1. No category

ISSN 1561-2422
ESTIMATING INFORMAL ECONOMY SHARE
IN RUSSIAN REGIONS
Pavel Vorobyev
Working Paper No E15/02
This project (No 113-062) was supported
by the Economics Education and Research Consortium
and funded by GDN
All opinions expressed here are those of the authors
and not those of the EERC, GDN and Government of Sweden
Research dissemination by the EERC may include views on policy,
but the EERC itself takes no institutional policy positions
Estimating informal economy share in Russian regions
Pavel Vorobyev 1
March 27, 2015
Abstract
Informal economy in Russian regions is measured using two approaches: 1) cross-section regression model for
electricity consumption in Russian regions; 2) augmented electricity dynamics approach.
1) Regression model is applied for electricity consumption in production of goods and services (total electricity
consumption less losses, less households’ consumption). Model was estimated on the basis of regional data in 2011.
It allowed estimating informal economy share in 67 Russian regions in 2011. The average informal economy share is
estimated at 40% with standard deviation 18 percentage points. These results show high positive correlation with usual
proxies for informal economy such as corruption, unemployment, and especially dependency of regional budget from
Federal transfers.
2) Augmented electricity dynamics approach is developed to estimate dynamics of informal economy share in
regions over 2004-2011. Comparing to traditional method in the literature, it takes into account changes in regional
industrial structure and electricity intensity of GRP. It leads to more accurate estimates. It has been shown that the
share of informal economy in Russia diminished from 55% in 2004 to 40% in 2011 due to the growth of formal sector.
Only 16 from 65 regions witnessed an increase in informal economy share over the period.
Keywords: informal economy, electricity consumption, Russia, regions, gross regional product
JEL classification: R110
Acknowledgements. I am grateful to EERC experts, especially to Prof. Gary Krueger and Prof.
Tom Coupe for their valuable comments and advise. I also appreciate comments made by
seminar participants, especially by Tigran Kostanyan and Dmitry Vorobyev. I am grateful to
Nikita Suslov, Anna Mishura for their useful recommendations. I gratefully acknowledge
financial support from the Economics Education and Research Consortium Grant No. 130621.The views expressed in this paper, and all remaining errors, are mine.
1
E-mail: [email protected]
1
Contents
1
Introduction ......................................................................................................................2
2
Overview of the literature .................................................................................................4
3
Regression model for regional electricity consumption ................................................... 11
4
3.1
Estimates of informal activity in Russian regions in 2011 ........................................ 15
3.2
Comparison with alternative indicators of informal economy .................................. 21
3.3
Informal economy and economic conditions in Russian regions .............................. 25
Dynamics of informal economy share ............................................................................. 26
4.1
Methodology ........................................................................................................... 27
4.1.1
Traditional electricity consumption method ...................................................... 27
4.1.2
Proposed augmented electricity consumption method ....................................... 28
4.2 Empirical results on dynamics of informal economy share in Russian regions over
2004-2011 ............................................................................................................................. 32
5
Conclusion ..................................................................................................................... 36
6
Bibliography .................................................................................................................. 39
7
Appendix........................................................................................................................ 42
1 Introduction
A lot of attention is drawn to the informal economy in Russia both in the mass-media and in
the Russian government. Anton Siluanov (the Russian finance minister) said that hidden sector
amounts to 15-20% of GDP which means that the budget system loses about 3 trn rubles of tax
incomes each year. Andrey Belousov (economic expert at the Russian President) has argued that
if small businesses go from the shade, its share in Russian economy will increase from 19% to 4050%. Now 18 mln people are employed in unofficial small business which accounts for a quarter
of total labor force in Russia. Government policy dealing with informal economy sometimes looks
inconsistent. For example, according to Oleg Savel’ev (Deputy Minister of Economic
Development) increased insurance payments (2 times since the beginning of 2013) have forced
2
426 thousand individual entrepreneurs to work unofficially. Finally government had to abandon
its previous decision.
Significant amount of firms in Russia use different methods to avoid paying taxes. It leads to
creation of fictive one-day-firms and increases demand for cashing in. Some commercial banks
are involved in these operations. Russian central bank is fighting with such banks. In November
2013 it withdrawn the license from Master-bank which was a big player in Russian banking market
and the biggest agent serving informal economy.
Estimates of informal economy share in Russia differ significantly from 19% (Rosstat, Ministry
of Finance) to 40% (Worldbank, Global financial integrity). It means that there is no consensus in
the literature.
Another important field of study is a regional dimension of informal economy. Existing
literature lacks regional estimates of informal economy in Russia. We could mention two regional
studies which were made in the form of master’s thesis: Komarova (2003) who used KaufmanKaliberda electricity consumption approach and Bilonizhko (2006) who applied MIMIC approach.
We could suppose that Russian regions are quite different in the size of informal sector. Regional
estimates would allow analyzing causes and consequences of informal sector on the basis of
regional panel data. For example, there is a number of studies on the relationship between
decentralization and informal economy (Teobaldelli D. (2010), Alexeev and Habodaszova
(2012)).
Electricity consumption method is usually used for estimation of informal economy; however
it has a number of drawbacks, for example, it does not take into account changes in industrial
structure and different factors of electricity intensity.
The main goal of this research is to estimate the size of unofficial (informal) sector in the regions
of Russian Federation using electricity consumption method. It implies two tasks:
•
To estimate the level of informal economy in Russian regions for a base year (2011)
•
To estimate dynamics of informal economy share in Russian regions
3
This report consists of three parts. Section 2 “Overview of the literature” is about different
existing methods of informal economy measurement and their results for Russia. It is concluded
that electricity consumption method has very good perspective to be applied on the regional level,
however it should be improved. Section 3 “Regression model for regional electricity consumption”
develops the model of electricity demand which takes into account a structure of economy,
electricity prices, weather, and natural gas prices. It is estimated on a cross-section of Russian
regions in 2011. Informal economy share is derived as a residual of this regression. Section 4
“Dynamics of informal economy share” is devoted to the augmented electricity dynamics model
which allows taking into account changes in the structure of economy. This methodology provides
estimates of informal economy share dynamics in Russian regions in 2004-2011. Thus we are able
to calculate informal economy share not only in 2011, but also in 2004-2010.
2 Overview of the literature
Unofficial economy can be defined following Kaufman and Kaliberda (1996): “From a
meaningful economic standpoint, we define an unofficial activity as the unrecorded value added
by any deliberate misreporting or evasion by a firm or individual.” More precise definition was
given in Schneider et al. (2010): “The shadow economy includes all market-based legal production
of goods and services that are deliberately concealed from public authorities for any of the
following reasons: (1) to avoid payment of income, value added or other taxes, (2) to avoid
payment of social security contributions, (3) to avoid having to meet certain legal labor market
standards, such as minimum wages, maximum working hours, safety standards, etc., and (4) to
avoid complying with certain administrative procedures, such as completing statistical
questionnaires or other administrative forms.”
Thus, for the needs of quantitative estimation, informal economy represents all existing
economic activities which are not properly registered by the government due to different reasons
4
from simple mistakes to tax evasion or other prohibited activities. Among factors which force
economic activities to informal sector are taxes, government regulation, corruption, etc.
Another problem is to find indicator which measures the absolute and relative size of informal
economy. It could be employment, output, value added. In our study we focused on value added
(or gross domestic product).
There are 3 main classes of approaches for measurement of unofficial economy: 1) direct
methods (micro surveys or tax audits); 2) indicator approach; 3) multiple indicators – multiple
causes model or MIMIC model.
•
Direct methods represent micro surveys or tax audits. This is the most relevant approach;
however it is very costly for implementation. Rosstat surveys Russian workers (about 69
thousand people) and publish number of informally employed people. Informal
employment treats as working for not registered enterprises. Another source of survey data
is BEEPS provided by EBRD. However survey data has a bias as people usually do not
want to reveal their informal activity.
•
Indicator approach. The method is based on the use of observable indicators which
correlate with total economic activity including informal. There are a number of such
indicators: consumption of electricity (Kaufman, Kaliberda (1996)), currency demand
(Tanzi (1983)), consumption of other resources.
•
Multiple indicators – multiple causes model or MIMIC model (Breusch (2005)). This
method is based on econometric estimation of regression where the share of unofficial
economy is a latent variable. This approach does not give us the absolute level of unofficial
economy, it produces only dynamics. Another problem is that we use assumptions on the
causes of unofficial economy which themselves should be tested.
Electricity consumption method is considered as the most prospective for the measurement of
informal activity. Electricity is the most important energy source in the Russian economy. In 2012
electricity consumption for final use in Russia was 47% of the total use of energy sources. All
5
economic activities require energy for equipment, lightening, heating. Electricity consumption
data is available from Rosstat with high level of details (regional, industrial dimensions).
The simplest modification of electricity approach is called “Electricity consumption model”
(ECM). It implies that total electricity consumption has a constant elasticity (usually unitary) to
the total economic activity. Thus, percentage point change of informal economy share is calculated
as a difference between growth rate in electricity consumption and growth rate of officiallyrecorded GDP (Kaufman, Kaliberda (1996)).
Such simple approach have certain biases which are discussed in Kaufman and Kaliberda
(1996): 1) electricity consumption could grow slower (in terms of growth rates) than GDP due to
fixed electricity costs; 2) growth rates of electricity consumption could deviate from growth rates
of GDP due to changes in industrial structure in respect to more and less electricity intensive
industries; 3) electricity consumption could grow slower that GDP due to the impact of the growth
of electricity prices. For sensitivity analysis Kaufman and Kaliberda suggested different scenarios
of informal economy depending on assumption of electricity elasticity to output.
Eilat and Zinnes (2002) introduced approach based on regression with factors of electricity
consumption: changes in electricity prices, share of industrial production in GDP and efficiency
of energy use. This approach was called “Modified electricity consumption model” (MEC). The
analysis was done for former Soviet Union (FSU) countries. Later Feige and Urban (2003)
replicated these results. The basic idea of MEC is to filter out the influence of other factors of
electricity consumption besides total economic activity. It was done with the help of regression of
the following type. The percentage change in electricity consumption was in the left-hand side of
equation. The right-hand side was constituted by 1) the percentage change of electricity prices, 2)
the percentage point change of industry share in GDP, 3) and the percentage point change in the
share of private sector in GDP (which is assumed to be a proxy for energy efficiency
improvements). Feige and Urban (2003) got the following results on the basis of a panel of
countries for 1994-1997 (47 observations):
6
̇ 𝑡𝑡
̇
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸
𝑡𝑡+0.005𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼
̇
̇
=0.035−0.026
𝑡𝑡 −0.0022𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃
𝑡𝑡
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡
(3.63) (−2.97)
Adj. R2 = 29%
(2.44)
(−1.94)
Residual of this regression is considered to be a true measure of total economic activity change.
Informal economy share dynamics was calculated as a difference between this residual and
officially recorded GDP growth rate.
ECM or MEC approaches are dealing with dynamics (or growth rate) of variables. This analysis
generates dynamics of informal economy share, but it fails to provide us with estimates of the
absolute volume of informal activities. The absolute volume of informal economy can be estimated
using electricity consumption regression in levels rather than growth rates. An example of such
analysis was done by Lackó (1998) who used the regression approach for household electricity
consumption in order to define the size of household informal activities. Lackó’s model (1998) is
described by the following equations:
𝑙𝑙𝑙𝑙𝐸𝐸𝑖𝑖 = 𝛼𝛼1 𝑙𝑙𝑙𝑙𝐶𝐶𝑖𝑖 + 𝛼𝛼2 𝑙𝑙𝑙𝑙𝑃𝑃𝑃𝑃𝑖𝑖 + 𝛼𝛼3 𝐺𝐺𝑖𝑖 + 𝛼𝛼4 𝑄𝑄𝑖𝑖 + 𝛼𝛼5 𝐻𝐻𝑖𝑖 + 𝑢𝑢𝑖𝑖
𝐻𝐻𝑖𝑖 = 𝛽𝛽1 𝑇𝑇𝑖𝑖 + 𝛽𝛽2 (𝑆𝑆𝑖𝑖 − 𝑇𝑇𝑖𝑖 ) + 𝛽𝛽3 𝐷𝐷𝑖𝑖
(1)
(2)
Where i - the country index,
𝐸𝐸𝑖𝑖 - per capita household electricity consumption in country i in Mtoe,
𝐶𝐶𝑖𝑖 - per capita real consumption of households without the consumption of electricity in country
i in US dollars (at purchasing power parity),
𝑃𝑃𝑃𝑃𝑖𝑖 - the real price of consumption of 1 kWh of residential electricity in US dollars (at
purchasing power parity),
𝐺𝐺𝑖𝑖 - the relative frequency of months with the need of heating in houses in country i,
𝑄𝑄𝑖𝑖 - the ratio of energy sources other than electricity energy to all energy sources in household
energy consumption
𝐻𝐻𝑖𝑖 - the per capita output of the hidden economy,
7
𝑇𝑇𝑖𝑖 - the ratio of the sum of paid personal income, corporate profit and taxes on goods and
services to GDP
𝑆𝑆𝑖𝑖 - the ratio of public social welfare expenditures to GDP, and
𝐷𝐷𝑖𝑖 - the sum on number of dependents over 14 years and of inactive earners, both per 100 active
earners.
Lacko’s method implies a cross-country estimation of regression (1) substituting the per capita
output of the hidden economy by equation (2). Regression estimates allow to calculate the
electricity consumption in the informal economy defined as follows: 𝛼𝛼5 (𝛽𝛽1 𝑇𝑇𝑖𝑖 + 𝛽𝛽2 (𝑆𝑆𝑖𝑖 − 𝑇𝑇𝑖𝑖 ) +
𝛽𝛽3 𝐷𝐷𝑖𝑖 ). In order to estimate the volume of activity in informal economy we should know electricity
intensity of output in informal economy. If it is unknown, then this approach can give us only
relative ranking of countries by the size of informal economy. In order to get absolute figures of
informal economy, we need to define the basis level. Lacko supposed informal economy share in
the USA on the level of 10.5% which was taken from external source. Then she managed to
calculate informal economy share in other countries using simple proportion to US level implied
by her regression estimates.
The main advancement in Lacko’s method was using factors of cross-country variance in the
level of informal economy (tax burden, social expenditures, number of dependents). It allows
separating informal economy and other factors which influence electricity consumption and
present in the residual of regression 1. However the list of informal economy determinants might
be incomplete. Thus, Lacko’s method estimates only a part of informal economy which is
attributable directly to a chosen set of factors.
Dynamic approaches (e.g. MEC approach) produce only dynamics of informal economy share:
how much is it increasing or decreasing. We cannot estimate the share itself: is it 20% or 40%.
That is why dynamic models require some external estimate of informal economy share in a basis
year. Then, estimated dynamics of informal economy share can be applied to a basis year and
8
produce informal economy share estimates in other years. Regression in levels (e.g. Lacko’s
approach) produces the level of informal economy itself.
There are several limitations for using Lacko’s approach in our study. Lacko’s approach is
designed for a cross-section of countries. When studying cross-section of regions, we may find
variation in tax burden insignificant because tax regime is almost equal in all Russian regions.
One might suggest using actual paid taxes in the region. Actually paid taxes are a function of tax
rate and informal economy share. Thus, we might have endogeneity bias. Moreover, the difference
in informal sector share can be determined by historical, geographic and other factors except
government policy. However these factors could hardly be measured and put into regression.
In our study we also implement regression analysis of cross-section electricity consumption
data. However, there are 3 differences to Lacko: 1) we use cross-section of regions instead of
countries; 2) we analyze electricity consumption in production of goods and services, while Lacko
was focused on households; 3) we do not model factors which affect informal output, we treat
informal economy as a residual of regression for electricity consumption. Thus, we do not restrict
analysis by assumptions on what factors impact informal output. Cross-regional data does not
provide us with sufficient variation of tax burden which is considered as the most important factor
for informal economy share.
Usual critics of electricity method (see for example Schneider, 2002) argues that not all
informal economy activities require electricity. Estimates based on electricity consumption reflect
informal activity only in electricity-dependent sectors. Nevertheless this analysis remains valuable
for efficient allocation of resources between formal and informal sectors. If informal economy
does not consume such material resource as electricity it would not lead to a shortage of resources
available for the formal sector.
9
Empirical estimates of unofficial economy in Russia
Unofficial economy in the Soviet Union was estimated at 12% of GDP (Alexeev et al. (2003)).
During transition period of 1990-s its share substantially increased according to the majority of
studies. In 1995 unofficial economy was estimated at 41.6% of GDP (Alexeev et al. (2003)).
Exhibit 1. Comparison of the estimates of informal economy share in Russia, % of GDP
90%
Schneider et al (2010)
80%
Global Financial Integrity
(2013)
Kaufman and Kaliberda (1996)
- Unity elasticity scenario
Johnson, Kaufman and Shleifer
(1997)
Alexeev, Pyle (2001)
70%
60%
50%
40%
30%
20%
Rosstat
10%
0%
1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
Ministry of Finance
Estimated share of unofficial sector in Russia in 2000-s is sizeable. Schneider (2006) reported
48.7% in 2002/2003 on the basis of DYMIMIC model and currency demand approach. Relatively
recent study by Worldbank published the same result – 43.6% during 1999-2007 (Schneider et all,
2010).
According to Rosstat about 19% of Russian workers were engaged in informal activities in
2012. Only 4% of workers were employed in informal sector in Moscow and 9% in Moscow
region. Informal workers are concentrated in several sectors: trade (34%), agriculture (26%),
construction (10%), manufacturing (9%) and transport & communications (8%).
High variation in estimates of informal economy in Russia suggests that there is a room for our
study. Literature lacks estimates of informal economy in Russian regions. At the same time,
regional budgets face significant deficits needing permanent transfer from federal budget.
Decreasing informal economy could lead to higher tax collection and solution for balanced
regional budgets. Informal economy measurement is the primary step for adequate policy design
10
aimed at moving business to official reporting and paying taxes. This study provides special
methodology which is tailored for particular Russian publicly-available regional data.
3 Regression model for regional electricity consumption
Electricity consumption was chosen as a proxy for total economic activity in a region.
Electricity is consumed by firms among other production inputs. However, there are activities
which do not consume electricity at all, for example, road transportation consuming gasoline as
energy source instead of electricity. Electricity consumption cannot be used as a proxy for such
activities what limits the applicability of the method based on electricity consumption.
Let us consider activities which consume electricity. Electricity consumption by an individual
firm consists of fixed and variable parts. Fixed part of electricity consumption is determined by
the total stock of production capacities. Fixed electricity consumption is required to maintain
capacities workable; it includes for example lightening rooms and heating. It does not change when
output is changed. Variable component represents direct electricity consumption for producing
output. Short-term electricity consumption depends on output (capacity utilization of existing
capacities).
Let us consider the model of electricity consumption in industry j in the region i consisting of
variable and fixed parts.
𝐸𝐸𝑖𝑖,𝑗𝑗,𝑡𝑡 = 𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡 + 𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡 𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
(3)
Where 𝐸𝐸𝑖𝑖,𝑗𝑗,𝑡𝑡 – electricity consumption in region i in sector j in year t;
𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 – electricity consumption in region i in sector j in year t per unit of output in this sector
𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡 ; 𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡 – electricity consumption in region i in sector j in year t per unit of capacities in this
sector 𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡 .
Total electricity consumption in a region is a sum of formal sector (with index f) and informal
(index u):
𝑓𝑓
𝑓𝑓
𝑢𝑢
𝑢𝑢
𝐸𝐸𝑖𝑖,𝑗𝑗,𝑡𝑡 = 𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡 + 𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡 𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡 + 𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
+ 𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡 𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
(4)
11
Total electricity consumption in the region is given by:
𝑓𝑓
𝑓𝑓
𝑢𝑢
𝑢𝑢
𝐸𝐸𝑖𝑖,𝑡𝑡 = ∑𝑗𝑗 𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡 + ∑𝑗𝑗 𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡 𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡 + ∑𝑗𝑗 𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
+ ∑𝑗𝑗 𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡 𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
(5)
In a cross-regional regression variable output reflects also variation in capacities. Regional
output variation is very significant. On the level of particular industries inter-regional differences
in output might be 10, 100, 1000 times and even more. This huge variation can be almost perfectly
explained by variation in capacities, variation in capacity utilization will play minor role.
𝑓𝑓
𝑢𝑢
𝐸𝐸𝑖𝑖,𝑡𝑡 = ∑𝑗𝑗 (𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 + 𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡 )𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡 + ∑𝑗𝑗 (𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 + 𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡 )𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
(6)
Let us rename (𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 + 𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡 ) as 𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 .
There is a strong cross-section correlation between output of different industries as all industries
are correlated with the size of the region. It causes multicollinearity problems. Also there could be
heteroscedasticity in residuals due to different size of regions. It is possible to solve these problems
𝑓𝑓
by dividing equation by formal gross regional output 𝑄𝑄𝑖𝑖,𝑡𝑡 .
𝐸𝐸𝑖𝑖,𝑡𝑡
𝑓𝑓
𝑄𝑄𝑖𝑖,𝑡𝑡
=
𝑓𝑓
∑𝑗𝑗 𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
𝑓𝑓
𝑄𝑄𝑖𝑖,𝑡𝑡
+
𝑢𝑢
∑𝑗𝑗 𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
𝑓𝑓
𝑄𝑄𝑖𝑖,𝑡𝑡
(7)
Thus our dependent variable will be electricity consumption per unit of formal gross regional
product or electricity intensity of the regional economy.
Informal economy is considered as omitted variable in our regression for electricity
consumption. Thus, informal economy can be reflected in regression in two ways: 1) through
omitted variable bias in regression coefficients; 2) in regression residuals.
Omitted variable bias depends on the correlation between informal economy and independent
variables. The most important bias is expected for variables which measure official output because
economic activity can be correlated in formal and informal sector due to common macroeconomic
drivers. In order to exclude macroeconomic factor from regression we divide regression by
regional gross domestic product (in formal sector).
In the right-hand side of equation there are shares of different industries in total regional output.
In the regression we cannot use all industries as their sum is equal to 1 for each region meaning
12
perfect multicollinearity in a cross-regional regression.
Moreover there are industries with
relatively low contribution to the cross-regional variance of electricity consumption per unit of
gross regional product. Thus it is reasonable to split industries into two parts: 1) significant
industries which should be estimated explicitly in a regression; 2) insignificant industries.
Electricity consumption by insignificant industries is approximated by their output multiplied by
the Russian average electricity intensity of those industries. We use Russian average instead of
regional average simply because there is no data on the regional level. Divided by GRP of a
particular region, this indicator could be used among independent variables in the right-hand side
of the regression.
Electricity consumption by industries is presented in the exhibit 2. Significant industries used
in a regression are marked with a dark color. They are 1) production of metals; 2) mining industry;
3) chemical industries; 4) electricity generation, gas and water distribution; 5) production of nonmetal mineral commodities (cements, bricks, etc.); 6) production of transport goods (passenger
cars, trucks, etc.); 7) paper production; 8) construction.
Transportation industry is labeled as insignificant despite high electricity consumption. The
reason is that transportation statistics is mixed with goods, people and telecommunications which
have significantly different electricity intensity. As a result, electricity intensity of transportation
is not stable in a cross-regional regression leading to insignificant coefficients.
Trade was assigned to insignificant industries also due to high volatility of electricity intensity
across different regions.
13
Exhibit 2. Electricity consumption by industries in Russia in 2011, GWt*h
160000
140000
120000
100000
80000
60000
40000
20000
0
Source: Rosstat, author’s calculations
Subdividing industries into significant and insignificant we can use the following representation
of electricity consumption in the region:
𝐸𝐸𝑖𝑖
𝑓𝑓
Where
𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
𝐸𝐸𝑖𝑖
𝑓𝑓
𝑄𝑄𝑖𝑖
𝑄𝑄𝑖𝑖
=
𝑓𝑓
∑𝑗𝑗∈𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝛾𝛾𝑖𝑖,𝑗𝑗 𝑄𝑄𝑖𝑖,𝑗𝑗
𝑓𝑓
𝑄𝑄𝑖𝑖,𝑡𝑡
=
𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
𝐸𝐸𝑖𝑖
𝑓𝑓
𝑄𝑄𝑖𝑖
+ ∑𝑗𝑗∈𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠
𝑓𝑓
𝛽𝛽𝑗𝑗 𝑄𝑄𝑖𝑖,𝑗𝑗
𝑓𝑓
𝑄𝑄𝑖𝑖
(8)
represents electricity consumption by insignificant industries.
Electricity intensities of significant industries are parameters 𝛽𝛽𝑗𝑗 (𝑗𝑗 ∈ 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠) which should be
estimated in a regression.
Besides industrial structure of economy regional electricity consumption is subject to the
following factors which could differ across regions.
•
Capacity utilization. The higher capacity utilization, the lower electricity intensity due
to the existence of fixed costs. In a regression we suppose that all regions have similar
capacity utilization for a particular industry. It allows us not include this factor into the
regression.
•
Electricity prices which could stimulate more efficient use of electricity.
•
Availability and prices of substitutes for electricity. If natural gas is very expensive
or there is no sufficient natural gas infrastructure, then region will rely more on
electricity.
14
•
Weather conditions: cold winter or hot summer could increase electricity consumption
for heating or air-conditioning.
Taking into account additional factors, we use the following regression specification:
𝐸𝐸𝑖𝑖
𝑓𝑓
𝑄𝑄𝑖𝑖
= 𝛽𝛽1
𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
𝐸𝐸𝑖𝑖
𝑓𝑓
𝑄𝑄𝑖𝑖
+ ∑𝑗𝑗=𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠
𝑓𝑓
𝛽𝛽𝑗𝑗 𝑄𝑄𝑖𝑖,𝑗𝑗
𝑓𝑓
𝑄𝑄𝑖𝑖
+ 𝛽𝛽2 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖 + 𝛽𝛽3 𝑃𝑃𝑖𝑖𝑒𝑒 + 𝛽𝛽4 𝑃𝑃𝑖𝑖𝐺𝐺 + 𝛽𝛽0 + 𝜀𝜀𝑖𝑖
(9)
Coefficient 𝛽𝛽1 is assumed to be 1 because we subtract insignificant industries from the total
electricity consumption. However, calculating electricity consumption by insignificant industries
𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
𝐸𝐸𝑖𝑖
we use Russian average electricity intensities. It does not guarantee that it will be accurate
for a particular region. We need to estimate in regression whether coefficient 𝛽𝛽1 equals 1 in order
to check appropriateness of our approach.
Electricity consumption in informal sector is assumed to be equal to the intercept plus the
residual:
𝑢𝑢
∑𝑗𝑗 𝛾𝛾𝑖𝑖,𝑗𝑗 𝑄𝑄𝑖𝑖,𝑗𝑗
𝑓𝑓
𝑄𝑄𝑖𝑖
= 𝛽𝛽0 + 𝜀𝜀𝑖𝑖
(10)
This regression equation (9) is estimated on the basis of cross-section data for Russian regions
in 2011.
3.1 Estimates of informal activity in Russian regions in 2011
The principal source of data is “The common interagency statistical information system”
(EMISS) presented on Rosstat website (http://www.fedstat.ru/indicators/start.do).
I used the data on 67 Russian regions and 28 industries: 14 aggregated economic sectors
(agriculture, mining, transport, educations, etc.) and 14 individual manufacturing sub-sectors
(food, metallurgy, machinery, etc.).
Official economic activity was measured by the gross regional product (GRP) and value added
in aggregated sectors published by Rosstat. GRP and value added nominal volumes in 2011 were
used. Rosstat does not provide GRP statistics by individual manufacturing industries. It was
15
estimated as a product of manufacturing GRP and a share of particular manufacturing industry in
the volume of manufacturing production.
Electricity consumption by production sectors is calculated as the total electricity consumption
in the region less losses in electricity grids and consumption by households.
Table 1. Descriptive statistics of the estimation sample (67 Russian regions in 2011)
Variable
Mean
Std. Dev
Min
Max
Electricity consumption in production of goods
and services per official GRP, KWt*h per ths
rubles
13.4
8.76
1.15
46.1
Electricity consumption by insignificant
industries per official GRP, KWt*h per ths rubles
6.171
2.525
0.87
13.47
252.8
42.56
151.72
380.000
Paper industry share in GRP
0.6%
1.0%
0.0%
6.3%
Chemical industry share in GRP
1.4%
1.9%
0.0%
9.7%
Non-metal minerals share in GRP
0.8%
0.7%
0.1%
4.0%
Metals industry share in GRP
3.0%
5.4%
0.0%
26.5%
Transport machinery share in GRP
1.6%
1.8%
0.0%
9.0%
Mining industry share in GRP
5.1%
9.4%
0.0%
49.5%
Electricity, gas, water supply share in GRP
2.9%
1.4%
0.4%
7.8%
5.2%
2.8%
1.3%
19.1%
Electricity price
Construction share in GRP
Source: Rosstat, author’s calculations
Results of cross-regional regression estimation for 2011 year are reported in the table 2 below.
Table 2. Results of regression for electricity consumption in production sectors per Gross
regional product, cross-section of Russian regions, 2011 year
Regressor
Electricity consumption by insignificant industries per
GRP
Electricity price
Paper industry share in GRP
Chemical industry share in GRP
Non-metal minerals share in GRP
Metals industry share in GRP
Coef.
Std. error
tstatistics
P>t
1.06
0.23
4.7
0.000
-0.029
0.012
-2.5
0.015
238.5
43.99
5.42
0.000
5.6
21.73
0.26
0.797
65.4
62.62
1.04
0.301
87.1
8.11
10.73
0.000
-15.1
22.01
-0.69
0.496
Mining industry share in GRP
24.7
5.05
4.89
0.000
Electricity, gas, water supply share in GRP
77.6
34.73
2.23
0.030
-22.4
16.22
-1.38
0.174
7.46
4.00
1.87
0.067
Transport machinery share in GRP
Construction share in GRP
Constant
Number of observations (regions): 67. R-squared: 89%. Adj. R-squared: 87%.
Source: Author’s calculations
16
Adjusted R2 of regression is 87%. It is very important indicator of regression for us because we
are going to use a residual for calculation of informal economy share. If R2 were low like 20%40%, it would mean either a) there is a huge informal economy variation or b) there are other
factors which were not captured in the regression, they exist in residual term. In this case, due to
high impact of other factors we cannot use residual term for calculation of informal economy share.
When R2 is high then influence of other factors on regression residual is as low as possible. It
allows us to use residual for estimation of informal economy.
According to regression results, electricity prices affect electricity consumption negatively (in
line with our hypothesis). Temperature and natural gas prices are not presented in the final
regression as they are insignificant. Industrial structure of the region has significant effect on
electricity intensity of GRP. Industries which influence positively electricity intensity are paper,
chemical industry, metals industry, mining industry, electricity production and water & natural gas
supply. The higher their share in the regional output, the higher electricity consumption per unit
of GRP. Transport machinery and construction sectors are insignificant factors for electricity
consumption intensity in the region. Results of initial regression which includes all factors (some
of them were dropped) are presented in appendix 2. Coefficient at electricity consumption of
“insignificant” industries is close to 1 which means that we calculated it correctly. Thus, we can
directly subtract it from dependent variable or leave it in independent variables.
Electricity consumption in the informal sector is captured by 𝛽𝛽̂0 + 𝑒𝑒𝑖𝑖 where 𝛽𝛽̂0 is an estimate
of the intercept in the regression and 𝑒𝑒𝑖𝑖 is regression residual for region i.
𝑢𝑢
∑𝑗𝑗 𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
𝑓𝑓
𝑄𝑄𝑖𝑖,𝑡𝑡
= 𝛽𝛽̂0 + 𝑒𝑒𝑖𝑖
(11)
Total informal output is estimated under assumption that it has the same electricity intensity as a
ratio of total electricity consumption to official GRP:
𝑢𝑢
𝑄𝑄𝑖𝑖,𝑡𝑡
=
�0 +𝑒𝑒𝑖𝑖 �𝑄𝑄𝑓𝑓
�𝛽𝛽
𝑖𝑖,𝑡𝑡
𝐸𝐸𝑖𝑖,𝑡𝑡
𝑓𝑓
𝑄𝑄
𝑖𝑖,𝑡𝑡
(12)
17
Informal economy share in GRP is easy to calculate:
𝑢𝑢𝑖𝑖 =
𝑢𝑢
𝑄𝑄𝑖𝑖,𝑡𝑡
𝑓𝑓
𝑢𝑢 +𝑄𝑄
𝑄𝑄𝑖𝑖,𝑡𝑡
𝑖𝑖,𝑡𝑡
(13)
Calculated informal economy share ranges from 3.8% to 85% with the average level of 40%.
85% seems unrealistically high. The histogram, table and the map with informal economy share
by regions in 2011 are presented below. There are 10 regions with informal economy share above
60%: Ingushetia, Moscow, Dagestan, Kalmykia, Altay Republic, Krasnodar region, Kabardino
Balkaria, Kaliningrad, Astrakhan, and Saint-Petersburg. Southern small regions are likely to have
high informal economy share because they have a large share of small business and low
government control over economic activity. Informal economy share in Moscow (79%) is likely
to be overestimated. It could be caused by large electricity consumption for lightning, air
conditioning and other requirements of a large city. The same bias is applicable for SaintPetersburg (estimated informal share at 60%). Thus, we need special study to estimate informal
economy share in these two cities. The lowest informal economy share is in Komi, Marii El,
Lipetsk region, Arkhangelsk region, Krasnoyarsk territory, Vologda region.
0
2
Number of regions
6
4
8
10
Exhibit 3. Distribution of Russian regions by estimated informal economy share in 2011
0
.2
.4
.6
Estimated informal economy share in GRP in 2011
.8
Source: Author’s calculations
18
Table 3. Russian regions ranking according to the share of informal economy in total GRP
(formal and informal), from the highest to lowest
#
Region name
Informal
economy
share, %
#
Region name
Informal
economy
share, %
1
Republic of Ingushetia
85%
36
Smolensk Region
36%
2
Republic of Dagestan
81%
37
Murmansk Region
35%
3
City of Moscow
79%
38
Novosibirsk Region
34%
4
Republic of Kalmykia
77%
39
Penza Region
34%
5
Altay republic
76%
40
Republic of Bashkortostan
34%
6
Krasnodar Territory
68%
41
Ivanovo Region
33%
7
Republic of Kabardino Balkaria
64%
42
Tomsk Region
33%
8
Kaliningrad Region
62%
43
Tula Region
33%
9
Astrakhan Region
61%
44
Ryazan Region
32%
10
City of St Petersburg
60%
45
Kurgan Region
32%
11
Republic of Adygea
57%
46
Leningrad Region
31%
12
Pskov Region
55%
47
Volgograd Region
31%
13
Khabarovsk Territory
54%
48
Kemerovo Region
31%
14
Kaluga Region
53%
49
Novgorod Region
30%
15
Stavropol Territory
53%
50
Kirov Region
30%
16
Primorsky Territory
52%
51
Saratov Region
28%
17
Republic of Northern Osetia Alania
51%
52
Kursk Region
28%
18
Tambov Region
51%
53
Republic of Karelia
28%
19
Rostov Region
50%
54
Republic of Udmurtia
26%
20
Tver Region
50%
55
Tumen Region
25%
21
Ulyanovsk Region
44%
56
Nizhny Novgorod Region
25%
22
Republic of Tatarstan
44%
57
Kostroma Region
24%
23
Republic of Mordovia
44%
58
Perm Territory
24%
24
Orel Region
44%
59
Sverdlovsk Region
23%
25
Voronezh Region
44%
60
Vologda Region
21%
26
Altay Territory
43%
61
Chelyabinsk Region
18%
27
Omsk Region
43%
62
Orenburg Region
14%
28
Samara Region
41%
63
Krasnoyarsk Territory
14%
29
Bryansk Region
41%
64
Arkhangelsk Region
13%
30
Vladimir Region
40%
65
Lipetsk Region
13%
31
Moscow Region
40%
66
Republic of Marii El
11%
32
Belgorod Region
39%
67
Republic of Komi
33
Republic of Buryatia
37%
34
Republic of Chuvashia
37%
35
Yaroslavl Region
37%
4%
Source: Author’s calculations
19
Exhibit 4. Map of Russian regions by estimated informal economy share in 2011
20
3.2 Comparison with alternative indicators of informal economy
We need to compare our results with alternative indicators of informal economy. We think that
credible results should correlate with other sources of estimates. We have considered a number of
benchmarks for informal economy; this paragraph summarizes the main conclusions.
Our estimates of informal economy share have 35% correlation with estimates by Rosstat
which measures the share of workers employed in the informal economy. It is important to
stress that we have a positive and significant correlation although it is not very high. The average
informal economy share in Russia is 19% according to Rosstat versus our 40%. We plot these two
indicators on a chart below. For Dagestan and Ingushetia Rosstat estimates are close to our results.
Exhibit 5. Comparison of two indicators of informal economy by Russian regions
Source: Author’s calculations
Sub-national Russian data of “Doing business rating” (2012) has a low correlation with our
estimates of informal economy. Two charts below demonstrate plotting our estimates against
“starting a business” and “dealing with construction permits” rankings.
21
Exhibit 6. Comparison with “Doing business” (2012) rankings by Russian regions
Source: Author’s calculations
There is a Corruption perception index for Russian regions for 2002 prepared by
“Transparency international Russia”. Unfortunately we do not have more “fresh” estimates.
However our estimates of informal economy share in 2011 show 35% correlation with Corruption
perception index for 2002. It supports reliability of our estimates because it is theoretically proven
that the higher corruption the higher informal economy share.
Exhibit 7. Comparison with “Corruption perception index for 2002” by Russian regions
Source: Author’s calculations
We calculated specific indicators which characterize the degree of violations by taxpayers in a
region. It is a ratio of cameral tax inspections which found violations to a total number of
cameral tax inspections in Russian regions in 2011. Higher amount of revealed violations should
correspond to higher informal economy share because the main motive for being informal is tax
22
evasion. We have found that the share of tax violations have 30% correlation with our estimate of
informal economy share. We should note that we could underestimate informal economy share in
Northern Osetia – Alania as it has extremely high reveled tax violations.
Exhibit 8. Comparison with the share of cameral tax inspections which found violations
in 2011 by Russian regions
Source: Author’s calculations
Usually residential housing prices are considered as an indicator of real incomes of population.
Thus we used them for comparison with our informal economy estimates. We calculated a ratio of
housing purchase price to the average monthly per capita income in a region. This indicator has
17% (low) correlation with informal economy share.
Exhibit 9. Comparison with the housing purchase price in 2011 by Russian regions
Source: Author’s calculations
23
We can conclude that our measure of informal activity has imperfect correlation with other
individual indicators. No one of these indicators measures informal economy precisely. Moreover
they are definitely impacted by other factors which do not relate to informal economy. However
we could suppose that all considered indicators should have something common which relates to
informal economy. In order to measure it we calculate the average of 3 indicators: informal
economy share by Rosstat, corruption perception index and housing price to income ratio.
Before averaging we standardized indicators deducting their cross-regional average and divided
by cross-regional standard deviation.
Exhibit 10. Comparison with the aggregate indicator (average of 3 standardized
indicators: Rosstat informal economy share, corruption perception, housing prices to income
ratio) in Russian regions
Source: Author’s calculations
This average of 3 indicators has 44% (pretty high) correlation with our estimate of informal
economy. This is the highest value among considered indicators. It supports that our measure of
informal economy is reliable in the sense that it reflects some common regional trends. It is
important that 1) correlation is not negative – we do not have completely unreasonable results, 2)
though positive, correlation is not very high meaning that our indicator is different from existing
proxies. Difference could come either from our advantage of informal economy measurement or
from our mistakes in measurement of informal economy.
24
3.3 Informal economy and economic conditions in Russian regions
In theory informal economy has significant impact on economic development. We are
interested to find any correlation between our estimate of informal economy share and economic
performance of Russian regions. We consider the following regional economic indicators: an
official unemployment rate, formal gross regional product (GRP) per resident, an investment share
in GRP. From fiscal point of view, it is interesting to find any correlation between the informal
economy and the regional public deficit or federal transfers to the regional budget.
Official unemployment rate is supposed to be positively correlated with informal economy
share because informal economy absorbs officially unemployed people. Correlation between these
two indicators in 2011 is 38% which is pretty high.
Exhibit 11. Comparison with the unemployment rate (by methodology of International
Labor Organization) by Russian regions in 2011
GRP per capita has negative correlation (-17%) with informal economy share which
corresponds to our expectations. The higher level of formal income in the region, the higher
opportunities in formal sector which becomes relatively more attractive than informal sector.
25
Exhibit 12. Comparison with GRP per capita by Russian regions in 2011
Transfers from federal to regional budgets (in terms of their share in total income of regional
budget) have the strongest correlation with informal economy share at 54%. Indeed, the higher
share of informal activities the lower ability of regional government to collect taxes. In order to
maintain necessary social spending such regions needs external financing from the Federal
government.
Exhibit 13. Comparison with transfers to the regional budget (as % of GRP) by Russian
regions in 2011
4 Dynamics of informal economy share
In the previous section we estimated the absolute level of informal economy in a given year.
Now we want to determine how it changed over time, i.e. what is dynamics of informal economy.
26
For this purpose we use different approach. It is based on calculation of statistical discrepancies
between dynamics of electricity consumption and official economic activity.
We need to explain why we need another method. One could suggest to estimate dynamics of
informal economy running panel regression. We think that this could lead us to contradicting
results. For example, regression method does not guarantee that estimated informal economy share
decreases when electricity consumption significantly falls and official output grows. For such
observations regression would try to decrease coefficient at official output rather than decrease
residual (which plays the role of informal economy).
4.1 Methodology
4.1.1 Traditional electricity consumption method
Let us start with basic relationship used by Kaufman and Kaliberda (1996). We consider the
model of electricity consumption Ei,t in total economy of the region i. It is supposed to be
proportionate to the total output Q i,t .
Ei,t = γi,t Q i,t
γi,t is electricity intensity.
(14)
Total output is a sum of official Qfi,t and unofficial Qui,t . It is easy to see that total output can be
expressed through the official output and the share of unofficial sector in the economy ui,t:
Qf
Q i,t = 1−ui,t where ui,t =
i,t
Qu
i,t
Qi,t
(15)
Thus, total electricity consumption can be expressed on the basis of official output
and informal economy share:
Qf
Ei,t = γi,t 1−ui,t
Taking logarithms and differentiating:
̇
Eı,ȷ,t
Ei,j,t
γı,ȷ,t
̇
=γ
i,j,t
+
ḟ
Qı,ȷ,t
f
Qi,j,t
i,t
+ u̇ i,j,t
(16)
(17)
27
Thus, growth rate of electricity consumption in the region is a sum of growth rate of electricity
intensity, growth rate of official output and the change in the share of unofficial economy.
Usually authors assume no changes in electricity intensity and use only difference between
electricity consumption and formal output. Kaufman and Kaliberda (1996) simplified this equation
to the following:
𝐸𝐸̇
𝑢𝑢̇ 𝑡𝑡 = 𝐸𝐸𝑡𝑡 −
𝑡𝑡
𝑓𝑓
𝑄𝑄̇𝑡𝑡
𝑓𝑓
𝑄𝑄𝑡𝑡
(18)
It is important to return electricity intensity into consideration. Actually, it can change in the
economy by a number of reasons. We can take into account changes which are associated with
unequal growth of industries with different electricity intensity. It could significantly improve
quality of results. For example, if during economic crisis industries with high electricity intensity
are hit the most, electricity consumption can fall higher than total gross regional product. In this
case, simple approach could lead us to the wrong conclusion that informal economy share
diminishes.
4.1.2 Proposed augmented electricity consumption method
I am proposing to use augmented method which takes into account changes in industrial
structure and trends in electricity intensity in the economy. It was developed by the author and
generally motivated by usual critics of Kaufman-Kaliberda approach.
Let us consider total electricity consumption in a region i as a sum of consumption by individual
industries denoted by index j. As we derived in section 3, total electricity consumption in the region
is a sum of variable part depending on current output and fixed part depending on a size of
capacities:
𝐸𝐸𝑖𝑖,𝑡𝑡 = ∑𝑗𝑗 (𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡 + 𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡 𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡 )
(19)
Capacity in the industry can be represented as output divided by capacity utilization rate CU.
Let us denote
𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 = 𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡 +
𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡
𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
(20)
28
Then we can simplify
𝐸𝐸𝑖𝑖,𝑡𝑡 = ∑𝑗𝑗 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
(21)
Let us express industry output using official output and the share of informal economy in that
industry 𝑢𝑢𝑖𝑖,𝑗𝑗,𝑡𝑡 :
𝑓𝑓
𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡 = 1−𝑢𝑢
(22)
𝑖𝑖,𝑗𝑗,𝑡𝑡
Using this for total electricity consumption, we get:
𝑓𝑓
𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
𝐸𝐸𝑖𝑖,𝑡𝑡 = ∑𝑗𝑗 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 1−𝑢𝑢
(23)
𝑖𝑖,𝑗𝑗,𝑡𝑡
Let us take a derivative of this equation by time:
𝐸𝐸̇𝑖𝑖,𝑡𝑡 = ∑𝑗𝑗
𝑓𝑓
𝑓𝑓
𝑓𝑓
𝜌𝜌̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡 +𝑄𝑄̇𝑖𝑖,𝑗𝑗,𝑡𝑡 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 +𝑢𝑢̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
1−𝑢𝑢𝑖𝑖,𝑗𝑗,𝑡𝑡
(24)
This equation means that increase in total electricity consumption is due to increase in
electricity intensity (which depends on capacity utilization as well), increase in official output and
increase in informal output.
Let us suppose that informal economy share 𝑢𝑢𝑖𝑖,𝑡𝑡 is equal across all industries; hence its
dynamics 𝑢𝑢̇ 𝑖𝑖,𝑡𝑡 is equal too. Multiplying eq. 28 by 1 − 𝑢𝑢𝑖𝑖,𝑡𝑡 and splitting up the sum in the right-
hand part:
𝑓𝑓
𝑓𝑓
𝑓𝑓
𝐸𝐸̇𝑖𝑖,𝑡𝑡 �1 − 𝑢𝑢𝑖𝑖,𝑡𝑡 � = ∑𝑗𝑗 𝜌𝜌̇ 𝑖𝑖 ,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡 + ∑𝑗𝑗 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄̇𝑖𝑖,𝑗𝑗,𝑡𝑡 + 𝑢𝑢̇ 𝑖𝑖,𝑡𝑡 ∑𝑗𝑗 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
(25)
From this equation we can express dynamics of informal economy share:
𝑢𝑢̇ 𝑖𝑖,𝑡𝑡 =
(1−𝑢𝑢𝑖𝑖,𝑗𝑗,𝑡𝑡 )𝐸𝐸̇𝑖𝑖,𝑡𝑡
𝑓𝑓
∑𝑗𝑗 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
−
𝑓𝑓
∑𝑗𝑗 𝜌𝜌̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
𝑓𝑓
∑𝑗𝑗 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
−
𝑓𝑓
∑𝑗𝑗 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄̇𝑖𝑖,𝑗𝑗,𝑡𝑡
𝑓𝑓
∑𝑗𝑗 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
(26)
𝑓𝑓
𝑓𝑓
Taking into account that ∑𝑗𝑗 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡 = 𝐸𝐸𝑖𝑖,𝑡𝑡 = (1 − 𝑢𝑢𝑖𝑖,𝑗𝑗,𝑡𝑡 )(𝐸𝐸𝑖𝑖,𝑡𝑡 ) we come to the following:
𝑢𝑢̇ 𝑖𝑖,𝑡𝑡 =
𝐸𝐸̇𝑖𝑖,𝑡𝑡
𝐸𝐸𝑖𝑖,𝑡𝑡
−
𝑓𝑓
∑𝑗𝑗 𝜌𝜌̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
𝑓𝑓
∑𝑗𝑗 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
−
𝑓𝑓
∑𝑗𝑗 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄̇𝑖𝑖,𝑗𝑗,𝑡𝑡
𝑓𝑓
∑𝑗𝑗 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 𝑄𝑄𝑖𝑖,𝑗𝑗,𝑡𝑡
(27)
Thus, a change of informal economy share equals to the growth rate of total electricity
consumption less the weighted growth of electricity intensity less the weighted growth of
29
production in formal sector. If there were no cross-industry differences in electricity intensities
𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 this equation would equal to Kaufman-Kaliberda equation.
Modeling changes in electricity intensity
Changes in official output can be measured directly using official statistics. However changes
in electricity intensity are unobservable and should be considered in more detail. Let us take
derivative by time of equation for electricity intensity (20):
𝜌𝜌̇ 𝑖𝑖 ,𝑗𝑗,𝑡𝑡 = 𝛾𝛾̇𝑖𝑖 ,𝑗𝑗,𝑡𝑡 + 𝜇𝜇̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡
1
𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
−
1
̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡
𝐶𝐶𝐶𝐶
𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡 𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡
(28)
Let us suppose that efficiency gains (or technological progress) are the same for variable and
fixed part of electricity consumption that is: 𝛾𝛾̇𝑖𝑖 ,𝑗𝑗,𝑡𝑡 = 𝜇𝜇̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡 , then
𝜌𝜌̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡 = 𝛾𝛾̇𝑖𝑖,𝑗𝑗,𝑡𝑡
1+𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
−
̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡
𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡 𝐶𝐶𝐶𝐶
(29)
𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡 𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
Thus the electricity intensity in industry j is a) depends on efficiency improvements measured
by decreasing 𝛾𝛾̇𝑖𝑖,𝑗𝑗,𝑡𝑡 and b) depends on the changes in capacity utilization (negatively). Growth of
capacity utilization has negative decreases electricity intensity due to existence of fixed electricity
costs. The relative importance of fixed electricity costs is measured by the ratio
𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡
𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
.
We can assume that 𝛾𝛾̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡 is decreasing by 1% per year. This rate corresponds to technological
progress in electricity use which is estimated at 1% per year by Bashmakov and Myshak (2012).
Dynamics of electricity intensity is directly connected with dynamics of informal economy share.
If we mistake by 1 percentage point in technological progress assumption, we automatically
mistake by 1 percentage point in informal economy share.
Dynamics of capacity utilization
̇
CU
i,j,t
CUi,j,t
was estimated on the basis of Rosstat data on particular
aggregated economic sectors in Russia. We suppose that dynamics for all regions is equal to the
Russian average within the same sector. The average annual growth rate of total value added in
Russian economy in 2004-2011 was 4.4%; annual average growth rate of capacities over the same
periods was 2.9%. Thus capacity utilization increased by 1.5% each year on average.
30
We should determine
𝛾𝛾
𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡
𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
. We can express it from equation (20)
𝜇𝜇𝑖𝑖,𝑗𝑗,𝑡𝑡
𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
= (1 − 𝜂𝜂)𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡
(30)
Where 𝜂𝜂𝑖𝑖,𝑗𝑗,𝑡𝑡 = 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 represents the share of variable cost in total electricity intensity. We should
𝑖𝑖,𝑗𝑗,𝑡𝑡
somehow estimate 𝜂𝜂𝑖𝑖,𝑗𝑗,𝑡𝑡 as there is no statistical data on it. It should be different across industries.
We can suppose that the higher electricity intensity of a particular industry, the higher share of
variable electricity consumption. The idea is that high-electricity intensive industries use
electricity as one of the main inputs for production. Thus electricity consumption is mainly variable
depending on the amount of output. Let us suppose that variable part ranges from some reasonable
maximal level 0.8 (our expert opinion) for electricity intensive industries to minimal level of 0.2
for service sectors. It can be formalized in the following equations:
𝜂𝜂𝑖𝑖,𝑗𝑗,𝑡𝑡
𝐾𝐾𝐾𝐾𝐾𝐾ℎ
0.2, 𝑖𝑖𝑖𝑖 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 < 7
⎧
𝑡𝑡ℎ𝑠𝑠 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟
0.08
+
0.011𝜌𝜌
,
𝑖𝑖𝑖𝑖
7
< 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 < 60
=
𝑖𝑖,𝑗𝑗,𝑡𝑡
⎨
𝐾𝐾𝐾𝐾𝐾𝐾ℎ
0.8, 𝑖𝑖𝑖𝑖 𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 > 60
⎩
𝑡𝑡ℎ𝑠𝑠 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟
(31)
Because numbers 0.2 and 0.8 are taken from our expert opinion, we are interested in how
results are sensitive to them. Let us also suppose that the average capacity utilization is around
70%. Now we can rewrite and approximate eq. (28):
𝜌𝜌̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡
𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡
𝜌𝜌̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡
𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡
≈(
=(
̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡
𝐶𝐶𝐶𝐶
𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡
𝐶𝐶𝐶𝐶
𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
𝛾𝛾̇
+ 𝛾𝛾𝑖𝑖,𝑗𝑗,𝑡𝑡
𝑖𝑖,𝑗𝑗,𝑡𝑡
1+𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
)𝜂𝜂𝑖𝑖,𝑗𝑗,𝑡𝑡 −
̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡
𝐶𝐶𝐶𝐶
𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
+ 1%)(0.08 + 0.011𝜌𝜌𝑖𝑖,𝑗𝑗,𝑡𝑡 ) −
̇ 𝑖𝑖,𝑗𝑗,𝑡𝑡
𝐶𝐶𝐶𝐶
𝐶𝐶𝐶𝐶𝑖𝑖,𝑗𝑗,𝑡𝑡
(32)
31
4.2 Empirical results on dynamics of informal economy share in Russian
regions over 2004-2011
The data used for informal economy dynamics estimation is similar to data for regression
analysis described in 3.2.
Electricity consumption by production sectors is calculated as the total electricity consumption
in the region less losses in electricity grids, less consumption by households. Statistics for losses
and household consumption is available for 2004-2010. Other years were estimated on the basis
of assumption of the constant share of losses in the total electricity consumption.
Electricity intensity of production sectors in 2011 was estimated as a ratio of electricity
consumption in a sector to the value added in that sector if Rosstat provided statistics on electricity
consumption by sector in the particular region. If these data did not exist, electricity intensity in
the industry/sector was set up at the average level for the whole Russia. Total electricity
consumption for manufacturing was available for all regions. Using this information, electricity
intensities of individual manufacturing industries were corrected by equalizing the sum of
estimated electricity consumption by individual industries to the total electricity consumption in
manufacturing taken from Rosstat. All this work was done for 2011 year only. Electricity intensity
in other years was calculated using assumption of the average electricity intensity change by -1%
per annum. It is assumed to be equal for all industries/sectors.
First of all, we calculated dynamics of informal economy share using data for the whole Russia.
Result is that informal economy share was decreasing during 2004-2011. This trend can be
explained 1) by the growth of official sector during economic expansion in Russia; 2) migration
of entrepreneurs from informal to official sectors in order to utilize opportunities in official sector;
3) positive changes in Russian tax regimes (introduction of the flat income tax rate which is among
lowest in the world). 2009 was the only year when informal economy share increased by 1
percentage point. This could be due to economic crisis which forced people to look for alternative
32
income sources apart from formal sector. Tax minimization through informal activity was also a
tool for business survival.
Exhibit 14. Dynamics of informal economy share in Russian Federation estimated by
augmented electricity consumption method, y-o-y, percentage points
Source: Author’s calculations
Let us take a look on the regional picture. Excluding few outliers we can see relatively plausible
dynamics of informal economy share. Cumulative dynamics of informal economy share in 20042011 lies in the range -30 - +30 percentage points across different regions. Only 16 from 65 regions
witnessed an increase in informal economy share over this period.
Exhibit 15. Distribution of Russian regions by an increase/decrease in informal economy
share in 2011 vs. 2003 (equals to cumulative change over 2004-2011)
18
16
16
12
12
10
9
9
8
7
5
1
1
0
(20%; 30%)
1
(50%; 60%)
(10%; 20%)
(0%; 10%)
(-10%; 0%)
(-20%; -10%)
(-40%; -30%)
-50%
0
0
(-50%; -40%)
2
(40%; 50%)
4
4
(30%; 40%)
6
(20%; 30%)
Number of regions
14
Cumulative increase of informal economy share in 2011 vs 2004
Source: Author’s calculations
33
42 out of 65 regions demonstrated decrease in informal economy share over 2004-2011. Thus,
majority of Russian regions achieved some progress in decreasing informal economy share. It was
done thanks to 2 reasons: 1) increasing absolute volume of formal sector which made informal
activities relatively less important, 2) decreasing absolute volume of informal economy (which
was a minor factor as we will show further). The highest decrease of informal economy is evident
for Kaluga region, Penza, Tula, Ivanovo, Kabardino Balkaria, Omsk region, Tambov region,
Irkutsk, Mordovia, Altay territory, Tatarstan, Bashkortostan, Moscow region. The highest increase
of informal economy share is registered for city of Moscow, Murmansk, Stavropol territory,
Tumen, Udmurtia, Tomsk.
It is interesting to compare results of two methodologies: 1) informal economy share in 2011
calculated on the basis of regression model and 2) cumulative increase of informal economy share
in 2004-2011 on the basis of augmented electricity consumption model (see exhibit below).
Surprisingly we do not see any correlation. It means that the level of informal economy share does
not determine any particular trend in its dynamics.
Exhibit 16. Comparison of results of two approaches to measurement of informal
economy share by Russian regions
Source: Author’s calculations
34
We can combine results of two methodologies to calculate informal economy share in Russian
regions in other years except 2011: let us take base level in 2011 from regression model approach
and then calculate 2004-2010 years on the basis of informal economy share dynamics. Results are
presented in Appendix 1.
Finally we can calculate informal economy in Russia as a sum of regional informal economies.
Our sample covers 66% of total Russian gross regional product. We excluded from analysis city
of Moscow and Saint-Petersburg which have rather ambiguous results on informal economy share.
Informal economy share has decreased from 55% in 2004 to 40% in 2011 with slight growth in
2009 during economic slump.
Exhibit 17. Informal economy share in Russia, % (on the basis of regional sample covering
66% of Russian GDP)
Source: Author’s calculations
We calculated volume of informal value added in 2011 rubles. It was relatively stable. Only in
2011 it decreased significantly. It means that observed decrease in informal economy share was
driven by increasing official output. Existing informal activities were not significantly affected.
Nevertheless the good thing is that informal economy did not attract new participants.
35
Exhibit 18. Volume of informal value added in Russia, in trn 2011rubles
Source: Author’s calculations
5 Conclusion
This study develops two approaches for estimation of informal economy share. The first method
is based on cross-regional regression model for electricity consumption in production sectors of
economy. The level of informal economy share is estimated as a difference between total actual
electricity consumption and electricity consumption implied by regression, i.e. by the level of
official economic activity.
In the second approach, dynamics of electricity consumption was studied. Traditional approach
by Kaufman and Kaliberda was augmented by taking into account changes in industrial structure
and intensity of electricity use which affect electricity consumption in the economy of the region.
Main conclusions from this analysis are the following:
•
Cross-section variation of electricity consumption per output in Russian regions is explained
by the model with electricity consumption per unit of GRP as dependent variable and a number
of independent variables: electricity prices, shares of particular industries in GRP (metals,
mining, chemicals, electricity generation, non-metal mineral products, transport machinery,
paper, and construction). Adjusted R2 for a sample of 67 regions in 2011 is 87%.
36
•
Level of informal economy share can be estimated as a residual of this regression. The average
level of informal economy share in 2011 is 40%, there is significant cross-regional variation
(from 4% to 85%).
•
The highest share of informal economy in 2011 is registered for Ingushetia, Moscow,
Dagestan, Kalmykia, Altay Republic, Krasnodar region. The lowest informal economy share
in 2011 was in Komi, Marii El, Lipetsk region, Arkhangelsk region, Krasnoyarsk territory,
Vologda region.
•
Our estimates of informal economy share have 35% correlation with estimates by Rosstat
which measures the share of workers employed in the informal economy.
•
Sub-national Russian data of “Doing business rating” (2012) has a low correlation with our
estimates of informal economy.
•
Our estimates of informal economy share in 2011 show 35% correlation with Corruption
perception index for 2002. We have found that the share of tax violations have 30% correlation
with our estimate of informal economy share.
•
Informal economy is positively correlated with unemployment (correlation in 2011 is 38% pretty high), negatively correlated with GRP per capita (-17%).
•
Transfers from federal to regional budgets (in terms of their share in total income of regional
budget) have the strongest correlation with informal economy share at 54%. The higher share
of informal activities the lower ability of the regional government to collect taxes. In order to
maintain necessary social spending such regions needs external financing from the Federal
government.
•
Majority of Russian regions experienced decline of informal economy share over 2004-2011.
Only 16 from 65 regions witnessed an increase in informal economy share over this period.
•
Informal economy share in the whole regional sample (which covers 66% of Russian GDP)
has decreased from 55% in 2004 to 40% in 2011 with slight growth in 2009 during economic
slump.
37
•
Declining share of informal economy can be explained by the growth of formal activities
rather than contraction of informal activities in absolute terms.
This study suggests that government should implement “facilitative” policies instead of
“prohibitive” which means deregulation and support of formal activities.
Estimates of informal sector in regional dimension can be used in a number of ways:
•
For the fiscal policy. Estimates of unofficial economy in Russian regions could be used
to assess a potential increase of the tax base by moving business from unofficial sector.
•
Incentives for regional institutional development. Comparison of regions by the
unofficial sector size gives incentives for the regional authorities to improve institutions
•
Federal investments allocation policy. Federal investments should be spent in regions
where unofficial economy is relatively low. Otherwise fiscal multiplier will be low due to
corruption and weak spillovers.
38
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41
7 Appendix
Appendix 1. Informal economy share in Russian regions (combined results of regression
model for 2011 and augmented electricity consumption dynamics approach)
№
Region name
1
Republic of Ingushetia
85%
2
Republic of Dagestan
81%
3
City of Moscow
4
Republic of Kalmykia
5
Altay republic
75%
67%
61%
67%
68%
71%
81%
76%
6
Krasnodar Territory
75%
67%
76%
77%
72%
74%
70%
68%
7
Republic of Kabardino Balkaria
87%
78%
83%
68%
67%
60%
60%
64%
8
Kaliningrad Region
9
Astrakhan Region
10
City of St Petersburg
11
Republic of Adygea
12
Pskov Region
13
Khabarovsk Territory
14
Kaluga Region
15
2004
2005
2006
2007
2008
2009
2010
2011
Results are considered as an outlier, another approach is needed
77%
62%
61%
Results are considered as an outlier, another approach is needed
57%
72%
64%
68%
63%
64%
71%
64%
55%
63%
65%
66%
60%
65%
48%
40%
54%
101%
96%
94%
85%
67%
73%
66%
53%
Stavropol Territory
21%
46%
66%
66%
55%
54%
50%
53%
16
Primorsky Territory
69%
75%
61%
67%
68%
64%
57%
52%
17
Republic of Northern Osetia Alania
62%
60%
53%
58%
48%
53%
57%
51%
18
Tambov Region
74%
70%
69%
67%
64%
48%
57%
51%
19
Rostov Region
57%
54%
52%
49%
60%
54%
53%
50%
20
Tver Region
21
Ulyanovsk Region
71%
59%
54%
47%
42%
59%
48%
44%
22
Republic of Tatarstan
74%
70%
68%
55%
54%
47%
46%
44%
23
Republic of Mordovia
79%
71%
69%
57%
42%
38%
36%
44%
24
Orel Region
57%
45%
37%
44%
38%
50%
53%
44%
25
Voronezh Region
59%
50%
43%
39%
46%
51%
47%
44%
26
Altay Territory
70%
65%
61%
56%
59%
50%
52%
43%
27
Omsk Region
74%
60%
60%
52%
56%
53%
47%
43%
28
Samara Region
37%
37%
31%
33%
23%
48%
40%
41%
29
Bryansk Region
70%
69%
55%
47%
42%
52%
51%
41%
30
Vladimir Region
56%
51%
49%
39%
32%
56%
47%
40%
31
Moscow Region
71%
68%
77%
69%
58%
68%
59%
40%
32
Belgorod Region
66%
61%
71%
67%
53%
50%
41%
39%
33
Republic of Buryatia
33%
43%
24%
27%
39%
29%
42%
37%
34
Republic of Chuvashia
56%
46%
43%
37%
33%
52%
43%
37%
35
Yaroslavl Region
31%
14%
20%
26%
31%
41%
44%
37%
36
Smolensk Region
47%
52%
52%
43%
48%
46%
41%
36%
37
Murmansk Region
17%
35%
38%
38%
42%
38%
35%
38
Novosibirsk Region
38%
31%
32%
27%
28%
39%
30%
34%
39
Penza Region
82%
80%
69%
59%
53%
45%
39%
34%
40
Republic of Bashkortostan
60%
58%
55%
50%
47%
40%
35%
34%
41
Ivanovo Region
70%
59%
46%
32%
35%
53%
48%
33%
42
Tomsk Region
16%
26%
32%
43%
50%
49%
38%
33%
50%
42
№
Region name
2004
2005
2006
2007
2008
2009
2010
2011
43
Tula Region
61%
52%
49%
45%
46%
36%
35%
33%
44
Ryazan Region
47%
48%
50%
39%
38%
39%
38%
32%
45
Kurgan Region
43%
37%
31%
30%
28%
32%
26%
32%
46
Leningrad Region
28%
47%
60%
41%
51%
33%
38%
31%
47
Volgograd Region
30%
30%
32%
32%
38%
36%
35%
31%
48
Kemerovo Region
39%
37%
35%
33%
35%
35%
34%
31%
49
Novgorod Region
28%
27%
31%
19%
19%
16%
24%
30%
50
Kirov Region
43%
42%
42%
41%
41%
38%
34%
30%
51
Saratov Region
46%
44%
40%
46%
36%
27%
26%
28%
52
Kursk Region
13%
16%
36%
29%
28%
31%
29%
28%
53
Republic of Karelia
54
Republic of Udmurtia
18%
20%
22%
22%
23%
28%
28%
26%
55
Tumen Region
19%
18%
22%
20%
24%
28%
26%
25%
56
Nizhny Novgorod Region
22%
19%
20%
20%
18%
21%
19%
25%
57
Kostroma Region
36%
41%
40%
34%
31%
35%
29%
24%
58
Perm Territory
59
Sverdlovsk Region
19%
17%
15%
13%
24%
29%
34%
23%
60
Vologda Region
31%
30%
24%
25%
31%
26%
25%
21%
61
Chelyabinsk Region
16%
13%
11%
8%
12%
18%
17%
18%
62
Orenburg Region
15%
19%
10%
11%
14%
63
Krasnoyarsk Territory
21%
19%
18%
15%
14%
64
Arkhangelsk Region
65
Lipetsk Region
10%
9%
14%
7%
9%
7%
10%
13%
66
Republic of Marii El
11%
16%
16%
21%
20%
3%
12%
11%
67
Republic of Komi
16%
14%
13%
13%
7%
5%
5%
4%
NA
28%
NA
23%
21%
21%
24%
NA
13%
Source: Author’s calculations
43
Appendix 2. Regression results with all possible independent variables
Regressor
Electricity consumption by insignificant
industries per GRP
Average temperature in heating season
Natural gas price
Electricity price
Share of houses with gas available
Metals industry share in GRP
Mining industry share in GRP
Transport share in GRP
Chemical industry share in GRP
Trade share in GRP
Electricity, gas, water supply share in GRP
Non-metal minerals share in GRP
Transport machinery share in GRP
Paper industry share in GRP
Construction share in GRP
Constant
Coef.
Std. error
t-statistics
P>t
1.283
0.25
5.2
0.000
0.252
0.051
-0.023
-0.052
89.3
27.8
-33.6
21.7
12.2
75.1
112.3
-9.8
212.7
-22.9
7.60
0.14
0.03
0.012
0.024
8.14
5.60
24.35
21.93
10.82
34.78
63.78
21.98
45.79
17.80
4.59
1.85
1.74
-1.95
-2.21
10.96
4.97
-1.38
0.99
1.13
2.16
1.76
-0.45
4.64
-1.29
1.66
0.070
0.087
0.057
0.032
0.000
0.000
0.174
0.327
0.264
0.036
0.084
0.657
0.000
0.204
0.104
Number of obs
67
R-squared
89%
Adj R-squared
87%
44