Econ 442 What we have done And what you’ll need to study 1 •  First set of lectures were on the background facts •  Maddison – the long term rise of the West •  The facts and ﬁgures –  Western Europe –  The Neo-‐Europes –  Japan’s late growth 2 •  Rodrik (lecture 1) –  More recent 20th century and a bit before. –  Growth in the modern era •  Since Both Maddison and Rodrik lecture 1 are both ﬁlled with Facts and Figures I will not ask quesQons directly on them. 3 •  Rodrik Lecture 2: “What can we learn from growth experiences” •  The important things here to review: •  Convergence: condiQonal, uncondiQonal, what are the facts on this •  There is something very special about manufacturing •  What do growth spurts look like, what tends to accompany them? •  Why don’t natural resource booms tend to lead to sustained development? 4 •  Channels through which growth can happen –  Fundamentals: broad investments in human capital and insQtuQons –  Manufacturing: UncondiQonal convergence –  Structural transformaQon: ReallocaQon of labor and producQve resources from agriculture to manufacturing and to a lesser extent services. •  Primary resources: Good or Bad? •  Role of government versus markets. DistorQons good or bad? 5 AJR Handbook •  The disQncQon between proximate and fundamental causes of growth/prosperity •  What do the “reversals” in the former colonies, but no reversals in the early developers suggest? •  What is the AJR explanaQon for colonial reversals? 6 I.V. •  What are instrumental variables? •  Why do we need instrumental variables when exploring insQtuQons and development? •  How does this reversal suggest the use of “instruments”? •  What is the “exclusion restricQon”? •  What are threats to the exclusion restricQon in the case of secler mortality? 7 •  Fully understand how AJR marshal the evidence (what evidence?) to make their case that insQtuQons are the fundamental cause of diﬀerences in cross-‐country prosperity •  The ﬁrst stage •  The second stage •  The threats to this being correct •  The counter-‐explanaQons 8 •  Their emphasis on insQtuQons leads to a whole set of quesQons regarding why insQtuQons don’t improve everywhere •  We didn’t deal too much with this but •  The poliQcal coase theorem and commitment problems were covered. 9 •  Don’t worry about Pande and Udry –  Just asking for more evidence on the micro-‐ foundaQons of the “insQtuQons” view •  Unbundling insQtuQons paper is important –  The type of insQtuQons might macer –  How we measure legal insQtuQons –  How we “idenQfy” exogenous variaQon in these –  How we put the whole thing together and use the legal measures with the poliQcal ones and come up with a ﬁnal comparison of the two 10 •  You should be clear on how the variaQon in legal insQtuQons is “exogenous” to the contemporary development outcomes •  Analogous to how the variaQon in seclement pacerns by the colonists is “exogenous” to the outcomes •  Again, threats to the exclusion restricQon are key •  What do we learn from this exercise in terms of the relaQve importance of the insQtuQonal types? 11 Geography •  Sachs and Warner •  Claim: Geography plays a key role in explaining SSA underdevelopment •  Geographic obstacles: –  Landlocked countries –  High proporQon tropical •  Disease environment worse •  Soils worse •  How do they support this claim about the importance of geography for growth? 12 Cross country growth regressions What are these? What are the independent variables in these? Possibility that the factors on the RHS are endogenous •  What this endogeneity does to the esQmates they are using. •  The level diﬀerence in “idenQﬁcaQon” between this work and the AJR work •  •  •  •  13 Gallup Sachs Mellinger •  Geography aﬀects poverty all over the world, not just SSA –  Landlocked, tropical, malaria, low populaQon density •  Evidence showing that most of the world’s tropical and landlocked countries are poor •  Level and growth rate regressions showing negaQve eﬀects of geographic factors •  Hard to link to export markets •  Even when policies are controlled for geography trumps •  Suggests re-‐orientaQon of aid 14 Rodrik Subramaniam Trebbi •  ComparaQvely assess fundamentals: –  InsQtuQons –  Geography –  Policy (i.e., trade policy) •  This complicated diagram is the story in a nutshell 15 16 1. Is Sachs: Geography to GDP 3. Is AJR ﬁrst stage: Sec. Mort. To InsQtuQons 8. Is AJR second stage InsQtuQons to GDP 2. Is Frankel/Romer ﬁrst stage: gravity equaQon •  4. Is Frankel/Romer second stage: Openness to GDP •  •  •  •  17 Punch line •  InsQtuQons Rule! •  (i) Direct eﬀect of insQtuQons on incomes is posiQve and large; •  (ii) Direct eﬀect of trade on incomes is negaQve (but staQsQcally insigniﬁcant); •  (iii) esQmated eﬀect of trade on insQtuQons is posiQve, but small 18 Geograpy (2): Ruggedness •  Why might “bad” geography be good? •  Direct negaQve eﬀects of ruggedness on outcomes (especially agricultural) •  In Africa also has posiQve eﬀect due to slavery •  Around 1/3 of Africa’s annual populaQon was enslaved and moved from 1400 to 1900 •  Rugged local terrain allowed evasion of slavers •  Nuanced story of geography aﬀecQng outcomes 19 IV strategy •  •  •  •  Ruggedness aﬀects slavery Slavery aﬀects insQtuQons InsQtuQons aﬀect outcomes Econometric Framework 20 Econometric Framework •  Hypothesis is that ruggedness has an eﬀect on current income that is the same for all parts of the world. 21 •  Empirical work of Nunn (2008) (others) shown Africa’s slave trades adversely aﬀected poliQcal and social structures of socieQes 22 •  Number of slaves taken from an area was reduced by the ruggedness of the terrain. 23 24 25 •  None of this uses the slaves data, but it all goes through when you also use the slavery esQmates •  Ruggedness generally bad, in Africa it is good •  All of this eﬀect explained by slave trades •  Take away – Geography macers – evidence for direct eﬀects is small. •  Evidence for indirect eﬀects is larger. Mechanisms? InsQtuQons, culture. 26 Culture •  The Issue well encapsulated by Easterly/Levine “Europeans carried with them besides general educaQon, scienQﬁc and technological knowledge, access to internaQonal markets, and human capital creaQng insQtuQons. They also brought ideologies, values, social norms, and so on. It is diﬃcult for us to evaluate which of these were crucial either alone or in combinaQon.” 27 Culture and Trust •  Cultural factor extensively studied is trust •  Degree to which individuals are able to rely on the benevolence of others in essenQally anonymous interacQons •  Measured via “trust” quesQon •  Why might “trust” aﬀect economic outcomes? •  How do studies claim the “trust” quesQon is validated? •  Trust Game 28 •  Glaeser and Shleifer correlaQon between trust quesQon and play in the trust game •  First papers in this area showed correlaQon between trust and GDP per capita •  How has the literature progressed beyond this? •  I.V. Culture/trust and the regions of Europe 29 Tabellini – Culture in Europe •  AJR: Historical InsQtuQons -‐> Contemporary InsQtuQons -‐> Economic Development •  Tabellini: Historical InsQtuQons -‐> Culture -‐> Economic Development •  How does he measure culture today? •  What is the assumpQon about cultural persistence •  What are the instruments? –  Literacy C19 –  Historical InsQtuQons 30 •  What is the exclusion restricQon? •  Importance of country ﬁxed eﬀects. –  Both instruments are regional –  Outcomes also regional 31 PrimiQve SocieQes Studies •  •  •  •  •  UlQmatum game Findings in West (students) ExpectaQons in small scale socieQes Findings there CorrelaQons with characterisQcs –  Village level •  How measured? –  Individual level •  De-‐emphasis on world religions component 32 Algan and Cahuc •  Short Qme series on cultural variables •  Problem is Qme invariant country speciﬁc factors in X-‐country regressions •  Parents good predictors of children’s trust •  Individuals know when ancestors came to US •  Individual age known •  Individual other determinants controlled for •  Asked trust quesQon •  Can run regression with changes in trust aﬀecQng changes in outcome (GDP/capita) 33 •  Note that change regression similar to running regression with country ﬁxed eﬀect •  Findings? 34 Easterly on Aid in Africa •  MDGs big push in recent years •  Idea is that small change wont work •  General tension between “incrementalist”or “marginal” view of aid’s role in Africa and “big push” or “transformaQonal” view •  Easier to test marginal projects that transformaQonal. Why? •  Poverty trap model as moQvaQon for transformaQonal view 35 •  Hard to test such models due to “idenQﬁcaQon” problems. •  Nunn and Qian study is well idenQﬁed. •  Eﬀect of aid on conﬂict. •  How does idenQﬁcaQon work here –  2 parts to this –  EﬀecQvely do a Diﬀ in diﬀ –  Understand intuiQvely •  NegaQve ﬁndings on “regular” recipients •  Mechanisms? 36 Female ReservaQons, Governance •  Women under-‐represented •  Indian system of randomized reservaQons •  Eﬀects can be well esQmated due to “RCT” type of program •  How to get an idea of “female” and “male” policy preferences •  Results in terms of direcQon of policy •  Due to gender or other factors in reservaQons –  Two ways in which authors show it is likely to be gender 37 Voter astudes and reservaQons •  MoQvaQon for reservaQons is changing percepQons of women as leaders •  Study percepQon eﬀects of men and women due to reservaQons •  IAT, indirect (vigneces, speeches), direct quesQons on women’s leadership •  Logic of IAT studies •  Findings on Male astudes regarding women leaders •  Findings on Male astudes regarding how well reserved women does in the village •  Measured eﬀects in terms of public goods, bribes 38 Turkish Mayoral ElecQons and Islamic ParQes •  Diﬃculty in inferring direct eﬀect of Islamic leaders (idenQﬁcaQon problem) •  How solved by using Regression DisconQnuity? •  Logic of inference when using R/D •  Link to idenQﬁcaQon with RCTs? •  Findings here on female educaQon (short term, long term) •  Likely mechanism •  Findings here on women in poliQcs and longer term success of islamic party 39 MVP project and evaluaQons •  Aim of MVPs •  Millenium goals and villages (how selected) •  Assessment planned by MVP –  Before v. ater •  Problems with before and ater studies –  AssumpQons necessary for these to be valid •  Consistency between such assumpQons and other changes in Africa 40 Doing it right •  ConstrucQng comparator villages while respecQng goals of the project •  SelecQng villages for treatment and control •  •  •  •  Measuring (what, when, where) pre-‐staQng What – not just MDGs When – before, ater, long ater Where – everywhere equally 41 Good Luck! 42