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Table 5 DID analysis results—based on OLS estimations

From: Impact of minimum wage on gender wage gaps in urban China

 

1990 vs. 1995

1990 vs. 2002

1990 vs. 2007

The initial years: 1990

Coeff.

SE.

Coeff.

SE.

Coeff.

SE.

 Male

0.1327***

0.0123

0.1552***

0.0135

0.1404***

0.0086

 year

0.0062

0.0140

0.4767***

0.0194

0.7921***

0.0146

 The treatment group

−0.2598***

0.0233

−0.0947***

0.0183

−0.2110***

0.0198

 Male*year

0.0319*

0.0181

0.0420*

0.0253

0.1616***

0.0185

 Male*The treatment group

0.0649**

0.0309

−0.1097***

0.0247

0.1361***

0.0254

 year*The treatment

−0.0817**

0.0385

−0.0144

0.0344

0.1712***

0.0409

 DIDterm

−0.0077

0.0503

0.0572

0.0468

−0.1125**

0.0508

 

1991 vs. 1995

1991 vs. 2002

1991 vs. 2007

The initial years: 1991

Coeff.

SE.

Coeff.

SE.

Coeff.

SE.

 Male

0.1249***

0.0133

0.1603***

0.0185

0.1194***

0.0112

 year

0.0207

0.0142

0.4845***

0.0214

0.8015***

0.0155

 The treatment group

−0.2753***

0.0230

−0.1201***

0.0223

−0.1263***

0.0206

 Male*year

0.0106

0.0191

0.0332

0.0284

0.1800***

0.0199

 Male*The treatment group

0.0252

0.0312

−0.1335***

0.0313

0.1060***

0.0306

 year*The treatment

−0.0819**

0.0383

0.0107

0.0367

0.0866**

0.0412

 DIDterm

−0.0568

0.0504

0.0809

0.0506

−0.0803 +

0.0536

 

1992 vs. 1995

1992 vs. 2002

1992 vs. 2007

The initial years: 1992

Coeff.

SE.

Coeff.

SE.

Coeff.

SE.

 Male

0.1232***

0.0133

0.1429***

0.0187

0.1209***

0.0110

 year

−0.0525***

0.0141

0.4128***

0.0214

0.7325***

0.0153

 The treatment group

−0.2555***

0.0217

−0.1353***

0.0229

−0.1432***

0.0210

 Male*year

0.0150

0.0191

0.0529*

0.0285

0.1809***

0.0198

 Male*The treatment group

0.1262***

0.0303

−0.1127***

0.0325

0.1174***

0.0298

 year*The treatment

−0.1364***

0.0383

0.0256

0.0370

0.1028**

0.0413

 DIDterm

−0.0548

0.0496

0.0593

0.0513

−0.0922*

0.0531

  1. Note:
  2. 1. Estimations using Kaitz index in models. Treatment group: the region where the gender wage gap is lowest before the MW implementation, and the Kaitz index is higher after the MW implementation
  3. 2. The other varibles such as education, experience years,han race,married are also estimated
  4. 3. *, **, *** statistically significant in 10%,5%,1% levels
  5. Source: Calculated using CHIP1995,CHIP2002,CHIP2007