14 their way into the research world. I will focus here on a single question recurring in policy circles: is there a dominating bias in favor of working on the alleviation of rural poverty rather than urban poverty?<sup>14</sup> Is the story the same across sectors (i.e. water vs. energy vs. telecoms vs. transport)? Are the needs of tomorrow’s poor well anticipated? Are tomorrow’s poor likely to be mostly urban?

There is plenty of evidence showing that poor people urbanize faster than the population as a whole, which implies that anticipating their needs will require a stronger focus on urban issues.<sup>15</sup> Yet in many regions, the sheer numbers argue otherwise. In Africa, for instance, more than two-thirds of the population remains rural, and it will likely be a while until the urban population becomes the majority. More generally, experiences across countries suggest that a majority of poor people will still live in rural areas long after most people in the developing world live in urban areas (Ravallion 2002). So, if there were a rural bias, would it not be rational?

This is not a new debate. Almost 30 years ago, Lipton (1977) and Mellor (1976) were concerned with the opposite question: was the urban bias of the international community rational? Ultimately, the choice of the priority is an empirical question (Reardon 2001; Sahn and others 2003). In some regions, such as Africa, rural infrastructure needs are likely to continue to drive the agenda (Mwabu and Thornbecke 2004). In others, such as many parts of Latin America and Asia, urban needs should take priority. Since the priority is an empirical matter, sector-specific poverty data on access for various income groups are needed—but are not available for most developing countries. There are reasonably good databases on the number of poor (although the measurement of poverty continues to be a source of conflict), but no database tells policymakers exactly what the poor are poor in. And even for countries with some data, problems exist.

From a sectoral viewpoint, Living Standard Measurement Surveys (LSMS), Demographic and Health Surveys (DHS), and household consumption surveys raise three types of problems.<sup>16</sup> First, the fact that they do not generate data for every country trying to get access to international aid aimed at reducing poverty limits the ability of the international community to monitor progress across countries. Second, the questionnaires in the available surveys are seldom comparable, and hence cross-country comparisons and performance benchmarking are quite approximate for many of the variables of interest to policymakers, particularly for infrastructure services. Third, there are significant differences in the quality of the data available for urban and rural areas. The World Bank has started working on standardized questionnaires, but there is still a long way to go.

Ignoring for a moment the data issues, there are some fundamental questions that have not been addressed by researchers. How consistent are the ideal strategies implicit in these differentiated needs assessments for the rural and urban poor with the strategies to maximize the

> 14 Many in the water community would argue against this , at least form the viewpoint of their sector. According to WHO statistics, for developing regions, rural areas have 5.3 times more unserved people for water supply and 3.6 times more unserved for sanitation compared with urban areas (WHO (2004)). This service gap is not really well correlated with the direction of the lending program of many donors. For instance,  in the World Bank’s lending portfolio between 1900 and 2001, **urban areas received nearly six times more in loan funds** than rural areas and this is not explained by unit cost differences.  According to the World Bank Evaluation Department (OED), each dollar spent on a rural water system provides approximately four times the population coverage offered by an equivalent urban investment, implying that an urban bias in this case prevails . I am grateful to J. Narkevic for this important footnote.

> 15 See Ravallion (2002) and Cohen (2004) for a discussion of population trends.

> 16 See also Sahn and Stifel (2004) for an interesting discussion of data issues in the African context and Sahn and others (2003) for a more general discussion that is directly relevant to assessments of the relative importance of infrastructure in the poor people’s expenditure patterns.