metadata: authors: 0: author: Infrastructure Department, Europe and
            Central As
count: Europe and Central Asia
docna: 0: docna: Dimensions of urban poverty in the Europe
            and Central Asia region
docty: Policy Research Working Paper
subtopic: Urban Housing and Land Settlements,Urban Poverty,Urban Governance and Management,Urban Housing,Urban Partnerships & Poverty,Hydrology,Economic Services to the Urban Poor,Municipal Management and Reform,Energy Demand,Energy and Environment,Energy and Mining,Inequality,Peri-Urban Communities
teratopic: Rural Development,Urban Development,Health, Nutrition and Population,Poverty Reduction,Transport
abstracts: cdata!: The objective of this study is to
            contribute to a better understanding of the extent and
            nature of poverty in urban areas in transition countries in
            Eastern Europe and the former Soviet Union, providing
            particular attention to the disparities within urban areas
            between capital cities and secondary cities, and focusing on
            dimensions of poverty related to provision of network
            infrastructure and energy services in cities. Household
            surveys carried out in 1998-2003 in 20 countries provided
            the data for the study. The study found substantial
            differences in urban areas between the capital and secondary
            cities, with households in secondary cities being worse off.
            In addition, secondary cities often had poverty indicators
            equivalent to, or worse than, those of rural areas,
            including in terms of access and quality (reliability) of
            infrastructure. The study confirmed that many households,
            especially in secondary cities, are
            "infrastructure-poor" because of unreliable and
            deteriorated services and that these households are hidden
            in studies that do not examine actual quality. Finally, the
            study found that income and infrastructure inequality are
            generally higher in urban areas, although inequality in
            secondary cities often was greater than that in the capitals.
repnme: repnme: Dimensions of urban poverty in the Europe
            and Central Asia region
display_title: Dimensions of urban poverty in the
            Europe and Central Asia region
listing_relative_url: /research/2006/08/7014497/dimensions-urban-poverty-europe-central-asia-region
topicv3: Water m1326522 137,Energy and Extractives m1326595 507,Poverty m1326687 695,Urban Poverty m1327411 710,Land and Housing m1327648 817
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Rural Development,Urban Development,Health, Nutrition and Population,Poverty Reduction,Transport
no_of_pages: 147
model_extractions: # **ACKNOWLEDGEMENTS**

This study was carried out by a multi-sectoral team from ECSIE, ECSPE and TUDUR under the supervision and guidance of Lee Travers (Sector Manager, ECSIE). Ellen Hamilton (ECSIE) managed the work. Team members include Deniz Baharoglu (TUDUR), Bill Denning (ECSIE), Christine Kessides (TUDUR), Alexandre Kolev (ECSPE) and Maka Lomaia (ECSIE). A number of people assisted the team with methodological and data questions including Michael Lokshin (DECRG), Diane Steele (DECRG) and members of ECA poverty teams too numerous to list here. A special thanks is due to Zurab Sajaia and Radwan Shaban (ECSPE) who were instrumental in resolving a number of data issues. Peer review comments were provided by Asad Alam (ECSPE), Daniela Gressani (LCRVP) and Judy Baker (TUDDR).
# **_CONTENTS_**

|1.<br>Introduction|1|
|---|---|
|1.1.<br>Motivation and objectives of the study|1|
|<br>1.2.<br>The context of urban poverty in ECA: the socialist legacy|1|
|1.3.<br>A framework for viewing urban poverty|4|
|2.<br>Measurement and Data Issues|5|
|2.1.<br>Data sources|5|
|2.2.<br>Selected indicators of income and non-income dimensions of well-being|6|
|2.3.<br>Measurement issues regarding infrastructure and urban poverty in household surveys|7|
|_2.3.1._<br>_Urban poverty may not be properly represented in sample surveys_|_7_|
|_2.3.2._<br>_Poverty indicators are not necessarily comparable between urban and rural areas_|_8_|
|_2.3.3._<br>_Poor coverage of infrastructure and energy in multi-topic questionnaires_|_9_|
|3.<br>Economic Overview|11|
|3.1.<br>Uneven economic recovery across the region|11|
|3.2.<br>Large disparities in national income and national income poverty|13|
|3.3.<br>Migration and urban change|16|
|4.<br>Revisiting the Extent and Nature of Urban Poverty|18|
|4.1.<br>Income poverty|18|
|_4.1.1._<br>_Comparisons of income poverty_|_19_|
|_4.1.2._<br>_Inequality by settlement area_|_23_|
|_4.1.3._<br>_Employment and labor force participation_|_24_|
|_4.1.4._<br>_Other sources of income and transfers_|_27_|
|_4.1.5._<br>_Household expenditure patterns_|_28_|
|4.2.<br>Non-income dimensions of poverty: access to infrastructure, energy and housing|28|
|_4.2.1._<br>_Infrastructure and energy services remain widely available but no-longer reliable_|_29_|
|_4.2.2._<br>_Households pay little for infrastructure and energy services and housing_|_32_|
|_4.2.3._<br>_The incidence of poor sanitary and environmental conditions is high_|_35_|
|_4.2.4._<br>_The links between access to infrastructure and energy services and income poverty_|_36_|
|_4.2.5._<br>_Housing context_|_41_|
|_4.2.6._<br>_Housing affordability_|_45_|
|_4.2.7._<br>_Low mobility rates_|_47_|
|_4.2.8._<br>_Implications for the urban poor_|_49_|
|4.3.<br>Human capital|54|
|4.4.<br>Social Capital, empowerment and security|56|
|5.<br>Conclusion|57|
|5.1.<br>Summary of results|58|
|<br>5.2.<br>Issues for policy makers|59|
|<br>_5.2.1._<br>_Strategic questions_|_59_|
|<br>_5.2.2._<br>_Sectoral issues_|_60_|
|_5.2.3._<br>_Questions related to data and methodology_|_61_|
|_5.2.4._<br>_Implications for future empirical research_|_61_|
# **_FIGURES_**

|Figure 1.1. Patterns of urbanization and growth in the transition economies and middle income count<br>1960-1990|ries,<br>2|
|---|---|
|Figure 3.1. Real GDP level in 2001 relative to 1989|12|
|Figure 3.2. Sectoral output growth between 1990 and 2000|13|
|Figure 3.3. GDP per capita ppp in ECA countries compared to other regions 2001|14|
|Figure 3.4. National poverty level in ECA countries and comparators|15|
|Figure 3.5. National poverty and GDP per capita growth|15|
|Figure 3.6. Urban poverty and GDP per capita growth|16|
|Figure 4.1. Share of poor in capital cities, other urban and rural areas in ECA countries|21|
|Figure 4.2. Percent of uneducated household heads who are poor|22|
|Figure 4.3. Percent of large families with 5 or more members who are poor|22|
|Figure 4.4. Percent of unemployed household heads by country groups|25|
|Figure 4.5. Poor unemployed household heads by settlement area|25|
|Figure 4.6. Percentage of poor household heads who are unemployed, by settlement type|26|
|Figure 4.7. Poor unemployed household heads – Distribution by country groups|26|
|Figure 4.8. Access to infrastructure and energy services in ECA in the early 2000s by location|29|
|Figure 4.9. Reliability of infrastructure and energy services in ECA in early 2000s|30|
|Figure 4.10. Water connection comparison in capital and other urban areas in ECA countries|30|
|Figure 4.11. Water reliability|31|
|Figure 4.12. Water connection versus reliability in ECA countries (capital)|31|
|Figure 4.13. Water connection versus reliability in ECA countries (other urban)|32|
|Figure 4.14. Payment for infrastructure and energy services|34|
|Figure 4.15. Payment rates for water and reliability of service in ECA capitals|34|
|Figure 4.16. Incidence of poor sanitation and environmental conditions|35|
|Figure 4.17. Distribution of basic infrastructure connections by quintiles in capital cities|36|
|Figure 4.18. Distribution of basic infrastructure connections by quintiles in other urban cities|37|
|Figure 4.19. Lack of access to inside toilets and use of dirty fuels by quintiles for ECA countries|37|
|Figure 4.20. Reliability of water and electricity by quintiles in capital cities and other urban areas|38|
|Figure 4.21. Reliability of water for richest and poorest quintiles in the capital cities|39|
|Figure 4.22. Reliability of water for richest and poorest quintiles in the secondary cities|39|
|Figure 4.23. Payment incidence by quintiles in capital cities|40|
|Figure 4.24. Payment incidence by quintiles in other urban cities|40|
|Figure 4.25. Space per capita in ECA capital cities and comparator countries|42|
|Figure 4.26. Comparison of residential density in Moscow and Paris by distance from city center|43|
|Figure 4. 27. Central water and electricity 24 hours a day|44|
|---|---|
|Figure 4.28. House price to income ratios in capital cities|47|
|Figure 4.29. House price to income ratios in secondary cities|47|
|Figure 4.30. Residential mobility rates for households in capital cities, other urban settlements and|rural areas<br>48|
|Figure 4.31. Ratio of one square meter housing per capita for the bottom quintile to square meters h<br>per capita for the highest quintile in capital cities and other urban areas|ousing<br>50|
|Figure 4.32. Residential mobility by the poorest and richest quintiles in capital cities|50|
|Figure 4.33. Residential mobility by the poorest and richest quintiles in other urban settlements|51|
|Figure 4.34. Health outcomes|55|
|**_TABLES_**||
|Table 1.1. Urbanization rates and urban primacy rates by country of ECA region, 2001|3|
|Table 2.1. Data sources by country and year|6|
|Table 2.2. Availability of infrastructure poverty related indicators in ECA|10|
|Table 3.1. Urbanization rate for Russians and other non-titular Slavs|17|
|Table 3.2. World urban population growth and urbanization change|18|
|Table 4.1. Measures of relative poverty by settlement area|20|
|Table 4.2. Inequality by settlement area|24|
|Table 4.3. Household expenditures housing and communal services|33|
|Table 4.4. The percentage of population that changed residence in the preceding five years|49|

# **_BOXES_**

|Box 2.1: Millennium Development Goals (MDGs) ..........................................................................................7|
|---|
|Box 2.2. What happened to public transportation?..........................................................................................11|
|Box 3.1. Migration in the Kyrgyz Republic ....................................................................................................18|
|Box 4.2 Categorical Privileges (L’goti) in Russia............................................................................................41|
|Box 4.3. Living without services in Georgian apartment buildings................................................................44|
|Box 4.4. Armenia’s vicious circle ...................................................................................................................46|
|Box 4.5. Housing as a coping mechanism in Armenia and Moldova..............................................................52|
|Box 4.6. The Emergence of slums in the peri-urban areas of Bishkek............................................................53|
|Box 4.7. Urban Poverty in Tomsk city, Russia ...............................................................................................54|
|Box 4.8. Strategies of the extreme poor for reducing food consumption in urban areas in Armenia..............56|
# **1. Introduction**

# **1.1. Motivation and objectives of the study**

The economic crisis in East and Central Europe (ECA) over the past decade, and the associated increase in poverty, have been well documented. ( _Transition_<sup>_1_</sup> _)_ The rise in income or expenditure poverty has resulted from the loss of enterprise jobs, the decline of agriculture, and cutbacks in public sector employment. Many elements of the safety net, such as housing and public services provided by government and formerly provided by state enterprises have sharply deteriorated, resulting also in deprivation in terms of the nonincome aspects of well-being. The effects of these phenomena on the urban population have been particularly stark—resulting in more dramatic rates of urban poverty in ECA than in other low or middleincome countries—an outcome that has been less well researched.

The aim of this study is to contribute to a better understanding of the extent and nature of poverty in urban areas of this region, giving particular attention to the disparities within urban areas between capital cities and secondary cities (drawing comparisons with rural areas where this is useful), and focusing on dimensions of poverty related to provision of network infrastructure and energy services in cities.<sup>2</sup> The paper is intended to fill gaps in knowledge about access of the poor to infrastructure and energy services, and about urban poverty across the region, by systematically using available survey data to develop a regional profile of these dimensions of poverty. The study was prepared as an input into ECSIE strategy and ECA poverty work and, as such, was intended to be of use to Bank staff in their work.

# **1.2. The context of urban poverty in ECA: the socialist legacy**

Urban poverty in ECA reflects a particular history and character of the urban context, rooted in the socialist legacy of these countries. ( _Commissars_<sup>3</sup> ) Relative to their GDP per capita, the transition countries are overurbanized—with a higher share of urban population than is typical for their income level, because of the planned drive towards industrialization under socialism (Figure 1.1). While central planning dictated the establishment and location of industrial firms, many of the normal developments that would accompany market-based urban growth and respond to household demands were suppressed. In particular, urban land was more heavily tied up in industrial use than is typical in market-based cities. Where privately owned, housing became a relatively illiquid asset because of regulations and other factors suppressing a housing market, but residents of state- or enterprise-owned housing also had little residential mobility.

While access to urban infrastructure of water and sanitation, electricity, and district heating was provided to a fairly high share (with almost universal coverage in some cases) of the urban population in most of the region at the time of transition, urban infrastructure was heavily subsidized and few systems were commercially viable as state subsidies were reduced. Shares of household expenditures on housing and utilities in the transition countries have risen several-fold since the transition, yet remain very low compared to OECD averages. Maintenance of the infrastructure facilities and services (as well as maintenance of (formerly) state-owned housing) has deteriorated to the point where reliability and even access are becoming significant welfare issues. Because most of the ECA economies were so heavily industrialized, with liberalization the inherited rigidities hampered the supply response in creation of jobs, housing, land and

> 1 World Bank, _Making Transition Work for Everyone: Poverty and Inequality in Europe and Central Asia_ .  Washington, D.C. 2000.  (hereafter Transition)

> 2 Even in developed countries, network infrastructure is not necessarily available in rural areas.  However, this does not necessarily mean rural residents lack adequate sources of heating, water and sanitation since viable solutions for rural areas may differ from those for urban areas.

> 3 World Bank, _From Commissars to Mayors: Poverty and Cities in Transition Economies_ .  Long version draft.  2000. (hereafter Commissars).
urban services. Poverty in the region has therefore been greater than an economic depression alone would have created.<sup>4</sup>

**Figure 1.1. Patterns of urbanization and growth in the transition economies and middle income countries, 1960-1990**

<!-- Start of picture text --> 70<br>1990<br>65<br>1990<br>A C<br>60<br>55<br>1960<br>B<br>50<br>1960<br>45<br>40<br>2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000<br>GDP per capita (1990 US$)<br>world except low income ECA<br>Urbanization (%)<br><!-- End of picture text --> Source: Commissars to Mayors, p. 7 Much of this poverty has appeared in urban areas of the region. In most analysis of poverty in most countries, poverty is found to be predominantly rural (with a higher incidence in rural areas even if the majority of the total poor are not rural), for a variety of economic, social and political reasons. Yet although urban economies in general offer individuals a wide range of opportunity though a deep and diverse labor market, and the relative density of urban settlement makes it possible for many services to be provided at lower cost and with greater quality than in the rural context, urban poverty remains a reality even in high income countries. Poverty in cities can result from in-migration of the poor from elsewhere; it can also result from cyclical or structural mismatching of workers with available jobs; and from institutional or governance failures, whereby access to assets such as housing and services does not respond to demand of some groups who become increasingly excluded and disadvantaged. In the ECA countries rural-to-urban migration is no longer significant, although urban-to-urban migration continues. Income poverty in cities is therefore more an issue of the economy’s response to transition and other shocks, and of growing inadequacies in services— all of which undermine residents’ sense of security and empowerment and raise their vulnerability. Much of the inadequate supply response reflects the partial (or rudimentary) progress of structural reforms in some of the countries, which cripples the urban economy’s ability to foster enterprise and ensure good matching of workers to jobs.

Apart from the overall high levels of urbanization, the distribution of urban population, economic activity and infrastructure were not balanced across the system of cities in the transition economies.<sup>5</sup> A common 4 Defining structural poverty as the difference between observed poverty rates and those implied by change in GDP alone, it has been estimated that a 1.3 percentage point increase in structural poverty is associated with every additional percentage point of over-industrialization. Based on data for 13 countries in ECA. Source _: Commissars, Box 5.1_ .

2
indicator of the concentration of urban population in the largest city, the primacy rate, does not suggest that the socialist regimes particularly favored the major (usually the capital) city. (See Table 1.1) Relative to other low and middle income countries, the primacy rate of countries in the region is not particularly high and the wide range of country values is largely in line with geographic size. Econometric analysis of a global country sample has revealed that urban concentration in general tends to rise then fall with per capita income and to decline with national scale, with increased openness to trade, and with political decentralization (or increased federalism) (Henderson, 2000). Based on this analysis of “optimum” levels of concentration at any given income level, it might be expected that prior to the transition, the ECA countries had a relatively high urban concentration; however, Henderson finds that at the time of transition the ECA countries in his sample were dramatically _less_ concentrated than their “expected” or “optimum” level.<sup>6</sup> Despite the presumable “pull” effect of highly centralized government favoring the capital city, socialist planning allocated industry in such a way that alternative urban areas grew more than a market economy would have permitted. However, secondary cities have suffered greatly during the transition from the decline of the noncompetitive state sector; and possibly lacking a strong natural economic and political base, these cities have been harder hit than the capital city, which can rely on government activities and growth of such competitive service sectors as the economy still sustains.

**Table 1.1. Urbanization rates and urban primacy rates by country of ECA region, 2001**

||**Urban Population**<br>(% of Total)|**Population in the Largest City**<br>(% of Urban Population)|
|---|---|---|
||2001|2001|
|**Region** (unweighted averages)|||
|**Balkans**|52|28|
|Albania|43|22|
|Bosnia and Herzegovina|43|31|
|Bulgaria|67|22|
|Croatia|58|42|
|Macedonia, FYR|59|36|
|Moldova|42|37|
|Romania|55|16|
|Serbia andMontenegro|52|30|
|**Caucasus**<br>|59<br>|51<br>|
|Armenia|67|55|
|Azerbaijan|52|47|
|Georgia|57|..|
|**Central Asia**<br>|40<br>|27|
|Kazakhstan|56|13|
|Kyrgyz Republic|34|43|
|Tajikistan|28|30|
|Turkmenistan|45|23|
|Uzbekistan|37|24|
|**EU Accession**|63|29|
|Czech Republic|75|16|
|Estonia|69|42|
|Hungary|65|28|
|Latvia|60|53|
|Lithuania|69|24|

> 5 _Henderson, Vernon. 2000. “How Urban Concentration Affects Economic Growth.” Policy Research Working Paper 2326. World Bank, Development Research Group, Washington, D.C._

> 6 _In another paper he finds Poland also relatively under-concentrated.  Uwe Deichmann and Vernon Henderson,_

_“Urban and Regional Dynamics in Poland,” Policy Research Working Paper 2457, World Bank Development Research Group, Washington, D.C. 2000._ 3
||**Urban Population**<br>(% of Total)<br>2001|**Population in the Largest City**<br>(% of Urban Population)<br>2001|
|---|---|---|
|Poland|63|14|
|Slovak Republic|58|15|
|Slovenia|49|26|
|**Slavic**|70|13|
|Belarus|70|24|
|Russian Federation|73|8|
|Ukraine|68|7|
|Turkey (_not a transition country_)|66|21|
|**Income Group** (weighted averages)|||
|Low income|31|17|
|Middleincome|52|15|
|Low &middleincome|42|16|
|Europe & Central Asia|63|15|
|High income|78|17|

Source: World Development Indicators, 2003

# **1.3. A framework for viewing urban poverty**

This paper views poverty in both income and non-income dimensions, as established in _WDR 2000/01_ and as reflected in the World Bank-supported poverty assessments that provide much of the material for this report. In addition the analysis draws upon a framework for understanding urban poverty and vulnerability (the risk of falling into poverty) in terms of three characteristics that imply a relative (though not absolute) distinction with rural poverty.<sup>7</sup> First, the urban economy is highly monetized so that a steady source of cash income is critical and cash expenditures required to avoid poverty. Second, the relative density of urban settlement increases the risks and importance of environmental health and safety measures, many of which are infrastructure related. Third, urban communities are generally more mobile and changeable, and urban social networks more diverse, than is typical in rural areas. Poverty and vulnerability are closely linked to the degree of command of multiple assets and in the urban context, adequate access<sup>8</sup> to housing, infrastructure, energy services. Public transport is an important determinant of whether households can be sufficiently mobile to take advantage of the urban labor market and find employment, as well as a contributor to health, safety, and quality of life.

The present report focuses on recent developments in income/expenditure poverty and the status of infrastructure/energy/housing as particularly relevant to urban poverty. The social dimensions of poverty and empowerment are discussed more briefly, only because available information is particularly weak in this area. The analysis proceeds from the following hypotheses:

- a) Living standards vary significantly across urban areas--notably, between the capital city and “other urban” (secondary cities), the distinction possible from most of the available household survey databases.  These differences are often greater than those between overall urban and rural averages; therefore, to understand patterns of poverty it is necessary to spatially disaggregate the data.

> 7 _Moser, C., M. Gatehouse, and H. Garcia.  1996.  “Urban Poverty Research Sourcebook Module I:  Sub-City Level Household Survey.”  Urban Management Program Working Paper Series 5.  UNDP/UNCHS (Habitat)/World Bank, Washington. D.C.  ; World Bank.  2002.  A Sourcebook for Poverty Reduction Strategies.  Volume 2.  Washington, D.C._

> 8 _Adequate access here means having the facility to acquire and exchange assets in the land and housing market, such as by moving from one city or urban zone to another in response to opportunity.   Note that access to housing assets, in the sense of private home ownership, is not necessarily the purview of upper income groups because state-owned housing under socialism was often a perquisite and so rental housing is not necessarily inferior to privately-owned._

4
- b) The “other urban areas” have poverty indicators equivalent to, or worse than, those of rural areas, including in terms of access and quality (reliability) of infrastructure.

- c) Although formal access to infrastructure and energy (e.g. utility connections) remains higher in urban areas than rural in most cases, many households, especially in secondary cities, are “infrastructure-poor” because of unreliable and deteriorated services, and these households are hidden by studies that do not examine actual quality.  To fully appreciate the welfare implications of inadequate infrastructure services, it is important to take account of the different housing circumstances and options available to urban as compared to rural households.

- d) Income and infrastructure inequality are generally higher in urban than in rural areas, and highest in capital cities.  Inequality may have significance for social perceptions of welfare.

It must be stressed, however, that there is no average ECA country and that the economies vary widely across all issues, although there are distinct similarities within the sub-regions (the Balkans, Caucasus, Central Asia, EU Accession, and Slavic countries).

The remainder of this report is organized as follows. Section 2 describes the data used for the empirical analysis and discusses some measurement issues. Section 3 provides an overview of the economic and demographic situation in Europe and Central Asia. The extent and nature of urban poverty in the region is then investigated in Section 4.

# **2. Measurement and Data Issues**

# **2.1. Data sources**

The sources of primary data were sample surveys of households within transition economies of the ECA Region. In most cases the surveys are administered by the statistical agencies within each country with technical assistance from donor organizations. The sophistication and usefulness of the household surveys undertaken in the Region have improved considerably during the 1989 - 2003 period. Most countries have a program of annual Household Budget Surveys (HBS). However the data available from this source were of poor quality until the sampling frameworks were improved in the mid to late 1990s. The HBS approach does not always allow for the calculation of welfare aggregates based on consumption so expenditures or income are used instead. Large flows in population within and among countries in the region in the early transition years also created sampling uncertainties. The most recent surveys have benefited from completion of new national censuses from 1999-2003.

This study used surveys from 20 countries in the ECA region (Table 2.1). The countries that were not included in the study include five of the EU candidate countries (the Czech Republic, Estonia, Latvia, Slovakia, Slovenia), as well as Croatia, Macedonia, Montenegro (although the more populous Serbia was included) and Ukraine. In the case of the first wave EU accession countries, data sets were not easily available and these countries were seen to be of lower priority in terms of future Bank-financed development work. Datasets of sufficient quality were not available for Croatia, Macedonia and Montenegro at the time the data were being assembled. Work on the Ukrainian dataset was not sufficiently advanced to determine the welfare (consumption) aggregate to be used. The final set of surveys that was used to provide the data used in this report is listed below. Annex 1 Measurement and Data Issues, provides more background and detail on the material presented in this chapter.

5
# **Table 2.1. Data sources by country and year**

||**Country**|**Date**|**Survey**|
|---|---|---|---|
|1|Albania|2002|Living Standard Measurement Study|
|2<br>3|Armenia<br>Azerbaijan|2001<br>2001|Integrated Living Conditions Survey<br>Household Budget Survey (new design)|
|4|Belarus|2001|Income and Expenditure Survey (newer design|
|5|Bosnia & Herzegovina|2001|Living Standard Measurement Study|
|6|Bulgaria|2001|Integrated Household Survey|
|7|Georgia|2001|Survey of Georgian Households|
|8|Hungary|2000|Household Budget Survey|
|9|Kazakhstan|2001|Household Budget Survey|
|10|Kosovo|2000|Living Standard Measurement Survey|
|11|Kyrgyz Republic|2001|Household Budget Survey|
|12|Lithuania|2000|Household Budget Survey|
|13|Moldova|2001|Household Budget Survey|
|14|Poland|2001|Household Budget Survey|
|15|Romania|2002|Family Budget Survey|
|16<br>17|Russia<br>Serbia|2001<br>2002|Russia Longitudinal Monitoring Study Round X<br>Poverty Household Survey|
|18|Tajikistan|1999|Living Standard Measurement Survey|
|19|Turkmenistan|1998|Living Standard Measurement Survey|
|20|Uzbekistan|2000|HouseholdBudget Survey|

# **2.2. Selected indicators of income and non-income dimensions of well-being**

For the purpose of this study, three different types of indicators were constructed, each representing a different dimension of poverty. The first type of indicator refers to income poverty and economic opportunities and includes the national absolute poverty rate, the relative poverty rate, and the householdhead unemployment ratio. The national absolute poverty rate refers to the percentage of households whose consumption lies below a pre-defined country--specific poverty line. The relative poverty rate corresponds to the households in the bottom quintile of national consumption per capita and is useful to assess the relative position of different groups in society. The household head unemployment ratio is the proportion of unemployed heads of household.

The second type of indicator relates to very approximate aspects of human capital and includes the incidence of activities interrupted due to health problems and the incidence of household heads with less than secondary education. These are very narrow dimensions of well-being but the advantage is that they can be easily constructed for, and compared across, a large number of countries.

The third type of indicator refers to other non-income dimensions of well-being: adequate shelter, light, heat, running water and sanitation. In urban areas where light, heat, running water and sanitation depend on access to local utilities, the surveys can be used to identify "delivery-based" indicators which show basic access to infrastructure services, level of service reliably available, living conditions and spending on payments for services. Since most of the surveys used in this study track access to network services, the data are of much less relevance for understanding living conditions in rural areas. As one example, a rural household that does not have access to piped water, may have a well in the front yard to meet its water needs.

6
# **Box 2.1:  Millennium Development Goals (MDGs)**

Several of the indicators used in this study are related to those mentioned in the Millennium Declaration adopted in September 2000 by the U.N. General Assembly, which set the Millennium Development Goals (MDGs) to be achieved by countries by 2015. In fact, among the MDGs, 2 include specific targets and indicators that relate directly to infrastructure and energy poverty. these are as follows:

- Goal 7 - Ensure environmental sustainability

- Target 10 - Halve by 2015 the proportion of people without sustainable access to safe drinking water � Indicator 29 - Proportion of population with sustainable access to an improved water source

- Target 11 - By 2020 to have achieved a significant improvement in the lives of at least 100 million slum dwellers

- Indicator 30 - Proportion of people with access to improved sanitation

- Indicator  31 - Proportion of people with access to secure tenure (urban/rural)

- Goal 8 - Develop a Global Partnership for Development

- Target 18 - In cooperation with the private sector, make available the benefits of new technologies, especially information and communications

- Indicator 47 - Telephone lines per 1,000 people

The MDGs were however mostly developed for the poorest countries in Africa and do not fit very well the situation in ECA countries, where quality, reliability and affordability of infrastructure and energy services may be more of an issue than actual provision (physical connection). ECA countries are also unusual in that for some MDGs for some countries, performance is deteriorating, not improving. A number of indicators more relevant to the region were therefore constructed for this study. Four broad types of desired indicators were identified, referring respectively to access, reliability, living conditions, and payment for services.

# **2.3. Measurement issues regarding infrastructure and urban poverty in household surveys**

This analysis of urban poverty, including its infrastructure and energy dimensions, relies on recent Living Standards Measurement Surveys (LSMS) surveys, and when these were not available, on Household Budget Surveys (HBS) surveys. LSMS and HBS surveys have been the most frequently used quantitative instrument for poverty monitoring and analysis in the region, as they are the only surveys that contain extensive information on household income and expenditures. The preference given to LSMS over HBS surveys lies in the fact that LSMS surveys usually cover a greater variety of topics, including infrastructure and energy poverty, and receive considerable care in terms of quality control.

Despite their advantages, there are a number of problems with LSMS and HBS surveys for the purpose of a comprehensive analysis of infrastructure and urban poverty in ECA. These problems are set-out below.

# **2.3.1. Urban poverty may not be  properly represented in sample surveys**

Although LSMS and HBS surveys in ECA countries have generally robust sampling frameworks, three groups are consistently under-represented or omitted entirely from the surveys: peri-urban dwellers, those who are homeless and Internally Displaced People (IDP)/refugees. The appearance of slums in the periphery of big cities is a new - and still not well recognized - phenomenon in some countries in the ECA region. Since these peri-urban areas are not administratively part of the city, residents do not appear on the rosters of the local authorities and are excluded from sampling within the official city boundaries. Large peri-urban settlements have been reported outside Bishkek and some Albanian cities, especially Tirana, as well as in the 7
countries of the former Yugoslavia. Exclusion of homeless populations occurs across ECA countries, as well as the rest of the world. They are a notoriously difficult population to include in a survey. Finally, countries in the region that have experienced conflict (notably, the Caucasus and the countries of the former Yugoslavia) typically under-sample IDP/refugee populations, although these populations are generally found in urban areas. As a result of under-representation of these groups, the true level of poverty in urban areas is likely underestimated.

**Peri-urban areas.** These are typically not treated adequately in household surveys because they are excluded from explicit consideration when setting up the sampling strata.  Formally established urban areas are covered in one strata.  Rural areas are covered in one or more strata which make use of sampling units selected from around the entire country.  Unless a peri-urban area happens to be chosen by random selection as one of the sampling units in the rural strata, it will not be included at all.  This random inclusion in the rural sample strata does not ensure proper coverage of peri-urban issues.  (For example, in Albania in the last ten years, ten percent of the national population has migrated to Tirana and is largely housed in peri-urban areas on the outskirts of the capital.)

**Internal structure of the city.** Similarly to the sampling problem of peri-urban areas is the issue of adequate understanding of specific sub-areas or neighborhoods within a city.  Urban activities take place in such intensity, concentration, and with substantial externalities that many different household welfare situations can exist in close proximity and yet be leading to different welfare outcomes.  This could be corrected through better sample strata design and higher numbers of households surveyed.  Alternatively, and to prevent over-burdening the national sample, separate urban surveys could be undertaken before the poverty analysis for the country is attempted.

**Capital city.** A related example of the inadequacy of the traditional approach to setting up the urban strata is the problem of analysis when the capital city is combined with other urban areas in the country.  Since the capital city has better access to national decision makers and international connections it is often better off than other cities.  This can introduce an overall upward bias in the urban welfare measures which can mask major problems in non-capital cities.  This effect is demonstrated by the analysis within this study.  Most household surveys undertaken within the last three years have solved this by providing separate strata for the capital city and other urban areas.  The poverty analysis work based on these surveys needs to consistently make use of this greater specificity and avoid lumping together the capital and other cities.

# **2.3.2. Poverty indicators are not necessarily comparable between urban and rural areas**

Since there is no single definition of what is a rural and urban settlement, great care needs to be given when comparing poverty indicators from LSMS and HBS data across urban and rural areas in different countries. The choice of a particular country-specific threshold for a rural/urban setting can have a non-negligible implication for the observed incidence of income and non-income poverty by rural-urban areas and makes comparison across countries problematic.

**One poverty line.** Generally poverty lines are calculated for the country as a whole.  A common problem with many poverty estimates derived from household surveys is that they do not take into account ruralurban price differences.  In the ECA region, out of the 20 countries investigated, only 12 had a welfare aggregate and/or a poverty measure which had been adjusted for price differences between rural and urban households.  Since the cost of living is usually higher in urban areas than in rural areas, in an income-based poverty measure, ignoring the relative price differences would lead to an overestimate of the true level of economic well-being in urban areas.  In addition the underlying "basket" of consumption used to estimate price differences generally does not reflect the larger differences in urban and rural consumption patterns. This exacerbates the underestimation of urban and the overestimation of rural poverty.  Also the regions used for the price calculations may correspond to administrative units which are inappropriate for isolating capital city, other urban, and rural differences such as whole provinces, states, or districts.

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**Access or connection to network-based utilities.** The presence or absence of a connection to a centralized network utility does not have the same welfare implications in rural areas as it does in urban areas.  In urban areas, households without connections to central water supply, central sewage, or central heating/natural gas have a lower quality of life than those with these services.  However, in rural areas this may not be the case as adequate substitutes such as well water may be available.  For example, no one would assume that rural households without district heating are deprived of heating.  Quite the opposite, district heating (and many other network services) only make economic sense in densely populated areas.  Access to district heating should never be used as a proxy for availability of heating for rural households.  Furthermore, as this study shows, connection to network utilities does not mean those services are provided and care should be taken to not assume that connection means provision.

# **2.3.3. Poor coverage of infrastructure and energy in multi-topic questionnaires**

Overall in the region, the coverage of infrastructure and energy poverty tends to be fairly poor. This is illustrated in Table 2.2 showing the availability of 26 desired indicators for 20 transition countries. Regionwide, out of the 26 desired indicators constructed for this study, only about 70 percent could be measured with recent available data. There are also large disparities across countries in terms of survey coverage of infrastructure and energy indicators. The percentage of desired indicators that could be measured ranged from 48 percent in the Belarus 2001 HBS to 89 percent in the Albania 2002 LSMS and Turkmenistan 1998 LSMS.

In general, LSMS surveys in the region provided much more comprehensive coverage of infrastructure and energy than the HBS surveys. The average coverage rate of the desired indicators was 78 percent in LSMS surveys, compared with only 64 percent in HBS surveys. The possibility to relate welfare outcomes with access to infrastructure and energy services was also much more limited in HBS than LSMS surveys. Among countries with available recent LSMS surveys, the coverage of infrastructure and energy poverty was the worst in Russia (48 percent) and the best in Albania and Turkmenistan (89 percent). Among those with HBS-type surveys only, the coverage was the poorest in Belarus (48 percent) and the most comprehensive in Georgia (85 percent).

Table 2.2. also shows great disparities in the dimensions of infrastructure and energy poverty that can be measured in the region. While most surveys provided information on the availability of infrastructure and energy services, few contained information on whether these services were reliable and paid for and even fewer provided information on the consumption of infrastructure and energy services.

Moreover, not all types of infrastructure and energy services were covered equally. In terms of availability, public transportation and electricity connections were the least well documented in the region, although for different reasons. In the case of public transportation, few surveys asked any questions and those questions were not comparable (Box 2.2). In the case of electricity, countries assume all households are connected, thus choose not to include this question. As regards reliability, information on the quality of district heating was extremely limited and even information on water and electricity was available from fewer than half the surveys. In terms of payment rates, the information provided for natural gas was extremely poor.

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# **Table 2.2. Availability of infrastructure poverty related indicators in ECA**

|**Indicator**|**% of**<br>**Surveys**|**Indicator**|**% of**<br>**Surveys**|
|---|---|---|---|
|**DELIVERY BASED INDICATORS**||**DEMAND BASED INDICATORS**||
|Availability||Potential demand|100%|
|Water connection|100%|||
|District heating connection|100%|**WELFARE BASED INDICATORS**||
|Natural gas connection|75%|Environmental||
|Electricity connection|50%|Lacking waste water treatment|75%|
|Telephone connection|100%|Lacking waste disposal|35%|
|Time/distance to nearest bus stop|35%|Using dirty fuels|85%|
|Car ownership|100%|Health||
|Reliability||Activities interrupted by health problems|70%|
|Potable water 24 hours per day|45%|Education||
|Potable water≤4 hours/day|35%|Head of HH with less than secondary<br>education|100%|
|District heating for 3 or more months per|25%|||
|year||||
|Electricity 24 hours per day|45%|Living Conditions||
|Electricity≤6 hours/day|30%|Crowding|95%|
|Affordability||Economic Opportunities||
|Reporting any payment for central water|85%|Unemployment|100%|
|Reporting any payment for district heat|85%|Security/Disruption||
|Reporting any payment for electricity|80%|Owning principal dwelling|100%|
|Reporting any paymentfor naturalgas|70%|Moved withinthelastfive years|35%|

Source: see Table 2.1.

Another important drawback is the fact that the infrastructure module is not tailored to reflect the specific conditions that differ in urban and rural areas. Most questions in infrastructure modules relate to central connections which only makes sense in urban areas, as discussed above. In rural areas, however, central connection is not necessarily desirable for all types of services (e.g., district heating) and the absence of connections does not necessarily mean poor access to basic services, as other types of measures are usually used.

In addition to those indicators which were reasonably expected to be available, and for which some were not available (the discussion of the 26 indicators above), is another issue. This concerns the indicators that were ideally desired but for which there was little possibility, at this time, that they would be available.

In the early stages of this research some 80 ideally desired indicators were developed. Initial screening of data availability reduced this set to the 26 indicators which were used for analysis. Annex 1 provides a table of these ideally desired indicators and the original framework used to develop the set of 80, as well as discussion of the problems encountered.

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# **Box 2.2.  What happened to public transportation?**

ECA countries began transition with a greater reliance on public transportation than is true in other regions. Subsequent years have seen the continued collapse of public transportation and an associated rapid motorization. Despite this background, and despite the importance of public transportation (especially for the poor), only 35 percent of surveys included the most basic indicator of public transport availability (distance to nearest bus stop). The surveys did not include questions about the use or quality of public transportation, such as number of trips or the average commuting time to work. Finally, although the surveys did include expenditures on public transportation, the large number of people who are exempted from payment or who simply do not pay makes the data of little use, since one cannot establish who rides public transportation to begin with. As a result, the authors reluctantly excluded public transportation from this study.

# **3. Economic Overview**

The overall impact of the transition on the state of the economy of countries in the ECA region is illustrated by the large changes in the most basic economic indicators – primarily, national incomes. The degree to which national income has been affected ranges widely among transition economies. There is a sharp divergence across the region, both in terms of output and level of national poverty.

# **3.1. Uneven economic recovery across the region**

Available data points to a large diversity across the region in the degree to which countries have recovered from the initial transition shocks. Figure 3.1 provides information on the change in real GDP level from 1989 to 2001 by countries and country groups.<sup>9</sup> As Figure 3.1 presents, by the end of 2001, five of the accession countries (Poland, Slovenia, Hungary, Slovakia, Czech Republic,) out of 8, reached and even exceeded their pre-transition GDP level. The success rate among other transition countries is lower. Of the seven Balkan countries, only Albania managed to exceed its pre-transition GDP level. Among the three Slavic countries, only Belarus did so and in Central Asia, this was true only of Uzbekistan. In nine countries (Romania, Kazakhstan, FYR Macedonia, Bulgaria, Kyrgyzstan, Latvia, Lithuania, Russia, Armenia), GDP levels were about 60-80 percent of their pre-transition level. And in six countries (Azerbaijan, Tajikistan, FR Yugoslavia, Ukraine, Georgia, Moldova), GDP levels stood at only 35 to 55 percent of their 1989 levels.

Besides the large changes in overall GDP, the patterns of growth in different sectors have also differed greatly. As shown in Figure 3.2, between 1990 and 2000, in most countries in the region, there was a large decline of output in industry, manufacturing, and agriculture, while there was an increase in services. The very weak performance in agriculture (almost universally negligible or negative growth, except for Albania and the Czech Republic) explains why urban-to-rural migration during the early transition years has stopped. Economic prospects remain better in the urban areas. However, the equally dismal record in manufacturing (except in the EU accession countries of Hungary and to a lesser extent, Slovakia, Slovenia and Estonia) has meant considerable unemployment especially from retrenchment in the traditional state-owned enterprises.

> 9 _For the purposes of this study, transition countries were classified into five groups:  first wave EU accession countries, Balkans, Slavic, Caucasus and Central Asia.  The use of these groups allows us to draw out broader patterns among countries that share similar patterns of urban development and face similar urban problems.  In developing these groupings, consideration was given to factors such as urbanization, level of income and economic structure. Classification of Moldova was problematic and its inclusion with the Balkans admittedly rather arbitrary.  In the case of Kazakhstan, the structure of the economy would suggest affiliation with Russia and the other Slavic countries, but Kazakhstan is less urbanized and ultimately it was included with the other Central Asian countries._

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Growth in services has been inadequate to make up for these declines in the primary and secondary sectors. Moreover, across the region economy wide employment losses have far outweighed production losses.<sup>10</sup>

**Figure 3.1. Real GDP level in 2001 relative to 1989** (1989=100)

<!-- Start of picture text --> 0 20 40 60 80 100 120 140<br>Poland<br>Slovenia<br>Hungary<br>Slovakia<br>Czech Rep.<br>Estonia<br>Latvia<br>Lithuania EU Accession<br>Albania<br>Croatia<br>Balkans<br>Romania<br>FYR M acedonia<br>Bulgaria Slavic<br>Serbia and M ontenegro<br>M oldova<br>Belarus Caucasus<br>Russia<br>Ukraine<br>Armenia Central Asia<br>Azerbaijan<br>Georgia<br>Uzbekistan<br>Turkmenistan<br>Kazakhstan<br>Kyrgyzstan<br>Tajikistan<br><!-- End of picture text --> Source: UNICEF MONEE project database

> 10 The World Bank.  2000. _Making Transition Work for Everyone: Poverty and Inequality in Europe and Central Asia._ Washington, D.C.

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**Figure 3.2. Sectoral output growth between 1990 and 2000**

(Average annual % growth) <!-- Start of picture text --> Percent<br>-25 -20 -15 -10 -5 0 5 10<br>Czech<br>Estonia<br>Hungary<br>Latvia<br>Lithuania<br>Poland<br>Slovak<br>Slovenia<br>Albania<br>Bulgaria<br>Croatia<br>Moldova<br>Macedonia<br>Romania<br>Armenia<br>Azerbaijan<br>Kazakhstan<br>Kyrgyz<br>Tajikistan<br>Turkmenistan<br>Uzbekistan<br>Belarus<br>Russia<br>Ukraine<br>Agriculture Industry Manufact. Services<br>EU Accession<br>Balkans/EE<br>Caucasus<br>Central Asia<br>Slavic<br><!-- End of picture text --> Source: WDI 2002

# **3.2. Large disparities in national income and national income poverty**

There are large disparities in GDP per capita across countries in the region. In 2001 GDP per capita ranged from nearly $1,000 in Tajikistan to above $8,000 in Lithuania. And 11 out of 16 countries for which data are reported were below the ECA average, i.e. GDP per capita of around $6,500. Although a wide divergence in incomes was evident even before transition, the uneven economic recovery and reform process has tended to widen the gap between the less developed and the more developed countries in the region. Most of the countries that experienced recovery or growth in real GDP since 1989 as shown on Figure 3.1, are in the upper middle income ranking (WDI), with the exception of Uzbekistan, while the poorest performers 13
according to that graph are in the low income ranking (with the exception of Latvia and Lithuania). In 8 out of 16 ECA countries reported in Figure 3.3, GDP per capita is now below the average of that in East Asia.

**Figure 3.3. GDP per capita ppp in ECA countries compared to other regions 2001**

<!-- Start of picture text --> 18000<br>16000<br>14000<br>12000<br>10000<br>8000<br>6000<br>4000<br>2000<br>0<br>Balkans Caucasus Central Asia EU Accession Slavic<br>Slovenia<br>Czek R.<br>Hungary Slovak R.<br>Estonia<br>Croatia Poland Upper mid<br>Lithuania<br>Latvia Belarus<br>Bulgaria Kazakhs Russia ECA LAC<br>Bosnia H Macedonia Romania MNA Middle Inc<br>Albania Turkmen Ukraine East Asia<br>Azerbaijan<br>Moldova Armenia Georgia Kyrgyz R Uzbek Low inc<br>Tajikistan<br><!-- End of picture text --> Source: WDI Besides important differences in national income across countries, the region is also characterized by large disparities in national poverty levels. Each country in the region has its own country-specific poverty line, and thus the differences in so-called “national” poverty rates across countries are not strictly comparable. However, because these national rates reflect what is widely accepted as the incidence of poverty in each country (see Section 4), it is interesting to see how these vary across countries in the region with different level of national income.

Figure 3.4 presents national poverty rates and GDP per capita for several countries in the ECA region and other countries with similar level of income. What is notable is that many ECA countries (Tajikistan, Kyrgyz Republic, Armenia, Azerbaijan, Romania and Moldova) reported in the figure are above the trend line with considerable margins in terms of poverty rates. This indicates that these ECA countries have higher poverty levels than would be expected at their levels of income. However, not all countries in the ECA region face the same incidence of poverty. While national poverty tends to be the highest in Tajikistan, the Kyrgyz Republic and Moldova, it is much less pronounced in Bulgaria, Albania, Belarus, Bosnia, and Kazakhstan.

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**Figure 3.4. National poverty level in ECA countries and comparators**

<!-- Start of picture text --> 100<br>TJ<br>90<br>80<br>70 MD<br>KG<br>60 AZ<br>AM<br>50<br>40 TM<br>30 UZ RO<br>20 AL<br>BY BA<br>10<br>KZ BG<br>0<br>0 2000 4000 6000 8000 10000 12000<br>GDP per capita ppp 2001<br>2000)<br>National poverty rates (1995,<br><!-- End of picture text --> AL=Albania; AM=Armenia; AZ=Azerbaijan; BA=Bosnia-Herzegovina; BG=Bulgaria; BY=Belarus; KZ=Kazakhstan; KG=Kyrgyz; MD=Moldova; RO=Romania; TM=Turkmenistan; TJ=Tajikistan; UZ=Uzbekistan Source: WDI and staff calculations There is also a relationship (although not very strong) between the change in GDP and poverty levels, whereby ECA countries with greater declines in GDP over the past decade show the highest rates of poverty incidence (Figure 3.5).

**Figure 3.5. National poverty and GDP per capita growth**

<!-- Start of picture text --> 100<br>TJ<br>90<br>80<br>MD<br>70<br>KG<br>60<br>AM<br>AZ 50<br>UZ 4 0<br>TM<br>RO BY 30<br>KZ 2 0 AL<br>10<br>BG<br>0<br>-10 -8 -6 -4 -2 0 2 4 6 8 10<br>Annual GDP percapita growth rate between 1990 and 2000<br>National poverty rate<br><!-- End of picture text --> AL=Albania; AM=Armenia; AZ=Azerbaijan; BG=Bulgaria; BY=Belarus; KZ=Kazakhstan; KG=Kyrgyz; MD=Moldova; RO=Romania; TM=Turkmenistan; TJ=Tajikistan; UZ=Uzbekistan Source: WDI and staff calculations An equivalent relationship between urban poverty rates and GDP growth is shown in Figure 3.6 below. Countries where GDP has fallen more sharply tend to be those with higher rates of urban poverty. This suggests that the prolonged economic recession in these countries has had strong impacts on urban households.

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**Figure 3.6. Urban poverty and GDP per capita growth**

<!-- Start of picture text --> 100<br>TJ 90<br>80<br>70<br>MD AZ<br>6 0<br>AM<br>KG 50<br>40<br>UZ 30<br>TM RO 2 0<br>B Y<br>10 AL<br>KZ<br>BG<br>0<br>-10 -8 -6 -4 -2 0 2 4 6 8 10<br>Annual GDP percapita growth rate between 1990 and 2000<br>Urban poverty rate<br><!-- End of picture text --> AL=Albania; AM=Armenia; AZ=Azerbaijan; BG=Bulgaria; BY=Belarus; KZ=Kazakhstan; KG=Kyrgyz; MD=Moldova; RO=Romania; TM=Turkmenistan; TJ=Tajikistan; UZ=Uzbekistan Source: WDI and staff calculations On the one hand, urban and national poverty rates are much higher than would be expected (notwithstanding their negative growth rates for the past ten years) in a few countries (Tajikistan, Azerbaijan, Armenia). On the other hand, urban and national poverty rates are lower than would be expected (given the rates of GDP change) in other countries (e.g. Albania, Romania, Bulgaria, Belarus, and Turkmenistan). These differences likely result from country-specific progress on structural reforms, wage policies and employment strategies.

Analysis of patterns and trends in poverty across the ECA countries reveals some commonalities, according to _Transition_ . The countries with the highest poverty incidence—Central Asia and the Caucasus, plus Moldova—have been those in which the progress of structural reform and liberalization has been very incomplete, and which have been least successful in switching from state enterprise to private sector-based output and employment. Employment has fallen even more sharply than output across the region, and in most countries the labor force participation has declined as well. Poverty outcomes have been worsened by policies that have contributed to sharply rising inequality in virtually all the transition economies. While part of this rise in inequality is a natural and necessary outgrowth of the shift to market-based wages and returns on assets and education, much of the increasing inequality reflects the gap between individuals stuck in nonproductive and publicly funded activities, and those able to exploit new opportunities.

# **3.3. Migration and urban change**

During the past decade, transition countries have experienced large international and domestic migration flows, which have resulted in unique pressures on urban areas. During the 1990s, three main groups comprised most international migrants in transition countries: (1) Russians and other Slavs who were returning to their historic homelands; (2) people seeking jobs in other countries; and (3) people displaced by conflict. Domestic migration consisted of two main groups, people seeking economic opportunities and internally displaced people.

The first group of international migrants resulted from Soviet policies to promote industrialization across the country. In order to build and run the new factories built in the republics, Russian and other Slavic (Ukrainian and Belarusans) managers and technical specialists were moved to the republics. As a result, at 16
the beginning of transition, Russians and other non-titular Slavs<sup>11</sup> comprised a substantial share of the population as can be seen in the table below. The table also shows that Russians and non-titular Slavs were overwhelmingly concentrated in urban areas. The share of Russians and non-titular Slavs living in urban areas ranged from 69 percent in the Kyrgyz Republic to over 90 percent in Azerbaijan, Estonia, Tajikistan, Turkmenistan and Uzbekistan.

**Table 3.1.  Urbanization rate for Russians and other non-titular Slavs**

(Ukrainians and Belarusans) in 1989

|Republic|Total Population|Urbanization rate for<br>total population (%)|Share of Russians &<br>other non-titular Slavs<br>(%)|Urbanization rate<br>for Russians/ non-<br>titularSlavs (%)|
|---|---|---|---|---|
|Armenia|3,304,776|67|2|86|
|Azerbaijan|7,021,178|54|6|93|
|Belarus|10,151,806|65|16|86|
|Estonia|1,565,662|71|35|92|
|Georgia|5,400,841|55|7|86|
|Kazakhstan|16,464,464|57|44|76|
|Kyrgyz|4,257,755|38|24|69|
|Latvia|2,666,567|71|42|85|
|Lithuania|3,674,802|68|12|89|
|Moldova|4,335,360|47|27|74|
|Tajikistan|5,092,603|33|9|93|
|Turkmenistan|3,522,717|45|11|94|
|Ukraine|51,452,034|67|23|87|
|Uzbekistan|19,810,077|41|9|94|

Source: 1989 Soviet Census.

In the aftermath of transition, industrial collapse and related economic shocks, as well as rapidly changing political situations, meant large numbers of Russians and other ethnic groups who were living outside their historic homelands opted to move. From 1989 to 1998, approximately 3 million ethnic Russians and 1 million ethnic Ukrainians returned to Russia or Ukraine.<sup>12</sup> The departure of large numbers of better off people meant that, on average, those who remained in the cities were poorer.

One of the results of the large international migration flows in the region can be seen in the table below. As the top half of the table shows, only 11 countries worldwide experienced absolute declines in urban populations from 1990-2002. And all 11 of those countries were transition countries.<sup>13</sup> From 1990-2002, of 187 countries for which data are available, only 18 experienced ruralization. Of these, 11 countries were found in the ECA region.<sup>14</sup> To a large extent, ruralization and absolute urban population decrease can be explained by high levels of emigration from urban areas, although in some cases population declines due to low fertility rates and conflict-related emigration are also important contributing factors.

> 11 _Slavs here refer to Belarusans and Ukrainians except for Belarusans in Belarus and Ukrainians in Ukraine, who are considered titular nationalities._

> 12 _United Nations. 2002.  “International Migration from Countries with Economies in Transition:  1980-1999.” Mimeo._

> 13 _Croatia, Czech Republic, Slovenia, Russian Federation, Ukraine, Lithuania, Bulgaria, Kazakhstan, Moldova, Estonia, and Latvia._

> 14 _Czech Republic, Russia, Kazakhstan, Slovenia, Estonia, Azerbaijan, Kyrgyz Republic, Uzbekistan, Tajikistan, Moldova and Latvia._

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**Table 3.2.  World urban population growth and urbanization change**

||**1960-1970**|**1970-1980**|**1980-1990**|**1990-2002**|
|---|---|---|---|---|
|Countries with growing urban<br>populations|189|188|191|188|
|Countries with shrinking urban<br>populations|0|4|1|11|
|Ofthese,in ECA|0|0|0|11|
|**Total**|**189**|**192**|**192**|**199**|
|Urbanizing Countries|178|180|174|169|
|De-urbanizing (ruralizing) countries|10|9|17|18|
|Ofthese,in ECA|0|2|4|11|
|**Total **|**188**|**189**|**191 **|**187 **|

Source: SIMA.

# **Box 3.1.  Migration in the Kyrgyz Republic**

Kyrgyz is atypical in the ECA region in that it is still primarily rural and rapidly urbanizing, resulting in a 45 percent increase in the number of residents in Bishkek alone in seven years. It is estimated that one third of the national population (one million people) has moved within the country over the past ten years, although the official system of residency registration has been unable to keep up with the changes and the requirement of residency permits ( _propiska_ ) is evidently not being enforced.<sup>15</sup> While rural to urban migration is normal for a country at this level of development, the government is concerned that the very rapid pace since the transition strains both the rural and urban economies, and therefore it seeks to manage (i.e., reduce and stabilize) the internal movements. What is interesting is that with large scale emigration of the Russian-speaking population from the Kyrgyz Republic and internal relocation of inhabitants from poor mountainous areas into the two main cities (Bishkek and Osh), the socioeconomic profile of these cities is becoming poorer and their human capital base is lower than before _._ Therefore, an explicit understanding of urban poverty is becoming more urgent.

According to a 2000 survey of migrants to urban areas (IOM 2001)<sup>1</sup> the economic motivation (search for employment) was paramount to their decision. They come mainly to the two largest cities and most report that their expectations were met, even when city life is hard. The main concerns expressed by the migrants surveyed were access to cash, housing and employment. When asked what conditions would impel them to return to their home area, respondents ranked civil strife and economic deterioration in the city as the main potential factors. However, those who reported a desire to return to their area of origin said they would do so if the government provided or guaranteed a well paid job, housing, loans or grants, or other financial incentives.

On balance, while acknowledging that the urban destination posed many difficulties (after the first-ranked economic concerns, in the second ranking were concerns regarding food and health care, followed by education of children, and last relations with authorities), three-quarters of the migrants were rather satisfied with their situation as “normal or passable”. Yet, the same study of migration concludes that government policy should focus on making conditions more attractive in the rural areas to stem the outflow—which would be highly impractical if the population demands heavy financial incentives. It is clear that while migrants find life in the city economically demanding they have a realistic assessment of the trade-offs they face and believe their prospects are better there. A message that should be taken from the Kyrgyz study is that the large internal flow of residents does create an increasing urban poverty risk and this needs to be addressed—but that this flow is itself a _de facto_ poverty reduction strategy that much of the population has chosen for itself, with considerable success.

Source:<sup>1</sup> International Organization for Migration (IOM), “Internal Migration in the Kyrgyz Republic”, January 2001.

The decline in the urban population share due to emigration conceals the extent to which rural to urban net migration continues. In Estonia, for example, analysis of internal migration data shows that internal migrants continued to move to the large cities and the immediately surrounding areas during the 1990s. However, the number of internal migrants moving to urban areas was far surpassed by the number of people emigrating from large cities.<sup>16</sup> In Kyrgyz, Kazakhstan, and Moldova researchers found a similar situation, namely that the high level of emigration from large cities concealed continued, and substantial, rural to urban domestic migration.<sup>17</sup> Box 3.1 provides more information about migration in the Kyrgyz Republic.

> _15 Under the Soviet system the propiska was necessary to acquire basic rights as a resident._

> 16 _Tammaru, Tiit.  2001.  “Urbanization in Estonia in the 1990s:  Soviet Legacy and the Logic of Transition.”  Post-_ _<u>Soviet Geography and Economics</u> 42, No. 7, pp. 504-518._

> 17 _IOM.  2001.  “Internal Migration in the Kyrgyz Republic.”  Mimeo.;  Rowland,R. 2001.”Regional Population_

18
Migration, whether rural-to-urban or international is an important coping strategy in a number of countries. In Armenia, 22 percent of the population live in households with at least one member who is permanently absent, most frequently in Russia and most likely in cities where jobs are easier to find.<sup>18</sup> In Albania, migration (both rural to urban and international) in search of work is the most important coping strategy and estimated remittances total 14 percent of GDP.<sup>19</sup> In Azerbaijan, large numbers of IDPs have moved to the capital, as have more traditional rural to urban migrants. The results have been substantial, albeit largely unofficial, growth in the capital, where as many as 50 percent of all people may now live.

# **4. Revisiting the Extent and Nature of Urban Poverty**

This section investigates the level, sources and forms of poverty in urban areas, paying particular attention to the disparities in urban areas between capital cities and secondary cities, including in access to infrastructure, energy and housing.

# **4.1. Income poverty**

# **4.1.1. Comparisons of income poverty**

The profile of income or consumption poverty is highly variable across ECA countries and the countries do not fit into one general pattern. It is clear, however, that the traditional dichotomy between rural and urban areas hides important disparities within urban areas between the capital city and secondary cities. Table 4.1. summarizes different poverty indicators in each country, according to the latest available year. Note that this table portrays relative poverty, that is, the share of each settlement area’s population falling below the lowest quintile of national income.

**Poverty incidence.** As is true in most of the developing world, the incidence of poverty, or headcount rate (first set of columns in Table 4.1), is considerably higher in rural than in overall urban settlements, with the notable exception of the Caucasus and Moldova (in Kosovo, the two areas are almost even).  The urban:rural poverty ratio (last column of the table) indicates this pattern by a ratio exceeding 1.00 in Armenia, Azerbaijan, Georgia and Moldova.  It is also striking for the present analysis that in each country, the poverty incidence in Other Urban settlements exceeds that of the Capital City (and in Tajikistan and Bosnia as well as the Caucasus and Moldova, exceeds that of the rural average).  This can also be seen by the relative poverty risk ratios (the fourth set of columns in the table), which compare the poverty incidence in each location to that of the country overall.  In most countries the poverty risk of residents in secondary cities is two to four times greater than that of residents in the capital.

**Degree of income poverty.** The poverty gap and severity indicator are two measures revealing how far the populations fall below income thresholds (see second and third sets of columns of Table 4.1).  Both indicators produce similar patterns, although the severity indicator shows less disparity between rural and urban averages than does the poverty gap.  Again, income poverty is seen to be worse in the rural areas, with the exception of the Caucasus, Moldova and Kosovo.  Among urban areas, poverty is significantly worse in secondary cities than in the capital, with the sole exception of Armenia.

> _Change in Kazakhstan during the 1990s and the Impact of Nationality Population Patterns:  Results  from the Recent Census of Kazakhstan.”  Post-Soviet Geography and Economics.  Vol., 42, No.8, pp. 571-614.; UNHCR.  No date. “The Republic of Moldova:  The Process of Migration in 1989-1996.”, Table 3.1._ <u>http://www.unhcr.md/artpdf/migrat.pdf</u> _<u>.  Accessed Nov. 26, 2003.</u>_

> 18 _World Bank.  2003.  Armenia Poverty Assessment._

> 19 _World Bank. 2003.  Albania Poverty Assessment._

19
# **Table 4.1. Measures of relative poverty by settlement area**

|Region|Povert|y Incidence|(headc|ount)|Gap||||Severit|y|||Povert|y Relative|Risk Ra|tio***|Urban/Rural<br>Poverty|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||||||||||||||||Incidence|
|C|Urban|Capital|Other|Rl|Urban|Capital|Other|Rl|Urban|Capital|Other|Rl|<br>Urban|Capital|Other|Rl|<br>Ratio|
|ountry|(all)|City|Urban|ura|(all)|City|Urban|ura|(all)|City|Urban|ura|<br>(all)|City|Urban|<br>ura|<br>|
|EUAccession||||||||||||||||||
|Hungary|17.29|13.21|18.89|24.80|3.68|2.60|4.10|5.71|1.20|0.80|1.36|2.03|0.86|0.66|0.94|1.24|0.70|
|Lithuania|13.64|8.22|15.28|33.45|3.13|1.60|3.60|9.28|1.07|0.44|1.26|3.62|0.68|0.41|0.76|1.67|0.41|
|Poland|13.34|2.04|14.10|30.58|2.82|0.20|3.00|7.58|0.92|0.03|0.98|2.74|0.67|0.10|0.70|1.53|0.44|
|Balkans/ EE||||||||||||||||||
|Albania|15.11|13.27|15.79|23.40|3.36|2.75|3.59|4.83|1.18|0.93|1.27|1.48|0.76|0.67|0.79|1.17|0.65|
|Bosnia*|14.19|...|23.98|20.56|2.89|...|5.79|4.94|0.92|...|2.11|1.65|0.71|...|1.20|1.03|...|
|Bulgaria|14.87|8.17|16.68|30.64|4.16|1.32|4.94|11.34|1.76|0.33|2.14|5.83|0.74|0.41|0.83|1.53|0.49|
|Kosovo|18.69|...|...|20.72|4.94|...|...|4.78|1.85|...|...|1.66|0.94|...|...|1.04|0.90|
|Moldova|21.85|8.28|34.99|18.95|6.15|1.72|10.43|4.80|2.61|0.58|4.57|1.86|1.09|0.41|1.75|0.95|1.15|
|Romania**|11.44|4.32|12.84|30.24|2.60|0.64|2.98|7.83|0.95|0.18|1.10|2.92|0.57|0.22|0.64|1.51|0.38|
|Serbia**|16.05|13.23|17.28|25.12|3.33|2.76|3.58|6.15|1.09|0.88|1.18|2.31|0.80|0.66|0.86|1.26|0.64|
|Caucasus||||||||||||||||||
|Armenia**|22.42|20.88|23.89|16.60|4.86|4.95|4.77|3.79|1.72|1.93|1.52|1.35|1.12|1.04|1.19|0.83|1.35|
|Azerbaijan|23.88|16.69|30.13|15.60|5.49|3.02|7.64|3.91|1.85|0.84|2.73|1.48|1.19|0.83|1.50|0.78|1.53|
|Georgia**|20.98|16.59|24.96|18.97|5.93|4.18|7.51|6.35|2.61|1.71|3.42|3.20|1.05|0.83|1.25|0.95|1.11|
|Central Asia||||||||||||||||||
|Kazakhstan|14.81|4.41|16.31|26.18|3.39|0.68|3.79|6.05|1.17|0.16|1.31|2.11|0.74|0.22|0.82|1.31|0.57|
|Kyrgyz Rep.|14.09|7.03|19.71|23.23|2.56|0.95|3.85|4.72|0.76|0.23|1.18|1.52|0.70|0.35|0.98|1.16|0.61|
|<br>Tajikistan|16.78|5.05|21.65|20.92|4.55|1.37|5.87|5.53|1.87|0.61|2.40|2.28|0.84|0.25|1.08|1.05|0.80|
|<br>Turkmenistan|9.53|...|12.45|27.95|2.20|...|2.87|8.02|0.78|...|1.01|3.31|0.48|...|0.62|1.39|0.34|
|Uzbekistan|15.60|5.59|18.63|22.64|3.30|1.18|3.94|5.63|1.12|0.40|1.34|2.41|0.78|0.28|0.93|1.13|0.69|
|Slavic||||||||||||||||||
|Belarus**|17.44|6.41|20.95|25.75|3.54|1.33|4.25|5.64|1.17|0.51|1.38|1.88|0.87|0.32|1.05|1.29|0.68|
|Russia|16.53|8.11|18.21|29.16|5.32|2.32|5.92|10.70|2.58|1.11|2.88|5.65|0.82|0.40|0.91|1.45|0.57|

The use of small type designates cells with fewer than 30 counts.

*    For Bosnia, the categories are urban, mixed and rural **  Per adult equivalent  consumption ***Relative to country poverty incidence (first column) Source: see Table 2.1

20
**Distribution of the income-poor population.** Figure 4.1 shows how the population in the lowest welfare quintile is distributed across settlement areas.  Two patterns should be noted.  First, the urban poor are overwhelmingly located in secondary cities in all countries, except for Armenia, Azerbaijan and Georgia where 20-30 percent of the poor are found in the capital city.<sup>20</sup> Second, the share of urban poor outnumbers the share of rural poor in six of the ECA countries for which we have data (Hungary, Armenia, Azerbaijan, Georgia, Belarus and Russia) and is equal in a seventh, Bulgaria.  In the case of Hungary, Belarus and Russia, the predominance of urban poverty results from the high level of urbanization since the incidence of urban poverty is lower than that of rural poverty.  However, in Armenia, Azerbaijan and Georgia both the incidence of urban poverty and the share of the urban poor exceed that in rural areas.

**Figure 4.1. Share of poor in capital cities, other urban and rural areas in ECA countries**

<!-- Start of picture text --> % rural other urban capital<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EU Accession Balkans/EE Caucasus Central Asia Slavic<br>Hungary Lithuania Poland Albania Bulgaria Kosovo* Moldova Romania Serbia Armenia Azerbaijan Georgia Kazakhstan Kyrgyz Republic Tajikistan Turkmenistan Uzbekistan Belarus Russian Federation<br><!-- End of picture text -->

* In Kosovo, households were classified as urban or rural.

Source: See Table 2.1

**Characteristics of the poor.** Two features which the household surveys in ECA find almost invariably to be associated with poverty are low education of the household head (less than secondary school completion) and large family size.  These characteristics can also be examined separately by settlement area to separate the effects of location.

The incidence of **low education among household heads** is found to be significantly greater in rural areas than in urban areas in all the countries with available data, as is the pattern worldwide. However, the rate of poverty among uneducated household heads is often greater in the urban settlements than rural, as seen in Figure 4.2. A lack of education is most associated with poverty in the secondary cities of Moldova, Azerbaijan, Georgia, Tajikistan and Belarus, the mixed areas of Bosnia, and in the capitals of Serbia and Armenia. This outcome presumably reflects the fact that uneducated household heads have less capability of competing for well-paying urban employment, in addition to lacking access to farm

> 20 And possibly in Bosnia as well, however, since the stratification used in the survey (urban, rural and mixed) was not comparable with that used in other ECA countries (capital, other urban and rural) so Bosnia was excluded from this discussion.

21
**Changes in urban poverty over time.** Several of the ECA countries, such as Armenia, Georgia and Moldova,<sup>21</sup> showed a sharp deterioration in urban poverty (also relative to rural poverty) around the time of the Russia macroeconomic crisis in the late 1990s, but the situation has improved somewhat since then. In Russia itself the urban population was affected by the crisis more harshly than the rural population.<sup>22</sup> This pattern reflects that urban economies are highly sensitive to macroeconomic fluctuations, which can ripple throughout the services sectors and public employment and therefore have a wide reach in cities. Price inflation in food and utilities, and fiscal retrenchment, the latter seen in wage arrears by public sector employers, also hit urban residents particularly hard.  At the same time, urban areas characterized by economic diversity can recover faster when general conditions improve.  This is the major reason why the capital cities, which offer more economic diversity than the secondary cities, have fared better in most countries.

# **4.1.2. Inequality by settlement area**

As is usually the case, income inequality as measured by the ratio of the richest quintile (Q5) to the poorest (Q1) within each settlement area is higher in urban areas than in rural, in all but six of the 20 countries for which these data can be obtained (the exceptions being Lithuania, Poland, Bosnia, Bulgaria, Serbia, and Kyrgyz Republic). (Table 4.2) The highest inequality might be expected to be found within the capital city because it is typically the center of the greatest wealth, but this is the case only for eight of the sampled countries.<sup>23</sup> Secondary cities are the most unequal in five countries: Hungary, Romania, Azerbaijan, Kazakhstan and Turkmenistan, although generally only by a slim margin.

Gini coefficients, which measure the distribution of income across all the quintiles, are highest (indicating greatest inequality) in urban areas for fourteen of the twenty countries and are the same as those in rural areas in a fifteenth, Bosnia. Countries with high urban Ginis are divided about equally between those with the highest Ginis in capital cities and those where the highest Ginis are to be found in other urban areas.

The countries generally do not show stark disparities in the measures of inequality across their settlement groups, but there are some notable exceptions. While the Gini coefficients (based on per capita consumption) within the three settlement groups hover in the range of 0.25-0.35 for most countries, the capital cities and/or other urban areas in Moldova, Azerbaijan, Georgia, Tajikistan, Turkmenistan and Russia score much higher ranging from 0.36 to 0.47. Rural areas are more equal and only Turkmenistan (0.36) and Russia (0.40) are above 0.35.

Additional evidence on non-income inequalities within settlement areas will be discussed below in section 4.2.

> 21 _Armenia Poverty Assessment 2003, Table 11.B.1, p. 7.;  Georgia Poverty Assessment 2002.; Poverty in Moldova (1997-2002).  Poverty Assessment Concept Note, 2003._

> 22 _Lokshin, Michael and Martin Ravaillion 2000 (background paper to Transition study)_

> 23 _Albania, Moldova, Georgia, Armenia, Tajikistan, Uzbekistan, Belarus and Russia._

23
**Table 4.2.  Inequality by settlement area**

|**Region**||**Gin**<br>|**i Coeffici**<br>|**ent***||**Rati**<br>|**o of Riche**<br>**(Q5) t**|**st Consu**<br>**o Poorest**<br>|**mption Q**<br>**(Q1)****|**uintile**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**Urban**<br>**(all)**|<br>**Capital**|**Other**<br>**Urban**|**Rural**|**Country**|**Urban**<br>**(all)**|**Capital**|**Other**<br>**Urban**|**Rural**|**Country**|
|**EU Accession**|||||||||||
|Hungary|0.29|0.30|0.29|0.28|0.29|4.27|4.23|4.26|4.24|4.28|
|Lithuania|0.31|0.31|0.30|0.32|0.32|5.00|4.99|4.94|5.11|5.12|
|Poland|0.33|0.31|0.32|0.32|0.33|5.33|5.26|5.24|5.34|5.43|
|**Balkans/EE**|||||||||||
|Albania|0.29|0.30|0.28|0.27|0.28|4.30|4.50|4.20|4.07|4.20|
|Bosnia ***|0.27|...|0.24|0.27|0.26|4.01|...|3.83|4.16|4.07|
|Bulgaria|0.28|0.25|0.29|0.32|0.30|4.46|4.16|4.52|4.85|4.64|
|Kosovo|0.29|...|...|0.28|0.28|4.60|...|...|4.49|4.55|
|Moldova|0.40|0.37|0.34|0.33|0.36|6.84|6.41|6.19|5.73|6.25|
|Romania|0.27|0.26|0.27|0.26|0.29|4.27|4.15|4.22|4.04|4.31|
|Serbia|0.29|0.28|0.29|0.30|0.30|4.46|4.53|4.42|4.99|4.68|
|**Caucasus**|||||||||||
|Armenia|0.28|0.31|0.24|0.27|0.28|7.60|7.42|7.60|6.27|7.01|
|Azerbaijan|0.40|0.38|0.40|0.32|0.36|6.60|6.50|6.44|6.47|6.50|
|Georgia|0.36|0.37|0.33|0.33|0.35|4.06|4.34|3.68|4.02|4.04|
|**Central Asia**|||||||||||
|Kazakhstan|0.29|0.26|0.29|0.27|0.29|4.45|4.23|4.44|4.16|4.42|
|Kyrgyz Republi|c 0.28|0.27|0.28|0.29|0.29|4.39|4.28|4.40|4.47|4.46|
|Tajikistan|0.36|0.36|0.33|0.30|0.32|6.20|6.46|5.79|4.95|5.42|
|Turkmenistan|0.40|0.29|0.40|0.36|0.41|8.48|...|8.63|8.39|8.89|
|Uzbekistan|0.29|0.28|0.27|0.25|0.27|4.42|4.64|4.22|3.98|4.36|
|**Slavic**|||||||||||
|Belarus|0.24|0.23|0.23|0.22|0.24|3.44|3.54|3.35|3.22|3.39|
|Russia|0.44|0.47|0.43|0.41|0.44|10.49|12.87|9.90|9.43|10.50|

* Headcount level **Household level

***Bosnia- urban, mixed, rural settlements

Source: see Table 2.1

# **4.1.3. Employment and labor force participation**

The relationship between unemployment and income poverty is not straightforward, especially when viewed across the different settlement types.<sup>24</sup> Looking first at the share of household heads who are unemployed, the figures are dramatically higher in the Caucasus (averaging 15-17 percent for the urban areas) than in the other country groups, where unemployment averages below 8 percent. Unemployment rates are also higher on average in Other Urban than the other two settlement areas in all the country groups except the Slavic countries (Russia and Belarus). Unemployed household heads are located predominantly in secondary cities in virtually all the countries, except for Moldova and Georgia where unemployed heads are found most commonly in the capital city, and Azerbaijan, Tajikistan and Uzbekistan, where unemployed heads predominate in rural areas.

> 24 _It should  be noted that sub-sample sizes are very small for several of the countries especially in Central Asia._

24
**Figure 4.4.  Percent of unemployed household heads by country groups**

<!-- Start of picture text --> Capital city Other Urban Rural<br>20<br>15<br>10<br>5<br>0<br>EU Accession Balkans/EE Caucasus Central Asia Slavic<br>%<br><!-- End of picture text --> Note: EU Accession - average among Hungary, Lithuania, Poland. Balkans-average among Albania, Bosnia, Bulgaria, Kosovo, Moldova, Romania, Serbia. Caucasus-average among Armenia, Azerbaijan, Georgia. Central Asia-average among Kazakhstan, Kyrgyz, Tajikistan, Turkmenistan, Uzbekistan. Slavicaverage among Belarus, Russia.

Source: see Table 2.1 Among the unemployed household heads, the relative poverty rate in Other Urban areas is slightly below that of rural areas but half again as large as that of the Capital (Figure 4.5). This suggests that the financial safety nets available to the unemployed are best in the capital cities. In the absence of an adequate public or private safety net, unemployment may be especially likely to raise poverty risk in the urban context because cash is needed for essential goods and services. The finding suggests that unemployment in the secondary cities may be of longer duration and so more likely to lead to poverty than is the case in the capitals, where a wider range of jobs is available and finding new work easier.

**Figure 4.5.  Poor unemployed household heads by settlement area** (Percentage of unemployed hh heads who are poor)

<!-- Start of picture text --> 40<br>30<br>20<br>10<br>0<br>Capital city Other Urban Rural<br>%<br><!-- End of picture text --> Note: average among 20 ECA countries. Albania, Armenia, Azerbaijan, Belarus, Bosnia, Bulgaria, Georgia, Hungary, Kazakhstan, Kosovo, Kyrgyz, Lithuania, Moldova, Poland, Romania, Russia, Serbia, Tajikistan, Turkmenistan, Uzbekistan Source: see Table 2.1 25
A further question is to what extent the poor are unemployed in each settlement (Figure 4.6). In each of the countries reviewed the unemployment rate of the poor is highest in urban areas (mainly the secondary cities) except for Bulgaria where the rural rate is slightly higher. In Georgia, the Kyrgyz Republic, and Tajikistan it appears highest in the capitals (although the sample sizes are very small). This indicates that urban poverty in income or consumption terms, especially in the secondary cities, reflects the failure of adequate new jobs to emerge to replace those lost since the demise of the Soviet Union. Several of the poverty assessments, such as that of Tajikistan, also refer to a new phenomenon of the “working poor,” that is, household heads who are employed but poor because of very low wages or nonpayment of wages.

**Figure 4.6.  Percentage of poor household heads who are unemployed, by settlement type**

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>Capital city Other Urban Rural<br>%<br><!-- End of picture text --> Note: average among 20 ECA countries. Albania, Armenia, Azerbaijan, Belarus, Bosnia, Bulgaria, Georgia, Hungary, Kazakhstan, Kosovo, Kyrgyz, Lithuania, Moldova, Poland, Romania, Russia, Serbia, Tajikistan, Turkmenistan, Uzbekistan Source: see Table 2.1 The population that is both poor and unemployed is overwhelmingly found in the secondary cities, with the main exception of three countries in Central Asia: Tajikistan, Turkmenistan and Uzbekistan.

**Figure 4.7.  Poor unemployed household heads – Distribution by country groups**

<!-- Start of picture text --> Rural Other Urban Capital city<br>100<br>80<br>60<br>40<br>20<br>0<br>EU Accession Balkans/EE Caucasus Central Asia Slavic<br>%<br><!-- End of picture text --> Note: EU Accession- average among Hungary, Lithuania, Poland. Balkans-average among Albania, Bosnia, Bulgaria, Kosovo, Moldova, Romania, Serbia. Caucasus-average among Armenia, Azerbaijan, Georgia. Central Asia-average among Kazakhstan, Kyrgyz, Tajikistan, Turkmenistan, Uzbekistan. Slavic-average among Belarus, Russia.

Source: see Table 2.1 26
The coincidence of high unemployment and poverty in Other Urban areas reflects the effect of closures and retrenchment of many of the state enterprises that were located there under socialism and became uncompetitive after liberalization—as in the extreme case of one-company towns. In many of the countries new alternative production has not emerged sufficient to absorb the laid off workers with adequate purchasing power and access to essential services. Even some highly educated individuals such as doctors and teachers in the public sector are resorting to supplementing their incomes with other services or subsistence agriculture as salary payments are delayed. Across the ECA region rates of labor force participation are declining, which may in part reflect some normal adjustment to individual preferences after the socialist regime but also may indicate discouragement.

# **Box 4.1:  Differentiation of welfare within the region of Tomsk (Russia)**

A case study of Tomsk, an oblast or administrative district in Western Siberia spanning the capital of Tomsk (population 484 thousand) and a range of other urban and rural settlements, illustrates the diversity of welfare across them. The study authors note that secondary urban areas can vary greatly depending on factors such as whether the cities or towns achieve alternative sources of employment when they lose a dominant industry, whether they are remote or have good transport access to other centers, the level of development and wealth in their surrounding subregion, opportunities for local migration or commuting, and access to subsidiary land cultivation to supplement urban incomes.

The growth of many of the urban centers in Tomsk oblast was promoted by government incentives, both monetary and nonmonetary, which were possible because of Soviet central planning. As is true elsewhere in Russia, a large number of these monoindustrial towns are not viable under market economic conditions. These towns and cities are increasingly distinguished from viable urban areas, which in Tomsk refers to those based on extraction of natural resources (i.e., oil towns) and the regional capital, which is economically more diversified. Residents of the non-oil-producing secondary towns are most vulnerable to economic dislocations because they not only lack the services and fiscal resources of larger cities and oil towns, but also have less access to subsistence agriculture than do their rural counterparts. As a result, the highest poverty rates and greatest poverty severity are found in the secondary, non-oil towns, where residents are substantially worse off than even rural dwellers. Location-related factors such as living in the capital city or the oil towns improves welfare outcomes, but these improvements are less significant than the welfare losses associated with living in secondary non-oil towns. At the same time, non-locational factors such as education and female gender of the household head appear more beneficial in the non-oil towns because there is still some diversity of opportunity available.

The Tomsk case illustrates the importance of differentiating among settlements to assess poverty conditions. However, the favorable conditions of oil towns, which depend on commodities where market prices fluctuate, could change in response to world supply and demand.

Source: Alexandrova, A., Hamilton, E., and Kuznetsova, P. (Forthcoming 2003). _Urban Pooverty in Tomsk Oblast._ Washington D.C.: World Bank.

# **4.1.4. Other sources of income and transfers**

As noted above, with the loss of previous sources of wage and salary employment since the transition, households in the ECA region have increasingly resorted to other sources of income including self production of food where the household has access to land, although sale of agriculture produce is generally not a major source of income for urban households.<sup>25</sup> Sale of real estate where possible (including privatization vouchers), and resorting to illegal or semi-legitimate activities such as prostitution are reported in many of the poverty assessments to be coping strategies of the poor especially in urban areas, as in the Ukraine and Moldova. Receipt of transfers, both official (e.g., pensions) and private funds, is also very important to the welfare of the poor and near-poor.

> 25 Ibid. and World Bank Poverty Assessments.

27
The Armenia poverty assessment provides a uniquely detailed analysis of household income sources by settlement and by consumption quintile. Although Armenia is not a typical example for ECA as it represents one of the worst economic conditions in the Region, this breakdown is still illustrative. In Armenia in 2001, transfers, especially pensions, comprised 22.7 percent of the incomes of the poorest consumption quintile in Yerevan and 25.5 percent in Other Urban areas, but only 18.6 percent in rural areas. Remittances were also a higher share of incomes of the lowest quintile in Other Urban areas than in the other settlements. In general, residents of the secondary cities showed a wider diversity of income sources than in the capital or in rural areas, where households depended more on labor earnings and farm income, respectively.<sup>26</sup> Since the Other Urban households’ total incomes were below those of their rural and capital city counterparts (both for the poorest and for total households), this income diversity represents coping effort rather than breadth of opportunity.<sup>27</sup>

# **4.1.5. Household expenditure patterns**

The Armenia poverty assessment also provides similar details on household expenditure or consumption patterns. Contrary to what might be expected, the poorest Yerevan households spend both a smaller amount (in drams per month) and smaller share of their total consumption on food (57 percent) than either their secondary city or rural counterparts (who spend 66 and 72 percent, respectively). However, the Yerevan poor spent over twice the share of their consumption on transportation and utilities compared to their counterparts in secondary cities and rural areas, whose shares were similar (4.5 and 4.8 percent, respectively).<sup>28</sup> Qualitative investigations of poverty in Romania find that among the rural populations the exchange of goods and services in kind is more prevalent than cash transactions, which are seen to come at a premium; this pattern was reported much less by urban populations.<sup>29</sup> The same study also found that the rural poor report themselves to be net lenders rather than net borrowers and feel that they benefit thereby because this behavior links them to informal networks of support. The urban poor respondents, however, see themselves as giving up more resources in private transfers than they receive. These alternative claims appear inconsistent, but they might reflect a perception by the urban poor that they are less well connected to reciprocal relationships and that they feel more vulnerable. The urban respondents reported having somewhat less trust in and cooperation with neighbors and others than did the rural respondents.<sup>30</sup>

# **4.2. Non-income dimensions of poverty:  access to infrastructure, energy and housing**

More than a decade of economic and political turmoil in the transition countries has resulted not only in much increased rates of income poverty and inequality, but also in a sharp deterioration in non-income dimensions of poverty. The quality of water, gas, heat, electricity and other infrastructure and energy services, as well as the housing stock, have deteriorated, although this has varied widely among transition countries. Deteriorating services and housing conditions have affected various parts of the population to differing degrees, which has meant some people have little or no access to basic services or live in especially poor housing. Access to services and housing conditions are important non-income dimensions of well being, which will be discussed in this section.

Urban residents, who are concentrated spatially and commonly live in multi-story apartment buildings, have been especially hard hit by the deterioration in infrastructure services. Historically, urban residents

> 26 Armenia: Poverty Assessment, May 29, 2003 draft.  Table 2.20, page 51.

> 27 Ibid, Table II.B.16, p. 18 of Descriptive Statistics annex.

> 28 Ibid., Tables II.B.12, p. 14 and II.B.13, p. 15.

> 29  “Mapped in or Mapped Out? The Romanian Poor in Inter-Household and Community Networks”, p. 20 and table 8a and 8b, appendix 1.

> 30 Ibid, p. 31.

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have been better provided with services. However, because they live in cities, urban residents have fewer coping options available to them if water or heat are not provided or if garbage is not collected. Urban populations have also been disproportionately affected by housing sector reforms, which primarily affected the formerly state-owned multi-family stock not privately owned houses in rural areas.

# **4.2.1. Infrastructure and energy services remain widely available but no longer reliable**

One important legacy of central planning is that access to basic services (as measured by network connections) remains widespread in the ECA region. Nonetheless, there are important differences according to the type of services, and by location. As shown in Figure 4.8, while electricity connections tend to be universal across ECA countries, connections to other basic services like water, district heating, natural gas and telephone vary a great deal by location. As regards water connection, while close to 100 percent of households in capital cities report having piped water inside their dwelling, this rate drops to about 80 percent for households in other urban areas and to only 40 percent for those in rural areas. The low rate in rural areas is misleading however, as many rural households may have access to adequate water outside the house.

Similarly, district heating and natural gas connections are much more limited in secondary cities and in rural areas. However, the findings about rural areas should be interpreted with great caution as district heating would never be the preferred heating method in sparsely populated areas and even in developed countries rural households commonly rely on bottled gas, not network gas.

With respect to telephone connections, there are also large disparities between capital cities, other cities and rural areas. While the ECA average connection rate reaches almost 80 percent in capital cities, the rate is down to 60 percent in other cities, and to less than 40 percent in rural areas.

**Figure 4.8. Access to infrastructure and energy services in ECA in the early 2000s by location** (% of households reporting access)

<!-- Start of picture text --> Capital Other urban Rural<br>100<br>80<br>60<br>40<br>20<br>0<br>Water District heating Natural gas Electricity Telephone<br>connection connection connection connection connection<br>%<br><!-- End of picture text --> Note: average among 20 ECA countries for water connection,19 ECA countries for district heating and telephone connection, 15 ECA countries for natural gas, 10 ECA countries for electricity. Source: see Table 2.1 What is remarkable, however, is that despite high connection rates, the reliability of basic services is becoming a serious challenge in the region. As shown in Figure 4.9, fewer than 50 percent of household with connections to water or electricity report that the service is available 24 hours per day in both 29
secondary cities and rural areas. In capital cities, the figure is also surprisingly low, with fewer than 65 percent of households having access 24 hours a day.

**Figure 4.9.  Reliability of infrastructure and energy services in ECA in early 2000s**

(of households reporting access, % receiving water or electricity 24 hours per day) <!-- Start of picture text --> Capital Other urban Rural<br>80<br>60<br>40<br>20<br>0<br>Potable water 24 hours per day Electricity 24 hours per day<br>%<br><!-- End of picture text --> Note: Average among 8 ECA countries for potable water; 8 for electricity.

Source: see Table 2.1 Available evidence also points to large disparities across countries in terms of availability and reliability of basic services, and within countries, between the capital city and other cities. Taking the example of water, one can see that the connection gap between the capital city and other cities is particularly pronounced in Moldova and Kyrgyz Republic, and to a lesser extent in Azerbaijan, Georgia, Kazakhstan, Turkmenistan and Uzbekistan (Figure 4.10).

**Figure 4.10. Water connection comparison in capital and other urban areas in ECA countries**

<!-- Start of picture text --> Capital Other urban<br>100<br>80<br>60<br>40<br>20<br>0<br>EU Accession Balkans/EE Caucasus Central Asia Slavic<br>%<br><!-- End of picture text -->

* Bosnia- urban & mixed settlements

Source: see Table2.1 30
The gap between connection and reliability of water system is also particularly pronounced in secondary cities in Albania, Armenia, Georgia, Tajikistan and Turkmenistan (Figure 4.13).

**Figure 4.13. Water connection versus reliability in ECA countries (other urban)**

<!-- Start of picture text --> Connection Reliability<br>100<br>80<br>60<br>%<br>40<br>20<br>0<br>Balkans/EE Caucasus Central Asia<br><!-- End of picture text -->

*Bosnia  mixed settlement

Source: see Table 2.1

# **4.2.2. Households pay little for infrastructure and energy services and housing**

**Changes in household expenditures for housing and utilities.** Despite widespread efforts to reform the housing sector by privatizing housing and reforming utility provision since the early 1990s, households in transition countries (especially in the FSU) devote smaller shares of expenditures to housing and related utility services than do those in OECD countries (Table 4.3).  At the beginning of transition, housing and utilities accounted for less than 3 percent of household expenditures in the FSU.  A decade later, housingrelated expenditures remained below 10 percent for most FSU countries.  Low tariffs, widespread exemptions and weak collection rates explain the low rate of spending on housing and utilities.  Only in Armenia, Kazakhstan and Ukraine did expenditures reach 10 percent or a bit above.

Changes in expenditure patterns for housing have been particularly dramatic in the Baltics, where housing and utilities accounted for less than 3 percent of expenditures at the beginning of transition as was true in the rest of the FSU. However expenditures by households for housing and utilities have increased sharply and now account for about 15 percent. This is primarily due to increased expenditures for energy. In Eastern Europe, expenditures were a bit higher at the beginning of transition, but have also increased and now average about 15-20 percent for households with much of the increase reflecting increased costs for energy. Eastern Europe is approaching OECD shares where households spend from 20-30 percent on housing and utilities.

**Non-payment is widespread.** Non-payment for infrastructure and energy services is widespread in the region, in particular for water, central heating and natural gas (Figure 4.14).  However, overall more than 20 percent of households do not even pay for electricity, where payment enforcement is relatively simple. Even in apartment buildings, individual households can be disconnected at little cost and electricity is commonly metered, unlike the other utilities.

In general, households with access to a given service in the capital are more likely to pay than those in secondary cities who, in turn are more likely to pay than those in rural areas. The reason behind low payment rates are multiple. This can be due to a large number of waivers for privileged groups, which may not necessarily be the most vulnerable. It can also reflect the legacy of the past, when services were 32
provided for free, and thus the difficulty to introduce a culture a payment for these services. Finally, in some cases, it can also be because the services are not affordable. Unfortunately, the LSMS and HBS data provide no information as to the reason for non-payment.

**Table 4.3. Household expenditures housing and communal services**

(percent of total household expenditures)

||**1991**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|
|---|---|---|---|---|---|---|---|---|
|**_Former Soviet Union_**|||||||||
|Armenia|1.7|3.3|5.9|8.5|8.6|12.2|10||
|Azerbaijan|1.4|0.9|0.8|1.4|||||
|Belarus|1.4|5.6|5.6|4.8|3.6|2.0|3.0|4.8|
|Georgia|2.1|||7.5|7.3|7.0|8.2||
|<br>Kazakhstan|1.8|5.9|7.2|10.6|9.8|13.5|11.9||
|Kyrgyzstan|1.3|4.2|4.4|4.7|4.4|4.3|4.9|5.7|
|Moldova|1.7|7.3|6.9||||||
|Russia|1.6|4.2|5.7|5.1|5.4|4.8|4.7|5.2|
|Tajikistan|1.3|1.9|||2.0|1.7|1.8|2.5|
|Turkmenistán|1.4|1.2|0.6|2.5|3||||
|Ukraine|2.2|8.0|11.9|12.5|11.3|8.2|6.5|9.3|
|Uzbekistán|1.7|0.8|||||||
|**_The Baltics_**|||||||||
|Latvia||13.9|14.3|15.0|16.6|17.3|16.3|14.3|
|Lithuania|1.7|14.8|11.8|12.3|12.3|12.9|13.5||
|Estonia|||17.8|18.8|18.1|17.7|15.3|14.9|
|**_Eastern Europe_**|||||||||
|Hungary||||||21.6|20.2|20.2|
|Poland||||||18.4|17.9|18.8|
|Bulgaria||||||15.9|16.3|15.7|
|<br>Slovenia||||||10.4|11.6|11.7|
|Romania||||||17.6|19.2|17.6|
|Croatia|||||10.8|13.3|13.3||
|Macedonia||12.4||||||11.3|
|Czech Republic|9.7|13.7|13.7|15.2|17.7|18.8|19.8|19.0|
|Slovakia||||||14.6|16.4|15.7|
|Serbia/Montenegro|12.4|9.8|12.4|13.6|13.4|14.8|11.1|9.6|
|**_OECD_**|||||||||
|Finland|21.1|25.3|25.3|25.8|25.5|25.6|25.5||
|Italy|17.5|19.4|19.9|19.6|19.4|19.5|19.9||
|<br>UK|18.3|19.1|18.7|18.5|18.2|18.2|18.3|18.7|
|Canada|23.8|24.7|24.5|23.7|23.4|23.0|23.3|23.3|
|Germany|20.1|23.4|24.2|24.5|24.3|24.3|||
|USA|27|28|28|28|28|28|29||

Source: For CIS (except Georgia for 1990-91) - CIS Statistical Handbook of Social and Economic Indicators, 2002; For Baltics - Statistical Yearbooks for each country; For Eastern Europe - Czech Statistical Office http://www.czso.cz/eng/redakce.nsf/i/home, except for Serbia and Montenegro (Yugoslavia Stat. Yearbook), Croatia (Stat Yearbook of the Rep. Of Croatia), and Macedonia (Stat. Yearbook of the Republic of Macedonia); Czech Republic: Czech Statistical Office. Indicators of Economic and Social Development 1990-2003. <u>http://www.czso.cz/eng/edicniplan.nsf/p/1404-03. Accessed on Nov. 22, 2003; USA: Consumer Expenditure</u> Survey, Bureau of Labor Statistics, U.S. Department of Labor; Canada: Statistics Canada (http://www.statcan.ca/); EU Countries: Eurostat Yearbook 2000; data for 2001 from Housing Statistics in the EU, 2002 Notes: Serbia and Montenegro data include Kosovo until 1999.

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**Figure 4.14.  Payment for infrastructure and energy services**

(% of household with access) <!-- Start of picture text --> Capital Other urban Rural<br>80<br>60<br>40<br>20<br>0<br>Any payment for Any payment for Any payment for Any payment for<br>central w ater central heating electricity natural gas<br>%<br><!-- End of picture text --> Note: average among 18 ECA countries for central water, 12 ECA countries for district heating and natural gas, 15 ECA countries for electricity.

Source: see Table 2.1

**Quality of services and payment levels.** Under-pricing of urban services leads to a situation where subsidies are necessary to maintain the provision of these services to the general public.  However, given the fiscal constraints in most of the transition countries, public funds are not available to cover even basic maintenance costs.  As shown in Figure 4.15 below, there is an observed positive correlation between low levels of payments for water in capital cities of several ECA countries and the quality of the service provision, that is estimated as the percentage of capital city residents receiving 24 hours water supply. Even if we do not know much about the reasons for (non)payment, still the correlation tells us clearly that better service reliability is related with better payments.  Presumably low payments rates and consequent uncovered costs lead to poor quality services.  One can also infer that in countries where there is not much enforcement for payment, services are becoming less reliable.

**Figure 4.15. Payment rates for water and reliability of service in ECA capitals**

<!-- Start of picture text --> 120<br>100 BG<br>BA KZ<br>80<br>GE<br>60<br>TM<br>40 TJ<br>AL<br>20<br>AM<br>0<br>0 20 40 60 80 100 120<br>Payment rates for water at capital cities<br>water for 24 hours<br>Ratio of households receving<br><!-- End of picture text --> AL=Albania; AM=Armenia; BA=Bosnia-Herzegovina; BG=Bulgaria; GE=Georgia; KZ=Kazakhstan; TJ=Tajikistan; TM=Turkmenistan Source: see Table 2.1 34
# **4.2.3. The incidence of poor sanitary and environmental conditions is high**

Poor sanitation and environmental conditions are also major problems in the region (Figure 4.16). In urban areas, sewerage and adequate waste disposal are especially important because large numbers of people live in close proximity to one another, while in rural areas lack of network sewerage and regular garbage collection may be less problematic. Although only 10 percent of households in capital cities lacked sewer connections, in secondary cities the share was much higher, about 25 percent. Regular garbage collection is even less common with more than 40 percent of urban households reporting burning, burying or dumping waste.

Another major issue is the use of dirty fuels for cooking and heating because of the negative effects on health and because of fire safety concerns, especially in multi-story apartment buildings. Surprisingly, about 20 percent of households in capital cities report purchasing dirty fuels (wood, coal or kerosene), while in secondary cities that rate was more than double, over 40 percent. The shares of households using dirty fuels are about the same as those who do not have access to gas, about 25 percent of all households in capital cities and 40 percent of those in secondary cities.<sup>31</sup>

**Figure 4.16. Incidence of poor sanitation and environmental conditions** (% households)

<!-- Start of picture text --> Capital Other urban Rural<br>80<br>60<br>40<br>20<br>0<br>Lacking w aste w ater Lacking w aste disposal Using dirty fuels<br>treatment<br>%<br><!-- End of picture text --> Note: Average among 15 ECA countries for lack of inside toilet, 6 for lack of regular waste collection; 16 for dirty fuels.

Source: see Table 2.1 In summary, available evidence in the region shows that despite relatively high connection rates to basic infrastructure in the region, there is a serious problem of reliability and non-payment. A large number of households in the region are also at risk of poor environmental and living conditions. Access to, and reliability of, infrastructure and energy services are lower in secondary cities than in the capitals. Households in secondary cities are also less likely to have access to sanitation and are more likely to use dirty fuels. Urban infrastructure poverty is particularly widespread in secondary cities.

> _31 Electricity is a relatively inefficient source of heating and is quite costly and it is unlikely that many households use it as a primary source of heating._

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# **4.2.4. The links between access to infrastructure and energy services and income poverty**

**Poor households are less likely to have access to basic infrastructure and energy services.** Figures 4.17 and 4.18 show the distribution of households with infrastructure connections by quintiles in capital cities and other urban cities respectively.  Clearly, income poverty and poor infrastructure coverage tend to go hand-in-hand, revealing the multiple and cumulative aspects of urban poverty.  As shown in these figures, there is an accumulation of disadvantages among the income poor.  In capital cities, households in the bottom quintile are 15-20 percent less likely to be connected to district heating or have a telephone and are about 5 percent less likely to have running water or network natural gas.

In secondary cities, the differences are larger, while the overall level of access is lower. Households in the bottom quintile lag those in the top by an average of nearly 30 percent for phone service, 16 percent for district heating and 10-15 percent for running water and piped natural gas. This indicates that poor households in secondary cities experience a greater level of inequality in terms of access to basic infrastructure and energy services than is true in the capital cities.

**Figure 4.17. Distribution of basic infrastructure connections by quintiles in capital cities** (% of households)

<!-- Start of picture text --> Q1 Q5<br>100<br>80<br>60<br>40<br>20<br>0<br>Water District heating Natural gas Electricity Telephone<br>connection connection connection connection connection<br>%<br><!-- End of picture text --> Source: see Table 2.1 Note: average among 20 ECA countries for water connection, 19 ECA countries for district heating and telephone connection, 15 ECA countries for natural gas, 10 ECA countries for electricity.

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**Figure 4.18. Distribution of basic infrastructure connections by quintiles in other urban cities**

(% of households) <!-- Start of picture text --> Q1 Q5<br>100<br>80<br>60<br>40<br>20<br>0<br>Water District heating Natural gas Electricity Telephone<br>connection connection connection connection connection<br>%<br><!-- End of picture text --> Source: see Table 2.1 Note: average among 20 ECA countries for water connection,19 ECA countries for district heating and telephone connection, 15 ECA countries for natural gas, 10 ECA countries for electricity.

**Poor households are less likely to have access to sanitation or clean fuels for cooking and heating.** Analysis of  households who have an inside toilet and those who use dirty fuels for heating and/or cooking shows that low income households consistently are worse off as can be seen in Figure 4.19 below.  In both capital cities and other urban areas, low income households are much less likely to have adequate sanitation and are much more likely to use dirty fuels.

**Figure 4.19.  Lack of access to inside toilets and use of dirty fuels by quintiles for ECA countries**

<!-- Start of picture text --> Q1 Q5<br>40<br>30<br>20<br>10<br>0<br>Capital Other Urban Capital Other Urban<br>No access to w aste w ater treatment Using dirty fuels<br>%<br><!-- End of picture text --> Source: Table 2.1 The above results are broken down by country in Annex 3. In both capital cities and other urban areas, high income households are consistently more likely to have adequate sanitation and in most countries the differences are substantial. In terms of the use of dirty fuels, the results are a bit more varied. In most countries low income households are more likely to use dirty fuels as would be expected. However, in Georgia, Azerbaijan, Kyrgyz and Turkmenistan, higher income households are more likely. In part, this may reflect the way the indicator was constructed since it was based on households reporting 37
expenditures for dirty fuels. Poor households in these countries may be more likely to gather firewood themselves than to purchase fuel, which could explain the results.

**Poor households are provided with lower quality infrastructure and energy services.** Among households with connections, there is also evidence in the region that the income poor are somewhat more likely to be affected by the low quality of services, although the differences are slight.  Aggregate data for the ECA region on the reliability of water supply systems and electricity supply for the lowest and highest income quintiles are presented in Figure 4.20.  In both the capital cities and in secondary cities, higher income households have somewhat more reliable water services than do lower income households; however, virtually no difference exists for electricity services.

**Figure 4.20. Reliability of water and electricity by quintiles in capital cities and other urban areas** (% of households)

<!-- Start of picture text --> Q1 Q5<br>80<br>60<br>40<br>20<br>0<br>Capital Other Urban Capital Other Urban<br>Potable w ater 24 hours per day Electricity 24 hours per day<br>%<br><!-- End of picture text --> Source: see Table 2.1 Note: Capital city data are based on 6 ECA countries; other urban data are based on 8.

Looking at a more disaggregated level, there is also evidence of disparities across countries in the extent to which poor household are disproportionately affected by lower service reliability (Figures 4.21 and 4.22). For instance, in capital cities, the gap between the poorest and the richest was greatest in Albania and Bosnia, but there was little difference between the poor and rich in Armenia, Bulgaria and Kazakhstan. In Georgia, higher income residents in the capital were actually a bit worse off, which likely reflects the overall lack of progress on water reform in Tbilisi and the city’s aging water system.

The differences between the highest and lowest income quintiles in secondary cities show a stronger pattern with high income quintiles benefiting from more reliable services in all countries except Albania and Armenia where reliability is about the same.

38
**Figure 4.21. Reliability of water for richest and poorest quintiles in the capital cities**

<!-- Start of picture text --> Lowest Highest<br>100<br>80<br>60<br>40<br>20<br>0<br>Albania Bulgaria Bosnia* Armenia Georgia Kazakhstan Turkmenistan*<br>Balkans/EE Caucasus Central Asia<br>%<br><!-- End of picture text -->

- For Bosnia, urban households

- **There were no Q1 households in the capital of Turkmenistan.

Source: Table 2.1

**Figure 4.22. Reliability of water for richest and poorest quintiles in the secondary cities**

<!-- Start of picture text --> Lowest Highest<br>100<br>80<br>60<br>40<br>20<br>0<br>Albania Bosnia* Bulgaria Armenia Georgia Kazakhstan Turkmenistan<br>Balkans/EE Caucasus Central Asia<br>%<br><!-- End of picture text -->

- For Bosnia, households in mixed areas.

Source: Table 2.1

**Poor households are somewhat less likely to pay for services than rich households.** As we have seen, the poor are less likely to have access to infrastructure and energy services, and there is some evidence that those who do have access receive poorer service.  One would expect service availability and quality to be related to payment.  Figure 4.23 and 4.24 show the payment rate (or share of households reporting making any payment for a given service) for different services in capital cities and other urban areas. Higher income households have a slightly higher payment rate than lower income households in capital cities, but the difference is a bit larger when payment rates in secondary cities are taken into account.  In both capital cities and other urban areas, the most striking pattern is the large number of people who do not pay at all.

39
**Figure 4.23. Payment incidence by quintiles in capital cities**

(% of households) <!-- Start of picture text --> Q1 Q5<br>100<br>80<br>60<br>40<br>20<br>0<br>Central w ater District heating Electricity Natural gas<br>%<br><!-- End of picture text --> Note: average among 18 ECA countries for central water, 12 ECA countries for district heating and natural gas, 15 ECA countries for electricity.

Source: see Table 2.1

**Figure 4.24. Payment incidence by quintiles in other urban cities**

(% of households) <!-- Start of picture text --> Q1 Q5<br>100<br>80<br>60<br>40<br>20<br>0<br>Central w ater District heating Electricity Natural gas<br>%<br><!-- End of picture text --> Note: average among 18 ECA countries for central water, 12 ECA countries for district heating and natural gas, 15 ECA countries for electricity.

Source: see Table 2.1 The prevalence of poorly targeted categorical exemptions from payments for different groups (i.e., pensioners or war veterans) may explain why so many people do not pay and why the differences between high income quintiles and low income quintiles are rather small. Box 4.2 below summarizes the development of categorical privileges in Russia, which have mushroomed in numbers and now benefit about 40 percent of the population. Categorical privileges are also found in many of the other transition countries, especially those of the FSU. They are notoriously poorly targeted and politically difficult to eliminate since they benefit large numbers of people and since much of the cost is not directly financed by the government but indirectly financed by the service providers through erosions in service quality and deterioration of the capital stock.

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# **Box 4.2 Categorical Privileges (L’goti) in Russia**

Reduced rent and utility rates (or “privileges”) date back to 1975 when a 50-percent reduction was introduced for some disabled war veterans and families of servicemen killed in action. In subsequent years, these reductions were expanded to include war veterans and other groups, such as specialists who lived and worked in rural areas and people working in hospitals for lepers located in rural areas.

A huge number of privileges have been introduced since 1991. Privileges were provided not only for services to the fatherland to Heroes of Russia and war veterans, to families with many children, disabled people and other similar groups, but they were also provided to people of particular occupations, such as customs officers, militiamen, prosecutors, army officers, judges and others. More then ten new laws providing for reduction of rent and utility rates for particular groups of citizens in 1991 through 2002, and more than 30 additions were introduced in them during the same period. The privileges provided in accordance with Soviet laws and resolutions are still in place.

In addition, many Russian city and regional governments have introduced local privileges to certain groups of citizens by their decisions (privileges to honored citizens, participants in operations in Chechnya, single mothers, people affected by natural disasters, etc.). As a result, more than 40% of Russians are now paying reduced rent and utility rates, according to the State Statistics Committee of the Russian Federation (Goskomstat).

Source: Institute for Urban Economics. “Overview of Legislation on Housing and Utility Sector in Russia.” <u>http://www.urbaneconomics.ru/eng/index.php</u> Accessed on Dec. 11, 2004.

# **4.2.5. Housing context**

In addition to access to services, access to adequate shelter comprises an especially important dimension of household well-being. This section reviews the quantity and quality of housing, considers housing affordability and mobility rates, and concludes by assessing the links between housing and income poverty.

**The region is well provided with housing but housing quality (services, location and maintenance) is deteriorating.** As a result of the severe recession in the region, transition countries experienced contractions in the housing capital stock during the 1990s.  A Bank study on housing and land market reforms (2001) shows that housing production dropped in parallel to declines in incomes in the region over the last decade.  Since households in most countries use housing as an investment, inflation and declines in real incomes and savings all affect housing supply and demand and the patterns seen in transition countries are not surprising.<sup>32</sup>

The quantity of housing when measured by floor space per capita is relatively high in ex-socialist countries, given their level of income. Households living in Eastern European cities are especially well off in terms of housing space per capita (Hegedus, Mayo and Tosics 1997). Our findings confirm this result. As the Figure 4.25 shows, when space per capita in the capitals of transition countries is compared with that in 34 other countries of similar incomes, all of the transition cities are above the trend line for the comparator cities.

But, when quality (measured by location, availability of services, housing maintenance and crowding) is taken into consideration, the situation is less clear. One problem is that under central planning, cities were built in response to government directives, not market forces. With transition and the introduction

> 32 Notably, the study also found much smaller than expected declines in a few countries, including Russia, which results from the continued priority the government places on housing construction over housing maintenance.

41
of markets, some cities are no longer viable. Remote mono-industrial settlements have been especially hard hit.<sup>33</sup>

**Figure 4.25. Space per capita in ECA capital cities and comparator countries**

(Line shows trend for comparator countries only) <!-- Start of picture text --> 35<br>BG<br>30<br>HU<br>25 GE AL BA RU BY PL<br>MD<br>KZ<br>LT<br>20 UZ AZ RO<br>KG<br>15<br>AM TM<br>TJ<br>10<br>5<br>0<br>0 2000 4000 6000 8000 10000 12000 14000<br>GDP per capita PPP (2001)<br>AL=Albania; AM=Armenia; AZ=Azerbaijan; BA=Bosnia-Herzegovina; BG=Bulgaria; BY=Belarus;<br>GE=Georgia; HU=Hungary; KG=Kyrgyz; KZ=Kazakhstan; LT=Lithuania; MD=Moldova; PL=Poland;<br>RO=Romania; RU=Russia; TJ=Tajikistan; TM=Turkmenistan; UZ=Uzbekistan<br>Space per capita (m2)<br><!-- End of picture text --> Source: WDI for GDP per capita levels; UN Habitat Global Urban Indicators for space per capita in comparator countries and household surveys for space per capita in transition countries (see Table 2.1).

Centrally planned cities also suffer from spatial misallocation of the capital stock (i.e., buildings), which resulted from decades of construction without reference to land values. The figure below compares residential density by distance from the city center in Paris (shown by a line) and Moscow (shown by shaded bars). In Paris, residential density is greatest near the city center, where land values are highest, and decreases with distance. In Moscow quite the opposite is true and the construction of large numbers of high-rise apartment buildings on the fringes of the city mean the greatest residential density is found on the least valuable land. The resulting spatial misallocation of housing creates costs for residents (who need to commute longer distances) and the city (which has to provide city services to remote locations). Perhaps more importantly, the construction of large amounts of poor quality housing in remote locations provides a concentration of cheap housing stock that is likely to be increasingly filled with the poor as better off residents move to better locations.

> 33 The non-viability of cities explains the tremendous out migration from cities in the far north and east in Russia. See also,  the Tomsk case study referred to in Box 4.1.

42
<!-- Start of picture text --> Comparison between Moscow and Paris Built-up Density (1990)<br>=< ;<br>260 fN\ 7 -<br>| Waneoneenaceeaeeee<br>BE<br>a<br>CS<br><!-- End of picture text -->
**Figure 4. 27. Central water and electricity 24 hours a day**

(for apartment dwellers only) <!-- Start of picture text --> Capital Other Urban Rural<br>100<br>80<br>60<br>%<br>40<br>20<br>0<br>Albania Bosnia* Georgia Armenia * Kazakhstan Kyrgyz Tajikistan Turkmenistan<br>Balkans/EE Caucasus Central Asia<br>Water Electricity Water Electricity Water Electricity Water Electricity Water Electricity Water Electricity Water Electricity Water Electricity<br><!-- End of picture text -->

* Bosnia urban, mixed, rural settlement.  * Armenia -electricity reliability not available.  * Kyrgyz water reliability not available

Source: see Table 2.1 As might be expected, apartment dwellers are generally more likely to be connected to network utility services than the rest of the population. But at the same time, service provision is not reliable and apartment dwellers have limited coping options, such as carrying water or using alternative power sources.<sup>36</sup> Despite being relatively well-off, many of the apartment dwellers are infrastructure-poor in terms of the adequacy of service given their limited alternatives. In most of the countries sampled, water and/or electricity service is available 24 hours a day to fewer than half of apartment residents. In Albania, fewer than 40 percent of apartment dwellers in the capital receive water 24 hours a day, while in other cities, the share drops to 25 percent. In Armenia, where 68 percent of capital city residents and 57 percent of those in other cities live in apartments, fewer than 15 percent receive uninterrupted water supply. In secondary cities in Georgia, water and electricity are regularly available to fewer than 20 percent of apartment residents and the collapse in services to apartments has made life very difficult as the Box 4.3 shows.

# **Box 4.3.  Living without services in Georgian apartment buildings**

Most municipal heating systems have stopped supplying households with hot water, heating, and also cooking gas. As a result, when electricity is available, households use electric heaters or hot plates to heat and cook. Lack of electricity makes it difficult to warm homes, prepare hot meals, watch television, or listen to radio; often telephones ceased to function as well. High-rise elevators stop running. Pumps stop supplying water to upper stories of high rise apartment buildings, forcing occupants to carry water up many flights of stairs every day. In winter, most people conserve heating fuel by moving into one room, and using alternative fuels and strategies to survive.

Source: Background paper on Georgia for “When Things Fall Apart.” 36 The survey data used for this study indicate that apartment dwellers who are connected to district heating generally do have 24 hour service. District heating in Turkmenistan, and natural gas in Georgia and Kazakhstan, were significantly more interrupted for apartment residents in other urban areas. (Data were very limited on natural gas.) 44
In addition to deteriorating service quality, multi-story apartment buildings have been largely unmaintained during the past 10 years (see Struyk 2000). This under maintenance results from the failure to complete housing reforms. As shown in the Armenia example described in Box 4.4, households who privatize apartments receive the benefits of ownership without assuming the responsibilities for maintenance.<sup>37</sup> In part, this is due to the legal framework, which often does not clarify ownership of common areas in apartment buildings. But this also results from the incentive framework set by local governments, which are concerned about the ability of households to absorb additional costs and consequently set tariffs below cost recovery levels and do not support efforts to improve payment discipline. Leaking roofs and internal piping and energy losses from poorly insulated buildings are the most prevalent problems. Buildings in East Europe and Central Asia generally use two to three times as much heat as buildings in comparable climates in Western Europe (World Bank 2003). Commuting and energy costs account for an estimated 10 percent of the region’s GDP (World Bank 2001). All of which suggests that a considerable part of the housing stock in transition countries functions inefficiently in meeting the population’s housing needs, which is especially problematic for the poor.<sup>38</sup> In summary: although in some countries the housing stock may have contracted, transition countries are better provided with housing than would be expected given their income levels. However, continued under maintenance of the housing stock, which is especially problematic in the multi-story apartment buildings where responsibility for maintenance of the common areas has not yet been clarified, means the stock depreciates from year to year. The problem, then, is not the amount of housing, but the quality of that housing, including access to services, location and maintenance. Lack of services is especially problematic for apartment dwellers who have fewer coping options. As is true with services generally, apartment residents in secondary cities are more likely to lack services than those in the capitals.

# **4.2.6. Housing affordability**

Although urban housing markets have begun to operate and private sector developers supply much of the housing, housing prices remain unaffordable to most people.<sup>39</sup> This results from a variety of factors including continued public ownership of most urban land, which limits land supply, the high cost of construction needed to meet local design standards, and the limited access to finance by either households or developers. In Moscow, housing prices increased by more than 40 percent from 1999 ($700/sq. m.) to 2002 ($1000/sq.m.). In nine other Russian cities, prices increased from 40 – 50 percent over the same period (Klepikova 2002). Real incomes have not increased at the same rate.

The problem of affordability can be seen in the high price of housing relative to income in capital and secondary cities. International experience suggests that a house price to income ratio of 4 or 5 should be considered reasonable, although it would mean that the housing market is tight and affordability levels are moderately low. Figure 4.28 provides the price to income ratio for capital cities. In cities such as Budapest, Moscow, Riga and Yerevan the ratio is around 5. However, in Belgrade, Sofia, Tbilisi, Vilnius and Zagreb the ratios are significantly high, indicating low levels of affordability. As Figure 4.29 shows, markets in the secondary cities do not work better than those do in the capitals.

> 37 And households that don’t privatize receive the benefits of ownership (security of tenure) also since

> 38 Tenure change and security of tenure do not overcome the problems of dead capital (de Soto 2000), although it may be a necessary condition to enable this to be mobilized (Jakobson 2000).

> 39 Although little evidence of the affordability of privately rented apartments is available, there is some reason to believe that privately renting households must use a greater share off their income for housing expenses than is true for households in privatized or social housing.

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# **Box 4.4.  Armenia’s vicious circle**

As a result of the local policy environment in Armenia, housing maintenance providers and residential customers are caught in a vicious circle. Low tariffs, poor payment enforcement and public subsidies mean apartment owners do not pay the full costs of their housing. As a result, apartment owners who establish a homeowners association (HOA)<sup>*</sup> to manage their building will pay more, which creates a strong disincentive to HOAs. At the same time, without HOAs, it is hard to enforce payments for maintenance since individual customers cannot be “disconnected” for non-payment. This situation has resulted in a cycle of mounting subsidies, arrears and deferred maintenance.

Although the central government has reduced its involvement in housing, the public sector’s role remains high because local governments continue to own and maintain the common areas in multi-story apartment buildings. There are two possible explanations for why local governments participate so actively in this sector. First, provision of shelter and vital communal services are seen to be part of the social contract with the population. Second, local government involvement provides opportunities to build political support, for example, by fixing a building that is falling down.

Maintenance fees set by local governments range from 50 to as much as 95 percent below the estimated costs, as shown in the table below. Low collection rates further compound the problem of low maintenance fees. At the beginning of 2003, only one-quarter of households paid their maintenance fees. As a result, substantial arrears owed to the municipal maintenance have accumulated. In Yerevan, arrears to maintenance providers were the equivalent of 1-3 years of total monthly billings.

As a result, virtually no capital repairs have been undertaken in recent years and even routine maintenance is rare. Interviews with representatives from local governments and Municipal Maintenance Companies found that most efforts are directed at emergency repairs performed on an as-needed basis. This work is financed by pooling the scarce fees from all buildings to cover the costs of repairs in a few buildings, which results in large cross-building subsidies that serve as a type of insurance for emergency repairs. Five years ago, the National Housing Policy Study estimated that the amount required to repair only the roofs and common hallways in multi-apartment buildings would be more than US$ 160 million, or nearly 10 percent of GDP for that year.

As the below table shows, capital repairs have dropped by a factor of ten from 1.3 percent of the housing stock in 1994 to 0.1 percent in 2000. Although world experience varies substantially from country to country, in the Netherlands it is assessed that capital repairs should average 1-2 percent per year.

## **Capital Repairs of Housing (as percent of total housing space)**

||**1994**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|
|---|---|---|---|---|---|---|---|
|Percent of all housing<br>space undergoing capital<br>repairs|1.3%|1.01%|0.05%|0.16%|0.03%|0.08%|0.11%|

_Table Source:_ National Statistical Service of the Republic of Armenia Source: Vecvagare, L. and E. Hamilton. 2003. “Multi-apartment housing in Armenia—an Issues Note.” ECSIE, the World Bank (mimeo).

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**Figure 4.28. House price to income ratios in capital cities**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>Belgrade Budapest Moscow Riga Sofia Tblisi Vilnius Yerevan Zagreb<br>House price to income ratios<br><!-- End of picture text --> Source: 1998 UN Habitat Global Urban Indicators

**Figure 4.29. House price to income ratios in secondary cities**

<!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Polish cities Russian cities Kazakh cities Bulgarian cities<br>Gdansk PoznanAstrakhan BelgorodKostromaNiz. NovgoNowomos Omsk SurgutV. Novgo Astana BourgasV. Tarnovo<br>House price to income ratios<br><!-- End of picture text --> Source: 1998 UN Habitat Global Urban Indicators

# **4.2.7. Low mobility rates**

Studies of residential mobility in the developed countries describe it as a mechanism by which households adjust their changing housing needs and improve labor opportunities. The major function of mobility is the process by which families adjust their housing to the housing needs that are generated by the shifts in family composition that accompany lifecycle changes (Rossi 1955). In addition to changes in family composition and consequent changes in housing needs, job changes are another significant determinant of mobility. Several studies indicate that labor market efficiency is enhanced through improved household mobility (Blanchard et.al. 1991; and WB 1993).

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Thus, the residential mobility rate is an indicator of the extent to which the housing sector enables households to adjust their changing needs or respond to the labor market. In this respect, it is important to understand the extent to which households are mobile in ECA cities, and whether there is a difference between different geographies, i.e. capital, secondary cities and rural areas. As shown in the below figure, a very small share of rural households moved during the past five years in all countries and the mobility rate for urban households<sup>40</sup> Comparison of residential mobility rates for capital city residents and those in secondary cities fails to identify any consistent trend. In terms of national mobility rates, Armenia, Georgia and Azerbaijan have the lowest rates for the preceding five years (6.8, 7.3 and 7.5 percent respectively), followed by Tajikistan (10.4 percent), Belarus (14.1 percent), Turkmenistan (16.3 percent), Albania (17.1 percent) and Kazakhstan (25.3 percent).

# **Figure 4.30. Residential mobility rates for households in capital cities, other urban settlements and rural areas**

(share of households moving in past 5 years) <!-- Start of picture text --> Capital Other Urban Rural<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Albania Armenia Azerbaijan Belarus Georgia Kazakh Tajikistan Turkmen<br>Mobility rates over the last 5 years<br><!-- End of picture text --> Source: see Table 2.1 When national mobility rates for ECA households are compared with those in other countries, ECA households are found to be less mobile than their counterparts in the US, Austria, Canada, New Zealand, Hong Kong and Turkey.<sup>41</sup> At the same time, household mobility rates in some ECA countries (Belarus, Turkmenistan, Albania and Kazakhstan) are not significantly lower than those in countries such as Spain and Austria. This said, given the enormity of political and economic changes in ECA countries, one would expect mobility rates to be higher in ECA countries (which have experienced tremendous political, economic and social upheaval during recent years) than those in Spain and Austria.

> 40 Residential mobility was defined as households that have moved one or more times during the previous five years. 41 In this study, the residential mobility rates calculated for ECA countries describe household (not population) rates. Since higher income households are not only smaller in size, but also more likely to have changed dwelling, the rates for the population would be lower than those for households.

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**Table 4.4. The percentage of population that changed residence in the preceding five years**

|**Country**|**Percent who moved**|
|---|---|
|Canada|47.6|
|Australia|47.1|
|United States|46.4|
|New Zealand|45.3|
|Turkey*|40.1|
|HongKong|38.3|
|Switzerland|36.0|
|Israel|29.8|
|Puerto Rico|29.6|
|Japan|22.6|
|Austria|20.1|
|Spain|18.5|

* Mobility rate for Turkey is only for intra city Ankara (Baharoglu, D. 1993).

Source: Long, L (1991) Residential mobility differences among developed countries.

# **4.2.8. Implications for the urban poor**

The earlier analysis has shown that poor urban households are less likely to have access to infrastructure and energy services and are more likely to have unreliable services when provided than is true for upper income households. Poor urban households are also less likely to have an indoor toilet and are more likely to use dirty fuels for cooking and heating (Sections 4.2.2-4.2.4).

**The poor live under somewhat more crowded conditions.** Although transition countries are generally well provided with housing space on a per capita basis, analysis of the data from the household surveys shows that lower income households have less space per capita than do high income households.  Figure 4.31 below shows the ratio of housing space in the top quintile to that in the bottom quintile in capital cities and other urban areas.  Values above one show places where the top quintile households have more housing space than the bottom quintile households.  In every case the bottom quintile lives under more crowded conditions, except in Georgia where the poorer households are very slightly better off.  In all but four countries (Hungary, Albania, Romania and Azerbaijan), the disparity in housing provision for rich and poor households is greater in secondary cities than in the capital, as is true in other countries in the world.

The above pattern suggests that although the region as a whole is relatively well housed, there are substantial disparities in housing space among income groups and that those disparities are generally more pronounced in secondary cities. As housing costs continue to increase and housing markets further develop, it is likely that the poor will live under more and more crowded conditions.<sup>42</sup>

> 42 Crowding is to some degree a relative concept since there is no agreed international standard as to how much floor space is acceptable.

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**Figure 4.31. Ratio of one square meter housing per capita for the bottom quintile to square meters housing per capita for the highest quintile in capital cities and other urban areas**

**Mobility rates are lowest for the poor.** Figure 4.32 shows mobility rates by lowest and highest quintiles for residents in the capital cities.  As expected, the rate of households who have moved during the past five years is substantially higher for upper income households than for lower income households.  The difference is particularly sharp in Armenia, Azerbaijan, Belarus, Georgia and Kazakhstan.

**Figure 4.32. Residential mobility by the poorest and richest quintiles in capital cities**

<!-- Start of picture text --> Poor mobility Rich mobility<br>40<br>30<br>20<br>10<br>0<br>Albania Armenia Azerbaijan Belarus Georgia Kazakhstan Tajikistan Turkmen<br>quintiles of the capital cities<br>Mobility by the pooerst and richest<br><!-- End of picture text --> Source: see Table 2.1 Although analysis of mobility rates in secondary cities generally finds upper income households to be more mobile, the patterns are less clear than those seen in capital cities (Figure 4.33). In Armenia, Azerbaijan, Belarus, Georgia, Tajikistan and Turkmenistan poor households are visibly less mobile than are rich households. However, in Albania and Kazakhstan, poor households are more likely to have moved during the last five years than are rich. In Albania, extensive rural to urban migration may explain the pattern. Rural residents who recently have moved to secondary cities may be poorer than other secondary city residents. In Kazakhstan, large numbers of ethnic Russians, Germans and Ukrainians—primarily living in urban areas--emigrated during the 1990s. They were replaced by ethnic Kazakhs from rural areas, who were likely poorer than the non-moving urban populations.<sup>43</sup>

> 43 For an analysis of population trends in Kazakhstan, see:  Richard Rowland.  2001.  “Regional Population Change

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**Figure 4.33. Residential mobility by the poorest and richest quintiles in other urban settlements**

<!-- Start of picture text --> Poor mobility Rich mobility<br>40<br>30<br>20<br>10<br>0<br>Mobility by the poorest and richest quintiles in other urban settlements Albania Armenia Azerbaijan Belarus Georgia Kazakhstan Tajikistan Turkmen<br><!-- End of picture text --> Source: see Table 2.1 If residential mobility is an adjustment mechanism to enable households to satisfy their housing needs and to improve their labor opportunities, then low income households in ECA cities, especially those in the capitals, are trapped in part due to housing sector conditions. Rigid housing markets make it difficult for households, especially lower income ones, to move to other cities for jobs, which contributes to unemployment. At the same time, decreasing real incomes have negative impacts on the housing demand. A study in Poland estimates that as much as 25% of its unemployment rate in 1992 occurred because workers could not find housing near the available jobs (Coricelly et. al 1995). High income groups are much more mobile and are approaching average mobility rates for some of the less mobile countries in Europe.

**Trapped in deteriorated living conditions—the development of urban slums.** Although residential differentiation by neighborhood in centrally planned economies was much less than in market societies, some differentiation did exist.  Development of real estate markets in transition countries has provided a mechanism for increasing the spatial concentration of different socio-economic groups.

Under central planning, housing was centrally built, allocated, owned and maintained. Citizens were constitutionally entitled to housing. Housing rents and maintenance fees were low and largely uniform so access to housing was not determined by ability to pay. Since housing was provided at little or no cost, demand for housing far out stripped supply and people waited for decades to receive a new unit (Smith, 1996).

The different types of housing differed by location and amenities. In urban areas, some families lived in privately-owned single-family houses, which commonly lacked access to basic infrastructure services and were of lower quality. Enterprises built multi-story apartment buildings of slightly lower quality for their employees, who lived in close proximity to one another and were more homogenous than the population at large. Cooperative housing, which required some self-financing, was inhabited by somewhat higher earners. Better quality housing was used to “reward” especially meritorious workers ranging from skilled specialists to Party nomenklatura, which also led to some residential differentiation. The elites were often located in central parts of the city in higher quality housing stock. Additionally, housing quality varied significantly by age of construction. Since a large area (micro-rayon) in a city was usually developed at in Kazakhstan during the 1990s and the Impact of Nationality Population Patterns: Results from the Recent Census of Kazakhstan.” Post-Soviet Geography and Economics 42, no. 8, pp. 571-614.

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one time, this meant that housing quality varied by location.<sup>44</sup> Furthermore, since land was not recycled for new uses, the city grew as rings of similar quality housing were added at one time.

Economic changes during the transition years meant major changes in the fortunes of cities and their residents. Transition countries began housing reform by privatizing housing, generally by simple giveaway schemes. Housing privatization programs created winners and losers depending on where people happened to be living at the beginning of transition. Those in well located, larger units in places with a sound economic basis received far more valuable assets than did residents of collapsing mono-industrial cities or remote and underserved suburbs. It is noteworthy that since most privatization programs have not yet closed, households living in social housing who have the right to privatize their units should also be considered winners or losers since they cannot be evicted, can easily privatize and sell, and also often sublet units on the private markets.

# **Box 4.5.  Housing as a coping mechanism in Armenia and Moldova**

In Armenia, Rosa, single mother of a six year-old son, sold the conveniently located two-room apartment where she had grown up for a one room apartment in a poor, badly serviced distant suburb. She was a pediatrician, but after moving into the distant suburb she also had to change her job, since she could not afford bus fare from her new apartment to her old job.

An old lady sold her apartment at the center of Yerevan, for cash to a rich person, with the agreement that she could live in it until her death.

Source: When the Things Fall Apart – WB 2002

***

Renting out even modest apartments, rooms -- or sometimes, just a bed -- forms an important source of income in urban settings in Moldova. The luckiest are those whose apartment happens to be in the higher priced, more fashionable center of the city, where they can demand higher rents. People often sell their bigger apartments and buy smaller ones, using the profit to support themselves. Parents sometimes exchange a large apartment to acquire separate apartments, for themselves and for a married child, especially if they lack the cash to simply buy an apartment. Sales are complicated by the fact that apartment owners can only sell after all housing bills have been paid. The sale has become even more difficult now that the number of apartments for sale has increased.

Alternatively, people double up to free their apartments; others move into a single room to free their other room up, often to students. A pensioner who had been selling clothing, dishes, and books just to survive, posted an announcement and found a student willing to pay 50 lei or the equivalent in food, per month. Graduates of urban technical institutes unable to find work in their own villages often return to the city to search for jobs; they are among the families forced to rent apartments.

Often, families move elsewhere to free up their own apartment for rent in order to pay off outstanding debts. One family accrued such a large electricity debt that the entire family, consisting of Maria, a 61 year old pensioner, her daughter, son-in-law, and nephew, moved from their two room apartment into the two-room apartment of another family (already occupied by a couple and their 16 year old son). Maria's family used the rent from their own apartment to clear their debts, although the pressure of seven people in two rooms forced them to leave before they had intended. Another family moved into the balcony kitchen of another family in order to rent out their own apartment. Sometimes renting one's apartment is risky. One couple discovered their lodger was bottling bootleg whisky to sell in the market. Similar experiences make many apartment owner reluctant to rent to anyone but personal acquaintances of themselves or their friends.

Source: De Soto and Dudwick. 1997. “Poverty in Moldova: The Social Dimensions of Transition.” Facing poverty, poor families lucky enough to privatize desirable housing in good locations have often sold their asset and moved to much cheaper housing stock on the outskirts of the city in order to make

> 44 Hamilton, E. 1993.  Social Areas under State Socialism:  The Example of Moscow.  Ph.D. Diss., Columbia University.

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ends meet, as is the case in the example from Armenia shown in the Box 4.5. Other households rent their units to generate income as shown in the Moldova example in the same box. In both cases the poor, who received a valuable asset, use this asset as a survival strategy.

At the same time, the development of housing markets provided a mechanism for people to move. Although mobility rates are still significantly below what would be expected given the tremendous changes in the region, mobility now is higher than it was before transition. The introduction of housing markets has meant people who wish to move, and who have the resources to do so, are able to move. Movers include not only people who leave depressed areas for more vibrant places, but also those who move within cities. Both kinds of movers potentially affect the development of poor neighborhoods or slums.

# **Box 4.6.  The Emergence of slums in the peri-urban areas of Bishkek**

Large internal migration flows in the Kyrgyz Republic in the past ten years have seen the emergence of new slums in the periphery of Bishkek—the so-called “Novostroiki.” Today there are 23 precarious settlements in Bishkek, and all of them were named by their residents. The largest has 4800 land parcels, while the smallest have about 100. Many internal migrants often lack the funds for the construction of real houses and therefore live in shoddy structures. Most of these settlements lack basic infrastructure services and are often located in areas where there are adverse environmental health-related impacts.

One of the most populated slums, the Ak-Bosogo settlement, has very serious problems with water supply. Another populated area, Bakai-Ata, is located close to the ash dump of the Bishkek power and heating station, which is the source of heavy pollution. Even a slight wind lifts ash into the air covering all houses and facilities in this residential area. Underground water is very close to the surface and causes destruction and flooding of houses during winter and fall. Other settlements like Altyn Beshik, Kolmo, Ak-Bosogom Aska-tash, Burdinsky, and Ak-Telek are also located in the lower part of the city and are flooded after rains or melting snow, which regularly destroys the houses. Drainage systems are also collapsing due to the lack of funds to repair these systems.

There are important differences across the “Novostroiki” in terms of the demographic characteristics of the residents, which reflect the various regions from where people migrated. In the Salam-Alik settlement, children comprise only 10 percent of the total population. In the Kelechek settlement, for instance, the children make up 70 percent of the total population. Kelechek is mostly inhabited by migrants from the Batken region, which has the highest birth rate in the country.

Source: Rakisheva, K. 2002. “Impact of the Internal Migration Upon the Poverty Problem.” Mimeo. As cited in World Bank - (2003). Kyrgyz Republic: Enhancing Pro <u>poor Growth.</u> In countries such as Kyrgyz, Albania and Azerbaijan, as a result of economic conditions and, in the case of Azerbaijan, the occupation of Azerbaijani territory by Armenia, large numbers of people have left their homes for the capital cities. Hundreds of thousands of relatively poor in-migrants have arrived in the capitals of these cities only to find few affordable housing options. As is the case in other places, the new arrivals have settled in the peri-urban areas where they build houses on unserviced lots. Interestingly, although tenure security is not generally an issue in ECA countries, peri-urban settlers are more vulnerable. In Albania and Azerbaijan peri-urban settlers generally lack any title to the land, although in the case of Azerbaijan most are internally displaced people, a group against which the government is unlikely to take action. In Bishkek, the city has handed out unserviced land plots to people deemed in need of housing (Box 4.6). Recipients build housing on these lots but houses have to be finished and approved by local authorities before people can legally reside in them.<sup>45</sup> Although less visible than peri-urban settlements, evidence increasingly suggests poor people are becoming more concentrated in already established neighborhoods of transition cities, i.e., those with

> 45 Albania Poverty Assessment, Kyrgyz Poverty Assessment and conversations with local officials in Azerbaijan in November, 2003.

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poor locations and low-quality housing stock. Economic crisis and impoverishment have led some others to use their property as a cushion.

A study of Tomsk city in Russia (Box 4.7 below) found that not only was poor quality housing stock concentrated in different parts of the city, but in many cases the locations of the poorest quality stock were remote or isolated from the city by industrial enterprises. Furthermore, these areas were poorly served by transportation services and less well provided with health and educational facilities. Clearly, these areas are most at risk of becoming slums.

# **Box 4.7.  Urban Poverty in Tomsk city, Russia**

A recent study of urban poverty in Tomsk City used geographic data about housing location and quality, industrial, health, and education facilities and transportation to identify neighborhoods at risk for becoming slums. One of the main conclusions of this study was that a household’s location within a city may have a significant impact on access to public goods, labor and geographical mobility. Households’ potential for smoothing consumption depends on the value of assets that are influenced by spatial factors. Even within one city, the living conditions and opportunities for residents vary significantly. The study found that although Tomsk is quite well off, pockets of deprivation exist within the city. Spatial isolation, or distance from the city center, is highly correlated with infrastructure-related factors that are considered risky in terms of welfare. Thus, more remote areas are less served by transportation or social services, such as health and education. Residents in these areas often live in poor quality housing located near industrial zones or enterprises, which are a source of noise and pollution. The results suggest a vicious circle is emerging within the city. As housing markets develop, poor-quality housing in remote and poorly served areas is worth the least and has the lowest asset value for its owners. Residents who have resources move into better sections of the city to be replaced by poorer residents, including migrants from rural areas and small towns, and the emergence of poor neighborhoods that can rightly be considered slums.

Indeed, as seen in Tomsk, the worst off micro-rayons (neighborhoods) are those with poor quality housing (often single family homes) in poor condition, which are located far from the city center. These micro-rayons likely share more with ordinary ‘industrial’ or ‘urban-type’ villages that are formally rural but consists of 3-5 storey multi-apartment houses and were usually built to accommodate workers of some factories than with more developed parts of the city. With the introduction of housing markets, one would expect that areas with poor quality housing, services and location would also be the areas where poor residents would increasingly be concentrated.

Alexandrova, A., et al. 2003.

In summary, the poor are not only more poorly housed, but also more likely to lack access to basic infrastructure and energy services. Mobility, while increasing, remains below levels in most developed countries and are especially low for the poor, who have few opportunities to move in search of jobs. In some cases, migration to cities has resulted in the rapid development of peri-urban areas, where poorer populations concentrate in self-built housing without basic infrastructure. In other cases, housing markets enable the better off to leave undesirable housing in poor neighborhoods for better housing in better locations. They are replaced by poorer families, who thus contribute to a process of change, which is resulting in increasing concentrations of the poor in some neighborhoods in cities.

# **4.3. Human capital**

Besides income and infrastructure poverty, it is important to look at health and education outcomes in urban areas and across settlement types, where information permits.

**Health.** By and large, the differences in health and educational outcomes between rural and urban areas, and within urban areas, between capital cities and secondary towns, reflect the differences in access to services.  In Armenia, for instance, the poverty assessment (World Bank, 2003) finds that rural infant and early childhood mortality rates exceeded urban rates and this was most likely the result of greater difficulties of access in rural areas, especially for the poor.  A study of non-income dimensions of poverty in ECA found that more urban than rural residents use the health services in Kyrgyz Republic and

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Moldova, though in Georgia more rural than urban residents reported getting medical care when needed.<sup>46</sup> A factor potentially reducing the effective access and use of medical services is the increased payments required. Moreover, in many countries health staff in secondary cities as well as rural areas have left the health facilities because of general deterioration in their incomes and working conditions. It is likely that the numerous economic and infrastructural problems in secondary cities have reduced much of the advantage in health access, quality and outcomes that they may have enjoyed. Figure 4.34 below indicates little difference across the settlement types regarding the percentage of households reporting that at least one member interrupted his/her activity due to health problems.

**Figure 4.34. Health outcomes** (% of households)

<!-- Start of picture text --> Capital Other urban Rural<br>25<br>20<br>15<br>10<br>5<br>0<br>Activities interrupted by health problems<br><!-- End of picture text --> Source: see Table 2.1 Note: average among 12 ECA countries.

**Education.** Access to education remains higher in urban areas than rural areas in the region.  In Albania (World Bank, 2003), the gap in enrolment rates between rural and urban areas was most evident in postprimary education, with less than 3 out of 10 secondary school-age children enrolled in rural areas compared to 7 in Tirana and 6 in other urban areas.  However, poverty assessments report that absenteeism and withdrawal of children from school for economic reasons is a growing issue in urban areas where poverty is a serious problem, such as in Armenia (particularly in the secondary cities).<sup>47</sup> In Tbilisi (Georgia), youths reported avoiding university classes because of fear of appearing poorly groomed or badly dressed.<sup>48</sup> In Albania, residents of peri-urban areas report serious inadequacies of access to health services and schools, as well as water and sanitation.  Concerns with physical insecurity deter female students.  In some of the newly formed peri-urban settlements illiteracy is becoming an emerging issue.  Overcrowding of schools is also becoming a problem in urban areas receiving large numbers of migrants from rural areas.<sup>49</sup> In short, conditions of educational access and quality are diverse and changeable within the countries of the region.

**Nutrition.** In several of the poverty assessments and qualitative poverty studies, poor households, especially poor urban households, report difficulties affording both food purchases and utility bills, and

> 46 “Tracking Non-Income Dimensions of Poverty in ECA”, PREM, HD Sector Unit, ECA Region, World Bank, April 7, 2003 draft,  table 2.4.

> 47 World Bank, “When Things Fall Apart: Qualitative Studies of Poverty in the Former Soviet Union”, p. 194. 48 Ibid, p. 217.

> 49 Ibid., p. 59-63.

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some report cutting back on energy use for food preparation (Armenia), reducing their food consumption (Georgia, Armenia—see Box 4.8), and limiting their dietary choices (Ukraine). Having access to a land plot for household food production is considered by many to be essential to meet their food needs (Tajikistan). In Moldova survey respondents count hunger among the worst aspects of their poverty. In Latvia, even housing vouchers were said to be sold for food.

# **Box 4.8.  Strategies of the extreme poor for reducing food consumption in urban areas in Armenia**

To adapt to insufficient food supplies, many of the poorest households, especially in urban areas, limit their food intake through out the year. Many families cut down on the amount they eat by reducing the number of meals eaten per day to commonly two meals a day or less in time of crisis. Rationing food is another adaptive mechanism that was widespread. One household explained it has a regimen of two potatoes per meal per person for the rest of the winter. Another household had an intricate system of rationing bread with the bed-ridden grandfather posted as the guardian of the bread bag. Households also cope by “tricking their stomachs” with warm “tea” without tealeaves or sugar, or drinking thin soups of boiled water and dried herbs.

Source: Elizabeth Gomart, “Social Assessment of the Poorest of the Poor in Armenia.” 1997.

In the ECA countries which are suffering the most prolonged economic dislocation and poverty, malnutrition has become a manifestation of poverty. In Kazakhstan the incidence of wasting is slightly higher in urban than in rural areas, although stunting is much greater among rural children.<sup>50</sup> Malnourishment was also found to be a serious problem in urban areas of Tajikistan in 1998.<sup>51</sup> Even in Romania, nutrition-poverty was found to be more prevalent among the urban poor (50 percent) than among the rural poor (28 percent).<sup>52</sup>

# **4.4. Social Capital, empowerment and security**

With the increase in income poverty and other deprivations, and with large scale internal and external migrations, the ECA countries are experiencing unprecedented stresses to their traditional social capital assets and attendant social problems. Residents in urban areas report feeling inadequately supported by social networks and little trust in government. Growing insecurity and worsening public safety no doubt contribute to the urban poor’s perceptions of disempowerment and social exclusion.

In Romania, rural households report having more “trust in most people” than do urban households. There is a large gap between urban and rural households regarding trust in either local or national officials, with the rural respondents reporting higher trust. Proximity to government does not necessarily entail service-urban residents and the rich are more likely to report having offered bribes to city hall employees. Cooperation with neighbors is higher for rural than urban respondents, although urban households tend to belong to more associations. This may indicate that the rural households have deeper and more functional ties to social networks than their urban counterparts do.<sup>53</sup> Contributing money or time to work on community projects is also more common among rural than urban localities.<sup>54</sup> In qualitative poverty surveys assistance to the urban poor is reported to come mainly from nongovernmental organizations, but even those are often lacking. Women respondents in Bishkek (Kyrgyzstan) said they relied on neither government nor NGOs for employment assistance, and 90 percent of those surveyed knew of no women’s organizations.<sup>55</sup> In Tajikistan, the urban Mahallah

> 50 “Tracking Non-Income Dimensions of Poverty in ECA”, table 2.1. 51 World Bank, Republic of Tajikistan Poverty Assessment, Report No. 20285-TJ, June 29, 2000, p. 22. 52 Romania Poverty Assessment 2003, p. 20.

> 53 Romania Poverty Assessment 2003, p. 24, 26. 54 “Mapped In or Mapped Out? The Romanian Poor in Inter-Household and Community Networks,” p. 36. 55 When Things Fall Apart, p. 41.

56
(Islamic religious leader) helps connect the local government with local communities, organizes and distributes social assistance to poor families, and rallies people to get involved in public works projects.<sup>56</sup> In Armenia, respondents complained of lack of information about government and private aid distribution, even blaming power outages in the cities for preventing timely distribution of information. Networks of reciprocity are said to have retracted to include only close family members. However, it is recognized that such kinship networks are becoming less effective and are often not available to the poorest households.<sup>57</sup> Similarly in Georgia, information about entitlement to assistance from public or private sources was often said to be unavailable and rarely provided by officials, leaving the poor to rely mainly on each other for help.<sup>58</sup> In Azerbaijan, internally displaced persons (IDP) live mainly in Baku and other urban areas, and they are among the poorest households in the country.<sup>59</sup> Street children have become a concern in Dushanbe (Tajikistan) since the early 1990s, although they usually live with their families and help support them by begging or other odd jobs.<sup>60</sup> Prostitution proliferates in Bishkek (Kyrgyzstan) and in Tbilisi (Georgia) among girls and women.<sup>61</sup> Crime is reported to be a growing issue in virtually all the countries. In Bosnia-Herzegovina, survey respondents attribute criminal activity to poverty (although said to be less often an effect of poverty than hunger and illness).<sup>62</sup> In Ukraine urban residents reported fear of leaving home due to crime and felt that criminals were gaining control of society.<sup>63</sup> In Moldova theft of goods from state owned enterprises was widely condoned because of the companies’ failure to pay salaries on time. Theft and violent crime, including by youth gangs, is said to be scaring people off the streets and reducing trust especially in urban communities.<sup>64</sup> In Latvia, respondents say that they no longer rely on the police for protection.<sup>65</sup> An aspect of poverty in the region which – like in many other countries – is more specific to urban areas relates to youth engagement in risky behaviors like drug taking and delinquency. In ECA, this particular aspect of urban poverty remains little documented, and this is probably because reliable quantitative data on substance abuse and crimes are not available. However, available evidence from qualitative or administrative sources confirms the increasing importance of risky behaviors among youth in the region.

# **5. Conclusion**

The last 15 years have brought profound changes to ECA countries as central planning was replaced by market forces and as large countries fragmented into many parts. Industry, manufacturing and agriculture shrank in nearly all countries and unemployment increased. The increase in services was inadequate to make up for the declines in the primary and secondary sectors.

Against this backdrop, the fortunes of ECA countries, both in terms of output and level of national poverty, have diverged sharply. Countries with the highest poverty incidence have been those in which the progress of structural reform and liberalization has been very incomplete. At the same time, countries where GDP has fallen more sharply are those with higher rates of urban poverty, suggesting that the prolonged economic recession in these countries has had strong impacts on urban households.

> 56 When Things, p. 85.

> 57 When Things, p. 146-152; 189-190.

> 58 When Things, p. 233, 254.

> 59 “An Update of Poverty Assessment and Consultations with the Poor in Azerbaijan, p. 51.

> 60 Tajikistan PA, p. 24

> 61 When Things Fall Apart, p. 45, 227

> 62 “Consultations with the Poor” National Synthesis Report, Bosnia and Herzegovina, p. 19.

> 63 When Things Fall Apart, p. 286.

> 64 When Things, p. 372.

> 65 When Things, p. 397.

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The objective of this study is to contribute to a better understanding of the extent and nature of poverty in urban areas of this region, providing particular attention to the disparities within urban areas between capital cities and secondary cities and focusing on dimensions of poverty related to provision of network infrastructure and energy services in cities.

# **5.1. Summary of results**

At the outset, the study proposed four hypotheses to be tested. The first postulated that living standards vary significantly across urban areas (especially between the capital and the secondary cities). The second proposed that secondary cities have poverty indicators equivalent, or worse than, those of rural areas, including in terms of access and quality (reliability) of infrastructure. The third stated that although formal access to infrastructure and energy remains higher in urban areas than rural in most cases, many households, especially in secondary cities suffer because of unreliable and deteriorated services, and are “infrastructure poor.” The fourth surmised that income and infrastructure inequality was generally higher in urban than in rural areas and highest in capital cities.

<u>The analysis found substantial differences in urban areas between the capital and secondary cities with households in secondary cities being worse off (hypothesis 1). At the same time, secondary cities are</u> home to 85 percent of all urban residents. Poverty incidence in secondary cities was higher (and in some cases even higher than in rural areas). In most countries the poverty risk of residents in secondary cities was two to four times greater than for residents in the capital. Among urban areas, the degree of poverty was significantly worse in secondary cities than for the capital, with the sole exception of Armenia. The urban poor were overwhelmingly located in secondary cities in all countries except the Caucasus where a large share of the poor were also found in the capital cities. The finding that poverty incidence and degree is worse in secondary cities likely reflects the relatively weak conditions of employment, limited economic diversification and fewer economic opportunities in these urban settlements.

Household heads in secondary cities were more likely to be unemployed than those in the capital or in rural areas, except in Russia and Belarus. Among the unemployed household heads, the relative poverty rates in other urban areas is only slightly below that of rural areas, but half again as large as that of the capital cities. This finding suggests that (i) unemployment in the secondary cities is of longer duration and so more likely to lead to poverty than in capitals; and (ii) financial safety nets available to the unemployed may be better in the capital cities. In Armenia, the only country for which this kind of data were available, in general residents of the secondary cities showed a wider diversity of income sources than in the capital or rural areas, where households depended more on labor earnings and farm income respectively. Since in Armenia this income diversity results from pensions and remittances, it represents coping effort rather than breadth of opportunity.

<u>The study also found that secondary cities have poverty indicators equivalent to, or worse than, those of rural areas, including in terms of access and quality (reliability) of infrastructure (hypothesis 2).</u> Throughout the region, household heads who are less educated and large families are consistently associated with poverty. Although the incidence of less educated household heads and the incidence of large families are greater in rural areas, the rate of poverty for each of those groups is higher in secondary cities than in rural areas. In the case of uneducated heads in secondary cities, the high rate of poverty reflects both lack of access to supplemental income from farm production as well as having less ability to compete for well paying urban employment. In the case of the high poverty rate for large families in secondary cities, the result likely reflects the need for urban families to have cash for essential goods and services. In terms of the reliability of water and electricity, the differences among the settlement types are 58
not very great, although secondary cities and rural places have less reliable services than do capital cities.<sup>66</sup> <u>The study results confirmed that many households, especially in secondary cities, are “infrastructurepoor” because of unreliable and deteriorated services (hypothesis 3)</u> and these households are hidden by studies that do not examine actual quality. Despite high connection rates, the reliability of basic services has become a serious challenge in the region. Infrastructure reliability is more of a problem in secondary cities than in capital cities. Even though residents in apartment buildings, the predominate kind of housing in urban areas, are generally better off, this finding is equally true for them. Apartment dwellers have even fewer coping options than do households in single family homes.

Environmental and sanitary conditions are poor for many urban households. More than 40 percent of all urban households report burning, burying or dumping household waste. Households in secondary cities also are much less likely to have access to adequate sanitation (i.e., an inside toilet) and much more likely to use dirty fuels than are capital city residents, both of which are indicators of poor living conditions. Regardless of location, poor households are nearly twice as likely to use dirty fuels and half as likely to have an inside toilet as are better off households.

<u>The final hypothesis examined in this study stated that income and infrastructure inequality are generally higher in urban than in rural areas, and highest in capital cities. The results of the study confirmed the first part of the hypothesis, namely that income and infrastructure inequality are generally higher in urban areas. However, inequality was not consistently highest in capital cities and inequality in secondary cities</u> often was greater than that in the capitals.

Income inequality was higher in urban areas than rural ones for about three-quarters of the countries used. Countries with higher urban inequality are divided about equally between those with the highest inequality in the capital cities and those with the highest inequality in the secondary cities. In terms of infrastructure provision, in capital cities, households in the bottom quintile were less likely to be connected to district heating, running water and telephone than were higher income households. However, the differences between connection rates for low and high income households were greater in secondary cities than in the capitals. A similar picture emerges for per capita housing space. Although the transition countries generally well provided with housing space on a per-capita basis, lower income households have substantially less space than do high income people. The disparity in housing provision for rich and the poor is greater in secondary cities than in capital.

# **5.2. Issues for policy makers**

# **5.2.1. Strategic questions**

The study findings concerning secondary cities, the emergence of slums and mobility have implications for broader strategy formulation in ECA countries.

**Problem of secondary cities.** <u>The study found urban poverty to be primarily a problem in the secondary cities, where the overwhelming majority of the urban population lives.</u> In general, little attention has been paid to secondary cities either in terms of CASs  or in terms of Bank-financed operations to support the

> 66 Comparison of infrastructure access rates in urban and rural areas is deceptive.  In rural areas, households may have access to acceptable alternative forms of infrastructure services such as well water instead of piped water. Urban residents, in contrast, have fewer options available to them if water or heat are not provided or if garbage is not collected.  For this reason, access rates for different settlement types are not easily compared.

59
strategy. One reason for overlooking poverty in the secondary cities may be that most poverty analyses fail to differentiate among urban settlement types. As a result, the better off capital cities conceal the degree of poverty in the secondary cities. This study has pointed to the importance of differentiating <u>between the capital and secondary cities in poverty work.</u>

**Problem of emerging slums.** Development of real estate markets in transition countries has provided a mechanism for increasing the spatial concentration of different groups – i.e. development of slums at periurban areas as well as in city housing stocks.  At the same time, the erosion of public transportation and other public services has meant that marginal neighborhoods are less likely to be well connected to the city fabric and public services and residents are more likely to face exclusion.  In peri-urban areas, ECA countries face a situation found in many other places, where the usual response involves slum upgrading. <u>Preventing the emergence of slums among the existing housing stock is a somewhat different problem and one that is perhaps unique to the region.  Policies relating to provision of public services and</u> transport, urban development, social assistance programs and the housing sector will all influence slum development.

**Problem of mobility.** <u>The study found that poor households were much less likely to have moved during the past five years than better off households, particularly in capital cities.  If residential mobility is an</u> adjustment mechanism to enable households to satisfy their housing needs and find jobs, as is true in other countries, then low income people in ECA cities are trapped due to both housing sector conditions and tight labor markets.

# **5.2.2. Sectoral issues**

The study also pointed to the interrelations between income poverty and poor infrastructure coverage, which go hand in hand in urban areas, revealing the multiple and cumulative aspects of urban poverty.

**Problem of deteriorating services.** <u>The results of this study point to the continuing erosion of</u> - <u>infrastructure services and means that for some countries meeting the infrastructure related MDGs may be a problem in the future.  The continued reliance on connection data, instead of reliability figures,</u> serves to conceal the degree of erosion in infrastructure service provision.  (As discussed in the data section below, information about reliability needs to be improved.)

<u>As service quality deteriorates, the region faces a major challenge since the poor quality of services provides few incentives for payment. At the same time, housing expenditures remain below 10 percent</u> for most countries, while the average is 20 percent for OECD countries. Low tariffs, widespread exemptions and non-payments explain the low rate of spending. Clearly, in this kind of environment, service providers are not able to invest in the rehabilitation of infrastructure facilities, perpetuating the vicious cycle of widening infrastructure poverty. This suggests the need to review carefully existing <u>tariffs, payment practices and subsidy systems (especially categorical exemptions) with the objective of improving quality</u> _<u>and</u>_ <u>access for the poor.</u>

**Problem of deteriorating apartment buildings.** In the transition countries, the quantity of housing (measured in terms of space per capita) is better than expected for their level of income.  However, the <u>quality of the stock, as measured in terms of access to services, location and maintenance, is becoming more problematic.  One illustration is the large number of apartment dwellers, who are generally better</u> off, who do not benefit from any better access to network utilities.  At the same time, regardless of privatization status, apartment buildings go largely unmaintained as a result of continued local government involvement in this part of the housing sector.   Neither the government, nor the households, however can afford to replace the existing apartment buildings.

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# **5.2.3. Questions related to data and methodology**

<u>This study was the first to explore systematically the use of household surveys in ECA countries for investigating urban poverty and infrastructure and energy dimensions of poverty. Here several important problems were identified:</u>

**Exclusion of peri-urban areas from the sampling framework** .  The exclusion of peri-urban areas from the sampling frameworks means peri-urban problems are not well incorporated into otherwise rich poverty analyses as well as contributing to a more general underestimation of the prevalence of urban poverty in countries such as Albania, Kyrgyz and Azerbaijan, where peri-urban areas are growing rapidly.

**Quality of infrastructure services.** This study has pointed to the deterioration of quality in provision of infrastructure services, however fewer than half of the surveys used included any questions about quality. <u>The survey questionnaires</u> should be improved to allow better coverage of the quality of basic infrastructure services.

**Inclusion of vulnerable urban groups in poverty analysis.** <u>Qualitative data should be used to complement quantitative diagnosis and provide information on vulnerable groups of urban dwellers who are often under-represented or omitted entirely (slum dwellers, homeless, IDPs/refugees) in quantitative poverty assessment.</u>

# **Address poverty comparability problems between rural/urban areas.**

- In about one-third of the surveys used for this study, income poverty measures were not adjusted for urban/rural prices differences.  This has the affect of overstating rural poverty and understating urban poverty.

- Tailor infrastructure modules in order to make infrastructure-poverty indicators relevant to the urban/rural context (e.g. network vs. non-network access; the comparison across urban/rural of network connections is meaningless, but most surveys only ask about network connections).

# **5.2.4. Implications for future empirical research**

The results of the study provide some lessons for further empirical work, not only in terms of addressing the identified data deficiencies, but also in terms of on-going and new analytical work.

- **Survey design.** As discussed above, peri <u>urban areas should be incorporated into the sampling framework.  In order to understand infrastructure service quality, additional questions should be included in the survey instrument.  A special effort will be needed to collect information about public</u> transportation, which is routinely excluded from on going surveys, but which is important for both urban and rural livelihoods.  At the same time, questions should be adjusted so as to be relevant to both the urban and the rural contexts.  Income poverty measures should be adjusted for urban/rural price differences.

**On-going empirical work.** <u>The study results point to several areas where ongoing poverty and related work would benefit from a more nuanced approach.  Analysis based on distinguishing between urban and</u> rural areas would be enhanced by disaggregating urban into the capital and secondary cities. Additionally, when survey data are used to compare urban and rural areas in terms of infrastructure services, care should be taken not to overestimate the importance of access to network services for rural inhabitants.

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**Future empirical work.** This study was a first step in understanding the dimensions of urban poverty by using existing survey data to provide an overview of the current situation.  This first step describes the current situation.  Subsequent work would be needed in order to provide understanding of the outcomes and to answer questions such as:  To what extent do institutional arrangements for service delivery explain the results?  How are fiscal and governance issues related to the outcomes? Has the recent improvement in the fiscal situation in some countries resulted in an improvement in service-delivery?  If not what are the implications?  How have changes in the social safety net contributed to poverty reduction in urban areas?  Why are mobility rates low and how does this affect urban poverty?  To what extent are slums emerging in ECA cities and how is this linked to country policies?

# **Annex 1. Measurement and Data Issues**

# **Annex 2. Country Data Tables**

# **Annex 3.  Payment Rates and Provisions of Different Infrastructure Services by Quintile and be Capital and Other Urban Areas**

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# **ANNEX  1. Measurement and Data Issues**

This annex will describe two aspects of the information used in this study. First, the obtaining of relevant survey data. Second, the establishment and application of meaningful indicators to present the data. Finally some observations on surveys and survey data will be offered, based on our recent experience.

# DATA SOURCES

The sources of primary data were sample surveys of households within transition economies of the ECA Region. Several possible sources for were considered for suitability. The survey types examined included: Living Standards Measurement Surveys (LSMS, assisted by the World Bank), Household Budget Surveys (HBS, also known as Income and Expenditure Surveys, assisted by the World Bank), Multiple Indicators Cluster Surveys (MICS, assisted by the UN), Demographic and Health Surveys (DHS, assisted by USAID), and national censuses of population.

The countries considered for data availability were all of those in the ECA region with transition economies, so all were considered except Turkey. In most cases the surveys are administered by the statistical agencies within each country with technical assistance from donor organizations. Other sources of data used in this study include: national official statistics, the World Development Indicators databank, the UN Habitat Global Urban Indicators databank, country Poverty Analysis reports, and other related literature.

The examination of household surveys lead to the compilation of a comprehensive and current listing of surveys undertaken in ECA Region, including survey details and technical and analytic contacts. In all around 200 surveys in 30 countries over the period from 1989 to 2003 were identified thorough desk research and discussion with other staff in Bank sectoral and research units.

The sophistication and usefulness of the household surveys undertaken in the Region have improved considerably during the 1989 - 2003 period. Most countries in the Region have a program of annual Household Budget Surveys (HBS). However the data available from this source, up until the late-1990's, was of poor quality largely because inadequate sampling techniques held over from the Soviet era. Until the sampling design was corrected these surveys were biased and misleading. The HBS approach does not always allow for the calculation of welfare aggregates based on consumption and expenditure or income must sometimes be used. The most recent LSMS surveys have benefited from a more rigorous sample frame being available from the national censuses conducted during 1999 - 2003.

LSMS and HBS surveys have been the most frequently used quantitative instrument for poverty monitoring and analysis in the region, as they are the only surveys that contain extensive information on household income and expenditures. The preference given to LSMS over HBS surveys lies into the fact that LSMS surveys usually cover a greater variety of topics, including infrastructure and energy poverty, and receive considerable care in terms of quality control.

After identifying the existence of these surveys, the list was screened according to relevance and availability of source datasets. A most likely survey was identified for each country, usually the most recent LSMS or "new design" HBS and examined for relevance. Relevance included both currency and coverage of urban and infrastructure issues. The primary consideration for urban data was the ability to separate out the capital city from other urban areas within the overall urban sample strata. The primary consideration for infrastructure data was the ability to derive level of service measures. This review is described in more detail below.

The next step was obtaining the selected source datasets and ensuring that the datasets included the necessary derived variables (especially the welfare aggregate used in Bank poverty work on the country) 63
**Annex 1 Measurement and Data Issues**

and national poverty lines, where used by the Bank. In the case of Russia, the survey used was the Russia Longitudinal Monitoring Survey (round X), as access to the official HBS was not possible at the time the analysis was carried out. In the case of Hungary and Poland, the welfare aggregates developed by the state statistical bodies were used. This step also involved extensive liaison with Bank staff in sectoral and research units to obtain the datasets and documentation necessary to make full use of the data.

Datasets for Czech, Estonia, Latvia, Slovakia, Slovenia were not obtained because they were not easily available to the study team and pursuing these data was considered a lower priority given that Central Europe and the Baltics were already represented in the datasets and the countries current or expected near term development prospects were reasonably good. Datasets of sufficient quality were not available for Croatia and Macedonia at the time the data were being assembled. The final set of surveys was used to provide the data used in this report is listed below.

Table 1

# ECA INFRASTRUCTURE AND URBAN POVERTY STUDY HOUSEHOLD SURVEYS INCLUDED IN THE ANALYSIS

||Country|Date|Survey|Urban - R<br>Adjustmen|ural Price<br>t|
|---|---|---|---|---|---|
|||||Price level<br>only|<br>Price and<br>consumption<br>basket|
|1|Albania|2002|LivingStandards Measurement Study|Y|N|
|2|Armenia|2001|Integrated LivingConditions Survey|Y|N|
|3|Azerbaijan|2001|Household Budget Survey(new design)|Y|N|
|4|Belarus|2001|Income and Expend. Survey(newer design)|Y|N|
|5|Bosnia & Herzegovina|2001|LivingStandards Measurement Study|Y|N|
|6|Bulgaria|2001|Integrated Household Survey|Y|N|
|7|Georgia|2001|Surveyof Georgian Households|N|N|
|8|Hungary|2000|Household Budget Survey|N|N|
|9|Kazakhstan|2001|Household Budget Survey|N|N|
|10|Kosovo|2000|LivingStandards Measurement Survey|Y|N|
|11|Kyrgyz Republic|2001|Household Budget Survey|Y|N|
|12|Lithuania|2000|Household Budget Survey|N|N|
|13|Moldova|2001|Household Budget Survey|Y|N|
|14|Poland|2001|Household Budget Survey|N|N|
|15|Romania|2002|FamilyBudget Survey|Y|N|
|16|Russia|2001|Russia Longitudinal Monitoring Study (RLMS),<br>RoundX|<br>N|N*|
|17|Serbia|2002|PovertyHousehold Survey( SMMRI, Dutch TF)|Y|N|
|18|Tajikistan|1999|LivingStandards Measurement Survey|N|N|
|19|Turkmenistan|1998|LivingStandards Measurement Survey|N|N|
|20|Uzbekistan|2000|Household Budget Survey|Y|N|

`*` In Russia (RLMS 2001) the price and consumption basket is adjusted on regional level not urban/rural.

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**Annex 1 Measurement and Data Issues**

# AVAILABILITY OF RELEVANT INFORMATION

The questionnaires from the most recent surveys were reviewed to determine their relevance for analysis of infrastructure and urban poverty issues.

|Table 2<br>ECA INFRASTRUCTURE AND URBAN<br>SCREENING FOR INFRASTRUCTURE R|POVERTY STUDY<br>ELEVANCE|
|---|---|
|Type of Infrastructure Service|Criteria|
|water, sewage, district heating, electricity,<br>naturalgas|connection, service, use, expenditures,|
|solid waste|type of service available,expenditures|
|transport|type of service available,expenditures|
|housing|type, ownership, condition, maintenance, additional<br>plot|

Level of service data came in various forms: data not available (question not asked) number of hours of operation per day less than 24 hours service available (yes/no) <u>Overall in the region, the coverage of variables to measure infrastructure and urban poverty was poor.</u> This is discussed in Chapter 2 of the main report and illustrated there in Table 2.2. Out of the 27 desired indicators constructed for this study, only about 70 percent could be measured with recent available data. There were also large disparities across countries in terms of survey coverage of infrastructure and energy poverty and the percentage of desired indicators that could be measured. A detailed listing of what questions, covering what aspects of infrastructure, were available by country is provided in Attachment 2 of this Annex.

Attachment 2 also shows that while most surveys provided information on the availability of infrastructure and energy services, few contained indications of whether these services were reliable and <u>paid for and even fewer provided information on the consumption of infrastructure and energy services. Not all types of infrastructure and energy services were covered equally. In terms of availability, public</u> transportation and electricity connections were the least well documented in the region, although for different reasons. The failure to ask about public transportation is especially noteworthy as the region remains heavily dependent on public transport. In the case of electricity, this reflects an assumption that all households are connected to electricity, hence there is no need for the question. As regards reliability, information on the quality of district heating was extremely limited and even information on water and electricity reliability was available from less than half the surveys. Finally, in terms of payment rates, the information provided for natural gas was extremely poor.

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**Annex 1 Measurement and Data Issues**

# SELECTION AND APPLICATION OF INDICATORS

The other main aspect of data preparation was the construction and application of a set of meaningful and credible indicators to measure and monitor poverty impacts of the infrastructure and urban variables. This meant establishing a preferred set of desired indicators and then working out in practical terms what indicators could be used in this study derived from the existing survey datasets. Finally the indicators were applied by examining the poverty effect of each indicator through the use of poverty measures and geographic disaggregation.

The framework for indicators originally proposed for measuring infrastructure and energy poverty is provided in Attachment 1 of this Annex. This is based on a three-way classification of the effects of infrastructure services, namely: Delivery-Based, Use-Based, and Welfare-Based, each representing a different dimension of poverty. The first type of indicators refers to infrastructure and energy poverty. These are "delivery-based" indicators which show basic access to infrastructure services, level of service reliably available, living conditions and spending on payments for services. The second type relates to potential and actual use of the services. The third type relates to very approximate aspects of economic opportunities and human capital and includes the incidence of activities interrupted due to health problems and the incidence of household head with less than secondary education. These are very narrow dimensions of well-being but the advantage is that they can be easily constructed for and compared across a large number of countries.

Based on data availability and after discussions within the study team and other interlocutors, the final set of indicators used in this study was derived. Attachment 2 of this Annex (already referred to) lists the final set of indicators used. Table 3 below highlights the difference between what indicators were desired compared to what indicators turned out to be possible. Note this list will be different in different regions depending on the surveys conducted.

The selected indicators were applied by examining the poverty effect of each indicator through the use of the relative poverty rate (quintile distribution). This was further disaggregated geographically by the use of a three-way classification: Urban - capital city only, Urban - all other urban, and Rural. This geographic detail allows the effect of the capital city to be isolated from urban conditions outside the capital.

The detailed results for each country are available in Annex 2 with a short description of each indicator. There are three pages of results for each of the 20 countries in the study.

|Table 3<br>ECA INFRASTRUCTURE AND URBAN<br>DESIRED AND POSSIBLE INDICATOR<br>INDICATORS DESIRED|POVERTY STUDY<br>S<br>INDICATORS USED|% of surveys<br>with this<br>information<br>available|
|---|---|---|
|DELIVERY BASED INDICATORS|||
|Availability|Availability of infrastructure/energy<br>service by type of service (formal and<br>informal)||
|Physical availability of<br>infrastructure/energy service by type of<br>service|Water connection *|100%|
||Districtheating connection *|100%|

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**Measurement and Data Issues**

# **Annex 1**

||Naturalgas connection *|75%|
|---|---|---|
||Electricity connection *|50%|
||Telephone connection *|100%|
|Distance to or daily time spent to access<br>infrastructure/energy service by type of<br>service (esp. water,fuel, public transport)|||
|water|generallynot available||
|fuel<br>|generallynot available<br>||
|public transport|Time/distance tonearest bus stop|35%|
|Physical/environmental constraints to reach<br>infrastructure/energy service, including<br>isolation and availability of transport<br>services|||
|average travel time or distance to nearest<br>settlement ofsame size|not available||
|average travel time or distance to nearest<br>settlement twice or more aslarge|not available||
|Private transport availability|Carownership|100%|
|Reliability|Is the infrastructure/energy service<br>reliable, by service (formal and<br>informal)||
|Level-of-service above minimum useful<br>threshold|Potable water 24 hours per day|45%|
||Potable water less than or equal to 4<br>hours/day|35%|
||District heating for 3 or more months<br>peryear|25%|
||Electricity24 hours perday|45%|
||Electricity less than or equal to 6<br>hours/day|30%|
|Frequency ofserviceinterruption|generallynot available||
|Predictability ofserviceinterruption|not available||
|Repairtimerequired|not available||
|Vulnerability to environmental risks|not available||
|Affordability|Is the infrastructure/energy service<br>affordable, by service (formal and<br>informal)||
|Price/Tariffcharged- formalservices|not available||
|Price/Tariffcharged- informalservices|not available||
|Measure of monthly income or expenditure<br>devoted toformalservice|Reporting making any payment for<br>centralwater|85%|
||Reporting making any payment for<br>districtheat|85%|
||Reporting making any payment for<br>electricity|80%|
||Reporting making any payment for<br>naturalgas|70%|

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**Measurement and Data Issues**

# **Annex 1**

|Measure of monthly income or expenditure<br>devoted toinformalservice|generally not available||
|---|---|---|
|Randomor regular nature ofpayment cycle|not available||
|Quality|Objective measures of the quality of the<br>infrastructure/energy service||
|Physical infrastructure|not available||
|Infrastructure/energy service|not available||
|USE BASED(Demand)INDICATORS|||
|Frequency ofuse orquantity used|||
|waterused|not available||
|electricity used|not available||
|number of public transport trips taken, by<br>purpose|not available||
|number of private transport trips taken, by<br>purpose|not available||
|Use of infrastructure/energy services in<br>household-based commercialactivity|not available||
|Potentialdemand|Persons per household|100%|
|WELFARE BASED (Outcome)<br>INDICATORS|||
|Satisfaction|Are beneficiaries satisfied with<br>availableinfrastructure/energy services||
|selfevaluationofdwelling unit condition|generallynot available||
|satisfaction questions (including, day to<br>day life, social services, infrastructure<br>services)|not available||
|Environmental|||
|outdoorwaterpollution|Lacking waste watertreatment|75%|
|outdoorsoilpollution|Lacking waste disposal|35%|
|outdoorairpollution|not available||
|indoorairpollution|Using dirtyfuels|85%|
|Health|||
|overall health|Regular activities interrupted by health<br>problems|75%|
|incidence of air-borne disease|somewhat available, all health-related<br>measures consolidated into the<br>interrupted activitiesindicator(above)||
|incidence of food-borne disease|" "||
|incidence ofcommunicable disease|" "||
|incidence ofsexually transmitted disease|" "||
|road trafficinjuries, deaths|not available||
|disturbance due tonoise|not available||
|Education|||

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