2 resultados para BAYESIAN ANALYSIS

em Academic Research Repository at Institute of Developing Economies


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The presence of a large informal sector in developing economies poses the question of whether informal activity produces agglomeration externalities. This paper uses data on all the nonfarm establishments and enterprises in Cambodia to estimate the impact of informal agglomeration on the regional economic performance of formal and informal firms. We develop a Bayesian approach for a spatial autoregressive model with an endogenous explanatory variable to address endogeneity and spatial dependence. We find a significantly positive effect of informal agglomeration, where informal firms gain more strongly than formal firms. Calculating the spatial marginal effects of increased agglomeration, we demonstrate that more accessible regions are more likely than less accessible regions to benefit strongly from informal agglomeration.

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With a newly constructed dataset on foreign firms in Japan for the period 1995-2008 from firm-level surveys, this paper estimates the impact of foreign firms on industrial productivity at the regional level. A Bayesian-model averaging approach is taken to account for model uncertainty resulting from various linkages between foreign firms and domestic industries. The results show that the foreign firms may contribute to industrial efficiency directly through their above-average productivity and indirectly through positive spillovers in intra-industry and local backward linkages. Forward linkages with foreign firms may have a negative impact on industrial productivity. However, these impacts depend on the nationality and entry mode of foreign investors. Aggregating foreign firms may mask their distinctive impacts on productivity.