2.2. Empirical strategy We concern two econometric tasks to estimate equation (6). Our first task is to deal with fixed effects, within . If is correlated with any of the covariates, pooled OLS estimation (POLS) would yield biased. To examine F-test, we show which estimator is more reliable between fixed effect estimation (FE) and POLS. Our second econometric task is potential endogeneity. While urban scale and income growth is correlated, the effect’ of primacy on income growth is ambiguous ‘causal (Henderson, 2003). Due to these correlation, estimation POLS of convergence could be also biased. Therefore, we should identify the causality between primacy and GRDP. Firstly, dependent variable or the error terms, is exogenous to predetermined covariates. According to Henderson (2003), we utilize lagged variables for primacy (,) to estimate our equation as follow: (7) Arguably, using predetermined covariates, however, is not strictly exogenous. We examine 2SLS and GMM estimation using instrument variables in order to take account of this potential endogeneity. It is important issue to choose good instrument variables. The validity of instruments take account of two conditions: first, good instruments are strictly correlated by covariates. Second, they are not shown correlation with the error-term, . These conditions imply that our instruments should be associated with primacy, and should not be related to regional income growth. We consider factors for sorting city as instruments satisfied above conditions. In urban economic literature, sorting city is referred why workers migrate to larger cities or primate areas. It means that city or region size is endogenously determined by individuals choice according to trade off benefits and cost in city (Combes et al, 2008; Behrens and 2014; Davis and Dingel, 2014). In free mobility, workers with heterogeneity skill want to earn higher wages, acquire knowledge accumulation, consume amenities, and take
2.2. Empirical strategy We concern two econometric tasks to estimate equation (6). Our first task is to deal with fixed effects, within . If is correlated with any of the covariates, pooled OLS estimation (POLS) would yield biased. To examine F-test, we show which estimator is more reliable between fixed effect estimation (FE) and POLS. Our second econometric task is potential endogeneity. While urban scale and income growth is correlated, the ‘causal effect’ of primacy on income growth is ambiguous (Henderson, 2003). Due to these correlation, estimation POLS of convergence could be also biased. Therefore, we should identify the causality between primacy and GRDP. Firstly, dependent variable or the error terms, is exogenous to predetermined covariates. According to Henderson (2003), we utilize lagged variables for primacy (,) to estimate our equation as follow: (7) Arguably, using predetermined covariates, however, is not strictly exogenous. We examine 2SLS and GMM estimation using instrument variables in order to take account of this potential endogeneity. It is important issue to choose good instrument variables. The validity of instruments take account of two conditions: first, good instruments are strictly correlated by covariates. Second, they are not shown correlation with the error-term, . These conditions imply that our instruments should be associated with primacy, and should not be related to regional income growth. We consider factors for sorting city as instruments satisfied above conditions. In urban economic literature, sorting city is referred why workers migrate to larger cities or primate areas. It means that city or region size is endogenously determined by individuals choice according to trade off benefits and cost in city (Combes et al, 2008; Behrens and 2014; Davis and Dingel, 2014). In free mobility, workers with heterogeneity skill want to earn higher wages, acquire knowledge accumulation, consume amenities, and take
S5-7-1_Jinwoo%20Dong.docx