Inequality in Russia during 1994-2005
PRELIMINARY AND INCOMPLETE
DO NOT CITE
October 31, 2007
Yuriy Gorodnichenko
UC California Berkeley
ygorodni@econ.Berkeley.EDU
Klara Sabirianova Peter
Georgia State University
ecoksp@langate.gsu.edu
Dmitriy Stolyarov
University of Michigan
stolyar@umich.edu
Abstract
We analyze the cross-sectional and time series behavior of some key household
and individual economic variables using the panel micro data from the Russia
Longitudinal Monitoring Survey for 1994-2005.
We compare our results to the official statistics and find evidence that the national
accounts may overstate the growth rate in living standards during the sample
period an understate the extent of economic collapse that occurred in 1998.
We also find that expenditure inequality has increased while expenditure level
was falling in 1994-1998 and that inequality in both income and expenditure has
decreased during the 2000-2005 economic recovery. Although the overall
expenditure inequality fell during the economic recovery, households in large
cities have gained ground relative to other groups. Households with higher
education have rapidly moved up in the expenditure distribution during 19941996, and held their ground in subsequent years.
1
1
Introduction
The goal of this paper is to construct the key variables describing the economic behavior
of Russian households and individuals and to analyze their cross-sectional dispersion and time
series patterns. Our primary data source is the Russia Longitudinal Monitoring Survey which is a
large panel data set covering the period between 1994 and 2005.
We construct measures of individual earnings, hours and wages, as well as householdlevel income, expenditure and consumption. It turns out that income and consumption measures
are affected in important ways by various adjustments that capture some of the unique features of
the Russian economy. One feature is that Russia is very geographically diverse, and some of its
regions are remote. In a cross-section, the cost of living by region can vary by a factor of three.
Inflation rates also differ widely by region, especially during years when inflation is high (see
Section 5). The second feature is that rural households in Russia (about 25 percent of our
sample) consume a significant amount of home grown food. On average, rural households
purchase less than three quarters of their food intake in the store and produce the rest at home.
These households are typically the poorest, and adjusting their consumption for home production
of food strongly affects the measures of consumption inequality.
We compare the levels of aggregate expenditure with other data sources and find that our
measures of nominal expenditure disagree with the official statistics. Our expenditure can be as
much as 40 percent lower than its official counterpart, and the gap between the two measures of
expenditure has widened significantly during 1995-2005. We think this can happen for two
reasons: (1) our sample does not include the very rich who own most capital in Russia, and the
inequality between the very rich and the rest of the population has grown; and (2) the coverage
of the shadow economy by the official statistics has improved substantially. If the coverage of
the economy by the official statistics was, in fact, improving over time, then the measurement
error in the national accounts was shrinking, and the national accounts are likely to overstate the
growth in living standards. For example, official data record a 20 percent drop in living standards
during 1995-1998, but our data suggest the drop was about 40 percent. During our sample
period, the official average annual growth rate of expenditure per capita is 6.5 percent, while in
our data it is just 3 percent.
2
Both expenditure and income inequality exhibit an interesting time pattern, which is
robust across the alternative inequality measures. Income inequality fell during the recovery of
2000-2005, and so did expenditure and consumption inequality. During the economic downturn
of 1994-1998, consumption inequality rose as expenditure level fell. We find that the rise in
consumption inequality is driven by the rise in inequality of food consumption within the urban
population. By contrast, during the economic downturn, non-food expenditure does not seem to
experience a clear pattern of rising inequality. During the recovery, however, the inequality in
non-food expenditure falls very significantly, especially for college educated and large city
populations.
Overall, large city dwellers are the group that experienced the most upward mobility
during the economic recovery, consistent with the story that the growth of economic activity was
concentrated in cities. The college educated group moved up in the distribution of consumption
during the early years of the downturn and held their ground since. This, perhaps, corresponds to
the more successful adjustment of the highly educated to the rapidly changing economic
environment. The within group inequality is the lowest for city population and the college
educated, and it is the highest for rural population and households with high school education or
less.
Our results on inequality have to be taken in the context of our sample. Household
surveys in Russia have been known to under-represent the extremely rich individuals. For
example, a related study by Guriev and Rachinsky (2006) compares income inequality measures
from household surveys with measures obtained from the tax data for the city of Moscow and
finds that inequality increases significantly when the extremely rich are accounted for. It is
therefore likely that our inequality measures are biased downwards if the sample is the whole
population. Although we find that inequality has declined in our sample during the economic
recovery, the growing discrepancy between our survey and the national accounts is, in principle,
consistent with a widening gap between the extremely rich and the rest. However, we cannot
state this conclusively, because the discrepancy between aggregate personal consumption and
survey micro-data may also arise due to a time trend in tax evasion behavior.
Beyond our results on inequality, we find some interesting patterns of employment rates
by age, particularly in the older population. For example, the employment rate for older Russian
males drops sharply well before their normal retirement age of 60: the average employment rate
3
for males between 25 and 52 is 75 percent, but this rate is just 60 percent for males between 53
and 60. It seems that Russian males are taking early retirement due to bad health or obsolescence
of skills. The employment rate for older females is much lower than that for males of the same
age, because female retirement age is 55. During the recovery, however, the employment rate of
older females rose from 40 percent to 52 percent, corresponding, perhaps, to the postponement of
retirement.
The focus of this paper is primarily on consumption inequality, motivated by the
comparative advantages that our data set offers in measuring expenditure and consumption. The
related literature can be grouped in three categories. The first one is the body of the recent
literature that analyzed consumption inequality in the United States and compared it with wage
and income inequality. This includes Cutler and Katz (1991), Mayer and Jencks (1993),
Attanasio and Davis (1996), Johnson and Smeeding (1998), Slesnick (2001), Blundell et al.
(2002) and Kruger and Perry (2006). The second category of related literature (e.g. Battistin
(2003) Attanasio et al. (2005)) studies the discrepancy between the US aggregate personal
consumption expenditure measured in the NIPA and the survey data. The focus of this literature
is on consistency of data definitions across the data sources and on survey methodology. While
we also find a sizeable discrepancy in expenditure levels between our data set and the official
statistics, our interpretation is that in the case of Russia the discrepancy may be driven by either
sample composition or income under-reporting in official statistics. The third category of related
literature is the studies of inequality and mobility in Russia.
The plan of the paper is as follows. Section 2 analyzes the time pattern of real income and
consumption levels and makes comparisons with the national accounts. Section 3 deals with
individual employment rates, hours and wages. Section 4 analyzes and compares several
alternative measures of cross-sectional inequality. Section 5 makes the adjustments of
consumption measures for regional inflation rates and home production. Section 6 and 7 describe
the between- and within-group differences in consumption.
1.1
Overview of the dataset
The analysis in this paper uses the Russia Longitudinal Monitoring Survey, which is a
panel dataset that includes detailed information on measures of income, consumption, household
demographics and labor supply. The data is collected annually, and our panel includes 10 waves
4
during the period 1994-2005, with the exception of 1997 and 1999, when the survey was not
administered.1 There were approximately 8,343-10,670 individuals who completed the adult
(age 14 and over) questionnaire and 3,750-4,718 households who completed the household
questionnaire in each round. These individuals and households reside in 32 oblasts (regions) and
7 federal districts of the Russian Federation.2
1.2
Overview of economic conditions in Russia during 1994-2005
Two aspects of economic conditions in Russia affect our interpretation of income and
consumption data in important ways. The first aspect is that Russia has experienced extremely
variable inflation.
250
200
150
100
50
0
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Figure 1 Annual CPI inflation rate, percent.
Figure 1 shows Russian CPI inflation rate by year. The early 1990s were a period of
hyper-inflation, as prices were liberalized in 1992. The year 1998 inflation spike corresponds to
the government default on sovereign debt and the abrupt devaluation of the national currency,
the ruble.
The second aspect is wage arrears during 1994-1998. Employee compensation and public
transfers were paid irregularly during this period, and were delayed by 3-5 months, on average.
1
In all plots, the 1997 and 1999 values are 2 point linear interpolations of the data points in adjacent years
Russia has 89 regions and 7 federal districts. The RLMS sample consists of the 38 randomly selected primary
sample units (municipalities) that are representative of the whole country.
2
5
2
Income and consumption levels
Our preferred measure of household income is after-tax earnings net of taxes plus public
and private transfers.3 Our preferred definition of household earnings is the sum of monthly
average earnings across all workers in the household. For employees, the earnings equal to labor
income. For self-employed, the earnings include labor income and capital income. Our data does
not allow to separate proprietor’s income into labor and capital portions. Even with ideal data,
such separation is difficult in principle, because the Russian tax system provides incentives to
distribute corporate profits as wages to owners (e.g. Guriev and Rachinsky, 2006).
Private and public transfers are both significant sources of income, and they account for
10.6 and 12.5 percent of income, respectively. Figure 2 plots the time series of after-tax labor
earnings net and gross of transfers (private and public) for the sample period 1994-2005.
10000
Earnings (after tax)
Earnings + private transfers
Earnings + private & pub.
transfers (preferred def.)
Consumption
8000
6000
4000
2000
0
1994
1996
1998
2000
2002
2004
Figure 2 Sample average household consumption and income levels, 2002 rubles per month (national CPI
deflator).
It is apparent that consumption significantly exceeds earnings, especially prior to 1998.
Since income from financial assets is zero or trivial for most households, we believe that this is
due to income under-reporting (see Gorodnichenko et al., 2007).
Another possible reason for the puzzlingly low level of average income may be wage
arrears. In the earlier period of the sample, wages were paid irregularly, and were delayed by an
average of 3-5 months during 1994-1998. Our definition of income as average monthly earnings,
3
See the Appendix for data definitions
6
rather than actual amount received in the past month, is supposed to control for wage arrears to
some extent.
The wage arrears during 1994-1998 and possibly time-varying income under-reporting
make us prefer consumption as a measure of the change in economic conditions over time.
The big slide and subsequent recovery in consumption are also evident from Figure 2.
The average level of consumption has barely risen from 1994 to 2005. At its trough in 1998, the
average household consumption was just 57 percent of its 1994 level.
2.1
Comparison with official statistics
To make comparisons with national statistics, one must be careful about using compatible
data definitions. Given that our income data may be affected by wage arrears and underreporting, we think that it is probably most informative to compare the total expenditure from
RLMS with household expenditure from the national accounts.4
To make the data definitions compatible, we only compare the expenditure on goods
from the two sources. Expenditure on services is harder to compare, because official aggregate
expenditure on services includes significant categories that are not covered by RLMS, such as
imputed rent from owner occupied housing and household consumption of public services (e.g.
universal health care and free education).
4
To construct the per capita expenditure, we used the unweighted data. We divided the sum of expenditures by the
total number of individuals in the sample. We have also compared our results (both weighted and unweighted) with
the official statistic for average expenditure per household member. The results are similar to those reported on
Figure 3.
7
4,000
1.40
1.20
3,000
1.00
0.80
2,000
0.60
Expenditure on goods, RLMS
1,000
Expenditure on goods, official
0.40
0.20
Ratio of official to RLMS (right scale)
0
1995
1997
1999
2001
2003
0.00
2005
Figure 3 Per capita expenditure on goods from RLMS and official statistics, 2002 rubles per month. Source:
RLMS and Russian Federal State Statistics Service.
Figure 3 shows that in most years the official expenditure exceeds the RLMS counterpart,
and that the gap between the two is growing over time (see the black line on Figure 3).
Superficially, this inconsistency seems puzzling, although the large and growing
discrepancy between household consumption surveys and national accounts is not unprecedented
– for example, Attanasio et al. (2005) report a 35 percent difference between Consumer
Expenditure Survey and NIPA Personal Consumption Expenditure for the US.
We think that the discrepancy seen in Figure 3 may arise for reasons other than
incompatible data definitions and survey methodology, and it may reflect inequality in asset
ownership in Russia. After the privatization of the early 1990s, ownership of Russian capital
stock capital became concentrated in the hands of a small number of extremely wealthy
individuals (e.g. Guriev and Rachinsky, 2005). It is likely that most owners of capital are not in
our sample – in fact, we find that income derived from assets f is negligible or households in our
sample.5 If this interpretation is correct, the gap in Figure 3 corresponds to expenditure of the
upper class of wealthy capital owners.
As a consistency check with an independent data source, we compare RLMS expenditure
on goods per capita with the same statistic from another survey of households administered by
Russian Federal State Statistics Service. The food and non-food expenditure from the two
5
On average, about 1 percent of RLMS households report positive income from rental property or dividends. The
share of these categories in aggregate income is less than 0.5 percent (see Mroz et al. 2005).
8
surveys are very close in some years, and differ by much less than they do in Figure 3 in any
year (see Figure 4). It is therefore likely that the aggregate personal consumption figure in the
national accounts also includes expenditure by a small an extremely wealthy group of population
that is not present in either sample.
1500
1200
900
600
Food, RLMS
Non-food goods, RLMS
300
Food, official
Non-food goods, official
0
1997
1999
2001
2003
2005
Figure 4 Per capita expenditure on food and non-food goods in RLMS and Federal State Statistics Service
survey of households (2002 RUR per month).
There are two different interpretations of the fact that the discrepancy between the RLMS
consumption measure and the official personal consumption expenditure is growing. One
interpretation is that the difference between the RLMS and the official total consumption figures
may represent the widening gap between the extremely wealthy and the rest of the population.
However, there is a second interpretation of this pattern. The official aggregate expenditure
numbers are constructed from receivables of final goods producers that are deposited in financial
institutions and/or reported to the government for tax purposes. Anecdotally at least, rampant tax
evasion and a large shadow economy whose financial flows by-pass the banking sector were
both salient features of post-Communist Russia. We think that the gap in Figure 3 is also affected
by the rate at which the shadow economic activity became legitimate. In one extreme case, there
could have been no change at all in the relative expenditure of the extremely wealthy and the
rest, and all of the growing difference between the two expenditure measures on Figure 3 could
have been driven by better coverage of economic activity by the official statistics. To separate
these two interpretations of the data, one would need an independent data source on the
expenditure of the very rich.
9
If, in fact, the fraction of the Russian economic activity covered by the official statistics
is growing, as Figure 3 suggests, the official statistics will likely overstate the average growth
rates of expenditure and income by recording businesses that entered the legitimate (measured)
sector as net additions to the size of the economy as a whole. The extent of this overstatement
may be fairly significant. For example, according to RLMS data, real per capita expenditure on
goods grew at an average annual rate of 3 percent during 1995-2005, whereas the official
statistics records a much higher, 6.5 percent annual growth. For the same reason, the true depth
of 1998 recession is also not fully reflected in the official statistics. For example, between 1995
and 1998, per capita real expenditure on goods fell by 20 percent in the official data and by
almost 40 percent in the RLMS.
We believe that the growth rate in expenditure recorded in RLMS (and the official survey
of households as well) are more representative of the overall change in living standards of the
vast majority the Russian population than the growth rates derived from national accounts. This
is because the national accounts also reflect expenditure of the tiny group of the very wealthy,
and the data may be subject to time-varying measurement error due to income underreporting.
3
Employment rates, hours and wages
Figure 5 depicts male and female employment rates by age group. For workers in the 35-
52 age interval, employment rates are fairly stable over time and are almost equal between males
and females. Younger females in the 25-34 age group, however, have lower employment rates
than males of the same age, as expected during the prime childbearing years. The behavior of
employment rates for the oldest, 53-60 age group highlights additional differences across ages
and genders.
Russian males typically qualify for a public pension at the age of 60, yet the employment
rate for the male 53-60 age group is much lower than that for other males. This difference could
be partly due to the effect of poor health on employment. Female employment rate among the
53-60 age group is significantly lower than that of males. This is probably because the age when
a person can qualify for a public pension is 55 for females and 60 for males; hence more females
in the oldest group may be retired. However, the employment rate among the oldest females in
the sample rises substantially after 1998, which probably corresponds to a decreasing fraction of
women retiring at age 55.
10
1
Female employment rate
Male employment rate
1
0.8
0.8
0.6
0.6
25-34
0.4
25-34
0.4
35-44
35-44
0.2
44-52
0.2
44-52
53-60
53-60
0
0
1994
1996
2000
2002
1994
2004
1996
2000
2002
2004
Figure 5 Employment rates by gender and age groups
The stability of employment rates seems remarkable given the depth of the 1990s
recession, although male employment rates show a slight decline during 1994-1998. As the
economy recovered, so did the employment rates, but the weaker recovery of employment rates
for males of age 44-60 is telling. Some older males with obsolete skills must have had a harder
time starting a new career after losing a job.
Figure 6 reports the distribution of actual hours worked per week within the 30 days prior
to the interview. The three lines on the figure are 10th, 50th and 90th percentiles of the distribution
of hours, year by year. The median hours for employed males and females are remarkably
similar and close to 40 hours per week. As expected the distribution of male hours has a fatter
upper tail, and the distribution of female hours has a fatter lower tail. Over time, the part time
work becomes less prevalent, especially for males, with almost 90 percent of employed males
working at least 30 hours per week after 1996.
11
Female hours
Male hours
70
70
p10
p50
p90
60
60
50
50
40
40
30
30
20
20
10
10
0
0
1994
1996
2000
2002
2004
1994
1996
2000
2002
2004
Figure 6 Distribution of actual hours worked
Figure 7 depicts the sample average hourly wages for males and females. The wages are
defined as the ratio of actual monetary income received from work within the past 30 days and
the actual hours worked. The time series pattern for wages, especially male wages, is similar to
that for income and expenditure (see Figure 2). The female to male wage ratio, averaging 0.7
over the sample period, has increased somewhat during 1996-1998, but overall exhibits no strong
time trend.
0.9
50
0.8
40
0.7
0.6
30
0.5
0.4
20
0.3
Males
10
0.2
Females
0.1
Ratio of female to male wage (right)
0
1994
0
1996
1998
2000
2002
2004
Figure 7 Sample average male and female wages, 2002 rubles per hour.
The next section looks at patterns of cross-sectional inequality.
12
4
Cross-sectional inequality
The time series for the Gini coefficients and percentile ratios for household income and
expenditure are shown on Figure 8. The average Gini coefficient is 0.44 for household earnings
gross of transfers (left panel of Figure 8, solid line). As expected, the addition of transfers to
earnings decreases income inequality in every cross-section. Income inequality decreases very
slightly over the sample period. It should be noted that the Gini coefficient on income may be
biased upwards during the earlier years due to wage arrears, so, if anything, the true income
inequality may exhibit an even bigger downward trend.
Expenditure inequality, by contrast, rises during 1996-1998 and subsequently falls close
to its 1994 level. The time series patterns on Figure 8 suggest a negative co-movement between
expenditure growth and expenditure inequality.
Gini coefficient
0.6
Percentile ratio
Earnings (after tax)
4
Consumption, p90-50
Earnings + transfers
Consumption, p50-10
3.5
Consumption
0.5
3
0.4
2.5
0.3
1994
2
1996
1998
2000
2002
2004
1994
1996
1998
2000
2002
2004
Figure 8 Gini coefficients for household income and expenditure, 1994-2005
The pattern of rising and falling expenditure inequality is also evident and somewhat
more pronounced if one looks at percentile ratios on the right panel of Figure 8. The much higher
50th to 10th percentile ratio means that the distribution of expenditure is asymmetric about its
median and has a fatter lower tail.
Given that Russian households have very low saving during the sample period, it is
somewhat surprising to see that income and expenditure inequality have different time patterns.
One reason for this may be time trends in home production, particularly households growing
13
their own food. For example, if an increasing number of households switch from store purchased
to home grown food during 1994-1998, this would make many of those households report low
food expenditure and may show up as a rise in expenditure inequality. We will examine the
relationship between trends in home production and consumption inequality in Section 6.
4.1
Raw, equivalized and residual inequality
We next compare three measures of income and expenditure inequality: raw, equivalized
and residual. The raw inequality measure is the standard deviation of the distribution of the
variable of interest, taken in logarithms. The equivalized measure is the same statistic, but for the
distribution of the household-level variable divided by the number of adult equivalents in the
household. The residual measure is the standard deviation of the distribution of residual in a
regression where the variable of interest (in logs) is regressed on four sets of explanatory
variables related to time, age of the head, education of the head, household composition and
region (oblast). See Appendix 2 for details.
σ (ln y T )
σ (ln c )
1.2
1.2
Raw
Raw
Equivalized
Equivalized
Residual
1
Residual
1
0.8
0.8
0.6
0.6
1994
1996
2000
2002
1994
2004
1996
2000
2002
2004
Figure 9 Raw, equivalized and residual inequality in income and expenditure
Figure 9 reports three alternative inequality measures for income and expenditure. The
equivalized dispersion for income is almost the same as raw dispersion, suggesting that
equivalized income is negatively correlated with household size. Indeed, since
Var (ln y T ) = Var (ln( y T / N )) + Var (ln N ) + 2Cov (ln( y T / N ),ln N ),
14
and the left hand side of the above equality is almost equal to first term in the right hand side, the
second and third tem in the right hand side must almost cancel out, thus the negative correlation.
The time pattern for the three inequality measures on Figure 9 is qualitatively similar to
that of Gini coefficient and the percentile ratio on Figure 8. The residual expenditure inequality
comes out somewhat lower, on average, than residual income inequality.
Figure 10 plots the contributions of the observable components to the overall dispersion
of income and expenditure. The observables on household composition and region have the most
of variance, the education component has a much lower dispersion, and age component has very
low dispersion. All the observables taken together explain 15-20 percent of the observed
dispersion in income and expenditure, so at least some observable components must be
negatively correlated.
σ (ln y T )
σ (ln c )
1.2
1.2
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
1994
1996
2000
2002
0
1994
2004
1996
2000
Raw
Residual
Age
Education
HH comp
Region
2002
2004
Figure 10 Dispersion of observable components compared to residual inequality
We next explore if the measures of inequality differ by age group.
4.2
Inequality by age group
(
)
Figure 11 depicts the time series averages for σ ln y T and σ (ln c ) and the respective
residual measures for four age group. We compute the inequality measures separately for each
group and year, and report averages across years.
15
σ (ln y T )
σ (ln c )
Raw dispersion
1.2
1.2
Residual dispersion
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
25-34
35-44
44-52
53-60
25-34
35-44
44-52
53-60
Figure 11 Income and expenditure inequality by age group
The dispersion of expenditure, both raw and residual, looks fairly similar across age
groups. The dispersion of income is lower for the oldest group compared to all others, perhaps,
because older households are likely to receive a higher fraction of their income from public
transfers.
5
Adjustments to measures of income and consumption
In the previous section (Figure 10), we saw that the regional components of the
household income and expenditure have substantial dispersion. This reflects a large geographic
variation in the cost of living in Russia. There is a lot of variation in inflation rates by region as
well. Figure 12 plots the interquartile range for the distribution of region-specific CPI inflation
rates across 80 Russian regions (oblasts). The figure shows, for example, that the 1995 difference
in the annual inflation rate between the oblast in the top quartile of inflation and the oblast in the
bottom quartile of inflation was over 12 percent. Not surprisingly, the years when inflation rates
differ the most by region are years of high inflation.
To make more accurate comparisons of living standards across regions, it is desirable to
adjust our measures of income and expenditure for region-specific inflation rates.
16
14
12
10
8
6
4
2
0
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Figure 12 Interquartile range for the distribution of regional inflation rates.
We do this adjustment by deflating nominal income and expenditure with regional, rather
than national, CPI. The comparisons of income and expenditure levels with national and regional
deflators are presented in Figure 13.
Consumption percentile ratios
Consumption and income levels
4
10000
p90-50, regional CPI
p50-10, regional CPI
p90-50, national CPI
p50-10, national CPI
8000
3.5
6000
3
4000
Income, regional CPI
2.5
Consumption, regional CPI
2000
0
1994
Income, national CPI
Consumption, national CPI
1996
2000
2002
2
1994
2004
1996
1998
2000
2002
2004
Figure 13 Comparison of expenditure and income levels and percentile ratios for national and regional CPI.
Levels are in 2002 rubles per month.
Predictably, the average levels of real income and expenditure disagree the most during
the years when inflation was highest, 1994 and 1998, and there is almost no difference between
the average levels after 2003. Regional CPI deflator makes the fall in living standard during
1994-1998 somewhat smaller (40 percent fall with regional deflator versus 43 percent with
17
national deflator), but it also reduces the average annual growth rate of household expenditure
during the sample period. In 2005, the regionally adjusted household expenditure was just over
90 percent of its 1994 level.
Regional CPI adjustment also matters for the average measures of expenditure inequality
in all years, as shown on the right panel of Figure 13. Expenditure inequality is lower with the
regional deflator, because households in poorer regions also face lower prices.
An additional factor that may affect inequality in Russia is production of food by
households for their own consumption. Home production of food was quite significant,
especially prior to 1998. On average, the value of home produced food was close to 7 percent of
household expenditure during 1994-1998, and subsequently it dropped to about 4 percent of
household expenditure. Given the sharp drop in food production activity after 2000, we can
expect that adjusting consumption for food grown at home may affect the time series pattern for
consumption inequality. Figure 14 compares the percentile ratios for unadjusted consumption
with that adjusted for home production of food.
4
p90-50, consumption + food production
p50-10, consumption + food production
p90-50, unadjusted consumption
3.5
p50-10, unadjusted consumption
3
2.5
2
1994
1996
1998
2000
2002
2004
Figure 14 Percentile ratios for consumption adjusted for food production at home.
Food production significantly reduces consumption inequality, especially at the lower
end of the distribution. This may be because households with low expenditure also tend to grow
more of their own food. The rise and fall in consumption inequality is still visible even with
adjusted consumption.
18
6
Between group differences in consumption
In this section, we look at consumption inequality in more detail. It turns out that the
composition of consumption varies dramatically by group and over time.
Ratio of home production to food expenditure
Share of food in expenditure
0.8
0.7
All sample
High school or less
Some college
College or more
Age 25-34
Age 44-52
Age 35-44
Age 53-60
City
Small town
0.5
0.4
Rural
0.3
0.6
0.2
0.5
0.1
0.4
0
1994
1996
1998
2000
2002
2004
1994
1996
1998
2000
2002
2004
Figure 15 share of food in expenditure and ratio of home production to food expenditure by group.
Figure 15 (left panel) shows the share of food in total expenditure by year and group. The
expenditure share of food falls dramatically for all groups during the sample period. It is unlikely
that the falling food share is due to income effects, as incomes were moving down and then back
up, while the food share kept falling. We think that the falling food share more likely reflects the
falling relative price of food. The relative price of food could have fallen for two reasons: food
imports during the earlier period and the rise in domestic agricultural productivity during the
later period.
Among all groups, the oldest households (age 53-60) as well as city population have the
highest shares of food in expenditure in almost all years. The last may be due to higher food
prices in big cities. The college educated group has one of the lowest shares of food in
expenditure, which perhaps reflects this group’s higher income.
The share of home-grown food varies dramatically across groups and over time, as the
right panel of Figure 15 details. For example, city population derived less than 5 percent of their
19
food from home production, while the rural population grew well over 40 percent of their food at
home.
The rural population may have been better insured against the high inflation of 19941998, because they receive a significant fraction their income in a form of goods rather than
money. The rural households were increasingly relying on home production for their
consumption of food during the recession years. After 2000, the rural households’ share of home
production in food consumption went down dramatically, perhaps reflecting better access of
rural households to markets where they can trade their home-grown food.
We next examine the relative positions of various groups in the cross-sectional
distribution of consumption. We adjust consumption for home production and compare the ratio
of group mean to sample mean, year by year.
1.4
1.2
1
0.8
0.6
High school or less
Some college
College or more
Age 25-34
Age 35-44
Age 53-60
Age 44-52
City
Small town
Rural
0.4
1994
1996
1998
2000
2002
2004
Figure 16 Adjusted consumption - the ratio of group mean to sample mean.
Figure 16 shows the position and mobility of groups in the distribution of adjusted
consumption. The college educated group is well above any other group in terms of
consumption, and has rapidly diverged from the rest of the sample during 1994-1996. Rural
population has lost ground during the 1994-1998 recession, despite their better insurance from
inflation. Two groups have clearly gained ground in the post 2000 economic recovery: the city
dwellers and the older households. The former is probably because the economic recovery was
20
concentrated in cities. The latter could be related to a sharp increase of employment rates for
older females that happened during the same period.
7
Within group differences in expenditure
As we have noted in the earlier section, the residual expenditure inequality is large and
has a distinct time pattern. We now examine the within group expenditure inequality to see what
is behind the time pattern of rising inequality during 1994-1998 and falling inequality in the later
years. For each household, we separate the total expenditure (unadjusted for home production)
into food and non-food categories and separately look at the time pattern for the dispersion of
expenditure within groups for food and non-food expenditure.
σ (ln c NF )
σ (ln c F )
1.4
1.4
All sample
High school or less
Some college
1.2
1.2
College or more
City
Small town
All sample
1
1
High school or less
Rural
Some college
College or more
0.8
0.8
City
Small town
Rural
0.6
1994
0.6
1996
1998
2000
2002
2004
1994
1996
1998
2000
2002
2004
Figure 17 Dispersion of non-food and food expenditure by group
Figure 17 presents the dispersion of raw food and non-food expenditure by group and
year. The differences in dispersion for the two expenditure categories are quite large. Non-food
expenditure has higher dispersion and a time-series pattern that is different from the behavior of
σ (ln c ) on Figure 9. Inequality in non-food expenditure does not rise between 1994 and 1998,
and subsequently it falls. The urban population has experienced the overall largest decrease in
non-food expenditure inequality.
The food expenditure inequality, by contrast, follows the rising and falling pattern that is
similar to σ (ln c ) . This, in itself, is not surprising, given that food accounts for more than half of
21
total expenditure for most years and groups. It is clear from Figure 17 (right panel), however,
that the increase in food expenditure inequality during 1994-1998 is largely driven by the
population of cities and towns, which together account for more than three quarters of the
sample. In the meantime, inequality in food expenditures within the rural group was falling
throughout the sample period.
For both food and non-food, expenditure inequality is the lowest among city households
and college educated, and it is the highest among the rural households and the least educated.
Conclusion
References
Attanasio, Orazio, Erich Battistin and Hidehiko Ichimura, “What Really Happened to
Consumption Inequality in the US?”, NBER Working Paper 10338 (March 2004).
Attanasio, O.P., and Davis, S.J. (1996), “Relative Wage Movements and the Distribution of
Consumption”, Journal of Political Economy, 104, 6, 1227-1262.
Battistin, E. (2003), “Errors in survey reports of consumption expenditures,” Working Paper
W03/07, Institute for Fiscal Studies, London.
Blundell, R., Pistaferri, L., and Preston, I. (2002), “Partial insurance, information and
consumption dynamics”, working paper W02/16, Institute for Fiscal Studies, London.
Cutler, D. and Katz, L. (1991), “Macroeconomic Performance and the Disadvantaged”,
Brookings Papers on Economic Activity, 2, 1-74.
Federal'naia sluzhba gosudarstvennoi' statistiki, Natsional'nye scheta Rossii: Statisticheskii
spravochnik, Moscow 2003, 2007.
Federal'naia sluzhba gosudarstvennoi' statistiki, Sotsial'noe polozhenie i uroven' zhizni naseleniia
Rossii, Moscow 2006.
Gorodnichenko, Yuriy, Jorge Martinez-Vazquez and Klara Sabirianova Peter, 2007. “Myth and
reality of flat tax reform: Micro estimates of tax evasion and productivity response in
Russia,” mimeo.
Guriev, Sergei M. Andrei Alexandrovich Rachinsky, “The Role of Oligarchs in the Russian
Economy” The Journal of Economic Perspectives, Vol. 19, No. 1. (Winter, 2005), pp.
131-150.
Guriev, Sergei M. Andrei Alexandrovich Rachinsky, “The Evolution of Personal Wealth in the
Former Soviet Union and Central and Eastern Europe”, UNU World Institute for
Development Economics Research, Research paper No. 2006/X.
Johnson, D. and Smeeding, T. (1998), “Measuring the Trends in Inequality of Individuals and
Families: Income and Consumption” (Mimeo, Bureau of Labor Statistics).
Krueger, Dirk and Fabrizio Perri, “Does Income Inequality Lead to Consumption Inequality?
Evidence and Theory”, Review of Economic Studies, vol. 73(1), pages 163-193.
Mayer, S. and Jencks, C. (1993), "Recent Trends in Economic Inequality in the United States:
Income versus Expenditures versus Well-Being" in D.Papadimitriou and E.Wolff (eds.)
22
Poverty and Prosperity in the USA in the Late Twentieth Century ( New York: St.
Martin's Press).
Mroz, T., L. Henderson, and B.M. Popkin. “Monitoring Economic Conditions in the Russian
Federation: The Russia Longitudinal Monitoring Survey 1992-2004.” Report submitted
to the U.S. Agency for International Development. Carolina Population Center,
University of North Carolina at Chapel Hill, North Carolina. April 2005.
Slesnick, D.T. (2001), Consumption and Social Welfare. Living Standards and Their
Distribution in the United States, Cambridge: Cambridge University Press
Appendix 1: Data definitions
After-tax earnings
Two measures of labor earnings are available in RLMS.
(1) Household earnings are reported by the reference person as after-tax payments received by
all household members from all places of work in the form of money, goods, and services in the
last 30 days (advantages – all household members; disadvantages – includes goods and services,
it is reported by the reference person who may not know earnings of all household members,
volatile during the period of wage arrears).
(2) Household earnings are constructed as a sum of monthly average (contractual) after-tax
money earnings received from primary and additional places of work by all individual
respondents within the household (advantages – money earnings, individual responses, less
volatility; disadvantages – includes only respondents).
We use the second measure of earnings, unless the individual data is missing. If the individual
responses are missing, we use the first measure, reported household earnings.
Private transfers
Private transfers include alimonies, government child care benefits, and 11 subcategories of
contributions from persons outside the household unit, including contributions from relatives,
friends, charity, international organizations, etc.
Public transfers
Public transfers include government pensions, stipends, unemployment benefits, and government
welfare payments.
Consumption
Consumption is defined as a sum of expenditures on non-durables in the last 30 days. Nondurable items include 50 categories of food at home and away from home, alcoholic and nonalcoholic beverages, and tobacco products, expenses on clothing and footwear, gasoline and
other fuel expenses, rents and utilities, and 15-20 subcategories of services (such as
transportation, repair, health care services, education, entertainment, recreation, insurance, etc.).
Adult equivalent
Real values
23
To adjust for monthly inflation, all flow variables are expressed in December prices of each year
by using a country average monthly CPI and the date of interview. If the date of interview is in
the first half of month, the previous month CPI is used. If the date of interview is in the second
half of month, the current month CPI is used. Next, the annual (December to December) CPI for
each 32 oblasts (regions) is applied to convert the flow variables into prices of December 2002.6
Finally, these real values are adjusted for regional differences in the cost-of-living by using the
regional value of fixed basket of goods and services.
Hours
Hours are actual hours worked within the last 30 days
Actual wages
Actual wage is the ratio of actual monetary income received from all jobs in the last 30 days to
the actual hours worked in the last 30 days.
Employment rates
Employment rate is the ratio of individuals who answered “yes” to the question “Are you
currently employed?” to the sample size for the corresponding year.
Appendix 2: Residual inequality
Variance decomposition
ln Yht
=
2005
∑
k =1995
β kyr,t ⋅ yrd k ,t
( year effect )
3
+ ∑ β kedu
,t ⋅ edukht
( education component )
k =1
2
k
+ ∑ β kage
,t ⋅ ageht
( age component )
k =1
5
2
6
k =1
k =1
k =2
+ ∑ β kNkid
⋅ Nkid kht + ∑ β kNsen
⋅ Nsenkht + ∑ β kNmem
⋅ Nmemkht
,t
,t
,t
3
8
k =2
k =2
obl
+ ∑ β kurb
,t ⋅ urbkht + ∑ β k ,t ⋅ oblkht
( household composition )
( regional component )
+ constant
where h and t index households (individual) and time and other variables are defined as follows.
Y is the variable of interest (e.g., household income)
yrdk,t is the year dummy variable equal to one if k = t and zero otherwise.
Eduk = education of the household head. Four dummy variables:
Edu1 = 1(years of education <8)
Edu2 = 1(years of education >=8 & years of education <=11)
6
Monthly regional CPI is not published in 1994-2001.
24
Edu3 = 1(years of education >11 & years of education <=14)
Edu4 = 1(years of education >14)
Edu1 is the base and hence is omitted from the regression
Age is the age of the household head
Nkidk = number of children (age<18) in the household. Five dummy variables
Nkid1 = 1 child
Nkid2 = 2 children
Nkid3 = 3 children
Nkid4 = 4 children
Nkid5 = 5 or more children
The (omitted) base is “no children”
Nsenk = number of senior (60+) household members. Two dummy variables
Nsen1 = 1 senior members
Nsen2 = 2 or more senior members
The (omitted) base is “no senior members”
Nmemk = number of household members. Six dummy variables.
Nmem1 = one HH member
Nmem5 = five HH members
Nmem6 = six or more HH members
The (omitted) base is “one household member”
Urbk = type of the location. Three dummy variables
Urb1 = city (oblast center)
Urb2 = town
Urb3 = village
The (omitted) base is “city”
Oblk = regional dummies. The omitted base is “Moscow”
The year component is yrd t =
2005
∑
k =1995
βˆkyr,t ⋅ yrd k ,t = βˆtyr,t
3
The education component is eduht = ∑ βˆkedu
,t ⋅ edukht
k =1
4
k
The age component is ageht = ∑ βˆkage
,t ⋅ ageht
k =1
The household composition component is
5
2
k =1
k =1
6
HHCht = ∑ βˆkNkid
⋅ Nkid kht + ∑ βˆkNsen
⋅ Nsenkht + ∑ βˆkNmem
⋅ Nmemkht
,t
,t
,t
k =2
3
8
k =2
k =2
ˆ obl
The regional component is oblht = ∑ βˆkurb
,t ⋅ urbkht + ∑ β k ,t ⋅ oblkht
25