Determinants of intraregional migration in Sub-Saharan Africa 1980-2000

Despite great accomplishments in the migration literature, the determinants of South-South migration remain poorly understood. In an attempt to fill this gap, this paper formulates and tests an empirical model for intraregional migration in sub-Saharan Africa within an extended human capital framework, taking into account spatial interaction. Using bilateral panel data between 1980 and 2000, we find that intraregional migration on the subcontinent is predominantly driven by economic opportunities and sociopolitics in the host country, facilitated by geographical proximity. The role played by network effects and environmental conditions is also apparent. Finally, origin and destination spatial dependence should definitely not be ignored

Determining Minimum Wages in China: Do Economic Factors Dominate?

Minimum wages may be an important instrument to reduce income inequality in a society and to promote socially inclusive economic growth. While higher minimum wages can support the Chinese transformation towards consumption driven growth, they can worsen the price competitiveness in export markets. As they differ throughout the country, this paper investigates their determinants at the regional level. In addition to a broad set of economic determinants, such as per capita income and consumption, consumer prices, unemployment and industrial structures, spatial effects are taken into account. They might arise for different reasons, including competition of local policymakers. The results show that the impact of economic variables declines, once spatial spillovers are considered. Although the minimum wage regulation pursues the relevance of economic factors in the determination of the appropriate levels, the actual development is largely driven by regional dependencies. As minimum wage standards set by local officials do not fully reflect the regional economic development, further reforms should be on the agenda

Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions

In any economic analysis, regions or municipalities should not be regarded as isolated spatial units, but rather as highly interrelated small open economies. These spatial interrelations must be considered also when the aim is to forecast economic variables. For example, policy makers need accurate forecasts of the unemployment evolution in order to design short- or long-run local welfare policies. These predictions should then consider the spatial interrelations and dynamics of regional unemployment. In addition, a number of papers have demonstrated the improvement in the reliability of long-run forecasts when spatial dependence is accounted for. We estimate a heterogeneouscoefficients dynamic panel model employing a spatial filter in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment, as well as a spatial vector-autoregressive (SVAR) model. We compare the short-run forecasting performance of these methods, and in particular, we carry out a sensitivity analysis in order to investigate if different number and size of the administrative regions influence their relative forecasting performance. We compute short-run unemployment forecasts in two countries with different administrative territorial divisions and data frequency: Switzerland (26 regions, monthly data for 34 years) and Spain (47 regions, quarterly data for 32 years)

Does Accounting for Spatial Effects Help Forecasting the Growth of Chinese Provinces?

In this paper, we make multi-step forecasts of the annual growth rates of the real GRP for each of the 31 Chinese provinces simultaneously. Beside the usual panel data models, we use panel models that explicitly account for spatial dependence between the GRP growth rates. In addition, the possibility of spatial effects being different for different groups of provinces (Interior and Coast) is allowed. We find that both pooling and accounting for spatial effects helps substantially improve the forecast performance compared to the benchmark models estimated for each of the provinces separately. It was also shown that effect of accounting for spatial dependence is even more pronounced at longer forecasting horizons (the forecast accuracy gain as measured by the root mean squared forecast error is about 8% at 1-year horizon and exceeds 25% at 13- and 14-year horizon)

Decentralization and regional government size in Spain

The aim of this paper is to investigate the impact of fiscal decen- tralization on the size of regional governments in Spain, by controlling for economies of scale, interregional heterogeneity and institutional framework. We study it over 1985 to 2004 using a panel dataset of seventeen spanish regions. The results can be easily summarized. Firstly, it supports the classic public goods theory of a trade-off-between the economic benefits of size and the costs of heterogeneity. Secondly, it doesn’t reject the “Leviathan” hypoth- esis and neither does the “common pool” hypothesis. Thirdly, by contrast, the paper partly rejects the “Wallis”’ hypothesis. It argues that government size is mediated by financial resources obtained through intergovernmental grants, consistent with welfare economics and positive economic policies. We conclude that later advances in the decentralisation process must be compatible with the goal of reducing fiscal imbalances that emanate from the vertical structure of fiscal power.info:eu-repo/semantics/publishedVersio

Likelihood Based Inference and Prediction in Spatio-Temporal Panel Count Models for Urban Crimes

PRELIMINARY DRAFT We discuss maximum likelihood (ML) analysis for panel count data models, in which the observed counts are linked via a measurement density to a latent Gaussian process with spatial as well as temporal dynamics and random effects. For likelihood evaluation requiring high-dimensional integration we rely upon Efficient Importance Sampling (EIS). The algorithm we develop extends existing EIS implementations by constructing importance sampling densities, which closely approximate the nontrivial spatio-temporal correlation structure under dynamic spatial panel models. In order to make this high-dimensional approximation computationally feasible, our EIS implementation exploits the typical sparsity of spatial precision matrices in such a way that all the high-dimensional matrix operations it requires can be performed using computationally fast sparse matrix functions. We use the proposed sparse EIS-ML approach for an extensive empirical study analyzing the socio-demographic determinants and the space-time dynamics of urban crime in Pittsburgh, USA, between 2008 and 2013 for a panel of monthly crime rates at census-tract level