Table 3 Multinomial logit results for migrant 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 | 0.0144*** | 0.0783*** | -0.0928*** | 0.0126*** | 0.0738*** | -0.0864*** |
| (0.00453) | (0.00884) | (0.00861) | (0.00462) | (0.00898) | (0.00863) | |
| Married | -0.00446 | 0.0904*** | -0.0860*** | -0.00155 | 0.0915*** | -0.0899*** |
| (0.00509) | (0.0115) | (0.0113) | (0.00521) | (0.0113) | (0.0109) | |
| Age | 0.00100*** | -0.00510*** | 0.00410*** | 0.00120*** | -0.00398*** | 0.00278*** |
| (0.000246) | (0.000574) | (0.000569) | (0.000252) | (0.000568) | (0.000558) | |
| Years of education | -0.000748 | -0.00473*** | 0.00548*** | -0.000751 | -0.00518*** | 0.00593*** |
| (0.000811) | (0.00179) | (0.00177) | (0.000857) | (0.00182) | (0.00178) | |
| Ownership-type firm last job | ||||||
| SOE | -0.00239 | -0.0947*** | 0.0971*** | -0.00241 | -0.0749*** | 0.0773*** |
| (0.00564) | (0.0127) | (0.0129) | (0.00569) | (0.0129) | (0.0129) | |
| Industry | ||||||
| Construction | -0.0229*** | 0.0352** | -0.0124 | |||
| (0.00386) | (0.0144) | (0.0143) | ||||
| Low-level service | -0.0272*** | -0.00363 | 0.0308 | |||
| (0.00515) | (0.0190) | (0.0189) | ||||
| Wholesale and retail trade | -0.0290*** | -0.0450*** | 0.0740*** | |||
| (0.00378) | (0.0160) | (0.0160) | ||||
| Hotel and catering services | 0.00114 | 0.0449*** | -0.0460*** | |||
| (0.00738) | (0.0159) | (0.0153) | ||||
| High-level service | -0.0149** | -0.0223 | 0.0372* | |||
| (0.00729) | (0.0210) | (0.0208) | ||||
| Occupation | ||||||
| Clerk and relevant personnel | 0.0819*** | 0.0692*** | -0.151*** | |||
| (0.0214) | (0.0200) | (0.0135) | ||||
| Commercial and service personnel | 0.0270** | 0.0634*** | -0.0904*** | |||
| (0.0130) | (0.0157) | (0.0132) | ||||
| Manufacturing and relevant personnel | 0.0723*** | 0.0524** | -0.125*** | |||
| (0.0209) | (0.0210) | (0.0160) | ||||
| City | ||||||
| Shenzhen | -0.00201 | 0.0132 | -0.0112 | |||
| (0.00849) | (0.0221) | (0.0220) | ||||
| Dongguan | -0.0227*** | 0.0162 | 0.00653 | |||
| (0.00702) | (0.0221) | (0.0221) | ||||
| Zhengzhou | -0.00587 | -0.0116 | 0.0175 | |||
| (0.00889) | (0.0215) | (0.0215) | ||||
| Luoyang | 0.0252* | -0.132*** | 0.107*** | |||
| (0.0143) | (0.0242) | (0.0250) | ||||
| Hefei | -0.0251*** | -0.173*** | 0.198*** | |||
| (0.00653) | (0.0200) | (0.0205) | ||||
| Bengbu | -0.0360*** | -0.0939*** | 0.130*** | |||
| (0.00580) | (0.0242) | (0.0245) | ||||
| Chongqing | 0.000241 | -0.0604*** | 0.0601*** | |||
| (0.00842) | (0.0193) | (0.0196) | ||||
| Shanghai | -0.0280*** | -0.185*** | 0.213*** | |||
| (0.00565) | (0.0181) | (0.0186) | ||||
| Nanjing | -0.0358*** | -0.291*** | 0.327*** | |||
| (0.00437) | (0.0157) | (0.0164) | ||||
| Wuxi | -0.0341*** | -0.382*** | 0.416*** | |||
| (0.00549) | (0.0149) | (0.0159) | ||||
| Hangzhou | -0.00813 | 0.0474** | -0.0393** | |||
| (0.00708) | (0.0184) | (0.0182) | ||||
| Ningbo | 0.0140 | 0.0891*** | -0.103*** | |||
| (0.0114) | (0.0239) | (0.0233) | ||||
| Wuhan | -0.00610 | -0.0663*** | 0.0724*** | |||
| (0.00743) | (0.0186) | (0.0188) | ||||
| Chengdu | -0.0176** | -0.00988 | 0.0275 | |||
| (0.00713) | (0.0200) | (0.0200) | ||||
| Pseudo R2 | 0.0113 | 0.0730 | ||||
| Observations | 4388 | 4388 | 4388 | 4381 | 4381 | 4381 |