1. Changing Relationship between Unemployment and Mortality in South Korea Chulhee Lee Department of Economics Seoul National University Kyeongbae Kim Department of Economics University of Chicago

2. Background  Major Questions  How do measures of health change over the business cycle?  How to explain the relationship between business condition and health?  How do the effects of business cycle on health change across times and places?  How to account for variations across times and nations?

3. Background  Effect of Unemployment on Health  Long-term adverse impacts on own health (Sullivan and Wachter 2009)  Mechanisms: decline in incomes and psychological distress

4. Background  Potential Negative Effects of Temporary Economic Booms  Changing opportunity costs of time  Decline in leisure, exercise, visiting physicians  Increase in hours of work, work intensity, work-related stress  Direct negative effects  Indirect effects through increased smoking and alcohol consumption  Increase in drinking and traffic  Rise in accident-causes death  Tight labor-market for health workers  Decline in the quality of medical care for elderly people

5. Literature  Ruhm (2000)  Estimating fixed effect model based on the U.S. state-level data from 1972  One percent increase in unemployment rate is associated with 0.54% decline in total mortality rate  More pronounced for deaths caused by traffic and other types of accidents, homicides, cardio-vascular diseases, liver diseases.  Ruhm (2003)  Unemployment rate is negatively related to the prevalence rate of various types of diseases.  Ruhm (2005)  Lowered rates of smoking and obesity, and increased leisure and physical activities during recessions.  Ruhm (2007)  One-percent increase in unemployment rate produces 0.75% decline in coronary heart disease death rates.

6. Literature International Evidence  Neumayer (2004): German state-level data,  Tapia Granados (2005): Spanish province-level data  Gerdtham and Ruhm (2006): OECD cross-country data  Lin (2009): cross-country data from 8 Asia-Pacific nations  Lee and Kim (2011): South Korean province-level data (1991-2009) Debate over Mechanisms  Changing behaviors caused by economic booms?  Smoking, obesity, number of physician visits: Ruhm (2000, 2005), Xu and Kaestner (2010)  Negative external effects of economic booms?  Miller et al. (2009): Cyclical deaths are concentrated outside of typical working ages.  Stevens et al. (2011): Cyclical fluctuations in mortality in the US are driven by fluctuations in the quality of health care staffs.

7. Literature Changing Relationship over Time?  McInerney and Mellor (2012)  US elderly mortality is counter-cyclical for 1994-2008.  Seniors report worse mental health and are no more engaged in healthier behaviors during recession.  Ruhm (2013)  US mortality shifted from strongly procyclical to being unrelated to macroeconomic conditions.  Cardio-vascular diseases and transport accidents  continue to be procyclical.  Cancers fatalities became strongly countercyclical.  Why? Not clear. It may reflect the increasing protective influences of financial resources as sophisticated (and expensive) treatments became available.

8. This Paper  Extending Lee and Kim (2011)  A longer period: 1989 to 2012.  Add province-specific time trend  Examining if the relationship between unemployment and mortality changed over time.  Looking for explanations for the shift over time.  Construct and analyze cause-specific data on death and health spending.

9. Model  Province Fixed-Effect Model for Mortality  Variables:  H: natural logarithm of the number of deaths per 100,000 in province j and year t.  E: unemployment rate  X: variables on age and education composition of the population in province j and year t.  R: time-invariant province effect  RTt: region-specific time trend  Observations are weighted by the size of population for corresponding province and year.  Clustered robust standard errors are reported.

10. Data  Mortality rate by age, sex, province, cause, and year  Number of deaths for each cell: Aggregated Cause of Death Statistics (Korea Statistical Information Service by Statistics Korea: KOSTAT)  Number of population for each cell: Aggregated statistics from the Population Projection of Korea  Mortality rate by education level  Number of deaths for each cell: Micro data on the 1991-2011 Cause of Death Statistics (the universe of death registration records)  Number of population for each cell: the share of the population with a particular education level calculated from the 2% micro samples of the censuses from 1985 to 2010. Interpolations were applied for the years between census years.  Unemployment rate for each province and year  Economically-Active Population Survey.

11. Table 1 Effects of Unemployment on Mortality Note: The dependent variable is the natural logarithm of the total mortality rate per 100,000 persons. All models include the shares of the population belonging to each three age categories and three education categories. Year and regional dummy variables are also controlled for. Clustered robust standard errors are in parentheses. Observations are weighted by the square root of the total population for the region and year. *** Statistical significance for 0.10 level. *** Statistical significance for 0.05 level. *** Statistical significance for 0.01 level. Model (1) 1989-2012 (2) 1991-2009 (3) 1989-2001 (4) 2002-2012 No region-specific trends-0.0127 (0.0077) -0.0116* (0.0059) -0.0011 (0.0038) 0.0106* (0.0054) With region-specific trends-0.0045 (0.0033) -0.0037** (0.0016) -0.0044 (0.0026) 0.0116*** (0.0029)

12. Figure 1 The Rolling Regression Result of Unemployment’s Effect on Mortality in 1989-2012 (Beginning of 10-Year Window)

13. Regression Results (1)  The entire period from 1989 to 2012.  Mortality changes were generally procyclical.  For the period from 1991 to 2009, it is significantly negative (confirming Lee and Kim 2011).  Rolling regressions for every 10-year interval from 1989 to 2012.  The coefficient for unemployment rate gradually increased over the 1990s, and then sharply rose in the early 2000s.  Regressions for two subperiods.  1989 to 2001: weakly procyclical.  2002 to 2012: strongly countercyclical.  Alternative Specifications  Allowing time lags

14. Table 2 Effects of Unemployment on Age-Specific Mortality Note: Age and education variables are those of each sex. Observations are weighted by the square root of the population of each group for the region and year. See Table 1. *** Statistical significance for 0.10 level. *** Statistical significance for 0.05 level. *** Statistical significance for 0.01 level. Age Group (1) All (2) Male (3) Female 1989-20012002-20121989-20012002-20121989-20012002-2012 All-0.0044 (0.0026) 0.0116*** (0.0029) -0.0044 (0.0031) 0.0097* (0.0046) -0.0040 (0.0028) 0.0128*** (0.0041) 4 year olds0.0214 (0.0181) 0.0066 (0.0685) 0.0319 (0.0202) -0.0865 (0.0910) 0.0133 (0.0229) 0.0888* (0.0480) 5-19 year olds-0.0112 (0.0143) 0.0268 (0.0446) -0.0293* (0.0149) 0.0321 (0.0575) 0.0291 (0.0281) 0.0212 (0.0662) 20-44 year olds-0.0025 (0.0053) 0.0095 (0.0143) -0.0025 (0.0059) 0.0310* (0.0158) -0.0012 (0.0075) -0.0301 (0.0333) 45-64 year olds-0.0046 (0.0031) 0.0148 (0.0089) -0.0065 (0.0040) 0.0094 (0.0098) 0.0017 (0.0036) 0.0277* (0.0144) 65 year olds-0.0011 (0.0034) 0.0095*** (0.0035) 0.0011 (0.0038) 0.0083 (0.0073) -0.0042 (0.0042) 0.0097* (0.0055)

15. Table 3 Effects of Unemployment on Mortality by Educational Attainment Note: Observations are weighted by the square root of the population of each group for the region and year. See Table 1. *** Statistical significance for 0.10 level. *** Statistical significance for 0.05 level. *** Statistical significance for 0.01 level. (1) All, 45 year olds (2) High school graduates or less, 45 year olds (3) College graduates or more, 45 year olds 1991-20012002-20111991-20012002-20111991-20012002-20 11 All-0.0018 (0.0023) 0.0105*** (0.0027) -0.0056 (0.0068) 0.0117*** (0.0034) 0.0027 (0.0087) 0.0133 (0.0189) Male-0.0021 (0.0023) 0.0075 (0.0044) -0.0093 (0.0075) 0.0096* (0.0047) -0.0013 (0.0098) 0.0113 (0.0187) Female-0.0014 (0.0035) 0.0130** (0.0049) -0.0065 (0.0061) 0.0132** (0.0053) 0.0720* (0.0374) 0.0250 (0.0664)

16. Table 4 Effects of unemployment on cause-specific mortality in 1989-2001 and 2002-2012 Disease Category1989-20012002-2012 Certain infectious and parasitic diseases0.0145 (0.0169) -0.0751* (0.0370) Cancers-0.0108** (0.0044) 0.0211*** (0.0066) Endocrine, nutritional and metabolic diseases-0.0016 (0.0128) -0.0094 (0.0357) Diseases of the nervous system0.0170 (0.0360) -0.0406* (0.0219) Diseases of the circulatory system-0.0010 (0.0076) 0.0205 (0.0160) Diseases of the respiratory system-0.0158 (0.0138) 0.0006 (0.0518) Diseases of the digestive system0.0052 (0.0116) 0.0485* (0.0253) Diseases of the genitourinary system-0.0403* (0.0227) 0.0329 (0.0522)

17. Table 4 Effects of unemployment on cause-specific mortality in 1989-2001 and 2002-2012 (Continued) Disease Category1989-20012002-2012 Vehicle and traffic injuries-0.0098 (0.0121) -0.0353 (0.0276) Falls-0.0699*** (0.0197) -0.0455 (0.0780) Drowning-0.0455 (0.0331) -0.0066 (0.0708) Fire, flames or hot objects-0.0113 (0.0557) -0.0813 (0.1860) Poisoning0.0963 (0.0729) -0.0411 (0.1487) Suicide-0.0192 (0.0242) 0.0123 (0.0200) Assault0.0536 (0.0346) 0.1163 (0.0872) Other external causes0.0072 (0.0229) 0.0395 (0.0619)

18. Appendix Table 5 Effects of unemployment on cancer mortality and cancer incidence in 2002-2010 Note: The dependent variable is the natural logarithm of the mortality rate or the cancer incidence rate per 100,000 population. All models include the shares of the population belonging to each three age categories and three education categories. Year and regional dummy variables are also controlled for. Clustered robust standard errors are in parentheses. Observations are weighted by the square root of the total population for the region and year. *** Statistical significance for 0.10 level. *** Statistical significance for 0.05 level. *** Statistical significance for 0.01 level. Cancer incidence Case fatality From cancer Cancer mortality No region-specific trends -0.0010 (0.0098) 0.0186 (0.0135) 0.0197** (0.0077) With region-specific trends 0.0011 (0.0151) 0.0266 (0.0179) 0.0282*** (0.0081)

19. Regression Results (2)  By gender and age.  Shift from procyclical to countercyclical mortality changes is more strongly revealed among older people (65 and older), especially the female elderly.  The negative effect of unemployment after 2002 is also significant for prime-aged males and middle-aged females.  By education  Shift to countercyclical mortality is found only for low-educated people.  By cause of death  Changing effect of unemployment is most clearly seen for cancer mortality: significantly negative to significantly positive.  Counter-cyclical change in cancer mortality was produced by changes in case fatality, not by the number of cases.  Digestive disease mortality after 2002 is also strongly countercyclical.  Rates of deaths caused by infectious disease and diseases of nervous system are procyclical after 2002.

20. What Explain the Changing Relationship?  Hypothesis  Pro-cyclical mortality: negative influences of economic booms (work-related stress, less healthy behaviors, lowered quality of health care. etc.) dominate positive effects (lower joblessness and increased earnings).  Turnaround: positive effects of prosperous business conditions became strong enough to dominate the potential negative influences of economic booms.  A possible candidate should satisfy the following conditions:  It strengthened the positive “income effects” of better economic conditions during the 2000s.  It’s effects were stronger for low-educated (low-income) individuals than for high-educated persons.  It’s impacts were confined to particular causes of death, such as cancers.

21. Extended Protection of National Health Insurance in the 2000s  Subsidization out-of-pocket (OOP) payment exceeding a certain limit (2000 to 2009)  January 2000: Subsidize 50% of OOP costs exceeding 120 million won for 30 days.  July 2004: Subsidize 100% of OOP costs exceeding 300 million won for 6 months.  July 2007: Lower the ceiling to 200 million won.  January 2009: 300 million won for one year; differentiate the ceiling income level (200m for lower 50%; 30% for middle 30%; 400m for upper 20%)  Extension of insurance coverage for serious diseases, including major cancers (2005)  2005: The official rate of the OOP expenses for cancers was reduced from 20% to 10%.  2009: it further diminished to 5%.

22. Effects of Extended Protection of NHI  Effects on Medical Utilizations  The subsidization of OOP expenses contributed to substantial increase in medical utilization of low-income people (Lee et al. 2013).  The extension of the insurance coverage in 2005 significantly increased the medical utilizations among cancer patients (Kim and Kwon 2014)  Potential Effects on Cyclical Mortality Changes  Prior to 2000, high OOP costs prevented low-income patients with serious illnesses from receiving high-quality medical services.  modest fluctuations in earnings wouldn’t matter.  Reforms in the 2000s  improved access to medical service but OOP expenses are still considerable burden  medical utilization and health are more strongly influenced by changes in income (and business conditions)

23. Empirical Tests of the Effects of the Extended Protection of NHI  Testable Hypothesis  The change in the relationship between unemployment and mortality should be more strongly revealed for deaths caused by diseases requiring larger medical expenses.  The turnaround should be more pronounced for the diseases for which the increase in the benefits per patient paid by the NHI is greater during the 2000s.  Constructing data on death and medical utilization for 298 disease categories  The number of patients and medical expenditures for 298 disease categories from the NHI Yearbooks.  Construct cause-specific mortality data using micro data on the Causes of Death Statistics, matching the disease classifications with those of the NHI Yearbooks.

24. Empirical Tests of the Effects of the Extended Protection of NHI  Estimating the magnitude of change in the coefficient for unemployment for 298 disease categories.  I: dummy variable indicating the former period (1991-2001)  Subscripts 0: entire period, 1: 1991-2001, 2: 2002-2011  Subscript i: type of disease  γ i2 : change in the effect of unemployment on mortality caused by disease i from the former to the latter period.  Estimating the effects of variables on medical utilizations on the estimated magnitude of the change  X: variables on medical utilizations

25. Table 5 The Correlation between Medical Utilization and the Changing Effect Note: The dependent variable is the degree of changes in the regression coefficients on log unemployment rates from 1991-2001 to 2002-2011, i.e.,. Explanatory variables are medical utilization in won by each disease (1 billion won), the number of patients (1 million people) and medical utilization per patient (1 million won). Observations are weighted by the square root of the number of 2011 deaths by each disease. *** Statistical significance for 0.10 level. *** Statistical significance for 0.05 level. *** Statistical significance for 0.01 level. The treatment amount, 2011 Change in the treatment amount, 2000-2011 Change in the patient payments, 2000-2011 (1)(2)(3)(4)(5)(6) Expenditure per patient 8.1178** (2.8201) 10.4313** (4.2798) -41.6397 (45.2960) Expenditure0.0539** (0.0247) 0.0652** (0.0294) 0.1611 (0.1142) Number of patients -0.0128* (0.0067) -0.0272** (0.0127) -0.0265* (0.0146)

26. Empirical Tests: Regression Results  The results are consistent with the hypothesis that the extended medical protections in the 2000s contributed to the change in the relationship between business cycle and mortality.  Medical expenditure per patient in 2011 is positively related to the size of the change in the effect of unemployment.  Change in medical expenditure per patient between 2000 and 2011 is positively related to the size of the change in the effect of unemployment.  Change in OOP expenses per patient between 2000 and 2011 is not correlated to the size of the change in the effect of unemployment.

27. Alternative Hypothesis  Advances in medical technology  Hypothesis suggested for the US (Ruhm 2013)  Matched well with the improvements in cancer treatments  Matched well with the effect of change in the medical expenditure on the change in the effect of unemployment.  However, this is inconsistent with the effect of change in OOP expenses per patient on the change in the effect of unemployment.  Inconsistent with the fact that the turnaround was stronger for low- educated (poorer) persons.  Weakening of negative influences of economic booms.  Hours of work, work-related stress, unhealthy behaviors (smoking and drinking), and pollutions became less procyclical or less serious threat to health during the 2000s?  Inconsistent with the results on age- and cause-specific mortality changes: changes in the work conditions and the quality of environment unlikely strongly affects the health of the elderly and cancer patients.

28. Implications  The relationship between business cycle and health may differ across times and places, depending on the relative strength of positive and negative influences of economic booms.  Possible determining factors include: medical technology, health insurance system, regulations on environment and labor market, and so on.