metadata: authors: 0: author: Abanokova,Kseniya
1: author: Dang,Hai-Anh H.
2: author: Lokshin,Michael M.
count: Russian Federation
docna: 0: docna: The Important Role of Equivalence Scales :
            Household Size, Composition, and Poverty Dynamics in the
            Russian Federation
docty: Policy Research Working Paper
subtopic: Inequality,Poverty Assessment,Poverty Diagnostics,Small Area Estimation Poverty Mapping,Poverty Impact Evaluation,Poverty Lines,Poverty Monitoring & Analysis,Health Care Services Industry
teratopic: Industry,Poverty Reduction
abstracts: cdata!: Hardly any literature exists on the
            relationship between equivalence scales and poverty dynamics
            for transitional countries. This paper offers a new study on
            the impacts of equivalence scale adjustments on poverty
            dynamics in the Russian Federation, using equivalence scales
            constructed from subjective wealth and more than 20 waves of
            household panel survey data from the Russia Longitudinal
            Monitoring Survey. The analysis suggests that the
            equivalence scale elasticity is sensitive to household
            demographic composition. The adjustments for the equivalence
            of scales result in lower estimates of poverty lines. The
            study decomposes poverty into chronic and transient
            components and finds that chronic poverty is positively
            related to the adult scale parameter. However, chronic
            poverty is less sensitive to the child scale factor compared
            with the adult scale factor. Interestingly, the direction of
            income mobility might change depending on the specific scale
            parameters that are employed. The results are robust to
            different measures of chronic poverty, income expectations,
            reference groups, functional forms, and various other specifications.
geo_reg_key: 517208,517191,82689,517198
repnme: repnme: The Important Role of Equivalence Scales :
            Household Size, Composition, and Poverty Dynamics in the
            Russian Federation
display_title: The Important Role of Equivalence
            Scales : Household Size, Composition, and Poverty Dynamics
            in the Russian Federation
listing_relative_url: /research/2020/06/32096795/important-role-equivalence-scales-household-size-composition-poverty-dynamics-russian-federation
topicv3: Poverty m1326687 695,Chronic Poverty m1326690 1155,Poverty Measurement and Analysis m1326941 707,Transitory Poverty m1327658 1154,Health m1504661 3480
publishtoextweb_dt: 2020-06-04T00:00:00Z
historic_topic: Industry,Poverty Reduction
origu: Development Data Group (DECDG)
no_of_pages: 55
author_keys: 0: author_key: 747967
1: author_key: 144523
model_extractions: Policy Research Working Paper 9270 The Important Role of Equivalence Scales Household Size, Composition, and Poverty Dynamics in the Russian Federation _Kseniya Abanokova Hai-Anh H. Dang Michael M. Lokshin_ Development Economics Development Data Group & Europe and Central Asia Region Office of the Chief Economist June 2020
Policy Research Working Paper 9270

# **Abstract**

Hardly any literature exists on the relationship between equivalence scales and poverty dynamics for transitional countries. This paper offers a new study on the impacts of equivalence scale adjustments on poverty dynamics in the Russian Federation, using equivalence scales constructed from subjective wealth and more than 20 waves of household panel survey data from the Russia Longitudinal Monitoring Survey. The analysis suggests that the equivalence scale elasticity is sensitive to household demographic composition. The adjustments for the equivalence of scales result in lower estimates of poverty lines. The study decomposes poverty into chronic and transient components and finds that chronic poverty is positively related to the adult scale parameter. However, chronic poverty is less sensitive to the child scale factor compared with the adult scale factor. Interestingly, the direction of income mobility might change depending on the specific scale parameters that are employed. The results are robust to different measures of chronic poverty, income expectations, reference groups, functional forms, and various other specifications.

This paper is a product of the Development Data Group, Development Economics and the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at hdang@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Important Role of Equivalence Scales: Household Size, Composition, and Poverty Dynamics in the Russian Federation**

Kseniya Abanokova, Hai-Anh H. Dang and Michael M. Lokshin<sup>*</sup> Key words: poverty, poverty dynamics, equivalence scale, Russia, panel survey JEL: I30, J10, O15

> * Abanokova (kabanokova@hse.ru) is junior research fellow with Higher School of Economics, National Research University, Russia; Dang (hdang@worldbank.org; corresponding author) is economist with the Analytics and Tools Unit, Development Data Group, World Bank and is also affiliated with IZA, Indiana University, and Vietam Academy of Social Sciences; Lokshin (mlokshin@worldbank.org) is lead economist with ECA Chief Economist’s Office, World Bank. We would like to thank Conchita D’Ambrosio, Sam Freije-Rodriguez, Jacques Silber, Rostislav Kapeliushnikov, Ambar Narayan, Sergey Roshchin, Ruslan Yemtsov, and participants at IARIW conference (Dresden), IARIW-HSE conference (Moscow), and seminars at the Laboratory for Labour Market Studies and the Centre for Labour Market Studies (Moscow) and the World Bank for useful feedback on earlier versions. We are grateful to Martin Biewen for sharing his Stata code. We would also like to thank the UK Department of International Development for additional funding assistance through its Strategic Research Program (SRP) and Knowledge for Change (KCP) programs. We also acknowledge support from the NRU-HSE Basic Research Program.
# **1. Introduction**

Obtaining comparable measures of household incomes across households of different sizes and composition—or converting these incomes on a common (equivalence) scale—is a crucial task for welfare measurement. Indeed, a large body of literature has demonstrated that there are substantial effects of scale adjustments on poverty and profiles of the poor for various countries at different income levels (Lanjouw and Ravallion, 1995; Peichl _et al._ , 2012; Bishop _et al._ , 2014). Equivalence scales are often estimated based on expenditure data; one major disadvantage of this method is that it requires strong identifying assumptions (Deaton and Paxson, 1998).

In this paper, we make several contributions to the literature on equivalence scales and poverty measurement. First, we estimate equivalence scales using an alternative source of data, subjective well-being data. While a growing literature has followed this approach using panel data, these studies mostly investigate data on life satisfaction and income satisfaction.<sup>1</sup> We analyze instead a subjective well-being question where individuals are asked to evaluate their own level of material welfare on a nine-point scale from "poor" to "rich". This question arguably better captures the multidimensional nature of welfare and is more related to household welfare than satisfaction variables (Ravallion and Lokshin, 2001 and 2002).

Second, we offer new and interesting findings regarding the dynamics of poverty given equivalence scale adjustments (scaling) on long-run household panel data from the Russian Longitudinal Monitoring Surveys (RLMS). It is well-known that policies to address short-term

> 1 Two main types of subjective well-being data have been analyzed in the economic literature. The first type asks respondents about a hypothetical minimum income level that is required to reach a specified level of well-being (e.g., Garner and Short, 2004). Since this method assumes that people know what their true minimum income level is, the hypothetical assessment of the situation may lead to interpretation issues of minimum income questions (Steiger _et al._ , 1997). The second type asks respondents to evaluate their level of satisfaction with life or income, and does not have such disadvantage (e.g., Biewen and Juhasz, 2017; Borah _et al._ , 2018). Our paper is more related to the second approach. But we also offer robustness checks using life satisfaction data that are collected in the same household surveys.

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static poverty are quite different from those for long-term chronic poverty. Yet, while these dynamics, by definition, requires analysis that must be based on panel data, the data used in the existing literature to investigate the effects of scaling on poverty measurement typically come from cross-sectional surveys (e.g., Newhouse _et al._ , 2017).<sup>2</sup> Such data do not provide a good understanding of how household demographics impact transient or chronic poverty, or to put it differently, how employing different scaling parameters affects household poverty dynamic patterns. To our knowledge, we are among the first to investigate the impacts of scale adjustments on poverty dynamics. As discussed later, we employ several different definitions of poverty dynamics for more robust analysis.

Furthermore, the RLMS offers panel data with longer time intervals than most existing studies. Longer-run panel data allow us to extend our analysis to broader definitions of households— including multigenerational households—and to better capture demographic changes caused by the formation of complex extended families.<sup>3</sup> Finally, the richer countries examined in existing studies, such as Germany, Switzerland or the United Kingdom, have a smaller household size on average than that of the Russian Federation. This different demographic structure implies that findings on the former countries may not necessarily apply to Russia. Furthermore, our study is especially relevant for Russia for two other reasons. Firstly, the equivalence scale currently embedded in the official poverty lines allows for unequal consumption needs but completely ignores the economies of scale in household size. A direct policy implication of no scale adjustment is that the official poverty lines would oftentimes

> 2 But see Dang, Jolliffe, and Carletto (2019) for a review of alternative poverty measurement methods in contexts where no panel data exist.

> 3 Only Borah _et al._ (2018) used longer panel data to analyze equivalence scales but their analysis was restricted to “classical households”, which consist of either a single adult or two partnered adults, with or without children for Germany.

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identify large families with children as those most in need of financial support, regardless of their actual living standards. Secondly, in his recent address to the federal assembly, the Russian president discussed the falling incomes of the country and the need to create favorable conditions to raise real incomes significantly.<sup>4</sup> But recent evidence also points to more upward mobility than downward mobility for the population over the past two decades (Dang _et al._ , 2019). Consequently, it would be useful for policy makers to monitor income trends correctly, and to understand whether, and to what extent, results can be affected by scale adjustments.

To our knowledge, Ravallion and Lokshin (2002) and Takeda (2010) are the only two other papers that estimate the relationship between household size and composition and subjective wellbeing in Russia using panel data. However, besides analyzing older data, these two papers use shorter panels and cross-sectional data, respectively. Consequently, their findings are likely biased by insufficient variation in household size and unobserved heterogeneity issues. We better control for unobservable characteristics by using a recently developed econometric technique, the fixedeffect-ordered-logit-type “blow-up and cluster” (BUC) estimator (Baetschmann _et al._ , 2015) that respects the ordinal nature of subjective well-being data. We also tested our results using more flexible econometric models.

Our results suggest that the elasticity is higher for adding another adult to a two-adult household than a child, and scaling results in lower estimates of poverty lines. We decompose poverty into chronic and transient components and find that chronic poverty as a share of total poverty, defined against an absolute poverty line, is positively related to the adult scale parameter. But chronic poverty is less sensitive to the child scale factor than the adult scale factor. Interestingly, income mobility can be classified as either upward or downward depending on the

> 4 See http://en.kremlin.ru/events/president/news/62582.

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specific scale parameters that are employed. Our results are robust to different measures of poverty, income expectations, reference groups, functional forms, and various other specifications.

This paper consists of seven sections. We briefly review the literature in the next section, before discussing our empirical strategy in Section 3. We subsequently describe the data in section 4, and present estimation results in section 5. We offer a wide range of robustness checks and further extensions in Section 6 before finally concluding in Section 7.

# **2. Brief Literature Review**

A number of studies estimate equivalence scales using panel subjective well-being data, but these studies mostly investigate data on life and income satisfaction and focus on richer countries that have panel data such as Germany or the United Kingdom (Charlier, 2002; Schwarze, 2003; Falter, 2006; Bollinger _et al._ , 2012; Biewen and Juhasz, 2017; Borah _et al.,_ 2018). Our brief overview of these studies, shown in Table A.1, Appendix A, offers several findings. First, although the magnitude of the estimated equivalence parameters differs considerably across studies, all the four studies for Germany find a lower weight for children than that of an additional adult. Only one study by Bollinger _et al._ (2012) finds that children in the United Kingdom are associated with diseconomies, but this result mostly applies to the first child. Second, although most studies suggest larger returns to scale than the (old or modified) OECD equivalence scales, non-parametric scales recently estimated for Germany by Biewen and Juhasz (2017) are fairly close to “squareroot” equivalence scales.<sup>5</sup> Third, equivalence parameters depend on the types of subjective data/questions used for analysis. For example, analyzing life satisfaction or minimum income data

> 5 The old OECD scale assigns a value of 1 to the first household member, 0.7 to each additional adult, and 0.5 to each child. The corresponding figures for the modified OECD scale are 1, 0.5, and 0.3. We discuss the definitions of the square root and other scales in Section 3.

5
leads to lower estimates of equivalence scales than using income satisfaction data (Charlier, 2002; Falter, 2006).

Yet, these findings may not necessarily apply to Russia, given the latter’s different demographic structures. We show four such indicators in Figure 1: the average household size (Panel A), single-person households as a percentage of the total population (Panel B), three-ormore-adults households as a percentage of all households (Panel C), and three-or-more-adults households with children as a percentage of all households (Panel D). Russia has the largest household size, which averaged at least 2.6 persons per household for the last 10 years, which is followed by the United Kingdom (2.3 persons), Switzerland (2.2 persons), and Germany (2 persons) (Panel A). Single-person households are also least common in Russia, accounting for less than 10 percent of the total population on average, while the corresponding figure for Germany is roughly twice higher at 20 percent (Panel B). The corresponding figures for the United Kingdom and Switzerland fall somewhere in between, with Switzerland catching up quickly with Germany. Figure 1, Panels C and D also display a clear cross-country difference in the proportion of extended households (i.e., households where multiple adults are present). While less than 10 percent of households in the other three European countries consist of three or more adults (with or without children) on average, the corresponding figure is at least three times higher for Russia.

# **3. Empirical Strategy**

# **3.1. Measuring Scale Elasticity**
error term. It was expected that satisfaction positively depends on income and negatively depends on household size.<sup>6</sup> Importantly, hi is the household’s equivalence weight that depends on the ee number of adults ( i ) and children i ), such that i i ; is the scale elasticity parameter to be estimated that also depends on the numbers of adults and children in the household. aa (kk h= aa + kk ee[0,1] In particular, when equals 1, we have the usual per capita household income variable (without any scale adjustment), and when ee equals 0.5, we have the square root scale. Equation (1) was first proposed by Schwarze (2003), which assumes that individuals evaluate their welfare level based ee on equivalent income rather than total household income when answering the satisfaction question.<sup>7</sup> Following Schwarze (2003), we also define as the equivalence scale elasticity of a aa household consisting of adults only, and as the scale parameter when there are children in the ee household, such that i . Both these parameters capture the effects of household size bb aa and composition. Parameter ee= ee −bbkk is a “baseline elasticity” that will be lowered _b_ times for each child aa in the household. The smaller ee is, the greater is the effect of household size. If _b_ is positive, aa children cost less than adults, and the opposite result holds vice versa.ee High values of intensify the effect of household composition when the household has many children. bb Plugging these values for hi and into Equation (1), we can rewrite it as ee i∗ i′ i ee β i i i β i i i (2) aa ii WW = xx θθ+ β ββYY −ββee (aa + kk ) + ββbbkk (aa + kk ) + αα + εε

> 6 These results are supported by empirical evidence from both richer and developing countries such as Germany and Great Britain (Van Praag and Ferrer-i-Carbonell, 2004) and Mexico (Rojas, 2007).

> 7 Compared to other models, the advantages of Equation (1) are that it is easy to implement, it differentiates between adults and children, and it permits estimates of a wide range of possible values of elasticity. This equation assumes a logarithmic relationship between equivalent income and subjective welfare (with decreasing marginal utility from equivalent income). We reexamine this relationship using the non-parametric approach of Biewen and Juhasz (2017) in the sensitivity analysis.

7
We apply the most recent statistical model, the BUC fixed-effects model that is developed by Baetschmann _et al._ (2015).<sup>9</sup> Consistent estimations of parameters ( ) are performed by collapsing ordered variables ( _J_ levels of i ) into binary outcomes for each choice (θθ, ββ _0_ , .., _J-1_ ). The conditional maximum likelihood estimator by Chamberlain (1980) can be subsequently applied to WW each of these binary choice models. By copying each observation _J-1_ times in the data set (i.e., “blowing-up” the sample size), so that for every _J−1_ copy of the observation, it is possible to dichotomize the dependent variable at each different threshold. This procedure helps avoid the (severe) loss of information as with the binary (Chamberlain) logit model with fixed effects. We use two-way clustering and cluster the standard errors at both the individual and household-wave levels.

The BUC approach was found to outperform other existing estimators (e.g., Riedl and Geishecker, 2014), but for robustness checks, we also estimate other models such as the pooled ordered logit (POL) model and the linear fixed effects model (FE OLS). While both these models likely yield biased results, they can provide some comparison estimates.<sup>10</sup> For example, empirical evidence for Germany suggests that the equivalence scale parameters in FE models are significantly reduced compared to the pooled regressions (Schwarze, 2003; Borah _et al._ , 2018), but the opposite result holds for Switzerland (Falter, 2006).

# **3.2. Chronic Poverty and Income Mobility**

> 9 Subsequently, Das and Van Soest (1999), Ferrer-i-Carbonell and Frijters (2004) and Baetschmann et al. (2015) introduced new estimators for the fixed effects ordered logit model using the extensions of existing binary choice panel data models. Baetschmann _et al._ ’s model is observed to outperform Das and van Soest’s estimator if some categories on the ordered scale have small sample size and Ferrer-i-Carbonell and Frijters’ estimator if the number of categories on the ordered scale is large (Riedl and Geishecker, 2012).

> 10 The POL provides biased estimates if the fixed effects are statistically significant, while the FE OLS does not model well the categorical dependent variable.

9
~~-~~ ( ~~-)~~ ~~-~~ € JC ~~=)~~ ~~-~~ <z) =11](1- ~~—)~~
(upward mobility) or _d_ (downward mobility), and _o_ = _n_ (unconditional mobility) or _c_ (conditional mobility).

We define the unconditional (probability of) upward mobility for the whole population as follows Note that this higher income category _k+1_ is not just the next higher income category, but can generally include any higher income category. The corresponding probabilities of unconditional downward mobility can be obtained by reversing the inequality signs in Equation (7) for individuals’ income level in the second year.

Focusing on the income category _k_ in year _1_ , we define the measure of conditional upward mobility for the whole population as follows<sup>12</sup>

# **4. Data**

We analyze the Russian Longitudinal Monitoring Survey (RLMS), which is an annual and nationally representative panel household survey. Our analysis covers 24 years (22 survey waves) from 1994 to 2017. We restrict the estimation sample to working-age adults, who are 16 years old or older, and exclude households where all members are younger. We also exclude households with an unusually large number of members (e.g., having more than five adults and three children).<sup>13</sup>

> 12 See Dang _et al._ (2019) for more discussion on these measures of mobility.

> 13 Such households represent less than 3% of the data. See Appendix A, Table A.3 for the distribution of household types. But we offer estimates using the whole unrestricted sample in Table 5. The results suggest that the scale parameters for children are lower when using a pooled model and even negative (but insignificant) when using fixed effect ordered logit. At the same time, the adult scale parameter is robust to using an unrestricted sample.

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Our outcome variable of interest, subjective wealth, captures individual responses to the following question on a scale ranging from one to nine: “Please imagine a nine-step ladder where on the bottom, the first step, stand the poorest people, and on the highest step, the ninth, stand the rich. On which step of the nine steps are you personally standing today?” We plot the distribution of this variable in Figure A.1 in Appendix A, which resembles a somewhat bell-shaped distribution.<sup>14</sup> There is also a reasonable degree of churning over time, with only about 40% of those who score in the range 3 to 5 keeping the same score for the next period. There are in total 44,010 individuals with 254,822 observations. We also offer robustness checks on our estimates by analyzing two other questions in the RLMS asking about satisfaction with life and personal economic conditions.

Our measure of income is the household’s total monetary income, which is temporarily deflated and adjusted for regional differences. To reduce the effects of outliers, we trim one-quarter of a percent of the data at both the top and the bottom of the income distribution and only keep individuals with a positive income level. For the other control variables, we include in all models: individual’s age (in groups), education level, marital status, employment status, health status, dummy variables indicating whether there are other household members with poor health, and per capita living space.<sup>15</sup> To estimate the pooled regressions, we additionally include individuals’ gender, nationality, and an extended set of regional variables. Table A.2 in Appendix A provides the summary statistics for the control variables.

> 14 Since responses with a score of eight or nine account for less than 1% of the sample, we combine these in one group. But we also estimate scale parameters without this aggregation and obtain similar results (results available upon request).

> 15 Frijters and Beatton (2012) show that age effects are better captured with more flexible forms (such as using 5-year age groups) rather than with age and age squared. But we also implement robustness checks with age and age squared and obtain similar results. Since unemployment and health variables may be considered endogenous variables (e.g., Oswald and Powdthavee, 2008; Kassenboehmer and Haisken‐De New, 2019), we re-estimate our scale parameters without these variables and obtain similar results.

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# **5. Estimation Results**

# **5.1. Scale Parameters**

We provide in Table 1 estimates for the equivalence weights of adults and children, using three models: the pooled ordered logit (POL), the linear fixed-effects (FE OLS), and our preferred BUC model. Compared to the FE OLS model, the number of individuals in the BUC model decreases by almost 13,000, since these individuals were observed only once in the RLMS, or their subjective welfare levels did not change during the period of study. In all three model specifications, the estimated parameters � and � have the expected signs and are both statistically significant, � aa although the statistical significance for ββee ββbb is slightly weaker at the 6 percent level for the BUC model. � is positive, indicating that households with a higher number of children need more ββbb resources and bear higher costs. ββbb Using the estimates from Table 1, Table 2 calculates the equivalence scales. Estimates based on the POL model yield 0.6 for the adult parameter and 0.08 for the child parameter _b_ , aa suggesting that the overall elasticity is higher for adding another adult than a child to a two-adult ee household. Controlling for unobserved individual heterogeneity in the panel models reduces by about one-third both the estimated equivalence scale parameter for adults (from 0.6 to 0.4) and for children (from 0.08 to 0.05). The scale elasticities estimated from the FE OLS and the BUC models are nearly the same, but we switch to presenting results using the BUC model in the subsequent discussion.<sup>16</sup> Our estimates suggest a larger scale impact for children on household income in Russia than in Germany and Switzerland (Schwarze, 2003; Falter 2006; Borah _et al._ , 2018), which can be

> 16 This result is consistent with that of Ferrer-i-Carbonell and Frijters (2004), who find little difference in estimates for the determinants of happiness in the FE Ordered Logit and FE OLS models, and with that of Riedl and Geishecker (2014) who show that linear and ordered fixed effect models offer similar estimates for the relative size of parameters.

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explained by more generous transfers to households with children in Russia. At the same time, our results are consistent with those for Germany and Switzerland in terms of the smaller effect of additional children compared to additional adults.

Figure 2 compares our preferred BUC estimated scales with some other common scales, including the simple per-capita adjustment, the square-root adjustment, the OECD scales, and the poverty line scale , each normalized to a single adult.<sup>17</sup> For each additional adult (or child), while the per capita and OECD scales display a constant marginal cost, our estimated scales, as well as the square-root scale, have a decreasing marginal cost. Compared to our estimated scales, all the other scales overestimate the weights for either an additional adult or an additional child. Interestingly, our estimated scales also provide lower elasticities than the equivalence scale embedded in the official poverty line for Russia, particularly for large-size households.

# **5.2. Adjusted Poverty Lines**

A natural question arises. What are the implications of these decreasing marginal costs for both adults and children for poverty measurement? We present in Table 3 our proposed populationweighted poverty lines for different family types, based on the estimated parameters of equivalence scales, and compare them with the official poverty thresholds employed by Rosstat. Our absolute poverty lines are derived from Rosstat’s official poverty thresholds for different age groups. Our relative poverty lines are computed as two-thirds of the median income per adult equivalent for each household type, using the parameters that account for differences in economies of scale and composition in the household. To make Table 3 easier to read, we leave out the standard errors (see Appendix A, Table A.5 for the full results).

> 17 We offer a comparison of our results with those in studies for Germany and Switzerland that use similar estimation methods in Appendix A, Table A.4.

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Table 3 suggests that our proposed poverty lines for households, in both absolute and relative terms, are generally much lower than the official poverty thresholds for the country. In particular, for our absolute poverty lines, the official poverty threshold ranges from 50 percent (for a twoadult household) to 160 percent higher (for a five-adult-no-children household) for households without any children. It ranges from 160 percent (for a one-adult-one-child household) to more than 200 percent (for a five-adult-one-child household) higher for households with children. The corresponding differences for our relative poverty lines are less, but are still considerable. The official poverty thresholds are from about 20 percent to 90 percent higher and 40 percent to 100 percent higher respectively for households without children and households with children.

We provide in Appendix A, Figure A.2 the poverty rates that corresponds to the official poverty line adjusted with the estimated equivalence scales in Table 2. Consistent with our previous discussion, the revised poverty rates based on the estimated equivalence scales are lower than the official poverty rates.

# **5.3. Poverty and Income Dynamics**

We start by examining in Figure 3 the extent to which the (headcount) poverty rate for Russia can be affected by the scale parameters. Again, the values of 1 and 0.5 for respectively aa correspond to the per capita scale and square root scale. The value of 0.1 for ee indicates an aa extremely large effect of household size. When _b_ increases from 0 to 0.1, it is a situation where for ee the same household size, households with children have a lower equivalence scale elasticity (i.e., a higher economy of size) than households without children. We also examine poverty using either the absolute poverty line (Panel A) or the relative poverty line (Panel B).

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Since the relative poverty line is adjusted to scaling by construction, it unsurprisingly provides the opposite scaling effects compared to the absolute poverty line.<sup>18</sup> Yet, Figure 3, Panel A shows that the poverty rate using the absolute poverty line can decrease by 9 to 15 percentage points (from 12 or 18 percent to 3 percent) if decreases from 1 to 0.5, depending on the child parameter aa values. The poverty rate subsequently remains almost the same, and decreases by one to two ee percentage points if decreases from 0.5 to 0.1. Figure 3, Panel B displays the opposite results aa where the poverty rate using the relative poverty line increases slightly by at most four percentage ee points if decreases from 1 to 0.5, again depending on the child parameter values. It then aa increases faster by four percentage points if ee decreases from 0.5 to 0.1. On the other hand, aa poverty is less sensitive to the choice of the child discount factor. It varies by at most 6 and 2 ee percentage points respectively for the absolute poverty line and the relative poverty line, when the child scale factor is varied from 0 to 0.1 and keeping fixed.<sup>19</sup> aa A natural question then arises. Are households with children always richer than those without ee children? The answer turns out to depend on the specific equivalence scales that are employed. For example, Table A.6 in Appendix A shows that for the same child scale parameter of 0.1, households with children are less poor if the adult scale parameter falls in the range [0.1, 0.5], but are poorer if the adult scale parameter falls in the range [0.6, 1]. Clearly, selecting a larger child discount factor, say at 0.1, will result in households with children being less poor than households with children for most values of the adult scale parameter. But still, if we set the latter at 1, households with children are poorer.

> 18 When we make scale adjustments for income, this results in changes to the population distribution of income and the relative poverty line. For example, most European countries set their relative poverty line at 60% of the national median equivalized disposable income.

> 19 We employ the range of [0, 0.1] for the child scale parameter since it is observed to be less than 0.1 in previous studies. For example, the scale elasticity for each additional child aged between 15 and 17 years was estimated to be 0.086 for Switzerland (Falter, 2006).

16
We turn next to examining poverty duration, which is defined as the average number of consecutive survey years (rounds) an individual spends in poverty. Figure 3, Panels C and D produce qualitatively similar results. Poverty duration is sensitive to changes in , and ranges aa from 1.8 to 2.6 years and from 2 to 2.7 years respectively with the absolute poverty line and the ee relative poverty line. But poverty duration is less sensitive to child scaling and varies by less than 0.2 year for both the absolute and the relative poverty lines.

We provide in Table 4 transient and chronic poverty estimates using Jalan and Ravallion’s (2000) method for three common poverty measures: the headcount poverty rate, the poverty gap index, and the squared poverty gap index. Table 4 shows that the shares of chronic poverty of total poverty are positively related to the adult scale parameter, regardless of the poverty measures we use. For example, for headcount poverty, the share of chronic poverty decreases by almost 10 percentage points when increases from 0.3 to 0.7. For the poverty gap and squared poverty gap, aa the corresponding figures are a-7-percentage-point and a 5-percentage-point increase. We plot the ee alternative chronic poverty measures (Foster, 2009; Duclos et al., 2010) against the scale factors in Appendix A, Figure A.3, which also shows that these measures are more sensitive to scale adjustments for adults than for children.

Figure 4 examines the relationship between scale parameters and unconditional income mobility (Panel A) and conditional income mobility (Panel B) (see Appendix A, Figure A.4 for the corresponding three-dimensional graphs). Three possible scenarios can happen with income mobility: more upward mobility (as represented by the area in orange), more downward mobility (as represented by the area in purple), and a mixed situation where neither upward mobility or downward mobility dominates (as represented by the gray area in between the two colors above). Interestingly, the selection of specific scale parameters can even change estimation results for 17
mobility. In particular, when income is measured on a per capita basis ( =1), there is always more aa upward unconditional mobility, regardless of the (different values for the) child parameter (Panel ee A). There is also mostly more upward conditional mobility, except for when the child parameter falls in the interval [0.09, 0.1] (Panel B). Yet when income is measured on a square-root scale, we have more upward unconditional mobility when the child parameter ranges from 0 to 0.03, a mixed situation when the child parameter ranges from 0.03 to 0.08, and even more downward mobility for the rest of the child parameter values. For conditional mobility, the square-root scale results in more downward mobility, for all values of the child parameter (Panel B). These results further emphasize the important role that equivalence scales have in determining estimation results with income dynamics.

# **6. Robustness Checks and Further Extensions**

# **6.1. Robustness Checks**

In addition, we examine a number of other robustness checks and extensions, which include income expectations, different reference groups, other satisfaction variables as dependent variables, measurement error in incomes, and no sample restrictions. We briefly summarize the results below.

Changes in household size or structure are typically expected and may affect subjective wellbeing well before their actual realization. We control for income expectations in the ( _t-1)_ period and find that this does not affect the estimates of baseline elasticity but slightly increases the child scale parameter up to 0.08 in the pooled model (Table 5, row 1).<sup>20</sup>

> 20 We analyze the answer to the following question in the RLMS “ _Do you think that in the next 12 months you and your family will live better than today or worse?_ ” The regression results are shown in Appendix A, Table A.7.

18
Relative income rather than total income may affect satisfaction, and if ignored, may result in biased estimates (Borah _et al._ , 2018). We include dummy variables to indicate the relative position of the household in the reference group`s distribution of household income quartiles (Appendix A, Table A.8). The reference group is determined for each year and consists of individuals living in households with a similar size in the same primary sampling units. To ensure stability, we only consider the number of households in the reference group as having 10 or more households. We report estimates of the scale parameters for the POL model only, since the variable used to define the reference groups is largely time-invariant, especially at the primary sampling unit level. Controlling for the reference group decreases the child scale parameter to 0.05 in the POL model but does not change the baseline elasticity. More importantly, we still obtain the earlier result that an additional child has a smaller effect compared to an additional adult (Table 5, row 2).<sup>21</sup> We also analyze the other satisfaction variables in the RLMS as alternative dependent variables for the subjective wealth variables, which are satisfaction with one’s life and satisfaction with one’s economic conditions. The estimated coefficients on household income and household size are still statistically significant as expected (Appendix A, Table A.9). To save space, we only report the scale parameters derived from the regressions for life satisfaction (Table 5, row 3). The estimation results of the BUC model are robust with the adult scale parameter of about 0.6 and child scale parameter about 0.04.<sup>22</sup> As a check on the total household income variable, we generate a new total household income by summing all the net incomes reported by household members (Appendix A, Table A.10). Yet, the estimated scale parameters of 0.3 for adults and 0.06 for children obtained from the BUC

> 21 The full regression results are shown in Table 1.

> 22 The adult scale parameter is still high when using satisfaction with economic conditions (0.8), but the child scale parameter decreases to 0.02.  The POL model also similarly provides a higher elasticity for adults (0.8), as well as for children (0.1) (Table 5, row 3).

19
models are close to our estimation results (Table 5, row 4). We use the unrestricted sample containing households with more than five adults and three children and estimate our main regressions. Estimation results for children are no longer statistically significant for the BUC model, and are only statistically significant in the POL model (Appendix A, Table A.11). At the same time, the estimates for the adult scale parameter remain similar at about 0.6 (Table 5, row 5).

# **6.2. Role of Pensioners**

Our earlier analysis has focused on household sizes and children, but has not discussed the impacts of elderly pensioners on the total household income. Pensioners may have disability or health issues and thus can impose significant costs on the household. On the other side, pensioners often consume less than a working-age adult and can contribute their pension salary to the household income. Our estimates from the RLMS suggest that the share of individuals (in total population) who received any pension in the past month hovers around 30 percent over the period 1994-2017. The majority of these pensioners (more than 70%) receive retirement or old-age pensions.

20
Although the interaction term for the household size and the number of pensioners is not statistically significant in both the pooled and BUC regressions (Appendix A, Table A.12), the total effect of pensioners is statistically significant and positive in the BUC model (Appendix A, Table A.13). But the inclusion of pensioners does not change the estimated scale parameters significantly: the adult scale parameter still varies between 0.4-0.6 and the child scale parameter is about 0.05-0.06 (Table 5, row 6).

# **6.3. Alternative Functional Form**

As an alternative to Equation (1), we can estimate a non-parametrical function recently proposed by Biewen and Juhasz (2017) as follows The estimated parameters for this scale are given in Appendix A, Table A.14. The table shows that the “non-parametric” scales for household types are smaller than those estimated using the parametric functional form as in Equation (1). The estimated equivalence weight of a second adult is 24 percent of the first adult, and the estimated equivalence weight of a child is 13 percent, or about half of the second adult. The child scale parameter is similar to our BUC estimates, and also to those obtained by Biewen and Juhasz (2017) for Germany.

# **7. Conclusion**

We estimate equivalence scales using unique subjective wealth data from Russia, and apply these scale adjustments to examine new poverty lines as well as the sensitivity of poverty dynamics. Our findings suggest that the country’s official poverty threshold ranges from 50 21
percent (for a two-adult household) to more than 200 percent (for a five-adult-one-child household) higher than our estimated poverty lines. The poverty rate varies for different adult scale parameters, but less so for children. The shares of chronic poverty of total poverty, defined against an absolute poverty line, are positively related to the adult scale parameter, regardless of the poverty measure. More interestingly, income mobility could be classified as either upward or downward depending on the specific scale parameters that are employed. Our results are robust to different measures of poverty, income expectations, reference groups, functional forms, and various other specifications. There is significant heterogeneity in terms of economic growth and demographic composition among the regions of Russia, which is caused by geographical differences in relative prices and consumption preferences. Since the RLMS data are, unfortunately, not representive at the regional level, we are unable to offer this analysis. But given a larger panel survey that is representative at the regional level, a promising direction for further research is to apply our scale adjustments to better understand the effects of equivalence of scale on the composition of poverty and poverty dynamics. Such knowledge is essential for regional poverty comparisons and the development of well-targeted policy interventions.

22
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26
**Table 1. Detailed regression results, RLMS 1994-2017**

|**Variables**<br>|**Pooled OL**|**FE OLS**|**BUC**|
|---|---|---|---|
|Ln household income (β)<br>|0.655***<br>_(0.011)_|0.249***<br>_(0.007)_|0.412***<br>_(0.079)_|
|<br>|-0.417***|-0.100***|-0.167***|
|Ln household size (−βea)<br>|_(0.023)_<br>|_(0.016)_<br>|_(0.034)_<br>|
||0.051***|0.012**|0.020*|
|Children# Ln household size (βb)|<br>_(0.008)_<br>|<br>_(0.005)_<br>|<br>_(0.010)_<br>|
||1.027***|0.404***|0.668***|
|Age 16-20|_(0.031)_|_(0.029)_|_(0.077)_|
|Ae 21-30|0.386***|0.178***|0.296***|
|g|_(0.021)_|_(0.021)_|_(0.057)_|
|A 3140|0.188***|0.062***|0.101***|
|ge -|_(0.019)_|_(0.014)_|_(0.036)_|
||-0.092***|-0.004|-0.006|
|Age 51-60|_(0.019)_|_(0.013)_|_(0.039)_|
||-0.009|0.057***|0.097|
|Age 61-70|_(0.025)_|_(0.019)_|_(0.065)_|
||0.103***|0.094***|0.159*|
|Age 71-80|_(0.028)_<br>|_(0.023)_<br>|_(0.083)_<br>|
||0.399***|0.330***|0.542***|
|Age 80+|_(0.038)_|_(0.030)_|_(0.086)_|
|Female|-0.027*|||
||_(0.014)_|||
|Russian nationality|-0.262***<br>_(0.024)_|||
||0.193***|-0.010|-0.014|
|Complete secondary|_0019_|_0012_|_0041_|
||_(.)_|_(.)_|_(.)_|
||0.298***|-0.043***|-0.071|
|Secondary + vocational|_(0.021)_|_(0.016)_|_(0.056)_|
||0.446***|-0.004|0.003|
|University and higher|_(0.023)_|_(0.022)_|_(0.082)_|
||-0.260***|-0.029*|-0.048|
|Single|_(0.023)_|_(0.017)_|_(0.037)_|
||-0333***|-0156***|-0263***|
|Divorced/widowed/separated|.<br>_(0.019)_|.<br>_(0.013)_|.<br>_(0.040)_|
|Unemloed/ot of labor force|-0.275***|-0.162***|-0.269***|
|pyu|_(0.015)_|_(0.009)_|_(0.030)_|
||-0.136***|-0.047***|-0.080***|
|Bad health|_(0.010)_|_(0.006)_|_(0.012)_|
||-0.075***|-0.013*|-0.022*|
|Other members with bad health|_(0.011)_|_(0.007)_|_(0.012)_|
||0.000|0.001|0.002|
|Log of per capita living space||||
||_(0.002)_|_(0.001)_|_(0.002)_|
|_Number of observations_|237,395|240,640|712,448|
|<br>_Log pseudolikelihood_|-403,224|-346,509|-263,848|
|_Number of individuals_|42,326|42,894|30,058|
|_Pseudo-R squared_|0.043|0.036|0.0285|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering (i.e., at the individual for the POL model and at the household-wave level for the FE OLS and BUC models). All regressions include year fixed effects, pooled model includes regional fixed effects (not reported).

*** p<0.01, ** p<0.05, * p<0.1

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# **Table 2. Scale elasticity parameters, RLMS 1994-2017**

||**Dependent variable:****_subjective wea_**|**_lth_**|
|---|---|---|
|**Scale parameters**<br>|**Pooled Ordered Logit**<br>**FE OLS**|**BUC**|
|Baseline elasticity<br>|0.636***<br>0.399***|0.407***|
|ea= βea/β<br>|_(0.032)_<br>_(0.060)_|_(0.088)_|
|<br>Additional child<br> <br>|0.078***<br>0.050**|0.048*|
|<br>b=βb/β<br>|_(0.012)_<br>_(0.021)_|_(0.026)_|
|<br>**_Overall elasticity _**e<br>**Note:**Standard errors in p<br>education level marital|0.636-0.078*_k_<br>0.399-0.050*_k_<br>arentheses are calculated using delta-method. All regressi<br>tatus employment status respondent`s poor health du|0.407-0.048*_k_<br>ons include age groups,<br>mmy whether there are|

**Note:** Standard errors in parentheses are calculated using delta-method. All regressions include age groups, education level, marital status, employment status, respondent`s poor health, dummy whether there are other household members in poor health, dummy indicating whether the person was employed at survey time and per capita living space and time effects as additional variables. Pooled model additionally includes gender, nationality and regional state effects.

28
**Table 3. Alternative poverty thresholds by household size in 2017 (in rubles per month)**

|**Household Type**|**Estimated with**<br>|**absolute line**<br>|**Estimated with**<br>|**relative line**<br>|**Official**|
|---|---|---|---|---|---|
||**Pooled OL **|**BUC**|**Pooled OL **|**BUC**||
||_Househo_|_lds without chi_|_ldren_|||
|One adult, no children|9,607|9,607|10,800|10,800|9,607|
|Two adults, no children|14,891|12,777|13,913|16,306|19,214|
|Three adults, no children|19,310|14,987|15,931|20,488|28,821|
|Four adults, no children|23,153|16,908|17,520|24,066|38,428|
|Five adults, no children|26,707|18,542|17,397|25,150|48,035|
||_House_|_hold with child_|_ren_|||
|One adult, one child|14,122|12,297|12,226|14,035|19,532|
|Two adults, one child|17,773|14,218|14,973|18,632|29,139|
|Two adults, two children|18,734|14,795|15,422|19,493|39,064|
|Three adults, one child|20,847|15,755|15,225|20,062|38,746|
|Three adults, two children|20,847|15,852|18,885|24,788|48,671|
|Four adults, one child|23,537|17,100|20,098|27,685|48,353|
|Four adults, two children|22,673|16,812|20,311|27,494|58,278|
|Five adults, one child|26,131|18,253|20,201|28,855|57,960|

**Note:** Population weights are applied. Standard errors for poverty rates are adjusted for complex survey design. Poverty line for reference “one adult” is defined as an average of minimum subsistence levels for working-age individual and pensioners in 2017. The level of absolute poverty line is 10899 rubles per month for working-age individual, is 8315 rubles per month for pensioner and is 9925 rubles per month for child in 2017. Relative poverty line is set on 60% of household size-weighted median equivalized income for each household type using RLMS data in 2017. Poverty lines of reference adult are adjusted with weights in Table 2 using BUC model (where baseline elasticity equals 0.407 and every child has a weight 0.048) and using Pooled Ordered Logit model (where baseline elasticity equals 0.636 and every child has a weight 0.078).

29
# **Table 4. Chronic and transient poverty by adult scale factors, Jalan-Ravallion**

# **decomposition, RLMS 1994-2017**

||<br>**E**<br>|**quivalent in**<br>|**come is com**<br>|**puted using**<br>||
|---|---|---|---|---|---|
||ea**_=0.3_**|ea**_=0.4_**|ea**_=0.5_**|ea=**_0.6_**|ea**_=0.7_**|
|**_Headcount Poverty_**||||||
|Total Poverty|0.085|0.1|0.119|0.142|0.17|
|Transient Poverty|0.036|0.041|0.045|0.05|0.054|
|Chronic Poverty|0.049|0.059|0.074|0.092|0.115|
|_Share of chronic poverty (%)_|_57.3_|_59.4_|_62.1_|_64.9_|_67.9_|
|**_Poverty Gap_**||||||
|Total Poverty|0.03|0.035|0.042|0.05|0.06|
|Transient Poverty|0.015|0.017|0.02|0.023|0.026|
|Chronic Poverty|0.015|0.018|0.022|0.027|0.035|
|_Share of chronic poverty (%)_|_49.6_|_50.7_|_52.3_|_54.5_|_57.2_|
|**_Squared Poverty Gap_**||||||
|Total Poverty|0.016|0.019|0.022|0.026|0.032|
|Transient Poverty|0.009|0.01|0.012|0.014|0.016|
|Chronic Poverty|0.007|0.009|0.01|0.013|0.016|
|_Share of chronicpoverty (%)_|_45.6_|_46.1_|_47.1_|_48.5_|_50.5_|

**Note:** Absolute poverty line is defined as a minimum regional subsistence level per person for each year. Both the poverty thresholds and household income are converted to constant 2011 rubles using regional CPI indices provided by the Rosstat. The child scale parameter is set at 0.04

30
**Table 5. The effect of alternative specifications on scale parameters estimates, RLMS 19942017**

|||**Poole**|**d OL**|**BUC**||
|---|---|---|---|---|---|
|**Sen**|**sitivity scenarios**|Baseline<br>elasticity|Additional<br>child|Baseline elasticity|Additional<br>child|
|**_1_**|Exectations|0.649***|0.080***|0.410***|0.050*|
||p|_(0.03)_|_(0.01)_|_(0.09)_|_(0.03)_|
|**_2_**|Rf|0.497***|0.057*|||
||eerence group|_(0.06)_|_(0.02)_|||
|**_3_**|Life satisfaction|0.762***|0.117***|0.659***|0.043*|
|||_(0.03)_|_(0.01)_|_(0.11)_|_(0.03)_|
|**_4_**|Measurement error|0.571***|0.089***|0.342***|0.056*|
|||_(0.04)_|_(0.01)_|_(0.10)_|_(0.03)_|
|**_5_**|Utitd l|0.577***|0.020*|0.306***|-0.013|
||nresrce sampe|_(0.03)_|_(0.01)_|_(0.09)_|_(0.02)_|
|**_6_**|Pensioners|0.560***|0.064***|0.352*|0.046*|
|||_(0.04)_|_(0.01)_|_(0.15)_|_(0.03)_|

**Note:** Standard errors in parentheses are calculated using delta-method. All regressions include the same controls as in Table 1.

31
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# **Figure 2. Comparison of different equivalence scales**

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# Headcount Poverty Rate

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**Figure 4. Scale Factors and Income Mobility, RLMS 1994-2017**

<!-- Start of picture text --> 1 1<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 More upward mobility .2<br>Mixed<br>.1 More downward mobility .1<br>0 .01 .02 .03 .04 .05 .06 .07 .08 .09 .1 0 .01 .02 .03 .04 .05 .06 .07 .08 .09 .1<br>Child parameter b Child parameter b<br>Adult parameter e Adult parameter e<br><!-- End of picture text --> 35
# **Appendix A: Additional Tables and Figures**

# **Table A.1: Overview of subjective scales estimated from panel data**

|**Author**|**Data**|**Welfare indicator**|**Subsample**|**Specification**|<br>**Weight give**|<br>**n to the additio**<br>**member**|<br>**nal household**|
|---|---|---|---|---|---|---|---|
||||||**2nd adult (1st**<br>**adult = 1)**|**1st child (in**<br>**2/1 adult hh)**|**2nd child (in**<br>**2/1 adult hh)**|
|||Stifti ith lif/||Pooled ordered logit|0,5|0.23/0.34|0.15/0.23|
|**Charlier (2002)***|GSOEP, 1984–91|asacon w e<br>Satisfaction with hh income|Sample of household heads|FE ordered logit(Das and van Soest(1999)approach)|0,43|0.20/0.28|0.10/0.16|
|||||RE ordered logit|0,5|0.26/0.39|0.19/0.28|
|**Sh 2003**|GSOEP 199299|Stifti ith hh i|Respondents who answered|Pooled ordered logit|0,34|0.17/0.30|0.08/0.14|
|**cwarze ()**|, –|asacon w ncome|at least twice are included|FE binarylogit|0,28|0.13/0.24|0.06/0.11|
|||||OLS|0,43|0.25/0.39|0.14/0.22|
|**Flt 2006****|SHP 19992002|Satisfaction with hh income|Households who answered at|Orderelprobit<br>|0,43|0.26/0.4|0.15/0.23|
|**aer ()**|, -||least twice are included|FE linear model|0,48|0.28/0.43|0.14/0.24|
|||Minimum IncomeQuestion||FE linear model|0,07|0.09/0.1|0.11/0.11|
|||Self-assessed financial|Individuals 18-80 yo<br>who are household heads or|FE ordered logit (Baetschmann et al. (2015) approach), men|0,15|1.12/1.64|0.68/1.22|
|**Bollinger et al.(2012)**|BHPS, 1991-2008|situation|the partner of the head with<br>or without minor children|FE ordered logit (Baetschmann et al. (2015) approach),<br>women|0,31|1.17/1.52|0.41/0.78|
|**Biewen and Juhasz**<br>**(2017)*****|GSOEP, 1999–2009|Satisfaction with hh income|Individuals≥17 yo living in<br>households with less than 6<br>members|Nonlinear FE ordered logit (based on Baetschmann et al.<br>(2015))|0,35|0,13|0,13|
||||Individuals≥18 yo, one- or|Pooled ordered logit|0.31-0.36|0.18-0.29/<br>0.29-0.37|0.10-0.27/<br>0.16-0.30|
|**Borah et al. (2018)******|GSOEP, 1984-2013|Satisfaction with hh income|two-adult-households with or|Nonlinear least squares|0.31-0.25|0.12-0.31|0.12-0.31|
||||without minor children|FE ordered logit (Baetschmann et al. (2015) approach)|0.26-0.15|0.16-0.13/<br>0.24-0.17|0.10-0.12/<br>0.15-0.14|

**Note:** GSOEP - German Socio-Economic Panel; SHP - Swiss Household Panel; BHPS - British Household Panel Survey

*Equivalence weights reflect period-specific equivalence scales based on satisfaction with income in case when the first child is 12 years old and the second child is 6 years old.

**Equivalence weights refer to the model with control variables

***Equivalence weights refer to OECD-type scale

****Equivalence weights reflect the cases without and with reference effect measured with household Mincer equation

36
# **Table A.2: Summary statistics, RLMS 1994-2017 (237 395 obs)**

||**Mean**|**_SD_**|
|---|---|---|
|Subjective welfare|3.83|_1.5_|
|Log of household income|9.93|_0.9_|
|Household size|3.14|_1.3_|
|Number of adults<br>|2.59|_1.0_|
|Number of children|0.56|_0.8_|
|age16_20|0.07|_0.3_|
|age21_30|0.19|_0.4_|
|age31_40|0.19|_0.4_|
|age41_50|0.17|_0.4_|
|age51_60<br>|0.16<br>|_0.4_<br>|
|age61_70|0.12|_0.3_|
|age71_80|0.08|_0.3_|
|age80plus|0.03|_0.2_|
|Female|0.58|_0.5_|
|Russian nationality|0.87|_0.3_|
|Incomplete secondary|0.21|_0.4_|
|Complete secondary|0.32|_0.5_|
|Secondary + vocational|0.25|_0.4_|
|University and higher|0.22|_0.4_|
|Single|0.16|_0.4_|
|Married|0.63|_0.5_|
|Divorced/widowed/separated|0.21|_0.4_|
|Have poor health|0.40|_0.5_|
|Other household members in poor health|0.53|_0.5_|
|Employed|0.61|_0.5_|
|Unemployed/out of labour force|0.39|_0.5_|
|Log ofpercapita living space (sqm)|3.10|_3.1 _|

_Note_ : data are unweighted 37
**Table A.3: Distribution of household types, RLMS 1994-2017**

|**No of adults**|**No of children**|**Share of sample (%)**|
|---|---|---|
|_1_|_0_|9.36|
|_1_|_1_|1.18|
|_1_|_2_|0.29|
|_1_|_3_|0.03|
|_2_|_0_|23.99|
|_2_|_1_|12.07|
|_2_|_2_|6.01|
|_2_|_3_|0.81|
|_3_|_0_|15.24|
|_3_|_1_|8.54|
|_3_|_2_|2.47|
|_3_|_3_|0.42|
|_4_|_0_|7.60|
|_4_|_1_|4.52|
|_4_|_2_|1.64|
|_4_|_3_|0.34|
|_5_|_0_|2.57|
|_5_|_1_|1.94|
|_5_|_2_|0.75|
|_5_|_3_|0.22|
|**_Total_**||**_100_**|
|**_Number of_**|**_observations_**|**_260,133_**|

_Note_ : data are unweighted 38
# **Table A.4: Comparison of different equivalence scales**

|**W**|**ihts**|**Per**|**Square-**|**Modified**|**RUS**<br>**Povert**|**Est**|**imated s**|**cales**|**Sch**<br>**(2**|**warze**<br>**003)**|**Fa**<br>**(2**|**lter**<br>**006)**|**Borah**<br>**(20**|**_et al._**<br>**18)**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**g**|**it**|<br>**t**|**OECD***|**y**||**_FE_**|||**_FE_**||**_FE_**|||
|||**capa**|<br>**roo**||**Line****|**_POL_**|<br>**_OLS_**|**_BUC_**|**_POL_**|<br>**_BL***_**|<sup>**_POL_**</sup>|<br>**_OLS_**|**_POL_**|**_BUC_**|
||1|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|
||2|2.00|1.41|1.50|2.00|1.55|1.32|1.33|1.34|1.28|1.43|1.48|1.31|1.26|
|**_Adults_**|3|3.00|1.73|2.00|3.00|2.01|1.55|1.56|1.59|1.47|1.77|1.86|1.53|1.44|
||4|4.00|2.00|2.50|4.00|2.41|1.74|1.77|1.78|1.63|2.06|2.18|1.71|1.58|
||5|5.00|2.24|3.00|5.00|2.78|1.90|1.93|1.98|1.76|2.31|2.47|1.87|1.70|
|**_1 Adult_**|1 Child|2.00|1.41|1.30|1.90|1.47|1.27|1.28|1.30|1.24|1.40|1.43|1.29|1.24|
|**_2 Adlt_**|1 Child|3.00|1.73|1.80|2.90|1.85|1.47|1.48|1.52|1.41|1.70|1.78|1.49|1.41|
|**_us_**|2 Children|4.00|2.00|2.10|3.80|1.95|1.51|1.54|1.59|1.47|1.85|1.90|1.59|1.51|
|**_3 Adl_**|1 Child|4.00|2.00|2.30|3.90|2.17|1.62|1.64|1.69|1.55|1.95|2.04|1.65|1.55|
|**_uts_**|2Children|5.00|2.24|2.60|4.80|2.17|1.62|1.65|1.71|1.56|2.04|2.11|1.71|1.62|
|**_4 Adl_**|1 Child|5.00|2.24|2.80|4.90|2.45|1.75|1.78|1.84|1.66|2.17|2.28|1.79|1.66|
|**_uts_**|2Children|6.00|2.45|3.10|5.80|2.36|1.71|1.75|1.82|1.65|2.21|2.29|1.82|1.71|
|**_5 Adults_**|1 Child|6.00|2.45|3.30|5.90|2.72|1.87|1.90|1.97|1.76|2.37|2.51|1.91|1.76|

**Note:** Household types whose population share is at least 1%. Children are defined as individuals aged below 16 years (OECD and Rosstat def.) *First adult has weight 1.0, every further adult 0.5, children 0.3.

**Working-age adult has weight 1.0, pensioner 0.8, children 0.9 *** Fixed Effects Binary Logit

39
# **Table A.5. Alternative poverty thresholds and corresponding poverty rates for Russia by household size in 2017**

|**Hosehold Te**|**Poverty**|**Line (in ru**|**bles)**|**Poverty Hea**|**dcount (in**|**percent)**|
|---|---|---|---|---|---|---|
|**u yp**|**Pooled OL**|**BUC**|**Official**|**Pooled OL**|**BUC**|**Official**|
||<br>_Hous_|<br>_eholds with_|<br>_out children_|<br>66|<br>66|<br>66|
|One adult, no children|9,607|9,607|9,607|.<br>_(1.41)_|.<br>_(1.41)_|.<br>_(1.41)_|
||14,891|12,777||3.7|2.0|9.1|
|Two adults, no children|_(343)_|_(784)_|19,214|_(0.76)_|_(0.46)_|_(1.58)_|
|Three adults, no children|19,310<br>_(735)_|14,987<br>_(1,618)_|28,821|1.8<br>_(0.61)_|0.8<br>_(0.40)_|12.7<br>_(2.93)_|
||23,153|16,908||2.7|0.7|10.0|
|Four adults, no children|_(1,127)_|_(2,304)_|38,428|_(1.25)_|_(0.70)_|_(2.56)_|
|Five adults, no children|26,707<br>_(1,470)_|18,542<br>_(2,990)_|48,035|0.0|0.0|7.0<br>_(3.70)_|
||<br>_Ho_|<br>_usehold wit_|_h children_||||
|O dl hild|14,122|12,297|19532|8.7|7.2|27.5|
|ne aut, one c|_(196)_|_(539)_|,|_(3.21)_|_(2.97)_|_(6.45)_|
||17,773|14,218||2.7|0.9|16.9|
|Two adults, one child|_(392)_|_(1,029)_|29,139|_(1.14)_|_(0.52)_|_(2.91)_|
|dl hild|18,734|14,795|4|4.1|0.4|30.8|
|Two auts, two cren|_(196)_|_(784)_|39,06|_(1.10)_|_(0.39)_|_(4.47)_|
|Thr dlt n hild|20,847|15,755|38746|4.1|0.9|21.0|
|ee aus, oe c|_(588)_|_(1,519)_|,|_(1.47)_|_(0.60)_|_(3.49)_|
||20,847|15,852||5.6|1.5|32.0|
|Three adults, two children|_(245)_|_(980)_|48,671|_(2.61)_|_(1.48)_|_(6.41)_|
|Four adults, one child|23,537<br>_(784)_|17,100<br>_(1,912)_|48,353|1.0<br>_(1.02)_|1.0<br>_(1.02)_|14.7<br>_(3.74)_|
|Four adults, two children|<br>22,673<br>_(343)_|<br>16,812<br>_(1,127)_|58,278|<br>0.0|<br>0.0|<br>20.0<br>_(7.61)_|
|Five adults, one child|26,131<br>_(931)_|18,253<br>_(2,255)_|57,960|0.0|0.0|11.0<br>_(5.31)_|

**Note:** Population weights are applied. Standard errors for poverty rates are adjusted for complex survey design. Poverty line for reference “one adult” is defined as an average of minimum subsistence levels for working-age individual and pensioners in 2017. The level of absolute poverty line is 10899 rubles per month for working-age individual, is 8315 rubles per month for pensioner and is 9925 rubles per month for child in 2017. Absolute poverty line of reference adult is adjusted with weights in Table 2 using BUC model (where baseline elasticity equals 0.407 and every child has a weight 0.048) and using Pooled Ordered Logit model (where baseline elasticity equals 0.636 and every child has a weight 0.078).

40
# **Table A.6. Differences in Poverty Rates for Households with and without Children for Different Equivalence Scales, RLMS 1994-2017**

**Panel A. Headcount poverty in households with children (percent)**

|**Child scale**<br>|||||**Adult s**|**cale par**|**ameter e**||||
|---|---|---|---|---|---|---|---|---|---|---|
|**parameter**|||||||||||
|<br>**b**|**0.1**|**0.2**|**0.3**|**0.4**|**0.5**|**0.6**|**0.7**|**0.8**|**0.9**|**1**|
|**0.01**|0.4|0.8|1.2|1.7|2.8|5.0|7.5|10.9|15.9|23.1|
|**0.02**|0.3|0.7|1.1|1.5|2.5|4.8|6.6|9.9|15.1|21.5|
|**0.03**|0.2|0.6|1.0|1.4|2.2|4.1|6.1|9.4|13.9|19.7|
|**0.04**|0.2|0.6|1.0|1.4|2.1|3.6|5.5|8.7|13.0|18.6|
|**0.05**|0.2|0.6|1.0|1.4|2.0|3.1|5.2|8.1|11.9|17.3|
|**0.06**|0.2|0.4|0.9|1.2|1.8|3.0|5.0|7.5|10.9|16.6|
|**0.07**|0.2|0.3|0.8|1.1|1.6|2.7|4.6|6.8|10.2|15.5|
|**0.08**|0.2|0.3|0.8|1.1|1.6|2.6|4.3|6.2|9.8|14.1|
|**0.09**|0.2|0.3|0.8|1.1|1.6|2.4|4.1|6.1|9.4|13.0|
|**0.1**|0.2|0.3|0.8|1.0|1.4|2.2|3.7|5.6|8.7|12.5|

**Panel B. Headcount poverty in households without children (percent)**

|**Child scale**<br>||||**A**|**dult scal**|**e parame**|**ter e**||||
|---|---|---|---|---|---|---|---|---|---|---|
|**parameter**|||||||||||
|<br>**b**|**0.1**|**0.2**|**0.3**|**0.4**|**0.5**|**0.6**|**0.7**|**0.8**|**0.9**|**1**|
|**0.01**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.02**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.03**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.04**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.05**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.06**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.07**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.08**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.09**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.1**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|

**Panel C. Absolute difference in poverty headcount between households with and without children (percent)**

|**Child scale**||||**Ad**|**ult scale p**|**aramete**|**r e**||||
|---|---|---|---|---|---|---|---|---|---|---|
|**parameter**|||||||||||
|<br>**b**|**0.1**|**0.2**|**0.3**|**0.4**|**0.5**|**0.6**|**0.7**|**0.8**|**0.9**|**1**|
|**0.01**|-2.5|-2.3|-2.3|-2.2|-1.4|0.3|2.1|4.4|7.3|11.8|
|**0.02**|-2.5|-2.4|-2.3|-2.4|-1.7|0.1|1.2|3.4|6.5|10.1|
|**0.03**|-2.6|-2.5|-2.5|-2.5|-1.9|-0.7|0.7|3.0|5.3|8.4|
|**0.04**|-2.6|-2.5|-2.5|-2.5|-2.1|-1.2|0.2|2.2|4.4|7.2|
|**0.05**|-2.6|-2.6|-2.5|-2.5|-2.2|-1.6|-0.1|1.6|3.3|5.9|
|**0.06**|-2.6|-2.7|-2.6|-2.6|-2.4|-1.7|-0.4|1.0|2.3|5.2|
|**0.07**|-2.6|-2.8|-2.6|-2.8|-2.6|-2.0|-0.8|0.3|1.5|4.1|
|**0.08**|-2.6|-2.8|-2.6|-2.8|-2.6|-2.2|-1.1|-0.3|1.1|2.7|
|**0.09**|-2.6|-2.8|-2.6|-2.8|-2.6|-2.4|-1.3|-0.4|0.8|1.7|
|**0.1**|-2.6|-2.8|-2.6|-2.9|-2.7|-2.5|-1.7|-0.9|0.0|1.1|

**Note:** Panel C provides the absolute differences in the poverty rate for the same combination of child and adult scale parameters shown in Panels A and B. Absolute poverty line is defined as a minimum regional subsistence level per person for each year. Both the poverty thresholds and household income are converted to constant 2011 rubles using regional CPI indices provided by the Rosstat.

41
# **Table A.7. The effect of expectations, RLMS 1994-2017**

|_Main variables_<br>**Pooled Ordered Logit**|**BUC**|
|---|---|
|Log of household income<br>0.623***<br>_(0.011)_|0.406***<br>_(0.079)_|
|Log of household size<br>-0.404***<br>_(0.022)_|-0.166***<br>_(0.034)_|
|Children*Log of household size<br>0.050***<br>_(0.008)_|0.020**<br>_(0.010)_|
|_Expectations in T-1 period (base – “Nothing will change”)_<br>You will live worse<br>-0.509***<br>_(0.016)_<br>You will live better<br>0.448***<br>_(0.013)_|-0.139***<br>_(0.017)_<br>0.152***<br>_(0.014)_|
|_Number of observations_<br>237,395|712,448|
|_Log pseudolikelihood_<br>-400,970|-263,550|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering (i.e., at the individual for the POL model and at the household-wave level for the BUC models). All regressions include the same controls as in Table 1.

42
# **Table A.8. The effect of reference group, RLMS 1994-2017**

|_Main variables_|**Pooled Ordered Logit**|**BUC**|
|---|---|---|
|Lo of household income|0.484***|0.282|
|g|_(0.021)_|_(0.213)_|
|Log of household size|-0.241***<br>_(0.034)_|0.007<br>_(0.117)_|
|Children*Lo of household size|0.028**|0.003|
|g|_(0.011)_|_(0.022)_|
|_Relative income (base – 1_<sup>_st_</sup>_quartile)_|||
|2<sup>nd</sup>artile|0.161***|0.071|
|qu|_(0.021)_|_(0.084)_|
|3<sup>rd</sup>til|0.310***|0.203|
|quare|_(0.025)_|_(0.144)_|
|4<sup>th</sup>quartile|0.477***<br>_(0.033)_|0.323<br>_(0.234)_|
|_Number of observations_|140,026|366,511|
|_Log pseudolikelihood_|-237,167|-135,630|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering (i.e., at the individual for the POL model and at the household-wave level for the BUC models). All regressions include the same controls as in Table 1.

43
# **Table A.9. The effect of welfare definition, RLMS 1994-2017**

|_Dependent variable_|_Satisfaction wit_|_h life_|_Satisfaction with ec_<br>_conditions_|_onomic_|
|---|---|---|---|---|
|_Main variables_|**Pooled Ordered**<br>**Logit**|**BUC**|**Pooled Ordered**<br>**Logit**|**BUC**|
|L f hhld i|0.606***|0.431***|0.863***|0.719***|
|og o ouseo ncome|_(0.010)_|_(0.077)_|_(0.013)_|_(0.112)_|
|||||-|
|Log of household size|-0.462***|-0.284***|-0.790***|0.569***|
||_(0.022)_|_(0.035)_|_(0.025)_|_(0.046)_|
|Children*Log of household|0.071***|0.018*|0.069***|0.015|
|size|_(0.007)_|_(0.011)_|_(0.008)_|_(0.012)_|
|_Number of observations_|240,383|525,832|211,032|446,937|
|_Log pseudolikelihood_|-329,968|-196,405|-286,373|-169,660|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering (i.e., at the individual for the POL model and at the household-wave level for the BUC models). All regressions include the same controls as in Table 1.

44
# **Table A.10. The effect of measurement error, RLMS 1994-2017**

||**Pooled Ordered Logit**|**BUC**|
|---|---|---|
|Lo of household income|0.558***|0.372***|
|g|_(0.010)_|_(0.070)_|
|L f hhld i|-0.318***|-0.127***|
|og o ouseo sze|_(0.022)_|_(0.032)_|
|Children*Lo of hosehold size|0.050***|0.021**|
|g u|_(0.008)_|_(0.010)_|
|_Number of observations_|242,768|733,754|
|_Log pseudolikelihood_|-413,485|-271,759|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering (i.e., at the individual for the POL model and at the household-wave level for the BUC models). All regressions include the same controls as in Table 1.

45
# **Table A.11. The effect of sample restriction, RLMS 1994-2017**

|_Main variables_|**Pooled Ordered Logit**|**BUC**|
|---|---|---|
|Lo of household income|0.640***|0.396***|
|g|_(0.011)_|_(0.079)_|
|L f hhld i|-0.369***|-0.121***|
|og o ouseo sze|_(0.022)_|_(0.033)_|
|Children*Lo of hosehold size|0.013*|-0.005|
|g u|_(0.007)_|_(0.009)_|
|_Number of observations_|245,777|746,710|
|_Log pseudolikelihood_|-418,712|-276,200|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering (i.e., at the individual for the POL model and at the household-wave level for the BUC models). All regressions include the same controls as in Table 1.

46
# **Table A.12. The effect of pensioners, RLMS 1994-2017**

|_Main variables_|**Pooled Ordered Logit**|**BUC**|
|---|---|---|
|Lo of household income|0.657***|0.409***|
|g|_(0.011)_|_(0.079)_|
|L f hhld i|-0.368***|-0.144***|
|og o ouseo sze|_(0.029)_|_(0.043)_|
|Children*Lo of household size|0.042***|0.019*|
|g|_(0.008)_|_(0.011)_|
|Pi*L f hhld i|-0.022|-0.044|
|ensonersog o ouseo sze|_(0.023)_|_(0.051)_|
|Nb f i|-0.040|0.079|
|umer o pensoners|_(0.027)_|_(0.060)_|
|_Number of observations_|231,972|692,336|
|_Log pseudolikelihood_|-394,096|-256,312|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering (i.e., at the individual for the POL model and at the household-wave level for the BUC models). All regressions include the same controls as in Table 1.

47
# **Table A.13. Total effect of pensioners by household composition, RLMS 1994-2017**

|**Total number of hh members**|**Number of pensioners**|**Pooled Ordered Logit**|**BUC**|
|---|---|---|---|
|_2_|_1_|-0.056***|0.049*|
|||_(0.01)_|_(0.03)_|
|_3_|_1_|-0.065***|0.033*|
|||_(0.01)_|_(0.02)_|
|_3_|_2_|-0.129***|0.067*|
|||_(0.02)_|_(0.03)_|
|_4_|_1_|-0.071***|0.022|
|||_(0.01)_|_(0.02)_|
|_4_|_2_|-0.142***|0.043|
|||_(0.03)_|_(0.04)_|
|_5_|_1_|-0.076***|0.013|
|||_(0.02)_|_(0.03)_|
|_5_|_2_|-0.152***|0.026|
|||_(0.03)_|_(0.06)_|

**_Note:_** Standard errors in parentheses are calculated using delta-method

48
**Table A.14. Results for non-parametric scales, RLMS 1994-2017**

|**Parameter**|**Coefficient**|
|---|---|
|a1k1<br>a2k0|1.422***<br>_(0.233)_<br>1.243***<br>_(0.105)_|
|a2k1<br>a2k2|1.374***<br>_(0.146)_<br>1.259***<br>_(0.161)_|
|a3k0|1.454***<br>_(0.143)_|
|a3k1|1.704***<br>_(0.187)_|
|a4k0|1.731***<br>_(0.198)_|
|a4k1|1.836***<br>_(0.241)_|
|a5k0<br>a5k1|1.968***<br>_(0.312)_<br>1.836***<br>_(0.326)_|
|_Number of observations_|74,627|
|_Log pseudolikelihood_|-237,360|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering. All regressions include the same controls as in Table 1.

49
# **Figure A.1. Estimation sample distribution of subjective welfare variable, RLMS 1994-2017**

<!-- Start of picture text --> 1 2 3 4 5 6 7 8 9<br>.25<br>.2<br>.15<br>Density<br>.1<br>.05<br>0<br><!-- End of picture text --> 50
**Figure A.2. Scale Factors and Headcount Poverty Rate, RLMS 1994-2017**

<!-- Start of picture text --> 80<br>Per capita income<br>70 Equivalent income<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Headcount poverty (%)<br>1994 1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->

**Note:** Population weights are applied. Standard errors are adjusted for complex survey design. Poverty line is defined as a minimum regional subsistence level per person for each year provided by the Rosstat. Household incomes are adjusted with equivalence scale weights from Table A.7 using BUC model (where baseline elasticity equals 0.407 and every child has a weight 0.048). Both the poverty thresholds and household incomes are converted to constant December prices of 2011 using regional CPI indices provided by the Rosstat. Real values of household incomes are also adjusted for regional differences in the cost-ofliving.

51
# Panel A: Absolute Poverty Line

# Panel B: Relative Poverty Line

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# Panel A: Unconditional Mobility

# Panel B: Conditional Mobility

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