Locked out? China s New Cooperative Medical Scheme and Rural Labour Migration

Transcription

1 Locked out? China s New Cooperative Medical Scheme and Rural Labour Migration Xuezhu Shi a, a Department of Economics, STICERD, London School of Economics, Houghton Street, London, WC2A 2AE, United Kingdom. x.shi2@lse.ac.uk Abstract Providing health insurance only to rural residents could increase the proportion of the population that stays in rural areas. This paper tests whether the new rural health insurance in China, the New Cooperative Medical Scheme (NCMS), has a job-lock effect on rural-to-urban migration. Using individual-level data from the China Health and Nutrition Survey (CHNS), I find that being enrolled in NCMS decreases the probability of being a migrant, especially for males. Utilising the CHNS dataset as well as data collected from provincial yearbooks in China, the results show that NCMS reduces the growth rate of the migration propensity withn counties, but no evidence that it reduces the county-level migration propensity. These results imply that NCMS gradually locks the rural labour force into rural areas and further hinders job mobility in China. Keywords: Health Insurance, Immigrant Workers, Public Policy JEL codes: I13, I18, J61, J68 1. Introduction In 2003 the Chinese government initiated a health insurance scheme to replace the old one, which hardly benefited anybody, to cover the health needs of the rural population. This scheme, New Cooperative Medical Scheme (NCMS), provides coverage for catastrophic This research uses data from China Health and Nutrition Survey (CHNS). I thank the National Institute of Nutrition and Food Safety, China Center for Disease Control and Prevention, Carolina Population Center, the University of North Carolina at Chapel Hill, the NIH (R01-HD30880, DK056350, and R01-HD38700) and the Fogarty International Center, NIH for financial support for the CHNS data collection and analysis files from 1989 to 2006 and both parties plus the China-Japan Friendship Hospital, Ministry of Health for support for CHNS 2009 and future surveys. I wish to thank Alan Manning, Joan Costa-i-Font, Camille Landais, Johannes Spinnewijn, Alain Trannoy and especially Frank Cowell for their comments and suggestions for this paper. Also, I want to thank Shawn Chen, Sarah Clifford, Shiyu Bo, Jiajia Gu, Anders Jensen, Yatang Lin, Panos Mavrokonstantis and other participants in the Public Economics WIP Seminar in LSE, the IAB Graduate School s 8th interdisciplinary Ph.D. workshop on Perspectives on (Un-) Employment and also the Winter School on Inequality and Social Welfare Theory in University of Verona. Preprint submitted to Elsevier February 28, 2016

2 illnesses for the rural population and aims to prevent the poverty caused by illness. 1 But it has one major restriction: rural residents can only get reimbursements from their health expenditure in hospitals which are in their place of residence as recorded in the hukou system. 2 During the implementation period of NCMS ( ), the number of migrants increased dramatically due to China s rapid urbanization process and increasing income differences between rural and urban areas. Could the new health insurance scheme lock people in rural areas despite the great temptation from working in urban areas in China? Given the large population base in China, even a small change in the migration trend might affect the migration behaviour of hundreds of millions of people. If the new scheme really constrains migration, it means the insurance policy might cause a large distortion in the migration labour market in China. The job-lock effects of different health insurance schemes have been noted in the U.S. context: Gruber and Madrian (1993) discuss the job-lock effect of health insurance portability; other papers examine the effects of health insurance, especially Medicare and retirement decisions (Gruber and Madrian 1995; Fairlie et al., 2013). Using individual-level data, the U.S. results show that the health insurance locks people in their current job. To date, there has been almost no discussion of possible labour market distortions by NCMS. Qin and Zheng (2011) discuss this issue but their data are limited to an individual-level dataset, the China Health and Nutrition Survey (CHNS), up to 2006 only (NCMS starts around 2003 or 2004), and it has no county-level data. This paper seeks to fill the gaps in the current literature by studying the restraining effect of NCMS in China. NCMS has similar effects when compared to non-portable health insurance provided in the U.S. The effect of NCMS includes the job-lock effect on individuals, as discussed in the U.S. context, and also covers the effects of health insurance on those who return to their hometown due to NCMS. The new scheme distorts people in the labour market more than the health insurance in the U.S. While focusing on individual career choices, the present paper also contributes to the existing literature by examining a younger working generation from a macro-level perspective with a novel dataset from provincial statistical yearbooks. The paper firstly studies how individuals, with an average age of about 30, change their probability of being a rural-to-urban migrant because urban workplaces usually do not provide insurance, while, back in their hometown, they can get NCMS. The individual-level 1 Source for NCMS information: (Content in Chinese), the State Council of the P.R.C. 2 Hukou is a person s record in the system of household registration. It includes information such as whether the person is a rural or urban resident, birthplace, age, gender and other basic personal information ( in Chinese). The classification of rural or urban residency is very difficult to change for rural residents. This restriction potentially prevents a sizable fraction of people in rural areas from benefiting directly from NCMS, especially for most of the inter-county or even inter-province rural-to-urban migrants. Whether this newly introduced health-insurance scheme induces possible distortions of the migrant labour markets is a particularly interesting question. 2

3 dataset is the CHNS dataset up to The migration propensity for each county, which is the total number of migrants divided by the total population in a county, might be affected. Through an analysis of county-level data, the results show that NCMS does not affect the migration propensity in a county directly, but the implementation of NCMS in rural areas has a negative effect on the growth rate of the migration propensity. The results from the individual-level data (CHNS) also support the proposition that NCMS has a constraining effect on rural migration. The results from both datasets suggest that NCMS is gradually restraining people in rural areas. The remainder is organized as follows. Section 2 provides background on the rural residents, the rural-to-urban migrants and different types of health insurance in China. Section 3 mainly focuses on the micro-level data and Section 4 discusses the macro-level evidence. Policy analysis and some conclusions are in Section Background on migrants and health insurance systems in China 2.1. The rural population and migrants The rural population, those with rural hukou (China s household registration), consists of three groups. Those who both live and work in rural areas are in Group 1: they benefit directly from NCMS because they live close to the hospitals from which they can get reimbursements on their health expenditures. Group 2 is made up of intra-county rural-to-urban migrants, who work in urban areas of the county in which they reside - some 20% of the total migrants (NBS report, 2012). 3 It is easy for people in Group 2 to commute between their hukou residence place and their workplace, so they can still benefit from NCMS. Group 3 consists of the rural-to-urban migrants who are the focus of this paper. They have rural hukou, but they work and live in urban areas far from their hometown. This category of migrants is non-seasonal because of the long distance between their workplace and their hometown. It is expensive and difficult for them to go back in the harvest season. Because the rural-to-urban migrants live far away from their hukou place, it is also difficult for them to get to the hospitals where they are entitled to reimbursements for health expenditures. According to the Report of Chinese Migrants in 2012 (NBS report), there are 208 million rural-to-urban migrants working outside their hometown: 85% of them cannot benefit directly from NCMS. 94% of rural migrants do not have a college degree and 80% of them do not even have a high school diploma. Moreover, 85% of them are not employed by companies providing welfare benefits. They have much less health insurance coverage compared to the urban residents and the rural residents who are not working outside their hometown. Despite its young average age, the migrant group is vulnerable to serious health problems including lower immunization rates, higher rates of infectious diseases, and maternal mortality (Barber and Yao, 2010). The occupational health risks that the migrants face are higher than for those with higher social economic status and white collar jobs (Herd et al., 2010), so the migrants are usually poor in health because their workloads are higher while 3 All information about migrants in Section 2.1 are from this report and similar reports from other years. 3

4 Note: RUMiC provides information on when the individual first migrates out as a rural-to-urban migrant. The earliest year for the first migration out dates back to To make this figure easier to read as well as to make the time span similar to the datasets used in this paper, I only show the number of new migrants from 1990 to Figure 1: The number of new migrants for each year (1990 to 2008, RUMiC) their incomes are comparatively lower than others (Chen et al., 2014). They can be easily dragged back to the poverty line if they fall ill and cannot afford health expenditures for illnesses. But the rural-to-urban migrants can always choose to go back to their hometown and participate in NCMS. While male migrants are the major group in the total rural-to-urban migrants, female migrants make up 35% of the total migrants (NBS report, 2009). The actual accident injury rate of males are higher than that of females and occupational injuries are also mostly among male rural residents "who have joined the workforce in the poorly regulated private mining, construction, and manufacturing firms" (Wang et al., 2008). These are the occupations that hire mainly male migrants (DPCB, 2007). So women are more likely to be under-insured than men because of the low occupational risk (Mou et al., 2013) and male migrants might be more likely to respond to the new health insurance in rural-areas. This hypothesis will be examined using micro-level data. The number of return migrants was also increasing around 2008 and 2009; however, it will not have a significant effect on my analysis. According to Xiwen Chen, one of the officials in the Rural Working Leading Group from the central government, there were about 20 million return migrants in 2009 due to the financial crisis. 4 These return migrants account for around 10% of total migrants. The total number of migrants actually increased by 1.9% in 2009, according to the official report of migrants in 2009, and the total number of intercounty migrants increased by 3.5%. The macro-level data in the government report cannot identify the new migrants and the return migrants. According to the longitudinal survey on 4 Website: contents in Chinese. 4

5 Rural Urban Migration in China (RUMiC), 5 there were 522 new migrants just migrated to cities in 2008, compared to 407 new migrants in The trend of an increasing number of new migrants is evident in Figure 1. It appears that the financial crisis in 2008 and 2009 did not greatly affect the increase of the new rural-to-urban migrants, especially the inter-county migrants. Age is another factor that might affect the number of return migrants. From the Statistics Report on Migrants in 2015 from NBS, 7 the average age of the rural-to-urban migrants increased from 35 in 2009 to 38.3 in For the age group above 50, this group counted 17.1% of the total rural-to-urban migrants in 2014 while it only possessed 4.2% in the total rural-to-urban migrants in Therefore, it is highly likely that most of the old migrants stayed in urban areas, even after their increasing years had made it more difficult for them to compete with young migrants New Cooperative Medical Scheme and other health insurance schemes NCMS covers the health expenditure for catastrophic illnesses of rural residents and aims at avoiding the possible poverty caused by illness in rural areas (Yi et al., 2009). Its principle restriction is that it only provides reimbursements for medical expenses for a person seeking medical services in hospitals located near his or her hukou residence. The scheme was launched gradually in 2003 and all counties in China were covered with NCMS by From 2003, each provincial government chose different counties within the province as pilot areas for each year. 8 Once a county became a pilot area, the local government would continuously provide NCMS to the people in the region from then on. The areas increased year by year after Figure 2 shows the distribution of treated counties across different implementation years. The health expenditure coverage for NCMS varies slightly across counties and has been increasing since its early implementation. First, NCMS provides a fixed subsidy to each person per year for all outpatient services that this person consumed in a year. This does not include outpatient services for chronic diseases. For any inpatient services, NCMS provides reimbursements (around 80 to 90 percent, but it varies across counties) for each inpatient treatment, but there is a cap on the amount that can be reimbursed per year. The subsidies as well as the cap on the reimbursements have been increasing since the early 5 The Longitudinal Survey on Rural Urban Migration in China (RUMiC) consists of three parts: the Urban Household Survey, the Rural Household Survey and the Migrant Household Survey. It was initiated by a group of researchers at the Australian National University, the University of Queensland and the Beijing Normal University and was supported by the Institute for the Study of Labor (IZA), which provides the Scientific Use Files. The financial support for RUMiC was obtained from the Australian Research Council, the Australian Agency for International Development (AusAID), the Ford Foundation, IZA and the Chinese Foundation of Social Sciences. 6 RUMiC only contains two waves, one in 2008 and one in In the 2009 data, it cannot capture all the new migrants who migrated in 2009 in the sample, so I use the number of new migrants up to year 2008 in the 2009 data set. 7 Website: 8 The selection of "pilot areas" is discussed in Section Figure 4 in the Appendix shows the increase in numbers of counties from 2003 to 2008 in the five provinces used in the macro data set. 5

6 Note: This distribution is based only on data collected from provincial statistical yearbooks. The figure only includes counties from the five provinces covered in this paper. There is also one county starting in 2002 and one county starting in Figure 2: The distribution of counties for different implementation years implementation years of NCMS. For outpatient services and medicines for chronic diseases, NCMS provides reimbursements depending on the disease type, and the coverage for these chronic diseases increases year by year. Hence, even for these young migrants, NCMS is also beneficial if they choose to enrol in this scheme. There are three main health schemes in China: Urban Resident Health Care Insurance, Urban Employee Health Care Insurance and NCMS. NCMS covers Group 1 and Group 2 discussed in Section 2.1. Urban Resident Health Care Insurance covers the urban hukou population but only those who are not employed, such as young students and senior residents. Urban Employee Health Care Insurance covers people who are employed in companies that offer this insurance in urban areas, regardless of whether the person is an urban or rural resident. This scheme counts as a welfare benefit for employees while most of the ruralto-urban migrants usually work in companies that lack welfare benefits. Combining these three insurance schemes, it is obvious that only Group 3 is not covered by public health insurance. Given their low income level, it is also impossible for them to buy commercial health insurance. 10 The summary of the coverage is in Table 1. NCMS is the only insurance scheme for which those migrants are eligible, so this scheme counts as an incentive for them to return to or to stay in their official hukou residence, rather than work in urban areas. This is the main reason for the potential restraining effect caused by NCMS. There was some coverage for rural-to-urban migrants in big cities, such as Beijing, Shanghai, Guangdong and Shenzhen, but these schemes were not compulsory for employers of migrants and were not well implemented (Barber and Yao, 2010). 10 Also the market for commercial health insurance was very limited during the implementation period of NCMS. 6

7 Group Urban residents Urban employees Rural-to-urban employees Rural residents Intra-county migrants Rural-to-urban migrants Heath insurance coverage URHI UEHI UEHI NCMS NCMS No HI Coverage Table 1: Different groups and health insurance coverages in China 3. Restraining effect on migrants: Evidence from CHNS 3.1. Main mechanism of the job lock by NCMS This paper mainly focuses on whether NCMS has an effect on constraining the rural-tourban migrants or potential rural-urban migrants in their hometown. The theory behind the empirical method is the simple compensating-differential model of Gruber (2000) which, in turn, is based on Rosen (1986). A modified form of the Gruber model is applied to the rural-to-urban migration context and the details of the model are in the Appendix. The migration decision under the compensating differential model is very simple. For those who have already migrated, if NCMS were to shorten the urban-rural income gap after decreasing the medical expenses in rural areas, then the number of those who want to move back to their hometown would increase. For these people in the rural areas, the number of people who want to migrate would also decrease. The important condition for these changes is that NCMS does not change the probability of getting sick for individuals, which is likely to be true Data sets, methods and regressions The CHNS is a comprehensive survey data set covering information regarding income, health care, medical expenditure, health insurance and other aspects. Data are collected by questionnaires filled out by households and the data are at the individual-level. This unbalanced longitudinal data set contains comprehensive information for households from nine different provinces for Hence, CHNS can be treated as a comprehensive representation of national data; it is used in two ways here. First, I use the individuallevel data originally provided by CHNS to examine the effect of being enrolled in NCMS on the probability of being a migrant. This method verifies the job-lock effect of NCMS on individuals. The second method of using CHNS is to generate a county-level CHNS dataset. This creates new macro-level data to compare to the analysis in Section 4. This method can also be used to examine whether county-level migration propensities are also affected by NCMS. In CHNS, I cannot distinguish the return migrants in the rural population; however, as discussed in Section 2.1, the return migrants only accounts for 10% of the total migrants. 11 Liaoning, Heilongjiang, Jiangsu, Shandong, Henan, Hubei, Hunan, Guangxi, and Guizhou. Guangdong is not included in this data set. There are nine waves: 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009 and, recently

8 Probability of individual job changes Utilising the CHNS in the first way, I use two key variables in the dataset: the variable that indicates whether an individual is seeking jobs somewhere else (labelled here as migrant) and the variable that shows whether an individual has NCMS (labelled here as havencms). I keep all the rural population entries, but it is difficult to identify whether a person is a return migrant in this data set. CHNS only provides information on whether an individual is now a migrant and how long this individual has been away from home. Since, for the years 1989, 1991, and 1993, there is no information about the migration behaviour in the data, I will only use 6 waves from 1997 to In order to be consistent with the later analysis at the macro level, the variable migrant only includes those rural residents aged between 16 and 45, 12 the main age range for the rural-to-urban migrants (NBS, 2012). To drop the intra-county migrants in the sample, I use the variable that shows how long an individual has been away from home in order to filter out these rural-to-urban migrants that can easily benefit from NCMS. Only those migrants who have been away from home for more than six months are included in the sample. Using the fixed-effect method with panel data, the possible unobservable individual selection problem can be solved. The main difference-in-differences regression is migrant d,t = α + βhavencms d,t + t v t p λ p + d µ d + ε d,t, (1) where d is the index for individuals and t stands for time. d µ d is the individual fixedeffect dummies and t v t p λ p is the year fixed effects (for 6 waves) interacted with the province (p) dummies (9 provinces in total). The error term ε d,t is robust and clustered at the individual-level. havencms d,t is the treatment and time interaction term in this equation. However, there might be a reverse causality between variables migrant d,t and havencms d,t even after controlling for the individual fixed effects. I adopt the Lei and Lin (2009) method and use a county-level NCMS enrolment variable as the instrumental variable for havencms d,t. Lei and Lin test the exogeneity of the county enrolment status in NCMS relative to the individual utilization of health services and confirm that the only thing which affects the implementation of NCMS is the central government policy change. In the migration context, the story is similar: individual choices on whether to migrate do not affect a county s decision to enrol in NCMS. The exogeneity of the instrumental variable is satisfied. The other requirement for the instrumental variable is that there must be a sufficent amount of correlation between the instrumental variable and the regressor. To create the instrumental variable, I generate earlytreated06 to get information on county-level enrolments. The detailed county-level data from CHNS are not available, so the data set is not precise on the date of NCMS implementation. To specify these dates, each county is classified as belonging to one of two groups: the early-treatment group or the late-treatment group. This classification requires a number of assumptions: first, those 12 CHNS data set includes people aged from 1 to

9 counties where there are sudden increases in numbers of people enrolled in NCMS in 2004 or 2006 should be classified as the early-treatment group. 13 Second, because CHNS only has data for the years 2004 and 2006 around the beginning of NCMS implementation (2003) and 2004 is near the beginning date of implementation, the year 2006 can be taken as a safe date to ensure that at least some of these counties have started NCMS. The dummy variable earlytreated06 sets to 1 for all individuals from early-treatment counties for the waves 2006, 2009 and 2011 and 0 for other individuals and waves. The first stage result is reported in Table 2 and the coefficient is positive and significant with large F -statistics. It establishes that the instrumental variable satisfies the correlation requirement. The reduced-form regression for this question then becomes migrant d,t = α iv + β iv earlytreated06 d,t + t v t p λ p + d µ d + ε iv d,t. (2) The results for the fixed-effect method with the instrumental variable are reported in Table 3, after controlling for the individual and the year times province fixed effects. The result is negative and significant for earlytreated06 d,t. The effects of NCMS on individual choices test the stock of migrants. It means that, on average, being enrolled in NCMS reduces the probability of one being a migrant by 6.5%. It is difficult to gauge how large this effect is. The negative 6.5% effect on individuals will also be the effect on the migration propensity for counties if I aggregate individuals by county. However, this individual effect cannot safely be generalized to the county level; accordingly additional county level regression analyses are reported in equations (3) and (4) below. Data used are the county-level CHNS in this section and also the yearbook data in Section 4. Different preferences on NCMS caused by gender differences can also be examined using the individual dataset. Traditionally, people believe that women are more risk-averse than men (Borghan et al., 2009), so the strong insurance preference of the female migrants might camouflage the fact that healthy male migrants do not want to be enrolled in the insurance scheme. However, the results in Table 3 show that males are more affected by NCMS, and the results are consistent with the results provided by Mou et al. (2013): female migrants are more likely to be under- or uninsured Migration propensity The second method of using CHNS is to create a county-level CHNS dataset. Using the variable migrant, I create a variable measuring the total number of eligible migrants in different counties. 14 Dividing the total number of eligible migrants by the total population for different counties, I get the migration propensity for each county P rop(migrants) i,t. I 13 If a county has less than 10 people in 2000 (2004) and has at least 50% increase in 2004 (2006), then I count this as a sudden increase and treat this county as an early-treatment county. Detailed summary statistics of numbers of people in each county for each year are presented in the Appendix. The percentage of individual enrolment for each county at different years is also presented in the Appendix. 14 Eligible migrants mean those migrants included in the sample for regression (2), which are aged between and who have been away from home for more than six months. 9

10 VARIABLES havencms d,t earlytreated *** (0.0141) individual FE Yes year x province FE Yes Observations Number of individuals 7516 R-squared F-statistic Note: Robust standard errors in parentheses.* significant at 10%; ** significant at 5%; *** significant at 1%. earlytreated06 is the instrumental variable for havencms, the treatment and time interaction term in the difference-in-differences method. This table is to test the first stage of the IV method, F -statistic is larger than 10. Table 2: The first stage result for the instrumental variable also create the growth rate of the migration propensity for each county. I construct average income per capita for each county to be used as the control variable. After dropping all the duplicates, I get the county-level CHNS. Due to the data limitation, CHNS county-level data only have 36 counties for the years 1997, 2000, 2004, 2006, 2009, and A differencein-differences county-level regression will be applied to the county-level CHNS. Again, since I cannot get the specific county data in CHNS, I will use earlytreated06 as the treatment and time interaction term. The regression for this county-level CHNS data used to examine the impact of NCMS implementation on the migrants propensityfatt for each county is: P rop(migrants) i,t = α+βearlytreated06 i,t +controls i,t + t v t p λ p + i µ i +ε i,t, (3) where i is the index for county and t stands for time. P rop(migrants) i,t is the propensity of rural residents in county i working outside their home county (county i) at time t. i µ i is the county fixed-effect dummies for each county and t v t p λ p is the year fixed effect interacted with the province (p) dummies (nine provinces in total). The error term ε i,t is robust and clustered at the county level. For the controls i,t, I control for the average income per capita for each county. To examine the impact of NCMS on the growth rate of the migrants propensity for each county, the regression is: Growthrate i,t = α + βearlytreated06 i,t + controls i,t + t v t p λ p + i µ i + ε i,t, (4) and all other variables have the same meaning as in equation (3) The results for the two regressions are reported in Table 4. It shows that the NCMS implementation does not have a significant negative effect on the migration propensity but 10

11 Male Female VARIABLES migrant d,t migrant d,t havencms ** ** (0.0327) (0.0520) (0.0399) individual FE Yes Yes Yes year x province FE Yes Yes Yes Observations Number of individuals R-squared Note: Robust standard errors in parentheses.* significant at 10%; ** significant at 5%; *** significant at 1%. earlytreated06 is the instrumental variable for havencms, the treatment and time interaction term in the difference-in-differences method. This table also shows the effect of havencms by gender. Males and females in this data set are nearly equally sampled in the data set. Table 3: Results from the fixed-effect IV regressions for migrants (also by gender) has a marginally significant negative impact on its growth rate. The negative NCMS impact on the growth rate of migration propensity using this CHNS county data should enhance the hypothesis that NCMS actually locks the rural-to-urban migrants in rural areas, although through a second-order effect. This piece of macro-level evidence can help specify how large the effect of NCMS implementation on migration is for each county. On average, NCMS could decrease the growth rate of migrants by 0.832% yearly. According to the 2009 Migration Report by NBS, the growth rate for the number of inter-county migrants is 3.5%. The average yearly impact amounts to more than a quarter of the yearly growth rate. Given the large population of the migrants group, around two million people would be affected each year. Analysing the micro-level results, NCMS affects individual migration choices through compensations for health insurance and reduces the probability of one s decision to become a rural-urban migrant. The reverse causality between having NCMS and being a migrant is eliminated by using the appropriate instrumental variable. The county-level CHNS evidence shows that NCMS has effects on the growth rate of the propensity of migrants for each county. The attrition problem might affect the individual-level results (examined in the next subsection), but it does not affect the county-level results. Both results suggest that the health insurance program encourages rural residents to stay in their hometown. However, as discussed in Section 2, the coverage of NCMS has been increasing since its implementation. The restraining effect of NCMS is also due to the increasing coverage of NCMS Attrition problem in panel data If I want to track individual behaviours over time, the attrition problem might affect the results. To get rid of the attrition problem, I keep only the individuals who are always in the data set from 1997 to The number of individuals drops from 7516 to 3115, 11

12 VARIABLES P rop(migrants) i,t Growthrate i,t earlytreated06 i,t * (0.0101) (0.4703) county FE Yes Yes year x province FE Yes Yes controls Yes Yes Observations Number of counties R-squared Note: Robust standard errors in parentheses.* significant at 10%; ** significant at 5%; *** significant at 1%. The control variable is the average income per capita for each county. earlytreated06 is the treatment and time interaction term in the difference-in-differences method. P rop(migrants) i,t is the county-level migration propensity and Growthrate i,t is the growth rate of the migration propensity. There are only 36 counties in this sample because of CHNS data limitations. The number of observations drops in the third column because for the growth rate of the migration propensity, it loses one-year of data due to the calculation. Table 4: Results from the county-level CHNS data about half of the sample remained. I applied the same regressions in this reduced sample and the results are in Table 5. Comparing the results in Table 3 and Table 5, it is clear that, apart from the number of observations, the two results are very similar. Therefore, it adds credibility to the micro-level evidence Mechanism check It is also necessary to test whether NCMS reduces health care expenditure. If NCMS really reduces expenditure, it means that NCMS functions through the mechanism described in the compensating-differential model. Recall that in this model, NCMS allows reimbursements from health care expenses in rural areas. The variable m30 in CHNS is the treatment costs of a person s illness or injury within four weeks of the survey date. Using this variable as the main dependent variable, the regression for testing the relationship between whether a person has NCMS and recent medical expenditure is m30 d,t = α + βhavencms d,t + controls d,t + t v t p λ p + d µ d + ε d,t, where all other variables have the same definitions as before. The instrumental variable earlytreated06 d,t is also used to avoid the reverse causality problem arising from the fact that people with higher medical expenditure are more likely to self-select into NCMS. The arguments for the instrumental variable are the same as before. The result for this IV fixed-effect regression is in Table 6. The deflated household income per capita is controlled because the decision on medical expenditure is more a household one than an individual decision in China (Wang et al., 2006). 12

13 Male Female VARIABLES migrant d,t migrant d,t havencms ** ** (0.0350) (0.0518) (0.0458) individual FE Yes Yes Yes year x province FE Yes Yes Yes Observations Number of individuals R-squared Note: Robust standard errors in parentheses.* significant at 10%; ** significant at 5%; *** significant at 1%. earlytreated06 is the instrumental variable for havencms, the treatment and time interaction term in the difference-in-differences method. This table also shows the effect of havencms by gender. Males and females in this data set are nearly equally sampled in the data set. In this attrition-bias-free CHNS data set, the sample size drops from around 7,000 to 3,115 observations. However, the results are similar to Table 3. Table 5: Results from the fixed-effect IV regression for migrants (by gender) from the attrition-bias-free CHNS data Table 6 shows that the number of observations decreases substantially when m30 is used as the dependent variable. This is inevitable because there are many missing entries in CHNS for the expenditure data. To make the sample consistent with the sample of migrants in regression 2 and to make sure the sample is large enough, the data set is limited to those aged between 16 and 50 and away from home for more than six months. I also need to exclude the outliers in the medical expenditure data. 15 The results show that, in this group, the people with NCMS on average spend less on health-care services than those not $300) yuan is a relatively large amount, larger than the monthly income (1417 yuan) of a migrant in 2009 Migrants Report by NBS. Because there are a lot of missing entries in medical data in the CHNS dataset and also the results show that being enrolled in NCMS only reduces the treatment costs for the illness within four weeks of the survey date, more evidence is needed to verify the channel. There are many references in the literature on the reduction in out-of-pocket expenditures by NCMS. Sun et al. (2009) find that NCMS decreases the out-of-pocket payments and significantly decreases the number of households below the poverty line after catastrophic illnesses, and they also draw a similar conclusion in their paper in 2010 (Sun et al., 2010). Wagstaff et al. (2009) present in the review of NCMS that NCMS increases the outpatient and inpatient utilisation and reduces the cost of deliveries. Combining the related literature and the results from the medical expenditure in this section, one may infer from this result 15 There are 40 outliers: 39 of them equal to and another one equals to The total number of available entries for medical expenditure is about The distribution including the outliers is displayed in the Appendix. From the graph, the distribution is not left skewed. Therefore, it should be valid to drop the outliers. 13

14 VARIABLES m30 d,t havencms -1,879* (1,091) individual FE Yes year x province FE Yes controls Yes Observations 1,627 Number of individuals 1,349 R-squared Note: Robust standard errors in parentheses.* significant at 10%; ** significant at 5%; *** significant at 1%. earlytreated06 is the instrumental variable for havencms, the treatment and time interaction term in the difference-in-differences method. The control variable is the household income per capita (cpi-adjusted). m30 d,t, is the treatment costs of a person s illness or injury within four weeks of the survey date. The number of observations drops from 16,692 to 1,627. Table 6: Result for the relationship between having NCMS and medical expenditures that NCMS decreases medical expenditure for the migrants, just as the mechanism of the model predicts. 4. Evidence from the macro-level data 4.1. Macro data set and main variables To endorse the results from the county-level CHNS data, I compile another novel data set from provincial statistical yearbooks. The macro-level data set consists of county-level data collected from the yearbooks of five provinces from 1998 to Only five provinces, Jiangsu, Gansu, Ningxia, Hubei and Shanxi, provide data on the number of migrants in their county-level provincial yearbooks, yet these provinces are important in migration and economic activities in China. Gansu, Hubei and Shanxi are in the top-ten list of the migrantexporting provinces (Chan, 2013), 16 and Jiangsu is the province with the second largest GDP in China. 17 The main variables collected are total number of workers in the labour force in rural areas, the total number of migrants in rural areas, Gross Domestic Product (GDP) at the county-level. The sample consists of inter-county rural-to-urban migrants and most of the migrants are aged between (NBS, 2012), which is consistent with the sample I used in 16 In the list, Chongqing needs to be included in Sichuan province because it is more a city than a province in terms of the land area. 17 Source: 14

15 the micro-level section. For all provincial yearbooks, in most of the years, total number of workers in the labour force in rural areas and Gross Domestic Product (GDP) are provided. The formats of the provincial statistical yearbooks are not completely consistent over a long time period, so that, in certain years for one or two provinces, the total number of migrants in rural areas is missing. Some imputations based on the data set are needed to fill in the missing values. 18 Another important variable is the date of NCMS implementation for different counties. In this data set, I can obtain exact information about NCMS implementation for all counties. The information is obtained from counties official documents on NCMS and also from news and documents from provincial governments. 19 Suburban areas are excluded from the regressions: each suburban area is classified administratively as a county, but it makes more sense to think of it as a prefecture-level city rather than a county in the rural areas. All data are at county-level: so the intra-county rural-to-urban migrants are counted as being in the labour force in other sectors (such as manufacturing, service, etc.) within the county total labour force. The migrants in rural areas in this macro-level data set are those inter-county or inter-provinces migrants. They cannot easily benefit from NCMS and this is the group on which NCMS might exert its locking effect. Data from Guangdong province are used as a placebo test for the main results. This province implemented an early version of NCMS in 1999 (Zheng, 2011), so it has been already treated with the new health insurance when NCMS was first implemented in other provinces. The results from the placebo test show that NCMS does not affect the migration trend negatively. The details of this placebo test are in the Appendix Empirical methods and results In this macro data set, the exact dates of NCMS implementation are provided, so the difference-in-differences method is easy to implement for testing these effects empirically. Different counties have different implementation dates, and hence, during the same period, some counties had implemented this scheme while others had not. There are a treatment group and a control group for comparison. The treatment group includes those counties with NCMS and the control group has those counties without NCMS in each year. Counties in these groups change in each year until finally all counties are in the treatment group. After controlling for county fixed-effects and year times province fixed-effects, the differences between the treatment group and the control group should be caused by the different timing of the policy implementation. This conclusion can only be reached if the time trends 18 The detailed information about imputation methods is in the Appendix. For example, if, for year 2000, the total number of migrants in rural areas is missing, but I have data for this variable for 1999 and 2001, and I use 1999 to 2001 data s growth rate to calculate 2000 s data. Also, if in the yearbooks, there is no data for the total number of migrants in rural areas, but it has all other sectors labour force data, I will approximate the total number of migrants in rural areas using the total number of the labour force in rural areas minus total numbers of all other labour forces. The means of calculation depend on different provincial statistical yearbooks. 19 Each government has one official document on the implementation date of the scheme. It is difficult to list all of them in this paper. 15

16 for these two groups were the same before implementing NCMS. The identifying assumption for the difference-in-differences method is the parallel trend for both the treatment and the control group before the policy implementation. The results from the differencein-differences method are valid only if the identifying assumption is not violated. In the previous county-level CHNS data set, because there is no detailed information on when exactly a county started NCMS, I cannot test for the parallel trend before the implementation of the scheme. For this new data set, I can test the parallel trend for different groups using the lead and lag year dummies of the initial NCMS implementation for each county. NCMS mainly depends on the central government policy and was emphasised mainly as a welfare benefit for rural residents instead of targeting the rural migrants (Yi et al., 2009). From the policy point of view, the exogeneity of NCMS implementation relative to the rural-to-urban migrants is confirmed. NCMS also needs to be exogenously assigned to each county in theory. However, the official requirements for the timing of implementation of NCMS in each county are vague. 20 The requirements are not related to the timing of the implementation. One main concern is that rich counties (with high GDP per capita) might have implemented NCMS earlier than poor counties. I classify counties into two groups based on the date of NCMS implementation: an early-treatment group and a late-treatment group. The early-treatment group includes counties that had NCMS implementation in 2003, 2004 and 2005 and the late-treatment group includes those counties with the implementation after Table 7 shows the summary statistics for these two groups before the implementation. 21 Comparing the summary statistics of the early-treatment and late-treatment groups in the years before the early NCMS implementation (2003 and 2004), there is around a 1 or 2 percentage-point difference in the migration propensity between these two groups. The earlytreatment group always has a higher percentage of migrants before 2000 and the difference is smaller after Therefore, in the short time period just before the early pilots, there is nearly no difference between these two groups. It is difficult to conclude that the migration propensity in a county, which is the main dependent variable in regressions, affects the implementation dates of NCMS. Focusing on the GDP per capita comparison for the two groups, the early-treatment group always has a higher GDP per capita than the other group. The difference is not very large, but it still suggests that GDP might affect the implementation of NCMS. So the parallel pre-trend of different counties (after controlling for fixed-effects) is the main identification channel in this case. I also control for the county GDP in the main regressions and test its correlation with the implementation date of NCMS. Figure 3 gives an overview of the impact of NCMS on the average migration propensity for the early-treatment and late-treatment group. It shows a negative lagged impact of NCMS 20 Website: (The content is in Chinese). The requirements are such that the county has sufficient ability managing health care resources or the county needs to have sufficient subsidies to help the implementation of NCMS, but they did not define what is sufficient in their requirements. 21 Urban areas and suburban areas are not included. 16

17 Average GDP per capita Average migration propensity Early-treatment Late-treatment Early-treatment Late-treatment % 5.08% % 4.22% % 3.87% % 4.15% % 6.75% % 5.72% % 6.25% % 8.97% % 8.01% Note: The early-treatment group includes counties that had NCMS implementation in 2003, 2004 and 2005 and the late-treatment group includes those counties with the implementation after For the years before NCMS implementation, the average migration propensities are similar between the early and late-treatment group. It corresponds to the migration percentage graph in Figure 3. Table 7: Summary statistics for the early and late treatment groups ( ) on the early-treatment group and also that the parallel pre-trend exists for the two groups. There are observable differences between these groups before 2000, and the gap decreases afterwards. The average migration propensity in the early-treatment group becomes less than the average propensity in the late-treatment group after This graph reflects the lagged negative effects of NCMS on migration in rural areas. The average migration propensity in the late-treatment group has a large drop in This also suggests the lagged negative effects of NCMS. The changes in the growth rate of the migration propensity for different treatment groups are shown in Figure 3. Note that this growth rate comparison figure is not a de-meaned time trend. The figure only aims at showing that the early-treatment group has lower growth rates compared to the late-treatment group in the years after the early NCMS implementation and before the late implementation. It needs more data after 2011 for a further check; however, in 2009, the health system reform in China also emphasised improving the health care insurance system for rural-to-urban migrants. 22 Around 2010 and 2011, large cities, such as Guangdong and Shanghai, either implemented the insurance specially for the ruralto-urban migrants or allowed these migrants to enrol in other insurance schemes in the cities. 23 This might explain the increasing migration propensity or the growth rate of the propensity in the years 2010 and 2011 in Figure 3. As discussed in Section 2, the financial crisis did not affect the trend of migration greatly. 22 Website: (in Chinese). 23 Shanghai: and Guangdong cancelled the migrants insurance system established in 2009 and allowed the migrants to enrol in urban employee insurance, website: 17

18 Note: The left one is the graph for the migration propensity and the right one is the graph for its growth rate. The blue line is the early-treatment group and the red line is the late-treatment group. In each graph, the red line on the left is at the year 2004, which is the average implementation year for the early-treatment group. The red line on the right is at the year 2007, which is the average implementation year for the late-treatment group. Figure 3: Overview of changes in the average migration propensity and its growth rate for the early and late-treatment groups In terms of other large-scale agricultural reforms that might affect the trend, the central government officially abolished the agricultural tax on January 1st, This reform was nation-wide, so the abolishment of agricultural taxation was implemented provincially from 2004 to 2006 (Section 2.2.4, Li 2015). If in the regressions, I control for the yeartime-province fixed effect, the effect of the tax reform will be captured by this fixed effect. This is one of the reasons that I am not using the straight year and province fixed effects. Another reason is that for different provinces from 1998 to 2011, the provincial leaders changed at least five times. Different provincial leaders also affect policy implementations of their province differently, depending on the closeness of their relationship with the central government (Chuang, 1995). Therefore, it is necessary to use the year times province fixed effect to capture the province s fixed effect that changes over time. To observe the basic results of NCMS overall impacts on the migration propensity for different counties, the regression function using the difference-in-differences method is: P rop(migrants) i,t = α + βncms i,t + controls i,t + t v t p λ p + i µ i + ε i,t, (5) where i is the index for county and t stands for time. Like Gruber and Madrian (1993) and other papers that test the job-lock effects for different insurance policies, I use a labouroutcome variable as the dependent variable. P rop(migrants) i,t is the propensity of rural residents in county i working outside their home county (county i) at time t. Similar to the definition in Section 2, it is the number of rural-to-urban migrants from a county divided by the total rural working population in the county, including the rural-to-urban migrants. 24 Website: 18

19 P rop(migrants) i,t is the main dependent variable and is used for analysing the stock of migrants. NCMS i,t is an indicator set to 1 when county i implements NCMS in year t. For example, if a county was chosen to be the pilot area for NCMS from 2005, then the NCMS i,t i µ i for this county will be 0 before 2005 and 1 for 2005 and the years afterwards. is the county fixed-effect dummies for each county and t v t p λ p is the year fixed effect (for 14 years from 1998 to 2011) interacted with the province (p) dummies (five provinces in total). The robust error term ε i,t is clustered at the county level. For the controls i,t, I control for GDP for each county at each year. 25 County GDP and the NCMS implementation dates are not statistically correlated after controlling for year-cross-province fixed effect, so including GDP as a control variable will not lead to the multicollinearity problems. 26 The result for this regression is in Table 8. The result shows an insignificant coefficient for the implementation of NCMS. VARIABLES P rop(migrants) i,t NCMS (0.0250) county FE Yes year x province FE Yes controls Yes Observations 3,069 Number of counties 257 R-squared Note: Robust standard errors in parentheses.* significant at 10%; ** significant at 5%; *** significant at 1%. The control variable is the county GDP at each year. NCMS is the treatment and time interaction term in the difference-in-differences method. Table 8: The result for the average effect of NCMS on P rop(migrants) i,t NCMS might take years to come into effect, but the variable NCMS i,t can only represent the aggregate average effect from the year of implementation and onwards. It is difficult to assess the previous and following years effects of the new insurance policy using this regressor. To test for the parallel pre-trend before the implementation and the effects of NCMS after, I created another dummy variable, F irstncms i,t. It equals 1 at the year of NCMS implementation in a county i and, in other years, the dummy equals 0. For example, if a county was chosen to be the pilot area for NCMS from 2005, then the F irstncms i,t dummy equals 1 only in 2005, and it equals 0 in all other years. In the regressions with leads and lags of F irstncms i,t, the identifying assumption (the parallel pre-trend) is equivalent 25 To rule out the multicollinearity between NCMS and GDP, the F -test statistic for all the following regressions is larger than Results for the correlation between GDP and implementation dates are in the Appendix Table.21. This shows the VIF for this regression is smaller than 5, which is further evidence to rule out the multicollinearity between NCMS implementation date and county GDP. 19

20 VARIABLES P rop(migrants) i,t F4.FirstNCMS (0.0111) F3.FirstNCMS (0.0088) F2.FirstNCMS (0.0185) F.FirstNCMS (0.0336) FirstNCMS (0.0299) L.FirstNCMS (0.0722) L2.FirstNCMS (0.0515) L3.FirstNCMS (0.0527) L4.FirstNCMS * (0.0545) county FE Yes year x province FE Yes controls Yes Observations 2,083 Number of counties 178 R-squared Note: Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. The control variable is the county GDP at each year. The missing counties are mostly counties from Shanxi province. It is because some counties in Shanxi implemented NCMS in 2006, and, for this year Shanxi province does not provide the yearbook for this year. Table 9: The results of the leads and lags effect of F irstncms on P rop(migrants) i,t to the insignificant leads regressors. With 4 leads and 4 lags of the variable F irstncms i,t, 27 the new regression is P rop(migrants) i,t = α + βf irstncms i,t + 4 m=1 β t mf irstncms i,t m + 4 n=1 β t+nf irstncms i,t+n + controls i,t + t v t p λ p +. (6) i µ i + ε i,t The results are shown in Table 9 and the graph for the coefficients is presented in Figure 4. Interpreting the results from Table 9 and Figure 4, the standard difference-in-differences pre-trend assumption is not violated: the coefficients of lead variables (FX.FirstNCMS) are insignificant and their values are close to zero and are smaller than those of lagged variables. The results show insignificant coefficients for F irstncms i,t, F irstncms i,t 1, F irstncms i,t 2, and F irstncms i,t 3, but a negative significant coefficient for F irstncms i,t 4. It is difficult to draw any conclusion about effects of the insurance policy here and I find no evidence of NCMS locking the rural migrations in their hometown. But this result does suggest that NCMS might have a lagged negative effect on the migration propensity. The difference between the number of counties in Table 8 and 9 is mainly because some counties in Shanxi 27 Results for different numbers for leads and lags are presented in the Robustness Check section in the Appendix. 20

21 Note: The confidence intervals are 90% confidence interval. F N stands of F irstncms. fx_f Ns indicate the lead variables and lx_f N represent the lad variables. Figure 4: The graph for the four leads and lags effect of F irstncms on P rop(migrants) i,t implemented NCMS in 2006 and for this year Shanxi province does not provide the yearbook for this year. The data set is from 1998 to 2011: in this period, China was experiencing rapid development and urbanization. The urban-to-rural income ratio increased from 2.5 to 3 and has been stagnating at a high level since 2007 (Sicular, 2013). There should have been an irresistible increase in rural-urban migration during this time (Shi, 2008). It is reasonable to believe that, although NCMS does not decrease the propensity of rural migrants for each county directly, it might slow down its growing trend. To test this effect, the dependent variable is changed to the growth rate of the migration propensity in a county, so the two regressions become: Growthrate i,t = α + βncms i,t + controls i,t + t v t p λ p + i µ i + ε i,t (7) and Growthrate i,t = α + βf irstncms i,t + 4 m=1 β t mf irstncms i,t m + 4 n=1 β t+nf irstncms i,t+n + controls i,t + t v t p λ p +. (8) i µ i + ε i,t The results are in Tables 10 and 11. Figure 5 shows the coefficients from the regression equation (8). The aggregate effects of NCMS are insignificant, whereas there are significant negative impacts of NCMS implementation on the Growthrate i,t for the years after NCMS implementation. 28 These results indicate that NCMS slows down the growing trend of the rural-urban 28 Except the second year after the implementation. 21

22 VARIABLES Growthrate i,t NCMS (0.779) county FE Yes year x province FE Yes controls Yes Observations 2968 Number of counties 254 R-squared Note: Robust standard errors in parentheses.* significant at 10%; ** significant at 5%; *** significant at 1%. The control variable is the county GDP at each year. NCMS is the treatment and time interaction term in the difference-in-differences method. The loss of counties is because the change of the main dependent variable. For some counties, some of the migration propensities are missing, so there are more missing entries when generating the growth rate of the migration propensity. Table 10: The result for the average effect of NCMS on Growthrate i,t migration in the years after implementation. The insignificant results in Table 10 might be due to the fact that NCMS takes time to come into effect. The rural residents who are currently working in urban areas also need time to quit their jobs and settle in their hometown if they want to move back for NCMS. The results also imply that the implementation of NCMS might have long-run negative effects on the migration propensity because it decreases the flow of migrants. NCMS could decrease the growth rate of migrants by as much as 1.72% on average in the third year after implementation. This is a large effect. As mentioned before, the growth rate for the number of inter-county migrants was 3.5% in The average third-year impact nearly equals half of the yearly growth rate. Generalizing the results to the whole country and, given the actual number of intercounty migrants in 2008 is 140,410,000 (NBS, 2012), if NCMS decreased the growth rate in 2009 by 1.72%, this would mean that 2,416,000 rural residents would be induced to stay in their hometown. 29 The decrement in the flow of migrants in just one year would be more than two-thirds of the total labour force of Singapore, Hong Kong or Massachusetts: the large absolute numbers arise from the large population base. 30 A 1.5 percentage decrease for the growth rate of the migration propensity for each year is reasonable for this policy change, considering the great trend of increasing migration during that period. The county-level CHNS evidence in Section 3 shows that NCMS has a larger and more significant effect on migrants than the evidence from this macro-level data. Less misreporting 29 Assume all counties implemented NCMS in Sources: (HKG), Force-In-Singapore-2013.aspx (SGP) and (USA) 22

23 VARIABLES Growthrate i,t F4.FirstNCMS (0.366) F3.FirstNCMS (0.600) F2.FirstNCMS (1.303) F.FirstNCMS (1.492) FirstNCMS (0.888) L.FirstNCMS ** (0.689) L2.FirstNCMS (1.061) L3.FirstNCMS ** (0.738) L4.FirstNCMS ** (0.556) county FE Yes year x province FE Yes controls Yes Observations 2036 Number of counties 178 R-squared Note: Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. The control variable is the county GDP at each year. The missing counties are mostly counties from Shanxi province. It is because some counties in Shanxi implemented NCMS in 2006, and, for this year Shanxi province does not provide the yearbook. Table 11: The results of the leads and lags effect of F irstncms on Growthrate i,t Note: The confidence intervals are 90% confidence interval. F N stands for F irstncms. fx_f Ns indicate the lead variables and lx_f N represent the lead variables. Figure 5: The graph for the four leads and lags effect of F irstncms on Growthrate i,t 23

24 in CHNS may contribute to this inconsistency. Also, the lack of information on a county s NCMS implementation date in the individual-level data could also be one of the causes for the inconsistent results. Analysing the lagged effects of NCMS, NCMS affects the growth rate of migration propensity for each county negatively. However, notice that there are two flaws in the macro-level dataset that might weaken the credibility of the results: under-reporting on the number of migrants on county-level and limited provincial coverage of the data set. For the first weakness, a county s government has an incentive to under-report the number of migrants for them to look good in comparison to other counties in provincial yearbooks of statistics (Cai, 2014). According to Koch-Weser (2013), the underreporting might also be due to unregistered migrants. When interpreting the results, these two main flaws of the macro data set should also be borne in mind. The micro-level data set can make up for these flaws. Therefore, I interpret the macro-level evidence jointly with the results from the county-level CHNS. Notably, the macro-level results using the statistical yearbooks and the county-level CHNS results are similar to each other. Both sets of the results show that NCMS implementation decreases the growth rate of the migration propensity for each county. The increasing coverage of NCMS might also enhance the restraining effect of NCMS and cause the negative lagged effects. An important takeaway from the results is that the implementation of NCMS has a negative impact on the rural-to-urban migrants. The robustness checks for different numbers of leads and lags are presented in the Appendix. The results from these checks show significant negative effects for different numbers of leads and lags for the growth rate of the migration propensity. These results also suggest that NCMS has a long-term effect on the migration rather than an immediate effect. To add more control variables, I also include the rural resident income per capita into the regressions. The regressions are also reported in the Appendix (Table.13). The results with rural income per capita controls present similar, but less significant results. 5. Conclusion Since the early 2000s, the welfare of rural residents has become an important topic for policy makers in China. From 2004, the "No. 1 Document", which is the first document jointly issued by the CPC Central Committee and the State Council for each year, has been 31 focused on improving farmers livelihoods. China s government launched NCMS with the aim of meeting the welfare needs of residents in rural areas. At the same time, the policy clearly has the potential to create an important side effect: it could seriously distort rural-to-urban migration in the labour market. However, NCMS was introduced during a period when China was experiencing rapid development and increasing income inequality between rural and urban areas. The increasing number of migrants since the early 2000s could appear to be counter-evidence to the potential job-lock effect of health insurance in China. Whether the health insurance can lock or constrain rural residents in their hometown during this period might appear questionable. 31 Website: 24

25 However, the individual-level evidence from this paper supports the hypothesis of a joblock effect of NCMS on labour migration in China. It also shows that NCMS has a larger impact on males than females in rural areas. Using the aggregated county level CHNS dataset, the result serves as the first piece of evidence to show that NCMS decreases the migration propensity for each county. However, the macro results suggest that the effect that this policy has on decreasing migration propensities is not as large as the same effect in other countries. The increasing average income difference between urban and rural areas alleviates most of the restraining effects of the health-insurance programme in this case. The lagged effects of NCMS on the growth rate of the migration propensity are negative and significant in the macro data set. It implies that NCMS has decreased the growth rate of the migration propensity and it takes time to come into effect. The results from the macro-level analysis suggest that, although NCMS does not directly decrease the migration propensity for counties, it has a long-term effect on reducing its growing trend. Combining the micro and macro results, the results imply that NCMS restrains rural-to-urban migrants in rural areas, but how large the effect is difficult to conclude. However, given the population of China, even a small change in the growth rate of the migration propensity can affect millions of people s migration behaviour. There are a few limitations of the analysis that qualify the interpretation of the results. First, the data sets are not comprehensive: only twelve of China s 23 provinces are covered and there are some missing years in the macro-data set and missing entries in CHNS. Second, the measurement error problem in both data sets is almost unavoidable when using survey data and yearbook data. Using the county-level and the individual-level data together, the combined results support the job-lock effect. This, to some extent, makes the misreporting less of a problem; but the problem is not completely eliminated and still needs to be borne in mind when interpreting the results. If there were a more complete and larger data set for migrant information in China, this would permit a structural analysis of the willingness to pay for NCMS in rural areas from both county government and individual perspectives. A more complex model could be used with the structural analysis. 32 NCMS provides better health care and reduces medical expenditure for rural residents, but also decreases job mobility in rural areas. In urban areas cheap manual force is still in high demand, but this labour force is shrinking. 33 If NCMS continues to tether potential migrants to their birthplace, it might hinder any further urbanization process. The government should provide a health insurance scheme particularly for those migrants who cannot be enrolled in the urban employee insurance. From 2010 onwards, the government made the enrolment of rural-to-urban migrants in Urban Resident Health Care Insurance easier for 32 If there were more data, the propensity-score matching method could also be used as another identification strategy. I apply the propensity-score matching method on the county-level data set. The results suggest the data set are not large enough for doing the matching after limiting the sample of the counties used in regression. Finally, the increasing coverage of NCMS might attenuate the results before. The pure implementation effect of NCMS may be amplified by the NCMS coverage increment. 33 Gabriel Wildau, China migration: At the turning point. 11e4-b feab7de.html#axzz3fQkZNtL0 25

26 those rural-to-urban migrants. 34 This is a useful step forward, but the ultimate goal for this policy might be difficult to achieve. It is likely that there will continue to be unregistered migrants working in urban areas, especially in middle-sized or small cities. It is also difficult to enforce the policy in small cities because, for these cities, local financial supports might not be enough for this policy. 35 From the previous description of the constraint for NCMS, it is clear that the hukou system is one of the main factors preventing the rural-to-urban migrants from enrolling in the health-insurance programme provided in the urban areas. If the hukou system were abolished and urban and rural people had the identical household registration type, NCMS should not affect individual migration decisions. The migrants could then be enrolled in any rural or urban health insurance scheme in China. The Chinese central government is now trying to abolish or at least relax the restrictions of hukou. 36 However, it is difficult for the government to do it quickly because the design of many other policies was based on the hukou system. It is easier for the central government to introduce a health insurance scheme only for the rural-to-urban migrants and use the new scheme to address the immediate health care needs of the large population of rural-to-urban migrants. References [1] S. L. Barber, L. Yao, Health insurance systems in China: A briefing note, Tech. rep., World Health Organization (2010). [2] L. Borghans, J. J. Heckman, B. H. Golsteyn, H. Meijers, Gender differences in risk aversion and ambiguity aversion, Journal of the European Economic Association 7 (2-3) (2009) [3] Y. Cai, China s Challenges, University of Pennsylvania Press, 2014, Ch. China s Demographic Challenges: Gender Imbalance, pp [4] K. Chan, The Encyclopedia of Global Human Migration, Blackwell Publishing Ltd, 2013, Ch. China: internal migration. [5] Y. Chen, Z. Yin, Q. Xie, Suggestions to ameliorate the inequity in urban/rural allocation of healthcare resources in China, International Journal for Equity in Health 13 (2014) / [6] J. H. Chung, Studies of central-provincial relations in the People s Republic of China: A mid-term appraisal, The China Quarterly 142 (1995) [7] DPCB, Report on injury prevention in China, Tech. rep., Disease Prevention and Control Bureau of Ministry of Public Health (2007). [8] R. W. Fairlie, K. Kapur, S. Gates, Job lock: Evidence from a regression discontinuity design, Industrial Relations: A Journal of Economy and Society 55 (1) (2016) [9] J. Gruber, Handbook of Health Economics, Vol. 1, Elsevier Science, 2000, Ch. Health insurance and the labor market, pp [10] J. Gruber, B. C. Madrian, Limited insurance portability and job mobility: The effects of public policy on Job-Lock, Tech. rep., NBER Working Papers 4479 (1993). [11] J. Gruber, B. C. Madrian, Health-insurance availability and the retirement decision, American Economic Review 85 (1995) [12] J. R. Harris, M. P. Todaro, Migration, unemployment and development: A dynamic two-sector analysis, American Economic Review 60 (1-2) (1970) Source: (in Chinese) 35 Source: (in Chinese). 36 Source: (in Chinese). 26

27 [13] R. Herd, Y.-W. Hu, V. Koen, Improving China s health care system, Tech. rep., Organization for Economic Cooperation and Development (2010). [14] I. N. Koch-Weser, The reliability of China s economic data: An analysis of national output, Tech. rep., U.S.-China Economic and Security Review Commission (2013). [15] X. Lei, W. Lin, The New Cooperative Medical System in rural China: Does more coverage mean more service and better health?, Health Economics 18 (2009) [16] L. Li, Popular Religion in Modern China: The New Role of Nuo, Ashgate Publishing, Ltd., [17] J. Mou, S. M. Griffiths, H. Fong, M. G. Dawes, Health of China s rural urban migrants and their families: A review of literature from 2000 to 2012, British medical bulletin 106 (1) (2013) [18] NBS, The report of rural migrants in China, Tech. rep., National Bureau of Statistics of China (2009). [19] NBS, The report of rural migrants in China, Tech. rep., National Bureau of Statistics of China (2012). [20] X. Qin, Z. Zheng, NCMS s impact on rural labour force: Analysis based on panel-data, China Rural Economics 10 (2011) [21] S. Rosen, Handbook of Labor Economics, Elsevier Science, 1986, Ch. The theory of equalizing differences, pp [22] L. Shi, Rural migrant workers in China: Scenario, challenges and public policy, Tech. rep., Policy Integration and Statistics Department, International Labour Office (2008). [23] T. Sicular, The challenge of high inequality in China, Tech. rep., Poverty Reduction and Equity Department, The World Bank (2013). [24] X. Sun, S. Jackson, G. Carmichael, A. C. Sleigh, Catastrophic medical payment and financial protection in rural China: Evidence from the New Cooperative Medical Scheme in Shandong province, Health economics 18 (1) (2009) [25] X. Sun, A. C. Sleigh, G. A. Carmichael, S. Jackson, Health payment-induced poverty under China New Cooperative Medical Scheme in rural Shandong, Health policy and planning 25 (5) (2010) [26] M. P. Todaro, A model of labor migration and urban unemployment in less developed countries, American Economic Review 59 (1969) [27] A. Wagstaff, M. Lindelow, G. Jun, X. Ling, Q. Juncheng, Extending health insurance to the rural population: An impact evaluation of China s New Cooperative Medical Scheme, Journal of health economics 28 (1) (2009) [28] H. Wang, L. Zhang, W. Hsiao, Ill health and its potential influence on household consumptions in rural China, Health Policy 78 (2006) [29] S. Wang, Y. Li, G. Chi, S. Xiao, J. Ozanne-Smith, M. Stevenson, M. Phillips, Injury-related fatalities in China: An under-recognised public-health problem, The Lancet 372 (9651) (2008) [30] H. Yi, L. Zhang, K. Singer, S. Rozelle, S. Atlas, Health insurance and catastrophic illness: a report on the New Cooperative Medical System in rural China, Health Economics 18 (2009) [31] L. Zheng, NCMS s impact on economic growth: Evidence from Guangdong, Contemporary Economic Research 12 (2011)

28 Appendix The compensating differential model The migration models of Todaro (1969) and Harris and Todaro (1971) focus on market equilibria in rural and urban labour markets. General equilibrium models also emphasise the importance of the unemployment rate in the urban labour market. However, my analysis focuses on partial equilibrium: whether these rural migrants want to come back to, or stay in, their hometown because of NCMS, given that they can find a job in urban areas. The migrants usually are guaranteed at least one job option in rural areas, farming. Gruber (2000) uses a model of pure compensating differential based on Rosen (1986). A modified form of the Gruber model is applied to the rural-to-urban migration context. Focusing on individuals in rural areas, an individual i has preferences over total income in urban areas M iu, or rural areas M ir and the consumption-related job indicator, D i. So the utility function for a rural migrant in urban areas is and in rural areas is U iu = U(M iu, D i ) U ir = U(M ir, D i ); M ir and M iu can take positive or negative values. D i is a binary indicator for the individual s job type, D i = 1 (jobs in urban areas), and D i = 0 (jobs in rural areas). The utility function is quasi-concave in M i. Total income equals wage, W iu or W ir minus health care expenditure, C i M i = W i C i, where we assume for now that health care expenses are the same in both urban and rural areas for simplicity; hence, C i = C iu = C ir. This assumption will be changed after introducing NCMS into the model. Wages in urban areas are usually higher than rural wages. So, the compensating variation (Z) is the difference between M iu and M ir when the individual is indifferent between working in rural or urban areas, U(M iu, 1) = U(M ir, 0), and Z = M iu M ir. The wage difference for an individual, W i, in urban and rural areas is W iu W ir under the assumption of identical urban and rural health care expenses, M i = M iu M ir = W i, where M i is the urban-rural income difference for individual i. The choice of working in urban areas can be summarized as D i = 0 if Z > M i and D i = 1 if Z M. 28

29 I use F (Z) for the cumulative distribution function of Z and f(z) for the associated probability density function. Aggregating from the individual-level to the county level, the fraction of rural population who work in urban areas is N D=1 = ˆ M f(z)dz = F ( M) = P (Z M) (.1) 0 and the fraction of rural population who remain in rural areas is N D=0 = ˆ f(z)dz = 1 F ( M) = 1 P (Z M), (.2) M assuming that demand in both urban and rural labour market is exogenous. Demand for rural migrant workers in urban areas, especially during the period of NCMS implementation, was growing fast (Shi, 2008). It is reasonable to assume that the labour markets in cities were large enough, so that the changes of numbers in migration in each county cannot affect the urban labour market. From equation (1), if W decreases, the fraction of the rural population who work in urban areas decreases. If a rural migrant joins NCMS, he/she can get reimbursements, B i, from health care expenses. The rural-urban income difference for this migrant after joining NCMS becomes: M i = W i + B i = M iu M ir + B i and, as the income difference decreases, health insurance should lead to a decrease in the rural-to-urban migrants. However, in reality, urban health care expenses are usually higher than rural expenses (Chen et al., 2014). This further reduces the income difference: M i = W iu C iu W ir + C ir + B i < M i, where C iu > C ir and it decreases the fraction of the rural-urban migrants in total rural population more than the case where the health care expenses are the same in both rural and urban areas. This is how health insurance affects migration behaviour through income changes. 29

30 Expansion of NCMS Figue.6 shows the gradual expansion of NCMS in the five provinces from 2003 to Figure.6: NCMS implementation from 2003 to 2008 Note: This graph corresponds to Figure 2: The distribution of counties for different implementation years. Only five provinces are shown in this figure. 30