A similar profile of poverty based on an assets index constructed from the 1997 and 2003 CWIQ surveys is provided in Diallo and Wodon (2007). The findings are very similar to the findings for consumption-based poverty in the GLSS surveys. Asset-based poverty measures are significantly higher in rural than in urban areas. Given that since most of the population still lives in rural areas, a majority of the poor are thus rural. Yet because the proportion of the population in rural areas decreased from 69 percent to 58 percent according to the CWIQ data, while 83 percent of the poor lived in rural areas in 1997, this proportion has decreased to 77 percent in 2003. By contrast, the share of the poor in rural areas has increase slightly with in the GLSS data. Still, both the CWIQ and the GLSS data suggest that about eight individuals in poverty out of ten live in rural areas. Asset-based poverty is also higher in all the other regions than in Accra and Ashanti, and many of the better off regions had a larger drop in the headcount index of asset-based poverty between 1997 and 2003 than the poorer areas of the country. In terms of demographic variables, Diallo and Wodon (2007) also provide a profile according to household size, the sex of the household head, and the age of the head. Small families (1 to 3 members) are better off than larger families (5-10 members or more than 10 members), as expected, but the differences tend to be small. The reason for such small differences is that asset-based poverty is based on a measure of total “wealth” of the household and not the wealth per capita. Hence, a higher household size does not have an automatic negative effect on the wealth measure, as is the case with consumption per equivalent adult. Women headed households are better off than men-headed households, in part because it is more likely to have women headed household living in urban areas. Households with heads under 20 years of age and over 60 are poorer than households in the middle range, probably because younger heads have not had the time yet to accumulate wealth, while older heads are more likely to be rural and a large family. Single and divorced (or separated) heads are less poor than heads in union. As with consumption-based poverty, the incidence of asset-based poverty is lower when the head is better educated and when the head is employed either in the public or formal sector. The differences in poverty by education level are very large. Households whose head has no education at all have a probability of being poor at 71.6 percent in rural areas, versus 10.2 percent for rural households with post-secondary or higher education. In urban areas, virtually all heads with a post-secondary or higher education are non-poor (headcount index of 2.3 percent), while the headcount is at 41.4 percent among household whose head has no education. Households whose head is an employer or owner tend to be poorer, but this is because most of them work in the agricultural sector as self-employed individuals. This is also why the “private sector” category identified in the CWIQ data shows much higher rates of poverty than the public and unstated/unemployed categories (those household head who can afford to be unemployed for some time are not typically among the poorest). Also, households whose head works in the commerce and services sectors as well as in mining or transportation tend be better off than their counterparts working in the agricultural sector. There is one surprising jump between 1997 and 2003 in the headcount index among rural households whose head is unemployed or did not stated its occupation, but this may due to misclassification in the survey, as it is unlikely that the unemployment rate among household heads doubled between the two years (said differently, a
proportion of rural households whose head is classified as unstated/unemployed in 2003 are probably working in the agriculture sector, which would explain the sharp rise in poverty).
Again as observed with consumption-based poverty, ownership of land also matters for poverty reduction, although apparently more so according to the estimates in urban areas than in rural areas. This is probably because land owners in urban areas are indeed wealthy, while in rural areas, those who do not own land tend to form an heterogeneous group made of both very poor households and wealthier households likely to engaged in the non-farm sector (this heterogeneity among those who do not own land in rural areas would explain why the differences in poverty measures according to land ownership are small there).
3.3 Poverty Map
Geographic poverty profiles based on the GLSS or CWIQ surveys are limited to broad areas, as the sample size of the survey does not enable analysts to construct valid estimates of poverty for example at the district level. However, policy makers may need finely disaggregated information at the level of city neighbourhoods, towns or villages in order to implement anti- poverty programs. A detailed map of poverty in Ghana has been constructed by combining household and survey data, following a methodology developed by Elbers, Lanjouw and Lanjouw (2002, 2003). The idea behind the methodology is rather straightforward. First a regression model of adult equivalent consumption is estimated using GLSS survey data, limiting the set of explanatory variables to those which are common to both that survey and the latest Census. Next, the coefficients from that model are applied to the Census data set to predict the expenditure level of every household in the Census. And finally, these predicted household expenditures are used to construct a series of welfare indicators (e.g. poverty level, depth, severity, inequality) for different geographical subgroups (although the idea behind the methodology is conceptually simple, its proper implementation requires complex computations). The latest Housing and Population Census was conducted in spring 2000. The questionnaire is relatively detailed but does not contain information on incomes or consumption. Yet it does contain data on individual characteristics (demography, education and economic activities) as well as on household dwelling characteristics. The Census database turns out more than 18.9 million individuals grouped into 3.7 million households. The Census field work grouped households into around 26,800 enumeration areas (EAs) of 138 households each on average. To construct the poverty map, the fourth round of the GLSS was used instead of the last survey, since the GLSS4 is closer in terms of date of implementation to the census than the GLSS5. The welfare index to be used in the regression models to construct the poverty map (expenditure per equivalent adult in real terms) is the same as the one used for poverty measurement.
The lowest administrative level for which a formal geographical definition is currently available is the 110 districts (this map could be in the future updated with the new 138 districts). The importance of the District Assemblies in the on-going decentralisation process makes district- level poverty figures fundamental. Those district-level poverty headcount estimates are presented in Figure 8 (at an ulterior stage, the poverty map could be disaggregated by council using the same techniques). These administrative units would be small enough for most decision making while being large enough to enable a statistically robust poverty maps to be computed.
Figure 8