Using an asset index to assess trends in poverty in seven Sub-Saharan African countries

1. Using an asset index to assess trends in poverty in seven Sub-Saharan African countries Frikkie Booysen, Servaas van der Berg, Ronelle Burger,Gideon du Rand & Michael von Maltitz Paper presented at IPC conference on The Many Dimensions of Poverty, 29-31 August 2005, Brasilia, Brazil

2. Outline • Background • Data • Method • Findings • Conclusions

3. Background • Income-based cross-country poverty comparisons difficult due to price conversions / fluctuations • Comparisons within countries across time often not possible due to insufficient or incomparable surveys • Data reliability an issue for many African countries’ official statistics • Worse for income/expenditure data because complexity of surveying

4. Background • Sahn and Stifel (2000) propose used of Demographic and Health Surveys (DHS) as solution to this problem • Standardization of surveys ensures comparability across time and space • Possession of assets, access to public services and characteristics of infrastructure easier to survey than income/expenditure

5. Data • Criteria for selection: three surveys available from late 1980s to early 2000s • DHS conducted in different years for different countries, thus survey years are not matched • To enable comparability over time: • First wave/baseline: 1987 - 1992 • Second wave: 1992 - 1997 • Third wave: 1998 - 2001

6. Data • Seven African countries in our sample: • Ghana • Kenya • Mali • Senegal • Tanzania • Zambia • Zimbabwe

7. Data • Variables included in asset index • TV ownership • Fridge ownership • Radio ownership • Bicycle ownership • Type of toilet facility • Type of floor material • Source of drinking water • Apart from a few peculiarities in access to slow-moving assets, data appears reliable… BUT there is an inherent urban bias?

8. Method • Multiple correspondence analysis used for constructing an asset index • More appropriate than PCA/factor analysis often used in literature • Aim is to find a number of smaller dimensions to capture most of information contained in original space • Each of these dimensions are the weighted sum of the original variables

9. Method • MCA weights were allocated based on pooling of countries for the baseline (first) period, using mca command in Stata 8.2 • Explain 94% of inertia • Logical distribution of weights across response categories, excl. “other categories”

10. Method • MCAPi = Ri1W1 + Ri2W2 + … + RijWj + … + RiJWJ , where MCAPi is the ith household’s composite poverty indicator score, Rij is the response of household i to category j, and Wj is the MCA weight applied to category j • Negative index values transformed into positive, non-zero values by adding 0.1785 to the index

11. Method

12. Method • Given the arbitrary transformation required to make all index values non-negative and the arbitrary poverty line, it was not deemed appropriate to calculate P1 and P2 • Poverty analysis confined to the poverty headcount ratio (P0) and the investigation of stochastic poverty dominance, using cumulative density curves or functions

13. Method • Employed three poverty lines… • 40th percentile of asset index • 60th percentile of asset index • Absolute poverty line: weighted sum of categories that is deemed as representing an adequate standard of living: • radio • bicycle • cement floor • public water • pit latrine • no refrigerator • no TV

14. Findings Number of unique values per quintile

15. Findings Asset index rankings compared to household consumption rankings (Uganda 1995)

16. Findings Asset index rankings compared to rankings based on education of household head (Uganda 1995)

17. Findings Poverty headcount across countries

18. Findings Poverty headcount over time by country

19. Findings

20. Findings

21. Approach • “In places the density curves are almost indistinguishable. In most cases therefore it is not possible to reach strong conclusions on trends and disparities in poverty, giving rise to uncertainty as to whether there has been progress in terms of the alleviation of poverty.”

22. Findings Poverty of what?

23. Findings

24. Findings OLS regression of country, time and place of residence on the asset index

25. Conclusions • Evidence that overall poverty declined in Ghana, Kenya, Mali, Senegal and Zimbabwe, but increased in Zambia over this period • Evidence that urban poverty declined in Ghana, Kenya, Mali, Tanzania and Zimbabwe, but increased in Senegal Zambia over this period

26. Conclusions , BUT caution required in interpreting results, given caveats of asset index approach… • Not a complete measure of welfare • Sensitivity of results to choice of poverty line • Urban bias of the asset index means that analysis of trends in rural poverty remains problematic • Aggregation conceals divergent shifts in underlying variables and complicates policy recommendations, e.g. increased access to private assets versus decline in access to public assets • Slow-moving nature of component variables: asset index not a good measure for assessing changes in welfare over short- to medium-term?