measured by “the ratio of FDI to GDP” . Fiscal revenue decentralization (): Cities with higher local fiscal revenue relative to provincial-level fiscal revenue may have different fiscal expenditure preference over other cities. Based on the approach of Jin and Zou (2002) and Wu and Lin (2012), we measure fiscal revenue decentralization by “the ratio of city’s budgeted fiscal revenue to the provincial-level budgeted fiscal revenue,”. The Proportion of government staff supported by public funds (Government Staff): Salary of staff and infrastructure investments are the two most important components of local fiscal expenditure in China. The higher the proportion of staff supported by public funds, the less fiscal resources are available for infrastructure investments (Yin and Xu, 2011). We consider the influence of the proportion of government staff, measured by “the ratio public funding government staffs to total population”.
2: Explanations and Statistical Descriptions of the Variables Variable Definition Observations Mean Standard Deviation Minimum Maximum Infrastructure investments Ratio of infrastructure investments to public expenditure 1260 6.700 5.992 0.002 61.615 Independent Variables: Officials Variables Age Secretary’s age 1260 50.986 3.997 39 67 Gender Secretary’s gender 1260 0.031 0.173 0 1 Ethnic Whether Secretary is from an ethnic minority 1260 0.060 0.240 0 1 Place Whether the secretary holds the 1260 0.074 0.262 0 1
Population Average population per square kilometer 1260 1152.152 1206.245 13.000 14052.410 Urbanization population divided by total population (%) 1260 62.794 22.482 13.052 100 Openness Ratio of FDI to GDP (%) 1260 3.540 5.766 0 134.592 Decentralization Ratio of prefectural fiscal revenue to provincial fiscal revenue (%) 1260 8.200 6.336 0 .945 33.315 Government Staff Ratio of government 1260 14.313 9.789 0.803 56.912
Staff Tests -0.444*** -0.444*** F-test 16.130*** Breusch and LM test 1487.100*** Moran’s I test 25.556*** 25.556*** Hausman test 3.337 3.337 Anderson corr. LM 256.343*** 103.581*** Sargan test 9.332 14.443 Note: ***, **, and * represent significance levels of 1%, 5%, and 10%, respectively.
following form: (A15) is the spatial autoregressive parameter of the disturbance term. Besides,, to account for intertemporal correlation in the disturbance term, is set to be: (A16) where is the vector and indexes a city’s individual effectthat is independent and identically distribution with zero mean and variance . is the vector formed by ; changes with cities and times; the obeying mean value is 0; and the variance of the IID process is . and are mutually independent and have a finite fourth moment. We take the following four steps to estimate Equation (6). First, we solve the endogeneity issue of the spatial lag. Rearranging Equation (6) to , it can be seen from this equation that ordinary least squares (OLS) estimators are inconsistent due to the correlation of dependent variable Y with error component . We turn to instrumental variables to obtain consistent estimators for parameter and residual . The valid instrumental variables are . The second step is to estimate the spatial autoregressive parameter . Based on the generalized moments method (GMM) proposed by Kapoor et al. (2007), let , , , and , we obtain six moment conditions: (A17) Given the estimator of error component from the first step, applying nonlinear least squares (NLS) we construct the sample moments conditions to consistently estimate , , and . The third step address the spatial correlation of error components The consistent estimators for , , and in the second step are , , and , respectively. We implement Cochrane-Orcutt procedures on all variables (denoted as in Equation (6) to get. Fourth, we implement IV estimation on the transformed data in a fixed or random effects panel data framework, respectively. Let and . Using data to conduct IV estimation, we get the random effect estimators for Equation (6). Denote. We use to conduct IV estimation on Equation (6) to get the fixed effect estimators. After implementing the non-identifiability and overfitting tests on SARAR fixed-effects (SARAR-FE) model and SARAR-FE model, we apply spatial Hausman test (Mutl and Pfaffermayr, 2011) to examine whether the empirical model is fixed or random effect.