CHIZHENG MIAO 2015:16. Self-employment and happiness in China

Transcription

1 CHIZHENG MIAO 2015:16 Self-employment and happiness in China

2 Self-employment and happiness in China Chizheng Miao Department of Economics and Statistics Linnaeus University SE Växjö Sweden Abstract Although the self-employment rate is high in many developing countries, the job quality of selfemployment has been little studied. Instead of using earnings, this paper uses life satisfaction as a proxy for individual total welfare. Using data from the China Family Panel Studies, we study the selfemployment effect on life satisfaction. We find the life satisfaction of self-employed men is significantly higher than that of wage-employed men; the life satisfaction of self-employed women is not significantly different from that of wage-employed women. To address the informality of labour market, our results suggest that there is no sign that the life satisfaction of the self-employed in the informal sector is significantly lower than that of wage-employed in the formal private sector for both men and women. Keywords: self-employment, life satisfaction, informality chizheng.miao@lnu.se. The author would like to thank the comments from Mats Hammarstedt, Lina Aldén, Helena Svaleryd, Dominque Anxo, seminar participants at the department of economics and statistics Linnaeus University, participants at labour market conference in Kalmar The author would also like to thank Institute for Social Science Survey of Peking University for providing the data.

3 1. Introduction In developing countries, self-employment constitutes a large share of labour force (Maloney, 2004, Parker, 2009). Since China s government enacted the economic reform to establish a market economy, selfemployment has been seen as an important source of economic growth and labour market improvement (Li and Zhao, 2011; Mohapatra, et al., 2007). For instance, Cui, et al. (2013) states that there were approximately 64 million self-employed in 2005 and that self-employment accounts for 10% of the urban labour force. However, existing studies of self-employment in China mainly stress its determinants whereas the outcome is relatively little known, especially of non-economic well-being. 1 Meanwhile, previous literature, such as Hamilton (2000), has shown that the economic return of selfemployment is on average lower than that of the wage-employed. Despite the earnings disadvantage, many people still choose to become self-employed, even in developed countries. Benz and Frey (2008) argue that the individual employment decision does not only depend on the value of outcome, such as earnings, but also on the processes leading to the outcome, known as procedural utility. Moreover, Lange (2012) has found that some characteristics of self-employment, such as autonomy and independence, are important in compensating for the earnings disadvantage of self-employment. Thus, earnings alone cannot fully capture the utility gain from the type of job people have. Instead of using earnings as a proxy for utility, the happiness literature argues that individual subjective well-being provides advantages over the traditional objective utility measure in terms of directly capturing border concept and measuring human well-being (Frey and Stutzer, 2002). 2 Therefore, the subjective selfreported individual satisfaction data have been favored by the self-employment literature to serve as a proxy for utility since it considers all the economic and non-economic aspects of the job. Among all satisfaction data, life satisfaction and job satisfaction are the most commonly used for the proxy of individual s welfare. Previous evidence from developed countries consistently shows that selfemployment is correlated with higher job satisfaction (Andersson, 2008; Blanchflower and Oswald, 1998; Lange, 2012). The literatures has found that this positive relationship can be explained by some characteristics of self-employment, such as schedule flexibility (Hundley, 2001), independence and autonomy (Cassar, 2010; Lange, 2012; Mångs, 2013). However, it is less known whether or not such job characteristics transfers into greater life satisfaction. On the one hand, a more flexibility and autonomy at work facilitate self-employed people to meet family demand and strengthen the work-life balance (Parasuraman and Simmers, 2001; Zwan, et al., 2015). On the other hand, self-employment demands more 1 For the determinants of self-employment, see Djankov, et al. (2006a, 2006b); Giulietti, et al. (2012); Li and Zhao (2011); Cui, et al. (2013); See Liu and Huang (2013) for the earnings comparison. 2 In this paper, we use the terms subjective well-being and happiness interchangeably. 1

4 professional involvement and time commitment, which can reduce well-being by increasing the conflicts between work and family. According to Parasuraman and Simmers (2001), the work-family conflicts can be time-based and strain-based conflict. The longer working hours among the self-employed could limit the time available for non-work activities such as home production and leisure, creating more time-based conflicts. In addition, self-employed people have to bear the direct responsibility for their businesses and face more volatile income than wage workers. This stress can be manifest in other areas of life, creating more strain-based conflict. Thus, the characteristics of self-employment would reduce personal well-being. Therefore, the effect of self-employment on total welfare, specifically life satisfaction, depends on the strength between the positive and negative forces. Previous evidence from developed countries show that the life satisfaction of self-employed is significantly higher than that of wage-employed (Andersson, 2008; Blanchflower and Oswald, 1998; Sevä, et al., 2015; Zwan, et al., 2015) However, whether such a positive relationship between self-employment and life satisfaction holds in developing countries is still questionable. Self-employment in developing countries is often categorized as bad jobs in terms of fewer job-related social protections and the association with poverty (Maloney, 2004). 3 Previous evidence from developing countries gives different results about the life satisfaction premium among the self-employed compared with the wage-employed (Aguilar, et al., 2013; Falco, et al., 2015; Markussen, et al., 2014). Moreover, despite China s fast-growing economy, life satisfaction has not increased much (Knight and Gunatilaka, 2011). Therefore, the main aim of the paper is to ask whether China s self-employed are more satisfied with their lives compared with the wage-employed. Moreover, in developing countries, a large proportion of the self-employed are associated with small-scale, low productivity and family-based enterprises. Those enterprises are considered as part of the informal sector (Maloney, 2004). 4 According to the International Labour Organization (ILO), one way to measure the informal sector is to use private unincorporated enterprises with the size of employment below a certain threshold, such as fewer than five employees. 5 The dualistic view of labour market in developing countries, such as Harris and Todaro (1970) and Loayza (1994), argues that all workers would prefer jobs in the formal sector that offer higher wages, greater job security and better working conditions. To avoid unemployment, people who are rationed out of formal wage sector have to accept the second best option: jobs with small firms in the informal sector. Thus, the dualistic view concludes that self-employment in 3 Both ILO and World Bank have similar definitions of good job or decent work, which the job shall respect labour standard and be protected by formal institutions (Maloney, 2004). 4 The definition of informality according to International Labour Organization is The informal sector enterprises are defined as production units operated by single individuals or household that are not constituted as separate legal entities independent of their owners and in which capital accumulation and productivity are low. (Perry, et al., 2007). 5 See, e.g. Hussmanns (2004) for a more detailed definition of the informal sector and how it is measured. 2

5 the informal sector is likely to be involuntary. In contrast, advocates of the competitive view, such as Maloney (2004), argue that the labour market in a developing country is well integrated and that working in the informal sector is a voluntary individual decision given the constraints people face, such as skill and wealth. This is supported by Falco et al. (2015), which show that job/life satisfaction of the self-employed (with no employees) is not significantly different from that of wage workers in Ghana s formal sector. They conclude that the labour market is less likely to be dual and that self-employment in developing countries is naturally similar to self-employment in a small business in a developed country, which is a desirable voluntary choice given individual constraints. To sum up, according to the dualist view if jobs in the formal sector are considered good jobs with better pay and working condition than jobs in the informal sector, then there should be a significant difference in the utility that derived from jobs between the two sectors with otherwise similar observed characteristics. Hence, the second aim of the paper is to test the dualist view by using life satisfaction data in China. The hypothesis is that if self-employment is regarded as bad and that if people are pushed into the sector, the life satisfaction of the self-employed in the informal sector should be significantly lower than that of wage-employed in the formal sector. To our best knowledge, no research has investigated the non-pecuniary return of self-employment in China with nationally representative data. Therefore, the main contribution of the paper to the debate on the job quality of self-employment is as follows: Unlike most previous studies that focus on advanced economies, we test the hypothesis of whether self-employed people in China enjoy greater life satisfaction than wage workers. To answer this question, we utilize the two waves of panel data from the China Family Panel Studies (CFPS) 2010 and CFPS is one of the few national representative data in China that provides rich information about non-economic well-being of Chinese residents not only crosssectional but also by time. CFPS is conducted by the Institute of Social Science Survey of Peking University and the data is collected mainly through face-to-face interview. 6 To study the self-employment effect on life satisfaction, we define individual as self-employed/wage-employed if the survey respondent answers she or he is currently working and the main occupation is self-employed/wage-employed at the time of interview. 7 We use self-reported life satisfaction as the main outcome variable and it is measured with five point scale. Although the focus of the paper is on life satisfaction, to have a better understanding of the job quality of self-employment, we also would like to test how self-employment relates to various 6 In 2012 CFPS survey, data is also collected through phone interview and the share is around 2.8%. 7 In the survey, respondents are firstly given the question on If you are currently working and then follows the questions on choices of main occupation among wage-employed, self-employed, farming. The design on main occupation is a little different in the two rounds survey. In 2010 survey, respondents selected the main occupation according to the most time people spend on the job if people several jobs at the same time. In 2012, respondents reported all the jobs she or he is currently involved and the time spent on each job. In this paper, we focus our analysis on people who is currently working with only one full-time job. Please refer to the data section for the detail discussion of how to identify the employment status. 3

6 domains of job satisfaction as a complement to baseline results. However, job satisfaction data is only available in the 2010 wave. The paper finds that the self-employed men are more satisfied with their lives compared with wageemployed men. However, we are not certain whether such a positive correlation between self-employment and life satisfaction reflects a causal relationship or it is due to the selection of unobservables. For women, we find that self-employment does not affect life satisfaction across model specifications. In addition, considering the informality feature of labour market in developing countries, we conclude there is no evidence that the life satisfaction of the self-employed in the informal sector is significantly worse than that of wage workers in the formal private sector. Finally, we show that self-employment is not associated with higher job satisfaction in China. However, self-employment is significantly positively correlated with men s satisfaction with their job income and working time. To sum up, our results suggest that selfemployment in developing countries is not inferior to wage employment and that self-employment is therefore more likely to be a voluntary choice. The remainder of the paper is organized as follows: In section 2, we give an overview of labour market in China. In section 3, we review the previous studies on self-employment and individual well-being. In section 4, we present the data and empirical strategy. In section 5, we show the econometric analysis. In section 6, we perform several robustness analyses. In the last section, we conclude. 2. Labour market and self-employment in China Since the economic reform in 1978, China has been moving from a planned economy to a market economy. The reform includes the allowance of private sector in the economy in both urban and rural areas but it only serves as a complement to the public sector (state-owned enterprises and the government sector). The private sector includes various forms of non-state-owned enterprises in which property is privately owned. The owner of the non-state enterprise can be broadly divided into two types based on the registration legal status of the business organization at Bureau of State Administration for Industry and Commerce ( Gongshangju ): the owner of private enterprise ( Siyingqiye ) and the owner of individual family business ( Getihu ). The major differences between individual family business and other forms of private enterprises is that the number of employees with the individual family business should be fewer than seven people and more importantly it is subject to fewer regulations, such as accounting and tax regulations, and the legal entity cannot be separated from the owner (Entwisle, et al., 1995; Park, et al., 2012). Therefore, self-employment with individual family business type in China is more likely to be small-scaled and less formal than other enterprises and usually is classified as the informal sector (Park, et al., 2012). 4

7 Under the centrally planned economy, the state-owned sector is the only employer in the labour market and its workers are fully protected with life-time tenure, known as the iron rice bowl. Associated with the legitimization of market economy in the early 1990s, the role of private sector in the economy has become progressively important and thereby restructured the labour market. Especially since the mid- 1990s, massive of unskilled workers were laid off due to the reform in the state-owned sector, which included the replacement of life-time tenure by a performance-based labour contract. Many of the laid-off workers became self-employed. Based on the national urban household data in 2000, 26.6% of selfemployed had been laid off and the self-employed are more likely to have been unemployed (Yueh, 2009). Meanwhile, the self-employment rate in China rose from less than 2% in 1981, peaking around 18% in 1999 and then dropping to around 12% in 2008 (Li and Zhao, 2011). Another feature of economic development is the change in the employment structure of the labour market, which a large part of the labour force moves from agriculture into other sectors. In China, employment in agriculture sector has declined from 70% in 1978 to around 40% in 2008 (Cao and Birchenall, 2013). In the non-agriculture sector, the private sector has overtaken the state sector in the importance of labour market. According to the OECD Economic Surveys China 2010, in 2008, state enterprises accounts for only about 7% of total non-agricultural employment and the share of public sector among the total non-agricultural employment is about 15%. In addition, the percentage of people employed in the informal sector is about 21.9% of non-agricultural employment according to ILO 2012 statistics based on survey data from six Chinese cities Theoretical background and literature review Many people move to self-employment sector even though on average they work more hours and face more uncertain income (Hamilton, 2000). The choice to become self-employed is thus not only due to pecuniary reasons (the relative earning difference between self-employment and wage employment) but also non-monetary job characteristics that people appreciate, such as autonomy. Self-employment could affect life satisfaction in two offsetting ways. On the one hand, literature from developed countries argues that some characteristics of self-employment such as a better work-life balance, more control in the workplace and being one s own boss could enhance the psychological well-being of the self-employed (Andersson, 2008; Benz and Frey, 2008; Parasuraman and Simmers, 2001). On the other hand, selfemployment demands more job engagement and thus could create more work-life conflict, more stress, income uncertainty and the direct responsibility for the success or failure of business (Parasuraman and 8 The share of informal employment of non-agricultural employment is about 32.6%. According to ILO, almost all persons in the informal sector are informal employed. However, not all informal employment belongs to informal sector since there are also persons employed informally in the formal sector. Source: Statistical update on employment in the informal economy, June 2012, 5

8 Simmers, 2001; Parker, 2009). Such factors would depress the effect of self-employment on individual well-being. In addition to this, the job quality of self-employment in developing countries can be further disadvantaged since self-employment is less subjective to formal institutional regulations and lack of job protection (Maloney, 2004), such as absence of job provided pension, medical insurance and unemployment insurance. Therefore, the net effect of self-employment on life satisfaction depends on which factor dominates. The empirical findings of the effects of self-employment on life satisfaction are mixed, especially in developing countries. Blanchflower and Oswald (1998) show that self-employment is positively correlated with both higher job and life satisfaction in Great Britain. Using the European Social Survey, Sevä et al. (2015) also show that life satisfaction is higher among self-employed people than wage-employed. Using two waves of the Swedish Level-of-Living survey data, Andersson (2008) finds that self-employment is positively correlated with life satisfaction but the effect disappears once individual unobserved heterogeneity is controlled for. Using the panel data analysis with German Socio-Economic Panel Study, Zwan et al. (2015) find that both life satisfaction and work satisfaction are significantly positively related to the switch from wage employment to self-employment. However, moving from wage employment into self-employment significantly decreases the satisfaction with leisure. Meanwhile, some studies have also realized that the self-employed can be a quite heterogeneous group and this shall be taken into account in the study. Binder and Coad (2013) have pointed out the importance of distinguishing the opportunity from the necessity type self-employment when analyzing the effect. They find that the life satisfaction premium exists only among the self-employed who had left paid employed. The evidence of the self-employment effect on life satisfaction from developing countries is rare and most of the findings are based on cross-sectional data. According to a survey data based on seven cites in both China and Russia, by a simple mean comparison, Djankov et al. (2006a, 2006b) show that Russian entrepreneurs are happier than non-entrepreneurs but that entrepreneurs (business owners with more than five employees) in China are not significantly happier. In Latin America, Aguilar et al. (2013) find that the self-employed are generally not significantly different from the wage-employed in terms of life and job satisfaction. They also argue that the benefits of self-employment, such as autonomy and flexibility, could be offset by the negative factors such as economic insecurity and less job stability. However, they also point out self-employment can be heterogeneous and different types of self-employment respond differently to job satisfaction, life satisfaction and income satisfaction. Using the data from 2012 Vietnam Access to Resources Household Survey, Markussen et al. (2014) show that self-employment in the rural farming sector is positively associated with life satisfaction but that the self-employment effect is not significant in other sectors. Moreover, they find that wage workers in both formal and informal sector are 6

9 less satisfied with their lives than the self-employed with own farm. Exploiting Ghana Urban Household Panel Survey, Falco et al. (2015) find that the higher job and life satisfaction premium exists only among self-employed with employees while the life satisfaction of self-employed without employees is not significantly different from that of the wage-employed. Moreover, they conclude there is no evidence to support the inferiority of the small firm informal sector in Ghana, where they define the informal sector as enterprise with fewer than 5 people employees. Regarding job satisfaction, the literature from developed countries has consistently noted a positive relationship between self-employment and higher job satisfaction (Andersson, 2008; Benz and Frey 2008; Blanchflower and Oswald 1998; Lange, 2012; Zwan, et al., 2015). Moreover, self-employment is correlated with other domains of job satisfaction, such as higher satisfaction with job pay and less satisfaction with job security (Clark, et al., 2008; Millan, et al., 2013). However, the result is less conclusive in developing countries (Aguilar, et al., 2013; Cassar, 2010; Falco, et al., 2015). To sum up, there is more evidence of the positive effect of self-employment on life satisfaction and job satisfaction in developed countries. Literature from developing countries tend to show there is no significant life and job satisfaction disparity between being wage-employed and self-employed, although in some circumstances that the self-employed report higher satisfaction. 4. Data and empirical strategy 4.1 Data In this study, we use the individual adult data from the 2010 and 2012 waves of CFPS. CFPS is a nationally representative longitudinal survey of Chinese households that has been conducted every other year since CFPS uses multi-stage Probability-Proportion-to-Size sampling method and integrates urban and rural populations to obtain a representative sample of the national population. 9 The target sample consists of about 16,000 households that cover more than 160 counties among the 25 provinces and direct-controlled municipalities. 10 The household survey data includes household level data, individual adult data and adolescent data. So far, the survey was implemented for two rounds. The response rate is 84% in the first wave survey (2010) and the individual follow-up rate is about 80% in the second wave survey (2012). The data consists of information on the economic and non-economic wellbeing of Chinese adolescents and adults. It also covers many domains, including employment status, 9 See Xie and Hu (2014) for more detail discussion of data sampling strategy. 10 The four direct-controlled municipalities are Beijing, Shanghai, Tianjin and Chongqing, which are equivalent to province level and directly controlled under the central government. The administrative of China are classified into four levels, which are ordered as follows: province level, prefecture level, county level and township level. 7

10 health, demographic and socioeconomic characteristics, which makes it suitable for this study. In addition, to get information about household level and children level, we linked the adult data with household and adolescent data. Among the total 16,000 households in the complete CFPS data, about 8000 are from five big provinces while the remaining 8000 households are from twenty small provinces. Because of oversampling among the five big provinces, therefore, we use the subsample from the complete CFPS data, which the subsample is called a nationally integrated sample and it consists of a subsample from the five big provinces and the whole sample from twenty small provinces. This nationally integrated sample is constructed by CFPS to make it representative of the national population (Xie and Hu, 2014). The sample consists of around 9000 households in each wave. Since we are interested in the disparity of well-being between the self-employed and the wage-employed, we exclude full-time students, unemployed people, unpaid family business helper, retiree. We also exclude any type of employment in agricultural sector due to the different characteristics of selfemployment in farming and non-farming business. 11 Due to the design of 2012 survey, we cannot precisely identify the main occupation for people who had more than one job at the time of interview. Thus, the main analysis is restricted to workers who have only one job at the time of interview, which is either self-employed or wage-employed. Moreover, previous research has shown a difference in happiness between part-time and full-time workers (Boo, et al. 2010; Booth and Van Ours, 2008). Therefore, we focus on full-time workers. We define full-time worker as individuals who work more than 120 hours a month (35 hours a week) on the job. We also put the upper bound of the number of working hours at 400 hours since few wage workers work more than 400 hours a month. However, we relax that time boundary in sensitivity analysis to make sure our results are robust. Finally, we set different lower bounds on the age for men and women because in China the official retirement age is 60 for men and 55 for women. 12 That means we define the working population as men between the ages of 18 and 60 and women between the ages of 18 and 55. In order to study the relationship between self-employment and non-economic well-being, we use selfreported life satisfaction as the outcome variable to measure overall well-being. In the survey, life satisfaction is measured on a five-point scale from very unsatisfied to very satisfied. None of the values between the two poles is labelled. The question is as follows: 11 In the CFPS data, the proportion of individuals that work in agriculture sector is very high. For instance, in the 2010 survey, this proportion is about 50%. 12 However, people are also allowed to have early retirement as early as their ages turn to 50 for women and 55 for men. In some cases, women can retire as early as age 50. For example, if the job is highly labour intensive. 8

11 Please answer according to your own situation ( 1 is very unsatisfied and 5 is very satisfied) How satisfied are you with your life? In addition to life satisfaction, we show other alternatives to measure individual subjective well-being, which are domains of job satisfaction. Unfortunately, those data are only available in the 2010 survey and thus we cannot perform panel analysis. The advantage of job satisfaction data is that it examines job satisfaction from several dimensions: overall satisfaction with the job, satisfaction with working time, satisfaction with job income and satisfaction with job security. 13 Again, a five-point scale is applied. (See appendix B for the survey questions on the outcome variables.). Since we are studying the effect of self-employment on life satisfaction, we identify an individual s main employment status from the survey questions. In the job section of the 2010 survey, if the respondent answered that she or he is currently working at the time of interview, then she or he is asked to select her or his main employment status based on the most time spend on the job. The options are: self-employed, wage-employed or farming. Then, respondents answer questions about their job and the firm. Meanwhile, if people have more than one job at the time of interview, the secondary job is also reported but the job information is less detailed. The job question design in 2012 survey is a little different from the 2010 one. Instead of asking about main employment status directly, the respondent is asked to give a job history (including employment status) between the survey years. Therefore, to be consistent with 2010 s definition of main employment status, we identify the respondent s main employment status according to the job that she or he had at the time of survey. 14 Since we do not know how respondents measure the amount of time they spent on the job when they answered the question about their main employment status in the 2010 survey, we limit our analysis to respondents who had only one job at the time of survey. Therefore, we have a consistent measure of the main employment status and we construct the selfemployment (wage-employment) variable from those questions. 13 Job security in this case means the probability of losing the job for the wage-employed and closing the business for the self-employed. With a simple question on overall job satisfaction, it is hard to assess what is exactly measured since job satisfaction could mean different things for different respondents. Millan et al. (2013) showed that selfemployment has positive effect on job satisfaction in terms of the type of work but negatively affects individual s satisfaction on job security. 14 In the job section of the 2012 survey, respondents need to report each job that they have done since And for each job, they have to report when does the job start and when does the job finish or if the job still exist at the time of survey. 9

12 4.2 Empirical strategy To answer the question of how self-employment affects happiness, one needs to find the counterfactual state of a self-employed individual if she or he was wage-employed. Ideally, the potential selection problem, as discussed by Blanchflower and Oswald (1998), could be mitigated by using panel data. However, given our small size panel data with a short time span, we therefore focus on pooled OLS analysis although it limits our interpretation to correlation. We focus on cross-sectional analysis for two reasons. First, we need large intra-person variations to be able to estimate the self-employment effect under the individual fixed effect model, which is less like to be plausible with small size panel data. This problem has been discussed in the previous study about life satisfaction by Andersson (2008). Secondly, even there is enough variation with small size panel data, those people who have switched employment status are likely to comprise a non-random group. This issue has been discussed by Rietveld et al. (2015) in their study of the self-employment effect on health. Although our preferred model is pooled OLS, given the availability of panel data, we also present individual fixed effect estimation as a complement to crosssectional analysis. However, we should be a little cautious with interpreting the fixed effect results given the small data size and short time span. In addition to that, previous literature, such as Hundley (2000), Parasuraman and Simmers (2001), have shown that women have different motivations to enter selfemployment from men, such as facility home production. Therefore, we perform regression analysis separately for men and women. The empirical model specification for respondent i and survey year t is: YY!" = ααssss!! + XX!!" ββ + εε!" Where YY!", in origin, is the ordered categorical life satisfaction variable with five-point scale. SSSS!" is the variable of interest and it is a dummy variable which indicates whether an individual is self-employed or wage-employed (1 if self-employed and 0 if wage-employed). XX!" is a set of control variables that includes the individual s socioeconomic characteristics that would affect her or his well-being, which we discuss in the next paragraph. In addition, we control for survey month and year fixed effect as well as county fixed effect to remove any time-invariant unobservables at the aggregate level. Ideally, a non-linear model, such as ordered probit/logit model, is preferable to the linear model since those models better reflect the ordinal nature of the data. However, there is no consensus on the non-linear fixed effect model. Ferrer-i-Carbonell and Frijters (2004) pointed out that the estimation results are less sensitive by treating the life satisfaction data as cardinal or ordinal. Therefore, in order for model comparison, we decide to use ordinary least squares estimation (OLS) and treat the dependent variable cardinally. A similar estimation strategy has been adopted by several earlier literatures on the study of happiness, such as Luttmer (2005) and Zwan et 10

13 al. (2015). We perform sensitivity analysis by using a non-linear ordered probit model, in which the model captures the ordinal nature of the dependent variable. In addition to life satisfaction and employment status, we include several control variables in the regression which have been identified by previous literatures. Those variables are important in studying life satisfaction and self-employment. This includes age, the presence of children under age 17 in the household, highest attained education, marital status, health status, living in urban or rural area. 15 Previous Chinese research shows that the ethnic minorities in rural areas are more likely to be happier than the ethnic majority Han group (Knight, et al., 2014). Therefore, we include an ethnic minority dummy in the regression. Moreover, some studies have shown that being a Communist Party member is associated with both a wage premium and higher life satisfaction (Appleton and Song, 2008; Appleton, et al., 2009; Markussen, et al., 2015). Thus we include a dummy variable Communist Party indicates whether a person belongs to the communist party or not. In China, all citizens are registered in the Hukou system, which is an official permission for people to live in one place and entitles them to local social benefits. There are four types of Hukou : agricultural/non-agricultural and urban/rural. Since we do not have information if people hold urban or rural Hukou, therefore, we only include a dummy variable Hukou status indicates if people hold non-agricultural Hukou or not. We further control if people are internal migrants or not. We define an internal migrant as someone whose registered province of residency in Hukou system is different from her or his current province of residency. We control for the household income instead of individual income mainly for two reasons. One reason is due to the lack of information on individual income for self-employed people in the 2012 survey. The other reason is that the selfreported income of self-employed can be unreliable as documented by other researchers (Blinder and Coad, 2013) and it also partly captures the self-employment effect on the outcome variable (Blanchflower and Oswald, 1998, Andersson, 2008). Blinder and Coad (2013) propose to use net equivalised annual household income as a proxy for wealth instead of individual income. However, we do not have such a measure in our data and therefore we control for household income instead. Furthermore, we control for some job-related characteristics that are common in life satisfaction studies (e.g., Falco, et al., 2015; Zwan, et al., 2015), such as working hours and job tenure. We control for average number of hours worked per month and its square to capture the non-linear effect of working hours (Falco, et al. 2015). In addition, we control for job tenure which indicates the number of year people who have had their current job. Finally, 15 Marital status is classified as married, single, or divorced. We group cohabit with married since they are very few people reported as cohabit. Like with Zwan, et al. (2015), we group widowed into the divorced group since the proportion of widowed people in our data is less than 1%. The self-assessment health is measured on a three-point scale with following options: my health condition is worse than last year, neither worse nor better than last year, and better than last year. We define the health dummy variable as 1 if a person reports her or his health is relative better than a year ago and 0 if she or he is as healthy as last year or if health is getting worse. 11

14 we also classify industries into four categories to control for industry fixed effect. Please refer to the table 1B in appendix B for detailed descriptions of variables. 4.3 Descriptive statistics Figure 1 Distribution of life satisfaction, men Figure 2 Distribution of life satisfaction, women Figure 1 shows the life satisfaction distribution for both self-employed and wage-employed men. As can be seen from the distribution, the proportion of life satisfaction scores of 4 and 5 are higher among the self-employed. Moreover, the share of people who score 2 or below is smaller among the self-employed than the wage-employed. In figure 2, we show the life satisfaction for women. Compared with men, the distribution between the self-employed and wage-employed is similar. We present the details of life satisfaction distribution for each group in table 1A in appendix A. 12

15 Table 1 Summary statistics Men Women (1) (2) (3) (4) (5) (6) Full sample SE WE Full Sample SE WE Life Satisfaction (1.01) (1.00) (1.01) (0.96) (0.96) (0.96) Self-employment (0.41) (0.37) Age (10.3) (9.36) (10.5) (8.89) (8.54) (8.90) Ethnic Minority (0.22) (0.22) (0.22) (0.23) (0.29) (0.22) Hukou Status (0.50) (0.44) (0.50) (0.50) (0.48) (0.50) Internal Migrant (0.19) (0.21) (0.19) (0.19) (0.20) (0.18) Single (never married) (0.31) (0.23) (0.32) (0.32) (0.21) (0.34) Married (0.33) (0.26) (0.35) (0.37) (0.27) (0.38) Divorced/Widowed (0.14) (0.12) (0.15) (0.20) (0.18) (0.20) Children (age<17) (0.50) (0.49) (0.50) (0.50) (0.50) (0.50) Health (0.31) (0.29) (0.32) (0.32) (0.37) (0.31) Illiteracy /Half Illiteracy (0.26) (0.29) (0.25) (0.27) (0.33) (0.26) Elementary School (0.35) (0.40) (0.34) (0.35) (0.41) (0.34) Middle School (0.48) (0.50) (0.47) (0.46) (0.50) (0.45) High School (0.42) (0.40) (0.42) (0.41) (0.39) (0.41) University (0.39) (0.23) (0.42) (0.43) (0.21) (0.45) Communist Party (0.37) (0.24) (0.39) (0.27) (0.11) (0.29) Urban (0.47) (0.49) (0.47) (0.43) (0.46) (0.42) Household Income (log)

16 Table 1 continued (0.95) (1.19) (0.85) (0.97) (1.22) (0.89) Job Tenure (log) (1.25) (1.04) (1.25) (1.14) (1.05) (1.13) Working Hours / Month (58.8) (64.7) (55.1) (59.4) (69.7) (53.7) Manufacture/Mining /Construction (0.50) (0.45) (0.50) (0.48) (0.35) (0.49) Transportation/Storage/Communication (0.30) (0.37) (0.28) (0.18) (0.18) (0.18) Retail/Whole Sale/Hotel/Restaurant (0.33) (0.47) (0.26) (0.45) (0.50) (0.41) Other Services Sector (0.45) (0.43) (0.45) (0.47) (0.44) (0.48) Observations Individuals Mean coefficients; Sd in parentheses; SE and WE stands for self-employed and wage-employed. We present the descriptive statistics in table 1. In total, our unbalance panel data includes 3766 for men and 2456 observations for women, corresponding to 2355 and 1473 individuals respectively. Looking at the average life satisfaction score, we find women are on average more satisfied with life than men. In our sample, the proportion of self-employment is about 22% for men and 16% for women. If we compare the self-employed with the wage-employed, on average, self-employed people are more likely to have no more than a middle school education. Self-employed people are much less likely to be Communist Party members compare than wage workers are. The sample also shows that wage workers on average have higher total household income than the self-employed. As in developed countries, self-employed in China work much longer hours than the wage-employed. Self-employed men on average work 254 hours a month; the wage earners work 223 hours (the corresponding figures for self-employed and wageemployed women are 268 hours and 218 hours respectively). These findings seem to be similar to those Giulietti et al. (2012), where they show that in China the self-employed on average worked 77.8 hours a week and wage earners only worked 58.7 hours. We present working hours distribution in Figures 1A and 2A in appendix A. The figures show that the distribution of working hours among the self-employed is more to the right than it is for wage workers (both full- and part-time). Therefore, it is plausible to believe that self-employed people on average work longer than wage workers in China as is observed in other countries. Regarding industry distribution, table 1 shows that the majority of self-employed are working in the retail, whole-sale, hotel and restaurant industries and most wage workers are in manufacturing, mining and construction. 14

17 5. Econometric analysis 5.1 Baseline results In this section, we present regression analysis on how self-employment affects life satisfaction. We show the results for men and women separately. Table 2 Life satisfaction and self-employment Panel A: Pooled OLS Analysis Panel B: Panel Analysis (1) (2) (3) (4) (5) (6) VARIABLES Men Men Women Women Men Women Self-employment 0.093** 0.101** (0.042) (0.047) (0.058) (0.065) (0.349) (0.378) Socioeconomic characteristics Age * ** 0.329* 0.329** (0.015) (0.015) (0.023) (0.023) (0.172) (0.129) Age square / ** 0.070** (0.019) (0.019) (0.032) (0.032) Ethnic Minority (0.097) (0.097) (0.110) (0.110) Hukou Status (0.050) (0.050) (0.057) (0.057) (0.340) (0.262) Internal Migrant (0.104) (0.104) (0.123) (0.123) Married * 0.142* (0.077) (0.077) (0.082) (0.083) (0.427) (0.498) Divorced/Widowed ** ** ** *** (0.139) (0.139) (0.139) (0.139) (0.636) (0.857) Health 0.094* 0.093* (0.052) (0.052) (0.061) (0.061) (0.195) (0.194) Children (age<17) (0.045) (0.045) (0.054) (0.054) (0.250) (0.216) Elementary School (0.081) (0.081) (0.090) (0.090) Middle School (0.077) (0.077) (0.089) (0.089) High School (0.082) (0.082) (0.097) (0.097) University ** (0.091) (0.094) (0.102) (0.106) 15

18 Table 2 continued Communist Party 0.147*** 0.128*** 0.156** 0.123* (0.048) (0.049) (0.071) (0.071) (0.290) (0.374) Household income (log) 0.101*** 0.102*** 0.127*** 0.121*** (0.021) (0.021) (0.027) (0.027) (0.065) (0.085) Urban (0.048) (0.048) (0.057) (0.057) (0.592) (0.469) Job characteristics Working hours * (0.002) (0.003) (0.009) (0.010) Square of Working hours (0.000) (0.000) (0.000) (0.000) Job tenure (log) ** (0.018) (0.024) (0.059) (0.071) Observations 3,766 3,766 2,456 2,456 3,766 2,456 R-squared Month Effect Yes Yes Yes Yes Yes Yes Year Effect Yes Yes Yes Yes Yes Yes County Effect Yes Yes Yes Yes Yes Yes Industry Effect No Yes No Yes Yes Yes Individual Effect No No No No Yes Yes Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The reference group is wage-employed, ethnic majority Han group, holding an agriculture Hukou, non-internal migrants, single with never married, household without any child under age 17, illiteracy/half illiteracy, non-communist Party member, rural area and people working in Manufacturing, mining and construction industry. In table 2, we show the results of OLS estimation on life satisfaction and self-employment for both men and women. Since we are mainly interested in whether or not self-employed people are more satisfied with lives than the wage-employed, we focus on interpreting the sign of the estimated effect rather than the quantitative meaning. In panel A, we treat the data as cross-sectional and use pooled OLS analysis; in panel B we take panel analysis. In all model specifications, we control for month, year and county fixed effect. We show cross-sectional analysis for men in columns 1 and 2. In column 1, we control for individual socioeconomic characteristics. The estimated coefficient on self-employment is and it is statistically significant at the 5% level, which indicates that people who are self-employed are significantly more satisfied with life than the wage-employed. In column 2, we future added job-related control variables, which are working hours and its square, job tenure and industry fixed effect. The estimated coefficient on 16

19 self-employment does not change in terms of sign and significance compared with the result in column 1. However, the coefficient on self-employment now reflects the life satisfaction differential between the self-employed and the wage-employed given individual characteristics, household income and job characteristics. Turning to the estimated coefficients on other control variables, as shown in column 2, they are generally similar to the findings from previous literature. Like the findings by Blanchflower and Oswald (2008), the signs associated with age and age square indicate a U shaped age effect on happiness, although the estimated effects are not statistically significant. Meanwhile, being a minority and an internal migrant is not associated with any happiness gain or loss. The life satisfaction of married people is not significantly different from that of people who have never married. However, like Zwan et al. (2015), we find that people who are divorced have significantly lower life satisfaction than people who never married. People who report they are healthier are more likely to have higher life satisfaction than others although the estimated effect is only marginally significant. Regarding education, our result shows no life satisfaction disparity across different education groups in China, which is consistent with other literature (Blinder and Coad, 2013; Zwan, et al., 2015). Consistent with Markussen et al. s (2014) findings from Vietnam, our result shows that being a Communist Party member is significantly associated with higher life satisfaction. As expected, a higher household income is significantly associated with more life satisfaction. Contrary to Falco et al. (2015) which show the effect of working hours on life satisfaction increases at a decreasing rate, our estimated coefficients on working hours and its square shows a negative correlation with life satisfaction although it is not statistically significant. One possible explanation is that our sample is restricted to full-time workers who work at least 120 hours a month and thus it is likely that a further increase in the number of hours lowers life satisfaction. Finally, the estimated effect on job tenure is not significant. Turning to women, in column 3, controlling for individual socioeconomic characteristics, we show that the estimated coefficient on self-employment is positive but not statistically significant, which indicates that self-employed women are not more satisfied with their lives than wage-employed women are. In column 4, we add future controls for job-related characteristics, the estimated effect does not change much compared with column 3. Thus, we conclude that self-employment is not correlated with higher life satisfaction among women in China. Regarding the control variables, in column 4, we show that age has a significant U shaped effect on life satisfaction. Being divorced significantly lowers people s happiness. Like men, being a Communist Party member and having a higher household income significantly positively correlated with life satisfaction. Furthermore, increasing working hours lowers women s life satisfaction although it is only marginally significant at 10%. Finally, increasing the job tenure significantly raises life satisfaction. 17

20 However, it is likely that the unobserved individual characteristics, such as optimistic and happier people self-selected into self-employment sector, which could bias our estimates. In order to take the timeinvariant unobservables into account, in panel B, we utilize the feature of panel data by including the individual fixed effect for both men and women. Under the individual fixed effect model, we use intraperson variations to estimate the effect. In the estimation, we exclude some variables that are less likely to vary within a short time although including those variables does not change our conclusion. In column 5, we show the estimated self-employment effect on life satisfaction is positive but no longer significant. In addition, the estimated coefficient is small and the standard error is large. In column 6, we show the life satisfaction of self-employed women is also not significantly different from wage-employed. Compared with the results from cross-sectional analysis, the insignificance of the estimated effect can be attributed to two things. First, as the literature has discussed, the cross-sectional results are driven by unobserved timeinvariant individual characteristics. For instance, if self-employed people are more optimistic, our crosssectional approach would bias upward. Secondly, the insignificance of the effect is due to the sample size. In other words, there is not enough variation on the change of employment status within a short time. Meanwhile, we cannot directly compare our panel results with other studies from developing countries due to the rare panel data evidence. The only exception is from Falco et al. (2015), but they focus on job satisfaction and they show the results are not sensitive to the inclusion of the individual fixed effect. In developed countries, using two waves of Swedish Level-of-Living survey, Andersson (2008) shows that the positive self-employment effect on life satisfaction disappears once time-invariant unobservables are controlled for. She concludes the insignificance of the estimate is more likely due to the lack of variation on the variables rather than the selection of happier people into self-employment. In addition, using a small size panel data, Rietveld et al. (2015) study the effect of self-employment on health and they argue that it is likely that people who have switched the employment status can be a non-random group and the individual fixed effect model is less informative. Therefore, given the limitation of the data, we treat the estimates from individual fixed effect model as a complement to our cross-sectional approach. To sum up, we find that self-employed men are significantly correlated with higher life satisfaction in China. However, we are less confident to interpret this positive effect as causal since the presence of unobservables could bias the estimated effect. For women, we see a slightly different pattern. Although women are on average happier than men, the results indicate that self-employment is not associated with greater life satisfaction. Moreover, the different self-employment effects on life satisfaction between men and women seem consistent with the argument about the gender difference in self-employment and life satisfaction by Parasuraman and Simmers (2001). They argue that the increased job involvement and time commitment with self-employment is more likely to exacerbate work-family conflict among selfemployed women since they assume more responsibility for the home than men do. In other words, the 18