Table 4 Multinomial logit results for urban sample – marginal effects
From: The costs of worker displacement in urban labor markets of China
| Only demographic and ownership controls | All controls | |||||
|---|---|---|---|---|---|---|
| Displaced | Quitters# | Stayers | Displaced | Quitters# | Stayers | |
| Demographics | ||||||
| Male | -0.0775*** | -0.0314*** | 0.109*** | -0.0723*** | -0.0228*** | 0.0951*** |
| (0.00732) | (0.00788) | (0.00976) | (0.00704) | (0.00800) | (0.00942) | |
| Married | -0.0336** | 0.0802*** | -0.0467*** | -0.0304** | 0.0921*** | -0.0616*** |
| (0.0153) | (0.0156) | (0.0162) | (0.0144) | (0.0157) | (0.0152) | |
| Age | 0.0115*** | -0.00538*** | -0.00613*** | 0.0108*** | -0.00534*** | -0.00547*** |
| (0.000546) | (0.000529) | (0.000636) | (0.000523) | (0.000529) | (0.000615) | |
| Years of education | -0.0229*** | -0.00108 | 0.0239*** | -0.0109*** | 0.00202 | 0.00885*** |
| (0.00153) | (0.00142) | (0.00171) | (0.00158) | (0.00152) | (0.00174) | |
| Ownership-type firm last job | ||||||
| Working in SOE in last job | 0.146*** | -0.146*** | 0.000392 | 0.137*** | -0.130*** | -0.00650 |
| (0.0120) | (0.00448) | (0.0118) | (0.0113) | (0.00514) | (0.0110) | |
| Industry | ||||||
| Construction | -0.0996*** | 0.0303 | 0.0694*** | |||
| (0.0123) | (0.0185) | (0.0196) | ||||
| Low-level service | -0.153*** | -0.00408 | 0.157*** | |||
| (0.00743) | (0.0131) | (0.0139) | ||||
| Wholesale and retail trade | -0.0653*** | 0.0279* | 0.0375** | |||
| (0.0127) | (0.0160) | (0.0181) | ||||
| Hotel and catering services | -0.115*** | 0.0370 | 0.0775*** | |||
| (0.0171) | (0.0245) | (0.0277) | ||||
| High-level service | -0.173*** | -0.0247** | 0.198*** | |||
| (0.00663) | (0.0116) | (0.0124) | ||||
| Occupation | ||||||
| Professional technicians | 0.0377 | 0.136*** | -0.174*** | |||
| (0.0300) | (0.0377) | (0.0259) | ||||
| Clerk and relevant personnel | 0.0369 | 0.114*** | -0.151*** | |||
| (0.0298) | (0.0372) | (0.0270) | ||||
| Commercial and service personnel | 0.107*** | 0.163*** | -0.269*** | |||
| (0.0343) | (0.0391) | (0.0214) | ||||
| Manufacturing and relevant personnel | 0.114*** | 0.124*** | -0.239*** | |||
| (0.0327) | (0.0379) | (0.0228) | ||||
| Other practitioner (difficult to classify) | 0.0601* | 0.152*** | -0.213*** | |||
| (0.0349) | (0.0416) | (0.0272) | ||||
| City | ||||||
| Shenzhen | 0.0436 | -0.0369* | -0.00671 | |||
| (0.0313) | (0.0211) | (0.0301) | ||||
| Dongguan | 0.0515* | -0.0396** | -0.0119 | |||
| (0.0284) | (0.0201) | (0.0281) | ||||
| Zhengzhou | 0.0498* | -0.144*** | 0.0947*** | |||
| (0.0260) | (0.0140) | (0.0264) | ||||
| Luoyang | 0.0887*** | -0.132*** | 0.0433 | |||
| (0.0332) | (0.0216) | (0.0346) | ||||
| Hefei | 0.0152 | -0.102*** | 0.0865*** | |||
| (0.0242) | (0.0161) | (0.0255) | ||||
| Bengbu | 0.120*** | -0.0976*** | -0.0229 | |||
| (0.0297) | (0.0191) | (0.0298) | ||||
| Chongqing | -0.000614 | -0.0804*** | 0.0810*** | |||
| (0.0226) | (0.0156) | (0.0236) | ||||
| Shanghai | 0.0738*** | 0.113*** | -0.187*** | |||
| (0.0242) | (0.0235) | (0.0228) | ||||
| Nanjing | -0.00453 | -0.110*** | 0.115*** | |||
| (0.0239) | (0.0155) | (0.0252) | ||||
| Wuxi | 0.0554** | -0.0344 | -0.0210 | |||
| (0.0281) | (0.0215) | (0.0286) | ||||
| Hangzhou | 0.0255 | -0.0742*** | 0.0486* | |||
| (0.0240) | (0.0168) | (0.0249) | ||||
| Ningbo | 0.0405 | -0.000688 | -0.0398 | |||
| (0.0288) | (0.0237) | (0.0294) | ||||
| Wuhan | 0.0785*** | -0.108*** | 0.0298 | |||
| (0.0247) | (0.0149) | (0.0245) | ||||
| Chengdu | 0.0675*** | -0.0295* | -0.0380* | |||
| (0.0228) | (0.0168) | (0.0221) | ||||
| Pseudo R2 | 0.1089 | 0.1853 | ||||
| Observations | 5953 | 5953 | 5953 | 5943 | 5943 | 5943 |