Jobs seri es Issue No. 4 - World Bank · The team worked with close guidance from David Robalino...

Labor Market outcoMes

kazakhstan

Jobs series

Issue No. 4

Achievements and Remaining Challenges

Victoria Strokova, Angela Elzir and David Margolis

1701501_Kazakhstan Labor Market.indd 1 6/2/17 12:07 PM

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Attribution—Please cite the work as follows: Victoria Strokova, Angela Elzir and David Margolis. 2016. “Kazakhstan Labor Market Outcomes: Achievements and Remaining Challenges.” A note prepared for Kazakh-stan’s “Jobs: Sector Specific Analysis” (a product of the Joint Economic Research Program). World Bank, Wash-ington, DC. License: Creative Commons Attribution CC BY 3.0 IGO

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iii

COntents1. IntroductIon 1

2. Jobs and ProductIvIty 3

3. demograPhIcs and Labor market outcomes 7Employment and Unemployment .................................................................................................................................... 10Determinants of Activity and Employment Type ............................................................................................................... 14Determinants of Wages .................................................................................................................................................. 17

4. access to Jobs 21Impact of Labor Markets on Poverty ................................................................................................................................ 23Labor Market Outcomes of the Bottom 40 Percent ......................................................................................................... 25

5. chaLLenges ahead and PoLIcy ImPLIcatIons 30

ANNEx A: ADDITIONAL TABLES AND GRAPHS ................................................................................................................ 32

ANNEx B: METHODOLOGY AND ADDITIONAL RESULTS ON INfORMALITY ...................................................................... 42

ANNEx C: METHODOLOGY Of THE BOTTOM 40 PERCENT ANALYSIS AND RESULTS ....................................................... 44

ANNEx D: METHODOLOGY AND RESULTS Of THE EARNINGS ANALYSIS ......................................................................... 50

REfERENCES ................................................................................................................................................................... 56


iv

ACKnOwLedgMentsThis note was prepared by Victoria Strokova (Economist, Jobs Group, co-Task Team Leader), Angela Elzir (Junior Professional Associate, Jobs Group), and David Margolis (Consultant, Jobs Group), with contributions from Aida Imanbekova (Consul-tant, Jobs Group), Aizhan Shamurzaeva (Consultant, Jobs Group), Aizhan Imasheva (Consultant, Jobs Group), Chengyan Gu (Consultant, Jobs Group), Shafique Jamal (Consultant, Jobs Group), and Judy Yang (Economist, Poverty and Equity). Valu-able comments and guidance were received from Namita Datta (Senior Private Sector Development Specialist, Jobs Group, co-Task Team Leader) and Thomas farole (Led Economist, Jobs Group). The note is a product of the Joint Economic Research Program.

The team worked with close guidance from David Robalino (Practice Manager, Jobs Group) and Naveed Naqvi (Program Leader, Human Development and Jobs, Central Asia). Peer reviewers were Roberta Gatti (Lead Economist, Social Protection and Labor), Christos Kostopoulos (Lead Economist, Macroeconomics and fiscal Management), and Steven R. Dimitriyev (Lead Private Sector Development Specialist, Trade and Competitiveness).

The work was carried out under strategic guidance from Saroj Kumar (Country Director), francis Ato Brown (Country Man-ager, Kazakhstan), and Ludmilla Butenko (former Country Manager, Kazakhstan).


1

1. IntrOduCtIOnThis note presents a detailed analysis of jobs in Kazakhstan at the macro and individual levels, including regional and socio-economic disparities. At the macro level,1 it includes a diagnostic of the links between economic growth, jobs, and productivity across different economic sectors. At the individual level,2 the analysis focuses on labor market outcomes of women and men, young and adult workers, residents of urban and rural areas, and people in the bottom 40 percent of the consumption distribution.3 It also presents a detailed analysis of determinants of employment and wages.

The results show that economic growth over the past decade has led to sustained job creation and rapid poverty reduction. The country has benefited from the global commodities boom to become one of the top 10 fastest growing economies in the world, achieving annual real per capita income growth of close to 7 percent. Kazakhstan is also characterized by strong labor market performance, even during periods of economic slowdown, with high labor force participation rates, low inactivity, and low unemployment. Increases in real wages have been the main contributors to pov-erty reduction—from 80 percent in 2001 to 15 percent in 2013 (measured by the PPP- corrected US$5 per capita per day). Prosperity was also shared over this period since the growth in consumption of the bottom 40 percent of the population outpaced that of the top 60 percent, resulting in upward mobility and a growing middle class.4

However, the economy remains highly natural resource-dependent and the concentration in capital intensive sectors means that the impact of growth on jobs remains relatively weak. Despite impressive strides, diversification remains a challenge for the country, with minerals, oil, and natural gas accounting for 73 percent of exports and 39 percent of GDP, which has important implications for the number and types of jobs being created. In particular, the dominance of capital-intensive extractive sectors in economic production has implications for the scale of job creation. As a result, the impact of growth on jobs remains relatively weak. Between 2003 and 2013, real GDP grew on average by 7 percent per year while employment expanded only by about 2 percent per year. furthermore, Kazakhstan faces the same competitive-ness challenges as other resource rich economies. Some of the labor market outcomes, such as the expansion of employ-ment in low-productivity non-tradable sectors (construction, services, etc.), can be attributed to the fact that domestic production of tradable goods remains uncompetitive.

Consequently, even though Kazakhstan experienced productivity-enhancing structural changes, jobs continue to be concentrated in low productivity activities. The concentration of investments in capital-intensive sectors means that the impact of growth on jobs remains relatively weak. In fact, while the sector with the largest relative decrease in employment was agriculture (below average productivity), there was little increase in the share of employment in high labor productivity sectors (mining and real estate activities). The main contributors to employment growth were low and below average productivity sectors, such as construction, education, wholesale and retail trade. Despite some reallocation

1 This analysis uses national accounts data and official government statistics on employment and utilizes a recently developed JobStructure tool. 2 This analysis utilizes most recent data for Kazakhstan: the Labor force Surveys (LfS) for 2010–13 and the Household Budget Surveys (HBS) for

2011–2013. 3 The definition of “bottom 40 percent” (B40) or “top 60 percent” (T60) refers to the distribution of per capita household consumption, in which

individuals are ranked by the expenditure of their household, deflated by regional price indices, based on data available in the 2011, 2012 and 2013 Household Budget Survey. Consumption aggregate used is the one developed by the ECAPOV team.

4 Azevedo, Joao Pedro, Sarosh Sattar, and Judy Yang. (2015). “Kazakhstan Economic Mobility and the Middle Class (2006–2013).” Manuscript in progress.


2

of labor in the last decade, a large share of the workforce remains in low productivity employment, particularly in agricul-ture. Self-employment remains relatively high at almost 30 percent of all employed.5

There are important variations in labor market outcomes and wages across regions and between different population groups, potentially exacerbated by limited geographic mobility. While there are relatively small differ-ences in labor force participation rates across regions and different population groups, there are major gaps in access to different types of employment. Individuals in the bottom 40 percent of the consumption distribution are more likely to live in lagging (mostly agricultural) regions, are more likely to be unemployed, or work in low productivity jobs, particularly as self-employed or personal farmstead workers.6 There are also important differentials in wage earnings across sector and regions, and by educational attainment, age and gender. These large wage differentials, especially across regions and sec-tors, indicate some constraints to labor mobility. Removing some of these constraints or lowering individual costs of mobility could help improve access to better jobs.

One of the important factors to ensure access to better jobs among the bottom 40 percent of the population is education. The education gap between individuals in the bottom 40 and top 60 percent of the consumption distribution may grow in the future. Although tertiary education has become more prevalent in the population, the gap in educational attainment between the bottom 40 and top 60 percent has widened in more recent generations. As the older generations leave the workforce and the younger ones enter, the skills gap is expected to widen. Therefore, improving the educational attainment and skills of those in the bottom 40 percent could allow them to access higher productivity, better paid jobs. Improving the relevance and quality of education overall is also needed so new entrants into the labor market can convert educational attainment into better skills and higher earnings.

Economy-wide institutional reforms continue to be needed to enable private sector job creation and diversifica-tion.7 While Kazakhstan has made impressive improvements in several areas, it remains critical to continue to improve the business environment. This includes reforming laws and regulations to address the specific obstacles faced by enterprises in different sectors; further strengthening the rule of law and improving the quality and effectiveness of service delivery; mak-ing room for the private sector and encouraging competition; improving the performance of the financial sector; and insti-tutionalizing a professional and merit-based civil service.8 It is important to note that a comprehensive government reform program called “100 Steps” was announced in 2015 with the aim to tackle some of these issues.

In addition, Kazakhstan could consider additional regional and sectoral policies targeted to the bottom 40 per-cent of the population. Broad-based policies that promote investments into the private sector are fundamental, but the relationship between overall investment and jobs is complex, not always resulting in the types of jobs that may benefit the bottom 40 percent or create jobs for the older workers or the highly skilled. While the government has been promot-ing investments in particular sectors, they are primarily targeted at relatively low labor intensity sectors (manufacturing) or the construction sector which mostly creates temporary jobs. Going forward, in addition to the broad-based reforms, Kazakhstan could consider more targeted policies that aim to promote jobs for specific population groups in given regions. These policies would seek to address barriers to job creation in particular sectors and regions and complement economy-wide policies.

The rest of the note is organized as follows: Section 2 discusses the relationship between economic growth, jobs, and productivity across different economic sectors. Section 3 discusses demographic trends and overall labor market outcomes. Section 4 focuses on assessing spatial and sectoral differences in access to jobs, including for those in the bottom 40 per-cent. Section 5 concludes with a discussion of challenges and broad policy implications.

5 While this share is actually not too high for low and middle income countries, it is almost twice as high as the average of the countries in the Organization of Economic Development and Cooperation (OECD) (16.8 percent) and is higher than the Europe and Central Asia (ECA) average of 20 percent.

6 Personal farmstead is defined as work on personal plots of land, either for self-consumption or trade/barter, or both, for at least one hour during the reference week.

7 World Bank. 2013. Beyond Oil : Kazakhstan’s Path to Greater Prosperity through Diversifying, Volume 2. Main report. Washington, DC.8 Ibid.


3

2. JObs And PrOduCtIvItyStrong economic growth in the past decade has contributed to robust employment gains, although the employment-growth elasticity has been low by international standards. Between 2003 and 2013, the economy has added 1.5 million jobs. During this period, real GDP grew on average by 7 percent per year, while employment expanded by about 2 percent per year (figure 1). This implies an elasticity of around 0.28, which is lower than the aver-age for Europe and Central Asia (0.48) and OECD countries (0.5) (figure 2). In fact, in 2013, employment grew by just 0.7 percent compared to 2012, while GDP grew 6 percent in real terms.

Figure 1GDP and employment growth, 2003–2013

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003–

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GDP Growth (annual %), 2003–2013

KAZ

Source: Authors’ calculations using World Development Indicators.

Figure 2Employment-growth elasticities, 2003–2013

Chile

Canada

Australia

Brazil

Kyrgyz Republic

Tajikistan

Malaysia

Uzbekistan

Armenia

Korea, Rep.

USA

Kazakhstan

Azerbaijan

Russia

Turkmenistan

Ukraine

China

Georgia

OECD members

ECA

0.0 0.2 0.4 0.6 0.8 1.0


Growth has been sufficient to create jobs, as the growth of employment was growing at a faster rate than the growth of the labor force. In the majority of countries, employment has been growing as fast as the labor force (fig-ure 3). However, in Kazakhstan, employment growth has been higher (2.1 percent) than the labor force growth (1.7 percent) between 2003 and 2013. As a result, unemployment has decreased during this period, as discussed in detail later.

Employment gains were driven by the services, construction, trade, and the education sectors. During 2003–2013, employment expanded in construction (contributing 21 percent of the total increase in employment), wholesale and retail trade (15 percent), education (18 percent): transportation and warehousing (10 percent), and other services (35 percent). Employment in manufacturing increased by 15 percent but contributed only 4 percent to employment gains. Employment


4

in the public administration and social sectors (such as health and education), both of which have a high share of public employment, contributed almost a third of employment increases during this period. Agriculture was the only sector that contracted, declining in absolute terms by 14 percent. Overall, reallocation of employment away from agriculture toward services progressed at a relatively slow pace, while job creation in industry was very low (Figure 4). The sectors with the highest elasticity of employment to value added during the same period were construction and education; while the lowest was wholesale and retail trade9 (Figure 5).

9 Given increasing output, low employment-growth elasticity could indicate increasing labor productivity, which is discussed next.

Figure 4Trend in employment by sector, 2003–2013

3.0

2.5

2.0

1.5

1.0

0.5

0.0

Mill

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s

2013

2004

2003

2005

2006

2007

2008

2009

2010

2011

2012

Agriculture

Construction

Other services

Trade

Mining

Public and social

Manufacturing

Source: WEO; Authors’ calculations using data from the Statistical Committee of RK.

Figure 3Employment and labor force growth, 2003–2013

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Total employed growth (annual %), 2003–2013

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Figure 5Employment-growth elasticities by sector, 2003–2013

(1.5) (1.0) (0.5) 0.0 0.5 1.0 1.5

Construction

Education

Transportation, storage and communications

Human health and social work activities

Manufacturing

Public administration and defence

Mining and quarrying

Wholesale and retail trade

Agriculture, forestry and fishing

Source: WEO; Authors’ calculations using data from the Statistical Committee of RK.


5

Growth in labor productivity has been the main contributor to GDP per capita growth. In Kazakhstan, GDP per capita grew by 9.1 percent annually between 2003 and 2013. Growing employment and participation contributed posi-tively, albeit marginally (0.46 and 0.37 percent, respectively), to the growth of per capita GDP. The bulk of the per capita value added growth came from increases in labor productivity (8.33 percent). The slightly declining share of the working age population contributed negatively to GDP per capita growth, but by a very small amount (0.06 percent) (Table 1).

Labor productivity growth has been driven mainly by the wholesale and retail trade as well as the mining sec-tor. The decomposition of value added per worker changes shows that the majority of labor productivity growth came from within sectors, such as mining and wholesale and retail trade, which contributed approximately 22 and 23.4 percent, respec-tively (Table 2). Other activities10 and manufacturing contributed an additional 17.5 and 7.6 percent, respectively. Agriculture contributed only 4 percent to labor productivity growth. Intersectoral shifts (i.e. changes in productivity due to reallocation of workers from less to more productive sectors) contributed about one fifth to overall labor productivity growth.

10 Other activities include the following sectors: public administration; education; health, public utilities; finance and insurance; real estate activities; accommodation and food services; professional, scientific and technical activities; arts, entertainment and recreation; and administrative and support service activities.

Table 1Decomposition of growth in per capita value added, 2003–2013

Period: 2003 to 2013

Change

% of Total

Change

% Yearly Contribution

to Growth

Change in per capita

value added 390.74 100.00 9.10

due to changes in

productivity 357.80 91.57 8.33

due to changes in

employment rate 19.70 5.04 0.46

due to changes in

participation rate 15.73 4.03 0.37

due to changes in

share of working age

population –2.49 –0.64 –0.06

Source: Authors’ calculations using JobStructure tool and data from Statistical Committee of RK.


6

While Kazakhstan has experienced some productivity-enhancing structural changes, a large share of the popu-lation continues to be employed in low productivity sectors. Despite a significant reduction in the share of employ-ment in agriculture (10 percentage points between 2003 and 2013), there are still about 2 million people, or a quarter of all employed, who remain engaged in this sector. furthermore, many of the other sectors that increased their share of employment, such as construction and education, also have below average productivity (figure 6). As a result, overall labor productivity remains low, especially for the non-oil sectors, and compared to countries with similar GDP per capita levels.11

11 World Bank. 2013. Beyond Oil : Kazakhstan’s Path to Greater Prosperity through Diversifying, Volume 2. Main report. Washington, DC.

Figure 6Sectors and structural change in Kazakhstan, 2003–13

–12% –10% –8% –6% –4% –2% 0% 2% 4% 6%

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0.8

0.6

0.4

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Manufacturing

Wholesale & retail trade

Accommodation & food service

Health & social work

Real estate activities

Mining

Financial & insurance

Transportation & communication

Other

Construction

Public administration & defence

Education

Change in employment share, 2003–2013

Note: The size of the bubble represents the sectoral employment shares in 2003.

Source: Authors’ calculations using data from Statistical Committee of RK.

Table 2Decomposition of value added per worker into sector changes and inter-sectoral shifts

Period: 2003 to 2013

Change % Contribution

Agriculture 19.35 4.01

Mining & Utilities 105.88 21.96

Manufacturing 36.72 7.62

Construction –1.09 –0.23

Wholesale & Retail 112.73 23.38

Transport & Communications 23.32 4.84

Other Activities 84.43 17.51

Not Defined 3.98 0.83

Intersectoral Shift 96.91 20.10

Total change in productivity

(value added per worker) 482.23 100.00

Source: Authors’ calculations using JobStructure tool and data from Statistical Committee of RK.


7

3. deMOgrAPhICs And LAbOr MArKet OutCOMes

Demographic trends have important implications on the labor market. In Kazakhstan, fertility rates dropped sig-nificantly in the 1990s and early 2000s. fertility increased sharply starting in 2005 and peaked in 2010 at 2.5 children per woman, constituting a short “baby boom” period. fertility subsequently started to decline and is projected to continue decreasing to the levels of the early 2000s (2 children per woman). These trends affect the size of the working age popula-tion and, therefore, of the labor force (figure 7).

Figure 7Trend in fertility, population pyramid (2010 and 2050) in Kazakhstan

3.5

3

2.5

2

1.5

1

0.5

01990 2000 2005 2010 2015 2020 2030 2050 2075 2100

Tota

l fer

tilit

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40

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Males Females

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100

90

80

70

60

50

40

30

20

10

0

2050

Males Females

1000 800 600 400 200 0 200 400 600 800 1000

Ag

e

Source: UN projections.


8

Figure 8Male labor force participation rates, 2013

0% 20% 40% 60% 80% 100%

Moldova

Belarus

Ukraine

Lithuania

Latvia

United States

Estonia

Azerbaijan

Canada

Russian Federation

Australia

Armenia

Chile

Georgia

Malaysia

Uzbekistan

Turkmenistan

Tajikistan

Kazakhstan

China

Kyrgyz Republic

Mexico

Source: WDI (labor force participation rate, 15+); Statistical Committee of RK.

Figure 9Female labor force participation rates, 2013

0% 20% 40% 60% 80%

Moldova

Malaysia

Mexico

Turkmenistan

Uzbekistan

Chile

Belarus

Ukraine

Armenia

Latvia

Lithuania

Kyrgyz Republic

Estonia

United States

Georgia

Russian Federation

Australia

Tajikistan

Canada

Azerbaijan

China

Kazakhstan

Source: WDI (labor force participation rate, 15+); Statistical Committee of RK.

At the same time, labor force participation rates are already high in Kazakhstan and unlikely to contribute much more to labor force growth. Almost 78 percent of men and 68 percent of women aged 15+ participate in the labor mar-ket.12 While women participate less than men, the share of women participating in the labor force is very high by international standards (figure 8 and figure 9). The share of individuals who are not in employment, education or training (NEET) is low throughout age cohorts, including among youth, with the exception of people aged 60–64 as they retire (figure 10). The number of youth entering the labor market increases sharply between the ages of 18–25, after which the activity rate flattens (figure 11). Around two-thirds of workers exit the labor market between the ages of 60 and 64. Women tend to exit the labor force earlier due to lower retirement ages.13

12 In 2013, 82.5 percent of men and 75.5 percent of women of working age, defined as those 15–64 years old, were in the labor force.13 Until recently, retirement ages were 63 years old for men and only 58 for women. However, in 2013 a law was signed gradually increasing the

retirement age for women from the current 58 to 63 years old within a decade.


9

Figure 10Status of working age population by age, 2013

100%90%80%70%60%50%40%30%20%10%0%

Ove

rall

15–1

920

–24

25–2

930

–34

35–3

940

–44

45–4

950

–54

55–5

960

–64

NEETEmployment Unemployment Inactivity

Age

Source: Authors’ calculations using LfS 2013.

Figure 11School to work transition, 2013

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Due to the fall in fertility in the 1990s and constant participation rates, the growth of the labor force in the next few years is expected to be much slower. Kazakhstan will experience a relatively slow growth in the labor force14 in the next few years (figure 12). In fact, while the labor force grew by 1.5 percent annually in 2003–2010, it is projected to increase only by 0.5 percent annually in 2015–2020, which means that it will increase by less than 60,000 persons per year. As a result, the pressure on the labor market may be reduced in the short run.15 The growth rate of the labor force will peak around 2030 when the “baby boom” generation enters the labor market (figure 12). This will mean that annually, the labor force will grow by as many as 135,000 people, requiring a much faster job creation pace during that period.

14 Assuming constant labor force participation rate of 72 percent.15 Assuming the current deterioration of the economic situation does not lead to a significant drop in job creation and a spike in unemployment. So far

the impact of the crisis on the labor market has been limited (World Bank Biannual Economic Update, 2015), but simulations presented later in the note indicate that unemployment may increase somewhat by 2020 should current economic trends persist.

Figure 12Projected labor force growth, 2010–2050

160,000

140,000

120,000

100,000

80,000

60,000

40,000

20,000

–

4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

2010

2012

2014

2016

2018

2020

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046

2048

2050

Tho

usa

nd

s

y/y LF increase, left axis Cumulative LF increase, right axis

Note: for working age population of 16+, assuming 72 percent labor force participation rate.

Source: Demographics projections and trends in working age population and labor force are based on the Statistical Committee of RK.


10

External migration flows remain relatively low, especially in comparison to the 1990s. Compared to the 1990s, migration flows have decreased dramatically in Kazakhstan. Estimates for official inflows and outflows are less than 30,000 persons per year, even though unofficial migration could be higher. It should be noted that during this period Kazakhstan has been actively pursuing a policy of encouraging repatriation of ethnic Kazakhs living abroad. Almost a million (944,500) ethnic Kazakhs immigrated to Kazakhstan during 1991–2013, which helped to smooth the decrease in the demographic growth observed in the country in the 1990s.16 While migration trends may change in the future,17 the impact of external migration on the labor market remains relatively low (figure 13).

EmploymEnt and UnEmploymEntEmployment rates have been traditionally high in Kazakhstan, especially for women, and unemployment has been declining steadily. The employment rate18 was 75 percent for men and 63 percent for women in 2013, which are very high levels by international standards.19 Employment rates are slightly higher in rural areas (figure 14). As a result of the rapid growth in the number of jobs, the unemployment rate was halved between 2001 and 2013, from 10.4 to 5.2 percent (figure 15). Long-term unemployment remains very low. The unemployment rate is slightly higher among women (5.9 per-cent compared to 4.6 percent among men) and urban areas (5.4 percent vis-a-vis 4.9 percent in rural areas) (figure 14). The recent economic slowdown has not yet affected employment in any significant way, with the unemployment rate holding steady at 5 percent in the first quarter of 2015.20

16 Source: Economic Research Institute. 17 for instance, a slowdown in neighboring countries may increase the number of labor migrants coming to Kazakhstan. 18 Calculated as the number of employed divided by the number of working age population (15+). 19 figure 44 in Annex A. 20 Data from the Statistical Committee of RK for Q1 of 2015.

Figure 13Official migration flows in Kazakhstan, 1991–2013

500

400

300

200

100

0

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

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nd

s o

f p

erso

ns

Emigrants Immigrants

70.438.1

477.1

299.5

24.4

Source: Economic Research Institute.


11

While unemployment among 15–24 year olds decreased dramatically, younger people (aged 25–34) do have somewhat higher unemployment rates. Youth unemployment fell drastically from almost 20 percent in 2001 to just under 4 percent in 2013, which can be attributed in large part to increasing enrollment in tertiary education.21 However, younger people aged 25–29 and 30–34 do have somewhat higher unemployment rates (approximately 7 percent compared to a national average of 5 percent) (figure 16). Even controlling for other characteristics, this age group is more likely to be unemployed. Similarly, unemployment is slightly less likely among males and the more educated (those with upper second-ary and tertiary education) (Table 5 in Annex A). Those living in urban areas are also slightly less likely to be unemployed.

21 Tertiary enrollment (gross) increased from 29 percent in 2000 to 45 percent in 2012 (WDI).

Figure 14Labor force status in working age population, 2013

Overall Male Female Urban Rural

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

Employment Unemployment Inactive

Note: Working age = 15–64 years of age.


Figure 15Unemployment rates, 2001–2013

25%

20%

15%

10%

5%

0%

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Unemployment rate, %

Long- term unemployment, %

Youth unemployment rate, % (15–24 years)

Source: Statistical Committee of RK.

Figure 16Unemployment rates by age, 2013

15–1

9

20–2

4

25–2

9

30–3

4

35–3

9

40–4

4

45–4

9

50–5

4

55–5

9

60–6

4

0%

1%

2%

3%

4%

5%

6%

7%

8%



12

While the majority of workers are wage employees, self-employment still remains relatively high. Although there has been a gradual shift from self- employment toward wage employment, the share of self-employment remains high at 29 percent of the employed (figure 17) and more than half of them (52.5 percent) are working in agriculture. In contrast, the share of self-employed is about 20 percent in ECA countries and 16.8 percent among OECD countries.22 Wage employment has increased from about 61 percent of total employment in 2003 to about 66 percent in 2013, mirrored by a decrease in the number of self-employed. Wage employment is significantly lower in rural areas (55 percent). Most of the difference in employ-ment between rural and urban areas is due to personal farmstead employment,23 although own-account workers and employ-ers are also relatively more frequent in rural areas. The share of public employment24 among wage employees is also high—at 42.3 percent—reflecting the big role, albeit decreasing,25 that the state continues to play as an employer.

Wage employment is high in almost all sectors, except agriculture and trade. Wage employment is nearly universal in mining, manufacturing, public administration, public utilities, and other services. Self-employment is more prevalent in retail and wholesale trade (commerce), construction, transport and communications, and agriculture (figure 18).

22 Source: WDI (ECA) and OECD (https://data.oecd.org/emp/self-employment-rate.htm), respectively. 23 Defined as those who have worked on their personal farmstead for at least one hour during the reference week. 24 Defined as those wage employees reporting state ownership of the organization they work in. 25 Analysis of HBS data shows that the share of public employment in total wage employment decreased by almost 10 percentage points from 2006 to

2013.

Figure 17Overview of the working age population in Kazakhstan, 2013

Working AgePopulation (15+)

12.5m; 74.4%

Active9.0m; 72.1%

Inactive3.5m; 27.9%

Wage Employee5.9m; 65.8%

Unemployed0.5m; 5.2%

Self-Employed2.6m; 29.0%

Public2.5m; 42.3%

Private3.4m; 57.7%

Agriculture1.4m; 52.6%

Non-Agriculture1.2m; 47.4%

Informal:14.4%

Formal:85.6%

Note: Self-employed include own-account workers, employers, farmstead workers, members of cooperatives and unpaid family workers.



13

The majority of self-employed in agriculture work on personal farmsteads, while self-employed in non-agriculture are mostly own-account workers. Almost two-thirds (64 percent) of self-employed in agriculture are those who work on per-sonal farmsteads. Only about 30 percent are own-account workers, while the rest are split among employers (4 percent), unpaid family workers (1.5 percent), and cooperative members (0.5 percent). Among self-employed in non-agricultural activities, 90 percent are own-account workers and another 8.5 percent are employers. Only a very small share are unpaid family workers (1 percent) and members of cooperatives (0.5 percent).

Informality is concentrated in self-employment, particularly in agriculture. A relatively small share (only 8.3 per-cent) of wage employees are informal, but over two-thirds of agricultural self-employment (which is largely personal farmstead work, as noted above) is informal26 (Table 3 and Annex 2). Very few employers are informal (only 3.4 per-cent), while about one fifth (18 percent) of non- agricultural self-employed are informal. However, it should be noted that measurement of informality for self-employment is somewhat less reliable.27 Due to this measurement issue and prominence of wage employment in Kazakhstan, this note primarily focuses on informal wage employment.

26 for wage employment, this note adopts a broad definition of informality based on two standard questions. first, a wage worker is considered informal if he or she does not have a written contract. Wage workers are also considered informal if their employer does not contribute to social insurance/the pension fund on their behalf. All unpaid family workers, cooperative members and personal farmstead workers are considered informal, and among the self-employed (employers or own-account workers), those whose enterprise is not registered are considered informal.

27 The question on registration changed in 2012 to make it applicable to all employment categories, whereas it previously applied only to the self-employed. As a result, the LfS shows a significant difference in the share of formal self-employed between 2011 and 2012.

Figure 18Distribution of employment by type and sector in Kazakhstan, 2013

Agricu

lture

Min

ing

and

extr

activ

esM

anuf

actu

ring

Publ

ic ut

ilitie

sCo

nstr

uctio

nCo

mm

erce

Tran

spor

t and

com

m.

Fina

nce,

insu

ranc

e an

d re

al e

stat

e

Publ

ic ad

min

istra

tion

Oth

er se

rvice

s

100%90%80%70%60%50%40%30%20%10%0%

Wage employment

Employer

Own-account self-employed

Farmstead worker

Note: Excluding unpaid family workers, which represent 1 percent of employed in agriculture and less than 1 percent in other sectors.



14

28

dEtErminants of activity and EmploymEnt typEMen and the more educated are more likely to participate in the labor market. Controlling for a set of characteristics,29 men are 10 percentage points more likely to be active than females. Besides gender, education plays a big role, with those with upper secondary and tertiary education being 17.7 and 15.6 percentage points, respectively, more likely to be active than those with lower secondary or less. Age is related to activity in a predictable manner: 15–19 year olds, who are over-whelmingly in school, are less likely to be active (by 15 percentage points), as well as those aged 55 and above, particularly those 60 years and older, who are 70 percentage points more likely to be inactive compared to 25–29 year olds. There are also some regional differences, with those in Astana particularly less likely to be active compared to the Akmola region, and those in urban areas are only slightly more likely to be inactive than those in rural areas (Table 5 in Annex A).

Those who are more likely to secure public wage employment compared to private formal employment appear to be different in several respects. Keeping other characteristics constant, males are significantly more likely to be employed in formal private sector jobs compared to women, who are more likely to work in the public sector (figure 19). Younger people (20–24 years old) are more likely to work in the private sector even compared to a relatively young cohort of 25–29 year olds. On the other hand, older people (40 and above) are much more likely to be in the public wage employment. While higher levels of education increase the probability of both public and private wage employment com-pared to lower secondary education, the effect is most dramatic for those with tertiary education, who are 31 percentage points more likely to be in the public sector. On the other hand, those with upper secondary education are much more likely to be in the private formal sector than those with lower secondary. There are also significant regional differences; however, while those in urban areas are more likely to be in the private formal sector compared to those in rural areas, this is not the case for the public sector, where there are no statistically significant differences between rural and urban areas.

28 Due to changes in the LfS questionnaire, the classification of public and private formal sector employment shifted between 2011 and 2012, so most of the analysis that distinguishes between these two types of formal employment focuses on 2013 only. Shares of employment by public/private among formal workers: 2010 = 32.8 percent/58.3 percent; 2011 = 33.0 percent/58.8 percent; 2012 = 42.6 percent/48.4 percent; 2013 = 42.3 percent/49.4 percent.

29 Multinomial logits estimated on the following outcomes: NEET (Not in Employment, Education or Training), in school or training, unemployment, farmstead worker, non-agricultural self-employed, public wage employment and private formal wage employment. Reference categories include female, 25–29 years old, lower secondary or less, rural resident, in Akmola region, and single person household. Informal wage employment and its determinants are considered separately.

Table 3Distribution of informality by job type, 2013

Type of Employment28

Share of Employed

Share of Total

Percent Informal

Public Wage Employment 29.35 42.30 0

Private Formal Wage

Employment 34.27 49.40 0

Private Informal Wage

Employment 5.76 8.30 100

Agricultural Self

Employment 16.11 52.61 69.31

Personal Farmstead 10.46 34.15 100

Non-Agricultural Employer 1.23 4.03 3.36

Non-Agricultural Self

Employment 13.28 43.36 18.17



15

Determinants of self-employment in agriculture and non-agriculture differ, but younger people tend to be more likely to engage in both. Not surprisingly, residents of urban areas are considerably less likely to be in agricultural self-employment while location (urban-rural) does not make a difference for non- agricultural self-employment (figure 20). Younger workers (15–19 and 20–24 years old) are more likely to be self-employed in agriculture compared to 25–29 year olds, but the number of such workers is very small.30 Younger people are also more likely to engage in non-agricultural self-employment, but there are also relatively few of them.31 Older workers, on the other hand, especially those over 60 years of age, are significantly less likely to be self-employed in non- agriculture than prime-age workers (25–29 year olds). Workers with tertiary education are less likely to be self-employed compared to those with lower secondary or less, especially outside of agriculture.

30 Approximately 16 percent of all self-employed in agriculture or about 232,000 workers. 31 Only 13 percent of all self-employed outside of agriculture or about 172,500 workers.

Figure 19Marginal effects for multinomial logit model, 2013

Urban

Almaty City

East Kazakhstan

Pavlodar

South Kazakhstan

Mangystau

Kyzylorda

Kostanay

Karaganda

Jambyl

Atyrau

Aktobe

Tertiary education

Upper secondary education

60–64 Years old

50–54 Years old

45–49 Years old

40–44 Years old

20–24 Years old

Male

Private formal wage employment Public wage employment

–40.00 –30.00 –20.00 –10.00 0.00 10.00 20.00 30.00 40.00

Interpretation: A male has 11.4 percentage points higher chance of having private formal wage employment than a female when both have the average characteristics of the working age population (15–64 years old).

Note: Only coefficients significant for both public wage employment and private formal wage employment are included, with the exception of urban which is only significant for private formal wage employment. Reference categories include female, 25–29 years old, lower secondary or less, rural resident, in Akmola region, and single person household.



16

Informal wage workers tend to be younger, less educated and more likely to be found in rural areas than for-mal private wage workers. Informal wage workers start working younger, and nearly a third of all informal workers are under the age of 30 (figure 21). In addition, only 13.5 percent of informal wage workers have a tertiary education, relative to 34.9 percent for formal wage workers (and 52.5 percent for public sector workers) (Table 6 in Annex B). Informal work-ers also drop out of education earlier. Less than half (46.4 percent) of informal wage workers live in urban areas, against 61.7 percent of public sector and 69.6 percent of formal private wage workers. On the other hand, very few people work in the public sector at young ages.

Figure 20Marginal effects for multinomial logit model, 2013

Urban

Almaty City

East Kazakhstan

Pavlodar

South Kazakhstan

Mangystau

Kyzylorda

Kostanay

Karaganda

Atyrau

Aktobe

Tertiary education

60–64 Years old

45–49 Years old

40–44 Years old

20–24 Years old

15–19 Years old

Male

Self-employment in agriculture Self-employment outside of agriculture

–15.00 –10.00 –5.00 0.00 5.00 10.00

Interpretation: A male has 3.75 percentage points higher chance of being self-employed outside of agriculture than a female when both have the average characteristics of the working age population (15–64 years old).

Note: Only coefficients significant for both self-employed in agriculture and non-agriculture are included. Reference categories include female, 25–29 years old, lower secondary education or less, rural resident, in Akmola region, and single person household.



17

Sector and location appear to play a large role for informality. Detailed statistical analysis32 shows that while certain determinants of informal status are very strong (there is almost no informal wage employment in mining, health and social services, education and public administration services, for example), other effects are less clear. for example, informality in agriculture is significantly higher than in other sectors with the exception of commerce, while informality is significantly lower in Astana and Almaty cities. However, outside of agriculture and the main cities, a wage worker is no more likely to be informal in rural areas than in urban areas. Similarly, the apparent differences in formality related to education levels appear to be largely due to the fact that the more educated workers are more likely to work in low-informality sectors and low-informality regions. Thus, it seems that people with an upper secondary education degree are more likely to be informal than those with a lower educational attainment, but these workers are also more likely to be wage employees. The combination of these factors implies that the level of educational attainment actually does not play a significant role in explaining formality among those who have wage jobs (Table 7 in Annex B).

dEtErminants of WagEs Similar to many other countries, an analysis of wages33 in Kazakhstan reveals a large gender gap and significant wage differentials by age (Annex 4). Men tend to earn more than women with identical characteristics; wages for men were 27–31 percent higher than for women over the 2011–2013 period. These estimates control for sector of work and worker characteristics so these wage gaps do not result from the fact that women are less likely to work in high-paying sec-tors such as mining, manufacturing or transportation, and are more likely to work in low paying sectors like commerce and other services (especially the education and health and social services sectors). They also do not reflect education differentials

32 See Annex B for more information about the methodology. 33 The analysis in this section exploits data from the 2011–2013 Household Budget Surveys to estimate the determinants of wages, overall and on a

sector-specific basis. It then uses the 2011–2013 Labor force Surveys to estimate how much individuals with different skill levels could expect to earn in different regions, and compares this to the actual distribution of employment of these skill levels in the different regions. In the absence of constraints, one should see a higher concentration of skills in the regions where these skills are the most highly rewarded; any deviation from this distribution is indicative of constraints to labor reallocation (see Annex D for more details on the methodology).

Figure 21Age distribution by formality status

20%

15%

10%

5%

0%

Shar

e o

f w

ork

ers

Age group

15–1

9

20–2

4

25–2

9

30–3

4

35–3

9

40–4

4

45–4

9

50–5

4

55–5

9

60–6

4

Public wage workers

Private informal wage workers

Private formal wage workers



18

or disproportionate work in rural areas.34 Holding these characteristics constant, women are paid less than men suggest-ing that there are other factors contributing to the gender wage gap. There are also significant wage differentials by age. Wages in Kazakhstan increase with age up to around 45 years of age, after which they tend to decline. This trend has been strengthening in recent years (figure 22). This age profile of wages is also typical of most countries, as it reflects the process of skills accumulation, depreciation and selection into and out of the labor market.

Additional skills—in the form of education—bring a wage premium, particularly for those with tertiary educa-tion. The estimated wage premium is 7–12 percent for an upper secondary education and 39–48 percent for a tertiary education (both relative to a lower secondary education or less).35 These estimates suggest that employers do value an upper secondary education more highly than simply a lower secondary education or less, but not by much. However, there is a significant premium in wages that comes with having a tertiary education. The lack of a significant premium for upper secondary education may be related to the low quality of education at secondary and upper secondary levels. At the inter-national level, despite high levels of enrollment and completion of secondary education, Kazakhstan fares poorly in educa-tion quality, as reflected in the poor, though markedly improved, performance on international student assessments,36 such as the OECD’s Programme for International Student Assessment (PISA). The 2012 PISA results suggested that Kazakhstani students underperformed compared to their peers in comparator countries in reading, mathematics and science (figure 67 in Annex A).

Not surprisingly, different sectors of the economy pay different wages, even after controlling for worker characteris-tics (figure 23).37 The mining and extractive industries sector pays the most on average, with its workers earning wages between 78 and 84 percent above what similar wage workers earn in agriculture in the same year.38 Manufacturing, transportation and communication, and finance, insurance and real estate are also relatively high paying sectors, while wage workers in agriculture, other services, and commerce earn the least. Not only do different sectors pay similar workers different amounts, they reward the same characteristics differently as well (Table 11 in Annex 4). Returns to skills, age, residing in an urban area or gender can vary

34 In fact, women make up a larger share of tertiary educated wage workers (56 percent) and slightly more than half of urban wage workers (51 percent), while men occupy 53 percent of wage jobs in rural areas.

35 Estimates from 2011–2013. See Table 11 in Annex D for results for 2013.36 Kazakhstan’s performance on PISA improved markedly since 2009, especially in math and science and also among the lowest achievers, but its overall

achievement remains significantly behind other countries with similar income per capita levels.37 Without controlling for other characteristics, the variation in wages within a sector can be quite large, although the ranking of sectors discussed here

holds on average. 38 There are few consistent trends over the 2011–2013 period, although wage gaps appear to be shrinking somewhat in finance, insurance and real

estate, transportation and communication, and public administration services.

Figure 22Wage differentials (relative to 15–19 year olds) by age group, 2011–2013

40%

30%

20%

10%

0%

15–1

9

20–2

4

25–2

9

30–3

4

35–3

9

40–4

4

45–4

9

50–5

4

55–5

9

60–6

4

2011 2012 2013

Source: Authors’ calculations using 2013 HBS.


19

dramatically across different industries. for example, gains to having a tertiary education degree range from 12 percent above a lower secondary education degree in the agriculture sector to 58 percent higher in manufacturing.

There are also significant regional and urban-rural wage differentials, mainly explained by the economic sec-tors prevalent in different parts of the country. In fact, urban residents earn 16–18 percent more than rural residents. Such rural-urban wage differentials are common around the world and reflect a higher level of labor demand in urban areas (Table 11 in Annex D). In terms of regional wage variation, the same worker earns 51 percent more than the national aver-age in Mangystau and 9 percent less than the national average in North Kazakhstan (figure 24), and these premia do not simply serve to cover differences in cost of living. Such large wage differences across sector and location of employment, controlling for worker characteristics, imply that there may be barriers to mobility which would allow workers to move where they can obtain higher paid jobs given the skills they have. Labor mobility is known to be low in Kazakhstan,39 while constraints to mobility could be cultural (desire to stay near the historic family residence), financial (not enough money to pay for a move or to acquire housing in the new location) or informational (not knowing where the high paying jobs are located).

Finally, while the public sector commands only a slight wage premium, it attracts a very high share of the ter-tiary educated. As was shown earlier, those with tertiary education are much more likely to work in the public sector com-pared to those with lower secondary education. In fact, half of those working in public wage employment have completed tertiary education compared to only 30 percent for private formal wage employment and just 10 percent of private non-formal wage employment.40 However, on aggregate, controlling for employee characteristics the public sector enjoys only a

39 Arias, Omar S.; Sanchez-Paramo, Carolina; Davalos, Maria E.; Santos, Indhira; Tiongson, Erwin R.; Gruen, Carola; de Andrade falcao, Natasha; Saiovici, Gady; Cancho, Cesar A. 2014. Back to work: growing with jobs in Europe and Central Asia. Europe and Central Asia Reports. Washington, DC: World Bank Group.

40 Based on LfS 2013.

Figure 23Trends in relative wages across sectors (relative to agriculture), 2011–2013

Min

ing

Man

ufac

turin

gPu

blic

utili

ties

Cons

truc

tion

Tran

spor

tatio

n an

d co

mm

unica

tion

Fina

nce,

insu

ranc

e an

d re

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stat

e

Publ

ic ad

min

istra

tion

serv

ices

Educ

atio

n

Health

and

socia

l ser

vice

sO

ther

serv

ices

Com

mer

ce

2011 2012 2013

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

Source: Authors’ calculations using 2013 HBS.


20

2 percent wage premium compared to the private sector (Table 11 in Annex D). Returns to higher education are high in some sectors with a large share of public employment, such as education (Table 12 in Annex D), but wages in those sectors tend to be on the lower side overall. Hence, wage differentials do not fully explain the appeal of the public sector. Public sector employment is likely to be associated with other benefits (such as better social benefits or social prestige, etc.) that make it particularly attractive to the well-educated.

Figure 24Wage differentials and cost of living (relative to Akmola) by region, 2013

Wages Cost of living

Aktob

e

Almat

y

Atyra

uW

est K

azak

hsta

n

Jam

byl

Karag

anda

Kosta

nay

Kyzyl

orda

Man

gyst

auSo

uth

Kazak

hsta

n

Pavl

odar

North

Kaz

akhs

tan

East

Kaz

akhs

tan

Astan

a Ci

tyAlm

aty

City

11%15%

25%

6% 7%10%

5%5%

51%

8% 6%2% 4%

43%

7%

28%

13%

–4% –4%–5% –7%–2% –2%0% 0%

–5% –6% –8% –9%

–1%

Note: The effects pictured are drawn from the overall regression, in which sectors are controlled for with a set of indicator variables, and not the sector specific regressions. Thus, this specification imposes the same differentials for all sectors.


21

4. ACCess tO JObs There are relatively small differences in labor force participation rates across regions, even when taking into account the gender dimension. The differences in participation rates and unemployment rates are not large in Kazakhstan, even between men and women (figure 25 and figure 26). The regions with the lowest participation rates are Almaty City and East Kazakhstan, at around 65 percent each, while the regions with the highest rates are Akmola and Zhambyl, at almost 80 percent. Similarly, female labor force participation rates are highest and lowest in those four regions.

Figure 25Working age population by region, 2013

0%

20%

40%

60%

80%

100%

Akm

ola

Akt

ob

e

Alm

aty

Aty

rau

Wes

t K

az.

Jam

byl

Kar

agan

da

Ko

stan

ay

Kyz

ylo

rda

Man

gys

tau

Sou

th K

az.

Pavl

od

ar

No

rth

Kaz

.

East

Kaz

.

Ast

ana

Cit

y

Alm

aty

Cit

y

Employed Unemployed Not in a labor force

Note: Working age population = 15–64 years old.

Source: Authors’ calculations using 2013 LfS.

Figure 26Labor force participation rates by gender and region, 2013

Male Female Total population

80%

60%

40%

20%

0%

Alm

aty

Akm

ola

Akt

ob

e

Aty

rau

East

. Kaz

.

Man

gys

tau

No

rth

Kaz

.

Pavl

od

ar

Kar

agan

dy

Ko

stan

ay

Kyz

ylo

rda

Sou

th K

az.

Wes

t K

az.

Zham

byl

Ast

ana

Cit

y

Alm

aty

Cit

y

Note: Working age population = 15–64 years old.

Source: Authors’ calculations using 2013 LfS.

However, the types of employment differ substantially across regions and are largely dependent on the eco-nomic sectors prevalent in different parts of the country (figure 27 and figure 28). Regions that are relatively special-ized in mining and extractive industries (Mangystau, Atyrau, Karaganda), as well as the cities of Almaty and Astana, have particularly high concentrations of wage employment, while agriculture-intensive areas like North Kazakhstan, Zhambyl and Kostanay had high shares of self-employed working in agriculture, including farmstead workers.


22

Figure 27Employment type by region, 2013

Wage formal public

Wage informal

Self-employed non-agriculture

Wage formal private

Self-employed agriculture

100%

80%

60%

40%

20%

0%

Aktob

e

Almat

y

Atyra

u

Wes

t Kaz

.

Akmol

a

Karag

andy

Kosta

nay

Kyzyl

orda

Man

gyst

au

Sout

h Kaz

.

Pavl

odar

North

Kaz

.

East

Kaz

.

Astan

a Ci

tyAlm

aty

City

Zham

byl


Figure 28Employment by sector and region, 2013

0%

20%

40%

60%

80%

100%

Services

Mining, manufacturing and construction

Agriculture

Akmol

aAkt

obe

Almat

yAty

rau

Wes

t Kaz

.Ja

mby

lKar

agan

daKos

tana

yKyz

ylor

daM

angy

stau

Sout

h Kaz

.Pa

vlod

arNor

th K

az.

East

Kaz

.Ast

ana

City

Almat

y Ci

ty


Figure 29Wage employment types within region, 2013

Astana

MANGYSTAU

ATYRAU

AKTOBE

KOSTANAI A KMO LA

PAV LODA R

ALMATY

ZHAMBYL

SOUTHKAZAKHSTAN

EA S TKA ZAKH S TAN

KARAGHANDY

KYZYLORDA

NORTHKAZAKHSTAN

WESTKAZAKHSTAN

IBRD 42766 | MARCH 2017This map was produced by the Map Design Unit of The World Bank.The boundaries, colors, denominations and any other informationshown on this map do not imply, on the part of The World BankGroup, any judgment on the legal status of any territory, or anyendorsement or acceptance of such boundaries.

General Services

Budget, Performance Review& Strategic Planning

Printing & Multimedia

Public Wage Employment

Private Formal Wage Employment

Private Informal Wage Employment

Note: Green = public wage employment; dark blue = private formal wage employment; light blue = private informal wage employment. Cities of Astana and Almaty excluded. Their shares of public wage employment, private formal wage employment and private informal wage employment are as follows: Astana (42 percent, 55 percent, 3 percent) and Almaty (41 percent, 54 percent, 5 percent).


Informal wage employment is much more prevalent in South Kazakhstan and in the agriculture, commerce and other services sectors. The South Kazakhstan region has the largest share of informal wage workers (almost a third), by a significant margin, while Astana and Almaty cities have a high share of formal workers (figure 29). Informal wage workers are employed primarily in the agriculture, commerce and other services sectors. In contrast, public employment is largely concentrated in education, health and social services and public administration (figure 30). formal private wage work, on the other hand, is not dominated by any particular sector.


23

impact of labor markEts on povErty Over the period 2006–2013, labor incomes contributed a large share to the decline in Kazakhstan’s poverty rate.41 Extreme poverty levels are now very low in Kazakhstan,42 and the share of the population living in poverty went down from 80 percent in 2001 to 15 percent in 2013, as measured by the PPP-corrected US$5 per capita per day.43 The main sources of observed poverty reduction were wage earnings and, to some extent, pensions (figure 31). Consistent with the considerable contribution of earnings to poverty reduction in 2006–2007 (6.8 percentage points), real wages increased significantly during this period but dropped in 2008 during the economic crisis (figure 32). Wage growth rebounded quickly after 2008, but at a significantly lower rate, thus contributing to a lesser extent to poverty reduction. Still, wages contributed 4.8 percentage points to the decrease in the poverty rate over the 2012–2013 period. Growth in consumption of the bottom 40 percent of the population outpaced that of the top 60 percent, resulting in increased upward mobility. As a result, the size of the middle class more than doubled between 2006 and 2013 (Azevedo, Sattar and Yang, 2015).

Continued poverty reduction will depend on wage increases, but recent wage growth has been outpacing labor productivity. Overall productivity growth over the past decade has been lower than wage increases, even at the sectoral levels (figure 33 and figure 34). In the period 2004–08, wage growth (9 percent annually) outstripped labor productiv-ity growth (5 percent annually) to some extent. However, after 2008, productivity growth stagnated (1 percent annually), but robust wage growth continued (7 percent annually). The sectors with the highest increases in wages are mainly low-productivity sectors (health & social services, education, public administration). In high productivity sectors (mining, real estate transactions, financial and insurance activities), productivity has fallen in recent years while wages have continued to increase (figure 33). Although growth in wages has contributed to poverty reduction and shared prosperity, the growing gap between labor productivity and wages raises concerns over competitiveness and sustainability.

41 Azevedo, Joao Pedro, Sarosh Sattar, and Judy Yang. (2015). “Kazakhstan Economic Mobility and the Middle Class (2006–2013).” Manuscript in progress.

42 The extreme poverty line is $1.25/day PPP per person.43 World Bank’s cross-country poverty lines for ECA countries include PPP-corrected US$5 per capita per day (ECAPOV).

Figure 30Sectors by formality status, 2013

Other services

Finance, insurance & real estate

Transport & communication

Public administration, utilities & social sectors

Commerce

Construction

Manufacturing

0%

20%

40%

60%

80%

100%

Public wageworkers

Private formalwage workers

Private informalwage workers

Note: Social sectors include education, health and social services.



24

Figure 33Trends in real wages and labor productivity index, 2004 = 100

280

260

240

220

200

180

160

140

120

100

80

60

40

20

0

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Real monthly wages index, 2004 = 100

Real VA per worker index, 2004 = 100

Note: VA = value added.

Source: Authors’ calculations using Statistical Committee of RK.

Figure 34Growth in real wages and labor productivity index, 2004–2013

Agricultu

re

Min

ing an

d quar

ryin

g

Man

ufactu

ring

Constructi

on

Wholes

ale an

d reta

il tra

de

Finan

cial a

nd insu

rance

activ

ities

Real e

state

tran

sacti

ons

Public

adm

inist

ratio

n and d

efen

se

Educa

tion

Health

and so

cial s

ervic

es

20%

15%

10%

5%

0%

–5%

–10%

–15%

Productivity growth, 2004–2008

Productivity growth, 2008–2013

Wage growth, 2004–2008

Wage growth, 2008–2013

Note: Labor productivity is measured by real value added per worker.

Source: Authors’ calculations using Statistical Committee of RK.

Figure 31Contribution to poverty reduction

4.0

2.0

0.0

–2.0

–4.0

–6.0

–8.0

–10.0

–12.0

–14.0

Pove

rty

Rat

es (

$5/d

ay)

(dif

fere

nce

, per

cen

tag

e p

oin

ts)

2006

–200

720

07–2

008

2008

–200

920

09–2

010

2010

–201

120

11–2

012

2012

–201

3

Share Employed

Pension

Wages

Agriculture

Shares of Adults

Social Assistance

Other Income

Source: Azevedo, Joao Pedro, Sarosh Sattar, and Judy Yang. (2015). “Kazakhstan Economic Mobility and the Middle Class (2006–2013)”. Manuscript in progress.

Figure 32Year to year change in real monthly wage index (2003 = 100)

18%

16%

14%

12%

10%

8%

4%

2%

0%

–2%

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Source: Azevedo, Joao Pedro, Sarosh Sattar, and Judy Yang. (2015). “Kazakhstan Economic Mobility and the Middle Class (2006–2013).” Manuscript in progress.


25

labor markEt oUtcomEs of thE bottom 40 pErcEnt 44

Two thirds of household incomes in Kazakhstan comes from wages, but the relative importance of other sources varies somewhat between the bottom 40 and top 60 percent of the population.45 Wages, on average, contribute two-thirds to household incomes, while self-employment and agricultural incomes contribute less than 10 percent com-bined. However, self-employment and incomes from agricultural activities, in particular, are more prominent for the bot-tom 40 percent, while pensions are a larger share of income for the top 60 percent. An additional 1.7 percent (top 60) to 2.5 percent (bottom 40) comes from social assistance programs. Other income sources, including financial income, make up less than 10 percent of total household income, even for the wealthiest households (figure 35).

Households in the bottom 40 percent are not less likely to get income from various sources, except pensions, but they receive smaller amounts for each type of income, except social assistance (Figure 36). The lack of income does not come from receiving each source of income less often, as bottom 40 households are more likely to receive at least some income from each different source except pensions (figure 37). However, the amounts received from each source, including wage and especially pension income, are smaller. Wage income of those in the bottom 40 percent was 38 percent less than the national average, whereas top 60 households earned wages that were 16 percent higher than the national average. Pension income is even more divergent, with bottom 40 households receiving 52 percent less than the average, while top 60 households receive 22 percent more, on average. This result is partially driven by demographics, as is shown above.

44 Unless explicitly stated otherwise, the statistics presented in this section are based on the working age (15–64 years old) population from the most recent (2013) Household Budget Survey.

45 As noted earlier, the definition of “bottom 40 percent” (B40) or “top 60 percent” (T60) refers to the distribution of per capita household consumption, in which individuals are ranked by the expenditure of their household, deflated by regional price indices, based on data available in the 2011, 2012 and 2013 Household Budget Survey. See Annex C for methodology.

Figure 35Sources of household income, by B40/T60 status, 2013

Wage income

Agricultural income

Social assistance income

Self-employment income

Pension income

Other income sources

Overall Bottom 40% Top 60%

0%

20%

40%

60%

80%

100%

Note: The welfare aggregate is household consumption per capita.

Source: Authors’ calculations based on the Kazakhstan Household Budget Survey.


26

Figure 38Trends in relative average total per capita income and per capita wage income by B40/T60 status, 2013

40%

20%

0%

–20%

–40%

–60%

2011 2012 2013

Total per capita income (bottom 40%)

Total per capita income (top 60%)



Household incomes have been slowly becoming more equal, and this has been driven by a convergence in wage income (Figure 38). Per capita total income for bottom 40 households increased, in relative terms, from 41 percent to 38 percent below the national average, while household income for the top 60 percent fell, in relative terms, from 18 per-cent to 16 percent above the national average. This is consistent with impacts on poverty presented earlier.

Figure 36Relative average per capita income by source and B40/T60 status, 2013

Social assistanceincome

Pension income

Agricultural income


Wage income

–60% –50% –40% –30% –20% –10% 0% 10% 20% 30%

Top 60% Bottom 40%



Figure 37Percentage of households with any income by source and B40/T60 status, 2013

Social assistanceincome

Pension income

Agricultural income


Wage income

0% 20% 40% 60% 80% 100%

Top 60% Bottom 40% Overall

Shar

e o

f h

ou

seh

old

s w

ith

an

y in

com

e b

y so

urc

e



Part of the reason behind the lower per capita income levels of bottom 40 households is due to larger household sizes relative to richer households (Table 8 in Annex 3). Average household size is 4.8 people among bottom 40 house-holds, but only 3.1 people amongst the top 60. This implies that whatever income is generated by earners in the household is spread over more people. Moreover, 31 percent of household members are under 15 in bottom 40 households versus only 15 percent in top 60 households. Although bottom 40 households also have more people in their prime wage-earning years, they have fewer retirement-age people. Neither children nor retirees generate labor income for the household directly, but retirees can contribute to household resources through pension benefits. This effect is clearly inequality-increasing in Kazakhstan, as income from pensions is far higher in top 60 than in bottom 40 households.

Bottom 40 households also reside in areas where earnings potential is lower. More than half (55 percent) of bot-tom 40 households reside in rural areas, whereas only 32 percent of top 60 households are found in rural areas. Moreover,


27

Figure 39Share of bottom 40 households in each region, 2013

Astana

Almaty

MANGYSTAU

ATYRAUAKTOBE

KOSTANAI A KMO LA

PAV LODA R

ALMATY

ZHAMBYL

SOUTHKAZAKHSTAN

EA S TKA ZAKH S TAN

KARAGHANDY

KYZYLORDA

NORTHKAZAKHSTAN

WESTKAZAKHSTAN

This map was produced by the Map Design Unit of The World Bank.The boundaries, colors, denominations and any other informationshown on this map do not imply, on the part of The World BankGroup, any judgment on the legal status of any territory, or anyendorsement or acceptance of such boundaries.

General Services

Budget, Performance Review& Strategic Planning

Printing & Multimedia

49.70 – 61.23

43.85 – 49.70

43.19 – 43.85

35.53 – 43.19

32.41 – 35.53

30.33 – 32.41

21.08 – 30.33

20.31 – 21.08

Astana City: 20.82

Almaty City: 12.32

Note: The welfare aggregate is household consumption per capita. Cities of Astana and Almaty excluded.


Figure 40Relative average income from wage employment by region, 2013

Astana

Almaty

MANGYSTAU

ATYRAUAKTOBE

KOSTANAI

PAV LODA R

ALMATY

ZHAMBYL

SOUTHKAZAKHSTAN

EA S TKA ZAKH S TAN

KARAGHANDY

KYZYLORDA

WESTKAZAKHSTAN

AKMO LA

NORTHKAZAKHSTAN

116.83 – 149.70

97.76 – 116.83

88.31 – 97.76

83.12 – 88.31

77.66 – 83.12

73.30 – 77.66

59.22 – 73.30

57.59 – 59.22

Astana City: 144.15

Almaty City: 147.68

Note: Cities of Astana and Almaty excluded.


bottom 40 households are concentrated in South Kazakhstan and East Kazakhstan, and make up the largest shares of the population in South Kazakhstan and North Kazakhstan (figure 39). However, these are the regions where wage employment provides the least income to households (figure 40). Conversely, the regions where wage employment provides the most income (Almaty City, Astana City and Mangystau) are also the regions with the fewest bottom 40 households.


28

Employed members of bottom 40 households disproportionately work in low paying sectors (Figure 41). Members of households in the bottom 40 are much more likely to work in the low-paying agriculture sector, and slightly more in the somewhat better paying construction sector. However, members of top 60 households are more likely to be found in high paying sectors such as mining, manufacturing, and finance, insurance and real estate. Part of this effect is related to the geo-graphic localization of bottom 40 households: as there are relatively few of them in Almaty City, Astana City and Mangystau, where the high paying jobs are found, it is normal that they are less likely to work in the high paying sectors.

Figure 41Sector of activity by B40/T60 status, 2013

Other services

Health and social services

Education

Public administration services

Finance, insurance and real estate

Transportation and communication

Commerce

Construction

Public utilities

Manufacturing

Mining

Agriculture

Top 60% Bottom 40%

0% 2% 4% 6% 8% 10% 12% 14% 16%



This difference in sectors can be related to a skills gap between top 60 and bottom 40 households, which is likely to grow in the future. Whereas 33 percent of household members in top 60 households have a tertiary educa-tion, the figure for bottom 40 households is only 20 percent. Conversely, bottom 40 households have a higher share of low skilled individuals, at the upper secondary (63 versus 56 percent), lower secondary (14 versus 9 percent) and primary or below levels (4 percent versus 2 percent). As higher skills bring more earnings capacity and better wages, the lack of skills may be contributing to keeping bottom 40 households poor. Although tertiary education has been more prevalent in top 60 households than in bottom 40 households across generations, the gap has widened in more recent generations (figure 42). Among 60–64 year olds, the difference was 11 percentage points, while among the most recent school-leaving cohort (25–29 year olds), the difference is 19 percentage points.46 If the skills gap continues to widen, recent trends toward reduced inequality could reverse, with the gap in income and expenditure between bottom 40 and top 60 house-holds potentially growing.

The combination of skills, household size and geography pushes individuals from bottom 40 households into different types of jobs than those occupied by individuals from top 60 households (Table 8 and Table 9 in Annex C). People from bottom 40 households are more likely to be out of the labor market or unemployed, and less likely to be found in wage employment. The difference in activity rates is driven primarily by the larger share of individuals in schooling and training among the bottom 40 percent. However, among active individuals, those from top 60 households are much more likely to be wage employees, while those in bottom 40 households are more often found in farmstead work and self-employment, or not employed at all (figure 43).

46 Conversely, upper secondary education accounted for 71 percent of people in the oldest generation of bottom 40 households (relative to 65 percent in top 60 households); the gap has widened in recent generations with 61 percent of poor 25–29 year olds having an upper secondary education versus only 45 percent of top 60 25–29 year olds.


29

Figure 42Educational attainment by age and B40/T60 status, 2013

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64

Age group

Upper secondary (top 60)

Upper secondary (bottom 40)

Tertiary (top 60)

Tertiary (bottom 40)



Figure 43Labor force status by B40/T60 status, 2013

Top 60% Bottom 40%

Private wage employee

Public wage employee

Non-agricultural self-employed

Non-agricultural employer

Farmstead worker

Unemployed

In school or training

NEET

0% 5% 10% 15% 20% 25% 30% 35% 40%



Not only do individuals in bottom 40 and top 60 households differ in terms of their characteristics, they also likely face a different set of constraints affecting their labor market outcomes (Table 10 in Annex C). A detailed statistical analysis47 of what makes people more likely to be wage employees relative to self-employment, farmstead workers or out of the labor force (for example) shows that most variables work in the same direction (e.g. women are more likely to be out of the labor force for reasons other than education or training and less likely to be in private wage employment for both bottom 40 and top 60 individuals).48 However, some factors play a larger role in the bottom 40 population than in the top 60 population (for instance, location of residence). This suggests that there may be differences in unobservable characteristics and constraints facing individuals from the bottom 40 and top 60 households, that cannot be captured with the existing data.

Individuals from bottom 40 households typically earn less than those from top 60 households from wage employ-ment and non-agricultural self-employment (figure 68 in Annex C). for both upper secondary and tertiary educated people, a larger share of members of bottom 40 households earn low wages and low income from self-employment than among members of top 60 households, and conversely a larger share of members of top 60 households earn high wages and high levels of income from self-employment.

Besides education levels, other observable characteristics cannot explain the differences in wages between those in the bottom 40 and top 60 percent (figure 69 in Annex C). After controlling for a large set of observable characteristics49 and the fact that individuals in wage employment differ from others along observable and unobserved dimensions, much of the difference between bottom 40 and top 60 individuals disappears, especially for those with upper secondary education. However, it appears that bottom 40 wage workers with tertiary education are more likely to be found at high incomes than would be expected given their education, gender, age, region, urban/rural residency and sec-tor of activity, while top 60 individuals earn slightly less than would have been expected, in particular at the top end of the wage distribution. The fact that such large unexplained variation remains further suggests a need for better data.50

47 The results of this analysis are presented in Table 10 in Annex C. The analysis of the determinants of job type was undertaken using a series of multinomial logit models. for more details, please refer to Annex C.

48 Note that the magnitude of coefficients presented based on HBS differs from LfS. LfS-based estimates presented earlier are likely to be more representative of the labor force. Hence, HBS-based estimates should be taken as indicative.

49 Including gender, 5-year age groups, education, region, urban residency and public/private sector employer.50 Household Budget Survey is the only household survey which has information on both labor market status and wages and other incomes, as well as

consumption of households. However, the set of labor market and demographic variables is a lot more limited compared to LfS.


30

5. ChALLenges AheAd And POLICy IMPLICAtIOns

In summary, despite a good labor market performance in the last decade, Kazakhstan continues to face several jobs and skills challenges. During the past decade, Kazakhstan benefited from the global commodities boom to become one of the top 10 fastest growing economies in the world. This sustained growth has enabled Kazakhstan to achieve rapid reductions in poverty rates, resulting from strong labor market performance: labor force participation and employment rates are high in Kazakhstan by international standards, especially for women; unemployment halved between 2001 and 2013, from 10.4 to 5.2 percent; and real wages doubled between 2003 and 2013. Despite these gains, Kazakhstan faces a num-ber of jobs challenges, which are related to some of the challenges resource rich economies face:

• Many people continue to be employed in low productivity jobs, particularly, in agriculture. Despite a significant reduction in the share of employment in agriculture, there are still about 2 million people, or a quarter of all employed, who remain engaged in this sector. furthermore, many of the other sectors that increased their share of employment, such as construction and education, also have below average productivity. As a result, overall labor productivity remains low, especially in the non-oil sectors, and compared to countries with similar GDP per capita levels.51 Despite efforts to increase import substitution in higher value added sectors, Kazakhstan’s tradable sectors have remained less competitive, in part due to the exchange rate which was boosted by the resource exports during the period of high oil prices.52

• There are significant inequalities when it comes to the types of jobs and earnings workers have access to across population groups. Men and more educated people tend to have better labor market outcomes, including more access to formal wage employment and higher wages. There are large differences in labor market outcomes and earnings between those in the bottom 40 percent compared to those in the top 60 percent, which cannot always be explained by observable characteristics, suggesting that there may be unobservable barriers that people in the bottom 40 percent face on the labor market. Significant wage differentials across regions and sectors, even after controlling for worker attributes, point to constraints to labor mobility, which appear to play a role in inhibiting some individuals from moving to access higher paid jobs.

• Education is a key determinant of labor market outcomes, but the gap in attainment between those in the bottom 40 percent and those in the top 60 percent is increasing, and low education quality may constrain labor productivity growth. Educational attainment is strongly linked with labor market outcomes and largely deter-mines access to wage employment and higher earnings. Although tertiary education has become more prevalent across generations, the gap between the top 60 households and bottom 40 households has widened in more recent genera-tions. Continued widening of the skills gap could result in a reversal of the recent trend toward reduced inequality. In addition, the low quality of education may hamper individuals’ prospects on the labor market as well constrain labor productivity growth. Despite high levels of enrollment and completion of secondary education, Kazakhstan does not provide a quality education, as reflected in the poor, though markedly improved, performance on international student assessments, such as the OECD’s PISA.

51 World Bank. 2013. Beyond Oil : Kazakhstan’s Path to Greater Prosperity through Diversifying, Volume 2. Main report. Washington, DC. 52 Under the pressures of low oil prices, the tenge has depreciated significantly in 2015, which could aid the price competitiveness of the economy.


31

Furthermore, the current economic slowdown is likely to increase unemployment and reduce real earnings in the short to medium term, with some groups being more affected than others. Kazakhstan’s GDP growth has slowed significantly due to a terms-of-trade (oil price) shock as well as the slowdown in Russia and the rebalancing of the China’s economy.53 Simulations show that with lower projected GDP growth in the next few years, unemployment may increase, particularly among older workers and the high skilled. This is despite the expected slowing down of labor force growth in the next few years.54

Removing constraints at the macroeconomic level might not be enough to address Kazakhstan’s jobs challenge, particularly for those in the bottom 40 percent of the population. Part of the solution to the jobs challenge may be in economy-wide policies that promote investments, innovation and economic diversification. However, the relationship between overall investment and employment is complex, not always resulting in the types of jobs that may benefit the bottom 40 percent. Reallocating a given amount of capital to a given sector can have very different consequences in terms of the number and composition of jobs created. Existing government programs such as the State Program for Industrial and Innovative Development (SPIID) for 2015–2019 and economic stimulus program “Nurly Zhol” are aimed at economic diversification and supporting the economy, respectively. However, these programs tend to favor capital intensive sectors or generate temporary jobs.55

Economy-wide reforms need to be complemented with sectoral and regional approaches. In addition to the reforms that target improvements in macro-economic management, investment climate, as well as business environment, it is impor-tant to focus on targeted sectoral policies that aim to promote jobs for specific population groups in given regions. This bottom-up approach implies that the government, in close consultation with social partners, also identifies sectoral policies for employment creation, such as targeted interventions to create jobs in specific regions/sectors. This entails mapping key sub-sectors and value chains within the economy to understand the potential for job creation and the types of bottlenecks and regulatory failures that would need to be removed to achieve it. This mapping would provide information about the types and level of investments that are necessary, the quantity of jobs that can be created, their composition in terms of skills, and their regional distribution.

Going forward, addressing the jobs challenge would involve consideration of policies and programs at three levels. These are further elaborated in the Jobs Strategy for Kazakhstan,56 which will help to enhance the impact of the Government’s policies, programs, and projects on the availability, diversity, quality, and sustainability of jobs.

1. facilitating the creation of new jobs through private sector investments, taking into account regional and population disparities in terms of labor market outcomes. This involves interventions to remove constraints to the creation and expansion of businesses—the main sources of jobs.

2. Upgrading production technologies and increasing the productivity of jobs in economic activities that are already underway, with a focus on lagging regions. To improve living standards, programs and policies to improve the pro-ductivity of existing jobs would be needed, with a focus on the poor. These could include programs that support self-employment and small-scale entrepreneurship, aiming to improve earnings, or policies that help upgrade workers’ skills and enhance their productivity.

3. Connecting individuals to jobs by facilitating labor market transitions; from inactivity or unemployment into jobs, or from low to high productivity jobs. This includes addressing constraints to mobility and reforming and expanding active labor market programs (counseling, intermediation, job search assistance, support to self-employment, and training). These programs are important to address information problems in the labor market and address skills mismatches.

53 Kazakhstan’s GDP growth slowed to 4.1 percent in 2014 and to 1.2 percent in 2015, while prospects for 2016 growth are 0.1 percent. Source: World Bank (2016). Kazakhstan first fiscal Management and Resilience Programmatic Development Policy financing. Program information document.

54 See Mohamed Ali Marouani, Bjorn Nilsson, Angela Elzir, Victoria Strokova and Namita Datta (2015) “Kazakhstan Jobs Impacts of Sectoral Investments: Simulations using Dynamic Computable General Equilibrium Model Applied to Kazakhstan”. A note prepared for Kazakhstan: Jobs: Sector Specific Analysis JERP (P153621).

55 These effects operate through two main channels: i) a substitution between capital and labor (as more capital reduces its rate of return, capital becomes cheaper relative to labor); and ii) the reduction of investments in other sectors, which reduces intermediate consumption from the sector in question.

56 World Bank (2016) Kazakhstan: Towards Development of a Jobs Strategy.


32

Annex A: AddItIOnAL tAbLes And grAPhs

Table A1Key Labor market indicators, Kazakhstan, 2013

Overall Male Female Urban Rural

All Adults (15+)

Total Population 12,534,701 5,917,084 6,617,617 7,079,761 5,454,940

Labor Force Participation Rate 71.76% 77.32% 66.79% 69.39% 74.85%

Employment/Population 68.04% 73.81% 62.88% 65.62% 71.18%

Working Age (15–64)

Total Population 11,387,615 5,494,509 5,893,106 6,376,155 5,011,460

Employment 8,480,801 4,340,221 4,140,580 4,626,300 3,854,501

Unemployment 466,939 207,858 259,081 266,814 200,125

Inactive 2,439,875 946,430 1,493,445 1,483,041 956,834

NEET 1,075,294 302,224 773,070 669,513 405,781

Employment Rate 74.47% 78.99% 70.26% 72.56% 76.91%

Unemployment Rate 5.22% 4.57% 5.89% 5.45% 4.94%

Percentage NEET 9.44% 5.50% 13.12% 10.50% 8.10%

Labor Force Participation Rate 78.57% 82.77% 74.66% 76.74% 80.91%

Working Age Employed

Wage Employment 69.60% 68.85% 70.38% 82.09% 54.60%

Employers 1.87% 2.57% 1.13% 1.25% 2.62%

Own Account Workers 17.95% 19.43% 16.40% 15.16% 21.30%

Farmstead Workers 10.19% 8.77% 11.68% 14.08% 20.72%

Source: 2013 LfS.


33

Figure A1Employment rates (15+), male and female, 2013

MexicoKazakhstan

ChinaKyrgyzstan

MalaysiaChile

TurkmenistanTajikistanAustralia

UzbekistanRussian Federation

AzerbaijanCanadaGeorgia

United StatesEstonia

ArmeniaUkraine

LatviaBelarus

LithuaniaMoldova

0% 10% 20% 30% 40% 50% 60% 70% 80%

Male

70%

KazakhstanChina

AzerbaijanCanada

AustraliaRussian Federation

TajikistanUnited States

EstoniaKyrgyzstan

LithuaniaUkraine

LatviaGeorgiaBelarus

ChileArmeniaMalaysia

UzbekistanMexico

TurkmenistanMoldova

0% 10% 20% 30% 40% 50% 60% 70% 80%

Female

60%

Source: ILO.


34

Table A2Marginal effects for multinomial logit model, 2013

NEET UnemployedPublic Wage Employment

Private Formal Wage Employment

Non-Ag Self-Employment

Ag Self-Employment

Male –10.35 *** –0.75 ** –4.81 *** 11.44 *** 3.75 *** –0.09 ***

15–19 Years Old 15.22 *** –4.06 *** –14.86 *** –3.22 4.70 ** 1.47 ***

20–24 Years Old 1.68 * –2.70 *** –4.42 *** 2.82 ** 1.78 * 0.18 ***

30–34 Years Old –1.11 –0.13 1.14 –0.78 1.73 * –0.09

35–39 Years Old –1.05 –1.67 *** 5.52 *** –1.24 –1.02 –0.04

40–44 Years Old –2.89 *** –2.20 *** 8.78 *** –3.21 *** 0.41 –0.08

45–49 Years Old –0.16 –2.64 *** 8.33 *** –4.47 *** 0.59 0.00

50–54 Years Old 1.83 ** –2.40 *** 6.69 *** –3.69 *** –0.81 –0.08

55–59 Years Old 18.77 *** –2.73 *** 1.50 –11.56 *** –3.65 *** –0.04

60–64 Years Old 69.99 *** –5.46 *** –20.39 *** –27.89 *** –11.01 *** –0.35 ***

Upper Secondary Education –17.70 *** –4.29 *** 9.93 *** 14.44 *** –2.25 –0.23 **

Tertiary Education –15.66 *** –5.71 *** 31.01 *** 4.62 –7.60 *** –0.78 ***

Aktobe 3.13 * –1.48 ** –14.03 *** 20.03 *** –3.19 ** –0.52 ***

Almaty 13.63 *** –1.01 –1.96 –7.46 *** –0.27 –0.36 ***

Atyrau 3.60 ** –1.92 *** –15.23 *** 28.08 *** –9.40 *** –0.82 ***

West Kazakhstan 10.67 *** –0.68 –3.97 ** –0.25 –0.84 –0.21 ***

Jambyl 10.95 *** –0.35 6.12 *** –19.67 *** 6.43 *** –0.03

Karaganda 6.83 *** –1.31 ** –9.39 *** 16.43 *** –9.90 *** –0.59 ***

Kostanay –1.42 –0.05 –11.50 *** 19.99 *** –3.75 *** 0.17 *

Kyzylorda 15.54 *** –1.85 *** 7.12 *** –18.86 *** 3.08 * –0.73 ***

Mangystau 15.09 *** –2.00 *** 16.55 *** –11.68 *** –11.77 *** –0.94 ***

South Kazakhstan 15.66 *** –0.05 –4.82 *** –18.90 *** 7.08 *** –0.25 ***

Pavlodar 3.24 ** –1.11 * –11.27 *** 24.03 *** –12.43 *** –0.19 ***

North Kazakhstan 3.74 ** –0.41 1.11 8.46 *** –10.44 *** –0.08

East Kazakhstan 11.62 *** –1.21 * –17.08 *** 15.57 *** –5.35 *** –0.20 ***

Astana City 25.03 *** –1.63 ** –10.71 *** 4.72 * –12.35 *** –0.94 ***

Almaty City 9.06 *** 0.40 –6.98 *** 15.67 *** –8.71 *** –8.66 ***

Urban 0.89 ** –0.96 *** –0.76 4.88 *** 0.60 –2.26 ***

2 Person Household –2.29 *** –1.27 *** 2.08 ** 2.82 *** –1.17 * 0.03

3 Person Household –2.04 *** –1.54 *** 2.41 *** 2.63 *** –0.07 –0.08

4 Person Household –2.62 *** –1.47 *** 3.26 *** 1.86 –0.29 –0.03

5 Person Household –0.86 –1.95 *** 3.13 ** –1.32 1.93 * 0.07

6 Person Household 0.91 –0.23 0.65 –2.35 1.22 0.16

7 Person Household –0.76 –3.79 *** –2.65 6.50 –0.17 0.25

8 Person Household –2.72 –2.38 –5.52 7.61 1.47 0.65

9 Person Household –6.85 *** –5.99 *** 7.95 11.33 –2.57 0.21

10+ Person Household –0.48 2.08 –15.94 ** –19.06 *** 9.53 1.50

Interpretation: A male has a 10.35 percentage point lower chance of being NEET (not in employment, education or training) than a female when both

have the average characteristics of the working age population.

Note: Multinomial logits estimated separately on the following outcomes: NEET, in school or training, unemployment, agricultural self-employed, non-

agricultural employer, non-agricultural self-employed, public wage employment and private wage employment. Reference categories were female,

25–29 years old, lower secondary or less, rural resident and one-person household. The models also include controls for region of residence.

*** signifies a marginal effect that is significant at the 1 percent level, ** signifies a marginal effect that is significant at the 5 percent level and

* signifies a marginal effect that is significant at the 10 percent level.


35

Figure A2Age by labor force status, shares, 2013

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

15–24 25–34 35–44 45–54 55–64

Wor

king

age

pop

.Em

ploy

edUne

mpl

oyed

In sc

hool

NEET


Figure A3Age by type of employment, shares, 2013

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

15–24 25–34 35–44 45–54 55–64

Wor

king

age

pop

.W

age

empl

oym

ent

Empl

oyed

Ow

n ac

coun

t wor

ker

Farm

stea

d w

orke

r


Figure A4Age by labor force status, number of people, 2013

9,000

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

Tho

usa

nd

s

15–24 25–34 35–44 45–54 55–64

Empl

oyed

Unem

ploy

ed

In sc

hool

NEET


Figure A5Age by type of employment, number of people, 2013

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

Tho

usa

nd

s

15–24 25–34 35–44 45–54 55–64

Wag

e em

ploy

men

t

Empl

oyer

Ow

n ac

coun

t wor

ker

Farm

stea

d w

orke

r



36

Figure A8Distribution of self-employed by age group, 2013

250

200

150

100

50

–

Tho

usa

nd

s

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

15–1

9

20–2

4

25–2

9

30–3

4

35–3

9

40–4

4

45–4

9

50–5

4

55–5

9

60–6

4

Total (left axis) Percentage (right axis)


Figure A9 Distribution of self-employed by education, 2013

600

500

400

300

200

100

–

Tho

usa

nd

s

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%


Less

than

12

year

sGen

eral

seco

ndar

yIn

itial

voc

atio

nal

Voca

tiona

l (sp

ecia

l)

Inco

mpl

ete

tert

iary

Com

plet

ed te

rtia

ry


Figure A6Job type by age for employed population, in %, 2013

100%

80%

60%

40%

20%

0%

15–24 25–34 35–44 45–54 55–64

Wage employment

Own account worker

Employer

Farmstead worker


Figure A7Job type by age for employed population, number of employed, 2013

3,000

2,500

2,000

1,500

1,000

500

–

Tho

usa

nd

s

15–24 25–34 35–44 45–54 55–64

Wage employment

Own account worker

Employer

Farmstead worker



37

Figure A10Distribution of self-employed by household size and gender, 2013

0%

10%

20%

30%

40%

50%

60%1

pers

on2

pers

ons

3 pe

rson

s4

pers

ons

5 pe

rson

sM

ore

then

5+

Mal

eFe

mal

e


Figure A12Distribution of farmstead workers by age group, 2013

140

120

100

80

60

40

20

–

Tho

usa

nd

s

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

15–1

9

20–2

4

25–2

9

30–3

4

35–3

9

40–4

4

45–4

9

50–5

4

55–5

9

60–6

4



Figure A11Distribution of self-employed by relation to head of household and gender, 2013

600

500

400

300

200

100

0

Head Spouse Children Parents Other ornot relate

Male Female


Figure A13Distribution of farmstead workers by education, 2013

500

400

300

200

100

0

Tho

usa

nd

s

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%


Less

than

12

year

sGen

eral

seco

ndar

yIn

itial

voc

atio

nal

Voca

tiona

l (sp

ecia

l)

Inco

mpl

ete

tert

iary

Com

plet

ed te

rtia

ry



38

Figure A14Distribution of farmstead workers by household size and gender, 2013

0%

10%

20%

30%

40%

50%

60%

1 pe

rson

2 pe

rson

s3

pers

ons

4 pe

rson

s5

pers

ons

Mor

e th

en 5

+

Mal

eFe

mal

e


Figure A16Distribution of wage employees by age group, 2013

1,200

1,000

800

600

400

200

–

Tho

usa

nd

s

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

15–1

9

20–2

4

25–2

9

30–3

4

35–3

9

40–4

4

45–4

9

50–5

4

55–5

9

60–6

4



Figure A15Distribution of farmstead workers by relation to head of household and gender, 2013

300

250

200

150

100

50

0

Head Spouse Children Parents Other ornot related

Male Female

Tho

usa

nd

s


Figure A17Distribution of wage employees by education, 2013

2,500

2,000

1,500

1,000

500

–

Tho

usa

nd

s

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%


Less

than

12

year

sGen

eral

seco

ndar

yIn

itial

voc

atio

nal

Voca

tiona

l (sp

ecia

l)

Inco

mpl

ete

tert

iary

Com

plet

ed te

rtia

ry



39

Figure A18Distribution of wage employees by household size and gender, 2013

0%

10%

20%

30%

40%

50%

60%1

pers

on2

pers

ons

3 pe

rson

s4

pers

ons

5 pe

rson

sM

ore

then

5+

Mal

eFe

mal

e


Figure A20Labor force status by education level, percentage, 2013

Employed Unemployed In school NEET

Less

than

12

year

sGen

eral

seco

ndar

yIn

itial

voc

atio

nal

Voca

tiona

l (sp

ecia

l)In

com

plet

e te

rtia

ryCo

mpl

eted

tert

iary

0%

20%

40%

60%

80%

100%


Figure A19Distribution of wage employees by relation to head of household and gender, 2013

3,000

2,500

2,000

1,500

1,000

500

0

Head Spouse Children Parents Other ornot related

Male Female

Tho

usa

nd

s


Figure A21Labor force status by education level, total number, 2013

Employed Unemployed In school NEET

Less

than

12

year

sGen

eral

seco

ndar

yIn

itial

voc

atio

nal

Voca

tiona

l (sp

ecia

l)In

com

plet

e te

rtia

ryCo

mpl

eted

tert

iary

3,500

3,000

2,500

2,000

1,500

1,000

500

–

Tho

usa

nd

s



40

Figure A22Education attained by type of employment, 2013

100%

80%

60%

40%

20%

0%

Wageemployment

Employer Own accountworker

Farmsteadworker

Less than 12 years

Initial vocational

Incomplete tertiary

General secondary

Vocational (special)

Completed tertiary


Figure A23Employment by sector and education, 2013

Min

ing

and

ext

ract

ive

indu

strie

sM

anuf

actu

ring

Publ

ic ut

ilitie

sCo

nstr

uctio

nTr

ansp

orta

tion

and

com

mun

icatio

n

Fina

nce,

insu

ranc

e an

d re

al e

stat

ePu

blic

adm

inist

ratio

n

Agricu

lture

Oth

er se

rvice

s

Com

mer

ce

Less than 12 years

General secondary

Incomplete tertiary

Completed tertiary

Initial vocational

Vocational (special)

100%

80%

60%

40%

20%

0%



41

Figure A24Comparison of PISA 2012 scores for select countries and ECA/OECD averages, 2013

520

500

480

460

440

420

400

380

360

Kaz

akh

stan

Mal

aysi

a

Ch

ile

ECA

Ru

ssia

Turk

ey

OEC

D

Mal

aysi

a

Ch

ile

Kaz

akh

stan

Turk

ey

ECA

Ru

ssia

OEC

D

Mal

aysi

a

Kaz

akh

stan

Ch

ile

Turk

ey

ECA

Ru

ssia

OEC

D

Reading Math Science

PISA

201

2 Sc

ore

One year ofschooling

432 425

393

Source: OECD, PISA 2012 Results (2012); WB, Strengthening Kazakhstan’s Education System (2014).


42

Annex b: MethOdOLOgy And AddItIOnAL resuLts On InfOrMALItymEthodologyMost of the statistics presented are weighted sample statistics. The weights used were drawn directly from the vari-ous years’ labor force surveys, and the years 2010 to 2013 were used.

The statistical analysis was based on a probit model with selection bias correction. The main equation explained the probability of formal wage employment (private or public) relative to informal wage employment. Since the set of individuals actually occupying a wage job (as opposed to self-employed or out of the labor force, for example) is not a random sample of the population, one has to correct the formal wage employment model estimates for the probability for being observed in wage employment. This is done through a (probit) selection equation for selection into wage employment. The disturbance terms between the selection equation and the formal wage employment equation can be correlated, and the system is esti-mated by maximum likelihood.

Table B1Characteristics of wage workers by formality status

All Wage Workers

Public Sector Wage Workers

Private Formal Wage Workers

Private Informal Wage Workers

Number of Workers 5,899,592 2,495,590 2,916,665 487,337

Share of All Wage Workers 100.0% 42.3% 49.4% 8.3%

Percentage Male 50.6% 43.6% 56.2% 53.7%

Average Age 38.0 38.8 37.6 36.7

Percentage with Lower Secondary or Below Education 0.8% 0.5% 0.8% 1.8%

Percentage with Upper Secondary Education 58.7% 47.0% 64.3% 84.7%

Percentage with Tertiary Education 40.5% 52.5% 34.9% 13.5%

Share Living in Urban Areas 64.4% 61.7% 69.6% 46.4%

Average Household Size 2.00 2.01 1.98 2.05

Contract and Social Security 100% 98% 0%

Contract Only 0% 2% 38%

Social Security Only 0% 0% 6%

No Contract or Social Security 0% 0% 56%



43

Table B2Marginal effects for probit model of informality with selection bias correction, 2013

Marginal Effect

Conditional Marginal Effect

Marginal Effect

Conditional Marginal Effect

Male 0.44 ** 0.49 *** Urban –0.51 –0.28

Age 15–19 –2.60 *** –2.86 *** Aktobe –2.56 *** –2.33 ***

Age 20–24 –0.85 ** –1.02 *** Almaty –2.37 *** –2.16 ***

Age 30–34 –0.28 –0.24 Atyrau –2.55 *** –2.30 ***

Age 35–39 0.19 0.23 West Kazakhstan –2.78 *** –2.55 ***

Age 40–44 –0.18 –0.10 Jambyl –1.97 *** –1.84 ***

Age 45–49 –0.73 ** –0.63 ** Karaganda –1.30 *** –1.10 ***

Age 50–54 –0.60 * –0.51 * Kostanay –2.71 *** –2.48 ***

Age 55–59 –0.59 * –0.63 ** Kyzylorda –2.06 *** –1.88 ***

Age 60–64 –2.21 *** –2.35 *** Mangystau –2.87 *** –2.62 ***

Upper Secondary Education 1.25 1.45 ** South Kazakhstan 1.51 * 1.05 *

Tertiary Education –1.62 –1.07 Pavlodar –1.78 *** –1.58 ***

Mining and Extractive Industries –10.42 *** –9.29 *** North Kazakhstan –2.01 *** –1.81 ***

Manufacturing –5.52 *** –4.92 *** East Kazakhstan –2.27 *** –2.07 ***

Public Utilities –8.43 *** –7.52 *** Astana City –2.59 *** –2.35 ***

Construction –1.16 ** –1.04 *** Almaty City –2.08 *** –1.87 ***

Commerce 1.83 *** 1.63 ***

Correlation

Coefficient

(Selection Effect) = 0.554 ***

Transportation and Communication –3.83 *** –3.41 ***

Financial, Insurance and Real Estate –4.21 *** –3.75 ***

Public Administration Services –8.69 *** –7.75 ***

Education –9.59 *** –8.55 ***

Health and Social Services –10.66 *** –9.51 ***

Other Services 0.01 0.01

Interpretation: Men have a 0.44 percentage point higher probability of being informal wage workers (as opposed to formal wage workers) than women

when in wage employment. Accounting for the fact that men are also more likely to be in wage employment and that those with a higher probability

of being in wage employment also have a higher probability of being informal (positive significant correlation coefficient), men have a 0.49 percentage

point higher chance than women to be seen in informal wage employment overall.

Notes: Reference categories include: female, age 25–29, lower secondary education and below, agriculture sector, rural and Akmola region. Exclusion

restrictions for the selection equation included a set of 10 indicators for household size.

*** signifies a marginal effect that is significant at the 1 percent level, ** signifies a marginal effect that is significant at the 5 percent level and

* signifies a marginal effect that is significant at the 10 percent level.


44

Annex C: MethOdOLOgy Of the bOttOM 40 PerCent AnALysIs And resuLtsmEthodologyThe descriptive analysis undertaken is based on weighted descriptive statistics. As the official sample weights were not provided, the World Bank poverty group designed a weighting scheme for the HBS that is representative of the number of households in each urban versus rural area in each region. There is, however, no calibration to a population census used in establishing sample weights, and thus the representativity of the data for particular population groups is not guaranteed.

The analysis of the determinants of job type was undertaken using a series of multinomial logit models. This approach assumes that there is an unobservable (latent) value to each possible outcome, and the outcome with the highest value is the one observed in the data. This value need not reflect exclusively preference parameters of individuals; it can also incorporate matching processes on the labor market and firm objectives. This approach implicitly assumes “independence of irrelevant alternatives,” in that factors that change the value of one alternative do not affect the relative rankings of other alter-natives among themselves. The multinomial logit approach assumes that each latent value is a linear function of the observable characteristics in the model and a random variable that follows a standard type-1 extreme value distribution.

The table presents marginal effects estimated from the multinomial logit models. Marginal effects describe how much the probability of a particular outcome changes when the variable in question changes by one unit (or goes from 0 to 1 in the case of indicator variables). As multinomial logit models are highly nonlinear, the estimated coefficients cannot be directly interpreted in terms of the probability of choosing a particular outcome. Moreover, the estimated marginal effects will typically depend on the reference values for the explanatory variables at which the calculations are made. This paper sets the covariates to the population average (overall, bottom 40 or top 60, depending on the model) and calculates the change in the probability of each outcome numerically.

Kernel density estimates of different income sources are also presented. Kernel density estimators are similar to his-tograms, in that they count the number of observations with a given value of variable in question (for example, log income from wage employment). They differ, however, in that they use a weighted count that includes the number of observations with values near the one being evaluated, with the weighting function (kernel) and set of points over which the weighted count is taken (related to the bandwidth) being chosen by the analyst. The figures use an Epanechnikov kernel, a bandwidth of 0.04 for wage income and a bandwidth of 0.1 for self-employment and agricultural income.

The kernel density estimates of residuals from a log wage regression were constructed using a Heckman (1979) selection-bias corrected wage regression. This model regressed, separately for 10 different sectors,57 log wage income on gender, 5-year age groups, education, region, urban residency and public/private sector employer. The selection equation

57 These effects operate through two main channels: i) a substitution between capital and labor (more easily available capital reduces its cost, making it cheaper relative to the price of labor); and ii) a reduction of investments in other sectors, which reduces intermediate consumption of goods and services produced by the sector receiving the injection of capital from the sectors from which the capital is taken.

These effects operate through two main channels: i) a substitution between capital and labor (more easily available capital reduces its cost, making it cheaper relative to the price of labor); and ii) a reduction of investments in other sectors, which reduces intermediate consumption of goods and services produced by the sector receiving the injection of capital from the sectors from which the capital is taken.

The sectors included agriculture, mining, manufacturing, public utilities, construction, commerce, transportation and communication, finance, insurance and real estate, public administration services and other services.


45

Figure C1Earnings distributions by education, income source and bottom 40 classification, 2013

Log (wage income (annual), relative to national average)

Wage Income—Upper Secondary Education

Overall Top 60 percentBottom 40 percent

.000028 .00003 .000032 .000034

0

100

,000

200

,000

300

,000

400

,000

500

,000

.000028 .00003 .000032 .000034

Log (wage income (annual), relative to national average)

Wage Income—Tertiary Education

Overall Top 60 percentBottom 40 percent0

1

00,0

00 2

00,0

00 3

00,0

00 4

00,0

00 5

00,0

00

.00018

Log (self-employment income (annual), relative to national average)

Self-Employment Income—Upper Secondary Education


0

10,

000

20

,000

3

0,00

0

40,0

00

.0002 .00022 .00024 .00026 .00028

.00018

Log (self-employment income (annual), relative to national average)

Self-Employment Income—Tertiary Education


.0002 .00022 .00024 .00026 .00028

0

10,

000

20

,000

3

0,00

0

40,0

00

also included indicators for household size, but did not include the public/private indicator. Unconditional expected log wage income was subtracted from observed log wage income, and kernel density estimators of this residual were calculated with an Epanechnikov kernel using a bandwidth of 0.1.


46

Figure C2Wage distributions after controlling for observables by education and bottom 40 classification, 2013

–100

Log (unexplained component of log (wage income), relative to national average)

Upper Secondary Education


–50 0 50 100

.01

.008

.006

.004

.002

0

–100

Log (unexplained component of log (wage income), relative to national average)

Tertiary Education


–50 0 50 100

.01

.008

.006

.004

.002

0


47

Tab

le C

1H

ou

seh

old

leve

l des

crip

tive

sta

tist

ics

2011

2012

2013

Ove

rall

Bo

tto

m 4

0%To

p 6

0%O

vera

llB

ott

om

40%

Top

60%

Ove

rall

Bo

tto

m 4

0%To

p 6

0%

Ave

rag

e H

ou

seh

old

Hea

d A

ge

47.7

46.1

48.4

47.9

46.0

48.8

48.0

46.3

48.7

Ave

rag

e H

ou

seh

old

Siz

e3.

74.

83.

13.

64.

83.

13.

54.

73.

0

Ch

ildre

n (

Un

der

15)

20.4

%31

.5%

15.6

%20

.7%

32.6

%15

.7%

19.6

%31

.4%

14.7

%

You

th (

15–2

4)16

.0%

17.0

%15

.6%

14.5

%15

.9%

14.0

%13

.9%

15.0

%13

.4%

Prim

e A

ge

(25–

54)

42.7

%40

.4%

43.8

%42

.7%

40.0

%43

.9%

43.5

%40

.6%

44.7

%

Old

er (

55–6

4)12

.4%

6.8%

14.8

%13

.3%

6.8%

16.0

%13

.5%

7.8%

15.9

%

Ret

iree

s (6

5 an

d A

bo

ve)

8.5%

4.3%

10.3

%8.

8%4.

7%10

.5%

9.5%

5.2%

11.3

%

Prim

ary

Edu

cati

on

or

Less

3.1%

4.2%

2.7%

3.0%

4.2%

2.5%

2.5%

3.6%

2.0%

Low

er S

eco

nd

ary

13.1

%17

.0%

11.4

%11

.6%

14.5

%10

.4%

10.3

%13

.8%

8.8%

Up

per

Sec

on

dar

y56

.3%

62.6

%53

.6%

57.1

%63

.0%

54.7

%58

.1%

62.6

%56

.3%

Tert

iary

27.4

%16

.2%

32.3

%28

.2%

18.3

%32

.4%

29.1

%20

.0%

32.9

%

Urb

an61

.1%

45.2

%68

.0%

61.3

%47

.9%

66.9

%61

.2%

47.8

%66

.8%

Akm

ola

4.9%

5.4%

4.7%

4.8%

5.3%

4.6%

4.8%

6.3%

4.2%

Akt

ob

e4.

6%4.

1%4.

8%4.

4%3.

9%4.

7%4.

2%3.

8%4.

4%

Alm

aty

10.2

%5.

7%12

.1%

10.4

%7.

4%11

.7%

10.8

%6.

4%12

.7%

Aty

rau

2.5%

2.6%

2.5%

2.4%

2.0%

2.6%

2.4%

2.2%

2.5%

Wes

t K

azak

hst

an3.

8%4.

4%3.

5%3.

6%4.

6%3.

2%3.

5%4.

4%3.

1%

Jam

byl

5.7%

8.5%

4.5%

5.6%

9.5%

4.0%

5.8%

8.4%

4.8%

Kar

agan

da

9.3%

8.0%

9.8%

9.1%

7.7%

9.8%

8.9%

8.5%

9.1%

Ko

stan

ay6.

4%6.

7%6.

3%6.

3%6.

6%6.

2%6.

2%6.

8%5.

9%

Kyz

ylo

rda

3.1%

3.0%

3.1%

3.1%

3.3%

3.0%

3.0%

4.0%

2.6%

Man

gys

tau

2.6%

3.4%

2.3%

2.5%

3.1%

2.3%

2.6%

1.5%

3.1%

Sou

th K

azak

hst

an12

.2%

20.5

%8.

6%12

.2%

21.9

%8.

1%12

.4%

22.5

%8.

2%

Pavl

od

ar5.

3%6.

0%5.

0%5.

2%5.

2%5.

2%5.

4%3.

9%6.

0%

No

rth

Kaz

akh

stan

4.2%

5.0%

3.8%

4.2%

4.6%

4.0%

4.0%

5.2%

3.4%

East

Kaz

akh

stan

10.0

%11

.3%

9.5%

10.3

%9.

1%10

.8%

10.3

%9.

7%10

.5%

Ast

ana

Cit

y4.

5%2.

7%5.

3%4.

9%3.

0%5.

8%4.

8%2.

8%5.

6%

Alm

aty

Cit

y10

.6%

2.6%

14.1

%10

.7%

2.8%

14.0

%11

.0%

3.7%

14.0

%

Rel

ativ

e A

vera

ge

per

Cap

ita

Wag

e In

com

e–4

1.0%

17.8

%–3

8.0%

16.0

%–3

8.1%

15.9

%

Self

-Em

plo

ymen

t In

com

e–1

7.5%

7.6%

–17.

0%7.

2% –

19.2

%8.

0%

Ag

ricu

ltu

ral I

nco

me

–13.

0%5.

6%–1

5.8%

6.6%

–16

.6%

6.9%

Pen

sio

n In

com

e–5

6.2%

24.3

% –

54.4

%22

.9%

–51.

9%21

.7%

Soci

al A

ssis

tan

ce In

com

e18

.0%

–7.8

%8.

7% –

3.7%

16.7

%–7

.0%

Not

es: P

er c

apita

mea

sure

s as

sign

a w

eigh

t of

1 t

o ea

ch h

ouse

hold

res

iden

t w

ith a

fam

ilial

rel

atio

n to

the

hou

seho

ld h

ead.

Rel

ativ

e av

erag

e co

mpa

res

the

aver

age

inco

me

from

a p

artic

ular

sour

ce in

the

sub

-pop

ulat

ion

to t

he o

vera

ll m

ean.

for

exa

mpl

e, a

vera

ge p

er c

apita

wag

e in

com

e in

hou

seho

lds

in t

he 4

0 pe

rcen

t po

ores

t pa

rt o

f th

e po

pula

tion

was

41

perc

ent

belo

w t

he

natio

nal a

vera

ge, w

hile

ave

rage

per

cap

ita w

age

inco

me

in h

ouse

hold

s in

the

ric

hest

60

perc

ent

was

17.

8 pe

rcen

t ab

ove

the

natio

nal a

vera

ge.


48

Tab

le C

2In

div

idu

al le

vel d

escr

ipti

ve s

tati

stic

s

2011

2012

2013

Ove

rall

Bo

tto

m

40%

Top

60%

Ove

rall

Bo

tto

m

40%

Top

60%

Ove

rall

Bo

tto

m

40%

Top

60%

Mal

e45

.9%

47.5

%44

.9%

45.7

%47

.7%

44.6

%45

.7%

47.7

%44

.6%

You

th (

15–2

4)25

.7%

28.0

%24

.4%

23.9

%26

.8%

22.3

%22

.7%

25.3

%21

.2%

Prim

e A

ge

(25–

54)

61.3

%62

.4%

60.7

%62

.2%

63.5

%61

.5%

62.9

%64

.1%

62.3

%

Old

er (

55–6

4)13

.0%

9.6%

15.0

%13

.9%

9.7%

16.2

%14

.4%

10.6

%16

.5%

Prim

ary

Edu

cati

on

or

Less

3.1%

4.0%

2.6%

3.3%

4.4%

2.6%

2.8%

3.9%

2.2%

Low

er S

eco

nd

ary

13.8

%17

.7%

11.6

%11

.6%

14.2

%10

.2%

10.2

%13

.2%

8.4%

Up

per

Sec

on

dar

y57

.3%

63.1

%54

.0%

58.6

%64

.1%

55.6

%59

.7%

64.2

%57

.1%

Tert

iary

25.8

%15

.2%

31.9

%26

.4%

17.3

%31

.5%

27.4

%18

.7%

32.2

%

Ag

ricu

ltu

re9.

3%15

.5%

6.2%

8.5%

12.9

%6.

3%8.

4%12

.8%

6.2%

Min

ing

3.6%

3.2%

3.7%

3.6%

3.4%

3.7%

3.6%

3.0%

3.8%

Man

ufa

ctu

rin

g4.

5%3.

9%4.

8%4.

2%3.

7%4.

5%4.

4%3.

9%4.

6%

Pub

lic U

tilit

ies

3.9%

3.2%

4.2%

3.9%

3.6%

4.0%

3.6%

3.0%

3.9%

Co

nst

ruct

ion

9.0%

10.1

%8.

4%8.

8%10

.0%

8.1%

8.8%

10.5

%7.

9%

Co

mm

erce

14.0

%14

.2%

14.0

%13

.9%

13.8

%13

.9%

13.7

%13

.7%

13.7

%

Tran

spo

rtat

ion

an

d C

om

mu

nic

atio

n10

.8%

9.6%

11.4

%11

.5%

10.8

%11

.8%

12.2

%11

.5%

12.5

%

Fin

ance

, In

sura

nce

an

d R

eal E

stat

e3.

5%1.

6%4.

4%3.

8%2.

3%4.

5%4.

1%2.

0%5.

1%

Pub

lic A

dm

inis

trat

ion

Ser

vice

s11

.5%

9.0%

12.7

%11

.1%

9.1%

12.1

%10

.6%

9.0%

11.3

%

Edu

cati

on

13.8

%13

.8%

13.8

%13

.6%

13.2

%13

.7%

14.0

%13

.6%

14.2

%

Hea

lth

an

d S

oci

al S

ervi

ces

5.9%

5.0%

6.4%

6.4%

5.5%

6.8%

6.1%

5.7%

6.3%

Oth

er S

ervi

ces

10.3

%11

.0%

10.0

%10

.8%

11.6

%10

.4%

10.5

%11

.2%

10.2

%

NEE

T14

.9%

18.1

%13

.1%

15.0

%18

.1%

13.3

%14

.3%

17.0

%12

.7%

In S

cho

ol o

r Tr

ain

ing

14.8

%16

.9%

13.5

%13

.9%

16.4

%12

.5%

13.2

%15

.6%

11.8

%

Un

emp

loye

d3.

5%4.

8%2.

8%2.

6%3.

3%2.

1%2.

4%3.

1%2.

0%

Farm

stea

d W

ork

er2.

3%3.

5%1.

6%2.

1%2.

6%1.

7%2.

4%3.

4%1.

9%

No

n-A

gri

cult

ura

l Em

plo

yer

0.1%

0.0%

0.2%

0.2%

0.1%

0.3%

0.2%

0.1%

0.2%

No

n-A

gri

cult

ura

l Sel

f-Em

plo

yed

7.2%

8.5%

6.4%

6.6%

8.0%

5.8%

6.9%

8.4%

6.1%

Pub

lic W

age

Emp

loye

e21

.9%

18.3

%24

.0%

22.2

%19

.0%

24.0

%21

.2%

19.0

%22

.4%

Priv

ate

Wag

e Em

plo

yee

35.3

%29

.9%

38.4

%37

.5%

32.4

%40

.3%

39.5

%33

.4%

42.9

%

Not

es: N

EET

= N

ot in

Em

ploy

men

t, u

nem

ploy

men

t, E

duca

tion

or T

rain

ing.


49

Tab

le C

3M

ult

ino

mia

l lo

git

mar

gin

al e

ffec

ts b

y b

ott

om

40

clas

sifi

cati

on

, 201

3

NEE

TU

nem

plo

ymen

tN

on

-Ag

ricu

ltu

ral

Self

-Em

plo

yed

Pub

lic W

age

Emp

loym

ent

Priv

ate

Wag

e Em

plo

ymen

t

Bo

tto

m 4

0%To

p 6

0%B

ott

om

40%

Top

60%

Bo

tto

m 4

0%To

p 6

0%B

ott

om

40%

Top

60%

Bo

tto

m 4

0%To

p 6

0%

Mal

e–3

1.8

–22.

0–0

.1–0

.24.

76.

4–9

.8–1

5.5

37.0

31.3

15–1

9 Y

ears

Old

4.3

10.2

6.5

8.9

–0.2

–3.2

–7.0

–11.

4–3

.6–4

.6

20–2

4 Y

ears

Old

0.3

–1.8

4.1

2.5

–0.2

0.2

–0.1

0.7

–4.1

–1.7

30–3

4 Y

ears

Old

–2.6

–1.5

–1.9

–0.9

2.5

1.0

5.3

3.6

–3.2

–2.3

35–3

9 Y

ears

Old

–7.9

–5.2

–3.1

–1.6

3.1

3.3

9.7

7.4

–1.8

–3.9

40–4

4 Y

ears

Old

–10.

3–6

.3–3

.3–2

.24.

04.

211

.78.

6–2

.1–4

.4

45–4

9 Y

ears

Old

–8.2

–6.8

–3.2

–1.9

2.1

5.4

15.6

12.4

–6.2

–9.1

50–5

4 Y

ears

Old

–5.9

–3.5

–2.9

–1.6

3.4

3.2

13.8

11.0

–8.5

–9.2

55–5

9 Y

ears

Old

8.8

12.0

–2.7

–1.7

0.2

1.1

10.2

4.4

–16.

5–1

5.7

60–6

4 Y

ears

Old

54.9

62.0

–3.3

–2.1

–3.7

–5.1

–9.0

–14.

3–3

9.0

–40.

6

Up

per

Sec

on

dar

y Ed

uca

tio

n–9

.9–6

.6–1

.9–0

.6–1

.2–1

.49.

89.

93.

2–1

.4

Tert

iary

Ed

uca

tio

n–1

3.7

–11.

1–2

.9–1

.8–4

.4–4

.540

.435

.0–1

9.2

–17.

5

Urb

an–4

.2–2

.50.

6–0

.1–0

.90.

3–7

.8–1

2.1

12.4

14.6

3 Pe

rso

n H

ou

seh

old

–0.2

3.4

–1.1

1.8

–2.5

0.8

0.7

0.2

3.2

–6.2

4 Pe

rso

n H

ou

seh

old

–0.9

5.6

–1.5

2.0

–1.8

2.1

4.0

0.7

0.2

–10.

4

5 Pe

rso

n H

ou

seh

old

0.3

8.0

–1.6

2.5

–0.9

2.5

3.2

–0.1

–1.0

–13.

0

6 Pe

rso

n H

ou

seh

old

1.3

9.7

–1.4

3.6

–0.6

3.7

5.9

0.0

–5.3

–17.

0

7 Pe

rso

n H

ou

seh

old

5.0

9.7

–1.3

3.3

–1.8

2.5

6.3

2.1

–8.1

–17.

6

8 Pe

rso

n H

ou

seh

old

4.5

8.3

0.2

7.1

–2.2

2.3

2.1

–2.8

–4.6

–14.

9

9 Pe

rso

n H

ou

seh

old

12.0

20.4

1.1

1.5

–0.6

0.0

–1.1

–2.2

–11.

4–1

9.8

10 o

r M

ore

Per

son

Ho

use

ho

ld9.

412

.5–0

.54.

0–1

.97.

9–0

.4–7

.5–6

.6–1

6.9

Inte

rpre

tatio

n: A

mal

e fr

om a

bot

tom

40

hous

ehol

d ha

d a

31.8

per

cent

age

poin

t lo

wer

cha

nce

of b

eing

in n

eith

er e

mpl

oym

ent,

une

mpl

oym

ent,

edu

catio

n or

tra

inin

g th

an a

fem

ale

whe

n

both

hav

e th

e av

erag

e ch

arac

teris

tics

of in

divi

dual

s in

bot

tom

40

hous

ehol

ds, a

nd a

mal

e fr

om a

top

60

hous

ehol

d ha

d a

22.2

per

cent

age

poin

t lo

wer

cha

nce

than

a f

emal

e w

ith a

vera

ge

char

acte

ristic

s of

indi

vidu

als

in t

op 6

0 ho

useh

olds

.

Not

e: M

ultin

omia

l log

its e

stim

ated

sep

arat

ely

on e

ach

Bott

om 4

0 C

lass

ifica

tion

on t

he f

ollo

win

g ou

tcom

es: N

EET

(Not

in E

mpl

oym

ent,

une

mpl

oym

ent,

Edu

catio

n or

Tra

inin

g), i

n sc

hool

or

trai

ning

, une

mpl

oym

ent,

far

mst

ead

wor

ker,

non-

agric

ultu

ral e

mpl

oyer

, non

-agr

icul

tura

l sel

f-em

ploy

ed, p

ublic

wag

e em

ploy

men

t an

d pr

ivat

e w

age

empl

oym

ent.

Ref

eren

ce c

ateg

orie

s w

ere

fem

ale,

25–

29 y

ears

old

, low

er s

econ

dary

or

less

, rur

al r

esid

ent

and

one

and

two

pers

on h

ouse

hold

s co

mbi

ned.

The

mod

els

also

incl

ude

cont

rols

for

reg

ion

of r

esid

ence

. fig

ures

sho

wn

are

roun

ded

to 1

sig

nific

ant

digi

t; m

argi

nal e

ffec

ts c

orre

spon

ding

to

coef

ficie

nts

that

are

not

sta

tistic

ally

sig

nific

ant

at t

he 5

% le

vel a

re s

how

n in

ligh

t bl

ue. T

here

are

no

“tru

e ze

ros”

am

ong

the

estim

ated

mar

gina

l eff

ects

.


50

Annex d: MethOdOLOgy And resuLts Of the eArnIngs AnALysIsmEthodologyEstimating the determinants of wages is different from simply calculating average wages for different types of individuals, as some characteristics tend to go together (for example, urban areas tend to concentrate more educated workers). Thus when one sees higher wages in urban areas, it is not clear whether this is due to urban employers paying all workers more, or more educated workers earning more and employers in urban areas paying comparably skilled workers the same amount as rural employers. Regression analysis allows one to statistically control for differences in all of the characteristics simultaneously in order to draw conclusions about how each individual factor affects wages.

The analysis here also attempts to address the problem of “selection bias”, using econometric techniques proposed by Heckman (1979). Since the objective is to study the determinants of wages, it is important to consider that some people are more likely to find wage jobs than others. Without special treatment, the estimates of the determinants of wages would be biased because only certain people (the “best”) among those unlikely to get wage jobs would actually be seen in wage employment, and we can only use the wages that we can measure in a regression.

The value added of the analysis presented here stems from the focus on the sector-geography type of constraints. The analy-sis undertaken here exploits the labor force survey, which is designed to be representative of the working age population, for studying the structure of employment. However, estimates from the household budget survey are needed to quantify incentives for mobility, as the labor force survey contains no information on earnings. By combining both surveys, one gets a more complete picture of the type of skills that are the most highly rewarded in each region and where the people with those skills actually live. This is necessary to assess the importance of mobility constraints for people of different skill levels. A “spatial” analysis of employment and skills can help guide more nuanced policy making at a regional level.

tEchnical discUssion of rEsUltsThe wage equations were estimated in logarithmic form following the technique for correction of selection bias proposed by Heckman (1979). In this technique, participation in the estimation sample is modeled using a dichotomous probit model, and an additional term is then introduced into the wage equation that corresponds to the expected residual from this selec-tion equation. When the disturbance term from the wage equation and the disturbance term from the latent model of the selection equation are jointly normally distributed, the expected residual takes on a form known as the “inverse Mills ratio”. Moreover, if only a selected subset of observations are used to estimate the wage model, inclusion of the inverse mills ratio purges the disturbance term of its correlation with the variables in the wage equation, thereby eliminating bias due to selection.

Once the coefficients are estimated for each sector, the average share of urban jobs and the average share of public sector jobs in region is calculated, as is the share of men and the share of the population in each age group for the whole country. Using these average values, expected log wages are calculated specific to each sector in each region for each education level by manipulating the set of indicator variables for region and education in each sector’s wage model. This provides expected log wages for all combinations of sector, region and education, and the exponential of the log wages is taken to give expected wages.


51

The available jobs in each region by sector are then estimated using the share of employment in the sector observed in each region. These shares are multiplied by the corresponding expected wages for the sector in the region, for each education level, and then summed across sectors to give a weighted average expected wage, where the weights reflect the observed share of jobs. This is how expected wages for each level of education in each region are calculated.

Kernel density estimates of wages by sector are also presented. Kernel density estimators are similar to histograms, in that they count the number of observations with a given value of variable in question (for example, log income from wage employment). They differ, however, in that they use a weighted count that includes the number of observations with values near the one being evaluated, with the weighting function (kernel) and set of points over which the weighted count is taken (related to the bandwidth) being chosen by the analyst. The figures presented use an Epanechnikov kernel and a bandwidth of 0.5.


52 Tab

le D

1R

egre

ssio

n r

esu

lts

fro

m H

eckm

an s

elec

tio

n-c

orr

ecte

d lo

g w

age

reg

ress

ion

s—20

13

All

Sect

ors

Ag

ricu

ltu

reM

inin

g

Man

ufa

ctu

rin

gPu

blic

U

tilit

ies

Co

nst

ruct

ion

Co

mm

erce

Tran

spo

rtat

ion

an

d

Co

mm

un

icat

ion

Fin

ance

, In

sura

nce

an

d R

eal

Esta

te

Pub

lic

Ad

min

istr

atio

n

on

Ser

vice

sEd

uca

tio

n

Hea

lth

an

d S

oci

al

Serv

ices

Oth

er

Serv

ices

Mal

e28

.8%

***

111.

9% *

**56

.2%

**

54.3

% *

**19

.1%

***

–1.3

%49

.0%

***

19.0

% *

**27

.6%

***

41.1

% *

**–1

4.4%

***

9.8%

28.3

% *

**

Ag

e 20

–24

8.5%

**

145.

0% *

**60

.2%

18.3

%–2

3.4%

*–1

.7%

–8.1

%–1

0.3%

97.8

% *

*–1

5.0%

107.

7% *

**–3

5.1%

*0.

8%

Ag

e 25

–29

14.6

% *

**23

0.7%

***

87.9

%37

.4%

**

–22.

5%–3

.3%

–7.6

%–1

.7%

131.

6% *

**–9

.7%

119.

0% *

**–3

5.4%

7.4%

Ag

e 30

–34

26.2

% *

**25

8.6%

***

123.

2%59

.0%

***

–13.

0%12

.6%

–2.9

%3.

0%18

2.4%

***

4.9%

149.

4% *

**–3

0.7%

16.8

%

Ag

e 35

–39

31.7

% *

**25

2.2%

***

125.

2%59

.5%

***

–10.

9%16

.8%

5.0%

14.0

%17

1.8%

***

12.2

%17

9.3%

***

–29.

6%22

.8%

*

Ag

e 40

–44

31.7

% *

**31

0.8%

***

118.

8%62

.1%

***

–7.8

%21

.0%

7.3%

9.6%

158.

8% *

**8.

1%19

2.4%

***

–29.

2%15

.8%

Ag

e 45

–49

31.8

% *

**25

2.9%

***

127.

5%59

.8%

***

–4.7

%28

.8%

3.6%

10.8

%17

1.6%

***

4.2%

201.

0% *

**–2

5.8%

16.4

%

Ag

e 50

–54

26.1

% *

**27

8.5%

***

113.

8%57

.9%

***

–11.

9%13

.9%

1.0%

9.0%

154.

7% *

**–4

.1%

195.

6% *

**–2

9.2%

10.0

%

Ag

e 55

–59

22.8

% *

**25

2.2%

***

118.

4%49

.8%

**

–13.

5%13

.5%

–3.1

%0.

8%17

9.8%

***

–0.7

%17

4.0%

***

–35.

5%8.

3%

Ag

e 60

–64

10.1

% *

**14

6.5%

***

85.2

%29

.7%

*–2

2.9%

6.0%

–9.9

% *

–1.6

%16

8.0%

***

–14.

0%10

9.6%

***

–30.

0%–1

0.6%

Up

per

Sec

on

dar

y11

.7%

***

–1.6

%15

.6%

***

32.3

% *

**12

.1%

***

13.8

% *

**4.

8% *

2.3%

33.6

% *

**8.

8% *

22.8

% *

**18

.6%

***

8.0%

***

Tert

iary

48.0

% *

**12

.3%

35.7

% *

**57

.8%

***

44.2

% *

**41

.2%

***

40.8

% *

**23

.7%

***

35.4

% *

**40

.1%

***

150.

9% *

**53

.7%

***

45.5

% *

**

Urb

an17

.4%

***

–48.

6% *

**20

.0%

***

31.4

% *

**19

.8%

***

14.0

% *

**17

.7%

***

13.8

% *

**–6

.7%

22.3

% *

**–9

.3%

**

13.2

% *

**11

.9%

***

Priv

ate

Sect

or

–1.9

% *

**–1

1.6%

***

14.5

% *

22.8

% *

*1.

6%–1

8.7%

***

–3.8

%–2

.1%

14.6

% *

**–5

.4%

***

0.4%

–7.0

% *

**3.

5% *

*

Tota

l Nu

mb

er o

f O

bse

rvat

ion

s11

5,43

111

6,01

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6,09

611

6,08

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6,09

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6,07

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6,06

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6,07

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5,99

611

5,96

311

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6,07

2

Ob

serv

atio

ns

in E

arn

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s M

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Not

e: D

iffer

ence

s fr

om c

ompa

rison

gro

up a

re c

alcu

late

d as

exp

(coe

ffic

ient

) min

us 1

. In

addi

tion

to t

he v

aria

bles

sho

wn,

the

wag

e m

odel

incl

udes

con

trol

var

iabl

es f

or r

egio

ns. T

he r

efer

ence

cat

egor

ies

are

fem

ale,

age

15–1

9, lo

wer

sec

onda

ry e

duca

tion

and

belo

w, r

ural

res

iden

cy a

nd p

ublic

sec

tor.

The

mod

el f

or s

elec

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into

wag

e em

ploy

men

t in

the

giv

en s

ecto

r ex

clud

es t

he p

rivat

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ctor

indi

cato

r bu

t in

clud

es a

set

of

indi

cato

rs f

or h

ouse

hold

siz

e (1

per

son

to 1

0 or

mor

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***

indi

cate

s st

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sig

nific

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at

the

1 pe

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** in

dica

tes

stat

istic

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igni

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ce a

t th

e 5

perc

ent

leve

l, *

indi

cate

s st

atis

tical

sig

nific

ance

at

the

10 p

erce

nt le

vel.


53Tab

le D

2R

elat

ive

exp

ecte

d e

arn

ing

s b

y ed

uca

tio

n le

vel,

sect

or

and

reg

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elo

w n

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n le

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fact

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on

stru

ctio

nC

om

mer

ce

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spo

rtat

ion

an

d

Co

mm

un

icat

ion

Fin

ance

, In

sura

nce

an

d R

eal

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te

Pub

lic

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min

istr

atio

n

Serv

ices

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cati

on

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an

d

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al

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ices

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er

Serv

ices

Akm

ola

–30

–88

–35

–49

2940

2843

118

7–5

8–2

04

Akt

obe

9–9

3–1

4–3

752

6883

7388

22–5

32

42

Alm

aty

–23

–92

–56

–46

3568

6844

8423

–57

–10

35

Aty

rau

33–8

924

683

8842

7212

425

–55

035

Wes

t K

azak

hsta

n–1

6–9

2–2

–41

3152

3043

119

13–5

5–3

17

Jam

byl

–25

–90

–43

–49

3350

3941

7422

–57

–618

Kar

agan

da3

–93

–6–2

163

8435

6094

24–5

9–2

40

Kos

tana

y–2

6–8

4–2

7–5

231

5518

5314

322

–59

–235

Kyz

ylor

da–1

0–9

3–3

4–5

016

815

4099

24–5

8–9

3

Man

gyst

au63

–94

502

101

143

117

132

145

37–4

811

80

Sout

h K

azak

hsta

n–2

4–9

0–2

7–5

343

4036

4490

21–5

5–9

23

Pavl

odar

–9–9

2–2

8–3

439

7529

4612

224

–53

–538

Nor

th K

azak

hsta

n–3

8–9

0–2

9–5

633

3212

4310

317

–56

–23

3

East

Kaz

akhs

tan

–24

–93

–27

–37

3630

1336

7015

–63

–919

Ast

ana

City

70–9

2–3

5–4

613

410

710

011

014

485

–53

2356

Alm

aty

City

53–9

322

–13

8198

8976

9452

–53

1361

Up

per

Sec

on

dar

y

Ove

rall

Ag

ricu

ltu

reM

inin

gM

anu

fact

uri

ng

Pub

lic

Uti

litie

sC

on

stru

ctio

nC

om

mer

ce

Tran

spo

rtat

ion

an

d

Co

mm

un

icat

ion

Fin

ance

, In

sura

nce

an

d R

eal

Esta

te

Pub

lic

Ad

min

istr

atio

n

Serv

ices

Edu

cati

on

Hea

lth

an

d

Soci

al

Serv

ices

Oth

er

Serv

ices

Akm

ola

–24

–86

–24

–41

4053

3262

187

16–7

3–1

45

Akt

obe

17–9

20

–28

6484

5997

148

32–7

09

44

Alm

aty

–18

–90

–49

–38

4684

7463

143

33–7

3–4

37

Aty

rau

44–8

744

2398

107

4795

196

35–7

17

37

Wes

t K

azak

hsta

n–1

0–9

014

–32

4267

3562

189

22–7

14

18

Jam

byl

–21

–88

–34

–41

4465

4460

129

31–7

20

19

Kar

agan

da11

–91

10–8

7610

240

8115

534

–73

5642

Kos

tana

y–2

0–8

2–1

5–4

541

7022

7422

032

–74

536

Kyz

ylor

da–5

–92

–24

–42

2519

2059

162

34–7

3–3

4

Man

gyst

au77

–93

7518

117

166

125

163

223

48–6

719

81

(con

tinue

d)


54 Tab

le D

3R

elat

ive

exp

ecte

d e

arn

ing

s b

y ed

uca

tio

n le

vel,

sect

or

and

reg

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(p

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nt

abo

ve/b

elo

w n

atio

nal

ave

rag

e fo

r ed

uca

tio

n le

vel)

(C

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Up

per

Sec

on

dar

y

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rall

Ag

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gM

anu

fact

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ng

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lic

Uti

litie

sC

on

stru

ctio

nC

om

mer

ce

Tran

spo

rtat

ion

an

d

Co

mm

un

icat

ion

Fin

ance

, In

sura

nce

an

d R

eal

Esta

te

Pub

lic

Ad

min

istr

atio

n

Serv

ices

Edu

cati

on

Hea

lth

an

d

Soci

al

Serv

ices

Oth

er

Serv

ices

Sout

h K

azak

hsta

n–2

0–8

8–1

5–4

554

5441

6415

131

–71

–324

Pavl

odar

–2–9

0–1

6–2

450

9234

6619

234

–70

240

Nor

th K

azak

hsta

n–3

5–8

8–1

8–4

944

4516

6216

827

–72

–17

4

East

Kaz

akhs

tan

–18

–92

–15

–27

4743

1855

125

24–7

6–3

20

Ast

ana

City

86–9

0–2

4–3

715

212

710

813

922

210

0–7

032

58

Alm

aty

City

65–9

242

096

117

9699

155

65–7

021

63

Low

er S

eco

nd

ary

Ove

rall

Ag

ricu

ltu

reM

inin

gM

anu

fact

uri

ng

Pub

lic

Uti

litie

sC

on

stru

ctio

nC

om

mer

ce

Tran

spo

rtat

ion

an

d

Co

mm

un

icat

ion

Fin

ance

, In

sura

nce

an

d R

eal

Esta

te

Pub

lic

Ad

min

istr

atio

n

Serv

ices

Edu

cati

on

Hea

lth

an

d

Soci

al

Serv

ices

Oth

er

Serv

ices

Akm

ola

–29

–92

–52

–60

6045

4980

3725

–86

75

Akt

obe

14–9

6–3

7–5

188

7578

118

1842

–85

3644

Alm

aty

–19

–95

–68

–58

6775

9581

1643

–86

2138

Aty

rau

38–9

3–1

0–1

612

796

6511

641

46–8

633

37

Wes

t K

azak

hsta

n–1

4–9

5–2

8–5

462

5852

8037

31–8

530

19

Jam

byl

–21

–93

–58

–60

6557

6277

942

–86

2620

Kar

agan

da7

–95

–31

–38

101

9257

101

2245

–87

3143

Kos

tana

y–2

4–9

0–4

7–6

262

6137

9352

42–8

731

37

Kyz

ylor

da–5

–96

–52

–61

4413

3476

2544

–86

225

Man

gyst

au65

–96

10–2

014

815

315

219

253

60–8

348

82

Sout

h K

azak

hsta

n–2

1–9

4–4

7–6

377

4658

8219

41–8

521

25

Pavl

odar

–6–9

5–4

7–4

872

8251

8439

45–8

527

41

Nor

th K

azak

hsta

n–3

7–9

4–4

8–6

565

3730

8027

36–8

63

4

East

Kaz

akhs

tan

–21

–96

–46

–50

6936

3271

734

–88

2221

Ast

ana

City

80–9

5–5

2–5

718

911

513

316

553

115

–85

6558

Alm

aty

City

59–9

5–1

1–3

212

410

612

012

122

77–8

552

64


55

Figure D1Expected wages by region and education level, 2013

Aktob

e

Akmol

a

Almat

y

Atyra

uW

est K

azak

hsta

n

Jam

byl

Karag

anda

Kosta

nay

Kyzyl

orda

Man

gyst

auSo

uth

Kazak

hsta

n

Pavl

odar

North

Kaz

akhs

tan

East

Kaz

akhs

tan

Astan

a Ci

tyAlm

aty

City

100

80

60

40

20

0

–20

–40

–60

Lower secondary and less Upper secondary Tertiary

Figure D2Wage distributions relative to the national average, overall and by sector, 2013

Overall

AgricultureMining

ManufacturingPublic utilities

Construction

–10

Log (wage income) relative to national average)

–5 0 5 10

.15

.1

.05

0

Overall Commerce

Transportation and communication Financial, insurance and real estate

Public administration servicesEducation

Health and social servicesOther services

–10

Log (wage income) relative to national average)

–5 0 5 10

.15

.1

.05

0


56

referenCes Arias, Omar S., Carolina Sanchez-Paramo, Maira E. Davalos, Indhira Santos, Erwin R. Tiongson, Carola Gruen, Natasha de

Andrade falcao, Gady Sajovici, and Cesar A. Cancho. 2014. Back to Work: Growing with Jobs in Europe and Central Asia. Europe and Central Asia Reports. Washington, DC: World Bank.

Azevedo, Joao Pedro, Sarosh Sattar, and Judy Yang. 2015. Kazakhstan Economic Mobility and the Middle Class (2006–2013). Manuscript in progress. Washington, DC: World Bank.

Marouani, Mohamed Ali, Bjorn Nilsson, Angela Elzir, Victoria Strokova, and Namita Datta. 2015. Kazakhstan Jobs Impacts of Sectoral Investments: Simulations using Dynamic Computable General Equilibrium Model Applied to Kazakhstan. A note prepared for Kazakhstan: Jobs: Sector Specific Analysis JERP (P153621), World Bank, Washington, DC.

World Bank. 2013. Beyond Oil: Kazakhstan’s Path to Greater Prosperity through Diversifying, Volume 2. Main report. Wash-ington, DC: World Bank.

———. 2014. Strengthening Kazakhstan’s Education System. Washington, DC: World Bank.

———. 2015. World Bank Biannual Economic Update. Washington, DC: World Bank.

———. 2016. Kazakhstan: Towards Development of a Jobs Strategy. Washington, DC: World Bank.


Jobs seri es Issue No. 4 - World Bank · The team worked with close guidance from David Robalino...

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Transcript of Jobs seri es Issue No. 4 - World Bank · The team worked with close guidance from David Robalino...