2007年-世界发展银行全球_Armenia___Geographic_Distribution_of_Poverty_and_Inequality_64页_1mb

Summary of the Document: Geographic Distribution of Poverty and Inequality in Armenia (June 2007)

Core Content

This document presents the results of a poverty mapping exercise conducted in Armenia in 2007, focusing on the geographic distribution of poverty and inequality. The study is a joint effort between the National Statistical Service of Armenia (NSSA) and the World Bank, utilizing data from the 2004 Integrated Living Conditions Survey (ILCS) and the 2001 Population Census. The goal is to provide detailed poverty and inequality estimates at the rayon level, which is the lowest administrative unit in Armenia, and to support policy-making and resource allocation at the local level.

Main Objectives

• To estimate poverty and inequality at smaller geographic levels than previously possible using existing data sources.
• To build local capacity within the NSSA for developing and updating poverty maps, ensuring their sustainable use in policy-making.

Key Information

Poverty Reduction Trends

• Armenia has experienced significant poverty reduction since 1998/99, with the poverty rate dropping from around 56% to below 30% by 2005.
• Extreme poverty decreased from 21% to less than 5%, a 75% reduction.
• Over 800,000 people were lifted out of poverty between 1998/99 and 2005.

Poverty Incidence by Region

• Urban areas outside Yerevan had the highest poverty incidence in 2005 (38%).
• Yerevan had a lower poverty rate (30.7%) compared to other urban areas (37.8%).
• Rural areas had a poverty rate of 28.3% in 2005, lower than urban areas outside Yerevan but higher than Yerevan.

Regional Disparities

• In 2004, poverty incidence varied significantly across marzs (regions).
• Shirak had the highest poverty incidence (48.8%), while Vayots Dzor and Yerevan had the lowest (28.9% and 3.6%, respectively).
• The Gini coefficient indicates that Yerevan and Lori had higher inequality levels than other marzs.

Poverty Mapping Methodology

• The poverty mapping technique combines household survey data (ILCS) and population census data.
• It involves three stages: data preparation, consumption model estimation, and prediction of welfare for census population.
• The method ensures reliable estimation of poverty and inequality at the rayon level by using regression models that account for geographic and socioeconomic factors.

Administrative Structure

• Armenia is divided into 11 marzs (regions), including Yerevan, and 929 communities.
• Most communities are rural, with populations under 5,000, while a few are large urban centers like Gumri and Malatia-Sebastia.
• Rayons are the next level of geographic aggregation after communities, used for more reliable poverty estimates due to the small size of many communities.

Key Variables and Correlates

• Accessibility, landownership, elevation, livestock population, education level, and distance to marz center are identified as key correlates of poverty.
• Areas with higher education levels and irrigable land tend to have lower poverty rates.
• High elevation and remoteness are associated with higher poverty levels.

Main Viewpoints

• The economic growth in Armenia has led to significant poverty reduction, but spatial disparities remain.
• Poverty is more prevalent in urban areas outside Yerevan than in rural or urban areas within Yerevan.
• Location plays a crucial role in determining welfare and poverty levels, influenced by factors such as access to markets, natural resources, and infrastructure.
• Data limitations at the community level make it difficult to produce reliable poverty estimates, hence the use of rayon-level data is more feasible.
• Capacity building is essential for the long-term use of poverty maps in policy-making.

Limitations and Considerations

• The ILCS data is not representative at the community level, and the census data lacks information on household consumption and income.
• The standard errors of poverty estimates are closely monitored to ensure reliability.
• The regression domains are constructed to group marzs with similar characteristics, ensuring sufficient data for meaningful analysis.
• The choice of poverty line does not affect the relative standing of geographic units, as the focus is on relative poverty.

Conclusion

The poverty mapping exercise provides a detailed understanding of the geographic distribution of poverty and inequality in Armenia. It highlights the importance of local-level data for effective policy-making and resource allocation, and emphasizes the need for capacity building within the NSSA to ensure the sustainable use of poverty maps. The results are expected to support targeted interventions and improve social protection mechanisms in the country.