Bosna i Hercegovina Agencija za statistiku Bosne i
Bosna i Hercegovina Agencija za statistiku Bosne i Hercegovine Contribution to Geospatial Analysis of Household Survey Data in Bosnia and Herzegovina Edin Šabanović, Assistant Director Sector for Statistical Methodology, Standards, Planning, Quality and Coordination Agency for Statistics of Bosnia and Herzegovina Workshop on Data Integration: Realising the Potential of Statistical and Geospatial Data Belgrade, Serbia, 21 -23 May 2019
Content • Background • Poverty and employment in Bosnia and Herzegovina - Evidence from household surveys • Geospatial presentation of basic socio-economic indicators in Bosnia and Herzegovina • Conclusions and future steps 2
Background (1) • Poverty is still one of the most pervasive problems in the overall human development • Millions of people live in extreme poverty, i. e. with less than the international poverty line of US$1. 90 a day or making just a little more than this amount • SDGs 2030 treat this problem within SDG 1 -No poverty • Poverty is multidimensional phenomenon and consists of various components 3
Background (2) • Employment status is one of the most important components, because it most affects poverty • The highest poverty rates in 2015 in Bi. H were among unable to work (38. 5%) and unemployed persons (26. 2%) • Unemployed persons in EU-28 faced a particularly high risk of poverty or social exclusion (67. 0 %) 4
Poverty in Bi. H - Evidence from LSMS and HBS (1) • First poverty assessments in Bosnia and Herzegovina were made in 2001 and 2004 within the Living Standards Measurement Survey (LSMS) • World Bank methodology of absolute poverty was applied • Consumption expenditure was used as a monetary measure of poverty • Following poverty and inequality indicators were produced: (i) Head Count; a) Gini coefficient; (ii) Poverty Gap; b) Gini coefficient using OECD scale; (iii) Severity of Poverty; c) Theil index; (iv) Shortfall. d) Entropy index; e) S 90/S 10; f) S 50/S 10; g) S 90/S 50. 5
Poverty in Bi. H - Evidence from LSMS and HBS (2) The majority of poverty and inequality indicators were disaggregated by: • • regions (Bosnian entities); type of location (urban, rural and mixed); age; war displacement status; education of the head of household; employment status of adults and household size. 6
Poverty in Bi. H - Evidence from LSMS and HBS (3) • Since 2004, Household Budget Survey (HBS) was designed for measuring of poverty • European methodology of relative poverty was applied • Consumption expenditure of households by COICOP was used as a monetary measure of well-being • Following poverty and inequality indicators were calculated: (i) Poverty incidence at household level; (ii) Poverty incidence at individual level; (iii) Poverty gap. a) Gini coefficient b) S 80/S 20. • HBS was conducted four times: 2004, 2007, 2011 and 2015 7
Poverty in Bi. H - Evidence from LSMS and HBS (4) The majority of these indicators were disaggregated by: • • • regions (Bosnian entities and Brcko district Bi. H); type of location (urban, rural); type of households; size of households; age of the head of household; sex of the head of household; education of the head of household; employment status of the head of household; age of household members; sex of household members. 8
Employment in Bi. H - Evidence from LFS • The first full-scale Labor Force Survey was conducted in 2006 and it was launched as regular annual cross-sectional household survey producing basic labor force indicators in line with definitions of the International Labor Organization (ILO) • The most used indicators of the employment of Bosnian population from labor force surveys are as follows: a) employment rate; b) unemployment and c) activity rate. • The majority of above mentioned indicators were disaggregated by regions (Bosnian entities), type of location (urban, rural), sex, age and education of respondents 9
User`s needs for socio-economic indicators in Bi. H • No one of these household-based surveys could provide indicators at levels of data disaggregation, which are lower than Bosnian entities, because of limited sample sizes • LSMS 2011: 5, 002 HHs; LSMS 2004: 3000 HHs • HBS: cca 7, 500 HHs • LFS: cca 8, 500 HHs • There are user`s needs for socio-economic indicators at lower levels of geospatial and administrative organization of the country • UNDP projects related to the regional disparity assessment in Bosnia and Herzegovina in 2010 and 2015 10
Geospatial presentation of basic socio-economic indicators in Bi. H (1) Following poverty indicators are selected from 2015 HBS: a) Poverty rate at household level b) Poverty gap c) Poverty rate at individual level d) Gini coefficient Three main employment indicators were selected from 2015 LFS: a) employment rate; b) unemployment and c) activity rate. a) Indicators are presented at level of 17 Bosnian cantons, regions or district b) Arc. Gi. S Ver. 10. 0 and QGIS are used for geospatial 11 disaggregation
Geospatial presentation of basic socio-economic indicators in Bi. H (2) 12
Geospatial presentation of basic socio-economic indicators in Bi. H (3) 13
Geospatial presentation of basic socio-economic indicators in Bi. H (4)
Geospatial presentation of basic socio-economic indicators in Bi. H (5)
Geospatial presentation of basic socio-economic indicators in Bi. H (6)
Geospatial presentation of basic socio-economic indicators in Bi. H (7)
Geospatial presentation of basic socio-economic indicators in Bi. H (8)
Conclusions and future steps • Paper opened a question of the production of socio-economic indicators at sub-entity level in Bi. H • Inceasing user`s need for subnational and sub-entity statistical data on living standards, poverty and employment • Limitation of household surveys for providing of such kind of disaggregated statistical data • Needs for sophisticated statistical techniques, which can combine survey data with those coming from population census • Pre-condition for the use of such techniques is that survey samples were selected from the census sampling frame, which is still not done in Bosnia and Herzegovina • New sampling frame in following 3 years • Common work of statisticians and specialists for geospatial presentations of statistical data needed
Thank you for the attention! Edin Šabanović edin. sabanovic@bhas. gov. ba
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