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from, household, Kazakhstan, that, redistribution, revenues, regions, than, with, oil-producing, which, have, this, 2002, 0,00, other, Almaty, more, production, higher, education, between, results, regional, 1996, social, households, Kazakhstan,, Table, pipeline


March 2006
Richard Pomfret
Professor of Economics
University of Adelaide
Adelaide SA 5005, Australia
ABSTRACT: Kazakhstan’s economy has been driven by an oilboom since the discovery of
large new oilfields coincided with the upturn of world oil prices at the turn of the century.
After reviewing the resource-curse literature, this paper uses high-quality microeconomic data
(the national survey of 12,000 households’ expenditure) to examine whether Kazakhstan’s
experience answers the question: Is natural resource abundance a blessing or a curse? We
assess the extent to which the benefits from the oilboom are retained in the oil-producing
regions, or spread evenly across the national economy, or are concentrated in the main
metropolitan centre, Almaty, which is geographically far from any oilfields but home to the
country’s elite. We then analyse the data to determine the transmission mechanisms (higher
wages, social transfers or informal income) from the oilboom to household expenditure.
* Paper presented at the British Association for Slavonic and East European Studies
(BASEES) conference in Cambridge (UK) on 1-3 April 2006. Section 3 reports research
done jointly with Boris Najman (CNRS-ROSES University of Paris I), Gaël Raballand
(World Bank) and Patricia Sourdin (University of Adelaide), presented at the 2006 American
Economics Association conference and available as Working Paper 2005/18 at
http://www.economics.adelaide.edu.au/research/wpapers/ We are grateful to Yelena
Kaluyuzhnova and to François Legendre for helpful comments on that paper. Patricia
Sourdin’s work on the 2002 household survey was supported by a University Research Grant
from the University of Adelaide.
In 2010, Kazakhstan will have joined the club of the ten largest oil exporters in the world. The
IMF is forecasting oil revenues of $99 billion over the next 45 years for this country of fifteen
million people; where the current GDP per capita is less than $2,000. The coming decades
will see a huge stimulus to Kazakhstan’s economy and potential for economic development.
The cross-country evidence, however, suggests that there may be pitfalls. Sachs and
Warner (1995) found a negative relationship between resource abundance and economic
growth in cross-country regressions. Subsequent contributions have refined the debate of oil
as a curse, establishing that the relationship is conditional (on variables proxying for
institutions or on democracy) and that the negative relationship is stronger for oil and minerals
than for agriculture.1 Papyrakis and Gerlagh (2004) obtain a negative coefficient on their
natural resource variable (share of minerals in GDP) in a simple conditional convergence
growth regression, but the coefficient becomes positive when measures of corruption,
openness and schooling are added to the right-hand side. This fits with the observation that
successful resource-rich countries like Norway or Australia or Malaysia have open economies
and low levels of corruption, but does not address the issue of whether resource-abundance
has fuelled corruption in countries like Nigeria or Venezuela.
The Sachs-Warner results also beg the next questions. What transmission mechanisms
make a resource boom a curse? How are oil revenues redistributed in an oil economy? How
should redistribution mechanisms be designed to benefit the population as a whole? As oil
exports increase, discontent in oil-producing regions and in the poorest regions of the country
grow, as oil-producing regions wish to retain a higher share of oil revenues while the poorest
regions want a larger redistribution of the revenues.2
The main innovation of this paper is the use of microeconomic (household survey)
data to analyse the impact of an oil boom. Kazakhstan is an interesting case study because the
oilboom has a clear starting point, the discovery of large new oilfields in the late 1990’s
coinciding with a sharp increase in the world price of oil, and its extent was unexpected.
Comparable high quality household survey data for 1996, before the start of the oilboom, and
since 2001 can be used to assess the extent to which the benefits from the oilboom were
retained in the oil-producing regions, spread evenly across the national economy, or
concentrated in the two metropolitan centres (Almaty, the former capital and current financial
centre and home to the country’s elite, and Astana, the capital since 1997).
The first section describes the oil sector in Kazakhstan, dating the oilboom and
assessing its importance for the economy. Section 2 presents data on poverty and living
standards figures before and after the oilboom, and uses the household survey data to identify
regional differences in the impact of the oilboom on living standards. The third section
defines three redistribution mechanisms, and gives a preliminary assessment of the
importance of each of the mechanisms in Kazakhstan. The fourth section concludes.
1. Kazakhstan Before and After the Oilboom
During the Soviet era, Kazakhstan’s two main economic pillars were agriculture and
livestock farming (over two-fifths of GDP in 1990) and coalmining and metal smelting
1 See, for example, the literature review and regression analysis in the first two sections of Sala-i-Martin and
Subramanian (2003), and Stevens (2003). Isham et al. (2003) distinguish between point-source resources (oil,
natural fertilizers and cotton) and coffee/cocoa, which have been associated with poor growth performance, and
other natural resources, which have not. This is the usual result, although Korhonen (2004) finds that the largest
negative effects on growth come from non-fuel extractive raw materials.
2 Nigeria has experienced this conjunction of discontents for decades (Ikein et al., 1998). In Russia,
approximately three-quarters of oil revenues flow to Moscow creating a struggle between the politicians in
regions of oil extraction and the central authorities (Dienes, 2002, 451).
(Pomfret, 1995, 80-5) Despite the fact that the start of oil extraction in Kazakhstan dates
back to the early twentieth century, oil production only started to grow significantly in the
1970s and 1980s and it stagnated during the 1990s (Figure 1).
Source: Kazakhstan Republic Statistical Agency.
When Kazakhstan became independent in December 1991, the oil sector confronted
major handicaps. First, prospecting for new reserves under the potentially oil-rich North
Caspian was delayed for several years by disagreements over delimitation of national
territories and by domestic wrangles over selling exploration rights to foreign firms
possessing the technology to explore the offshore fields. Second, the pipelines were
controlled by the Russian state-owned pipeline company, Transneft, which overtly
discriminated against Kazakh oil. Russia claimed rights to part of the oil from the largest
oilfield, Tengiz, and through Transneft controlled the only existing pipeline about whose
access no commitment had been made in the Tengiz joint venture agreement.3 Transneft
engaged in monopsonistic practices such as artificially high assessments of technical losses,
arbitrary long route allocations, and other discriminatory pricing including absence of a
quality bank which would recognize the higher quality of Tengiz oil; the net effect was that
transit tariffs for Kazakhstan’s crude were typically double those for Russian crude
(International Monetary Fund, 2002; 2003b). High transportation costs have been a severe
obstacle to the development of other oilfields, such as the Kumkol oilfield in the Qyzylorda
region in central Kazakhstan.4
3 The 1990 Tengizchevroil joint venture was the largest foreign investment agreement in Soviet history. After
the dissolution of the USSR, Kazakhstan assumed the 50% state share of the joint venture, and spent several
years negotiating the sale of parts of this share. Today the ownership structure is Chevron (operator) 50%,
ExxonMobil (25%), Republic of Kazakhstan/Kazmunaigaz (20%), LukArco (5%).
4 Kumkol’s expansion has been constrained by transport costs of around $12/bbl (International Monetary Fund,
2003b, 9). Hurricane Hydrocarbons of Canada (renamed PetroKazakhstan in 2003) has been exporting oil by
railcar to China and to the Transneft Russian pipeline system. Costs would have been reduced to $8 if
PetroKazakhstan could have joined the Caspian Pipeline Consortium, which opened a new pipeline to the
Despite the problems, Kazakhstan has attracted the largest amount of foreign direct
investment (FDI) per capita among CIS countries.5 Apart from Chevron’s investment in
Tengiz, negotiated in 1990, FDI was sluggish in the first half of the 1990s. However, from
1996 to 2000, FDI exceeded one billion dollars a year and since 2001 it has exceeded two
billion dollars, with over 85 percent going to natural resource activities. In 2003 investment
flows into the oil sector reached $4 billion (or 13% of GDP).6
Oil output and exports began to grow rapidly after 1999 (Figure 1 and Table 1). The
turnaround in was driven by rising oil prices (from under $10 per barrel in 1998 to over $60 in
2005) and discovery of the huge Kashagan offshore field. Although Kazakhstan only reached
agreement with Russia in 2002 over delimitation of the Caspian Sea bed, oilfields explored in
the late 1990s led to identification of oilfields which will come online in the first decade of
the twenty-first century with huge potential production levels.
Table 1: Oil and Gas Output and Exports, 1998-2002
1998 1999 2000 2001 2002
Oil production (mmt) 25.6 29.4 35.4 39.3 47.3
Oil exports (mmt) 20.4 23.7 29.4 31.7 39.5
Oil exports ($m.) 1,650 2,164 4,429 4,463 5,157
Oil exports (in % of GDP) 7.5 12.8 24.1 20.2 20.9
World oil price ($/bbl) 13.1 18.0 28.2 24.3 24.9
Natural gas production (bcm) 7.9 9.9 11.5 11.6 13.1
Source: International Monetary Fund (2003b, p.8 (oil) and p.72 (gas)).
As prices and output soared, the pipeline situation was also becoming more favourable
to Kazakhstan. The opening in autumn 2001 of the first privately owned and commercially
operated pipeline, the Caspian Pipeline Consortium (CPC), provided an alternative route
through Russia, which cut transport costs from Tengiz in half.7 Other oil producers in
Kazakhstan have also benefited from the CPC, as competition led to reductions in the costs of
using the Transneft pipeline. In early 2003, a 450 km pipeline was completed to link the
Uzen oilfield, operated by the Chinese National Petroleum Company, to the CPC. Further
diversification of pipeline routes was provided by completion in December 2005 of a 998 km
pipeline from Atasu, in central Kazakhstan, to the Chinese border; construction of other
sections of the Kazakhstan-China pipeline will link the major oilfields of western Kazakhstan
to the Chinese national pipeline network Also in 2005 the BTC pipeline from Baku to
Ceyhan on Turkey’s Mediterranean coast was completed; the BTC is of little immediate
benefit to Kazakhstan but is likely to become an important outlet as Azerbaijan’s oil
Russian Black Sea coast in 2001, but it was induced by the Kazakhstan government to sign on to construction of
a 700 km link to the existing pipeline network which would reduce its transport costs to $9.5 per barrel. In 2003,
the company signed an agreement to sell 1 mmt per year to Tehran in exchange for Iranian crude on the Gulf. In
2005 PetroKazakhstan was bought by the Chinese National Petroleum Corporation, so its oil’s future destination
is likely to be China
5 Cumulative FDI into Kazakhstan 1989-2002 was $13,568 million or $938 per capita, the highest in the CIS,
although less than FDI in Poland, Hungary and the Czech Republic (data from European Bank for
Reconstruction and Development, Transition Report 2003)..
6 FDI is likely to be even higher in the rest of the first decade of the twenty-first century. Kashagan development
costs are estimated (by the US Department of Energy, at www.eia.doe.gov/emeu/cabs/kazak.html) at $29 billion.
7 After the dissolution of the USSR, the CPC consortium (then consisting of Transneft, Kazakhstan and Oman)
was awarded the rights to transport oil from Tengiz to the Black Sea, but negotiations dragged on how much
Chevron should pay towards construction. After Mobil bought 25% of Tengiz and LUKoil/Arco purchased 5%,
the Tengiz partners together with other investors took a half-share in the CPC. Today the CPC is half-owned by
the governments of Russia (24%), Kazakhstan (19%) and Oman (7%), and the other half is divided among
ChevronTexaco (15%), LUKoil (12.5%), ExxonMobil (7.5%), Rosneft/Shell (7.5%), Agip (2%), British Gas
(2%), Kazakhstan Pipeline Ventures (1.75%) and Oryx Caspian Pipeline (1.75%).
production is projected to plateau rather rapidly while Kazakhstan’s production should
continue to expand after 2015.
Oil has had a strong positive impact on Kazakhstan’s economic growth. After a
miserable economic performance during the 1990s (Pomfret, 2005), real GDP achieved
double-digit growth in the early 2000s (Table 2).
Table 2: Output Growth and Inflation 1991-2005 (per cent)
1991 1992 1993 1994 1995 1996 1997 1998 1999
Growth in Real GDP -11 -5 -9 -13 -8 1 2 -2 3
Inflation 79 1,381 1,662 1,892 176 39 17 7 8
2000 2001 2002 2003 2004 2005 2004;
Growth in Real GDP 10 14 10 9 9 9 103
Inflation 13 8 6 6 7 7
Source: European Bank for Reconstruction and Development: Transition Report, 2005, p. 48
and 58.
Notes: 2004 = preliminary actual figures from official government sources. 2005 = EBRD
Oil production generates major revenues for Kazakhstan, especially since the tax code was
amended in 2004.8 Since 2000, current fiscal receipts from the oil sector, excluding one-time
payments, have accounted for almost 20% of general government revenue (IMF, 2004, 18).
In 2003, oil exports brought almost $2 billion to the budget revenue. By 2010, revenues
stemming from oil exports should reach more than $7 billion, and the IMF is forecasting
undiscounted revenues of $270 billion over the next 45 years (IMF, 2004, 27), equivalent to
$99 billion discounted at 5 percent or $6,600 per capita.
Oil production in Kazakhstan is geographically concentrated in two oblasts, Mangistau
and Atyrau, on the shores of the Caspian Sea in the country’s far west. After the start of
extraction operations in Tengiz, Atyrau became the country’s main oil centre. The huge
offshore Caspian field of Kashagan, whose operational base is in Atyrau oblast, is often
described as the largest new oilfield discovery for over thirty years, with an estimated 45
billion barrels of which 8-13 billion are recoverable with existing technologies. Unexplored
areas of the north Caspian may also contain large reserves. In the last decade, Aksai in West
Kazakhstan, with the Karachaganak oil and (primarily) gas field, Aktobe, and Kyzylorda with
the Kumkol field in central Kazakhstan, have emerged as new (or relatively new) oilproducing regions, although oil production in these oblasts is expected to remain limited. In
sum, although the oil-producing areas are not all isolated from pre-existing towns, they are far
8 The main sources of revenue are the state’s share of export earnings, taxes on producers, and other taxes.
Under production-sharing agreements (PSAs) the share of crude oil exports by consortia attributed to Kazakhstan
is determined after deduction of the cost recovery expenses, which may not exceed 75% of revenues prior to
payback and 50% post payback. The tax on oil producers depends upon the type of tax regime of the contract.
PSA and excess profit tax (EPT) contracts entered into force after 1996 and before January 1, 2004 are
unequivocally grandfathered (Ernst and Young, 2005, 17). Under a PSA the foreign oil company, at its own
risk, is expected to pay all initial exploration and development costs, while the government receives a share of
the field’s production, in cash or in kind, without making any investment. The key factors determining the
foreign oil company’s profits are the ratio of initial costs to revenues, procedures to determine profits, and the,
the company’s share of the profits. Under a PSA tax regime, foreign companies are exempted from excise,
property tax, land tax and vehicle tax. In an EPT regime, the company is liable to pay bonuses, royalty, excess
profits tax and a rent tax on export of crude oil, and “top up tax”. Additionally, there are taxes on profits,
indirect taxes such as VAT, excise and customs duties, and other taxes such as environmental fees, property tax,
land tax, vehicle tax and other fees and licenses.
from the country’s metropolitan centres of Almaty (in the extremer south-east) and Astana (in
the centre-north).
2. Poverty and Living Standards in Kazakhstan Before and During the Oil Boom
This section uses two sources of information, regional descriptive statistics and LSMS data, to
provide preliminary assessments of the impact of oil revenues. Oil-producing regions seem
not to have experienced any sustained employment growth, and poverty and inequality remain
worse in oil-producing regions than in non-oil regions. The most surprising results from
analysis of the LSMS data is that in 2002, in the midst of an oil boom, location in the oilproducing western region is not associated with higher living standards, and indeed the
relative position of households in those regions was worse than in 1996.
The direct employment impact of the oilboom is limited. In 2002 the unemployment
rate in all the producing-regions was above the national average; direct employment in the oil
sector was estimated to be less than 50,000 people, including employees working in the
refining sector, which is equivalent to less than one percent of Kazakhstan’s active population
of 7.4 million (Abdiev, 2003, 478).9 Oil production expands construction activities, but only
on a temporary basis, eg. construction of the CPC pipeline was positive for employment in
western Kazakhstan between 1998 and 2000 but after the inauguration of the pipeline building
activities shrank.
Table 3: Urban and Rural Poverty Rates by Oblast, 2002
Total Urban Rural
Kazakhstan Average 24.2 15,6 34.7
Aktöbe 22.6 6.9 43.8
Atyrau 34.1 27.5 44.4
Kyzylorda 32.3 22.1 48.0
Mangystau 39.8 28.8 84.6
West Kazakhstan 28.0 11.4 39.0
Astana city 2.2 2.2 Almaty city 4.1 4.1 Akmola 18.6 15.0 21.3
Almaty oblast 36.3 25.8 40.8
East Kazakhstan 20.0 13.9 27.9
Jambyl 35.8 31.3 39.1
Karagandy 19.3 16.0 33.1
Kostanay 22.3 12.9 33.4
North Kazakhstan 14.3 4.3 20.7
Pavlodar 21.6 12.2 34.9
South Kazakhstan 27.5 22.3 30.4
Source: UNDP (2004, 58 and 62).
9 The fifty companies included in Kompass Kazakhstan (the largest company directory in Kazakhstan, in which
all the major oil companies (foreign and national) oil companies are included) in the sector of oil production only
have 41,500 employees. The largest employers in this sector are mainly branches in which Kazmunaigaz is a
major shareholder such as Ozenmunaigaz (12,500 people), Embamunaigaz (12,000) or Mangghystaoumunaigaz
(4,400). Kazmunaigaz has 400 employees at his headquarters in Astana. As far as joint foreign-Kazakh
companies or foreign operators in Kazakhstan are concerned, Tengizchevroil has 2,800 employees,
Karachaganak 500 and Petrokazakhstan 900. For the foreign companies, which do not operate oilfields (such as
British Gas, Total, or Lukoil), the presence on the ground is limited to a dozen or so people to represent the
interests of the company
Except in Aktöbe oblast, the poverty headcount in all oil-producing regions remains
above the national average (Table 3). The highest poverty rates among the five oil-producing
oblasts are in the oldest, and largest, producers, Mangistau and Atyrau. Despite the
importance of oil production in Mangistau, almost 40% of the population of the oblast is poor,
and Mangistau has a higher poverty headcount than even Jambyl, the oblast with the lowest
regional product per capita. In contrast, in Astana and Almaty cities only 2.2 and 4.1% of the
population lives below the poverty line (UNDP, 2004, 58).
In all of Kazakhstan’s oblasts the poverty headcount is higher in urban areas than in
rural areas, but the rural-urban difference is especially pronounced in the oil-producing
oblasts, where the poverty headcount is two to three times higher in urban compared to rural
areas. In the oil-producing regions, cities may benefit from oil rents, e.g. in Mangistau oblast
the town of Aktau has a poverty headcount of 18% which is well below the regional average
of 40%. At the narrower geographical level, producing oil in a rayon is not a guarantee of
lower poverty. In the three oblasts mixing oil-producing and non-oil-producing rayons
(Aktöbe, Kyzylorda and West Kazakhstan), only four out of ten rural oil-producing rayons
experience less poverty than the regional average poverty headcount (Ivashenko, 2004).
Inequality has substantially grown in oil-producing regions. These regions lie above
the national average (Table 4). The discrepancy between poor and rich is especially acute in
Atyrau region, and to a lesser extent in Mangistau region.
Table 4: Gini Coefficient in 2002
Gini Coefficient
Kazakhstan Average 0,33
Aktöbe 0,34
Atyraou 0,43
Qyzylorda 0,32
Mangghystaou 0,36
West Kazakhstan 0,35
Astana city 0,31
Almaty city 0,29
Jambyl 0,29
South Kazakhstan 0,28
Source: Abdiev (2003, 147).
Thr remainder of this section reports results from applying a human capital model to
explain the determinants of per capita household expenditure in Kazakhstan during the 1990s
transition era with a year from the sustained growth period (2002).10 Data availability in the
1990s was hampered by the poor quality of the inherited household budget surveys. External
researchers have relied almost exclusively on the one-off 1996 LSMS survey, and the starting
point for the present exercise is the analysis of that dataset by Anderson and Pomfret (2002;
2003). In 2001 the National Statistical Agency revised the household budget survey using
sampling techniques and questionnaires comparable to those of the LSMS, although the data
are now collected continuously and reported quarterly and annually rather than for the twoweek period of the 1996 survey. This paper reports results using the 2002 data, and our
intention is to supplement it with analysis of the 2005 survey as that becomes available.
In all of the formerly centrally planned economies, the transition to more market-based
systems was accompanied by changes in labour markets and in the determinants of household
expenditure levels. Human capital variables, which are consistently significant determinants
10 Other examples of use of the dataset are Anderson and Pomfret (2002; 2003), Rama and Scott (1999), and
Verme (2001).
of earnings in established market economies, became more important. In Central Asia this
pattern was accompanied during the 1990s by a large increase in the cost (in terms of lower
per capita household expenditure) of large family size, especially the presence of children.
Large regional differences in household expenditure, ceteris paribus, also indicated that
national labour markets were not yet established in Central Asia. In Anderson and Pomfret
(2002; 2003), these three sets of variables (education, family size and location) were
consistently significant in various specifications, while other demographic characteristics such
as the ethnicity, age, health or marital status of the head of the household were seldom
statistically significant and had little explanatory power.
The dependent variable is household expenditure per capita, based on a headcount of
household members11 and the reported expenditures on goods (excluding vehicles), food,
health, education and other services, housing, utilities, communication, and transportation.12
In the estimating equation, per capita household expenditure is determined by the level of
human capital, the number of household members, and the household’s location. The
education level attained by the head or the highest-educated household member is assumed to
be indicative of the household’s human capital.13 Household composition is measured by
three variables describing the number of children under the age of 18, the number of elderly,
and the number of non-elderly adults in the household.14 Location of the household is
measured by five region-specific dummy variables, with Almaty, the largest city, as the
omitted category for regional location: the Central region is Akmola and Karaghandy oblasts
and Astana City, the South is Jambyl, Kyzylorda and South Kazakhstan oblasts, the North is
Kostanai, Pavlodar and North Kazakhstan oblasts, the East is East Kazakhstan and Almaty
oblasts, but not Almaty city, and the West is Aqtöbe, Atyrau, West Kazakhstan and
Mangistau oblasts, where most of the country’s oil-production is located. . .
Summary statistics for the two years are reported in Table 5. The main change in
location is an increase in the proportion of households in the oil-producing western region and
a decline in the proportion in the Centre and North. The number of people per household
increased slightly from 3.59 to 3.69, due to an increase in working age adults per household
and a smaller increase in the elderly, partially offset by a smaller number of children.15 In the
11 Anderson and Pomfret (2002) test the sensitivity of the results to this assumption (ie. assigning equal
expenditure weight to all children and adults in the household) by estimating the model with an alternative
dependent variable in which children, women and the elderly are assigned lower expenditure weights than prime
working age adult men. The results do not change in any significant way. The numerical results might also be
sensitive to the implicit assumption of no scale economies in the provision of household services; adjusting for
economies of size with a scaling such as E* = E/nθ, where E is household expenditure and n is family size, would
soften the conclusion about household size and perhaps affect other results. However, Jovanovic (2001) reports
that varying θ within a plausible range did not alter his results for Russia in any significant way.
12 The aggregate level of household expenditures is not of interest in the present context because we are trying to
understand the determinants of relative living standards. Expenditure is preferred to income because the arrears
problem in former Soviet republics during the 1990s meant that income often came in lumps so that many
households reported zero income during the two-week survey period. We also expect under-reporting to avoid
tax or other impositions to be less prevalent for expenditure. Non-purchased items, such as food grown on
household plots, are valued and included in expenditure. Because the log of expenditure more closely follows a
normal distribution, the estimating equations are semi-logarithmic regressions of the log of per capita
expenditure on household characteristics.
13 Education is characterized by five levels: higher education (university and postgraduate), Tecnikum education,
vocational or other technical training, completed secondary education, and incomplete secondary schooling. In
analysing the 1996 situation Anderson and Pomfret found no significant difference between using education
variables based on the head of household’s education and using the highest-educated person. For 2002, we use
highest-educated person because, rather than following a consistent definition, the surveyors appear to have
treated the person who answered the questionnaire as the head of household.
14 For 1996 Anderson and Pomfret (2002) defined a person as elderly if he or she was eligible for a state pension,
ie. at age 60 for a man and age 55 for a woman. For 2002 “elderly” is defined as aged 60 or over.
15 This reflects the demographic patterns of the 1990s when the birthrate fell and the death rate rose. It also
might be influenced by emigration patterns, as a disproportionate number of elderly were among the Germans
and Slavs who left Kazakhstan during the 1990s.
education categories the major change has been the fall in the portion reporting vocationaltechnical education.16 The proportion of households without anybody who completed
secondary education is higher in 2002 than in 1996, although there appear to be some
anomalous entries in this category.17
Table 5: Household Surveys: Summary Statistics
Variables 1996 2002
Per capita expenditure: 4963.76 (3515.27) 112,524 (75,999.73)
Education of Most Highly Educated:
University (%) 26.8 24.9
Tecnikum (%) 33.1 32.9
Vocational-technical (%) 26.6 12.9
Completed secondary (%) 7.8 19.9
Incomplete secondary (%) 5.7 9.4
Location of household:
Central (%) 20.7 19.5
South (%) 18.1 18.8
West (%) 8.5 12.5
North (%) 22.3 19.5
East (%) 21.0 21.0
Almaty city (%) 9.4 8.8
Household composition:
Number of children 1.263 (1.228) 1.167 (1.244)
Number of elderly 0.414 (0.676) 0.460 (0.685)
Number of non-elderly adults 1.914 (1.119) 2.060 (1.386)
Sample size (households) 1,890 12,000
Notes: Standard deviations of continuous variables are in parentheses. Expenditures are in national currency
units (tenge); note that the two surveys’ observation periods differ so that the nominal tenge values are
not comparable even apart from problems of measuring inflation.
Table 6 reports the regression results for 1996 and 2002. Although the datasets are not
a panel, the sampling techniques were the same and the results for the two years should be
comparable.18 The three groups of variables, which dominated in 1996, remain statistically
significant in 2002, but the magnitude of the coefficients changes considerably.
16 This is consistent with other evidence from Central Asia and elsewhere that during the 1990s much of the
specialized lower-level technical training from the Soviet era had no market value in the transition economy.
People ceased taking such courses, and in some cases may no longer have claimed this type of training as an
education. The drop in the vocational-technical category is largely matched by an increase in the number
reporting completed secondary as their highest level of education.
17 In 2002 the average per capita expenditure level for households in the lowest education category is over
114,000 tenge, which is above the sample average and higher than for any other education category apart from
those with university degrees. The reason for this anomaly appears to be the presence of a few households
reporting no education but having high expenditure levels; twenty-ight of the households reporting nobody with
completed secondary education had income levels around 600,000 tenge, ie. over six standard deviations above
the sample mean
18 The only major difference is in the control group for human capital. In analysing the 1996 data, Anderson and
Pomfret used the incomplete secondary schooling as the omitted education category, but with the 2002 data this
led to generally insignificant coefficients. The reported regression results for 2002 use completed secondary
Table 6: Household Expenditure Model: Kazakhstan, 1996 and 2002.
1996 2002
Variables Coefficient t-statistic Coefficient t-statistic
Intercept 8.542* 89.60 12.19* 488.62
University 0 .272* 5.62 0.069* 4.53
Tecnikum 0.167* 3.63 0.057* 3.97
Vocational-technical training 0.114* 2.56 0.020* 1.13
Completed secondary -.001 -0.02 -- - Location of household:
Central -0.036 -0.70 -0.527* -23.43
South -0.447* -8.38 -0.971* -42.16
West 0.089 1.43 -0.626* -25.92
North 0.295* 5.67 -0.720* -31.72
East (not Almaty city) 0.038 0.74 -0.742* -33.02
Household composition:
Number of children -0.174* -14.04 -0.023* -5.02
Number of elderly -0.116* -3.82 -0.017* -1.97
Number of non-elderly adults -0.058* -4.18 -0.012* -2.87
R-square 0.30 0.17
F-statistic 47.14* 223.44
Sample size 1,890 10,716
An asterisk indicates significant at the 5% level.
Family size continues to be negatively related to household living standards, but the
magnitudes are much smaller in 2002 and there is little distinction between the age groups.
Whereas in 1996 having an extra child was the largest cost in terms of lower per capita
household expenditure and an elderly person brought the next highest cost, the impact of these
two age groups in 2002 differs little from that of an additional working-age adult.
Education remains important. In 2002 having a university or Tecnikum educated
person in the household is associated with 6-7% higher per capita household expenditures,
ceteris paribus, than having nobody educated beyond completed secondary education. The
changes in the magnitudes of the effect of different levels of human capital between 1996 and
2002 are difficult to assess because there is a difference in definition (household head in 1996
versus highest-educated person in 2002) and in control group between the first and last
columns of Table 6.19 Nevertheless, it does appear that the returns to greater skill and
education levels were lower in 2002, which is surprising.
The location variable shows the most striking differences between 1996 and 2002. In
1996 a household located in the North had on average a 30% higher living standard than a
education as the control variable, and, because of the anomalies reported above, the lowest education group (with
9.4 percent of households) is omitted; the results for the other variables are almost identical to the results when
the entire sample is used.
19 In regressions using the entire sample and having incomplete secondary education as the control, the
coefficients on all education levels apart from university did not differ from zero at the five percent significance
similar household in Almaty and a household located in the South had a 45% lower living
standard than one in Almaty ceteris paribus, while the other regions were not significantly
different from Almaty. In 2002 households in all locations outside Almaty had significantly
lower living standards than otherwise similar households in Almaty. The difference is still
most pronounced, negatively, in the South, but the situation of households in the North and
East is significantly worse than Almaty in 2002 whereas they were better off than Almaty
households in 1996. The improved position of the Central region (relative to all other regions
except Almaty) may have been due to moving the capital to Astana, located in the Centre, and
the substantial public construction associated with that decision. The most striking aspect of
the location results is that in 2002, in the midst of an oilboom, location in the oil-producing
western region is not associated with higher living standards.
3. A Typology of Redistribution Oil Revenues in an Oil Economy
In Kazakhstan (as in most oil exporting countries), oil is produced in few regions of the
country: five out of fourteen oblasts, and there are 21 oil-producing rayons out of the
country’s 158 rayons (cities excluded).20 Concentration of oil production in a limited number
of districts should enable us to assess the preliminary impact of the oil boom in Kazakhstan.
Early revenues from the oilboom appear to have benefited cities in non-oil producing regions,
whereas people living in rural areas or in oil-producing rayons do not seem to benefit from
this rent. Consequently, oil revenues have strengthened the inequality gap between rich and
poor people in the country. An assessment of the redistribution mechanisms could partially
explain why cities benefit more from oil revenues than urban areas.
Why should oil revenues be redistributed in an oil economy?21 As Isham et al. (2003,
3) point out, oil is a “point-source” natural resource, which means that it is extracted from a
narrow geographic and economic base. First of all, it is worth noting that oil revenues are not
wholly redistributed in Kazakhstan. Indeed, a large share of oil revenues is allocated to the
National Fund for the Republic of Kazakhstan.22 Second, commentaries on Kazakhstan
emphasize the high levels of corruption, particularly associated with the appropriation of oil
rents by the elite. Third, oil-producing regions may disproportionately benefit from additional
revenues whereas poor regions could be left out of these revenues flows. Growing inequality
between regions as a result of an oilboom justifies redirection of oil revenues to the centre,
which may ensure efficient redistribution of oil revenues by producing economies of scale in
the production of a public good and by insuring against region-specific shocks (Ahmad and
Singh, 2003, 2-3). However, balance between oil-producing and poorest regions of an oil-rich
country is difficult to reach; as equalization and stabilization mechanisms performed by the
centre start to operate, oil-producing regions express discontent because they wish to keep
20 Aqtöbe region: 3 (out of 17), Atyrau region: 7 (all rayons of the oblast), West Kazakhstan: 4 (out of 12),
Kyzylorda region: 3 (out of 7), Mangistau region: 4 (all rayons of the oblast). We identified oil-producing rayons
combining detailed maps of oilfields and oblasts.
21 A trade-off exists between spending and saving oil revenues. Volatile oil revenues may lead to poor public
investment decisions. Also, an oil boom might have deleterious effects via Dutch disease mechanisms (ie. an
appreciating exchange rate makes production of other traded goods unprofitable). In this paper, these
macroeconomic effects are ignored, and the focus is on redistribution mechanisms which can be analysed with
household data..
22 The National Fund was established in 2001, with the main objectives of reducing the impact of volatile world
market prices and smoothing the distribution of oil-wealth over generations. Initially, the authorities identified
twelve major companies in the natural resources sector, but this figure was reduced to six in 2004 and the list
limited to petroleum companies. Flows consist of a savings component equal to 10 percent of the budgeted
baseline revenue invariant to price changes and a stabilization component that includes all revenues above the
baseline price, fixed at $19/bbl. The Fund’s capital is supplied by shares of government income from the oil
sector, royalties, bonuses and revenues from PSA. The Fund is invested in foreign equities. Thus, a large share
of oil revenues is allocated to the NFRK, which had accumulated $5 billion in late 2004 (or approximately 17 %
of GDP). See, Kalyuzhnova and Kaser (2005) and IMF (2004:19).
locally a larger share of oil revenues and poorer regions of the country express discontent
because they wish to benefit more from redistribution schemes.
To analyse the redistribution mechanisms in Kazakhstan, we distinguish three
categories based on two main questions: who redistributes oil revenues, and how is it
First, Official public redistribution encompasses taxes and revenues stemming from
oil production shared locally as well as financial transfers from the centre. In oil-producing
regions, revenue-sharing schemes generally replace financial transfers from the centre, unlike
what happens in the poorest regions. Local governments may redistribute oil revenues
through social transfers. Regional budgets in oil-producing oblasts have largely benefited
from oil revenues. However, transfer mechanisms have been put in place and are increasingly
important for lagging regions, especially in the South of the country. Between 1997 and
2002, budget revenues of the five oil-producing regions increased by 280% whereas budget
revenues of the other regions increased by 180% (Table 7). Regional authorities in oilproducing regions have increasingly used fines and quasi-fiscal policy as a means to increase
regional revenues. Previously, central authorities levied greenhouse emission rights, but this
has become the mandate of regional authorities and, probably as a result, environmental fines
increased by 400% in 2004 compared to the previous year. Besides, some taxes are collected
locally like social and income taxes, although oil-producing regions transfer a major share of
revenues collected - more than 40% of total expenditures of Mangistau and Atyrau oblasts.
Table 7: Regional Budgets 1997-2002
1997 2000 2002
Aktöbe 0,98 14,6 1,02 24,5 1,02 17,1
Atyrau 0,98 9,0 1,12 36,1 0,92 22,7
Kyzylorda 1,00 47,9 0,98 40,4 1,03 27,3
Mangistau 0,99 9,6 1,05 32,3 0,94 20,1
West Kazakhstan 0,96 26,3 1,05 18,7 1,00 24,0
Astana city 1,00 74,4 1,00 40,3 0,96 32,9
Almaty city 1,05 8,6 0,99 22,7 1,01 18,6
Akmola 1,02 48,7 1,02 29,8 1,01 31,3
Almaty region 1,00 23,3 1,02 25,2 1,00 30,9
East Kazakhstan 1,01 21,9 1,01 31,2 0,96 22,1
Jambyl 0,99 30,7 1,00 33,5 0,97 42,1
Karaghandy 0,90 15,2 1,13 20,6 0,96 16,9
Kostanaï 0,96 16,5 1,02 16,7 1,00 22,0
Pavlodar 0,92 18,7 1,03 17,0 0,96 20,8
North Kazakhstan 1,00 25,4 1,04 28,1 0,98 29,2
South Kazakhstan 1,00 25,1 0,99 27,6 1,00 30,8
Oil Regions Average 0,99 17,4 1,07 30,7 0,97 22,0
Kazakhstan Average 0,99 19,8 1,04 26,6 0,98 23,6
Source: Smailov (2001) and Abdiev (2003).
Several regions (oil-producing or not) have succeeded in benefiting from revenues of
the oil boom. Social expenditures per capita are on average higher in oil-producing regions
than non-oil producing regions (Figure 3). However, Kazakhstan remains a centralized state
and local fiscal autonomy in Kazakhstan appears limited (Dabla-Norris, Martinez-Vasquez
and Norregaard. 2000). Fiscal federalism with revenue-sharing arrangements was tentatively
developed (McLure 2000), but the implementation seems to vary across regions and criteria to
identify benefiting regions are obscure. On average, the share of official transfers in regional
revenues is higher in most poor regions (Figure 4).23 Thus, poor regions mainly depend on
official transfers from the central authorities. As expected, official transfers do not reach oilproducing regions (except Kyzylorda region). Two facts remain disturbing about the equal
redistribution of oil revenues: central and southern regions of the country mainly benefit from
those transfers (Jambyl, South Kazakhstan, Almaty, Akmola and Kyzylorda) and not the
northern regions, which are equally poor, and Astana also benefits from those transfers
(almost one-fifth of regional revenues).
Figure 3: Oblasts Social Expenditures per Capita
Ak töb
e At yr au Kz ylo
rd a Ma ng ist
au W
es t K
az ak hs ta n As tan
a c
Al m aty
ci ty Aq mo la Al m aty
re gio
n Ea st Ka za kh sta
n Zh am by l Ka ra ga nd a Qo st an aï Pa vlo
da r No rth
az ak hs tan
So uth
az ak hs ta n Source: Data from Sarsenov (2005).
Figure 4: Share of Transfers in Regional Revenues
Aq m ola
Ak to be Al m at y At yr au Ea st Ka za kh sta
n Zh am by l W
es t K
az ak hs ta n Kz ylo
rd a Qa ra ga nd a M
an gu ist
au Pa vlo
da r No rth
az ak hs ta n So ut h Ka za kh sta
n As ta na Al m at y c
Ka za kh sta
n av er ag e T
ra n sf er s/ R
ev en u es (
in p er ce n ta g e) Source: calculated on average for the period 2000-2003 from official data in Shokmanov (2004).
23 The difference between the poorest and richest regions in terms of budget revenues is declining, whereas in
terms of regional product per capita the difference is widening. Regarding regional budget revenues, in 1997,
the ratio between the richest and the poorest regions was equal to 5.4 and, in 2002, it decreased to 3.6. Regarding
regional product per capita, this ratio was equal to 5.6 and, in 2002, it increased to 10.3.
Second, company redistribution includes direct, indirect and induced revenues that oil
companies invest or spend locally. In the case of the oil sector, there is no need for large
employment or for social programs (or “social assets”), so this type of redistribution may be
geographically limited. Oil companies’ headquarters, especially in the case of non-operating
foreign companies, employ a limited number of staff. In terms of value added, oil production
makes a difference. Since 1997 the growth of regional product per capita has been above the
national average for all oil-producing regions, except for Kyzylorda oblast (Figure 5). The
difference is especially great for the old oil-producing regions such as Mangistau and Atyrau
regions (three and four times above the national average). However, the largest increases in
regional product were recorded in Astana and Almaty cities.
Figure 5: Regional Product Per Capita
1997 1998 1999 2000 2001 2002
eg io n al p ro d u ct p er c ap it a (i n T
en g e) Atyrau
Source: Smailov (2001) and Abdiev (2003).
Oil companies try to contribute to the development of communities where they
operate. However, taking into account the immense needs, these projects only address a
fraction of the needs in infrastructure. According to the PSA agreements, consortia are
requested to invest in social infrastructure projects (SIP). Regional authorities propose local
development projects, which should reflect the real needs local communities. Despite their
possible local impact, AgipKCO’s investments only represent 1.25% of the revenues of
Mangistau and Atyrau regions and Karachaganak Petroleum Operating (KPO) 6,5% of the
revenues of West Kazakhstan region(Abdiev, 2003, 426). Until now, AgipKCO has mainly
financed the building of schools, hospitals, gas pipelines to villages and other infrastructure.
Annually, this consortium spends $5 million (or 0,15% of regional GDP) in Mangistau and
Atyrau regions.24 KPO invests annually $10 million in West Kazakhstan (or 0,8% of regional
Third, unofficial redistribution results from two factors: not registered household
activities and informal “leakage”. First, oil production generates money and people inflows.
In order to fulfill the new demands, individuals or households may start small businesses.
Self-employment can therefore be the result of oil production. Second, revenues may be used
for political or individual purposes, ie. by informal “leakage” oil revenues are redistributed to
the economy. This type of redistribution is obviously the least easy to measure, but the “oil
24 This amount will grow to $18 million per year when Kashagan is fully operational (or 1% of expenses to
develop Kashagan oilfield).
curse” literature refers to such mechanisms, emphasizing their negative impact on institutions
and consequently on growth.25
An example of unofficial redistribution concerns the bargaining and negotiation
processes between oil companies and local and central authorities. The quotas of local
employees were previously agreed in Astana (in the Ministry of Labor and Social Protection),
but have now become the mandate of hakims (regional governors). This bargaining process is
said to increase revenues redistribution in the circles around hakims in oil-producing regions.
Unofficial incomes are underestimated in surveys. However, when one compares
incomes and expenditures, a large share of expenditures is not covered by corresponding
incomes. The size of this gap can be treated as a proxy for the importance of unofficial
income for a household. Specifically, if the household’s total expenditures are more than
twice as large as the total incomes, then we assume that the household participates in some
kind of informal activities.
To analyze the three types of redistribution (official public, company redistribution
and informal redistribution), the following mutually exclusive categorical variables are
constructed for each household:26
1. Official public redistribution is the dominant mechanism if the income from wages is
less than the income from social transfers, and total household expenditures are lower
than twice the total incomes of the household. In a household that fulfils both
conditions, we conclude that the household essentially benefits from official public
redistribution (social transfers). This category represents 12.5% of the sample.
2. Company redistribution is the dominant mechanism if the income from wages is
greater than the income from social transfers and if total household expenditures are
less than twice total income. About 44 percent of the sample is in this category, which
is indicative of how little importance the formal sector has in Kazakhstan, and how
difficult it is to tax wages.
3. Informal redistribution is the dominant mechanism if total household expenditures are
more than twice the total incomes. In this case we conclude that the household
essentially benefits from informal redistribution, i.e. undeclared activities. The
informal redistribution group represents 43.5 % of our sample.
Cross-tabulating the redistribution dummy on some of the main individual characteristics of
the household head (see Appendix) yields some preliminary results.
As might be expected, social redistribution goes primarily to women, the elderly and
the less educated. First, women benefit more (58.5%) from social transfers. This is quite
normal because women during the transition are usually a more vulnerable part of the society
especially when they are single with children. For both formal and informal redistribution,
males more frequently (about 55%) have access to a job and income. A second observation is
that older household heads receive more social transfers than others. Third, less educated
heads of household rely more on social redistribution. Company redistribution is relatively
more prevalent in non-oil rayons, and informal redistribution is more prevalent in oil
producing rayons.
The results of a multinomial logistic regression to analyze the three different
redistribution mechanisms are reported in Table 7. The three mechanisms (social, enterprise,
and informal redistribution) are mutually exclusive, and are assumed to be not correlated. We
use a simple multinomial regression without any conditionality, assuming that individuals are
25 Ross (2001) describes in details the effects of oil on political economy. According to Ross, the “rentier
effects” may characterize the link between oil and a type of rule. In this case, “governments use oil revenues to
reduce social pressures that would otherwise result in demands for greater accountability” (IMF 2004, 12).
Robinson and Torvik (2005) argue that governments may finance “white elephants” because such projects
produce political benefits.
26 Redistribution may combine several types of redistribution, but this paper focuses on the main source of
incomes, which means that the three types of redistribution are defined to be mutually exclusive.
free to choose the redistribution they want, which is of course a simplification.
Table 7: Multinomial Logistic Regression Results
Variables Coefficient Standard error p-value
1. Official public redistribution
sex -0,08 0,04 0,06
age 0,05 0,00 0,00
married -0,32 0,04 0,00
Education -0,26 0,01 0,00
Almaty -1,49 0,12 0,00
Astana -1,77 0,39 0,00
Medium city 0,24 0,08 0,00
Small town 0,34 0,07 0,00
Rural 0,99 0,05 0,00
Oil production 0,07 0,10 0,45
Size of the household -0,19 0,01 0,00
Constant -1,88 0,12 0,00
3. Informal redistribution
sex 0,12 0,03 0,00
age 0,01 0,00 0,00
married -0,33 0,03 0,00
Education -0,13 0,01 0,00
Almaty 0,11 0,05 0,03
Astana 1,30 0,09 0,00
Medium city -0,05 0,06 0,42
Small town 0,44 0,04 0,00
Rural 1,30 0,03 0,00
Oil production 0,23 0,06 0,00
Size of the household -0,13 0,01 0,00
Constant -0,08 0,07 0,29
Number of observations 32,229
LR chi2(22) 6,618.2
Prob > chi2 0.0000
Pseudo R2 0.104
Notes: The dependent variable is the redistribution variable defined in the text. Category 2 (enterprise
redistribution) is the control group
Compared to the control category (enterprise redistribution), social redistribution is
positively associated with residence in a small town or in a rural area and is negatively related
to the household head’s education level or residence in Almaty or Astana. Comparing the
incidence of social redistribution to that of enterprise redistribution, the effect of residence in
an oil-producing rayon is not statistically significant. Informal redistribution is positively
related to residence in a small town or in a rural area and negatively related to the household
head’s education level, but, unlike social redistribution, it is positively related to residence in
the capital city and to residence in an oil-producing rayon.
In sum, the results of analysis of the 2002 household survey data to distinguish
between channels for redistribution of the oil boom benefits suggest that little happens
through higher wages in oil-producing districts or through social transfers, but that “informal”
earnings (captured by households having much higher expenditures than incomes) are more
important in the oil districts than in the country as a whole. The informal redistribution may
include small-scale agriculture, which could explain the rural area effect. Both social and
informal redistribution are more prevalent for less educated household heads and small town
inhabitants relative to company redistribution, and there is some evidence that low skilled
people and rural area inhabitants are not benefiting from the oil sector development, which is
consistent with reports from the Tengiz region of complaints by the local population that they
are not employed in oil production. Households in the two large cities seem to benefit from
the oilboom mainly through informal redistribution; informal earnings are even more
prevalent for households in Astana, the new national capital, and to a lesser extent in Almaty,
the financial capital, than they are in the oil-producing regions.
4. Conclusions
Whether resource abundance is a curse or a blessing depends upon the nature of the resource
and on variables reflecting institutions and governance. Of all resources, oil appears to
produce the most extreme outcomes, from Nigeria to Norway. Kazakhstan is interesting
because the scale of the future oil boom was scarcely anticipated during the 1990s and
because key institutions remain in embryonic and malleable form. The evidence from this
case study is preliminary, as the story is still unfolding; political and institutional
developments during the decade after independence created a situation where political
economy mechanisms could turn oil wealth into a curse.
The household survey analysis presented in this paper gives a preliminary assessment
of the impact of oil revenues expansion. The benefits have not been redistributed evenly
across the country. The oilboom has not resulted in higher average living standards in the oilproducing regions, but has been associated with higher living standards in the metropolitan
centres where the country’s elite lives. As a Kazakh living in Sarykamys, a settlement near
Tengiz oilfield, said: “we have a lot of oil, but we’re not the masters of this oil”, expressing a
feeling many people in Kazakhstan share. Unofficial redistribution seems to be the main
transmission channel of redistribution of oil revenues. A complementary analysis would be
needed, especially in oil-producing regions, to distinguish if it results from unregistered
household small activities or “leakage” of oil revenues.
Use of oil revenues remains an evolving process. In Kazakhstan, the early stage of
the oilboom means that the jury still has a long wait before determining whether oil will be a
blessing or a curse.
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Appendix: Descriptive Statistics by Type of Redistribution
1. Type of Redistribution
Redistribution type Number of people Percentage
1 - Official Public 4,039 12.5
2 - Company 14,166 44.0
3 - Informal 14,024 43.5
Total 32,229
2. Gender of household head
Gender Official Public Company Informal Total
Male (in %) 134 (41.5) 2,268 (55.7) 1,956 (54.1) 4,358 (54.4)
Female (in %) 189 (58.5) 1,801 (44.3) 1,659 (45.9) 3,649 (45.6)
Total 323 4,069 3,615 8,007
3. Age of household head
Average age Standard deviation Number of households.
Official Public 48.5 9.0 323
Company 44.9 8.4 4069
Informal 45.3 8.4 3615
Total 45.2 8.5 8007
4. Education of household head
Redistribution Primary only Secondary University Total
Official Public 48 (14.9%) 239 (74.0%) 36 (11.2%) 323
Company 187 (4.6%) 2,922 (71.8%) 960 (23.6%) 4,069
Informal 297 (8.2%) 2,791 (77.2%) 527 (14.6%) 3,615
Total 532 (6.6%) 5,952 (74.39%) 1,523 (19.0%) 8,007
5. Cross Table Oil/Non-Oil rayons
Redistribution Non-oil rayons Oil-producing rayons All households
Official Public 303 (4.0%) 20 (5.6%) 323 (4.0%)
Company 3,924 (51.3%) 145 (40.9%) 4,069 (50.8%)
Informal 3,425 (44.8%) 190 (53.5%) 3,615 (45.2%)
Total 7,652 355 8,007
Source: own calculations using LSMS Kazakh data for 2002..

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