Run away? Air pollution and emigration interests in China

Abstract

This paper investigates the impact of air pollution on people’s interest in emigration. Using an online search index on “emigration” which is positively correlated with its search volume, we develop a city-by-day measurement of people’s emigration sentiment. We find that searches on “emigration” will grow by approximately 2.3–4.8% the next day if today’s air quality index (AQI) is increased by 100 points. In addition, such an effect is more pronounced when the AQI level is above 200, a sign of “heavily polluted” and “severely polluted” days. We also find that such effect differs by destination countries and by metropolitan areas.

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Notes

  1. 1.

    Regarding whether the net “brain drain effect” is detrimental or beneficial to the source country remains controversial. For example, Vidal (1998) suggests that emigration to a higher return to skills country may encourage people in the source country to invest in human capital; Chen (2006) argues that the relaxation of restrictions on the emigration of high-skilled workers will damage the economic growth of a source country in the long run, although a “brain gain” may happen in the short run. Beine et al. (2008) find that most countries combining low levels of human capital and low migration rates of skilled workers tend to be positively affected by the brain drain, whereas the brain drain appears to have negative growth effects in countries where the migration rate of the highly educated is above 20% and/or where the proportion of people with higher education is above 5%. Agrawal et al. (2011) indicate that the emigration of highly skilled individuals weakens local knowledge networks (brain drain) but may help remaining innovators access valuable knowledge accumulated abroad (brain bank).

  2. 2.

    See http://www.oecd.org/els/mig/World-Migration-in-Figures.pdf.

  3. 3.

    See http://www.economist.com/news/china/21601305-more-middle-classes-are-leaving-search-cleaner-slower-life-yearning-breathe.

  4. 4.

    See http://www.oecd.org/els/mig/World-Migration-in-Figures.pdf.

  5. 5.

    Another example is that Chinese buyers spent more than 221 billion U.S. Dollars on property in the U.S. alone, between April 2013 and March 2014. See http://www.rfa.org/english/news/china/flood-02122015104709.html

  6. 6.

    A growing body of literature studies the health impacts of air pollution. For example, Chay and Greenstone (2003) and Currie and Walker (2011) estimate the significant effects of air pollution on the infant mortality rate, premature births, and low birth weight using the U.S. data. Schlenker and Walker (2015) focus on a shorter time span of the impacts and show the contemporaneous health impacts of air pollution for various population cohorts. Using China’s data, Chen et al. (2013) find that the higher concentration levels of total suspended particulate (TSP) due to the winter heating policy in north China is responsible for approximately 5.5 years of lower life expectancy. Zhang et al. (2015) show that air pollution significantly reduces short-term hedonic happiness.

  7. 7.

    See http://www.civic-exchange.org/en/publications/164987357.

  8. 8.

    We assume that emigration searches by people who have planned to move are less likely to be affected by temporary shocks, such as air pollution.

  9. 9.

    Although we do not use GT for our empirical analysis in this paper, it is worth noting that GT and Baidu Index have a very high correlation (0.84 and 0.78, respectively) when searching universities and companies in China as suggested by Vaughan and Chen (2015). In this paper, we assume that the search index algorithm is similar between Baidu Index and GT since Baidu does not publicize its methodology on constructing the Index.

  10. 10.

    The numbers refer to https://www.cnnic.net.cn/hlwfzyj/jcsj/index.htm

  11. 11.

    See https://en.wikipedia.org/wiki/Baidu

  12. 12.

    We also collect data for six pollutants, namely PM2.5, SO2, NO2, PM10, CO, and O3. However, we still use the AQI as the major measurement of air quality in our analysis, because it scientifically considers the concentration levels of different pollutants. More importantly, in our data, we find that PM2.5 only accounts for about 45% of polluting days as a major pollutant. The other pollutants, such as PM10, O3, and NO2, accounts for about 27, 19, and 3.5% of polluting days as major pollutants, respectively. Therefore, pollutant-specific concentrations may not be representative enough as a measurement for overall air quality. Please refer to http://www.cnemc.cn/publish/106/news/news_25941.html for the new Ambient air quality standard (GB3095-2012).

  13. 13.

    Please refer to Table 2 of the Technical Regulation on Ambient Air Quality Index available at http://210.72.1.216:8080/gzaqi/Document/aqijsgd.pdf.

  14. 14.

    In our analysis, we find that the results from OLS regressions and Poisson regressions all point to similar conclusions.

  15. 15.

    To avoid the perfect multicollinearity problem in the regression, we omit the first category A Q I 1 in Eq. 2.

  16. 16.

    Please refer to Table 2 of the Technical Regulation on Ambient Air Quality Index available at http://210.72.1.216:8080/gzaqi/Document/aqijsgd.pdf.

  17. 17.

    The impact of AQI on people’s emigration decision could be either physiological or behavioral or both. We try to disentangle these two mechanisms by conducting regression discontinuity style regressions at the cutoffs of various AQI levels to examine whether search intensity changes significantly right above and below the cutoff points. We find that the estimated coefficient is sensitive to the econometric setting of the regression discontinuity design, therefore we do not find robust evidence for the potential behavioral effects at the cutoffs. The results are available upon request.

  18. 18.

    We cannot cluster the standard errors at the city level as in previous regressions because there are only five cities with PM2.5 measurement from the U.S. Embassy and the Consulates. Therefore, we adopt robust standard errors for these regressions.

  19. 19.

    Please refer to Table 1 of the Technical Regulation on Ambient Air Quality Index available at http://210.72.1.216:8080/gzaqi/Document/aqijsgd.pdf.

  20. 20.

    We check the robustness of our results using the concentration of each pollutant, including PM2.5, PM10, SO2, NO2, CO, and O3. The results are reported in Appendix Table 13. We find that the results are largely consistent with our main results in terms of both significance and magnitude. It should be noted that the coefficient on CO is much larger than the rest of the pollutants, because the mean value of the CO concentration is much lower than the other pollutants as shown in Table 1.

  21. 21.

    http://www.rfa.org/english/news/china/flood-02122015104709.html

  22. 22.

    http://www.ccg.org.cn/Research/View.aspx?Id=512

  23. 23.

    The accumulated number of emigrants is approximately 9.34 million by the end of 2013, of a total population of approximately 1.4 billion.

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Acknowledgement

We are grateful to Sumit Agarwal, Yongheng Deng, Shuaizhang Feng, Shihe Fu, Xiaobo Zhang, Klaus F. Zimmermann (the Editor), two anonymous reviewers, and seminar participants at the National University of Singapore for their valuable comments and suggestions. Thanks to Qingyue Open Environmental Data Center (https://data.epmap.org) for support on Environmental data processing. Qin acknowledges financial support from the Academic Research Fund - Tier 1 (WBS: R-297-000-129-133). Zhu acknowledges financial support from the National Natural Science Foundation of China (Project 71603103). All remaining errors are ours.

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Correspondence to Yu Qin.

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The authors declare that they have no conflict of interest.

Funding

This study was funded by Ministry of Education, Singapore (grant number R-297-000-129-133) and the National Natural Science Foundation of China (grant number 71603103).

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Responsible editor: Klaus F. Zimmermann

Appendices

Appendix Figure

Fig. 7
figure7

Linear relationship between search volume and Baidu Index. Data source: zhihu.com

Appendix Table

Table 13 Impacts of Different Pollutants on Baidu Index of “Emigration”

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Qin, Y., Zhu, H. Run away? Air pollution and emigration interests in China. J Popul Econ 31, 235–266 (2018). https://doi.org/10.1007/s00148-017-0653-0

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Keywords

  • Emigration
  • Air pollution
  • China
  • Online searches

JEL Classification

  • Q53
  • Q56
  • R23