicate that there are substantial market entry costs associated with
importing. One possible explanation for these empirical facts is that more productive firms self–
select themselves into importing markets. To test the empirical validity of this argument, I plot the
productivity distribution of firms in the periods prior to some of them start importing. Figure 2
shows the plot of the probability and cumulative densities of firm productivities on the vertical
axis, and a normalized firm productivity (in logarithmic scale) on the horizontal axis. Here, the
normalization is achieved by dividing the actual firm productivity by industry average to which the
firm belongs with the objective of accounting for possible industry idiosyncrasies and the relative
position of the firm in the industry. The first panel depicts that the density function of importing
firms lies to the right of the productivity density of non-importing firms for all the time lags
considered. Relatedly, in the second panel the cumulative density function of importing firms lies
below that of non-importing firms. These features of the productivity distributions indicate that
importing firms were more productive before becoming importers compared to firms currently not
importing. This result implies that there is a selection of more productive firms into importing
activity, in accordance with the presence of substantial market entry costs of importing.
To use a standard approach in testing for the self-selection hypothesis, I run a regression of
4
Previous findings on African firms show substantial learning-by-exporting. In the Ethiopian case, using the
same data source as in this paper, Bigsten and Gebreeyesus (2009) document a significant productivity gain of
15-26% from exporting over the period 1996-2005.
90
lagged values of productivity tf pi,t−s on current import status Mi,t , and control variables such as
firm capital ki,t−s , employment size li,t−s , export market participation Xi,t−s as well as year δt and
industry τ fixed effects.
tf pi,t−s = β0 + β1 Mi,t + β2 ki,t−s + β3 li,t−s + β4 Xi,t−s + δt + τ + i,t ; s = 1, 2, 3
Table 8 presents estimates of percentage differences in productivity between current importers and
non-importers periods prior to some of them becoming importers. I find a positive and highly
significant estimate for the current period dummy implying that these firms were actually more
productive even before they start importing.
The standard approach focuses only on a single moment of the productivity distribution. To
further establish the self-selection argument, I undertake the Kolmogorov-Smirnov test. This test
uses all the information in the empirical productivity distribution. Th test proceeds as follows.
Let x1 , x2 , ..., xn0 and xn0 +1 , xn0 +2 , ..., xn0 +n1 be random samples of size n0 and n1 independently
drawn from the cumulative distribution functions Ω0 (x) and Ω1 (x). The distribution functions
Ω0 (x) and Ω1 (x) represent the cumulative productivity densities of importing and non-importing
firms, respectively. To test whether or not the two distributions are identical, I do a two-tailed
test for the null hypothesis H0 : Ω1 (x) − Ω0 (x) = 0 against the alternative H1 : Ω1 (x) − Ω0 (x) 6= 0
where x ∈ R. The test statistic is given by D∗ = max(|Ω1 (x) − Ω0 (x)|). Similarly, the first order
x
stochastic dominance of the productivity distributions is checked by testing for the null hypothesis
H0 : Ω1 (x) − Ω0 (x) = 0 against the alternative H1 : Ω1 (x) − Ω0 (x) ≤ 0 where x ∈ R. And, the
test statistic becomes D∗ = max(Ω1 (x) − Ω0 (x)). In both cases Ω0 and Ω1 are replaced by the
x
P
P
i ≤x)
i ≤x)
and Ωn1 = I(i:x
respectively.5
empirical distribution functions Ωn0 = I(i:x
n0
n1
The Kolmogorov-Smirnov test results are shown in Table 9. The two-tailed test rejects the
hypothesis that currently importing and non-importing firms have the same productivity distribution. Relatedly, the one–tailed test fails to reject the null hypothesis that the productivity
distribution of importing firms stochastically dominates that of the non-importing counterparts.
These results provide support to the argument that the current importers were more productive
than their non-importing counterparts even before the former started importing.
In conclusion, the summaries from the transition matrix and the estimates from the probit and
least squares regressions clearly indicate the existence of substantial market entry costs which lead
to the selection of more productive firms into importing.
5
Learning-by-importing
In this section, I test for the learning-by-importing hypothesis by adopting a structural approach
that addresses several estimation issues. In the test, I distinguish between static and dynamic
5
For a discussion on the test procedure, see Delgado et al. (2002).
91
effects of importing and estimate them separately.
5.1
Technology
I closely follow Kasahara and Rodrigue (2008) and specify the production technology of firm i at
time period t as:
βk u β u s β s βe
Yi,t = Ai,t Ki,t
Li,t Li,t Ei,t
"Z
N (di,t )
m (j)
θ−1
θ
θ
# θ−1
βm
dj
;θ > 1
(1)
0
where Yi,t refers to output, Ai,t technology parameter, Ki,t capital, Lui,t and Lsi,t unskilled and skilled
labor, Ei,t energy and m(j) a composite of domestic and foreign of intermediate goods. The β’s are
elasticities of output with respect to inputs of production; θ > 1 elasticity of substitution between
f
f
d
d
denote the number of
and Ni,t
where Ni,t
any two intermediate goods, and N (di,t ) = Ni,t
+ di,t Ni,t
domestically produced and imported intermediate goods, respectively, and di,t = 1 is an indicator
function if the firm uses imported intermediates. In this specification, I treat different varieties of
intermediate goods as horizontally differentiated with no quality difference.
Assume that both domestic and foreign intermediate goods are produced and used symmetrically. That is m̄ units of each intermediate good variety j are used, and the total material input
used by firm i in time period t is Mi,t = N (di,t )m̄. After rearranging the terms, the production
function is now given by:
βm
βm
βk u β u s β s βe
Yi,t = Ai,t N (di,t ) θ−1 Ki,t
Li,t Li,t Ei,t Mi,t
βm
βk u βu s βs βe
βm
= e(ωi,t +i,t ) N (di,t ) θ−1 Ki,t
Li,t Li,t Ei,t Mi,t
(2)
βm
where e(ωi,t +i,t ) N (di,t ) θ−1 is a residual term of the production function. The residual term consists
of a component which represents the impact of imported intermediate inputs due to variety effect
βm
N (di,t ) θ−1 , a firm’s total factor productivity ωi,t , and unobserved shock i,t which denotes, say,
measurement error. Imposing a specific functional form on the evolution equation of ωi,t , it is
possible to capture learning-by-importing; the effect on productivity of imported inputs one period
after the firm imports.
5.2
Decision problem
As in the Olley and Pakes (1996) model, a firm faces three decision problems at the beginning
of each time period. First, it compares the sell-off value of exit with the continuation value of
operation. If the firm decides to exit, it gets a sell-off value of Φ. If the firm stays in, it chooses
levels of freely variable inputs labor and energy, and makes capital investment and import decisions.
Assuming that current investment is productive in the next period, capital evolves according to
ki,t = (1 − δ)ki,t−1 + ii,t−1 . Firm productivity is known to firms and follows, conditional upon
92
survival χi,t , a controlled first-order Markov process ωi,t = E [ωi,t | ωi,t−1 , di,t−1 , χi,t = 1] + ζi,t . The
maximum expected discounted reward for a firm at a time period is given by the Bellman equation:
(
n
V (ωi,t , ki,t , di,t−1 ) = max Φi,t , max πt (ωi,t , ki,t , di,t ) − Ck (ki,t , ii,t ) − Cd (di,t , di,t−1 )
di,t ,ii,t
Z
+β
V (ωi,t+1 , ki,t+1 , di,t ) dF (ωi,t+1 , ki,t+1 , di,t | ωi,t , ki,t , di,t , χi,t = 1)
) (3)
o
where ωi,t , ki,t and di,t refer to the state variables, π(.) indirect profit function, Ck (.) investment cost
function, Cd (.) sunk or fixed costs of using imported intermediates depending on previous period
import status, and β the discount factor. Solving the above dynamic programming problem of
firm i yields three policy functions: exit rule χi,t = ω t > ωit (ki,t , di,t−1 ), discrete import di,t =
dt (ωi,t , ki,t , di,t−1 ), and investment demand ii,t = it (ωi,t , ki,t , di,t−1 ) functions. The dependence of
the policy functions on the import variable follows from treating previous period import decision as
a state variable. Its inclusion as an additional state variable is to capture any dynamic productivity
effect of imported intermediates.
5.3
Empirical strategy
I now present the empirical procedure used to estimate parameters of the production function and
transition equations of the model. Logarithmic transformation of the production function and
inclusion of the discrete import variable di,t to capture the static effects of imported intermediates
βm
ln(N (di,t )) yields
θ−1
u
s
yi,t = βk ki,t + βu li,t
+ βs li,t
+ βe ei,t + βm mi,t + βd di,t + ωi,t + i,t
(4)
From a controlled first-order Markov process of productivity and assuming a linear approximation,
we have
ωi,t = E [ωi,t | ωi,t−1 , di,t−1 , χi,t = 1] + ζi,t
(5)
= τ + ρ ωi,t−1 + γ di,t−1 + ζi,t
where the innovation term ζi,t which is independent of ωi,t−1 and di,t−1 and with a known distribution. In the productivity equation, I condition on survival probability to control for endogenous
selection of firms in the data.6 Equations (4) and (5) allow us to test both the static and dynamic
effects of importing on firm output and productivity. That is, if βd > 0, it implies that using
imported intermediates immediately improve output for a fixed quantity of inputs in production.
On the other hand, γ > 0 indicates a dynamic productivity gain, and the long-run effect can be
γ
summarized as 1−ρ
.
6
Gebreeyesus (2008) finds an annual firm turnover rate of (average of entry and exit rates) 22% over the years
1996-2003 in Ethiopian manufacturing. This strongly suggests the need to account for firm attrition in the data.
93
Given that a firm does not make input decisions independent of it productivity, and ωi,t is not
observed in the data, estimation of equation (4) by ordinary least squares raises econometric issues
due to the endogeneity of input choices. For this purpose, I adopt the widely used estimation algorithm developed by Levinsohn and Petrin (2003) in which material inputs are used as a proxy for
unobserved firm productivity. I exploit the relationship that demand for material inputs depends
on observed capital and unobserved productivity mi,t = mt (ki,t , ωi,t , di,t ). Under the monotonicity
of mt (.), the unobserved productivity can be expressed in terms of the observables capital, material
inputs and import as ωi,t = ωt (mi,t , ki,t , di,t ). The resulting estimating equation becomes:
u
s
yi,t = βu li,t
+ βs li,t
+ βe ei,t + ϕt (mi,t , ki,t , di,t ) + i,t
(6)
where ϕt (mi,t , ki,t , di,t ) = βk ki,t + βm mi,t + βd di,t + ωt (mi,t , ki,t , di,t ). I estimate equation (6) by least
squares in which ϕt (.) is approximated using a third-order polynomial function, and with industry
and time fixed effects included. In so doing, βu , βs and βe are consistently estimated. This is
because the source of correlation between the freely variable inputs and a firm’s productivity has
now been controlled by the polynomial approximation, and these inputs are also uncorrelated with
i,t by construction. Because ki,t , mi,t and di,t are collinear with the terms in the polynomial
approximation, βk , βm and βd are unidentified in this stage.
The identification assumptions to estimate βk , βm and βd crucially depends on timing. Since
ki,t is determined at t − 1, it is uncorrelated with the innovation term in productivity, ζi,t , giving
rise to an exogenous variation in ki,t used for identifying βk . Because the firm choose mi,t at the
same time ωi,t is observed, mi,t is not independent of ζi,t . However, ζi,t is uncorrelated with mi,t−1
for it is decided at t − 1, and this condition is used to estimate βm . Identification of βd comes from
the orthogonality between ζi,t and di,t−1 . That is, even if di,t is correlated with ωi,t , the innovation
term ζi,t should have no correlation with past import status di,t−1 , which was decided at t − 1.
After estimating equation (6) and recovering φi,t ≡ ϕ̂t (ki,t , mi,t , ωi,t ) = βk ki,t + βm mi,t + βd di,t +
ωi,t 7 , I run a probit regression of Pi,t = P r(χi,t = 1) = χt (ki,t , ki,t−1 , di,t−1 )) where χt (.) is approximated linearly in its arguments. The probit estimation yields firms’ predicted survival probabilities
P̂i,t for a given level of capital, productivity, and previous period import status.
Afterwards, I substitute φi,t in (5) and obtain the following estimating equation:
φi,t = τ + βk ki,t + βm mi,t + βd di,t + ρ(φi,t−1 − βk ki,t−1 − βm mi,t−1 − βd di,t−1 )
+ γdi,t−1 + Ωt (φi,t−1 − βk ki,t−1 − βm mi,t−1 − βd di,t−1 , P̂i,t ) + ζi,t
(7)
where Ωt (.) is included to control for firm attrition in the data. Equation (7) is estimated by a
non-linear least squares technique. For the purpose of comparison, I also estimate equation (7)
without correcting for endogenous selection of the firms.
7
Ackerberg et al. (2006) argue that using the moment conditions in the residuals of ζi,t instead of ζi,t + i,t yields
precise and more stable estimates. This is due to the additional variance term associated with i,t .
94
To investigate whether intensive use of foreign varieties improves productivity, I invoke the
symmetry assumption regarding the production and employment of intermediate goods. From
the assumption, it follows that the ratio of imported to total intermediate inputs is given by:
f
Mi,t
N (1)m̄−Ni,t (0)m̄
N (1)−N (0)
= i,t Ni,t (1)m̄
= i,t Ni,t (1)i,t . This ratio can be interpreted as the fraction of imported
Mi,t
inputs both in number and value in the total material input used in the production. Introducing
this ratio into the production function and productivity equation, we obtain:
yi,t = βk ki,t + βu Lui,t + βs Lsi,t + βe ei,t + βm mi,t + βd ni,t + ωi,t
ωi,t = E [ωi,t | ωi,t−1 , ni,t−1 , χi,t = 1] + ζi,t
(8)
(9)
= τ + ρ ωit−1 + γ ni,t−1 + ζi,t
where ni,t−1 = log
f
Mi,t−1
Mi,t−1
. And, the identification assumptions and estimation steps proceed in
the same way as in the discrete case.
To summarize, the parameter vector of interest θ = (θy , θω ) comprises production function
parameters on capital, unskilled and skilled labor, energy and material θy = (βk , βl , βs , βe , βm , βd ),
and productivity transition parameters θω = (ρ, µ).
5.4
Result
This section discusses the least square (OLS), fixed effects (FE) and Levinsohn–Petrin (LP) estimation results.
Columns (1)–(4) of Table 10 present parameter estimates where import is treated as a discrete variable. The OLS results in column (1) show that all estimates of the output elasticities
are positive and significant. The magnitudes of these elasticities are also consistent with most
findings in the productivity estimation literature. Importantly, the coefficient on discrete import
variable is positive and significant implying that there is a productivity gain due to importing,
approximately 6.18%. It is well known that least squares estimation of production functions is
plagued by endogeneity problems. Assuming time–invariant firm effects, demeaning of the estimation equation yields a new estimating equation free of endogeneity. Under this assumption, I run
the fixed effects regression and the results are shown in column (2). The FE estimates are very
close to their OLS counterparts in terms of sign and statistical significance. However, there are
size differences between them. As expected, FE estimates on capital, skilled labor and material
inputs are lower than the OLS estimates. The only exception is the estimate on unskilled labor
which becomes higher with the FE estimation suggesting a downward bias in the OLS estimate
of the coefficient on unskilled labor. From the FE estimates we see that there are no immediate,
significant productivity gains from using imported inputs, albeit a positive estimate.8
8
Consistent estimation of the input elasticities under OLS and FE prevents the estimation of ρ and γ.
95
Least squares and fixed effects regression impose restrictive assumptions. Consistent estimation
of the parameters using OLS requires no correlation between freely variable inputs and serially
uncorrelated firm productivity whereas FE assumes firm-specific, time-invariant unobserved productivity. Additionally, with these techniques it is not possible to endogenize productivity and
distinguish between static and dynamic gains. To overcome these limitations and to impose a
richer structure, I adopt the LP estimation algorithm.
In columns (3) and (4), LP estimates of the coefficients on freely variable inputs are similar to
OLS and FE estimates in their statistical significance. However, the LP estimates are smaller than
their OLS counterparts but larger than FE estimates except for unskilled labor. Coefficients on
capital, material inputs and import are estimated first without controlling for survival probability
of firms and then after taking into account firm survival using the predicted probabilities from
probit model. The estimates are shown in columns (3) and (4), respectively. They are very
similar in statistical significance, direction and magnitude. They show that firms experience an
immediate decline in productivity due to importing, 0.8%. This is not unexpected in the light of
previous findings showing that firms might need to adjust their production structure to benefit
from the availability of cheaper and probably better imported intermediates.9 It is also shown
that there is a strong persistence in the evolution of productivity, ρ = 0.78 and 0.65, and there
are dynamic productivity gains due to importing, 1.1-1.2%. Long-run effects of importing predict
a firm productivity improvement around 3.5-4.9%.
In columns (5)-(8) I present the estimation results in which import is treated depending on how
intensive is the use of foreign varieties among importing firms. Both the OLS and FE estimates
display similar patterns as their counterparts in the discrete cases. The only exception is the
significance of the import variable under FE regression. The LP estimates show that a 100%
increase in the share of imported input increases firm productivity by 2.1% immediately and 0.7%
in the period after. In the long-run, the productivity gain is approximately 4-22%. Note that
the long run productivity gain increases substantially when an endogenous selection of firms is
addressed in the estimation.
To see the time path, I simulate the productivity path (loosely defined as βd di,t + ωi,t ) of a
hypothetical firm over time. In the simulation, I use the LP estimates while ignoring the unobserved
shocks which firms experience in each period. Figure 3 shows the evolution of productivity of a
firm that starts importing at period 1 and continues to do so afterwards. We see that the firm
experiences a momentary decline in productivity. However, after some time the firm adjusts its
production structure and is able to enjoy the productivity gains from importing. We observe
that correction of an endogenous selection of firms gives rise to a rapidly converging path, albeit
at lower level. In Figure 4, I repeat the same exercise in which import intensity is considered
instead. Here, the hypothetical firm starts using imported inputs at period 1 and these inputs
9
For instance, for Danish manufacturing firms, Smeets and Warzynski (2013) find a temporary decline yet a
continual improvement in firm productivity due to importing.
96
constitute 0.47% of the materials used in production, which is the average import share in the
data. We observe a significant productivity improvement over time. We notice that correcting for
the survival probability of firms makes a substantial difference.
All in all, the results from the empirical analysis show that there are significant productivity
gains from importing. When considering the import participation of firms only, we expect overall
productivity improvements ranging from zero as in a FE estimation to 3.5-4.9% as in a LP estimation. Considering how intensive the employment of imported intermediate affects productivity,
we see that there are both immediate and long–term benefits associated with a more intensive use
of imported inputs.
The main results of the above empirical analyses highlight the fact that even though there are
temporary declines in productivity, the firm ultimately benefits from importing, and even more so
if it intensifies the relative employment of imported varieties vis-à-vis the domestic ones. However,
in view of a significant portion of firms importing, and their production activities are characterized
by intensive use of imported intermediates, the estimated productivity gains are relatively small.
This strongly suggests the limited absorptive capacity—the capabilities and efforts to assimilate
the knowledge embodied in the imported inputs—of firms in the economy.
6
Conclusion
The vast majority of the literature on firm globalization has been restricted to advanced economies
and a few developing countries in Asia and Latin America. African manufacturing firms have
been greatly neglected because of lack of available accounting information and trade statistics.
Even among a handful of existing studies, utmost focus has been on exporting. In this respect,
the literature on African manufacturing remains largely incomplete, especially in light of high
import–to–GDP ratios and import shares of manufacture in these economies. In this paper, with
the objective of filling this void in the literature, I use a unique panel dataset from Ethiopian
manufacturing. A simple description of the data uncovers that most firms source production
inputs from the world market. This illustrates that firms heavily rely on imported inputs partly
due to limited availability of domestically manufactured inputs. Additionally, I find a positive link
between importing and productivity and other firm performance measures.
Examination of the direction of causality in the import-productivity relationship shows that
more productive firms self-select themselves into importing indicating significant sunk and fixed
costs of importing. Additionally, to test the causal effect of importing on firm productivity, I use
a framework in which the static and dynamic effects are estimated separately. The results provide
evidence of learning-by-importing albeit an initial temporary decline. Furthermore, intensive use
of imported inputs is associated with a greater productivity improvement among importing firms.
However, the small size of the productivity gains demonstrates the limited absorptive capacity of
firms in the economy. This feature is consistent with findings, mostly in economic growth literature,
97
which emphasize the mismatch between human capital of domestic workers and technological
content of imported inputs as a hindrance to technology diffusion to the least developed countries.
98
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100
Table 1: Sectoral composition of Ethiopian economy
GDP
Agriculture†
Manufacturing
‡
Service§
Year
% Growth
% GDP
% Growth
% GDP
% Growth
% GDP
% Growth
6.59
1996
12.43
55.35
16.96
5.64
3.23
34.03
1997
3.13
58.87
2.00
7.96
2.97
27.83
4.51
1998
-3.46
53.54
-9.64
5.74
0.38
33.84
7.22
1999
5.16
49.60
3.40
6.25
8.71
37.10
8.31
2000
6.07
48.71
3.05
6.11
6.66
38.84
11.20
2001
8.30
46.56
9.62
6.35
3.81
40.32
4.37
2002
1.51
42.50
-1.88
6.33
1.81
43.51
5.89
2003
-2.16
40.93
-10.48
6.33
1.21
44.91
9.10
2004
13.57
43.17
16.94
5.97
7.27
42.68
4.72
2005
11.82
45.61
13.54
5.36
13.03
41.33
12.30
2006
10.83
46.80
10.91
5.10
10.30
40.44
12.92
2007
11.46
46.38
9.45
5.03
9.93
40.91
15.98
2008
10.79
49.40
7.50
4.49
9.26
39.44
16.68
2009
8.80
49.60
6.36
4.19
8.62
39.93
14.92
2010
12.55
45.64
5.13
4.38
9.21
43.97
17.04
2011
11.18
45.57
9.01
4.07
9.24
43.75
13.08
1996–2011
7.62
48.02
5.74
5.58
6.60
39.55
10.30
Source: World Development Indicators, World Bank
†
‡
§
Agriculture corresponds to ISIC–Rev.3 divisions 1–5.
Manufacturing corresponds to ISIC–Rev.3 divisions 15–37.
Services correspond to ISIC–Rev.3 divisions 50–99.
101
Table 2: Components of Ethiopian international trade
Export†
Import†
% Merchandise trade‡
Year
Level
% GDP
% Growth
Level
% GDP
% Growth
Manuf. Export
Manuf. Import
1996
750.1
9.35
7.30
1,318.0
16.43
16.16
9.65
84.49
1997
992.0
11.99
32.26
1,559.5
18.84
18.33
6.76
66.61
1998
1,079.6
13.51
8.83
1,720.7
21.54
10.34
6.71
77.24
1999
1,015.6
12.09
-5.93
2,081.9
24.78
20.99
9.78
70.90
2000
1,083.6
12.16
6.69
2,158.5
24.22
3.68
13.43
65.19
2001
1,169.5
12.12
7.93
2,312.3
23.96
7.12
14.31
73.85
2002
1,249.3
12.75
6.83
2,635.4
26.90
13.97
11.38
64.03
2003
1,290.4
13.46
3.29
2,657.2
27.72
0.83
3.83
70.80
2004
1,635.8
15.02
26.77
3,475.4
31.92
30.79
4.58
71.97
2005
1,858.4
15.27
13.61
4,367.2
35.87
25.66
5.36
68.51
2006
1,893.7
14.04
1.90
4,991.0
36.99
14.28
13.75
76.41
2007
1,935.0
12.87
2.18
4,873.6
32.41
-2.35
9.01
60.18
2008
1,934.3
11.61
-0.04
5,236.0
31.43
7.44
8.65
71.53
2009
1,938.5
10.69
0.22
5,297.6
29.22
1.18
8.91
68.69
2010
2,826.3
13.85
45.80
6,882.5
33.73
29.92
10.37
65.27
2011
3,856.0
17.00
36.43
7,289.4
32.14
5.91
8.77
67.13
–
12.99
12.13
–
28.01
12.77
9.08
70.18
1996–2011
Source: World Development Indicators, World Bank.
†
‡
Export and import of goods and services in million constant 2005 USD.
Manufacture items comprise commodities in SITC–Rev.3 sections 5–8 excluding non–ferrous metals.
102
103
32
28
12
51
19
54
27
445
Chemicals
Rubber and plastic
Non-metallic products
Fabricated metals
Furniture
Others
Total manufacturing
42
Leather products
Printing and publishing
15
Wearing apparel
13
24
Textiles
Wood products
128
# Firms
Food and beverage
Industry
4.72
0.00
1.85
0.00
1.96
0.00
0.00
0.00
7.69
21.43
6.67
20.83
2.34
% Exporters
1996
67.87
92.59
57.41
89.47
21.57
100
100
71.88
69.23
80.95
66.67
83.33
64.06
% Importers
826
35
95
39
95
65
38
48
12
61
19
12
307
# Firms
14.77
14.29
0.00
2.56
3.16
3.08
21.05
2.08
16.67
45.90
21.05
50.00
20.20
% Exporters
2011
64.89
82.86
65.26
82.05
22.11
92.31
89.47
87.50
75.00
70.49
73.68
91.67
58.31
% Importers
Table 3: International trade participation of firms in Ethiopian manufacturing
5.86
2.57
0.58
1.23
1.49
0.43
2.77
0.13
1.85
32.01
13.56
26.95
5.46
% Exporters
69.74
92.00
79.10
85.55
26.29
95.29
93.35
85.27
51.08
82.24
75.72
71.10
59.89
% Importers
1996-2011
Table 4: Export and import activity premia
Export-only
Import-only
Two-way
TFP
23.078∗∗∗
(0.055)
5.660∗∗∗
(0.014)
31.832∗∗∗
(0.033)
Output per worker
46.020∗∗∗
(0.110)
8.211∗∗∗
(0.027)
77.311∗∗∗
(0.060)
Capital per worker
43.952∗∗
(0.150)
-14.790∗∗∗
(0.052)
39.440∗∗∗
(0.088)
Material per worker
39.449∗∗
(0.143)
4.146
(0.031)
68.109∗∗∗
(0.068)
Energy per worker
4.701
(0.164)
7.838∗∗
(0.032)
38.321∗∗∗
(0.077)
Employment size
170.102∗∗∗
(0.121)
40.020∗∗∗
(0.023)
612.849∗∗∗
(0.059)
Sh. of skilled worker
-1.75
(0.077)
4.407∗∗∗
(0.013)
13.058∗∗∗
(0.027)
Bootstrapped standard errors with 500 replications in parentheses.
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Table 5: Transition probabilities of firm activities
Domestic
Status t
Status t+1
Export-only Import-only
Two-way
Domestic
Export-only
Import-only
Two-way
67.19
13.75
13.76
4.01
1.41
33.75
0.22
6.61
30.71
13.75
84.30
12.42
0.69
38.75
1.71
76.95
Cross-sectional
Average
28.59
1.14
64.86
5.40
104
Table 6: Probit estimation of export and import participation: marginal effects
Export (Xi,t )
Import (Mi,t )
(1)
(3)
(4)
0.030∗∗∗
(0.006)
-0.004∗∗
(0.002)
0.023∗∗∗
(0.004)
–
0.029∗∗∗
(0.003)
-0.005∗∗
(0.002)
0.021∗∗∗
(0.004)
0.035∗
(0.019)
0.302∗∗∗
(0.006)
Yes
Yes
(2)
0.008∗∗∗
(0.003)
0.003∗∗∗
(0.001)
0.009∗∗∗
(0.002)
0.116∗∗∗
(0.004)
–
0.008∗
(0.003)
0.003∗∗∗
(0.001)
0.009∗∗∗
(0.002)
0.116∗∗∗
(0.004)
-0.003
(0.004)
Yes
Yes
Yes
Yes
9,020
tf pi,t−1
ki,t−1
li,t−1
Xi,t−1
Mi,t−1
Year FE
Industry FE
Obs.
0.303∗∗∗
(0.006)
Yes
Yes
9,020
Bootstrapped standard errors with 500 replications in parentheses.
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Table 7: Firms switching their importing status
Switching
frequency
# Firms
% Firms
0
1,413
60.13
1
425
18.09
2
282
12.00
3
96
4.09
4
68
2.89
5
26
1.11
5+
40
1.71
-
2,350
100
Note: Switching frequency refers to the number of times a firm
changes its importing status over the sample period.
105
Table 8: Self–selection into importing
tf pi,t−3
tf pi,t−2
tf pi,t−1
11.981∗∗∗
(0.019)
11.166∗∗∗
(0.019)
9.442∗∗∗
(0.018)
8.728∗∗∗
(0.018)
10.072∗∗∗
(0.015)
9.564∗∗∗
(0.015)
Xi,t−3
-
29.557∗∗∗
(0.038)
-
-
-
-
Xi,t−2
-
-
-
28.501∗∗∗
(0.034)
-
-
Xi,t−1
-
-
-
-
-
23.293∗∗∗
(0.031)
Yes
Yes
0.445
Yes
Yes
0.450
Yes
Yes
0.445
Yes
Yes
0.450
Yes
Yes
0.456
Yes
Yes
0.459
Mi,t
Year FE
Industry FE
Adj.R2
Obs.
5,832
7,118
9,020
Bootstrapped standard errors with 500 replications in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
106
Table 9: Kolmogorov-Smirnov test for equality of productivity distributions
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
∗
H0 : Ω1 (x)-Ω0 (x) ≤ 0
H0 : Ω1 (x)-Ω0 (x)= 0
Year
tf pi,t−3
tf pi,t−2
tf pi,t−1
tf pi,t−3
tf pi,t−2
tf pi,t−1
0.211∗∗∗
0.298∗∗∗
0.242∗∗∗
0.155∗∗
0.210∗∗
0.158∗∗
0.266∗∗∗
0.224∗∗∗
0.234∗∗∗
0.205∗∗
0.093
0.212∗∗∗
0.129∗∗
0.262∗∗∗
0.211∗∗∗
0.192∗∗∗
0.207∗∗∗
0.138∗∗∗
0.209∗∗∗
0.234∗∗∗
0.222∗∗∗
0.216∗∗∗
0.274∗∗∗
0.133∗∗
0.154∗∗∗
0.179∗∗∗
0.141∗∗
0.249∗∗∗
0.272∗∗∗
0.196∗∗∗
0.195∗∗∗
0.244∗∗∗
0.170∗∗∗
0.269∗∗∗
0.166∗∗∗
0.246∗∗∗
0.226∗∗∗
0.168∗∗∗
0.148∗∗∗
0.131∗∗∗
0.122∗∗
0.142∗∗∗
-0.025
-0.007
-0.004
0.000
-0.020
-0.030
-0.003
-0.009
-0.008
-0.009
-0.031
-0.003
-0.015
-0.022
-0.001
-0.014
0.000
-0.003
-0.014
-0.008
-0.006
-0.008
-0.009
-0.008
-0.005
-0.011
-0.023
-0.006
-0.022
-0.003
0.000
-0.022
-0.016
-0.002
-0.010
-0.005
-0.012
-0.012
-0.015
-0.006
-0.002
-0.006
p < 0.01, ∗∗ p < 0.05, ∗∗∗ p < 0.01
107
Table 10: Production function parameters
Import participation
OLS
FE
(1)
(2)
βu
0.136∗∗∗
(0.008)
0.142∗∗∗
(0.017)
βs
0.128∗∗∗
(0.008)
βe
Import intensity
OLS
FE
(5)
(6)
0.115∗∗∗
(0.007)
0.112∗∗∗
(0.011)
0.128∗∗∗
(0.020)
0.115∗∗∗
(0.007)
0.089∗∗∗
(0.017)
0.103∗∗∗
(0.007)
0.128∗∗∗
(0.010)
0.071∗∗∗
(0.019)
0.103∗∗∗
(0.007)
0.100∗∗∗
(0.006)
0.064∗∗∗
(0.007)
0.091∗∗∗
(0.005)
0.074∗∗∗
(0.006)
0.042∗∗∗
(0.007)
0.091∗∗∗
(0.005)
βk
0.059∗∗∗
(0.004)
0.033∗∗∗
(0.006)
0.042∗∗∗
(0.001)
0.043∗∗∗
(0.001)
0.070∗∗∗
(0.005)
0.040∗∗∗
(0.007)
0.037∗∗∗
(0.002)
0.038∗∗∗
(0.002)
βm
0.673∗∗∗
(0.009)
0.567∗∗∗
(0.015)
0.668∗∗∗
(0.002)
0.669∗∗∗
(0.002)
0.709∗∗∗
(0.009)
0.571∗∗∗
(0.017)
0.684∗∗∗
(0.002)
0.685∗∗∗
(0.002)
βd
0.060∗∗∗
(0.014)
0.012
(0.022)
-0.008∗
(0.005)
-0.008∗
(0.005)
0.093∗∗∗
(0.007)
0.103∗∗∗
(0.011)
0.021∗∗∗
(0.002)
0.021∗∗∗
(0.002)
ρ
-
-
0.777∗∗∗
(0.008)
0.654∗∗∗
(0.046)
-
-
0.811∗∗∗
(0.009)
0.968∗∗∗
(0.056)
γ
-
-
0.011∗∗∗
(0.004)
0.012∗∗∗
(0.004)
-
-
0.007∗∗∗
(0.001)
0.007∗∗∗
(0.001)
N
LP
(3)
(4)
8,282
LP
(7)
5,190
Bootstrapped standard errors with 500 replications in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
108
(8)
Figure 1: Geographical orientation of Ethiopian international trade
Sub−Saharan Africa
Middle East & North Africa
1995
2000
2005
2010
20
0
0
30
40
2
50
60
4
40
70
80
6
60
High−Income
1995
2005
2010
1995
Central & South Asia
2005
2010
2
10
1.5
20
1995
2000
2005
2010
0
0
0
5
.5
5
10
1
15
2000
Latin America & Caribbean
15
25
East Asia & Pacific
2000
1995
2000
Export share
2005
2010
1995
Import share
Source: World Development Indicators Database, the World Bank
109
2000
2005
2010
Figure 2: Productivity distribution by firm import status
One−period lag
.6
.4
.4
pdf
pdf
5
−10
−5
0
5
−10
−5
0
5
−10
−5
0
5
−5
0
5
−10
−5
0
5
.8
0
.2
.4
cdf
.6
.8
0
.2
.4
cdf
.6
.8
.6
cdf
.4
.2
0
−10
1
0
1
−5
1
−10
0
.2
.2
0
0
.2
pdf
.4
.6
Two−period lag
.6
Three−period lag
Importers
Non−importers
110
Figure 3: Productivity effect of importing: import participation
0.02
No survival correction
Survival correction
0.015
0.01
0.005
0
−0.005
−0.01
0
5
10
15
20
25
111
30
35
40
45
50
Figure 4: Productivity effect of importing: import intensity
0.4
No survival correction
Survival correction
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0
5
10
15
20
25
112
30
35
40
45
50
Appendix A: Data construction
The Central Statistical Agency of Ethiopia conducts an annual survey of large and medium–scale
manufacturing firms. The scope of the survey is delimited to public and private establishments
with at least 10 employees and which use power–driven machinery. In the survey, an establishment
is defined to be the whole of the premises under the same ownership or management with a specific
location address. Below is a list of the definitions of the variables used to construct the final dataset.
1. Gross value of production: includes the sales value of all products of an establishment net of
changes in the inventory of finished goods, fixed assets, and others during the reference period.
The value denotes the market price inclusive of indirect taxes but exclusive of subsidies.
2. Fixed capital assets: refers to assets with a productive life at least one year. It also includes
production of fixed assets for a firm’s own use. They are valued as the beginning period book
value and new capital expenditures less the part sold, disposed and depreciated during the
reference year.
3. Working proprietors, active partners and family workers: includes all unpaid persons who
actively participate in the operation of the establishment.
4. Administrative and technical employees: refers to salaried directors and managers, technicians, research workers, engineers, scientists, accountants, and other office staff.
5. Production workers: consists of persons directly engaged in fabricating, processing, assembling, maintenance, repair, and other associated activities.
6. Seasonal and temporary workers: includes persons who are not regularly on the payroll of
the establishment.
7. Basic wages and salaries: includes all payments made to employees during the reference year.
It excludes commissions, bonuses, social security contributions, insurance, and professional
and hardship allowances.
8. Materials: includes all raw and auxiliary materials which are consumed during the reference
year. Local raw materials are those produced locally, and imported raw materials those
produced in other countries and obtained directly or from local sources. The costs include
factory gate purchase price, transport charges, taxes and other incidental costs.
9. Industrial cost: includes the costs of raw materials, fuels, electricity and other supplies
consumed, costs of industrial services rendered by others, costs of goods bought and resold
without any transformation.
113
Appendix B: Tables
Table B.1: Geographical orientation of Ethiopian merchandise exports
Year
High-income†
Sub-Saharan
Africa
Middle East
&
North Africa
East Asia
&
Pacific
Central
&
South Asia
Latin America
&
Caribbean
Others‡
1996
80.78
0.18
10.21
1.51
0.00
0.00
6.53
1997
80.70
2.93
10.42
0.94
0.00
0.00
3.64
1998
82.80
0.86
9.51
0.94
0.00
0.00
4.78
1999
70.76
1.67
14.45
1.71
1.89
0.13
9.40
2000
80.90
0.60
14.21
1.03
3.17
0.08
0.02
2001
35.79
0.00
55.58
4.14
3.43
0.00
1.07
2002
71.64
0.57
15.91
3.11
7.56
0.00
1.21
2003
61.61
1.76
29.58
1.74
4.63
0.07
0.62
2004
75.19
2.62
8.00
3.74
5.57
0.23
4.65
2005
69.85
3.09
11.36
11.60
3.38
0.15
0.57
2006
69.81
3.75
9.25
11.51
3.67
0.05
1.96
2007
73.31
5.09
8.04
6.82
6.16
0.02
0.55
2008
74.49
5.87
7.03
6.40
5.25
0.14
0.82
2009
56.93
2.54
7.30
13.88
4.11
0.01
15.23
2010
58.16
1.40
6.82
14.99
4.51
0.03
14.09
2011
62.94
0.58
6.88
13.02
4.24
0.16
12.19
1996–2011
69.10
2.09
14.03
6.07
3.60
0.07
4.83
Source: Word Development Indicators, World Bank.
†
‡
All high-income countries in each region are excluded from their respective regions.
Includes trade with unspecified partners or with economies not covered by World Bank classification.
114
Table B.2: Geographical orientation of Ethiopian merchandise imports
Year
High-income†
Sub-Saharan
Africa
Middle East
&
North Africa
East Asia
&
Pacific
Central
&
South Asia
Latin America
&
Caribbean
Others‡
1996
69.36
3.31
4.25
3.59
3.76
0.70
13.62
1997
74.43
2.17
1.10
7.64
5.35
0.17
7.35
1998
75.10
1.59
1.70
5.98
5.62
0.31
7.56
1999
75.39
2.31
1.38
4.73
7.78
0.34
8.06
2000
49.93
1.75
22.70
8.45
7.21
0.22
9.73
2001
60.20
0.92
11.20
8.80
7.31
0.52
11.05
2002
58.92
1.18
5.95
10.75
8.79
0.58
13.84
2003
59.39
1.31
4.73
12.44
9.14
0.76
12.24
2004
57.29
0.86
3.93
12.82
8.95
0.60
15.56
2005
58.43
1.80
3.07
14.22
9.68
0.74
12.06
2006
54.72
2.22
3.97
15.74
9.38
1.29
12.68
2007
48.78
1.18
2.41
23.76
12.24
2.09
9.54
2008
42.61
2.35
1.76
19.01
15.46
0.69
18.12
2009
33.04
2.52
2.74
20.89
9.52
0.92
30.36
2010
35.91
3.35
3.53
16.89
6.10
0.64
33.58
2011
35.14
4.15
3.69
11.99
8.06
0.40
36.58
1996–2011
55.54
2.06
4.88
12.36
8.40
0.69
15.75
Source: Word Development Indicators, World Bank.
†
‡
All high-income countries in each region are excluded from their respective regions.
Includes trade with unspecified partners or with economies not covered by World Bank classification.
115
116
Chapter 4
Imported Inputs and Firm Absorptive
Capacity in Ethiopian Manufacturing
117
Imported Inputs and Firm Absorptive Capacity in Ethiopian
Manufacturing∗
Kaleb Girma Abreha†
Abstract
What is the role of firm absorptive capacity in trade-driven knowledge transfer? Using
a firm-level dataset from Ethiopian manufacturing over the period 1996-2011, I find that
imported inputs are associated with higher productivity only in firms with greater absorptive
capacity. This result provides firm-level evidence on why international knowledge transfer to
the least developed countries, characterized by limited absorptive capacity, are slower in pace
and smaller in magnitude.
JEL codes: F14, D22, L60
Keywords: Imported inputs, firm productivity, absorptive capacity, Ethiopia
∗
I am grateful to the Central Statistical Agency of Ethiopia for making the data accessible. I acknowledge
financial support from the Tuborg Foundation. The usual disclaimer applies.
†
Department of Economics and Business, Aarhus University, Denmark, E-mail: [email protected]
118
1
Introduction
It is now widely accepted that technological progress is the key to sustained economic growth
and development. To this end, countries devote substantial resources to R&D activities. Findings
in this field of research have shown that although these activities are highly concentrated in a
handful of countries, there is significant international knowledge transfer which leads to substantial
economic gains.
A seminal paper by Coe and Helpman (1995), using data on a group of OECD countries, shows
that there are significant gains associated with trade-driven technology transfer. In a subsequent
paper, Coe et al. (1997) document that the knowledge spillovers are not entirely limited to developed economies in that developing countries greatly benefit from their trade partnership with
countries which actively undertake R&D activities.
Other studies establish that the stock of physical and human capital, and institutions determine the pace and the size of technological innovations and diffusions across countries. In their
analysis of the impact of human capital on income per capita growth rate, Benhabib and Spiegel
(1994) illustrate the positive role of human capital in the rate of technology diffusions in addition
to promoting local technological innovations. Relatedly, Acemoglu and Zilibotti (2001) show that
technological developments and domestic workers’ skill mismatches lead to productivity differences
across countries even when they have equal access to technology. Los and Timmer (2005) find empirical evidence which pinpoints that technological assimilations are primarily driven by capital
intensification which generates a potential for knowledge spillovers. Given that capital intensifications vary substantially across countries, they show that technological assimilations are slower
than the predictions from the standard economic growth models. Coe et al. (2009) emphasize
the importance of institutional factors on the heterogeneity of R&D spillovers across countries.
Acharya and Keller (2009) identify differences in geographical distance between trading partners
and nature of goods traded as a source of asymmetry in the extent of technology transfers.1
There exist some studies that look into the role of absorptive capacity in trade-driven knowledge
spillovers. At the firm level, Augier et al. (2013) identify the importance of absorptive capacity
(measured by skill intensity) in determining the degree of learning-by-importing for Spanish manufacturing firms. Similarly, Yasar (2013) considers trade in capital goods among Chinese firms and
shows that imported capital inputs have a larger impact on productivity in importing firms with
greater absorptive capacity.
In this paper, I consider the Ethiopian manufacturing sector to examine trade-driven knowledge
transfer at the firm level. Ethiopian manufacturing makes an interesting case for several reasons.
First, the sector is relatively technologically backward indicating a great potential for technology
1
The role of absorptive capacity—which represents countries’ and firms’ capabilities and efforts to assimilate
technologies and commonly measured by R&D expenditures, human capital and institutions—as a determinant of
knowledge spillovers has been a subject of extensive research in the FDI literature (see e.g. Durham 2004; Girma
2005; Xu 2000). Also see Crespo and Fontoura (2007) for a survey of the literature on the topic.
119
transfer from other countries. Second, the country trades dominantly with technologically advanced economies making international knowledge spillover a possibility and trade an important
conduit for such a spillover; 55% of Ethiopia’s imports come from these countries. Third, manufacture imports constitute 70% of the import trade of the country, and more than 60% of the
manufacturing firms rely on imported inputs. This provides an opportunity to investigate whether
such a heavy reliance on imported inputs is translated into productivity gains.2 Lastly, despite
these potentials, the sector faces a shortage of skilled employees that potentially limits the extent
to which firms benefit from imported inputs.
Using the same dataset as in this paper, Abreha (2014) finds that there are productivity
gains from using imported inputs in Ethiopian manufacturing. However, these gains are small as
compared to similar findings in other developed and developing countries. For instance, Kasahara
and Rodrigue (2008), for Chilean manufacturing firms, find a productivity improvement of 11.1%
and 2.6% under the ordinary least squares and fixed effect regressions, respectively. Besides, using a
control function approach, they report static (21.4%) and dynamic (2.4-4.1%) productivity gains.
For Ethiopian manufacturing firms, the comparable estimate show a 6.18% and no significant
productivity gain under least squares and fixed effects regression respectively. And, the control
function estimates reveal an immediate, temporary decline and a 1.1-1.2% dynamic productivity
gain.
There are at least two explanations, besides the economic development of the source countries
of the imported inputs, as to why the productivity gains are rather small. First, it may be
the case that trade in capital goods rather than intermediate materials is the main channel for
international technology diffusion. On the other hand, importing firms may have limited absorptive
capacity, which refers to the ability and effort such as skill intensity and R&D investment to exploit
the knowledge embodied in imported inputs. Testing the empirical validity of these alternative
explanations is hindered by lack of information on trade in capital goods at the firm level. At the
same time, there is no available data on firm R&D expenditures. Within this context, this paper
examines the role of absorptive capacity (measured by the share of skilled workers in a firm) in
the productivity effect of imported inputs.
For this purpose, I estimate a Cobb-Douglas production function in which import status and
absorptive capacity are included as additional variables. The parameter estimates show that
imported inputs are associated with higher productivity, after controlling for the quantity of inputs
used in production, if firms have the necessary skill composition to absorb the embodied knowledge
in those inputs. This result suggest that mere access to technology is not a sufficient condition for
transfer. In the light of the country’s lack of human capital and less effective ancillary institutions,
this finding provides firm-level evidence on the limited nature and slow pace of technology transfer
to the least developed countries. It must be stressed that this empirical exercise does not establish
2
Abreha (2014) summarizes the sectoral composition of the Ethiopian economy and the geographic orientation
of its international trade.
120
a causal relationships rather it only displays cross-sectional correlations.
This paper is closely related to a series of contributions on firm globalization and economic
growth. It is linked to Augier et al. (2013) and Yasar (2013) who show the pivotal role of firm
absorptive capacity regarding the productivity implications of import trade in capital goods and
intermediates. Unlike these studies, the focus here is on a least developed country where there is
a great potential for technology transfer yet limited absorptive capacity. Additionally, many empirical investigations on absorptive capacity and international knowledge diffusion are undertaken
primarily at an aggregate level and predominantly confined to OECD countries and a few emerging
economies. In this respect, this paper is one of the first studies to provide firm-level evidence from
African manufacturing.
The rest of the paper is organized as follows. Section 2 describes the data. Section 3 presents
the econometric model and the estimation techniques. Section 4 discusses the results. Section 5
concludes.
2
2.1
Data description
Data source
The Central Statistical Agency of Ethiopia (CSA) conducts annual large- and medium-scale manufacturing surveys. The surveys cover all firms in the manufacturing sector with at least 10
employees and which use power-driven machinery. Classification of firms into manufacturing sector is based on ISIC-Rev.3, and it includes industries 15-37 at the 2-digit level. The focus is on
the time period 1996-2011. The dataset provides detailed information on the level of production,
local and export sales and fixed assets of firms. It also contains information on local and imported
material inputs and energy expenses (electricity, fuel and charcoal). After excluding seasonal and
temporary workers due to infrequent report of their number, I categorize workers into two broad
subgroups: skilled (administrative and technical employees) and unskilled (apprentice and production workers). In this study, a firm’s absorptive capacity is approximated by the share of skilled
employees in its workforce.3 Because the complementarity between skilled workers and materials
is not obvious as it is in the case of capital goods, I attach in the appendix a list of raw material codes by industry groups compiled by CSA. Finally, all nominal values are deflated using a
consumer price index, which is extracted from the World Development Indicators database of the
World Bank.4
To restrict the empirical analysis only to firms with real economic activity, I exclude those with
zero or unreported level of production, fixed asset, material input, energy expense and employment.
3
In the literature, absorptive capacity is defined in a variety of different ways. For instance, at the firm level
absorptive capacity can refer to skill intensity, R&D expenditure and size. At the aggregate level, it may also
include a stock of human capital, infrastructure and level of development of legal and financial institutions.
4
See Abreha (2014) for a detailed description of the data and construction of the variables used in the analysis.
121
I also exclude firms appearing only once over the entire sample period. The final dataset comprises
2,350 firms and 12,510 firm-year observations.
2.2
Absorptive capacity
Table 1 reports summary statistics on firm absorptive capacities over the sample period. It shows
that there are few outlying cases of absorptive capacities as indicated by large differences between
the minimum and the maximum values. However, comparison of the mean and the median values
demonstrates that the distribution of absorptive capacities is not greatly skewed. This is also shown
by the size of the standard deviations relative to the mean values. At the same time, we notice that
the mean exceeds the median implying that most firms have absorptive capacities below average,
and on balance, these firms constitute around 55% of the firms in the manufacturing sector. Over
time, we observe that there are no fundamental changes in the distribution of absorptive capacities
with the exception of a small rise in the share of firms with absorptive capacity below the mean.
3
Methodology
To explore any effect of imported inputs on the production efficiency of firms and the role of
absorptive capacity in this respect, I specify a Cobb–Douglas production function:
u
s
yi,t = β0 + βlu li,t
+ βls li,t
+ βk ki,t + βe ei,t + βm mi,t + βd di,t−1 + βs si,t−1 + βds di,t−1 si,t−1 + δt + τ + i,t (1)
u
s
where yi,t refers to output, li,t
unskilled labor, li,t
skilled labor, ki,t capital, ei,t energy, mi,t material
inputs, di,t−1 a dummy variable taking a value of 1 if a firm used imported in the previous period,
si,t−1 absorptive capacity at the beginning of period t, δt and τ year and industry fixed effects, and
i,t an iid error term. All the variables in equation (1) are in logarithmic scale with the exception
of di,t−1 , si,t−1 and the fixed effects.
Regressors of the parameters βd , βs and βds are one-year lagged to reduce the endogeneity
problem coming from a likely correlation between the import dummy and the error term in the
production function. If βd > 0, it shows that importing is positively associated with higher level
of output after controlling for overall quantity of materials used in production. If βs > 0, it
means that a firm whose production characterized by greater absorptive capacity exhibits greater
productivity conditional on its employment size, capital holding, volume and origin of material
inputs used. On the other hand, βds > 0 indicate that importing has a larger impact on the
production activity of a firm with greater absorptive capacity. Also, the interaction term indicates
that an increase in absorptive capacity has a larger effect in importing firms compared to otherwise
similar non-importing firms.
As an additional test on the productivity effect of importing, I explore whether or not firms
have different output elasticities based on their absorptive capacities. For this purpose, I adopt
a threshold regression and use a sample splitting technique developed by Hansen (2000). This
122
technique splits a sample into different regimes based on a threshold variable. In running the
threshold regression, I do not exploit the panel structure of the data. This is due to unavailability
of an estimator in the case of unbalanced panel data, albeit Hansen (1999) develops an estimator
for a non-dynamic balanced panel. Also, reducing the data to a balanced panel is inappropriate
due to high firm turnover rate in the sector.5
I define the production function for the threshold regression as:
yi,t

β l + β k + β e + β m + β d + = β 0 x + ; s ≤ γ
l1 i,t
k1 i,t
e1 i,t
m1
i,t
d1 i,t
i,t
i,t
i,t
1 i,t
=
0
β l + β k + β e + β m + β d + = β x + ; s > γ
l2 i,t
k2 i,t
e2 i,t
m2
i,t
d2 i,t
i,t
i,t
i,t
2 i,t
(2)
where β1 = (βl1 , βk1 , βe1 , βm1 , βd1 )0 and β2 = (βl2 , βk2 , βe2 , βm2 , βd2 )0 denote output elasticity vectors, γ a threshold parameter, xi,t = (li,t , ki,t , ei,t , di,t ) an input vector in which li,t now denotes
unskilled labor, si,t a threshold variable based on absorptive capacity (= share of skilled workers),
and i,t an error term.
Define a threshold effect δn = β1 − β2 , an indicator function Ii,t (γ) = I{si,t ≤ γ} and xi,t (γ) =
xi,t Ii,t (γ). Equation (2) can be written as
0
0
yi,t = β xi,t + δn xi,t (γ) + i,t
(3)
where β = β2 . After stacking the observations for each firm and time period in a vector, the
regression equation becomes:
Y = Xβ + Xγ δn + (4)
The sum of squared errors function from equation (4) is given by:
Sn (β, δ, γ) = (Y − Xβ − Xγ δ)0 (Y − Xβ − Xγ δ)
(5)
which is minimized through concentration. That is, conditional on γ, equation (4) is linear in β
and δn which allows estimation by ordinary least squares. The slope estimates β̂(γ) and δ̂(γ) are
obtained from a regression of Y on Z =[XXγ ]. The sum
of squared errors function from this conditional regression becomes Sn (γ) ≡ Sn β̂(γ), δ̂(γ), γ = (Y − Z(Z 0 Z)−1 Z 0 Y )0 (Y − Z(Z 0 Z)−1 Z 0 Y ).
Then, the threshold value is estimated by minimizing Sn (γ) with respect to γ: γ̂ = argmin Sn (γ),
γ
which can implemented through a grid search over the quantiles of si,t .
Once β, δn and γ are estimated, the next step is to test for the significance of the threshold
effect; that is, H0 : β1 = β2 against H1 : β1 6= β2 . Given that γ is not identified under the H0 ,
the hypothesis test is nonstandard. In this respect, Hansen (1996) develops a heteroskedasticity
robust Lagrange Multiplier test for which the correct p-values are calculated through a bootstrap
procedure.
5
Gebreeyesus (2008) documents that the Ethiopian manufacturing exhibits a turnover rate of 20-22% annually.
123
Conditional upon rejecting the null hypothesis (that is, there is a threshold effect), the next step
is to test whether or not γ is equal to a specific threshold value; H0 : γ = γ0 against H1 : γ 6= γ0
where γ0 is the true parameter value. Assuming that i,t follows an iid normal distribution, the
test is undertaken using the likelihood ratio statistic:
LRn (γ) = n
Sn (γ) − Sn (γ̂)
Sn (γ̂)
Because this test statistic does not have a standard χ2 distribution, Hansen (2000) calculates the
correct asymptotic critical values. The test rejects the null hypothesis for large values of LRn (γ).
This test statistic is then used to construct a confidence interval for the threshold estimate.
4
Result
Column (1) of Table 2 shows that the estimates on unskilled and skilled labor, capital, energy and
material inputs are positive and significant. The size of these coefficients is within the range of most
findings in the productivity literature. We see that the effect of using imported inputs depends on
a firm’s absorptive capacity, and importing has a greater impact in firms with higher absorptive
capacity. For example, for a firm with an absorptive capacity equal to 0.37, which is the average
absorptive capacity in the data, the effect of imported inputs on output production is 8.10%. In
a rather extreme case, imported inputs have no effect if a firm has no absorptive capacity at all,
as shown by the statistically insignificant βd . Relatedly, βs is not statistically different from zero,
and it shows that the share of skilled workers in the past does not affect output after controlling
for the number of skilled employees in the production function.
In column (2), I include the square of a firm’s absorptive capacity as an additional regressor.
This is motivated by the fact that firms with high absorptive capacity may own better technology
vis–à–vis imported inputs, and therefore there is little or no expected technology transfer from
using imported inputs.6 The insignificance of βss in this regression demonstrates that there is no
evidence of limited scope for knowledge transfer because of proximity to the technology frontier in
the case of Ethiopian manufacturing.
In columns (3)–(6), I divide firms into quantiles based on their absorptive capacities and estimate the model. The coefficients on different types of labor, capital, energy and materials are
positive, significant and similar across quantiles. We see that βd is insignificant and small for
firms located in the lowest quantiles of the absorptive capacity distribution. On the contrary, βd
becomes statistically significant and larger in size for firms in the upper quantiles. This variation
in size and significance suggest that absorptive capacity plays a role in the productivity effect of
importing.
6
At the aggregate level, Falvey et al. (2007) find that countries closest to or farthest from the technology frontier
gain less from international transfer although absorptive capacity facilitates cross-country knowledge transfers.
124
Table 3 reports the threshold regression estimates.7 The threshold parameter γ is estimated to
be 0.403. Figure 1 plots firms’ absorptive capacities against critical values and the test statistic from
the testing H0 : γ = γ0 against H1 : γ 6= γ0 . We see that the likelihood ratio statistics lie below the
95% critical value for the interval [0.375, 0.406] forming the confidence interval for γ. Compared to
the threshold estimate reported in Yasar (2013), the estimate here is larger in magnitude and has
a narrower confidence interval.8 Besides, this threshold estimate exceeds the average and median
values reported in Table 1 implying that most firms have limited absorptive capacity which prevents
them from exhaustively reaping the benefits associated with using imported inputs.
Based on the threshold value, the whole sample is split into two regimes, and then the output
elasticities are estimated. We see that the coefficients on βl , βk , βe , and βm do not vary across
the two regimes and they therefore can be considered as regime–independent. The parameter
of interest βd is significant under both regimes but its statistical significance and magnitude is
greater for firms with absorptive capacity above the threshold value. We notice that for firms in
the second regime the βd estimate is almost twice as large. Imported inputs are associated with
2.9% and 5.3% increases in output production for firms located in the first and the second regimes,
respectively. And, this difference is statistically significant as can be seen from the LM test which
rejects the null hypothesis that there is no threshold effect.
5
Conclusion
In this paper, I emphasize the role absorptive capacity plays in the transfer of technology through
imported inputs. It is shown that the benefits of imported inputs depend on the skill composition
of importing firms. The results reveal that a greater absorptive capacity is associated with a higher
productivity and larger benefits of imported inputs. However, this correlation is very weak at the
lower end of the absorptive capacity distribution. The threshold estimate shows that most firms
have limited absorptive capacity to exploit, to a considerable extent, the embodied knowledge in
the imported inputs.
Despite the pervasiveness of importing and intensive use of imported inputs among Ethiopian
manufacturing firms, the benefits of enhanced access to technology are confined to only firms
with at least a minimum skill intensity requirement. This finding provides firm-level support to
prevailing macroeconomic evidences that identify limited absorptive capacity of the least developed
countries as an impediment to technology spillovers.
The above results only illustrate cross-sectional correlations between absorptive capacity and
the productivity effect of imported inputs. As commonly known in the productivity estimation
7
Estimation of the threshold regression and hypothesis testing are implemented using a Matlab code written
by Bruce E. Hansen, which is available from the author’s website http://www.ssc.wisc.edu/~bhansen/progs/
progs threshold.html.
8
Yasar (2013) also reports IV estimate of the threshold parameter. This new estimate is smaller but still with a
wider confidence interval.
125
literature, there is a bevy of econometric concerns coming from the endogeneity of input choices.
An interesting future research area will be to deal with these issues by adopting an estimation
strategy that exploits the panel structure of the data.
126
References
Abreha, K. G. (2014). Importing and Firm Productivity in Ethiopian Manufacturing. FREIT
Working Paper # 843 .
Acemoglu, D., & Zilibotti, F. (2001). Productivity Differences. Quarterly Journal of Economics,
116 (2), 563–606.
Acharya, R. C., & Keller, W. (2009). Technology Transfer Through Imports. Canadian Journal
of Economics, 42 (4), 1411–1448.
Augier, P., Cadot, O., & Dovis, M. (2013). Imports and TFP at the Firm Level: The Role of
Absorptive Capacity. Canadian Journal of Economics, 46 (3), 956–981.
Benhabib, J., & Spiegel, M. M. (1994). The Role of Human Capital in Economic Development
Evidence from Aggregate Cross-country Data. Journal of Monetary economics, 34 (2), 143–
173.
Coe, D. T., & Helpman, E. (1995). International R&D Spillovers. European Economic Review ,
39 (5), 859–887.
Coe, D. T., Helpman, E., & Hoffmaister, A. (1997). North-South R&D Spillovers. Economic
Journal , 107 (440), 134–149.
Coe, D. T., Helpman, E., & Hoffmaister, A. W. (2009). International R&D Spillovers and Institutions . European Economic Review , 53 (7), 723–741.
Crespo, N., & Fontoura, M. P. (2007). Determinant Factors of FDI Spillovers – What Do We
Really Know? World Development, 35 (3), 410–425.
Durham, J. (2004). Absorptive Capacity and the Effects of Foreign Direct Investment and Equity
Foreign Portfolio Investment on Economic Growth. European Economic Review , 48 (2), 285–
306.
Falvey, R., Foster, N., & Greenaway, D. (2007). Relative Backwardness, Absorptive Capacity and
Knowledge Spillovers. Economics Letters, 97 (3), 230–234.
Gebreeyesus, M. (2008). Firm Turnover and Productivity Differentials in Ethiopian Manufacturing.
Journal of Productivity Analysis, 29 (2), 113–129.
Girma, S. (2005). Absorptive Capacity and Productivity Spillovers from FDI: A Threshold Regression Analysis. Oxford Bulletin of Economics and Statistics, 67 (3), 281–306.
Hansen, B. E. (1996). Inference When a Nuisance Parameter is not Identified under the Null
Hypothesis. Econometrica, 64 (2), 413–430.
Hansen, B. E. (1999). Threshold Effects in Non-dynamic Panels: Estimation, Testing, and Inference. Journal of Econometrics, 93 (2), 345–368.
Hansen, B. E. (2000). Sample Splitting and Threshold Estimation. Econometrica, 68 (3), 575–603.
Kasahara, H., & Rodrigue, J. (2008). Does the Use of Imported Intermediates Increase Productivity? Plant-level Evidence. Journal of Development Economics, 87 (1), 106–118.
Los, B., & Timmer, M. P. (2005). The ‘Appropriate Technology’ Explanation of Productivity
Growth Differentials: An Empirical Approach. Journal of Development Economics, 77 (2),
517–531.
Xu, B. (2000). Multinational Enterprises, Technology Diffusion, and Host Country Productivity
Growth. Journal of Development Economics, 62 (2), 477–493.
Yasar, M. (2013). Imported Capital Input, Absorptive Capacity, and Firm Performance: Evidence
from Firm-level Data. Economic Inquiry, 51 (1), 88–100.
127
Table 1: Summaries on firm absorptive capacity
Year
1996-2000
2001-2005
2006-2011
1996-2011
Minimum
Maximum
Mean
Median
Std. dev.: overall
Std. dev.: between
Std. dev.: within
0.04
0.93
0.38
0.36
0.18
-
0.02
0.93
0.38
0.36
0.19
-
0.01
0.96
0.36
0.33
0.20
-
0.00
0.94
0.37
0.34
0.19
0.16
0.13
% Firms below mean
53.80
54.80
56.33
55.06
Note: For each subsample of time periods, the reported summaries are calculated as simple averages of
the individual years. For details, see Table A.1 in the Appendix.
128
Table 2: Production function parameters
All
βlu
βls
βk
βe
βm
βd
βs
βds
βss
R2
Obs.
Quantiles
(1)
(2)
1st Quantile
2nd Quantile
3rd Quantile
4th Quantile
0.140∗∗∗
(0.009)
0.118∗∗∗
(0.003)
0.059∗∗∗
(0.003)
0.101∗∗∗
(0.006)
0.673∗∗∗
(0.008)
-0.0002
(0.032)
-0.073
(0.065)
0.219∗∗∗
(0.071)
–
0.140∗∗∗
(0.009)
0.118∗∗∗
(0.003)
0.059∗∗∗
(0.003)
0.101∗∗∗
(0.006)
0.673∗∗∗
(0.008)
-0.0002
(0.032)
-0.022
(0.140)
0.219∗∗∗
(0.071)
-0.059
(0.137)
0.150∗∗∗
(0.029)
0.084∗∗∗
(0.026)
0.047∗∗∗
(0.008)
0.103∗∗∗
(0.011)
0.668∗∗∗
(0.016)
0.017
(0.031)
–
0.117∗
(0.068)
0.157∗∗
(0.063)
0.064∗∗∗
(0.008)
0.092∗∗∗
(0.013)
0.641∗∗∗
(0.017)
0.061∗∗
(0.031)
–
0.003
(0.066)
0.267∗∗∗
(0.069)
0.065∗∗∗
(0.007)
0.101∗∗∗
(0.011)
0.672∗∗∗
(0.017)
0.116∗∗∗
(0.028)
–
0.126∗∗∗
(0.023)
0.148∗∗∗
(0.025)
0.050∗∗∗
(0.008)
0.105∗∗∗
(0.011)
0.702∗∗∗
(0.014)
0.155∗∗∗
(0.031)
–
–
–
–
–
–
–
–
–
0.94
0.94
0.92
2,179
0.94
2,228
0.95
2,336
0.94
2,277
9,020
Robust standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Industry and time fixed effects are
included in these regressions.
129
Table 3: Threshold regression
Absorptive capacity
≤γ
Threshold Estimate γ
0.95 Confidence Interval
>γ
0.403
[0.375, 0.406]
βl
βk
βe
βm
βd
R2
Obs.
0.227∗∗∗
(0.010)
0.050∗∗∗
(0.004)
0.075∗∗∗
(0.006)
0.700∗∗∗
(0.008)
0.029∗∗
(0.014)
0.238∗∗∗
(0.012)
0.052∗∗∗
(0.005)
0.111∗∗∗
(0.008)
0.694∗∗∗
(0.012)
0.052∗∗∗
(0.019)
0.922
7,805
0.935
4,705
LM test for no threshold effect test statistic
LM test for no threshold effect p–value
316.115
0.000
Robust standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Industry
and time fixed are included in these regressions.
Figure 1: Confidence interval construction for the threshold parameter
300
LRn(γ)
95% Critical
250
Likelihood Ratio
200
150
100
50
0
0.1
0.2
0.3
0.4
0.5
0.6
Threshold Variable: Absorptive Capacity
130
0.7
0.8
0.9
1
Appendix: Tables
Table A.1: Summary statistics on firm absorptive capacity
Year
Minimum
Maximum
Mean
Median
St. dev.
% Firms below
mean
absorptive cap.
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
0.04
0.05
0.03
0.04
0.04
0.02
0.03
0.02
0.03
0.02
0.02
0.01
0.02
0.02
0.00
0.00
0.94
0.95
0.94
0.89
0.93
0.91
0.92
0.96
0.95
0.91
0.93
0.98
0.95
0.97
0.97
0.95
0.37
0.38
0.38
0.37
0.38
0.40
0.39
0.38
0.37
0.37
0.37
0.36
0.36
0.37
0.34
0.36
0.36
0.36
0.36
0.34
0.36
0.38
0.36
0.36
0.35
0.35
0.33
0.33
0.33
0.33
0.30
0.33
0.18
0.19
0.18
0.17
0.19
0.19
0.19
0.18
0.20
0.18
0.19
0.20
0.20
0.21
0.19
0.20
53.71
53.33
53.78
53.49
55.28
54.51
54.03
55.74
56.30
53.41
56.81
56.26
55.68
56.34
56.82
56.05
1996–2011
0.02
0.94
0.37
0.35
0.19
55.10
131
MAJOR RAW MATERIALS CODES BY INDUSTRIAL GROUP
DESCRIPTION OF RAW MATERIALS
RAW
MATERIALS
U/M NAME
U/M CODE
CATTLE Ÿwƒ
0327
HEAD /lØ`/
83
COFFEE (UNMILLED)
0401
KG,QN,TON
02,03,04
EDIBLE OIL
0318
LT,KG,BARREL,TON
65,02,81,04
FLOUR
0103
KG,QN,TON
02,03,04
GLUCOSE
0167
KG, TON
02,04
MAIZE (UNMILLED)
0009
KG,QN,TON
02,03,04
MEAT
0095
KG, TON
02,04
MILK (POWDER)
0323
KG, TON
02,04
MILK (RAW)
0006
LT,HL
65,66
OIL SEEDS /¾pvƒ IKA‹/
0101
KG,QN,TON
02,03,04
ORANGE
0007
KG, TON,CAN*
02,04, (09-14)
PULSES /Ø^Ø_/
0324
KG,QN,TON
02,03,04
SUGAR
0112
KG,QN,TON
02,03,04
SUGAR CANE
0032
02,04,06
TEA LEAVES
0402
KG,TON,
‘000TON
KG,QN,TON
VEGETABLES
0147
KG,QN,TON
02,03,04
WHEAT (UN MILLED)
0008
KG,QN,TON
02,03,04
YEAST
1223
KG,QN,TON
02,03,04
ALLCOHOL
0136
LT,HL
65,66
BARLEY /Ø_ Ñwe/
0335
KG,QN,TON
02,03,04
CARBON DIOXIDE
0176
KG, TON
02,04
ESSENCE
0111
KG, TON,UNIT**
02,04 (18-25)
GRAPE & RAISING /¨Ã”/
0021
KG, TON
02,04
FOOD
02,03,04
BEVERAGE
DESCRIPTION OF RAW MATERIALS
U/M NAME
U/M CODE
HOPS /Ñ@j/
RAW
MATERIALS
0033
KG, TON
02,04
MALT /wpM/
0114
KG,QN,TON
02,03,04
MOLASSES
0113
KG,QN,TON
02,03,04
SUGAR
0112
KG,QN,TON
02,03,04
0037
KG,QN,TON
02,03,04
TOBACCO
TOBACCO LEAVES
Can 100gm Code 09
“ 240gm
“
10
“ 340gm
“
11
“ 350 gm
“
12
“ 400gm
“
13
“ 850gm
“
14
Unit ** Coca-Cola code
Fanta
“
Mirinda
“
Pepsi
“
Sprite
“
Teem
“
Tonic
“
Average
“
21
22
19
18
23
20
24
25
RAW
MATERIALS
U/M NAME
U/M CODE
RAW COTTON Ø_ ØØ
0501
KG,QN,TON
02,03,04
ACRYLIC (YARN)
0054
KG, TON
02,04
CHEMICAL & DYESTUFF
0331
KG, TON
02,04
COTTON (LINT) ¾}ÇSÖ ØØ
0058
KG, TON
02,04
COTTON (WASTE)
COTTON (YARN)
0057
0061
KG, TON
KG, TON
02,04
02,04
FABRICS
0062
MT,SQM,’000MT
28,51,29
FIBRE (ACRYLIC)
0320
KG, TON
02,04
FIBRE (POLYESTER)
0319
KG, TON
02,04
JUTE (FIBER)
0052
KG, TON
02,04
NYLON
0064
KG, TON
02,04
SISAL (LEAVES)
0326
KG, TON
02,04
WOOL (WASTE)
0056
KG, TON
02,04
CHEMICALS
0315
KG, ON,TON
02,03,04
HIDES & SKINS
0038
KG, TON
02,04
LEATHER LINING /¾ÝT Ñu` qÇ/
0117
SOF,’000SQF
47,48
LEATHER SOLE
0115
KG, TON
02,04
LEATHER UPPER
0116
SOF,’000SQF
47,48
LEATHER GARMENT
0287
SQF
47
PLASTIC SOLE
0118
PAIRS, DOZZEN
42,45
PVC FOR SOLE
0102
KG, TON
02,04
SHEEP & GOAT SKINS
0039
PCS, ‘000PCS, DOZ
31,32,45
DESCRIPTION OF RAW MATERIALS
TEXTILES
LEATHER & FOOTWEAR
DESCRIPTION OF RAW MATERIALS
RAW
MATERIALS
U/M NAME
U/M
CODE
CHIPWOOD
0158
PCS
31
FORMICA
0328
PCS
31
LOG /Ó”É & ”Úƒ/
0322
Cub.m
53
PLYWOOD /¢UüMd„& óÃ²=ƒ/
0140
PCS
31
PLUNK /×¨<L/
0126
Cub.M
53
VENEER
0125
SQM
51
BOXING PAPER
0329
KG, TON
02,04
CHEMICALS
0316
KG, TON
02,04
PAPER
0214
02,04,88,87
PULP
0222
KG,TON,REEM,
DESTA
KG, TON
WASTE PAPER
0227
KG, TON
02,04
CAUSTIC SODA
0171
KG, TON
02,04
COPPER ELECTRIC WIRE
0405
KG, TON
02,04
FATTY ACID
NATURAL & SYNTHETIC RUBBER
RESINS
SODIUM COMPOUND
CARAFINE WAX
POLYTHELINE
PVC
TALLOW /V^/
0201
0232
1112
0186
1111
0100
0102
0099
KG, TON
KG, TON
KG, TON
KG, TON
KG, TON
KG, TON
KG, TON
KG, TON
02,04
02,04
02,04
02,04
02,04
02,04
02,04
02,04
PETROLEUM JELLY (VASILINE)
0403
KG, TON
02,04
PARAFFINE
0404
KG, TON
02,04
POLYEHER POLYOL
0106
KG, TON
02,04
TDI (TOLUERE ISOLYANTE)
0107
KG, TON
02,04
KG, TON
02,04
WOOD & FURNITURE
PAPER & PRINTING
02,04
CHEMICAL
TIN CATALYST
DESCRIPTION OF RAW MATERIALS
RAW
MATERIALS
U/M NAME
U/M CODE
CEMENT
0267
KG, QN,TON
02,O3,04
CLAY g¡L ›ð`
0266
Cub.m
53
CULLET FLINT ¾c=T>”„ Ø_ n
0317
KG, TON
02,04
CRAVEL /ÖÖ`/
0269
Cub.m
53
GYPSUM /Ëf/
0276
KG, QN,TON
02,O3,04
LIME STONE /¾•^ É”ÒÃ/
MARBLE U’u[É
PUMICE
0275
0278
0272
KG, TON
KG, TON,M3
Cub.m
02,04
02,04,53
53
SAND
0270
Cub.m
53
SILICA SAND/ SAND STONE
0325
KG, TON
02,04
SODA ASH
0170
KG, TON
02,04
STONE FOR GRAVEL
0119
Cub.m
53
ALUMNUM
0330
KG, TON
02,04
CHEMICAL FOR METAL
0217
KG, TON
02,04
CROWN TIN PLATE
0286
SHEET,000SHEET
GAL VANIZED COILS
0289
KG, TON
02,04
IRON BARS ²”Ó w[ƒ/ ôa w[ƒ
0290
KG, TON
02,04
IRON (BILLETS)Éu<Mu<M w[ƒ
0337
KG, TON
02,04
IRON (SCARP) l`Ø^ß w[ƒ
0215
KG, TON
02,04
PIG IRON
0288
KG, TON
02,04
STEEL SHEETS q`qa Se]Á ØpM
0291
KG, TON,PCS
02,04,31
WIRE ROD
0282
KG, TON
02,04
ZINC
0285
KG, TON
02,04
NON METAL
METAL
ONE METER CUBE LIME STONE = 0.286 TON
DEPARTMENT OF ECONOMICS AND BUSINESS
AARHUS UNIVERSITY
SCHOOL OF BUSINESS AND SOCIAL SCIENCES
www.econ.au.dk
PhD Theses since 1 July 2011
2011-4
Anders Bredahl Kock: Forecasting and Oracle Efficient Econometrics
2011-5
Christian Bach: The Game of Risk
2011-6
Stefan Holst Bache: Quantile Regression: Three Econometric Studies
2011:12
Bisheng Du: Essays on Advance Demand Information, Prioritization and Real Options
in Inventory Management
2011:13
Christian Gormsen Schmidt: Exploring the Barriers to Globalization
2011:16
Dewi Fitriasari: Analyses of Social and Environmental Reporting as a Practice of
Accountability to Stakeholders
2011:22
Sanne Hiller: Essays on International Trade and Migration: Firm Behavior, Networks
and Barriers to Trade
2012-1
Johannes Tang Kristensen: From Determinants of Low Birthweight to Factor-Based
Macroeconomic Forecasting
2012-2
Karina Hjortshøj Kjeldsen: Routing and Scheduling in Liner Shipping
2012-3
Soheil Abginehchi: Essays on Inventory Control in Presence of Multiple Sourcing
2012-4
Zhenjiang Qin: Essays on Heterogeneous Beliefs, Public Information, and Asset
Pricing
2012-5
Lasse Frisgaard Gunnersen: Income Redistribution Policies
2012-6
Miriam Wüst: Essays on early investments in child health
2012-7
Yukai Yang: Modelling Nonlinear Vector Economic Time Series
2012-8
Lene Kjærsgaard: Empirical Essays of Active Labor Market Policy on Employment
2012-9
Henrik Nørholm: Structured Retail Products and Return Predictability
2012-10
Signe Frederiksen: Empirical Essays on Placements in Outside Home Care
2012-11
Mateusz P. Dziubinski: Essays on Financial Econometrics and Derivatives Pricing
2012-12
Jens Riis Andersen: Option Games under Incomplete Information
2012-13
Margit Malmmose: The Role of Management Accounting in New Public Management
Reforms: Implications in a Socio-Political Health Care Context
2012-14
Laurent Callot: Large Panels and High-dimensional VAR
2012-15
Christian Rix-Nielsen: Strategic Investment
2013-1
Kenneth Lykke Sørensen: Essays on Wage Determination
2013-2
Tue Rauff Lind Christensen: Network Design Problems with Piecewise Linear Cost
Functions
2013-3
Dominyka Sakalauskaite: A Challenge for Experts: Auditors, Forensic Specialists and
the Detection of Fraud
2013-4
Rune Bysted: Essays on Innovative Work Behavior
2013-5
Mikkel Nørlem Hermansen: Longer Human Lifespan and the Retirement Decision
2013-6
Jannie H.G. Kristoffersen: Empirical Essays on Economics of Education
2013-7
Mark Strøm Kristoffersen: Essays on Economic Policies over the Business Cycle
2013-8
Philipp Meinen: Essays on Firms in International Trade
2013-9
Cédric Gorinas: Essays on Marginalization and Integration of Immigrants and Young
Criminals – A Labour Economics Perspective
2013-10
Ina Charlotte Jäkel: Product Quality, Trade Policy, and Voter Preferences: Essays on
International Trade
2013-11
Anna Gerstrøm: World Disruption - How Bankers Reconstruct the Financial Crisis:
Essays on Interpretation
2013-12
Paola Andrea Barrientos Quiroga: Essays on Development Economics
2013-13
Peter Bodnar: Essays on Warehouse Operations
2013-14
Rune Vammen Lesner: Essays on Determinants of Inequality
2013-15
Peter Arendorf Bache: Firms and International Trade
2013-16
Anders Laugesen: On Complementarities, Heterogeneous Firms, and International
Trade
2013-17
Anders Bruun Jonassen: Regression Discontinuity Analyses of the Disincentive
Effects of Increasing Social Assistance
2014-1
David Sloth Pedersen: A Journey into the Dark Arts of Quantitative Finance
2014-2
Martin Schultz-Nielsen: Optimal Corporate Investments and Capital Structure
2014-3
Lukas Bach: Routing and Scheduling Problems - Optimization using Exact and
Heuristic Methods
2014-4
Tanja Groth: Regulatory impacts in relation to a renewable fuel CHP technology:
A financial and socioeconomic analysis
2014-5
Niels Strange Hansen: Forecasting Based on Unobserved Variables
2014-6
Ritwik Banerjee: Economics of Misbehavior
2014-7
Christina Annette Gravert: Giving and Taking – Essays in Experimental Economics
2014-8
Astrid Hanghøj: Papers in purchasing and supply management: A capability-based
perspective
2014-9
Nima Nonejad: Essays in Applied Bayesian Particle and Markov Chain Monte Carlo
Techniques in Time Series Econometrics
2014-10
Tine L. Mundbjerg Eriksen: Essays on Bullying: an Economist’s Perspective
2014-11
Sashka Dimova: Essays on Job Search Assistance
2014-12
Rasmus Tangsgaard Varneskov: Econometric Analysis of Volatility in Financial
Additive Noise Models
2015-1
Anne Floor Brix: Estimation of Continuous Time Models Driven by Lévy Processes
2015-2
Kasper Vinther Olesen: Realizing Conditional Distributions and Coherence Across
Financial Asset Classes
2015-3
Manuel Sebastian Lukas: Estimation and Model Specification for Econometric
Forecasting
2015-4
Sofie Theilade Nyland Brodersen: Essays on Job Search Assistance and Labor Market
Outcomes
2015-5
Jesper Nydam Wulff: Empirical Research in Foreign Market Entry Mode
2015-6
Sanni Nørgaard Breining: The Sibling Relationship Dynamics and Spillovers
2015-7
Marie Herly: Empirical Studies of Earnings Quality
2015-8
Stine Ludvig Bech: The Relationship between Caseworkers and Unemployed Workers
2015-9
Kaleb Girma Abreha: Empirical Essays on Heterogeneous Firms and International
Trade
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