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Transition Report 2013 Stuck in transition?

CH5 180sq

Facts at a glance

OVER 35% of variation in wealth in some transition countries is explained by circumstances at birth.

GENDER GAPS are greatest in the areas of employment, firm ownership and management across most countries observed.

Cover 180sqV2

 

PLACE OF BIRTH is the main driver of inequality with regard to wealth.

PARENTAL EDUCATION is the main driver of inequality of opportunity with regard to tertiary education.

RIGID LABOUR MARKET STRUCTURES and weak education systems restrict opportunities for young people.


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Economic inclusion

Regional gaps

The final stage of the analysis attempts to measure regional inequality in terms of institutions, education and services, which probably reflects inequality of opportunity linked to people’s place of birth and place of residence within a country. This involves addressing the following two complications.

  • First, internationally comparable data on institutions, education and services are rarely available at the regional level.
  • Second, where such data exist, indices of intra-country inequality will depend on the definition of administrative regions, which may differ widely across countries. Consider two countries with identical intra-country inequality at the level of local institutions. These will appear to have very different levels of internal inequality if one country is divided into 10 regions, while the other is divided into just three. The level of inequality measured in the latter will be lower, because inequality within a region is not recorded.

To circumvent these problems, the next analysis is based primarily on LiTS (2010) data at the level of primary sampling units (PSUs). Imagine PSUs as micro-regions, each numbering about 20 respondent households, which are spread across a country to give a representative impression of the country as a whole. The fact that the PSUs are collectively representative and of equal size solves the problem that comparing administrative regions of different sizes may create spurious differences in inequality.

In addition, the LiTS contains plenty of information on households’ perceptions of local institutions and services, which is internationally comparable. The main disadvantage, though, is that it does not contain data for the SEMED countries.

The analysis focuses on four dimensions: differences in the quality of local institutions; access to, and quality of, services (such as utilities or health care); labour markets (local unemployment and the extent of informal employment); and education (quantity and perceived quality). With the exception of the quantity of education, which is drawn from an extensive regional-level dataset – see Gennaioli et al. (2013) – all data are drawn from the 2010 LiTS (see Table 5.5).

Table 5.5

Regional inclusion gaps – dimensions and indicators

Dimension Indicators Source
Quality of, and trust in, local institutions Corruption in administrative, health and education systems LiTS (2010)   
Quality of administrative, health and education systems
Trust in local government
Satisfaction with local government
Access to services Access to water LiTS (2010)  
Access to heating
Perception of quality of health care system
Labour markets Unemployment LiTS (2010) 
Formal or informal job?
Education Years of education Gennaioli et al. (2013)
Perception of quality of education system LiTS (2010)

Regional inequality is measured in two ways: a Gini coefficient based on means for PSU (regional-level) data; and the difference between the mean of the top quintile of regions (that is to say, the 20 per cent at the top of the regional distribution for an indicator) and that of the bottom quintile. For the LiTS data, which comprise 50 PSUs in most countries, this means comparing the top ten PSUs (ranked according to a specific indicator) with the bottom ten. For the Gennaioli et al. (2013) data, the top and bottom regions were combined in artificial regions representing about 20 per cent of the population at both ends; means were then calculated and compared for these combined regions.

Although conceptually the benchmark against which inequality is measured is perfect equality, regions may be different as a result, for example, of geography and resource endowments. Therefore, the benchmarks against which gaps are measured are set empirically, based on the lower end of the observed distributions for the top-to-bottom difference and the Gini coefficient of each indicator (see Annex 5.2). The two resulting gap measures per indicator are subsequently averaged.

Table 5.6 shows the results. Across institutional dimensions regional gaps are largest in relation to labour markets, particularly in SEE countries, the Caucasus, Tajikistan and Uzbekistan. Gaps for access to local services are “medium” to “large” across most EBRD countries of operations – except for Belarus and Slovenia, where they are “negligible”. Regional gaps with regard to the quality of local institutions are mostly “medium” – with the exception of Bosnia and Herzegovina, Serbia and Uzbekistan, where they are “large”.

There are “small” education gaps in most CEB countries and about half of the SEE region, but Egypt, FYR Macedonia, Georgia, Moldova, Morocco, Serbia, Turkey and Uzbekistan all have “large” gaps.

Table 5.6

Inclusion gaps for regions

Country Institutions Access to services Labour markets Education
Central Europe and the Baltic states    
Croatia Medium Medium Small Medium
Estonia Small Medium Negligible Small
Hungary Medium Small Large Small
Latvia Small Medium Small Medium
Lithuania Medium Large Small Small
Poland Medium Medium Medium Small
Slovak Republic Medium Small Medium Small
Slovenia Small Negligible Small Small
South-eastern Europe    
Albania Medium Medium Large Small
Bosnia and Herzegovina Large Large Large Small
Bulgaria Medium Medium Medium Medium
FYR Macedonia Small Medium Large Large
Kosovo Medium Large Large Small
Montenegro Medium Medium Large Small
Romania Medium Large Medium Medium
Serbia Large Medium Large Large
    
Turkey Medium Large Medium Large
    
Eastern Europe and the Caucasus    
Armenia Medium Medium Large Medium
Azerbaijan Medium Small Large Small
Belarus Medium Negligible Small Negligible
Georgia Negligible Large Large Medium
Moldova Medium Large Large Large
Ukraine Medium Medium Medium Small
    
Russia Medium Small Small Medium
    
Central Asia    
Kazakhstan Small Small Medium Medium
Kyrgyz Republic Medium Large Medium Small
Mongolia Negligible Medium Medium Medium
Tajikistan Medium Large Large Small
Turkmenistan not available not available not available not available
Uzbekistan Large Medium Large Large
Southern and eastern Mediterranean    
Egypt not available not available not available Large
Jordan not available not available not available Small
Morocco not available not available not available Large
Tunisia not available not available not available not available
Comparator countries    
France Small Medium Medium Medium
Germany Negligible Large Negligible Medium
Italy Large Medium Negligible Small
Sweden Medium Small Small Small
United Kingdom Medium Small Small Large

Source: See Table 5.5.
Note: See Annex 5.2 for methodology.

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