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Poverty in Russia: A Bird's-Eye View of Trends and Dynamics in the Past Quarter of Century.

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Hardly any recent study exists that broadly reviews poverty trends over time for Russia. Analyzing the Russian Longitudinal Monitoring Surveys between 1994 and 2019, we offer an updated review of poverty trends and dynamics for the country over the past quarter of century. We find that poverty has been steadily decreasing, with most of the poor having a transient rather than a chronic nature. The bottom 20 percent of the income distribution averages an annual growth rate of 5 percent, which compares favorably with that of 3.3 percent for the whole population. Income growth, particularly the shares that are attributed to labor incomes and public transfers, have important roles in reducing poverty. Our findings are relevant to poverty and social protection policies. JEL: C15, D31, I31, O10, O57
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Poverty in Russia: A Bird’s-Eye View of Trends and Dynamics in the Past
Quarter of Century
Kseniya Abanokova and Hai-Anh H. Dang*
July 2021
Abstract
Hardly any recent study exists that broadly reviews poverty trends over time for Russia. Analyzing
the Russian Longitudinal Monitoring Surveys between 1994 and 2019, we offer an updated review
of poverty trends and dynamics for the country over the past quarter of century. We find that
poverty has been steadily decreasing, with most of the poor having a transient rather than a chronic
nature. The bottom 20 percent of the income distribution averages an annual growth rate of 5
percent, which compares favorably with that of 3.3 percent for the whole population. Income
growth, particularly the shares that are attributed to labor incomes and public transfers, have
important roles in reducing poverty. Our findings are relevant to poverty and social protection
policies.
JEL: C15, D31, I31, O10, O57
Key words: poverty, poverty dynamics, income growth, income mobility, RLMS, Russia
* This is a forthcoming chapter in Jacques Silber (Eds.) Handbook of Research on Measuring Poverty and
Deprivation with Edward Elgar Press. Abanokova (kabanokova@hse.ru) is junior research fellow with Higher
School of Economics, National Research University, Russia; Dang (hdang@worldbank.org) is senior economist with
the Data Production and Methods Unit, Development Data Group, World Bank and is also affiliated with GLO, IZA,
Indiana University, International School, Vietnam National University, Hanoi, and Vietam Academy of Social
Sciences. We would like to thank the UK Foreign Commonwealth and Development Office (FCDO) for funding
assistance through a Knowledge for Change (KCP) Research Program. We also acknowledge support from the NRU-
HSE Basic Research Program.
2
1. Introduction
The transition processes following the breakup of the former Soviet Union have received
considerable attention in economic literature (Milanovic, 1998). Indeed, Russia suffered severe
economic declines in early transition stages compared to formerly planned economies (Svejnar,
2002). Yet, the country could sharply reduce poverty in the subsequent period, particularly during
the past two decades.
In this short paper, we examine three main features of poverty in Russia over the past 25 years:
i) its general trends, ii) poverty dynamics, including income mobility, and iii) some key driving
factors behind the dramatic reduction in poverty. While we rely on official data for the (headcount)
poverty rate, we mostly analyze the Russian Longitudinal Monitoring Survey (RLMS-HSE
1
) from
1994 to 2019 for poverty dynamics. We briefly discuss inequality issues in an appendix, and we
also supplement our analysis with reference to the relevant studies. We show that Russia could
solidly reduce poverty during the past two decades thanks to the country’s transient nature of
poverty and significant income mobility. Although increases in labor incomes contributed the most
to poverty decrease, public transfers played an important role after the 1998 financial crisis
especially for the extremely poor.
2
Hardly any recent study exists that broadly reviews poverty trends over time for Russia.
Lokshin and Yemtsov (2013) is the single exception.
3
But compared to this study, we study a
longer time span starting from early 1990s to the present and we examine different indicators of
1
https://rlms-hse.cpc.unc.edu and http://www.hse.ru/rlms
2
We loosely refer to individuals with welfare levels much below the poverty line as the “extremely poor”.
3
Ovcharova and Biryukova (2018) offer another review that focuses on the methodology of poverty estimation and
its changes from 1992 to 2014. We provide an overview of some selected studies on poverty in Russia since 2000s in
Appendix A, which we classify into several headings such as poverty measurement, income mobility, and subjective
well-being. We also pay special attention to the data that these studies used.
3
poverty dynamics, income mobility (and inequality), and decomposition techniques. These helps
paint a richer picture of poverty trends.
We discuss in the next section the evolution of poverty, its levels and dynamics, and income
growth. We subsequently discuss in Section 3 the drivers of poverty changes using decomposition
analysis before offering some further thoughts and concluding in Section 4. We provide a brief
review of some selected key studies on poverty in Russia since the 2000s in Appendix A, additional
analysis (including inequality trends) in Appendix B, and technical details of the poverty measures
that we employ in Appendix C.
2. Poverty Evolution
2.1.Trends in Poverty
The landmarks in the evolution of poverty in Russia are intricately linked to the various
post-transition macroeconomic shocks and recessions the country experienced since the early
1990s. Indeed, although poverty in Russia was not a new phenomenon that can be attributed
exclusively to market reforms (Klugman and Braithwaite, 1998), price liberalization in the early
1990s resulted in sharply increased poverty compared to the late 1980s.
4
The transition recession
in 1992-93 with continuing ruble inflation caused incomes to collapse when three out of ten people
were estimated to be living in poverty. From its peak in 1992, the official poverty rate fell from
33.5 percent to 22.4 percent in 1994 and then increased again after the financial crisis of 1994
(Figure 1, Panel A).
The downward trend in poverty reduction took place against the upward trend of GDP in
the same period (Figure 1, Panel B). Indeed, after a period of GDP contractions, the Russian
4
Milanovic (1998) finds that the headcount poverty rate increased from 2 percent in 1987-88 up to 50 percent in 1993-
95. Commander et al. (1999) also observes that poverty was over 50 percent in 1992.
4
economy showed signs of recovery in 1997 but subsequently contracted again. Rising
unemployment and wage arrears during 1994-1996 were regarded as most damaging for poorer
households (Klugman and Kolev, 2001).
We plot in Figure 2 the evolution of poverty trends and average household incomes during
the period 1994-2019 and mark the major events that are widely considered to be associated with
significant changes in poverty. This figure suggests that all the poverty indicators peaked in 1998,
when the financial global crisis hit the Russian economy.
5
Since the social protection system failed
to protect the most vulnerable (Lokshin and Ravallion, 2000), increased poverty rates were
accompanied by sharp rises in the depth and severity of poverty. The severity index, which is more
sensitive to the extremely poor, almost doubled, indicating that poorer households were hurt the
most during this period.
The post-1998 period saw steadily decreasing poverty as household incomes recovered. In
contrast to the pre-1998 period, the poverty gap index was reduced faster than the headcount index,
and the severity of poverty index was reduced even more rapidly. This indicates that the extremely
poor benefited more than the average poor household during this recovery period. By the end of
2003, all the poverty measures fell down to the same level as in 1994. Liquidity problems in the
banking sector slowed down economic growth in Russia in 2004 (World Bank, 2005), but living
standards continued to increase, and poverty kept declining after this year.
After a decade of solid growth, Russia was hit by the global economic crisis in 2008. This
resulted in the economy shrinking by almost 8 percent in 2009. Although the crisis caused incomes
to decline, there were much milder increases in poverty compared to earlier periods.
6
In 2014,
5
We employ the Foster-Greer-Thorbecke poverty measures. Further details are provided in Appendix C (C1).
6
Criticism has been raised over the official poverty measurement approach. We offer a more detailed discussion on
these issues in Appendix B, Part 1. Notably, the official poverty lines changed over time, not only when prices
changed, but also when the composition of the reference basic needs basket changed due to rising living standards.
5
Russia's economy experienced two shocks. First, oil prices dropped significantly. Second, Russia
became subject to economic sanctions by developed economies resulting from the Russia-Ukraine
geopolitical conflict. The subsequent income decline starting in 2015 caused povertyfor the first
time since the 1998 crisisto increase to 13 percent in this year. But poverty appeared to have
started on a downward trend in the more recent years, reaching 12 percent (or more than 18 million
poor individuals) in 2019.
Given the important points with poverty evolution in 1998, 2004, 2009 and 2015 shown in
Figure 2, we divide the 1994-2019 period into five sub-periods of roughly equal lengths for better
analysis.
7
These include i) the transition period with financial collapse in 1998 (1994-1998), ii) the
first years of economic growth (1998-2004), iii) the period of accelerated economic growth (2004-
2009), iv) the global crisis and stagnation period (2009-2014), and v) the most recent period (2014-
2019).
Table 1 suggests that the shares of households trapped in chronic poverty become smaller
over time, falling from 28 percent in the 1994-98 transition period to 5 percent in the most recent
period.
8
For another comparison, this chronic poverty rate (28 percent) was less than half of the
transient poverty rate (59 percent) during the transition period. In the 1998-2004 period, the
chronic poverty rate (24 percent) decreased to roughly one-third of the transient poverty rate (70
percent) in the same period. The relative difference between these two poverty rates significantly
Because of this, the official Russian poverty lines varied in real terms between years. The revisions of the poverty
lines were regarded as helping increase poverty in 2000, 2005 and 2013 (Ovcharova and Biryukova, 2018).
7
Dividing into sub-periods also helps reduce potential effects due attrition issues with the long-run RLMS panel data.
Alternatively, synthetic panel methods can be employed to analyze poverty mobility where actual panel data are
inadequate (Dang, Jolliffe, and Carletto, 2019).
8
We describe poverty persistence according to the portion of individuals that are always, sometimes, or never poor
across a survey’s rounds for each of the five shorter periods. A transiently poor person in this context is someone who
is not poor in all periods but only in some periods, while a chronically poor person is poor throughout the period
(Hulme and Shepherd, 2003).
6
widened in the most recent period, where less than 5 percent of population were chronically poor,
and 34 percent of the population were transiently poor.
Taken together, Table 1 shows that in the past quarter of century 1994-2019, the majority
of people that were considered poor were in fact transiently poor rather than chronically poor. This
result reflects the significant flows into and out of poverty and large extents of income fluctuations
in Russia, which are masked by analysis of cross-sectional data and are only revealed by more in-
depth analysis of panel data. This result is also consistent with the findings in studies that analyze
earlier periods (Commander et al., 1999; Lokshin and Ravallion, 2004).
2.2. Income Growth and Mobility
Significant income mobility (or instability) was considered the reason that explains why
transient poverty was so high for Russian households in the early 1990s (Commander et al., 1999;
Jovanovic, 2001). Our updated analysis for the period 1994-2019, shown in Table 2, shows that
although a considerable degree of income mobility exists in each period, individuals are less likely
to move up by more than one income quintile in recent periods.
9
Slowdown in mobility is
noticeable with the poorest quintile: 36 percent of the poorest quintile in remain in the poorest
quintile in the period 1998-2004, but this figure increases by around half to 55 percent in the period
2014-19 (Appendix B, Table B1). This increase is larger than the corresponding immobility rate
of 30 percent for the two periods in the general population (i.e., 33 and 43 percent of households
remain in the same income quintile across two time periods).
Yet, Figure B4 in Appendix B shows that economic growth during the past 25 years has a
strong pro-poor nature. The bottom 20 percent of the income distribution grew by 5 percent
9
Different mobility measures are discussed in Appendix C (C2).
7
annually, while the corresponding figure for the top 20 percent of the distribution did not exceed
4 percent. The income of the poorest five percent of the income distribution grew by 6.8 percent
per year from 1994 to 2019, while the corresponding figure for the richest five percent was much
lower at 2 percent. These numbers compare favorably to an average annual growth rate of 3.3
percent (and an average annual growth rate of 4.3 percent for the median income).
10
3. Understanding Changes in Poverty
Inequality has been decreasing for Russia for the period 1994-2015 (Dang et al., 2020).
Our analysis using the updated RLMS data for the period 1994-2019 further confirms this finding.
This decreasing trend in inequality implies that poverty reduction in the past 25 years can be driven
by either growth or redistribution of in household incomes, or both. Consequently, we decompose
the changes in poverty into a growth component and a redistribution component separately for
each of the five sub-periods. To keep the overall level of poverty as a function of real mean incomes
and the Lorenz function only, we use two fixed poverty lines: the “2005 poverty line” and the
“2013 poverty line”.
Figure 3 shows that relatively, the growth component took the dominant role in reducing
poverty for all the periods and for both the poverty lines (the absolute numbers are shown in
Appendix B, Table B2).
11
But while the growth component accounted for at least three-fourths
(75%) of the changes in poverty, its importance diminished over time. For example, using the 2013
poverty line, while income growth explained more than 100 percent of the changes for the two
10
These results are consistent with the findings in Dang et al. (2020) that the poorest tercile experienced a growth rate
that was more than 10 times that of the richest tercile, leading to less long-term inequality than short-term inequality
during the period 19942015.
11
We use Datt and Ravallion’s (1992) decomposition (Appendix C (C3)). Both the 2005 and 2013 poverty lines are
provided by Rosstat https://rosstat.gov.ru/folder/13397, which we convert them to 2019 prices in our analysis. We do
not show the decomposition for the period 2014-19 in Figure 3 because poverty changes were not statistically
significant in this period.
8
periods 1994-98 and 1998-2004, it explained 88 percent in 2004-09 and 75 percent in 2009-14.
The fact that the redistribution component became increasingly important over time also implies
that income redistribution policies might have become more effective for poor households. This
finding is consistent with falling incomes in the recent years as discussed earlier.
12
Further analysis suggests that the most important contributor to poverty reduction was
growth in labor income per adult (Appendix B, Figure B3).
13
In periods of substantial declines in
poverty, including 1998-2004, 2004-09, and 2009-14, changes in labor income and employment
explained more than 70 percent of the change in poverty (Appendix B, Table B3). During 1994-
98, the period with increasing poverty rate, decreasing labor incomes accounted for more than 80
percent of the poverty increase. Another important factor was public transfers, which took a
relatively smaller role in explaining changes in poverty but were more beneficial for the extremely
poor. Although changes in public transfers explained less than 6 percent in the poverty decrease
during the post-crisis period 1998-2014, they accounted for a greater share of the decreases in
poverty gap and poverty severity (Appendix B, Figure B2).
4. Conclusion
We provide a broad overview of poverty trends and dynamics in Russia in the past quarter
of century. Since the early 1990s, poverty in Russia declined by around two-thirds, from 34 percent
in 1994 to 12 percent in 2019. This latter figure is equivalent to more than 18 million people
earning an income below the poverty line. Interestingly, most of the poor were transiently poor
12
These results are further confirmed when we estimate the elasticity of poverty to income growth, which steadily
increases in magnitude over time (Appendix B, Table B4). The largest value of the growth elasticity of poverty can
be observed in 2014-2019, when one percent increase in income reduced the poverty rate by around three percent.
13
We use Shapley decomposition proposed by Azevedo et al. (2012) (see Appendix C (C4)). Notably, switching from
a part-time job to a full-time job, from a lower-skill job to a higher-skill job or staying in the formal sector is found to
be positively associated with income growth, but a transition from the private sector to the public sector is negatively
associated with income growth (Dang et al., 2020).
9
rather than chronically poor. Furthermore, economic growth during the past 25 years has a strong
pro-poor nature. The bottom 20 percent of the income distribution grew by 5 percent annually,
which compare favorably to an average annual growth rate of 3.3 percent for the whole population.
We find that income growth was most important for poverty reduction in Russia, but social
protection policies including public transfers were effective in helping the extremely poor.
Redistribution policies can also be more useful, particularly in periods when incomes were
declining.
10
References
Commander, S., Tolstopiatenko, A., & Yemtsov, R. (1999). Channels of Redistribution: Inequality
and Poverty in Russia's Transition. Economics of Transition, 7(2), 41147.
Dang, H. A. H., Jolliffe, D., & Carletto, C. (2019). "Data Gaps, Data Incomparability, and Data
Imputation: A Review of Poverty Measurement Methods for Data-Scarce Environments".
Journal of Economic Surveys, 33(3): 757-797.
Dang, H. A. H., Lokshin, M. M., Abanokova, K., & Bussolo, M. (2020). Welfare dynamics and
inequality in the Russian Federation during 19942015. European Journal of Development
Research, 32(4), 812-846.
Hulme, D., & Shepherd, A. (2003). Conceptualizing chronic poverty. World Development, 31(3),
403-423.
Jovanovic, B. (2001). Russian roller coaster: Expenditure inequality and instability in Russia,
199498. Review of Income and Wealth, 47(2), 251-271.
Klugman, J., & Braithwaite, J. (1998). Poverty in Russia during the transition: an overview. The
World Bank Research Observer, 13(1), 37-58.
Klugman, J., & Kolev, A. (2001). The Role of the Safety Net and the Labor Market on Falling
Cash Consumption in Russia: 1994–96 A Quintile‐Based Decomposition Analysis. Review of
Income and Wealth, 47(1), 105-124.
Lokshin, M., & Ravallion, M. (2000). Welfare impacts of the 1998 financial crisis in Russia and
the response of the public safety net. Economics of Transition, 8(2), 269-295.
---. (2004). Household income dynamics in two transition economies. Studies in Nonlinear
Dynamics & Econometrics, 8(3).
Lokshin, M., & Yemtsov, R. (2013). Poverty and Inequality in Russia in Alexeev, Michael, and
Shlomo Weber, eds. Oxford Handbook of the Russian Economy. Oxford University Press,
2013.
Milanovic, B. (1998) Income, inequality, and poverty during the transition from planned to market
economy. Washington, DC: World Bank.
Ovcharova, L. & Biryukova S. (2018) Poverty and the poor in post-soviet Russia In Poverty,
Politics and the Poverty of Politics. BR Publishing Co: 151-175
Svejnar, J. (2002). Transition economies: Performance and challenges. Journal of Economic
Perspectives, 16(1), 3-28.
World Bank. (2005). Russian Economic Report. World Bank Working Paper No. 35602.
11
Table 1. Proportion of Individuals That Are Always, Sometimes, or Never Poor, RLMS-HSE
1994-2019
Periods
Always
Poor
Sometimes
Poor
Sometimes poor
as % of ever
poor
1994-1998
28.3
59.3
67.7
1998-2004
23.5
69.5
74.8
2004-2009
8.9
57.9
86.6
2009-2014
3.9
40.3
91.2
2014-2019
4.8
33.5
87.4
Note: Monetary income per capita is taken as the welfare measure and calculated for the entire population using total
household incomes, divided by the number of household members. Incomes are adjusted to 2019 constant rubles.
Incomes for rounds 5, 6 and 7 are divided by 1,000 to account for the nominal revaluation of the ruble in January
1998. The official poverty line as a minimum subsistence level at regional level is used. “Sometimes poor” out of
those who are ever poor are those who are poor in any wave including those always poor.
12
Table 2. Estimates of Income Mobility, RLMS-HSE 1994-2019
Periods
Upward mobility
by more than
one quintile
Upward
mobility by
one quintile
Immobility
Downward
mobility by
one quintile
Downward
mobility by more
than one quintile
Unconditional
1994-1998
14.6
20.5
34.6
18.0
12.4
1998-2004
17.9
18.1
32.9
18.4
12.7
2004-2009
10.6
15.9
38.3
22.8
12.3
2009-2014
11.7
20.3
40.9
17.4
9.7
2014-2019
10.7
21.2
42.6
17.0
8.6
Conditional
1994-1998
23.1
24.9
34.6
22.9
21.9
1998-2004
27.0
21.3
32.9
24.1
23.0
2004-2009
17.4
19.5
38.3
27.9
20.3
2009-2014
18.6
24.6
40.9
22.3
17.0
2014-2019
16.9
25.8
42.6
21.9
15.1
Note: Monetary income per capita is taken as the welfare measure and calculated for the entire population using total
household incomes, divided by the number of household members. Incomes are adjusted to 2019 constant rubles.
Incomes for rounds 5, 6 and 7 are divided by 1,000 to account for the nominal revaluation of the ruble in January
1998. The quintile thresholds are obtained from the cross-sectional sample for each year, which are subsequently used
for analysis of the panel sample. All numbers are weighted with population weights, where the second survey round
in each period is used as the base year.
13
Figure 1. Growth and Poverty in Russia 1992-2019
Source: Rosstat, WDI
Note: The ticks on Panel A are referred to official revisions of the poverty line.
14
Figure 2. Evolution of Poverty in Russia, RLMS-HSE 1994-2019
Note: Monetary income per capita is taken as the welfare measure and calculated for the entire population using total
household incomes, divided by the number of household members. Incomes are adjusted to 2019 constant rubles.
Incomes for rounds 5, 6 and 7 are divided by 1,000 to account for the nominal revaluation of the ruble in January
1998. All numbers are weighted with population weights. The official poverty line as a minimum subsistence level at
regional level is used.
15
Figure 3. Growth and Redistribution Decomposition of Changes in Headcount Poverty
(percent of total change), RLMS-HSE 1994-2019
Note: Monetary income per capita is taken as the welfare measure and calculated for the entire population using total
household incomes, divided by the number of household members. Incomes are adjusted to 2019 constant rubles.
Incomes for rounds 5, 6 and 7 are divided by 1,000 to account for the nominal revaluation of the ruble in January
1998. All numbers are weighted with population weights. Period 2014-2019 is not shown because changes in poverty
headcount were not statistically significant during that period.
16
Appendix A: Selected Studies on Poverty in Russia since the 2000s
No Authors Data Overview
1Lanjouw et al., 2004 HEIDE
Poverty profiles are sensitive to economies of scale in consumption and significant
changes in demographic profiles appear to be at values below 0.4. The impact of
relative price changes increases economies of scale in consumption significantly.
2 Lokshin and Ravallion, 2004 RLMS-HSE 1994-1998
Households generally bounce back from transient shocks, though the adjustment
process is slower for poorer households. Households with children, single-parent
households, and with poorly educated heads tend to have a lower long-run incomes.
The presence of elderly people has a negative impact on total household income.
3 Ravallion and Lokshin, 2006
RLMS-HSE, Rosstat,
2002
Regional poverty lines were tested for utility consistency that is based on their
consistency with nutritional requirements and found to be not utility consistent. P eople
living at the poverty line in different demographic or geographic groups do not have
the same level of welfare.
4 Wall and Johnston, 2008 RLMS-HSE 1996–2004
Asset index can be used to identify the poor population when no income or
expenditure data is available. Quintile approach is used to set the poverty line.
5 Gibson et al., 2008 RLMS-HSE 1992-2001
Measurement bias in the CPI affects the measurement of poverty rate after 1998
crisis. According to bias-adjusted data, the crisis is preceded by some years of growth,
rather than the decline that is apparent in the official data.
6 Takeda, 2010 RLMS-HSE 1994, 2002
Poverty rates measured with Engel's food share method and subjective economic well-
being method can be used for poverty evaluation. Official approach underestimates
poverty rate.
7 Denisova, 2012 RLMS-HSE 1994-2009
While larger families are doing better when getting out of poverty, the presence of
children increases chances to get into poverty and decreases chances to leave it. High
share of adults with university degree and living in urban areas reduce entry to
poverty and increases exit from it.
8 Abanokova et al., 2020 RLMS-HSE 1994-2017
Chronic poverty and poverty dynamics are sensitive to the scale parameter, regardless
of the poverty measure. Income mobility could be classified as either upward or
downward depending on the specific scale parameters that are employed.
9 Ravallion and Lokshin, 2001 RLMS-HSE 1994-1996
Household income is a highly significant predictor of individual's subjective economic
welfare. Becoming unemployed or sick lower subjective economic welfare, even if
there is full replacement of the income loss shocks.
10 Ferrer-i-Carbonell and Van Praag, 2001 RUSSET 1997, 1998
While subjective measures of poverty showed strong consistency and are comparable
between each other, measures based on the respondents’ feelings of income poverty
should be prefered. Well-being poverty is lower than income poverty measured using
a subjective question.
11 Ravallion and Lokshin, 2002 RLMS-HSE 1994-1996
Differences between the subjective and objective types of welfare data. For example,
60% of the poorest eighth of adults in terms of cur- rent household income relative to
the poverty line did not place themselves on either the poorest or second poorest
rungs of the subjective ladderThe discrepancies with self-rated welfare are due in part
to the weight- ing of the demographic and geographic variables that go into the
Russian poverty lines used for assessing diGerences in needs at a given income.
12 Frijters et al., 2006 RLMS-HSE 1995-2001
Changes in real incomes were important in explaining the swings in life satisfaction in
the post-transition period. Life satisfaction rises significantly in response to moving
from unemployment to employment, and falls in response to wage arrears, poor
health and marital dissolution.
13 Nivorozhkin et al., 2010 NOBUS 2003
Differences in the perception of income and perception of poverty across settlements
of different size. People in larger settlements require more money to make ends meet
than those living in smaller settlements.
14 Dang et al., 2019 RLMS-HSE 2001-2017
Did not find poverty adaption for life satisfaction and subjective wealth. Longer
poverty spells being associated with more dissatisfaction. Women, those who are
living in rural areas or foreign born adapt less, particularly for longer poverty
duration
15 Ravallion and Lokshin, 2000 RLMS-HSE 1992-1996
Support for redistribution is higher amongst those who expect their welfare to fall,
while resistance to redistribution is strongest amongst those who have been on a rising
consumption path over recent years, and expect incomes to raise.
16 Lukiyanova and Oshchepkov, 2012 RLMS-HSE 2000-2005
While there was pro-poor growth during that period, inequality decreased only
slightly. Relative and absolute mobility are significantly higher than in developed
countries. Mobility is higher and mostly smoothes out income differences at the very
top and the very bottom of the distribution.
17 Nissanov and P ittau, 2016 RLMS-HSE 1992-2008
Shrinking of the middle class in the years 2000–2008 lead to high degree of
polarization and affected incomes below the median. The mass of the distribution
moved mostly to lower quantiles of the income distribution.
18 Dang et al., 2020 RLMS-HSE 1994-2015
Decreasing inequality was caused by pro-poor growth. Transition to a full-time job or
a higher-skills job is positively associated with reducing downward mobility, while
transition to the formal sector, a full-time job, or a higher-skills job is positively
associated with higher income levels.
19 Borisov and P issarides, 2020 RLMS-HSE 1994-2016
Intergenerational correlations between the parents’ and children’s income is higher
than in Nordic countries, but at the same level as in US, UK and France. Education
explained about 20% of the overall correlation, while living area and unobservable
charactristics contributed equal amounts of 40% each.
Poverty Measurement (incl. poverty dynamics)
Income m obility/growth
Subjective poverty and poverty adaptation
17
References
Abanokova, K., & Lokshin, M. (2015). Changes in household composition as a shock‐mitigating
strategy. Economics of Transition, 23(2), 371-388.
20 Lokshin and Ravallion, 2000 RLMS-HSE 1996-1998
Identification of "gainers" and "losers" among the poor poplation due to 1998 crisis.
Social safety net did not respond efficiently to 1998 crisis in order to protect people
from poverty.
21 Lokshin et al., 2000 RLMS-HSE 1992-1996
Co-residence with relatives was coping strategies single-parent families used druing
economic instability. Higher labor and non labor incomes increased the likelihood that
the single parent family lives separately from other relatives.
22 Klugman and Kolev, 2001 RLMS-HSE 1994-1996
Negative changes in labor market, such as wage arrears, and weak state welfare
programs accounted for a substantial part of the welfare decline during 1994-1995.
The rise in unemployment and increase in wage arrears were much more important in
explaining the decline in bottom than at the top of the distribution.
23 Skoufias, 2003 RLMS-HSE 1994-2000
Households was able to protect their consumption from 1998 crisis by adjusting non-
food expenditures. Households differed in their ability to protect themselves from
shocks and combined self-insurance strategies of borrowing, adjusting their labour
supply and selling assets, with informal strategies, such as networks.
24 Jahns et al., 2003 RLMS-HSE 1992-2000
Women have higher rates of both overweight and obesity than men. While there was
no negative effect of economic reforms on macronutrient intake, there was income
effects on the diet and overweight status of Russian men.
25 Lokshin and Yemtsov, 2004 RLMS-HSE 1996-1998
Type of survival strategy during 1998 crisis depends on the level of human capital in
the household. The higher the household human capital, the more likely it chooses
active strategies. Social protection system was not able to protect the most vulnerable
efficiently.
26 Kuhn and Stillman, 2004 RLMS-HSE 1994-2000
Private transfers used as coping strategies. Transfers largely flow from elderly and
“empty-nest” households to younger households. Transfers helped young adults as
they transition to the job market and the most vulnerable elderly respondents.
27 Skoufias and Quisumbing, 2005 RLMS-HSE 1994-2000
Ability to smooth consumption is positively associated with the level of household
consumption and negatively associated with the incidence of poverty. Adjustments in
labour supply and selling assets helped households to spread risk over time.
28 Mu, 2006 RLMS-HSE 1994-2003
Households can partially protect their consumption from income shocks. Their ability
to smooth consumption correlates with the level of assets at the initial period for rural
households and with education level of household members for urban households.
29 Stillman and Thomas, 2008 RLMS-HSE 1994-2000
There was no negative effect of 1998 crisis on nutritional status of households.
Switching to cheaper diets and lower quality of calories were the strategies households
used to maintain energy intake.
30 Gerry and Li, 2010 RLMS-HSE 1996-2000
Households with children and unemployed members are the most vulnerable group to
income shocks. Ability to smooth consumption depends on welfare level and
education. Informal networks and home production are the important coping strategies
households used to protect themselves.
31 Abanokova and Lokshin, 2015 RLMS-HSE 1994-2011
Changes in household structure were important coping strategy during 1998 and 2008
crisis. Households that experienced decline in their incomes were more likely to
increase their size compared to households whose post-crisis income did not change
or increased.
32 Kolenikov and Shorrocks, 2005 Rosstat 1995
Regional differences in contributions of income and inequality to poverty. Inequality
has a greater impact on the poverty rate than real income per capita in about half of
the regions
33 Gerry et al., 2008 RLMS-HSE 2000-2004
Urban-rural gap in poverty levels and rates of poverty decline. Those living in urban
areas enjoying a higher decline of poverty than those in rural areas. Observable
characteristics explained less than a fifth of rural–urban poverty gap and did not affect
the rate of poverty decline.
34 Zubarevich, 2019 Rosstat 2000-2017
Regional differences in income, poverty levels and rates of poverty reduction.
Substistance minimum level, income inequality and urbanization level are significant
factors of regional differences.
35 Rutherford and Tarr, 2008
National Accounts, HBS,
RLMS-HSE, 2003
Welfare gains from accession to the World Trade Organization. While all households
across income distibution would gain from the accession to the WTO, poor
households gain slightly more than rich households and rural households gain less than
urban households.
36 Kapelyuk, 2015 RLMS-HSE 2006-2011
The effect of minimum wage policy on poverty. Minimum wage increase reduced the
incidence of poverty and the transitions into poverty but the size of this effect was
moderate
Effect of reforms/policy programs on poverty
Welfare impacts of the shocks and coping strategies
Regional Poverty
18
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Cash Consumption in Russia: 1994–96 A Quintile‐Based Decomposition Analysis. Review of
Income and Wealth, 47(1), 105-124.
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20
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21
Appendix B: Additional Analysis, Tables, and Figures
Part 1. Poverty Measurement in Russia
Official methods of collecting data and its accuracy in calculating poverty indicators have
been criticized in the literature (Klugman and Braithwaite, 1998; Clarke, 2000; Wall and Johnston,
2008). RLMS-HSE data on incomes and expenditures show a qualitatively similar trend to that
from the official data, although official data sources dramatically underestimate poverty rates
during the 1990s (Figure B1). The differences can be explained by Rosstat’s adjustments to make
the distributions of survey incomes close to those of macroeconomic data.
Although welfare indicators in the RLMS-HSE are found to be prone to underreporting
and misreporting (Aivazian and Kolenikov, 2001; Gorodnichenko et al., 2009; Murashov and
Ratnikova, 2016), we use total household monetary income as the main welfare measure in our
analysis. While consumption data can be more appropriate for measuring poverty in Russia
(because of less measurement error and income underreporting in the early 1990s), changes to
consumption items in the RLMS-HSE after 2000 make consumption variable incomparable over
time. Although the official poverty rates are lower than the RLMS-HSE in 2004-2012, they are
close to each other between 2012-2019. The official poverty estimates are consistently higher than
those based on the RLMS-HSE equivalence adjusted data in the late 2000s.
Figure B1. Poverty in Russia, ROSSTAT vs. RLMS-HSE, 1992-2019
Source: Rosstat (1992-2019), RLMS-HSE (1994-2019)
Note: Consumption per capita is defined as monthly average household expenditure on items for the
purpose of consumption. These include items purchased, consumption from own production and income in
kind, goods and services purchased by the household to be given to private persons or bodies as gifts or
allowances, expenditures on durable goods. Household incomes are adjusted with equivalence scale
weights where baseline elasticity equals 0.407 and every child has a weight 0.048. Both the poverty
22
thresholds and household incomes are converted to constant prices of 2019 using CPI indices provided by
the Rosstat.
Part 2. Additional Tables, and Figures
Table B1. Estimates of Income Mobility for Households in the Bottom Quintile, RLMS-HSE
1994-2019
Periods
Upward mobility by more
than one quintile
Upward mobility
by one quintile
Immobility
1994-1998
30.8
26.1
43.1
1998-2004
39.0
24.9
36.1
2004-2009
31.8
15.2
53.0
2009-2014
23.4
26.0
50.6
2014-2019
20.8
23.9
55.2
Note: Monetary income per capita is taken as the welfare measure and calculated for the entire population using total
household incomes, divided by the number of household members. Incomes are adjusted to 2019 constant rubles.
Incomes for rounds 5, 6 and 7 are divided by 1,000 to account for the nominal revaluation of the ruble in January
1998. The quintile thresholds are obtained from the cross-sectional sample for each year, which are subsequently used
for analysis of the panel sample. All numbers are weighted with population weights, where the second survey round
in each period is used as the base year.
23
Table B2. Datt-Ravallion Decomposition of Changes in Poverty Headcount, RLMS-HSE
1994-2019
Period
Growth
Redistribution
Total Change (p.p.)
2005 Poverty Line
1994-1998
23.1
-3.1
20.0
1998-2004
-34.0
-1.0
-35.0
2004-2009
-27.6
-7.2
-34.9
2009-2014
-5.9
-1.8
-7.8
2014-2019
0.5
-1.1
-0.6
2013 Poverty Line
1994-1998
16.0
-2.0
14.0
1998-2004
-25.6
0.1
-25.5
2004-2009
-31.9
-4.3
-36.2
2009-2014
-9.9
-3.3
-13.2
2014-2019
1.3
-1.2
0.0
Note: Monetary income per capita is taken as the welfare measure and calculated for the entire population using total
household incomes, divided by the number of household members. Incomes are adjusted to 2019 constant rubles.
Incomes for rounds 5, 6 and 7 are divided by 1,000 to account for the nominal revaluation of the ruble in January
1998. All numbers are weighted with population weights.
24
Table B3. Shapley Decomposition of Changes in Poverty Headcount, RLMS-HSE 1994-2019
Period
Share of
adults
Labor
income
Home
production
income
Capital
income
Public
transfers
Private
transfers
Total
change
(p.p.)
2005 Poverty Line
1994-1998
-0.5
18.5
0.0
0.1
2.7
1.7
22.5
1998-2004
-3.5
-28.1
-1.0
-0.2
-3.5
-1.2
-37.6
2004-2009
-1.1
-21.7
0.0
-0.0
-6.0
-0.8
-29.7
2009-2014
1.3
-6.5
-0.3
-0.0
-1.8
-0.2
-7.4
2014-2019
0.8
-1.8
0.5
0.0
0.3
0.2
0.1
2013 Poverty Line
1994-1998
-1.2
14.1
-0.1
0.2
1.4
1.4
15.7
1998-2004
-1.9
-23.3
-1.0
-0.2
-2.2
-1.1
-29.6
2004-2009
-1.6
-24.1
-0.1
-0.0
-5.7
-0.7
-32.2
2009-2014
1.1
-9.2
-0.2
-0.1
-2.6
-0.3
-11.2
2014-2019
1.2
-1.9
0.7
-0.0
-0.3
0.4
0.1
Note: Disposal income per capita is taken as the welfare measure and calculated for the entire population using total
household disposal incomes, divided by the number of household members. Incomes are adjusted to 2019 constant
rubles. Incomes for rounds 5, 6 and 7 are divided by 1,000 to account for the nominal revaluation of the ruble in
January 1998. All numbers are weighted with population weights.
25
Table B4. Elasticity of Poverty with Respect to Average Income Growth, RLMS-HSE 1994-
2019
Periods
Poverty
Headcount
Poverty Gap
Poverty Gap
Squared
2005 Poverty Line
1994-1998
-0.7
-1.0
-1.1
1998-2004
-0.8
-1.1
-1.2
2004-2009
-1.8
-1.8
-1.7
2009-2014
-2.5
-2.5
-2.5
2014-2019
-2.8
-2.8
-3.0
2013 Poverty Line
1994-1998
-0.4
-0.8
-1.0
1998-2004
-0.5
-0.9
-1.1
2004-2009
-1.4
-1.7
-1.7
2009-2014
-2.3
-2.4
-2.4
2014-2019
-2.6
-2.7
-2.8
Note: Monetary income per capita is taken as the welfare measure and calculated for the entire population using total
household incomes, divided by the number of household members. Incomes are adjusted to 2019 constant rubles.
Incomes for rounds 5, 6 and 7 are divided by 1,000 to account for the nominal revaluation of the ruble in January
1998. All numbers are weighted with population weights.
26
Figure B2. Contribution of Public Transfers to Changes in Different Poverty Measures
(percent of total change), RLMS-HSE 1994-2014
Note: Disposal income per capita is taken as the welfare measure and calculated for the entire population using total
household disposal incomes, divided by the number of household members. Incomes are adjusted to 2019 constant
rubles. Incomes for rounds 5, 6 and 7 are divided by 1,000 to account for the nominal revaluation of the ruble in
January 1998. All numbers are weighted with population weights. Period 2014-2019 is not shown because changes in
poverty headcount were not statistically significant during that period.
27
Figure B3. Component Decomposition of Changes in Headcount Poverty (percent of total
change), RLMS-HSE 1994-2014
Note: Disposal income per capita is taken as the welfare measure and calculated for the entire population using total
household disposal incomes, divided by the number of household members. Incomes are adjusted to 2019 constant
rubles. Incomes for rounds 5, 6 and 7 are divided by 1,000 to account for the nominal revaluation of the ruble in
January 1998. All numbers are weighted with population weights. Period 2014-2019 is not shown because changes
in poverty headcount were not statistically significant during that period.
28
Part 3. Trends in Inequality
Changes to Russia’s economy in the early 1990s led to significantly increased inequality,
which remained high in the early stages of the transition and reached its peak in 1996 with a Gini
ratio of 0.48 (Figure B5, Panel B).
14
High inequality throughout the transition was a major source
of rising poverty (Kolenikov and Shorrocks, 2005). The percentile ratios between the median
income and the lowest (or highest) income percentiles reached a maximum in the period 1996-
1998.
All inequality measures started falling since 2000, after staying relatively high during
1994-1998.
15
Although poorer households suffered relatively more income loss during the crisis,
they have caught up with richer households after 2005 (Figure B5, Panel A). The 90th/ 10th
percentiles income ratio also decreased, reflecting strong income growth for the poorest. The Gini
index fell from 0.47 in 1994 to 0.3 in 2019. Other inequality indexes even show a steeper decrease
than the Gini index. In particular, the Atkinson index of inequality, which is more sensitive to
changes at the bottom of the income distribution, decreased from 0.33 in 1994 to 0.14 in 2019.
14
Gini coefficient increased from 0.26 in the late 1980s to just below 0.40 by the early 1990s (Svejnar, 2002). High
inequality in the early 1990s was fueled by a combination of factors including privatization and subsequent growth of
the private sector on one hand, and unequally distributed social safety net and progressive taxation on another hand
(Commander et al., 1999).
15
Regional differences in growth rates in Russia has increased since the beginning of transition (Fedorov, 2002), but
decreasing inequality during 1994-2015 was mostly caused by pro-poor growth rather than redistribution (Dang et al.,
2020).
29
Figure B4. Growth Incidence Curve, RLMS-HSE 1994-2019
Note: Monetary income per capita is taken as the welfare measure and calculated for the entire population using total
household incomes, divided by the number of household members. Incomes are adjusted to 2019 constant rubles.
Incomes for rounds 5, 6 and 7 are divided by 1,000 to account for the nominal revaluation of the ruble in January
1998. All numbers are weighted with population weights. The median is the income growth of the 50th percentile and
the mean is the growth of average per capita income.
30
Figure B5. Trends in Percentile Ratios and Inequality Indices, RLMS-HSE 1994-2019
Note: Monetary income per capita is taken as the welfare measure and calculated for the entire population using total
household incomes, divided by the number of household members. Incomes are adjusted to 2019 constant rubles.
Incomes for rounds 5, 6 and 7 are divided by 1,000 to account for the nominal revaluation of the ruble in January
1998. All numbers are weighted with population weights.
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Datt, G. & Ravallion, M. (1992). Growth and Redistribution Components of Changes in Poverty
Measures: A Decomposition with Applications to Brazil and India in the 1980s. Journal of
Development Economics, 38, 275-296.
Fedorov, L. (2002). Regional inequality and regional polarization in Russia, 199099. World
Development, 30(3), 443-456.
Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures.
Econometrica, 761-766.
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Gorodnichenko, Y., Martinez-Vazquez, J., & Sabirianova Peter, K. (2009). Myth and reality of
flat tax reform: Micro estimates of tax evasion response and welfare effects in Russia. Journal
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131-147.
32
Appendix C: Brief Review of Relevant Poverty Measurement Techniques
C1. Aggregate Poverty Measures
For a particular year, FGT indices (Foster et al. 1984) are defined as follows:

  (C1)
where z is the poverty line, is the measure of living standards of individual i, N is the population
size and is the indicator function equals to 1 if individual is poor and equals to 0
otherwise. Parameter α is the poverty aversion parameter: larger values give greater weight to
poorer individuals. The headcount ratio is the case when α = 0, poverty gap index is the case when
α = 1, squared poverty gap if α = 2.
C2. Mobility Measures
We provide the following discussion on mobility measures based on Dang et al. (2020).
Let yj and zjk respectively represent individuals’ income (consumption) and the income threshold
k in year j, where j= 1 or 2, and k= 0, 1,…, K, with a higher number for k indicating a higher
income threshold. As is the usual practice, both yj and zjk are expressed in logarithmic form. The
minimal and maximal thresholds and correspond to - and + respectively. Let 
represent the population’s relative mobility measure of interest, where l= u (upward mobility) or
d (downward mobility), and o= n (unconditional mobility) or c (conditional mobility).
We define the unconditional (probability of) upward mobility for individuals in income
category k (
) as its probability of moving to a higher income category in the second year.
   (C2)
Note that this higher income category is not just the next higher income category, but can
generally include any higher income category. If we condition individuals’ movement on their
income levels in the first period, we can obtain the corresponding conditional version of upward
mobility
   (C3)
Put differently,
represents individuals’ unconditional (or joint) upward mobility for
both periods considered together, while
 represents their conditional probability of upward
mobility that is conditional on the fact that their income level is in income category k in the first
year.
We similarly define the corresponding probabilities of unconditional and conditional
downward mobility by simply reversing the inequality signs in the two equations above for
individuals’ income level in the second year.
   (C4)
   (C5)
33
Aggregating over the k income categories gives us the measure of unconditional upward
or downward mobility for the whole population


 (C6)
Further aggregating over the unconditional upward and downward mobility categories
gives us the general measure of unconditional mobility for the whole population
  (C7)
However, note that for the conditional mobility measures , a similar aggregation
formula as that in Equation (C7) does not hold because of the different conditions (denominators)
in Equations (C3) and (C5). But if we focus on the income category k in year 1, we can have the
following conditional mobility measure for this specific income category

 (C8)
To derive the measure of conditional upward and downward mobility for the whole
population, we respectively use the following equation instead
 
 (C9)
 

 (C10)
Thus, there is no general measure of conditional mobility for the whole population that
corresponds to in Equation (C7). A closely related, but opposite measure of mobility is
immobility (i.e., individuals remain in the same income category in both periods). For the
unconditional mobility measures  or defined above, we can simply subtract them from one
to obtain the corresponding unconditional immobility. For the same reason as earlier discussed,
we can only apply the same procedure to the conditional mobility index
in Equation (C8) to
obtain its corresponding conditional immobility index.
C3. Datt-Ravallion Decomposition of Poverty Changes
Poverty can be determined by mean income of the distribution - , fixed poverty line -
and the structure of relative income inequalities presented by Lorentz curve - :
(C11)
where α can take three possible values: 0 (or the headcount index), 1 (or he poverty gap index) and
2 (or the squared poverty gap index).
The level of poverty between two periods may change due to a change in the mean income
or due to a change in relative inequalities:
 (C12)
Datt and Ravallion (1992) splits the change in poverty into impact of income growth
(difference in mean income), redistribution component (difference in relative income shares) and
error term:
34
(C13)
where the growth component of poverty,
the inequality component of poverty for t = 0.
(C14)
where the growth component of poverty,
the inequality component of poverty for t = 1.
The growth component gives the impact on poverty change in the mean income while
holding the Lorenz curve constant at the reference level. The redistribution component gives the
change in poverty due to a change in the Lorenz curve while keeping the mean income at the
reference level. The residual (R) measures the effect of interaction between growth and
redistribution terms on poverty.
Using the Shapley values to decompose of the impact of growth and redistribution and to
eliminate residual:
(C15)
C4. Shapley Decomposition by Components of Welfare
Let`s define individual income yj as a function of household income per-capita:
 (C16)
Income per adult can be written as the sum of labor income,, and nonlabor income
, where nonlabor income includes public social transfers, pensions, remittances and other
private transfers:





  (C17)
where is the number of employed adults.
Let F( . ) be the cumulative density function of the income distribution. We can write any
distributional statistic θ as a function of each of the components above:



 (C18)
where 
 and 



To address path-dependence, Azevedo et al. (2012) propose to calculate the cumulative
decomposition in every possible order and then average the results for each component. For
example, the contribution of transfers will be:
35






 (C19)
A counterfactual unconditional distribution of the welfare is measured by changing each
component at a time to calculate their contribution to the observed changes in welfare.
... This minimum subsistence level is also used to define eligibility for social welfare assistance. See also Abanokova and Dang (2021) for a recent discussion of general poverty trends in Russia. ...
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