Suchergebnisse

This paper analyzes the climatic factors that affected food security in the West African Sahel in 2001–2017. We estimate the impact of droughts and floods on the four dimensions of food security defined by the Food and Agricultural Organization of the United Nations, based on a panel data model controlling for socioeconomic and political factors. Droughts and floods negatively affect food security with floods causing more damage. Since socioeconomic and political factors, especially conflicts, also play an important role, food security in the West African Sahel cannot be explained only by climatic problems, so that coordinated policies must be based on the four dimensions of food security.

AbstractThis paper examines the pull and push factors of interregional flows in Tunisia, focusing on the role of differences in human capital. Using data from the 2004 and 2014 Tunisian censuses, we estimate a gravity model augmented with proxies capturing high‐ and low‐skilled level human capital and an original nighttime light proxy for regional GPD. We consider both unemployment rate differential and road distance as multilateral resistance variables, and we add cultural and quality of life variables. We find that the macroeconomic variables are the main determinants of interregional flows. As for human capital, high skilled level acts as a pull factor while low skilled level acts as a restraining factor in the destination regions. Furthermore, non‐economic variables have a significant impact on migration flows: Tunisians leave their origin regions not only in search of job opportunities but also in search of better living conditions.

International audience ; We extend the literature on Cohesion Policy effectiveness by considering how the cohesion policy affects both within regional disparities and economic growth. For that purpose, a panel database of 205 NUTS2 regions of the UE-25 for 2000-2014 is used. We estimate panel data regressions with fixed effects and a spatial autoregressive term in order to control for unobservable characteristics and spatial dependence. Our results emphasize a trade-off between within and between regional disparities for EU-25 regions over the 2000-2014 period.

International audience ; How do countries respond to other countries when setting the level of their environmental expenditures? Using data from 1995-2017 on a sample of 28 OECD countries, we examine the nature and extent of strategic interactions in environmental expenditures among OECD countries using a spatial Durbin model including economic and political control variables and both economic and spatial weight matrices reflecting several interaction mechanisms. The results show the existence of significant positive spatial dependence in environmental spending suggesting that OECD countries consider their neighbors' behavior when making policy choices related to environmental expenditures. We also find that the most populous countries or those with high unemployment tend to spend less for environment while countries with a large urban population set higher levels of environmental expenditures. The results are robust to the inclusion of strong cross-sectional dependence under the form of common factors.

International audience ; How do countries respond to other countries when setting the level of their environmental expenditures? Using data from 1995-2017 on a sample of 28 OECD countries, we examine the nature and extent of strategic interactions in environmental expenditures among OECD countries using a spatial Durbin model including economic and political control variables and both economic and spatial weight matrices reflecting several interaction mechanisms. The results show the existence of significant positive spatial dependence in environmental spending suggesting that OECD countries consider their neighbors' behavior when making policy choices related to environmental expenditures. We also find that the most populous countries or those with high unemployment tend to spend less for environment while countries with a large urban population set higher levels of environmental expenditures. The results are robust to the inclusion of strong cross-sectional dependence under the form of common factors.

International audience ; We extend the literature on Cohesion Policy effectiveness by considering how the cohesion policy affects both within regional disparities and economic growth. For that purpose, a panel database of 205 NUTS2 regions of the UE-25 for 2000-2014 is used. We estimate panel data regressions with fixed effects and a spatial autoregressive term in order to control for unobservable characteristics and spatial dependence. Our results emphasize a trade-off between within and between regional disparities for EU-25 regions over the 2000-2014 period.

AbstractThe estimates for the human capital effect in cross‐country growth regressions have been subject of considerable controversy. We argue that human capital is intrinsically a multidimensional construct. We construct human capital measure by combining available alternative proxies via confirmatory factor analysis. Using panel data endogenous quantile regression methods we analyse the whole conditional growth distribution by simultaneously accounting for the potential endogeneity of human capital and country‐specific effects. Our results conform to theoretical expectations and we are able to demonstrate the beneficial effect of both the measurement approach and the endogeneity correction on the derivation of theoretically consistent estimates.

SummaryIn this paper, we revisit the analysis of cross‐country convergence by combining spatial econometrics and panel quantile regressions to estimate conditional β‐convergence models. Moreover, we use both exogenous and endogenous weight matrices. Our results show that indeed the effects of initial per capita income, investment rate, population growth and human capital on growth rates vary considerably across the estimated quantiles. Convergence is not a generalized phenomenon across the conditional growth distribution. Moreover, while using exogenous spatial weight matrices does not substantially alter the findings found in a‐spatial models, it appears that endogenous weights dramatically affect the estimates of the convergence process.