Politics, information and the urban bias

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Abstract

Governments in many LDCs skew public resources towards urban sectors, despite a majority of citizens residing in rural areas. This paper develops a novel political argument for this urban-bias phenomenon in a framework where all voters, rural and urban, have equal voice, but differ in their access to information. We argue that this difference is sufficient to give governments an incentive to inefficiently over-allocate resources towards urban areas. The bias is shown to worsen during adverse economic times, leading to increased migration. We also examine how voter informativeness affects efficiency of the electoral process in weeding out incompetent governments.

Introduction

In many developing countries, public resources are often skewed in favor of urban areas, despite the fact that urban residents make up only a small fraction of the total population. This observation of an ‘urban bias’ in resource allocation was first made by Lipton (1977) in his seminal work on urbanization in developing countries. Twenty-five years later, the issue is still an important one. According to the Rural Poverty Report (2001), over 70% of the world's poor live in rural areas, and even in 2025, over 60% of the poor are likely to be of rural origin. In this paper, we examine the incentives of a government with electoral imperatives, in distributing resources between the urban and rural sectors, and ask the following question: Why may a government in a country with a predominantly rural population have an incentive to skew resources away from the rural sector towards the urban one? Exploring this issue helps address some of the factors that affect urbanization and migration in developing countries.

An examination of rural–urban differences in the provision of almost any public good in LDCs, reveals striking disparities in favor of urban areas (see Table 1 in Appendix C).1 These disparities may arise for several reasons. For one, such urban–rural disparities may well be partly efficient (at least for some public goods) and arise due to a lower cost of providing urban public goods (see Arnott and Gersovitz, 1986). On the other hand, Lipton (1977) and Bates (1981) argue that at least part of this disparity arises due to the influence and lobbying power of the urban elite. In their view, more effective urban lobbying results in an inefficient amount of resources being allocated towards urban areas, in line with the preferences of the urban elite.2 While lobbying factors are more likely to play a role in influencing government policy towards a relatively small, cohesive group of citizenry in the form of subsidies, price controls or tariff protection, the urban-bias phenomenon appears much more pervasive. Many public services such as basic health clinics, subsidized primary education, access to water and sanitation seem less likely to attract active lobbying efforts by any focused lobby group.

What distinguishes our approach from the explanations outlined above then, is that we focus on addressing the urban-bias question in a non-lobbying framework, where all agents (be they in rural or urban areas) have equal voice. Thus, the emphasis of our analysis is on precisely the wide spectrum of public goods where simple lobbying factors are less likely to be at work. In particular, this paper focuses on a distinct mechanism, namely, the difference in the information sets of urban and rural residents. In our argument, information plays a functional role—it enables citizens to draw an accurate link between observed public good outcomes and the role of the government's ability in bringing about those outcomes. Urban residents have an information advantage that may arise due to several factors: greater average wealth, higher education, better access to the media as well as a stronger urban focus in media coverage. Even if both rural and urban residents observe public good outcomes equally well, this information advantage implies that urban residents are better positioned to evaluate the role of the government's ability in achieving a given outcome.3 In our political economy framework, with a government interested in getting re-elected, it is this advantage of urban residents that may result in an urban bias in the allocation of public resources.

In our benchmark case, we analyze government resource allocation, when both rural and urban public good outcomes are affected by sector-specific shocks, as well as by the government's administrative competence. Differences in resource allocation arise due to rural–urban differences in the observability of these sector-specific shocks. For example, rural residents may be relatively poorly positioned to ascertain the relative importance of government neglect versus other exogenous shocks in engendering a low output in rural areas. In contrast, urban residents, being better educated and with the support of an active media as a watchdog, are likely to hold a guilty government culpable much more accurately and faster.4 Realizing this, a government that wishes to retain power (i.e. maximize its chances of getting re-elected) will tend to use a disproportionate share of the resources in generating more favorable urban outcomes. Thus, the electoral imperatives of the government translate the information advantage of urban areas into a bias in resource allocation toward such areas, even though they contain a minority of citizen-voters. Our analysis suggests that this bias is likely to be strongest when the government is relatively new and not yet politically well entrenched.

However, being better informed is not always advantageous to the urban resident. Under some conditions, this greater information availability can also serve to reverse the skew in resource allocation. We observe this on analyzing a version of the preceding model with economy-wide shocks (as against sector-specific shocks). Such a global shock could be, for instance, an economy-wide recession triggered by a world-wide financial crisis, an external threat, or a change in the country's credit-rating. Typically, urban residents tend to be more aware of such adverse shocks than rural voters. Now, we show that the effect of such information asymmetry is that any urban bias in resource allocation is reduced under good economic conditions and exacerbated in bad times. During good times, a higher output is appropriately discounted by the informed urban voter as being due to a favorable exogenous shock (rather than due to government competence), but not so by the uninformed rural voter. Therefore, in such times, the government has more to gain by improving rural outcomes and it does so by devoting more resources to the rural sector. In contrast, a negative shock results in the opposite effect—it increases the bias in resource allocation in favor of urban areas. Suggestive evidence from the Philippines provides support for this prediction. Examining immunization coverage in the Philippines following the negative global shock of the Asian Financial crisis of 1998, we find that the drop in coverage was much bigger in the least informed provinces than in the more informed ones.

It is interesting to note that our structure also provides a framework to study the effects of voter informativeness on the efficacy of a democratic political system. As is well recognized, elections play an important role not only in controlling moral hazard on the part of incumbent governments, but also serve the purpose of weeding out incompetent governments and retaining efficient ones.5 While the moral hazard aspect of the problem manifests itself in the form of an urban-bias, given the heterogeneity in information sets across voters, the framework developed in this paper is also well suited to deal with the important role of information and elections in mitigating the adverse selection problem. Thus, we ask the question: if voters have more information available about the economic environment, will the political system become more efficient in weeding out low ability incumbents? Somewhat strikingly, our analysis suggests that this need not always be the case. Under some circumstances, an increase in information about the economic environment makes the relatively less informed voters put more weight on their own imperfect observations in determining the political outcome. This may outweigh the effect of an increase in the information's precision so that over a range, the political system may in fact become less efficient in weeding out incompetent politicians, despite an increase in overall information.

A bias in public good provision also has implications for individual migration decisions. Given that citizens' concern with the quality of public services often influence their choice of residence, our analysis makes several points relevant to the literature on rural–urban migration. First, it highlights the simple, but relatively neglected fact that rural–urban migration may be driven not just by differences in employment opportunities (Harris and Todaro, 1970), but also by a politically induced bias in public good provision. Second, it predicts that migration rates and levels of urbanization are likely to increase under adverse conditions in the economy, due to an exacerbation of the urban-bias in such situations. Both these predictions fit well with the phenomenon of ‘urbanization without growth' as observed in many developing countries in the last few decades (see the World Bank, 2000, Fay and Opal, 2000).

The paper is laid out as follows. Section 2 describes the benchmark model, demonstrates the basic result of a bias in sectoral resource allocation, and also examines the efficacy of the political system in throwing out incompetent governments and reelecting competent ones. Section 3 analyzes the effects of economy-wide shocks on the direction and magnitude of the bias in resource allocation. Section 4.1 discusses the implications of our analysis for rural–urban migration. Section 4.2 outlines various mechanisms that may drive a wedge in information availability between the rural and urban residents, and also provides some supportive evidence. Finally, Section 5 concludes.

Section snippets

The benchmark model

We construct a stylized political economy model of sectoral resource allocation. We begin by describing the key elements of our model.

Resource allocation under common global shocks

Economies are buffeted by shocks and uncertainties of many kinds. In the previous section, we demonstrated how an urban bias in resource allocation could arise due to differences in access to information availability about region-specific inputs. However, this is surely only part of the picture. Economies can also be hit by economy-wide shocks which affect all regions. For instance, countries suffer from adverse balance of payments crises and economy-wide recessions. A collapse in access to the

Extensions and discussion

Our analysis has focused on examining the implications of differences in information availability across a heterogeneous population on sectoral resource allocation. Two distinct questions immediately come to mind: first, why may such differences in information exist? and second, do our results of bias in resource allocation have any implications for the theory and pattern of migration in developing countries? In this section, we discuss both of these issues, starting with the latter.

Conclusion

This paper emphasizes a distinct mechanism that underlies urban bias in government resource allocation—namely, differences in information across urban and rural residents. In analyzing this informational channel, the paper also contributes more broadly to the literature analyzing the impact of information on the efficacy of the political system.

We show that greater information availability may result in the government allocating disproportionately more resources towards the urban citizen at the

Acknowledgements

This paper has benefited from comments by V. Bhaskar, Karla Hoff, Debraj Ray, two anonymous referees and seminar participants at Ohio State University, Williams College, the 2001 NEUDC conference at Boston University, the 2002 CEA meetings at Calgary and the 2003 APET conference at Duke University.

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