metadata: authors: 0: author: Pittau,M. Grazia
1: author: Zelli,Roberto
count: Kazakhstan
docna: 0: docna: Measuring the Middle Class in Kazakhstan : A
            Subjective Approach
docty: Policy Research Working Paper
subtopic: Inequality,Health Care Services Industry,Educational Sciences,Poverty Reduction Strategies,Economic Growth,Industrial Economics,Economic Theory & Research
teratopic: Industry,Education,Macroeconomics and Economic Growth,Poverty Reduction
abstracts: cdata!: This paper proposes a model-based
            approach to estimate income boundaries for identifying the
            middle class in Kazakhstan over 2003-15. The approach
            exploits the subjective evaluation of Kazakhstan households
            about their social status, relating self-declared social
            class membership to income. Income data come from the
            Kazakhstan Household Budget Survey, which also includes a
            specific module on quality of life and perceived social
            status. As social status is intrinsically an ordinal
            response, the paper estimates a proportional odds model with
            income as the key explanatory variable. Although other
            factors influence the self-perception of being in the middle
            class, income is by far the most important determinant.
            Benchmarking on 2013, the estimated middle class lower bound
            is $14 at 2011 purchasing power parity and the upper bound
            is $52. The Kazakhstan middle class has increased massively
            in size and income concentration. The increase is
            essentially due to a growth effect rather than a
            redistributive cause.
geo_reg_key: 517208,517191,82689,545134,517198
repnme: repnme: Measuring the Middle Class in Kazakhstan :
            A Subjective Approach
display_title: Measuring the Middle Class in
            Kazakhstan : A Subjective Approach
listing_relative_url: /research/2018/08/30374435/measuring-middle-class-kazakhstan-subjective-approach
topicv3: Economic Growth m1326241 471,Poverty m1326687 695,Education m1327048 470,Income Inequality m1503891 2668,Health m1504661 3480
publishtoextweb_dt: 2018-08-27T00:00:00Z
historic_topic: Industry,Education,Macroeconomics and Economic Growth,Poverty Reduction
no_of_pages: 28
author_keys: 0: author_key: 1365119
model_extractions: WPS8568 Policy Research Working Paper 8568 Measuring the Middle Class in Kazakhstan A Subjective Approach _M. Grazia Pittau Roberto Zelli_ Poverty and Equity Global Practice August 2018
Policy Research Working Paper 8568

# **Abstract**

This paper proposes a model-based approach to estimate income boundaries for identifying the middle class in Kazakhstan over 2003–15. The approach exploits the subjective evaluation of Kazakhstan households about their social status, relating self-declared social class membership to income. Income data come from the Kazakhstan Household Budget Survey, which also includes a specific module on quality of life and perceived social status. As social status is intrinsically an ordinal response, the paper estimates a proportional odds model with income as the key explanatory variable. Although other factors influence the self-perception of being in the middle class, income is by far the most important determinant. Benchmarking on 2013, the estimated middle class lower bound is $14 at 2011 purchasing power parity and the upper bound is $52. The Kazakhstan middle class has increased massively in size and income concentration. The increase is essentially due to a growth effect rather than a redistributive cause.

This paper is a product of the Poverty and Equity Global Practice. 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/research. The authors may be contacted at grazia.pittau@uniroma1.it and roberto.zelli@uniroma1.it.

_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
# Measuring the Middle Class in Kazakhstan: A Subjective Approach

M. Grazia Pittau and Roberto Zelli<sup>12</sup> JEL: D31; C25; I30.

Keywords: Middle class; Income distribution; Kazakhstan.

> 1 Affiliated with Sapienza University and the World Bank Poverty group.

> 2 This paper draws on the work of the Kazakhstan middle class analysis project led by Sarosh Sattar, task team leader. We are grateful to Nicholas T. Longford for invaluable help, especially in data processing. We would like to thank Francisco Ferreira and Maria Ana Lugo for their useful comments and suggestions. We also thank Sarosh Sattar, Luis Felipe López-Calva, William Hutchins Seitz and participants at the ECAPOV HBSS seminar in April 2017 at the World Bank, Washington DC. A special thank to Aibek Baibagysh Uulu for providing assistance and clarification when needed.
# **1. Introduction**

Kazakhstan has achieved rapid poverty reduction since independence. Based on the international poverty line of $1.90 a day at 2011 international prices, headcount poverty fell from 10.5 percent in 2001 to 0.04 percent in 2013.<sup>3</sup> However, the rapidity of the change has generated questions about what “poverty” and “middle class” mean.

An emerging middle class is critical because of its potential as an engine of growth (Easterly 2001). Historically, those in the ‘middle’ have vigorously accumulated both physical and human capital (OECD 2011a, 2011b). By driving consumption and domestic demand, consolidating Kazakhstan’s incipient middleincome group into a stable middle class would contribute to a solid foundation for economic progress. The expansion of the middle classes has also contributed to democratic movements and progressive but moderate political reforms, especially those that promote inclusive growth.

While most economists agree that middle class status is characterized by a relatively high income, there is no consensus on where to draw the line, since living above the poverty line does not necessarily ensure middle-class status.

The very concept of middle class has been extensively debated in the economic and sociological literature (for a review, see Atkinson and Brandolini 2013). Sociologists tend to identify the middle class in terms of its functional position in the society. Economists, however, tend to identify it in terms of thresholds in the income or consumption distribution. These thresholds can be either relative (e.g., percentiles or percentage of median income) or absolute (a certain amount of income per day or month).

In advanced economies, where the middle class usually comprises households in the middle of the income distribution, economic thresholds are identified using percentiles or values around the median income, such as 0.75 and 1.25 times the median (Pew Research Center 2016). In developing economies, scholars opt for absolute thresholds because median incomes do not necessarily identify middle class

> 3 World Development Indicators, World Bank (https://datacatalog.worldbank.org/dataset/worlddevelopment-indicators).

2
households in terms of living standards.

Recently, a “vulnerable” class, located between the poor and the middle class, has been defined (López-Calva and Ortiz-Juarez 2014). Vulnerability relates to the risk of falling into poverty over time due to adverse events. For instance, a vulnerable household may not have sufficient resources to continue to acquire all the necessities to maintain an adequate living standard over a reasonably long period. Even in countries like Kazakhstan where extreme poverty is very low, the presence of a large vulnerable group may prevent households with median income— those in the middle of the income distribution—from becoming middle class. Assessing vulnerability, Ferreira et al. (2013) used per capita income of US$10 to US$50 a day in 2005 PPP terms to define the middle class in Latin America and the Caribbean.

Can the US$10–US$50 range be exported and applied directly to Kazakhstan? While the range is solidly grounded for Latin America, it may not be appropriate elsewhere. For instance, the same method applied to Nigeria estimates the lower threshold at US$3 per capita a day (Corral, Molini, and Siwatu 2015).

For this reason, we have devised an approach to estimate country-specific thresholds for identifying the Kazakhstan middle class. Our method, which is based on an absolute income approach, estimates lower and upper bounds based on how Kazakhstan citizens perceive their own status relative to their reported income. One module of the 2013 Kazakhstan Household Survey (see Section 2) dealt directly with self-declared social class. Our analysis focuses on 2013 because the module appeared only in that round of the survey, but we have extended the analysis to 2014 and 2015 and have also looked back to 2003, 2006, and 2010 to better describe the profile of the middle class and analyze possible changes in the Kazakhstan middle class over a longer period.

In what follows, Section 2 discusses the Household Budget Survey of Kazakhstan. Section 3 details the econometrics method for estimating the lower and upper income bounds that define the middle class. Section 4 highlights the characteristics of the middle class, and Section 5 explores factors beyond income that could influence the self-perception of being middle class. After controlling for these factors, income remains the most important determinant of middle-class 3
status, giving support to the income-based approach. Section 6 reports the evolution in size and in income shares of the middle class between 2003 and 2015. Section 7 sets out the conclusions.

# **2. Data Issues**

This analysis is based on data from the Household Budget Survey of Kazakhstan (HBS) conducted periodically between 2003 and 2015 by the Statistics Agency of the Republic of Kazakhstan (www.stat.gov.kz) and harmonized by the World Bank Poverty and Equity Group for Europe and Central Asia (ECA). Survey samples were generally 12,000 households, except in 2010, when the sample was doubled to 24,000. Households are currently interviewed quarterly about sources of income and amounts received, but the evolution of the sampling design introduced inconsistencies into our analysis. For example, in earlier surveys a household provided data for only one quarter, so annual income is extrapolated by multiplying household income by four. Later, each household was contacted twice, in either quarters 1 and 2, or 3 and 4.

The households in the samples are associated with sampling weights. Sampling designs are summarized in Table 1. The surveys are stratified using a geoadministrative division of the country into 16 regions further divided into their urban and rural areas (see Table 2). The rural-urban subsample sizes, which were similar in the surveys for 2013 through 2015, roughly reflect the distribution of population in the region. Because the cities of Astana and Almaty are entirely urban areas, there are 30 rather than 32 strata. Within a stratum there are at most two distinct values of the sampling weight.

**Table 1: KHBS Survey Sampling Designs**

|Year|Households|Strata|Quarters|
|---|---|---|---|
|||(Regions)|per household|
|2003|12,000||4|
|2006|12,000|30(16)|1|
|2010|24,000|30(16)|2|
|2013|12,000|30(16)|4|
|2014|12,000|30(16)|4|

4
<u>30(16)</u> 2015 12,000 <u>4</u> A small number of data entries are missing. Because we are concerned mainly with household income, Table 3 summarizes the income data gaps related to households that are represented in only some quarters. Of course, a household may move out of the domain or the country or be dissolved by death, so some of the missing data cannot be regarded as nonresponse. In any case, because there are so few gaps, we make no provisions to address this issue. Similarly, we cannot establish whether some changes in the composition of the households are a result of a genuine change or nonresponse.

**Table 2: The Regions**

||**Sample Si**|**zes (2013 – 15)**||**Samp**|**le Sizes**|
|---|---|---|---|---|---|
|**Region**|**Rural**|**Urban**|**Region**|**Rural**|**Urban**|
|Akmola|480|360|Karaganda|360|600|
|Aktobe|480|360|Kostanay|450|360|
|Almaty|480|240|Kyzylorda|360|240|
|Almaty City|—|898|Mangystau|270|330|
|Astana City|—|660|North Kazakhstan|390|270|
|Atyrau|240|300|Pavlodar|480|360|
|East Kazakhstan|480|420|South Kazakhstan|480|300|
|Jambyl|420|270|West Kazakhstan|420|240|

**Table 3: Missing Household Income Data**

|||**Households**|**with data for**|||
|---|---|---|---|---|---|
||**1 quarter**|**2 quarters**|**3 quarters**|**4 quarters**|**All**|
|2003|_90_|_160_|_96_|11 645|11 991|
|2006|12 000|—|—|—|12 000|
|2010|_183_|23 816|—|—|23 999|
|2013|_45_|_107_|_49_|11 799|12 000|
|2014|_54_|_81_|_54_|11 809|11 998|
|2015|_123_|_93_|_56_|11 713|11 985|

_Note_ : Gap counts are in italics.

The income of a household consists of the total of the income of each 5
member from all sources. Sources are listed in the survey questionnaire. In all analyses, household incomes are annualized by aggregating the quarterly data and are in per capita terms. Incomes have been regionally deflated on the basis of the unit values of food reported in the survey.

# **3. Identifying the Middle Class in Kazakhstan**

## **3.1 The Absolute Income-based Approach**

The absolute approach identifies the middle class as those households with income or consumption within a specific range, fixing a lower threshold of particular income (e.g., $10 per household member) and a corresponding upper threshold (e.g., $50). Thresholds are usually set in purchasing power parity (PPP) terms for the sake of international comparison.

Since the welfare aggregate for official poverty in Kazakhstan is income (World Bank, Country Poverty Brief, October 2017), we focus on income aggregate.<sup>4</sup> The fundamental question is naturally how to set the thresholds; to some extent, the choices for the middle-class range are arbitrary. Banerjee and Duflo (2008) acknowledged use of an ad hoc range of values considering two groups of households: the lower middle class whose daily per capita expenditures valued at PPP are between $2 and $4, and the upper middle class between $6 and $10. Ravallion (2010) sets the lower bound of a “developing world middle class” at the global poverty line ($2 per day) and the higher bound ($13) at the U.S. poverty line. Milanovic and Yitzhaki (2002), in defining the global middle class, set the lower threshold equal to the mean earnings in Brazil ($12 per day) and choose the upper bound of $50 per day—the average income in Italy, the least wealthy G7 member.

Given the rate of absolute poverty in Kazakhstan, it is not appropriate to categorize anyone who is not poor as middle class, as Banerjee and Duflo (2008) and Ravaillon (2010) did for a group of developing countries. It is more appropriate to follow the vulnerability approach, which defines the middle class as those who are not vulnerable to falling into poverty in a few years’ time, as López-Calva and

> 4 On average, however, income and consumption are in balance. In 2013 the log-balances, defined as the logarithms of the ratios of consumption and income, have a mean of −0.0113, corresponding to consumption that is 1.13% lower than income, and standard deviation of 0.335. Details on the income and consumption distributions are available upon request.

6
Ortiz-Juarez (2014) and Ferreira et al. (2013) have done for Latin America and the Caribbean. López-Calva and Ortiz-Juarez (2014) set the lower bound of middle-class income in Latin America as the maximum income that ensures economic stability. Households were defined as economically stable and invulnerable only if the probability of their falling below the national poverty line within five years was 10 percent or less. The income level associated to the 10 percent probability was defined as the lower bound of the middle class.

Using longitudinal data in their study of Latin America and the Caribbean, Ferreira et al. (2013) established as the lower threshold of the middle class the value of US$10 a day in 2005 PPP terms. This estimation was corroborated by a subjective approach in which respondents were asked to report their social class; the majority of respondents with that income identified themselves as middle class rather than poor. The upper bound was established as $50 (2005 PPP international dollars) as a reasonable level that excludes the richest 2% of Latin American households.<sup>5</sup> Though the US$50 threshold is fairly arbitrary, it has since gained ‘authority’ by being widely adopted.

Although these fixed thresholds are in PPP terms, only to a limited extent can they be used to define the middle class in Kazakhstan because they have been attributed to countries at different stages of social and economic development. Country-specific thresholds are therefore preferable. However, in Kazakhstan the method proposed by López-Calva and Ortiz-Juarez (2014) cannot be applied directly for two reasons: (1) it would yield an extremely low probability of falling into poverty because of the very low poverty rate in Kazakhstan, and (2) household longitudinal data for at least four years are not available. In the next section, therefore, we explore a new methodology for estimating country-specific thresholds for identifying the middle class in Kazakhstan.

# **3.2 Estimation of the thresholds**

Our approach to estimating absolute thresholds for identifying the Kazakhstan middle class exploits individual perception of social status and relates self-declared social class to income. We follow a vulnerability-poverty approach by identifying a

> 5 The upper threshold lies in the 92nd percentile for Chile, the 97th for Mexico and the 98th for Peru.

7
vulnerable group between the poor and the middle class. Income thresholds are estimated using the _Quality of life of the population_ module in the 2013 survey in which respondents were asked to report their social class. Once the thresholds were estimated for 2013, we calibrated the corresponding lower and upper bounds for the years for which this information was not available.

The subjective module of the questionnaire asked heads of household to place themselves in ordered classes: Poor; Not poor, but not middle class; Middle class; Top middle class, and Rich<sup>6</sup> (see Table 4).

# **Table 4: Responses to the Question on Self-Perceived Social Status,** **<u>2013</u>**

|**What social group would you refer the household you head to?**|**Responses**<br>**(%)**|
|---|---|
|Low-income (do not have enough funds for food, clothes and footwear)|3.7|
|Not poor, but not middle-class (enough funds to buy food-products, clothes<br>and footwear, pay for housing and utility services, however we encounter<br>difficulties with purchase of durable goods)|49.8|
|Middle class (tier, level): we do not encounter any difficulties with purchase of<br>food-products, main non-food products and services, but have insufficient<br>funds to purchase additional dwelling (apartments, houses, summer house),<br>an expensive car and etc.|44.3|
|Top-middle class (tier, level): we consume high-quality products, live in<br>comfortable conditions, have work, our own income-generating business<br>and/or property, but we do not have enough free time for recreation|2.2|
|Prosperous (rich): have enough resources (knowledge, health, finance,<br>property, time) for comfortable life||

Figure 1 shows correspondences between self-declared social class and per capita income as recorded in the survey Household Income and Expenditure Module. Not surprisingly, relatively few respondents (5.9%) placed their households in the extreme classes 1 (Poor, 3.7%) and 4/5 (Prosperous, 2.2%). The majority declared their households “Not poor but not middle class”—i.e., vulnerable—(49.8%), followed by those who assigned themselves to the middle group (44.3%). Although

> 6 Since few identified their households as “Top middle class” or “Rich,” ECA-POV merged them into a single “Prosperous” category.

8
<!-- Start of picture text --> o» “uA as 3 a ed. io<br>Poor Vulnerable Middle Class Properous<br>Social class (self-declared)<br><!-- End of picture text -->
<!-- Start of picture text --> T<br>oa ,<br>Prob{Vulnerable} ' é é ‘ \ Prob{Middle. class}<br>) — / ‘ — |<br>oy y<br>S'‘<br>1<br>4<br>' i ‘<br>4<br>TM a \<br>°o 1 \<br>r \<br>1 ‘<br>‘ i i"<br>Ss Prob{Poor} é ‘ % \ Prob{Rich}<br>\—. / % . A\\<br>* , ‘ ‘ . TM ~ {i{<br>o _--" c1.5 c2.5 c3.5 7<br>3 -- / ---+-<br><!-- End of picture text -->

Income
Due to identification issues, the most widespread statistical software programs estimate a “traditional” parametrization of model (1) in which _z i_ = _βx i_ + _e i_ and _e i_ ∼ logistic( _x i ,_ 1). However, estimating all the thresholds in the same scale of income requires a slightly different parameterization (Gelman and Hill 2007). Using this parameterization, we can directly interpret the cutpoints _c_ 1 _._ 5, _c_ 2 _._ 5 , and _c_ 3 _._ 5 of model (1) as thresholds on the same scale of income and the standard deviation of the error terms _σ_ as the gradual transition from one class to the next.

The estimated cut-points (intercepts) make it possible to estimate the probability of being Poor, Vulnerable, Middle Class, and Prosperous. For example: Prob{yi = poor|xi} = Prob{xi + ei ≤ c1.5} = Φ(c1.5 −xi) ,

e<sup>w</sup> where Φ(w) = is the logistic distribution function.<sup>8</sup> 1+e<sup>w</sup> To reduce uncertainty in estimating the thresholds we incorporate into the second class the 441 respondents (3.7%) who identify themselves as poor, ending up with three social classes—Poor/Vulnerable, Middle Class, and Prosperous—and two thresholds, those for the lower and upper bounds of the middle class, to be estimated. Estimation of model (1) in 2013 yields a lower income threshold of 474,000 tenge and an upper of 1,772,000 tenge, which correspond to the 56th and the 99th percentiles of the weighted income distribution, and a size of 43.5 percent.<sup>9</sup> These values correspond to US$14.00 and US$52.20 per day in 2011 international dollars. Using the consumption price index for 2013 with the base in 2011 as deflator, annual values in tenge at current prices have been converted to 2011 international dollars using the 2011 PPP conversion factor for private consumption (World Bank, International Comparison Program database), and then divided by

> 8 Depending on the assumption on the distribution of the error terms, logistic or Gaussian, we estimate a logistic or a probit regression model. Logit and probit differ in how the function that links the categorical outcome and the linear predictor is defined. The logistic model uses the cumulative distribution function of the logistic distribution, while the probit model uses the cumulative distribution function of the standard normal distribution. Both methods yield similar, although not identical, inferences.

> 9 We fit the ordered logistic model using the bayespolr (bayesian “proportional odds logistic regression”) function, which is part of the ARM package in R (Gelman et al., 2016). For robust estimation the model was fitted with trimming: we trimmed the top and the bottom 2% for each social class. Estimation of the probit model leads instead to a lower threshold equal to 474,900 tenge and an upper threshold of 1,639,100 tenge corresponding to a size of the middle class equal to 43.2 percent.

11
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Table 5 shows the incidence in 2013 of different household types in the middle class and in the whole population. Whenever the value is greater than one, the household type is over-represented in the middle class; when less than one, it is under-represented. In 2013 households with retired members and those headed by a female were over-represented. Families with at least one child were underrepresented, and the presence of children in the family has a severely negative effect on the possibility of the household being in the middle class. For example, the percentage of households with more children than adults is 16 percent in the whole population but only 5.7 percent of middle-class families.

**Table 5: Distribution of Household Types, Middle Class and Total Population**

|**Type**|**Description**|**Middle**<br>**class**|**Total**<br>**pop.**|**Ratio**|
|---|---|---|---|---|
|||(1)|(2)|(1)/(2)|
|Ret1|At least one retiredperson in the HH|31.2|27.9|1.12|
|Ret2|At least two retiredpersons in the HH|7.3|6.3|1.16|
|Chd1|At least one child in the HH|51.9|70.0|0.74|
|Chd2|At least two children in the HH|18.0|37.5|0.48|
|ChdM|More children than adults in the HH|5.7|16.0|0.36|
|Fem|Female head of HH|56.0|50.7|1.11|
|Emp0|No income from employment in the HH|20.3|21.3|0.95|

Here we examine patterns of consumption of Kazakh households at different levels of income, to evaluate whether the poor and vulnerable, the middle class, and the rich have different consumption patterns. Consumption is recorded in monetary amounts for 14 non-overlapping categories of goods and services. For convenience, we classify them into four groups (see Table 6): Food, Essentials, Optional items, and Luxuries. The classification is somewhat arbitrary, also because it is constrained by the data recorded. For example, the category ‘Travel’ includes everyday travel to work (essential), occasional shopping and social meeting in a nearby town (optional), and long-distance travel for holidays (luxury).

14
**Table 6: Consumption Groups and Categories**

||Group||Category|
|---|---|---|---|
|A|Food|a|Food<br>|
|B|Essentials|c<br>d<br>f|clothing<br>housing/utilities<br>transport|
|||m|rent|
|||b|alcohol and tobacco|
|||e|furnishing/hh equipment|
|C|Optional Items|g<br>i<br>k<br>l|communication<br>education<br>miscellaneous<br>health|
|||h|recreation|
|D|Luxuries|j<br>n|hotels/restaurants<br>durables|

Figure 5 relates the components of consumption to income. The smoothed fractions of the consumption (within total household consumption) are plotted against household per capita log-income. The vertical lines correspond to the lower and upper income thresholds estimated for the middle class. The plot shows that throughout the income range, food (A) is the dominant component, although the fraction spent on food decreases with prosperity (from 57 to 27 percent), consistently with the Engel’s law. Within the middle-class thresholds, the food share goes from 46.6 to 35.3 percent. Throughout the range of income, the shares of necessities (B) and optional items (C) also increase, but more slowly. Within the middle-class range, the shares of B and C are almost stable, at around 27 and 20 percent, respectively. The share of luxuries (D) is very low until around 270,000 tenge and then rapidly increases. Within the middle-class income range, the share of luxuries varies from 7.7 to 14.9 percent.

15
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education, occupation, and some other demographic characteristics. These variables combine information from a thoughtful merger of responses to different sections of the 2013 HBS questionnaires.

We estimate different logistic regression models for three sets of predictors: income and access to facilities, other demographic characteristics, and region of residence. Table 7 shows estimated coefficients and corresponding standard errors for the three models. Income is mean-centered and scaled by two times its standard deviation, so that the resulting coefficient can be interpreted like those of binary predictors (Gelman 2008) and the relative importance of each predictor can be evaluated.

**Table 7: Estimated Likelihood of Being in the Middle Class, 2013,**

**Logistic Regression Results**

||**Mode**|**l A**|**Mode**|**l B**|**Mode**|**lC**|
|---|---|---|---|---|---|---|
||Coeff.|s.e.|Coeff.|s.e.|Coeff.|s.e.|
|Income|0,926|0,067|***<br>1,7 31|0,090 ***|2,297|0,103 ***|
|Safe district|0,850|0,068|***<br>0,765|0,071 ***|0,501|0,07 5 ***|
|Chronic disease|-0,215|0,041|***<br>-0,230|0,043 ***|-0,054|0,048|
|Access togas|0,385|0,039|***<br>0,400|0,043 ***|0,125|0,061 **|
|Land line or cellphone|0,311|0,125|**<br>0,244|0,129 *|0,451|0,138 ***|
|Access to PC at home|0,345|0,039|***<br>0,144|0,044 ***|0,247|0,048 ***|
|Age: 20-35|||-0,136|0,054 **|-0,078|0,059|
|Age:50-60|||0,170|0,052 ***|0,144|0,057 **|
|Age: 60-80|||-0,054|0,080|-0,106|0,087|
|Education:primary|||0,115|0,272|0,063|0,325|
|Education: tertiary|||0,254|0,047 ***|0,288|0,052 ***|
|Urban|||-0,208|0,046 ***|-0,060|0,053|
|Female|||-0,27 5|0,042 ***|-0,219|0,048 ***|
|Ret1|||0,168|0,060 ***|0,155|0,065 **|
|Ret2|||0,244|0,091 ***|0,284|0,099 ***|
|Emp0|||-0,182|0,060 ***|-0,264|0,065 ***|
|HH size: 1 component|||-0,835|0,087 ***|-0,980|0,095 ***|
|HH size:3components|||0,340|0,062 ***|0,354|0,068 ***|
|HH size:4components|||0,518|0,066 ***|0,524|0,07 3 ***|
|HH size:5components|||0,699|0,076 ***|0,647|0,085 ***|
|HH size: 6 or more components|||0,7 34|0,078 ***|0,600|0,089 ***|
|Aktobe|||||2,710|0,140 ***|
|Almaty|||||-0,494|0,132 ***|
|Almaty city|||||0,154|0,126|
|Astana city|||||0,051|0,129|
|Atyrau|||||1,129|0,139 ***|
|East Kaz|||||0,922|0,113 ***|
|Jambyl|||||1,539|0,125 ***|
|Karaganda|||||-0,860|0,125 ***|
|Kostanay|||||-1,256|0,148 ***|
|Kyzlorda|||||1,196|0,126 ***|
|Mangystau|||||0,412|0,139 ***|
|North Kaz|||||0,918|0,121 ***|
|Pavlodar|||||1,624|0,114 ***|
|South Kaz|||||1,282|0,123 ***|
|West Kaz|||||0,7 39|0,130 ***|
|(Intercept)|-1,47 4|0,141|***<br>-1,389|0,156 ***|-1,093|0,114 ***|

*** Significant at 0.1%; **significant at 1%; *significant at 5%.

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In Model A, the probability of belonging to the middle class is estimated as a function of: household per capita income; safety of the district where respondents live (0 unsafe, 1 safe); presence of a person in the household with a chronic disease (proxy for access to health care); access to gas (1 yes, 0 no); access to communication (having a landline or at least one cell phone); and access to a PC at home (proxy for access to the Internet). Income is the most significant predictor in terms of size. Living in a safe area, having access to gas, communication and a PC increase the probability that the household is middle class, though the probability decreases when at least one member has chronic health problems.

As evident from Model B (Table 7), sociodemographic characteristics like a man being the household head, middle-aged, or having reached a tertiary education have a significant positive impact. Families receiving one or two pensions (Ret1 and Ret2) also increase the probability to belong to the middle class. Instead, living in an urban area, not having income from employment (Emp0), and the household head being a woman reduce the probabilities. After controlling for these characteristics, income is by far the strongest predictor.

In Model C we finally control also for the region of residence, since the distribution of opportunities can be geographically unequal. As expected, there is a strong regional effect: living in Almaty, for example, reduces the probability of being in the middle class and Akmola (the baseline) raises it; residents of Jambyl or Pavlodar are more likely to consider themselves middle class than residents of Akmola. After controlling for place of residence, income is still clearly the factor that does most to shape the middle class in Kazakhstan. Figure 6 shows the estimated coefficients of Model C and their standard errors.

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-2 —1 0 | 2 a| Income ; — Sate district (ee Chronical disease coat Access to gas —— Landline or cell phone — Access PC at home = Age 20-35 _ Age 50-60 _ Age 60-80 — Primary Educ i Tertiary Educ — Urban — Female _ | Rett — Ret2 os Empo — HH size: 1 comp — HH size: 3 comp ion HH size: 4 comp — HH size: 5 comp —_ HH size: 6 comp a
<!-- Start of picture text --> : o — unsafe and no telephone<br>ao --- safe and no telephone<br>GD wo a vos Safe and telephone<br>wo . .<br>& Oo oe<br>o r<br>x-<br>— Qo .<br>ial “<br>So<br>-1 0 1 2 3 4<br>Standardized Household Income (‘000 KZT)<br><!-- End of picture text -->
in 2011 international dollars are constructed to be all the same.

**Table 8: Estimated Middle Class Thresholds, 2003–15**

||**Lowe**<br>|**r Bound**|**Uppe **<br>|**r Bound**|
|---|---|---|---|---|
|**Year**|**Annual Per**<br>**Capita**<br>**Income,**<br>**’000 KZT**|**Income Per**<br>**Day,2011**<br>**US$ PPP**|**Annual Per**<br>**Capita**<br>**Income**<br>**’000 KZT**|**Income Per**<br>**Day,2011**<br>**US$ PPP**|
|2003|211.1|14.0|789.4|52.2|
|2006|263.6|14.0|985.6|52.2|
|2010|393.2|14.0|1,470.1|52.2|
|2013|474.0|14.0|1,772.0|52.2|
|2014|505.8|14.0|1,891.0|52.2|
|2015|539.5|14.0|2,016.7|52.2|

Based on the estimated thresholds, Table 9 reports how many households have qualified within the intervals since 2003, along with the share of overall income the middle class received. The results confirm that since 2003 the middle class of Kazakhstan has significantly expanded—from 3.7 percent of households in 2003 to 44.4 percent in 2015. The trend appears to be almost consistent throughout the period: between 2003 and 2006 there was considerable growth in the percentage of Kazakhstan households that moved from poor/vulnerable to the middle class; between 2006 and 2010 the middle class doubled; from 2010 to 2013 this group continued to grow substantially, continuing to grow slightly into 2015. In terms of income, the middle class had only 12.1 percent of total disposable income in 2003 but in 2006 it shot up to almost one-third, reached half in 2010, and accounted for almost two-thirds in the last few years.

The consistent growth of the middle class parallels that of the per capita GDP and the rapid reduction in poverty experienced by the country after the period of economic turmoil immediately following independence (see Figure 8).

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<!-- Start of picture text --> e eo oe<br>So — Poverty ratio (national poverty line) o<br>T+ +® GDPMiddle Classper capita Size =°<br>° _~ ~TM a<br>a ee °o<br>—_ oo a)\ e Ooo -_—+<br>7\x8<br>38\ os &<br>2 \\ o8 8<br>=> & \ ® 9<br>g | a<br>fs) e ao<br>©<br>o TM. Ss 0<br>e ee ——_ 2°o<br>oOo =+<br>2004 2006 2008 2010 2012 2014<br>Year<br><!-- End of picture text -->
can be attributed to redistribution without growth. The last three columns of Table 9 show the decomposition of the change of the middle-class share (Δ share) into the growth effect and the redistribution effect over different sub-periods, using the initial year of each sub-period as the reference year.

**Table 9: Evolution of the Kazakh Middle Class: Size, Shares of Total Income, 2003–15, Percent**

|**Year**|**Middle**<br>**class**<br>**(share %)**|**Share of**<br>**Total**<br>**Income**|**Δ share**<br>**middle**<br>**class**|**Growth**<br>**effect**|**Redistribution**<br>**Effect**|
|---|---|---|---|---|---|
|2003|3.7|12.1||||
|2006|15.4|30.9|11.7|12.8|-1.1|
|2010|31.2|50.3|15.8|14.0|1.8|
|2013|43.5|61.7|12.3|12.0|0.3|
|2014|44.2|62.1|0.7|1.1|-0.4|
|2015|44.4|62.7|0.2|-0.2|0.4|

Overall, the growth component played the prominent role in increasing the share of the middle class, especially in the first decade of the 2000s. The redistribution component marginally contributed to increase the middle class between 2006 and 2013, but it had a negative effect between 2003 and 2006 and in 2013-2014. Redistribution was responsible for the slight increase of the middle group in the last years (2014-2015) that would otherwise have reduced due to the growth effect.

# **7. Conclusions**

What constitutes the middle class is hotly debated; different concepts result in different conclusions about the size of the middle class and its evolution. Taking an income-based approach, in this paper we propose a way to estimate absolute thresholds for identifying the middle class in Kazakhstan. Instead of taking boundaries from recent studies for other countries, we formulated a model-based approach to estimating country-specific boundaries. The approach relies on how citizens identified their own status in response to a module of the 2013 HBS questionnaire.

Our main findings are these:

- Taking an absolute approach, for 2013 we estimated absolute boundaries of

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about $14 and $52 at 2011 PPP international prices.

- Some groups of households, such as families with one or more retired members are over-represented in the middle class, and others, such as families with more children than adults or with no income from employment, are underrepresented.

- Consumption aggregates and income aggregates do not differ significantly. Shares of consumption groups (food, essentials, optional items, and luxuries) show different patterns between poor/vulnerable, middle and prosperous classes.

- The Kazakh middle class has increased massively in size and in income concentration. Between 2003 and 2010 the middle class expanded from 3.7 to 31.2 percent of the population. Thereafter it continued to grow substantially, reaching 43.5 percent by 2013 and stayed relatively stable into 2015. The increase in the size of the middle class is essentially due to a growth effect.

- Among factors beyond income we found to influence the self-perception of being middle class were access to a household gas supply, to communication, and to the Internet, living in safe areas, and being educated. Household size and regional residence also play a significant role. Nevertheless, income was by far the most important determinant of middle-class status, which supports the income-based approach.

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