Table A1.
Dependent variable | ||||||
---|---|---|---|---|---|---|
Variable | Description | Obs | Mean | SD | Min | Max |
Time | Number of years since roll-out | 2032 | 1.757874 | 0.95823 | 0 | 3 |
Independent variables (census data in 2010) | ||||||
Variable | Description | Obs | Mean | SD | Min | Max |
elderly | Proportion of residents aged > 60 | 2016 | 0.121858 | 0.02741 | 0.044787 | 0.245302 |
primeage | Proportion of residents aged 45-59 | 2016 | 0.182595 | 0.0336 | 0.084766 | 0.281767 |
Independent variables (China Data Center, averaged from 2006 to 2008) | ||||||
Variable | Description | Obs | Mean | SD | Min | Max |
population | Population with local Hukou (10,000) | 2032 | 46.96807 | 34.68808 | 0.673333 | 221.4667 |
gdppc | GDP per capita (10,000) | 2098 | 1.3671 | 1.338048 | 0.158216 | 17.55029 |
vaddedprim | Proportion of primary industry added value in GDP | 2032 | 11.72115 | 9.843006 | 0.126667 | 58.84 |
netrevenue | Net revenue per capita of the local government (10,000) | 2032 | −0.13074 | 0.121803 | −1.48527 | 0.209573 |
bed | Number of beds per 10,000 people in hospitals and orphanages | 2052 | 36.98728 | 19.73719 | 3.465704 | 210.4423 |
Independent variables (Party secretary characteristics) | ||||||
Variable | Description | Obs | Mean | SD | Min | Max |
ifhometown | If it is the birth city | 2198 | 0.040902 | 0.198145 | 0 | 1 |
age | Age | 2123 | 54.07195 | 3.494404 | 43 | 62 |
age50 | Age below 50 | 2123 | 0.119379 | 0.324366 | 0 | 1 |
age54 | Age 50-53 | 2123 | 0.253475 | 0.435179 | 0 | 1 |
age58 | Age 54-57 | 2123 | 0.46852 | 0.499212 | 0 | 1 |
ageover58 | Age above 58 | 2123 | 0.158626 | 0.365477 | 0 | 1 |
Gender | Gender | 2129 | 0.980472 | 0.138428 | 0 | 1 |
ifminority | Minority or not | 2111 | 0.072667 | 0.259697 | 0 | 1 |
College | highest degree = B.A | 2161 | 0.265246 | 0.441627 | 0 | 1 |
Master | highest degree = M.A | 2161 | 0.595151 | 0.491043 | 0 | 1 |
Phd | highest degree = PhD | 2161 | 0.139603 | 0.346702 | 0 | 1 |
partyschool | Graduated from the Party School or not | 2184 | 0.364161 | 0.481368 | 0 | 1 |
major_agri | Major in agriculture | 2138 | 0.069506 | 0.254409 | 0 | 1 |
major_humss | Major in humanities / social science | 2143 | 0.818317 | 0.385726 | 0 | 1 |
major_tech | Major in science or technology | 2139 | 0.291262 | 0.454513 | 0 | 1 |
major_medicine | Major in medicine | 2138 | 0.002242 | 0.047315 | 0 | 1 |
Independent variables (other sources) | ||||||
Variable | Description | Obs | Mean | SD | Min | Max |
Ifpoor | National poverty-stricken county | 2032 | 0.283957 | 0.451027 | 0 | 1 |
Auto | Autonomous county,state or region | 2032 | 0.30561 | 0.460779 | 0 | 1 |
Data sources: 1) county-level statistics (other than demographic characteristics) were drawn from the China Data Center at University of Michigan http://chinadatacenter.org/; 2) county demographic characteristics were drawn and averaged from China’s census data in 2000 and 2010; 3) prefecture-level party secretary information database 2000-2013 were compiled by China Insurance and Social Security Research Center at Fudan University (2015); 4) the national poverty-stricken county list in 2009.