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Social networks and mental health outcomes: Chinese rural–urban migrant experience


Over the past two decades, more than 160 million Chinese rural workers have migrated to cities to work. They are separated from their familiar rural networks to work in an unfamiliar, and often hostile, environment. Many of them thus face significant mental health challenges. This paper is the first to investigate the extent to which migrant social networks in host cities can mitigate these adverse mental health effects. Using unique longitudinal survey data from Rural-to-Urban Migration in China (RUMiC), we find that network size matters significantly for migrant workers. Our preferred instrumental variable estimates suggest that a one standard deviation increase in migrant city networks, on average, reduces the measure of mental health problems by 0.47 to 0.66 of a standard deviation. Similar effects are found among the less educated, those working longer hours, and those without access to social insurance. The main channel of the network effect is through boosting migrants’ confidence and reducing their anxiety.

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  1. While migration may adversely affect migrants’ mental health condition, migrants are often found to be a selected group who are in general mentally stronger (see, for example, Lou and Beaujot 2005 for Canada; Janisch 2017 for Australia).

  2. Despite these potential mental health challenges, a large number of rural people migrated to the city to work due mainly to the large wage gaps between rural and urban regions.

  3. Based on our data, the average share of urban locals among migrants’ social networks is around 32–34%.

  4. There are 2861 county-level jurisdictions in China.

  5. Please refer to Thoits (2011) for the detailed channels through which social networks improve mental health in the main effect model and stress-buffering model.

  6. We choose to include the growth of GDP and minimum wage rather than the level, given the rapid economic development in China and the importance of the adaption of one’s subjective well-being (Brickman et al. 1978; Di Tella et al. 2010).

  7. Our results are robust if we do not control for individual occupation and industry affiliation dummy variables and Djt. The results are available upon request.

  8. In this paper, we mainly investigate migrant mental health condition in the destination cities, and we take the decision of migration as given here. However, how the migration decision correlates to mental health and social networks is important. Due to the data unavailability (i.e. RUMiC Rural Household Surveys do not include most rural home towns of migrants surveyed in RUMiC Migrant Household Surveys), we are unable to fully address this self-selection issue. Nevertheless, in Section 5, we will discuss how this potential self-selection bias could affect our results.

  9. We discuss the measure of social networks in Section 4.

  10. For the measurement error issue, as long as the instrumental variable is uncorrelated with the measurement error of the network measure, the IV estimator is consistent. In the FEIV case, the consistency holds when the demeaned IV is uncorrelated with the measurement error of the demeaned network measure.

  11. For detailed discussion of these data, see Appendix B.

  12. These newly formed networks in cities do not have to coincide with migrants’ pre-migration existing networks. Chinese culture normally regards people one meets in a foreign land (for example, destination cities for most rural migrants) from one’s own home town as more reliable, trustworthy, and hence more likely to form social network with, regardless of whether the person one met was known pre-migration.

  13. It is still debated in the health literature whether long-term rainfall affects mental condition for one particular population (see e.g. Henríquez-Sánchez et al. 2014; O’Hare et al. 2016).

  14. Connolly (2013) uses rainfall during the day of the interview as well as the rainfall one-day before the interview in a subjective well-being regression and finds that the one-day before rainfall variable is not statistically significant.

  15. As the agricultural income can be seen as the opportunity cost of non-agricultural work, the daily wages of day-labour in villages should be positively correlated with agricultural income and thereby can be treated as its proxy.

  16. Note that the last potential violation of exclusion restriction is likely to bias our IV estimate downward, because rainfall is likely to be negatively correlated with mental health problems via a preference for city life, and it is also negatively correlated with social networks.

  17. The 2010 wave does not include information on migrants’ mental health, so it is not included in the analysis. We stop at 2012 because of the availability of the rainfall data. Up to now, the fully cleaned rainfall data are only available up to 2010.

  18. In China, there is a large fraction of migrant workers living in workplaces, such as dormitories of manufacturing factories and construction sites. These migrants are usually not sampled (or undersampled) in the other migrant household surveys, because the sampling frames of those surveys are all based on residential address. For example, the proportions of self-employed migrants in CHIP 1999 and 2002 and CULS 2002 are 64%, 66%, and 52%, respectively, and the proportions of production workers among migrants in CHIP 1999 and 2002 are around 7%. In contrast, the 2005 Mini Census shows that only 20% of migrant workers are self-employed, and 55% are production workers. Given these large differences, the RUMiC project conducted a census on workplaces in the aforementioned 15 cities to record the number of rural migrants in each workplace address. Based on this census information, the RUMiC project team then constructed the sampling frame. This sampling strategy leads to a more representative sample of rural–urban migrants. For example, in 2008 MHS, 22% of migrants are self-employed, and 40% are production workers, which is closer to the 2005 Mini Census than other migrant surveys. For more details of the sample representativeness and the RUMiC workplace-based census, see Gong et al. (2008). After being sampled in the workplaces, the home address and other contact details (e.g. telephone number and hometown address) of respondents are recorded during the interview for tracking purpose. For more details of tracking, please see Xue (2015).

  19. We are fully aware that due to attrition the panel sample is not representative. However, the panel sample still represents an important group of migrants and provides us with useful information. Our analysis shows that the attrition in the MHS survey is mainly caused by return migration or migrants moving to other cities. The stayers are those migrants who are more likely to be married, are better educated, and are willing to stay in cities permanently if policy allows them to. They are also likely to have better mental health conditions and to have larger economic gain from migration. Thus, the panel analysis is relevant to this group of migrants.

  20. The details of the GHQ 12 questions are presented in Appendix B.1.

  21. In the robustness check, we also consider the Caseness GHQ score, another measure of mental health problem that is used in the literature (e.g. Clark and Oswald 1994). The Caseness GHQ score counts the number of items for which the respondent reported “fairly” or “highly” stressed. It ranges from 0 to 12. Similar to the Likert score, a larger Caseness GHQ score indicates worse mental health condition. We choose the Likert score in the main analysis because it has better distributional properties (i.e. with less skewness and kurtosis, Graetz 1991), which may make the inference more reliable.

  22. RUMiC data also allow us to construct network size in rural areas. In the main analysis, we do not control for migrant hometown network size as it may be endogenous and bias the coefficients of other variables. But in the robustness check, we check whether the results are sensitive to controlling for it. We show that such an inclusion does not change the results (see panel A in Table 6).

  23. Around 10.6% of respondents did not provide accurate information on home counties. For these respondents, we match the closest weather station to the location of their home prefecture. Also, 0.1% of respondents did not provide precise information on home prefecture. These observations are excluded from the analysis. The average distance between the location of the local county/prefecture government and the nearest weather station is around 35 km. We also tested the robustness if we include the observations which do not have precise information on home prefecture and use the nearest weather station to the province government to proxy their hometown rainfall information. The results are very similar to the results in Section 5 and available upon request. In the sample, around 1.7% of observations are from home counties with only a single observation. After matching rainfall information, we also merge these counties with the neighbouring counties. The results remain similar if we do not merge these home counties and are available upon request.

  24. Potentially, we could assume that the rest of the household members have the same social network as the head or spouse. In the robustness check section, we provide results based on the sample under this assumption.

  25. Of all the variables, the home village daily wage for day labour has the most missing values, accounting for more than 63% or 1130 observations. In the unreported results, we tried to use the average village daily wage from the same home county/district to impute the missing values. The results are very similar and are available upon request.

  26. We also check the robustness by excluding the observations whose contacts are more than 50, and the results are shown in panel D of Table 6. An alternative way to account for potential non-linearity in the effect is to add square or inverse terms of social networks in the regression specification. However, we choose not to do so because of the weak instrumental variable problem when multiple endogenous variables are used.

  27. One Chinese yuan is equal to about 0.14 to 0.16 US dollars during 2008 to 2012.

  28. Around 5–6% of respondents reported the distance as 0 km. To include these observations, we add 1 km to the distance to make the inverse feasible. We also tried to add the logarithm of 1 km plus distance or the square term of distance in the regression. But the first stages are weak.

  29. Because the IV estimates generated by the pooled sample and the representative sample are largely similar, in this section, we report the IV estimates based on the pooled sample and the panel sample. The results using the representative sample are available upon request from the authors.

  30. These calculations are based on the fact that the mean value of the rainfall instrumental variable is 4.75 mm, and the average size and the standard deviation of migrant networks are 13.48 and 17.57, respectively, as shown in Table 1.

  31. Note that Stock and Yogo (2005) suggest that in the two IV cases the critical value of the F test for the instruments being strong is 19.93 when standard error is not clustered and is not corrected for heteroskedasticity (i.e. the plain standard error). In our case, if we use the plain standard error, the F statistic is about 29.

  32. The correlation coefficient of the two instrumental variables is − 0.05.

  33. The FEIV estimates are larger than the IV estimates in panel A of Table 4. This is perhaps partly because FEIV estimation removes the bias caused by unobserved individual characteristics, such as preference for city life (see discussion in Section 3), and partly because the sample used is a selected group of migrants.

  34. The comparison of the results between the representative sample (column (2)) and the panel sample (column (3)) of Table 2 is indicative of the possibility that social networks have a stronger effect on those less mentally healthy people. The coefficient from the representative sample is larger than the panel sample, which has fewer people who have mental problems, due to attrition.

  35. Note that this possibility can only explain the difference between the OLS and cross-sectional IV estimates and cannot explain the difference between the FE and FEIV estimates.

  36. There is another issue which needs to be discussed before we turn to further analysis. In the empirical strategy section, we assumed that the migration decision is exogenous. This, however, may not be the case. If it is the case that mentally unhealthy people, with a lower possibility of establishing social networks in cities, are also less likely to migrate, the results obtained from our sample are likely to underestimate the true effect of social networks, as social networks seem to have a larger effect on mental health for less mentally healthy people (see the comparison between the representative and panel samples of Table 2). If on the contrary, mentally unhealthy people are more likely to migrate who also have smaller social networks in cities, our results could be overestimates of the true effect. This latter case is less intuitive though.

  37. We also constructed the Likert score of each dimension by adding up the relevant items and estimated the effect of social networks. The results show that for IV estimation, the estimated effects for all three dimensions are negative and statistically significant. For FEIV, the results are precisely estimated for anxiety and confidence, but not for anhedonia and social dysfunction. The full results are available upon request from the authors.

  38. Cornaglia et al. (2015) also follow this classification to investigate the relationship between mental health and education decisions.

  39. The representative sample and panel sample have similar proportions of migrants who do not have access to social welfare.

  40. We also further include the number of hukou friends to check the robustness, and the results remain similar and are available upon request.


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We would like to thank Tue Gorgens, Bob Gregory, Jenny Williams, the Editor Klaus Zimmermann, and three anonymous referees and seminar and conference participants at Australian National University and the 2014 Australasian Econometric Society Annual Meeting for their invaluable comments.


This study was funded by the Australian Research Council for RUMiC project (ARC grant number LP066972, LP140100514, and DP0988572) and Guangzhou Social Science “13-fives plan” Grant (2019GZYB25).

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Appendix A: Tables and figures

Table 7 OLS estimates of impact of rainfall on migration decision
Table 8 OLS and fixed effect estimates of network effect on mental health problems
Fig. 4
figure 4

Unconditional relationship between social networks and mental health problems for the three samples. Source: 2008, 2009, 2011, and 2012 waves of the Migrant Household Survey in RUMiC project

Fig. 5
figure 5

Unconditional relationship between attrition and mental health problems. The x-axis shows the pre-determined mental health problem to attrition (i.e. the characteristics recorded when the respondent first participated the survey). Each dot represents a cell which is at a value of GHQ 12 Likert score, except the last dot represents a cell with the Likert score no less than 20 due to the small sample size. The y-axis shows the proportion of stayers in each cell. The stayers are those respondents who were tracked at least once after they participated the survey. Source: 2008, 2009, 2011, and 2012 waves of the Migrant Household Survey in RUMiC project

Appendix B: Data appendix

B.1 General health questionnaire 12

The questions in General Health Questionnaire 12 are as follows: “In the last few weeks have you ever had the following feelings?

  1. 1.

    Concen Have you been able to concentrate on whatever you’re doing?

    (1) been able to concentrate; (2) attention occasionally diverted; (3) attention sometimes diverted; (4) attention frequently diverted, not been able to concentrate;

  2. 2.

    NoSleep Have you lost much sleep over worry?

    (1) never; (2) occasionally; (3) fairly often; (4) very often;

  3. 3.

    Useful Have you felt that you were playing a useful part in things?

    (1) true so; (2) to some extent; (3) rarely; (4) not at all;

  4. 4.

    Decide Have you felt capable of making decisions about things?

    (1) very capable; (2) quite capable; (3) not quite capable; (4) not capable at all;

  5. 5.

    Strain Have you felt constantly under strain?

    (1) never; (2) slightly; (3) considerably; (4) seriously;

  6. 6.

    Diffic Have you felt you couldn’t overcome your difficulties?

    (1) never; (2) slightly; (3) considerably; (4) seriously;

  7. 7.

    Activ Have you felt your normal day-to-day activities are interesting?

    (1) very interesting; (2) fairly interesting; (3) not very interesting; (4) not interesting at all;

  8. 8.

    Probs Have you been able to face up to problems?

    (1) always; (2) most of the time; (3) sometimes; (4) rarely;

  9. 9.

    Depress Have you been feeling unhappy or depressed?

    (1) never; (2) slightly; (3) considerably; (4) seriously;

  10. 10.

    NoConf Have you been losing confidence in yourself?

    (1) never; (2) slightly; (3) considerably; (4) seriously;

  11. 11.

    Wthless Have you been thinking of yourself as a worthless person?

    (1) never; (2) slightly; (3) considerably; (4) seriously;

  12. 12.

    Happy Have you been feeling reasonably happy, all things considered?

    (1) very happy; (2) fairly happy; (3) not so happy; (4) not happy at all”

B.2 Data details of appendix Table Table 7

The sample used in Appendix Table 7 is extracted from the 2008 and 2009 waves of the RUMiC rural household survey. We restrict the sample to households whose agricultural income per household member in the previous year is not more than 50,000 yuan to reduce the potential measurement error. We also exclude respondents who are younger than 16 or older than 65, because these respondents are unlikely to migrate. The rainfall data in Table 7 is constructed in the way described in Section 4.

B.3 Data source of minimum wage

The minimum wage data are extracted from the online websites. We browsed the following websites to obtain the minimum wage change.

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Meng, X., Xue, S. Social networks and mental health outcomes: Chinese rural–urban migrant experience. J Popul Econ 33, 155–195 (2020).

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  • Mental health
  • Social networks
  • Migration
  • China

JEL Classification

  • I12
  • I15
  • J61