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, August 2005, Brasilia, Brazil

2. Outline Background Data Method Findings Conclusions
How presentation is structured

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
Data = major obstacle for this type of analysis

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
Data = major obstacle for this type of analysis

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:
Second wave:
Third wave:

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
Owns a radio
0.294
Does not own a radio
-0.234
Owns a TV
1.568
Does not own a TV
-0.103
Owns a fridge
1.630
Does not own a fridge
-0.099
Owns a bicycle
0.022
Does not own a bicycle
-0.006
Flush Toilet
1.147
Pit latrine
-0.087
No toilet
-0.308
Earth floor
-0.270
Cement floor
0.359
Smart floor
1.830
Piped water
0.877
Public water
-0.037
Surface water
-0.223
Well water
-0.229
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 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
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. Number of unique values per quintile
Findings
Number of unique values per quintile
Quintile 1 6
Quintile 2 18
Quintile 3 78
Quintile 4
128
Quintile 5
463
Total
693

15. Household consumption always or continuously in deficit
Findings
Asset index rankings compared to household consumption rankings (Uganda 1995)
Household consumption always or continuously in deficit
13-item asset index (40th percentile poverty line)
Poor
Non-poor
1,005
1,140
1,334
3,998

16. Household head has no education or primary education only
Findings
Asset index rankings compared to rankings based on education of household head (Uganda 1995)
Household head has no education or primary education only
13-item asset index (40th percentile poverty line)
Poor
Non-poor
2,007
117
3,604
1,574

17. Poverty headcount across countries
Findings
Poverty headcount across countries
Country
Mean asset index
Poverty headcount
Asset index rank
WDI $2
WDI rank
Ghana
0.267
71.7 5
75.2 4
Kenya
0.187
76.2 3
62.3 7
Mali
0.147
85.3 2
90.6 1
Senegal
0.319
60.9 6
63.1
Tanzania
0.108
89.3
72.5
Zambia
0.217
73.2
90.1
Zimbabwe
0.308
60.8
83.0
To what extent is it possible, with the limited (dummy) variables available to us that are not already incorporated into the asset index, to statistically “explain” differences in asset welfare? Table shows a number of OLS regressions of MCA baseline-weighted assets index, regressed on location (urban-rural), country, and time (period), Particularly noticeable in these regressions are:
The importance of urban location for asset wealth In Equation 1, a full 35% of the variation in asset wealth can be explained by spatial factor alone
The poor performance of Tanzania, the reference country; compared to Tanzania, all other countries show positive and statistically highly significant coefficients, indicating better performance than Tanzania, in all the equations in which the country dummies enter
The good performance of Zimbabwe and Ghana
By far the largest part of what can be explained by these variables (around 40%) can be ascribed to the explanatory role of location. Variation between countries is a much smaller factor than variation between urban and rural areas
Over time, there is improvement in the wealth index

18. Poverty headcount over time by country
Findings
Poverty headcount over time by country
Country
Period 1
Period 2
Period 3
Asset index trend
WDI trend
Ghana
83.2
72.5
64.6 -
Kenya
79.9
78.8
71.4 +
Mali
95.6
88.8
80.9
Senegal
75.8
59.5
57.3
Tanzania
88.4
88.9
92.1
Zambia
69.6
74.3
75.2
Zimbabwe
63.5
63.7
57.0
To what extent is it possible, with the limited (dummy) variables available to us that are not already incorporated into the asset index, to statistically “explain” differences in asset welfare? Table shows a number of OLS regressions of MCA baseline-weighted assets index, regressed on location (urban-rural), country, and time (period), Particularly noticeable in these regressions are:
The importance of urban location for asset wealth In Equation 1, a full 35% of the variation in asset wealth can be explained by spatial factor alone
The poor performance of Tanzania, the reference country; compared to Tanzania, all other countries show positive and statistically highly significant coefficients, indicating better performance than Tanzania, in all the equations in which the country dummies enter
The good performance of Zimbabwe and Ghana
By far the largest part of what can be explained by these variables (around 40%) can be ascribed to the explanatory role of location. Variation between countries is a much smaller factor than variation between urban and rural areas
Over time, there is improvement in the wealth index

19. Findings
Discrimnatory power weak at lower levels due to high number of non-unique values

20. Findings
Discrimnatory power weak at lower levels due to high number of non-unique values

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?
Lorenz dominance more robust than Theil and Gini,but not practical in most cases – lines crossing – thus use aggregated measures like Theil and Gini

23. Findings
Now look at rural-urban differences to try and explain observed variation in poverty across countries /time

24. Findings
OLS regression of country, time and place of residence on the asset index
Equation 1
Equation 2
Equation 3
Equation 4
Urban
0.344**
0.334**
Ghana
0.159**
0.122**
0.113**
Kenya
0.079**
0.090**
0.081**
Mali
0.039**
0.033**
0.018**
Senegal
0.211**
0.152**
0.140**
Zambia
0.109**
0.061**
0.054**
Zimbabwe
0.200**
0.164**
0.154**
Period 2
0.014**
Period 3
0.044**
R-squared
0.36
0.07
0.40
0.41
To what extent is it possible, with the limited (dummy) variables available to us that are not already incorporated into the asset index, to statistically “explain” differences in asset welfare? Table shows a number of OLS regressions of MCA baseline-weighted assets index, regressed on location (urban-rural), country, and time (period), Particularly noticeable in these regressions are:
The importance of urban location for asset wealth In Equation 1, a full 35% of the variation in asset wealth can be explained by spatial factor alone
The poor performance of Tanzania, the reference country; compared to Tanzania, all other countries show positive and statistically highly significant coefficients, indicating better performance than Tanzania, in all the equations in which the country dummies enter
The good performance of Zimbabwe and Ghana
By far the largest part of what can be explained by these variables (around 40%) can be ascribed to the explanatory role of location. Variation between countries is a much smaller factor than variation between urban and rural areas
Over time, there is improvement in the wealth 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?