Analyzing Poverty

There are numerous ways to analyze poverty and present findings: First, by comparing the characteristics of the poor and the non-poor; second, by comparing the poverty status of important groups in the country; third, by comparing poverty over time and analyzing changes; and finally by analyzing the determinants of poverty. This page briefly presents these alternative techniques.

Comparing the characteristics of different poverty groups (poor and non-poor)

A first way to draw a poverty profile is to analyze the characteristics of the different income or consumption groups. This allows to get a better understanding of who the poor are, and what are the differences between the poor and the non-poor. The profile may include information on who the poor are, where they live, what they do, how they earn a living, how they access and use government services, and what their living standards are in terms of health, education, nutrition, and housing, among other topics.

It is important that the data gathered in the profile to describe the living conditions of the poor be placed in the political, cultural, and social context of each country. In other words, qualitative and historical information as well as institutional analysis is necessary to give meaning to, and complement, the profile.

When doing such analysis, it might be useful to separate the tabulations for those groups which are expected to be very different.

In Table A below, we present information on households’ education, employment and access to services in Ecuador by urban and rural areas. Table A shows that the poor have on average lower education levels, and lower access to services. However, on average, the same proportion of households is engaged in the informal sector among the poor and the non-poor (although patterns differ in urban and rural areas).

When looking at urban and rural areas separately, it appears that access to services such as electricity is very similar for the poor and non-poor in urban areas. This dimension is not a correlate of urban poverty. When carrying out such type of analysis, one should remember that we are only looking at averages, which can hide very large variations, e.g. some of the poor might be very educated while some of the non-poor have low education attainments.

Table A:

Comparing poverty between different groups

The poverty profile focuses on presenting the poverty characteristics of various household groups. The choice of the types of groups will be driven by some ex-ante knowledge of important dimensions (where qualitative data can help) or by dimensions which are relevant for policies. For instance, geographic location, age or gender might be dimensions along which policies can be developed. Another dimension which can provide useful insights for policy elaboration is the link between employment and poverty. This could indicate which sectoral pattern of growth would have the highest impact on poverty. 

There are three main ways to present a poverty profile.

1. Poverty measures according to household groups: The first and most common way to present poverty data is to give poverty measures for various household groups. For example, Table B below, shows that households without education have higher poverty incidence than those with higher levels of education in Malawi. Another example is provided below in Table C which shows that households living in Barisal in Bangladesh had a poverty incidence of 60 percent in 1996, as compared to 53 percent for the country as a whole.
2. Contribution of various household groups to poverty measures: An alternative way to present a poverty profile consists in assessing how various household groups contribute to the overall poverty in the country. The contribution of a household group to overall poverty is a function of that group’s population share and the incidence of poverty in the group. Table C shows that the population living in the Barisal division represents 7 percent of the population, and the headcount index is 60%, against a national average of 53%. Therefore, the share of all the poor living there is 8 percent (8=7*60/53). In the case of Madagascar, Table 3 shows that 14 of the country’s poor live in urban areas (14 = 21 * 47/70).
3. Relative risk: Poverty measures can be translated into relative risks of being poor for different household groups. These risks provide the probability that the members of a given group will be poor in relation to the corresponding probability for all other households of society (all those not belonging to the group). In Madagascar, Table C indicates that urban households are 39 percent less likely to be poor than non-urban (i.e. rural) households (0.39 = 1 – 47/77), while rural households are 63 percent more likely to be poor than non-rural (i.e. urban) households (0.63 = 1 – 77/47). Similar calculations could be carried out relative to the entire population or to a specifically selected group.

Table B:

Table C:

While certain variables like education, health, and access to service will almost always be part of a poverty profile, the relevance of many variables will depend on country circumstances and on the data source available. The profile should, if possible, identify the major production and consumption characteristics of the poor: whether the rural poor farm their land, are agricultural wage laborers, or work in various non-farm activities; whether the urban poor work as wage employees or as micro-entrepreneurs in the informal sector.

Data on asset holdings by the poor are also relevant, as are their production technologies, use of inputs, and access to social and infrastructure services. Information on the composition of poor people’s consumption, including their access to public goods, is also valuable. Cross-links to other forms of poverty, such as lack of education, health care, and security, can also be established.

• The extent to which a detailed poverty profile can be constructed depends on the type of data available. (See Data for measurement)
• If the survey is designed to be representative at the level of relatively small geographic areas (e.g. the district level), the various measures could also be presented graphically on a poverty map.
• Poverty comparisons across countries are difficult for several reasons. For a discussion of the issues, see The Global Numbers Poverty Debate.

Comparing poverty over time

If consecutive rounds of a household survey, several separate surveys, or a survey with a panel component are available, changes in income poverty over time can be assessed. (See Data for measurement). This requires poverty measures which are comparable and which reflect differences over time in the cost-of-living across regions. (see Issues in Measuring Poverty).

When several rounds of survey are available, the analyst can investigate changes in the regional distribution of poverty or in the major characteristics of the poor, such as ethnicity, gender, age, urban and rural location, employment, access to social programs and basic services, etc. Although the various population groups identified in the first period of time should clearly form the basis of the analysis over time, it is also important to investigate whether or not ‘new’ groups of poor people have appeared. This is particularly relevant for countries that undergo rapid changes linked to such factors as economic reforms, conflicts, natural disasters, and epidemics such as HIV/AIDS.

For example, Figure A below compares the headcount indices of poverty by sector of employment in Burkina Faso in 1994 and 1998. The incidence of poverty declined for those employed in export agriculture and for households without working members, and increased for all other categories. This type of results can provide insights about the stability of poverty characteristics and about the relevance of various policies, including the use of targeting devices.

Figure A:

One can also look at changes in the characteristics of different poverty groups. For example, the distribution of access to services in the base year can be compared with the distribution of services in the second year. The patterns can then be compared to uncover whether changes made in the supply of the services have been pro-poor.

The concept of relative poverty risk introduced in the previous section can also be applied to the analysis of changes in poverty over time using repeated cross-section surveys. The objective is to examine whether, over time, the relative poverty risk of specific population groups decreases or increases.

It is also possible to decompose a national change in poverty into the effects of changes in poverty within groups or between groups/sectors. This allows the analyst to assess whether poverty has changed because poverty within certain groups has changed or because people have moved to more affluent or poorer groups. More specifically, the national change in poverty is decomposed into intra-sectoral effects (changes in poverty within sectors), inter-sectoral effects (changes in population shares across sectors), and interaction effects (correlation between sectoral gains and population shifts -- depending on whether people tend to move to sectors where poverty is falling or not).

For technical details, please see the Technical Note: Measuring Poverty and Analyzing Changes in Poverty Over Time .

Analyzing the determinants of poverty

Poverty and poverty changes are affected by both macro- and microeconomic variables. Within a microeconomic context, the simplest way to analyze the correlates of poverty consists in using regression analysis in order to see the impact on poverty of a specific household/individual characteristic, while holding constant all other characteristics.

Analysis of correlates of poverty can be carried using multivariate income and consumption regressions. In these regressions, the logarithm of consumption or income (possibly divided by the poverty line) is typically used as the left-hand variable. Right-hand explanatory variables span a large array of possible poverty correlates, such as education of different household members, number of income earners, employment characteristics, household composition and size, and geographic location. Special care must be taken when including variables that themselves are likely a function of income or consumption availability, for example access to basic services. The regressions will only return results for the degree of association or correlation and not for causal relationships.

It is important to note at this stage that numerous correlates or determinants of poverty are not quantifiable. For some other variables, one might only be able to use a proxy, which might not fully reflect the underlying dimensions. The method used here is only able to take into account those dimensions which are quantifiable or for which a proxy is available. It is also important to note that the various coefficients obtained from a regression will have different degrees of significance.

These multivariate regressions will estimate the partial correlation coefficient between income or consumption per capita and the included ‘explanatory’ variables while holding all other impacts constant. For example, the results could tell us how strongly an additional year of education for the household head or his spouse is associated with a change in income or consumption per capita while holding gender, employment, age, location, and all other possible influences constant. They can tell us, then, much more than the simple relative poverty risks discussed in the previous section since a high relative poverty risk of a specific population group could in fact be attributable to individual characteristics such as education rather than to a group characteristic.

Table D below shows an example of such a regression in Cote d’Ivoire. It indicates that education plays a different role in urban and rural areas (where it does not seem to significantly influence consumption), as do different types of assets. In rural areas, infrastructure has substantial predictive power—households located in villages that are nearer to both paved roads and public markets are better off, as are households living in areas with higher wage levels. The results present further questions about the quality of education in rural areas and to the importance of rural infrastructure in helping families grow out of poverty, that could be addressed in putting together a poverty reduction strategy.

Table D:

Several variations of these multivariate income regressions can be used to examine the correlates of the income of the poor. Poverty analysis focuses on correlates of income and expenditure at the lower end of the distribution rather than the correlates at the top end. One can then perform different regressions for each quintile, or quartile, of the population. Whether these regressions can be conducted will in part depend on the sample size of the survey. Alternatively, the regression can examine structural differences in parameter estimates for different income or expenditure groups.

When multiple cross-sectional surveys are available, the same regression can be repeated for different years to see how the association of certain correlates with income or consumption varies over time. Variations over time will be reflected in changes in coefficients or parameters. The results of repeated cross-section regressions can also be used to decompose changes in poverty between changes in household characteristics, and changes in the returns to (or impact of) these characteristics. Another possibility is to use parameters from the regression model obtained for year one in order to predict household income or consumption in year two, and to compare this prediction with the prediction obtained using the regression estimates for year two applied to the data for year two. The differences in the predictions with the two models can then be analyzed, and one can test whether changes in income between years is due to changes in structural conditions or changes in the behavior of households between the two years.

Apart from income and consumption regressions, several other types of multivariate regressions can provide additional insights into the determinants of poverty. These can in particular be applied to other dimensions of poverty, such as child nutrition, mortality, morbidity, literacy or other measures of capabilities. The techniques are also sometimes applied to understand the determinants of employment and labor income and to estimate the returns to education. They can also be used to better understand agricultural production patterns by estimating agricultural production functions (which relate production with information on type of crops grown per area, harvest, inputs into agricultural production, and input and output prices).