dc.description.abstract | Many studies suggest that it appears that unemployment tends to cluster in regional labor markets. Reviewing literatures about Taiwan’s regional unemployment, such a spatial effect has not been examined intensively. Therefore the two main questions that this dissertation shall seek to answer are: First, what are the determinants in regional unemployment rates? Does there exist a positive spatial spillover in the regional labor market of Taiwan? Second, what are possible influences to the variance of regional unemployment rates?
To facilitate such a study, a panel dataset of 22 regions in Taiwan from 1982 to 2005 was compiled for the first issue. Spatial autoregressive modeling with instruments to examine the possible factors to regional unemployment rates is adopted. Differing from the current literature, regional-specific characteristics, the macro-environment and spatial spillovers are simultaneously considered in discussing the determinants to regional unemployment rates. Besides the first–order lag variable of the spatially-weighted average unemployment rates which is employed in the current literature, this study constructed a second-order lag variable of spatially-weighted average unemployment rates to specifically account for the impact of distances for the first time.
It is confirmed that spatial dependences problem can be effectively eliminated after accounting for the second-order lag of the spatially-weighted average unemployment rates. The empirical results show that although regional characteristics still have certain explanatory power in affecting regional unemployment, the spatial interactions between regions are confirmed to be the key factors in Taiwan’s regional unemployment rates. The spatial spillovers decayed with distance as the regional unemployment disparities declined. Therefore, it can be confirmed that there exists a regional unemployment clustering effect in Taiwan. In addition, from the empirical results in this dissertation, the urban regions exhibit large positive spatial spillovers from their neighboring regions, that is, from their surrounding counties. Additionally, urban regions have higher unemployment rates and are more significantly affected by business cycles not by human capital. Moreover, when the surrounding county and its business center (corresponding major city) are re-classified as the same regional labor market, these regional labor markets show more spatial clustering on the geographical distribution.
Furthermore, it is important to note that this is the first time in the literature that the possible influences to the variance of regional unemployment rates are discussed. An AR model is utilized to explore the possible influences of the variance of regional unemployment rates in Taiwan from 1988 to 2005. Although human capital does not affect regional unemployment rates significantly as we mentioned before, nevertheless, improvement in labor quality may be the key factor to reduce regional economic inequality in Taiwan’s regional labor markets. These dispersions between regions consistently have positive affects on the variance of regional unemployment rates, especially the deviation of the manufacturing sector between regions. It is worthwhile to mention that through empirical estimation, improvements in the highway transportation network may actually worsen the distortion between regions.
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