53 research outputs found

    Spatial Inequality and Development - Is there an Inverted-U Relationship?

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    This paper studies the hypothesis of an inverted-U-shaped relationship between spatial inequality and economic development. The theory of Kuznets (1955) and Williamson (1965) suggests that (spatial) inequality first increases in the process of development, then peaks, and then decreases. To test this hypothesis I have used a unique panel data set of spatial inequalities in 55 countries at different stages of economic development, covering the period 1980-2009. Parametric and semiparametric regressions are carried out using cross-section and (unbalanced) panel data. The results provide strong support for the existence of an inverted U, but importantly I also find spatial inequalities to increase again at very high levels of economic development. Although many factors may be contributing to this rise, one explanation rests on the process of tertiarization, i.e., the structural shift from industrial production towards a service base in the highest-developed economies.regional inequality, Kuznets curve, panel data, semiparametric estimates

    Regional Inequality and Decentralization - An Empirical Analysis

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    This paper analyzes the impact of political and fiscal decentralization on regional inequalities using a unique data set which covers 56 countries at different stages of economic develop-ment. Cross-section and panel data estimations show that decentralization decreases regional inequalities in general. However, estimations using an interaction variable approach imply that the effect depends on the level of economic development. While rich countries benefit from decentralization with regard to a more equal regional income distribution, decentralization may lead to higher regional inequalities in developing and emerging economies. The results are pointing in the same direction for measures of fiscal and political decentralization implying that both - autonomy in decision making and fiscal authority - are decisive in this context. Thus, when fostering decentralization in developing countries - as proposed by international development agencies - the potential negative redistributional consequences should be taken into account.regional inequality, decentralization, panel data

    Aid, Growth and Devolution

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    This paper examines whether the federal structure of aid-receiving countries matters in explaining aid effectiveness. Following the decentralization theorem, the devolution of powers should increase aid effectiveness, since local decision-makers are better informed about local needs. At the same time, decentralization has reverse effects, e.g., through coordination problems, excessive regulation, administrative costs and local capture. Using panel data for up to 60 countries, we find that aid is less effective or even harmful in decentralized countries. Our results imply that donor countries should carefully consider how both anti-poverty instruments - financial assistance and decentralization - work together.foreign aid, growth, decentralization

    Decentralization and Foreign Aid Effectiveness: Do Aid Modality and Federal Design Matter in Poverty Alleviation?

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    This paper empirically studies the impact of decentralization on foreign aid effectiveness. For this purpose, we examine a commonly used empirical growth model, considering aid modality as well as different measures of political and fiscal decentralization. Our panel estimations reveal that fiscal decentralization negatively impacts aid effectiveness, while measures of political decentralization have no significant effect or even a positive one. This result is robust for grants and overall ODA, while the growth impact of other aid types is not generally conditional on decentralization. We therefore conclude that donor countries should carefully consider how both anti-poverty instruments - foreign assistance and decentralization - work together.foreign aid, growth, decentralization

    Regional inequality and decentralization – an empirical analysis

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    This paper analyzes the impact of political and fiscal decentralization on regional inequalities using a unique data set which covers 56 countries at different stages of economic development. Cross-section and panel data estimations show that decentralization decreases regional inequalities in general. However, estimations using an interaction variable approach imply that the effect depends on the level of economic development. While rich countries benefit from decentralization with regard to a more equal regional income distribution, decentralization may lead to higher regional inequalities in developing and emerging economies. The results are pointing in the same direction for measures of fiscal and political decentralization implying that both -autonomy in decision making and fiscal authority- are decisive in this context. Thus, when fostering decentralization in developing countries -as proposed by international development agencies- the potential negative redistributional consequences should be taken into account

    Decentralization and the Shadow Economy: Oates Meets Allingham-Sandmo

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    This paper studies the impact of decentralization on the shadow economy. We argue that decentralization may decrease the size of the shadow economy mainly through two transmission channels: (1) Decentralization enhancing public sector efficiency (efficiency effect), and (2) decentralization reducing the distance between bureaucrats and economic agents, which increases the probability of detection of shadow economic activities (deterrence effect). Using various measures of fiscal, political and government employment decentralization in a cross-section of countries, we find the deterrence effect to be of more importance. The deterrence effect is stronger, the lower the degree of institutional quality. Remarkably, we find no robust evidence of the efficiency effect.decentralization, shadow economy

    Human Lights

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    Satellite nighttime light data open new opportunities for economic research. The data are objective and suitable for the study of regions at various territorial levels. Given the lack of reliable official data, nightlights are often a proxy for economic activity, particularly in developing countries. However, the commonly used product, Stable Lights, has difficulty separating background noise from economic activity at lower levels of light intensity. The problem is rooted in the aim of separating transient light from stable lights, even though light from economic activity can also be transient. We propose an alternative filtering process that aims to identify lights emitted by human beings. We train a machine learning algorithm to learn light patterns in and outside built-up areas using Global Human Settlements Layer (GHSL) data. Based on predicted probabilities, we include lights in those places with a high likelihood of being man-made. We show that using regional light characteristics in the process increases the accuracy of predictions at the cost of introducing a mechanical spatial correlation. We create two alternative products as proxies of economic activity. Global Human Lights minimizes the bias from using regional information, while Local Human Lights maximizes accuracy. The latter shows that we can improve the detection of human-generated light, especially in Africa
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