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Pros And Cons Of Spatial Mismatch

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In the following section, I review the empirical evidence of the pro and cons on spatial mismatch hypothesis. In addition, I discuss the application of the data and previous empirical strategy to investigate the effect of spatial mismatch on the level of employment and earnings as well as commuting patterns. 3.1. Factors that cause spatial mismatch None of earlier studies that examined the interaction between housing location and unemployment until Kain (1968) proposed the idea of racial segregation in the U.S. housing market as the main cause of spatial mismatch problem. To support his idea, Kain simply used multiple regression models and tested the survey data of Chicago and Detroit Area Traffic Study in two different years – 1956 and 1952, …show more content…

By examining U.S 1980 Public Use Micro Samples (PUMS) data which are more powerful than survey-based samples, he includes neighborhoods across metropolitan areas to avoid error in sample selection and then estimates a regression model with a set of more controlling variables on individual characteristics such as age, gender, education, and race. Additionally, the author exploited intercity variations in non-white residential centralization. To get more consistency, Weinberg also performs instrumental variable (IV) regression. Further, he found that the effect of non-whites residential centralization is greatest for large metropolitan areas, for the young and elderly, and for those with low education levels. Hence, non-white residential centralization has been an important variable in explaining their employment status. This result is similar to Kain’s results and supports the spatial mismatch hypothesis. However, the analysis in this research does not include the variation of employment growth by job sector. This is important information in order to identify in which job sector the non-white are difficult to enter the labor …show more content…

One of the explanations is related to Kain’s prediction where job decentralization has reduced working opportunities for inner-city workers. However, this prediction is unclear when the youth are included in the observation as they do not work in blue-collar jobs and the number of the youth population in inner-city do not represent workers who compete for low-quality jobs (Ihlanfeldt & Sjoquist, 1990). To improve this measurement error, the authors construct a new measure of job access which is different from previous works to test the relationship between the nearness of jobs and youth job probability. In addition, they include the use of a richer set of control variables and separately estimate the models by race, age, and employment status in different metropolitan areas based on the Public Use Micro Sample (PUMS) U.S. 1980 Census of Population data. Later, the authors found that the relative distance to job locations have a strong effect on employment status regardless the race, age, and enrollment status. The important issue which remained unclear from this study is whether or not the absence of nearby jobs affect employment status for inner-city youth workers due to limited information about job opportunities, high distance commuting to the nearest jobs locations, and reluctant for working in unfamiliar

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