title: The impact of mental health on labour market outcomes in China.
abstract: BACKGROUND
  Mental illnesses account for 20% of the total burden of disease in China. Yet, health policy in China has not devoted much attention to mental health problems and their impact on Chinese society.
  
  
  OBJECTIVE
  The objective of this paper is to investigate the impact of mental health status on labour market outcomes, such as employment and income, and provide evidence about some of the economic consequences of mental illnesses.
  
  
  METHODS
  Using the China Health Surveillance Baseline 2001 Survey and an instrumental variables estimation approach, we address possible reverse causation between work and mental health. To estimate the impact of self-reported mental health status, we use the two-part model, the first part estimating a logit equation for the probability of being employed and the second-part estimating an ordinary least squares (OLS) model on the log of individual income condition on being employed. We use a list of symptoms of mental disorders to constitute a measure of mental health status. Our identification strategy relies on instruments that measure average mental health status by zip code other than the observed individual to implement an instrumental variables model.
  
  
  RESULTS
  Both men and women suffer a significant reduction in the employment rate and annual income if the average mental health deteriorates at a population level. The mental health index has a positive and significant effect on the likelihood of being employed. Our findings are consistent with what has been found in industrialised countries.
  
  
  CONCLUSIONS
  This is the first empirical study that reveals that poor mental health status can be disruptive of labour market activities in China. A rapid rise of mental and behavioural problems in population reflects the transition to a market economy and indicates pressing problems that have gone unrecognised and unaddressed. The negative economic consequences in labour market outcomes suggest a potential gain from preventing and curing the mental disorder. Our study about the impact of mental health on labour market participation adds value to the effort of evidence-based, decision-making process by the Chinese government. IMPLICATIONS FOR HEALTH CARE PROVISION AND POLICIES: A larger effort is required from the Chinese government and society in providing individuals with mental illnesses easier access to mental health care and better treatment. Allocating more resources to prevention and intervention and changing societal attitude towards individuals with mental illnesses should be important components in China's mental health policy. IMPLICATIONS FOR HEALTH CARE RESEARCH: A quantitative analysis on how much economic gain can be achieved from treatment and the trade-off between costs and benefits of treating mental disorders in China needs to be conducted in the future. In addition, further study on understanding the mental health delivery system in China should be conducted. By investigating care seeking, organisation of treatment and financing of care delivery, we may be able to identify high priority investments in mental health care.
authors.affiliation: Department of Global Health and Social Medicine, Harvard Medical School, 800 Boylston Street-47 th Floor, Boston, MA 02199, USA. chunling_lu@hms.harvard.edu
authors.forename: Chunling
authors.initials: C
authors.lastname: Lu
authors.forename: Richard G
authors.initials: RG
authors.lastname: Frank
authors.forename: Yuanli
authors.initials: Y
authors.lastname: Liu
authors.forename: Jian
authors.initials: J
authors.lastname: Shen
mesh_terms: D000293:Adolescent; D000328:Adult; D002681:China / Q000453:epidemiology; D004651:Employment / Q000191:economics / Q000523:psychology* / Q000706:statistics & numerical data*; D005260:Female; D006305:Health Status Indicators; D006306:Health Surveys; D006801:Humans; D008297:Male; D001523:Mental Disorders / Q000191:economics / Q000453:epidemiology* / Q000523:psychology*; D008603:Mental Health / Q000706:statistics & numerical data*; D008875:Middle Aged; D012044:Regression Analysis; D012307:Risk Factors; D017678:Sex Distribution; D012959:Socioeconomic Factors; D055815:Young Adult