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

Outline Background Data Method Findings Conclusions

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

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

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: Data

Seven African countries in our sample: Ghana Kenya Mali Senegal Tanzania Zambia Zimbabwe 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? Data

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 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” Owns a radio0.294 Does not own a radio Owns a TV1.568 Does not own a TV Owns a fridge1.630 Does not own a fridge Owns a bicycle0.022 Does not own a bicycle Flush Toilet1.147 Pit latrine No toilet Earth floor Cement floor0.359 Smart floor1.830 Piped water0.877 Public water Surface water Well water-0.229

Method MCAP i = R i1 W 1 + R i2 W 2 + … + R ij W j + … + R iJ W J, where MCAP i is the i th household’s composite poverty indicator score, R ij is the response of household i to category j, and W j is the MCA weight applied to category j Negative index values transformed into positive, non-zero values by adding to the index

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 P 1 and P 2 Poverty analysis confined to the poverty headcount ratio (P 0 ) and the investigation of stochastic poverty dominance, using cumulative density curves or functions

Method Employed three poverty lines… 40 th percentile of asset index 60 th 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

Quintile 16 Quintile 218 Quintile 378 Quintile 4128 Quintile 5463 Total693 Findings Number of unique values per quintile

Findings Household consumption always or continuously in deficit 13-item asset index (40 th percentile poverty line) PoorNon-poor Poor 1,0051,140 Non-poor 1,3343,998 Asset index rankings compared to household consumption rankings (Uganda 1995)

Findings Household head has no education or primary education only 13-item asset index (40 th percentile poverty line) PoorNon-poor Poor 2, Non-poor 3,6041,574 Asset index rankings compared to rankings based on education of household head (Uganda 1995)

Findings Country Mean asset index Poverty headcount Asset index rank WDI $2 WDI rank Ghana Kenya Mali Senegal Tanzania Zambia Zimbabwe Poverty headcount across countries

Findings CountryPeriod 1Period 2Period 3 Asset index trend WDI trend Ghana Kenya Mali Senegal Tanzania Zambia Zimbabwe Poverty headcount over time by country

Findings

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.”

Findings Poverty of what?

Findings

OLS regression of country, time and place of residence on the asset index Equation 1Equation 2Equation 3Equation 4 Urban0.344**0.334** Ghana0.159**0.122**0.113** Kenya0.079**0.090**0.081** Mali0.039**0.033**0.018** Senegal0.211**0.152**0.140** Zambia0.109**0.061**0.054** Zimbabwe0.200**0.164**0.154** Period ** Period ** R-squared

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

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?