A Meta-Analysis of the Urban Wage Premium
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1 A Meta-Analysis of the Urban Wage Premium Ayoung Kim Dept. of Agricultural Economics, Purdue University November 21, 2014 SHaPE seminar 2014 November 21, / 16
2 Urban Wage Premium Urban Wage Premium (UWP) Higher earnings in urban areas than in rural areas Potential economic benefits to urban workers? SHaPE seminar 2014 November 21, / 16
3 Urban Wage Premium Triggers of wage gap Individual characteristics: Education, experience, gender, race, union membership, or other factors (Labor economics) Urban economics and regional science consider: Macro-/micro-foundational, spatial aspects Clustering human capital and industrial agglomeration Compensation for congestion and pollution SHaPE seminar 2014 November 21, / 16
4 Urban Wage Premium Literature review The existence of the UWP in a considerable amount of studies Glaeser and Mare (2001), Combes et al. (2008), etc. Working in a city is associated with higher wages The extent of the UWP varies with literature due to different data types, variable measures, or model specifications The source of the UWP: differences in living cost, spatial sorting, agglomeration economies, faster human capital accumulation, or compensation of dis-amenity (Yankow et al., 2006) The UWP decrease by controlling for a variety of confounding factors (Combes et al., 2010; Heuermann et al., 2010) SHaPE seminar 2014 November 21, / 16
5 Urban Wage Premium Literature review: interesting results Figure: Empirical findings of the UWP from 8 studies A wide range of estimates on the UWP across countries or time periods What makes different results among the studies? SHaPE seminar 2014 November 21, / 16
6 Urban Wage Premium Research questions & contributions Q1. What gives different results among the studies? The different research designs lead to various results: what different factors in previous studies affect the results? To provide a comprehensive systematic review of wage premium as an economic benefit of urban labor through meta-analysis Melo et al. (2009) and de Groot et al. (2009) Q2. Do any factors, not presented by the previous studies, explain the differences of the UWP? To identify what variables affect the variation of UWP after controlling for the heterogeneity of study features Different (changes) urban system Country/temporal effect SHaPE seminar 2014 November 21, / 16
7 Meta-Analysis:meta-regression Meta-analysis: meta-regression Meta-regression: potential keys to explain differences in the study results Conceptual equation: Estimates = f (characteristics of studies within & between, information from outside the literature) Essential condition: Sufficient and valid literature having the same questions or hypotheses in a given topic Literature should test the existence of the UWP and should estimate effects of urban-related variables SHaPE seminar 2014 November 21, / 16
8 Meta-Analysis:meta-regression Data for meta-regression Data for the meta-regression include studies characteristics and information from outside literature Table: Descriptive statistics of the literature SHaPE seminar 2014 November 21, / 16
9 Meta-Analysis:meta-regression Meta-regression SHaPE seminar 2014 November 21, / 16
10 Meta-Analysis:meta-regression Meta-regression Response variable: r-based effect size for both continuous and discrete measures r = t t 2 + df and Var(r) = (1 r 2 ) 2 n 1 when not reporting t-value, standard error can be used Explanatory Variables: Operational features (measurement of urban-related variables or controlling variables) Temporal and geographical features (time-period, country, or percentage of urban or the largest city population) Dummies for most covariates, but population as a continuous variable SHaPE seminar 2014 November 21, / 16
11 Meta-Analysis:meta-regression Meta-regression model Hedges et al. s (2012) random effects model r ij = X ij β + η j + ɛ ij Unbalanced panel type with the different numbers of estimates (or r ij ) from each study Estimation approach: Feasible Generalized Least Squares To handle dependence and heterogeneity through the robust variance estimation in meta-analysis Pooled OLS as the baseline regression SHaPE seminar 2014 November 21, / 16
12 Results Preliminary results by pooled OLS SHaPE seminar 2014 November 21, / 16
13 Results Important findings The variables for between-studies are more likely to be significant The UWP declined over time feasibly due to: Improved information & communication technologies Reduced transportation costs Increase in the UWP when population is unevenly distributed decrease in the UWP when a large portion of population lives in several urban areas Increase in the UWP when more people are clustered in the largest city SHaPE seminar 2014 November 21, / 16
14 Implications & further studies Extension and checks More literature to build a data set More variation between literature What other factors can explain the variation of the UWP as well as the urban population variables? Estimation of unbalanced panel model Publication selection bias SHaPE seminar 2014 November 21, / 16
15 Questions and Comments SHaPE seminar 2014 November 21, / 16
16 go back SHaPE seminar 2014 November 21, / 16
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