Network effects on labor contracts of internal migrants in China: a spatial autoregressive model
Badi Baltagi (),
Ying Deng () and
Xiangjun Ma ()
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Ying Deng: University of International Business and Economics
Xiangjun Ma: University of International Business and Economics
Empirical Economics, 2018, vol. 55, issue 1, 265-296
Abstract This paper studies the fact that 37% of the internal migrants in China do not sign a labor contract with their employers, as revealed in a nationwide survey. These contract-free jobs pay lower hourly wages, require longer weekly work hours, and provide less insurance or on-the-job training than regular jobs with contracts. We find that the co-villager networks play an important role in a migrant’s decision on whether to accept such insecure and irregular jobs. By employing a comprehensive nationwide survey in 2011 in the spatial autoregressive logit model, we show that the common behavior of not signing contracts in the co-villager network increases the probability that a migrant accepts a contract-free job. We provide three possible explanations on how networks influence migrants’ contract decisions: job referral mechanism, limited information on contract benefits, and the “mini-labor union” formed among co-villagers, which substitutes for a formal contract. In the subsample analysis, we also find that the effects are larger for migrants whose jobs were introduced by their co-villagers, male migrants, migrants with rural Hukou, short-term migrants, and less educated migrants. The heterogeneous effects for migrants of different employer types, industries, and home provinces provide policy implications.
Keywords: Contract; Co-villager network; Spatial autoregressive logit model; Internal migrants (search for similar items in EconPapers)
JEL-codes: O15 R12 J41 (search for similar items in EconPapers)
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Working Paper: Network Effects on Labor Contracts of Internal Migrants in China: A Spatial Autoregressive Model (2017)
Working Paper: Network Effects on LaborContracts of Internal Migrants in China- A Spatial Autoregressive Model (2017)
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