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Licensed Unlicensed Requires Authentication Published by De Gruyter July 24, 2020

The Impact of Marital Status on Job Finding: A Field Experiment in the Chinese Labor Market

Gergely Horváth

Abstract

This paper studies the impact of marital status on job finding in China using the correspondence methodology. Fictitious CVs are sent to job advertisements through an online job board website, focusing on financial and accounting jobs, and the callback rate is measured. We vary the gender and marital status on otherwise identical CVs. The previous literature suggests that being married has a negative impact on the labor market outcomes of females, but a positive impact for males. In contrast, for the Chinese labor market, we do not find a significant effect of marital status on job finding for either gender.

JEL Classification: C93; J16; J71

Corresponding author: Gergely Horváth, Division of Social Sciences, Duke Kunshan University, No. 8 Duke Avenue, Kunshan, Jiangsu Province, 215316 China, E-mail:

This research was supported by the National Natural Science Foundation of China (NSFC), under the 2019 NSFC Research Fund for International Young Scientists program, grant nr. 71950410622. We also acknowledge the financial support of Xi’an Jiaotong Liverpool University under the SURF program, reference Nr. 201950. We thank Xiahoan Guo, Kefei Huang, Ruiou Li and Mengqi Qiu for their excellent research assistance, as well as Xinyu Jiang and Tony So for comments and suggestions.


Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 71950410622

Funding source: Xi’an Jiaotong Liverpool University

Award Identifier / Grant number: 201950

Appendix

Table 3:

Marginal effects from (homoskedastic) probit regression.

Dependent variable: callback (yes = 1; no = 0)AccountingFinance
Marital status (married = 1; single = 0)0.0095 (0.0140)−0.0333** (0.0169)
Gender (female = 1; male = 0)0.0262* (0.0139)−0.0654*** (0.0171)
Marital status * Gender−0.0163 (0.0193)0.0248 (0.0248)
Quality (high = 1; low = 0)0.0272*** (0.0105)0.0253** (0.0126)
Wage (the average value of the wage range stated by the company in the advertisement)−0.0000053** (0.000002)−0.0000039* (0.000002)
Number of person the firm intends to hire−0.0003 (0.0013)0.00139* (0.0007)
Company size dummies (omitted: less than 20)
 Between 20 and 99−0.0307 (0.0274)0.0252 (0.0258)
 Between 100 and 499−0.0281 (0.0277)0.0302 (0.0261)
 Between 500 and 999−0.0213 (0.0312)0.0106 (0.03)
 Between 1000 and 9999−0.0248 (0.0298)0.0304 (0.028)
 Above 9999−0.0236 (0.0372)0.0477 (0.036)
Company ownership dummies (omitted: private)
 State-owned−0.00792 (0.0317)0.0764** (0.0325)
 Foreign0.00648 (0.0258)−0.0214 (0.0339)
 Joint venture−0.0327* (0.0190)−0.0265 (0.0229)
Dummies for expected experience in years (omitted: less than 1 year)
 Between 1 and 30.00314 (0.0172)−0.0165 (0.0153)
 Between 3 and 50.0105 (0.0166)−0.0545*** (0.0177)
 Between 5 and 10−0.0143 (0.0175)−0.109*** (0.0212)
 Above 10−0.0211 (0.0312)−0.076 (0.0891)
City (Shanghai = 1; Xi’an = 0)−0.0459*** (0.0111)−0.103*** (0.014)
Industry dummiesYesYes
Number of observations30933170

  1. Standard errors in parentheses, ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1.

  2. Note: The table shows the marginal effects on callback from (homoskedastic) probit model estimations. The control variables include industry dummies with categories: financial services and banking; real estate; IT and media; agriculture and energy; trade and transportation; services and education; manufacturing; medical bioengineering.

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Supplementary Material

Supplementary material to this article can be found online at https://doi.org/10.1515/bejeap-2019-0364.

Received: 2019-10-21
Accepted: 2020-05-22
Published Online: 2020-07-24

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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