Income Distribution and Poverty

Attitudes toward the incorporation of distributional and poverty reduction objectives in project decision making have changed over time with shifts in aid fashion. The original methodology was developed and applied initially in the 1970s during the World Bank Presidency of Robert McNamara, with poverty reduction as the main focus of development assistance. However, during the 1980s this focus became diluted, with the emphasis turning to macroeconomic reforms and debt rescheduling. It reappeared again in the late 1990s partly as a result of the perceived neglect of the poor during the Structural Adjustment era and with the persistence of large pockets of poverty in many countries, despite years of development aid. However, as discussed below, this has been an area of controversy and in practice systematic weighting of project outcomes has rarely been carried out.

Both Little and Mirrlees (particularly their 1974 book) and UNIDO (1972) recommended the use of a weighting system to distinguish between benefits to the rich and those to the poor, with the latter being weighted at more than unity and the former at less than unity. This approach was set out more formally in Squire and van der Tak (1975) who showed how distributional weights could feed into a variety of parameters.10 _{Initially, the principal focus was on an adjustment to the}

shadow wage on the grounds that the main increase in consumption created by a project would be through payment to unskilled workers in excess of their opportunity cost. Since the labor surplus argument was applied, it was assumed that the gap between formal sector wages paid by a project and their alternative marginal product would be high and that this extra income would be consumed. The main application of distributional weights in both Little and Mirrlees (1974) and Squire and van der Tak (1975) was therefore through the economic price of labor.

9 _{Discussed separately in Chapter 3.}

10 _{Ray (1984) explained much of the underlying welfare theory and formalized many of the}

This procedure of introducing weights into the economic price of labor has been highly controversial and complex, with many adjustments introduced in a single equation. The most important shortcoming is the lack of objectivity in selecting the elasticity that reflects the declining social worth of an extra income as the consumption level of the project beneficiaries rises.11 _{The approach utilizing differential weights is only}

partial because any project will have distributional effects that go beyond just the extra income going to unskilled workers, for example, with gains to consumers, investors, and governments. A comprehensive analysis requires disaggregating the net income change created by a project—its economic net present value (NPV)—into changes for different groups. These income changes can then be decomposed into changes in consumption and savings and, if necessary, different weights can be applied to these. Thus, for a project involving four groups of stakeholders, for example A (workers), B (consumers), C (investors), and D (government):

Economic NPV = PV_A + PV_B + PV_C + PV_D (3) where PV is the present value of income and subscripts refer to the four groups. The savings and consumption components of these income changes can be identified given the marginal propensity to save (s) for each group. Thus, the present value of the total change in savings (PVS) will be:

PVS = s_A*PV_A +s_B*PV_B + s_C*PV_C + s_D*PV_D (4) where s_A, s_B, s_C and s_D are the marginal propensities to save of the four groups. Similarly, the present value of the total change in consumption (PVC) will be

PVC = (1–s_A)*PV_A + (1–s_B)*PV_B + (1–s_C)*PV_C + (1–s_D)*PV_D (5) Once all income changes created by a project are disaggregated in this manner, weights can be applied to each component and the sum will give a new weighted economic NPV.

This comprehensive approach to distribution was set out originally in UNIDO (1972) and applied with distributional weights in UNIDO (1980), which was one of the rare attempts to conduct a comprehensive distribution-weighted appraisal. Without the use of weights, it forms the

11 _{Attempts were made to infer a value for this elasticity from the degree of progressiveness in}

the tax system, but this approach is flawed because if taxation were optimally designed to meet distributional objectives there would be no need to use a weighting system in the first place.

basis of current attempts to trace through the full distributional effects of projects (see the discussion in Chapter 9, for example). However, the use of weights to differentiate the social worth of benefits going to, or costs borne by, different income or social groups has not been adopted in any significant operational way. The World Bank, which undertook the initial research and published the Squire and van der Tak book, experimented with the use of weights in some research, but ruled out incorporating them in operational work by the early 1980s (Devarajan et al. 1996).

There were several reasons why the widely publicized and much debated distributional weighting scheme faltered. First, was its complexity, with project teams finding it difficult in practice to trace through the set of income changes created by a project. Second, given the subjective nature of the weight there existed a serious risk of inconsistency across countries or sectors which could distort decisions. Third, some questioned the welfare basis of a weighting system relying on subjective elasticity values.12 _{Lastly, and most fundamentally, it was}

argued that project selection was a very ineffective means of influencing the distribution of income and that fiscal policy and the allocation of public sector expenditure are much more effective means of addressing distributional issues. Hence, despite the existence of a widely cited early literature, the use of distributional weights was never formally applied by international agencies or national governments.

The revived focus on poverty reduction at the end of the 1990s saw the method of distribution analysis, as laid out in UNIDO (1972), applied in a number of studies, but without the application of weights.13 _{In tracing}

through the gains and losses to different groups, this analysis draws on the identity:

Economic NPV = Financial NPV +

(Economic NPV – Financial NPV) (6) This indicates that there are two sets of income flows created by a project: those arising from its financial effects (for example, profits, subsidies, and taxes), and those generated by the difference between economic and financial prices (for example, between the shadow wage and the wage actually paid, and between the shadow exchange rate and

12 _{Harberger (1978), for example, argued that the weighting scheme gave implausibly high weights}

to some groups and implausibly low ones to others. There are still some like Brent (2006) who have argued consistently that equal weighting for all is also subjective and that there is a strong theoretical case for some form of weighing.

the market exchange rate).14 _{Simply using the same data as in a basic}

economic analysis, it is possible in principle to trace through the income effects for different groups.(The mechanics of doing this are illustrated in Chapter 9).

Disaggregating income changes for different stakeholder groups in an unweighted form is potentially very useful. It allows an assessment of whether the return to investors is sufficient for a project to be financially sustainable. By estimating the change in government income, it allows an assessment of the fiscal impact of a project. It allows an estimate of how the poor are affected by a project and thus the extent to which a project helps achieve the objective of poverty reduction.

Poverty impact analysis involves estimating the proportion of income going to different groups (such as consumers, workers, or investors) that will accrue to individuals or households who are below the national poverty line. Thus, following the notations used earlier, the poverty impact (PI) will be;

PI = p_A*PV_A + p_B*PV_B + p_C*PV_C + p_D*PV_D (7) where p is the proportion of income going to the poor. PI can be presented as either an absolute value or as share of the total net benefits of the project, so the poverty impact ratio (PIR) is

PIR = PI/Economic NPV (8) This approach is recommended in ADB (1997) and Belli et al. (2001). ADB (2001) gives practical guidance on how this might be done. The advantage of this approach is that it avoids the use of weights which are inevitably controversial and although approximate, first-round income changes created by a project can be estimated with no more data than that from the original financial and economic analysis. However, in the absence of further information some of the groups involved may be very aggregate and in some cases the share of income going to the poor may be known only as an approximation.15 _{Furthermore, the analysis will}

14 _{The choice of the price numeraire matters in distribution analysis. By definition, financial NPV}

will be at domestic prices. However if the economic NPV is at world prices the two sets of income flows will not be directly comparable. It will be necessary either to convert financial flows to world prices (which is potentially confusing) or to calculate the economic NPV using the domestic price numeraire.

15 _{In some important sectors the full distributional impact may be very difficult to establish. The}

benefits from a road project are a good example. Some will accrue to haulers if their costs fall; some will accrue to producers if additional output is induced by the road; some will accrue to consumers if prices are reduced through falling transport costs or extra production; and some will accrue to workers if employment expands. Disentangling these effects will be extremely complex and for many projects would require a major research study.

normally fail to pick up second-round indirect and non-monetary effects, if any. The problem of the indirect dimension is highlighted since there is a need to make an assumption as to how the poor are affected by additional government income, since government income spent on the project would otherwise be spent elsewhere with some poverty impact. To find the true net impact of a project on poverty, this poverty reduction opportunity cost must be taken into account.

Currently, distribution analysis of this type is perceived as critical for two types of projects. Firstly, for projects with targeted interventions for poverty reduction, it is possible to draw up a table of gainers and losers and estimate (even approximately) how many of each group are poor. This is useful in operationalizing, at the project level, widely-stated donor concerns over poverty reduction. Second, distribution analysis is also applied to projects involving more than one country, for example, cross-border roads or power export projects (Adhikari and Weiss 2004). The distribution of net benefits between participating countries is an important aspect of the appraisal of such projects since it sheds light on the fairness and sustainability of cost sharing and pricing arrangements. Hence, for regional cooperation projects, distribution analysis between collaborating countries has become an important part of the appraisals (see Chapter 9).