Job Market Prospects of Breast vs. Prostate Cancer Survivors in the US: A Double Hurdle Model of Ethnic Disparities

  • Shelley I. White-MeansEmail author
  • Ahmad Reshad Osmani
Original Paper


Labor market presence of cancer survivors has been significantly improved as medical technology revolutionized cancer-specific diagnoses and treatments. However, less understood are post-cancer variations in job market outcomes of racial and ethnic minorities in the US. Using a theoretical framework derived from family labor supply decision models and taking advantage of the rich data in the 2008–2014 Medical Expenditure Panel Survey (MEPS), this study employs a double-hurdle empirical model of labor force participation and hours worked to evaluate the employment decisions of Black and Hispanic cancer survivors. Hispanic and Black breast cancer survivors were less likely to be employed by 4% and 7.5%, respectively, when compared with Whites. Black prostate cancer survivors were 8% less likely to work than Whites, with nonsignificant differences between Hispanic and White prostate cancer survivors. Once employed, Black and Hispanic breast cancer survivors worked an extra 4 and 6 h than Whites, while Hispanic prostate cancer survivors worked 5 fewer weekly hours than Whites. In addition, our estimates indicate the significance of job types in labor market outcomes post-cancer. Employment of minorities in blue collar or service occupations decreased employment hours of survivors. Labor market disparities for minorities amplifies the socio-economic and familial burden of cancers. This timely work motivates informed public policy to reduce unexamined consequences of chronic conditions among minorities.


Breast cancer Prostate cancer Double hurdle model Medical expenditure panel survey Employment disparities 

JEL Classification

I1 J15 J22 J71 


Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Acemoglu, D., & Angrist, J. D. (2001). Consequences of employment protection? The case of the Americans with Disabilities Act. Journal of Political Economy, 109(5), 915–957. Scholar
  2. Acs, Z. J., & Armington, C. (2004). The impact of geographic differences in human capital on service firm formation rates. Journal of Urban Economics, 56(2), 244–278. Scholar
  3. Anderson, P. M., & Levine, P. B. (1999). Child care and mothers’ employment decisions. New York: National Bureau of Economic Research. Scholar
  4. Artazcoz, L. a., Borrell, C., Benach, J., Cortès, I., & Rohlfs, I. (2004). Women, family demands and health: the importance of employment status and socio-economic position. Social Science and Medicine, 59(2), 263–274. Scholar
  5. Ashenfelter, O., & Heckman, J. (1974). The estimation of income and substitution effects in a model of family labor supply. Econometrica: Journal of the Econometric Society, 42, 73–85. Scholar
  6. Ashing-Giwa, K. T., Gonzalez, P., Lim, J. W., Chung, C., Paz, B., Somlo, G., et al. (2010). Diagnostic and therapeutic delays among a multiethnic sample of breast and cervical cancer survivors. Cancer, 116(13), 3195–3204. Scholar
  7. Ashing-Giwa, K. T., Padilla, G., Tejero, J., Kraemer, J., Wright, K., Coscarelli, A., et al. (2004). Understanding the breast cancer experience of women: A qualitative study of African American, Asian American, Latina and Caucasian cancer survivors. Psycho-Oncology, 13(6), 408–428. Scholar
  8. Baicker, K., Finkelstein, A., Song, J., & Taubman, S. (2014). The impact of Medicaid on labor market activity and program participation: Evidence from the Oregon Health Insurance Experiment. American Economic Review, 104(5), 322–328. Scholar
  9. Baldwin, M. L., & Johnson, W. G. (2000). Labor market discrimination against men with disabilities in the year of the ADA. Southern Economic Journal, 66, 548–566, Scholar
  10. Banegas, M. P., Guy, G. P. Jr., de Moor, J. S., Ekwueme, D. U., Virgo, K. S., Kent, E. E., et al. (2016). For working-age cancer survivors, medical debt and bankruptcy create financial hardships. Health Affairs, 35(1), 54–61. Scholar
  11. Bayer, P., & Charles, K. K. (2018). Divergent paths: A new perspective on earnings differences between Black and White men since 1940. The Quarterly Journal of Economics, 133(3), 1459–1501. Scholar
  12. Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. New York: National Bureau of Economic Research. Scholar
  13. Berg, P., Appelbaum, E., Bailey, T., & Kalleberg, A. L. (2004). Contesting time: International comparisons of employee control of working time. ILR Review, 57(3), 331–349. Scholar
  14. Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. American Economic Review, 94(4), 991–1013. Scholar
  15. Blundell, R., Ham, J., & Meghir, C. (1989). Unemployment and female labour supply. In Unemployment in Europe (pp. 9–36).
  16. Blundell, R., & Meghir, C. (1987). Bivariate alternatives to the Tobit model. Journal of Econometrics, 34(1–2), 179–200.Google Scholar
  17. Bound, J., Schoenbaum, M., & Waidmann, T. (1996). Race differences in labor force attachment and disability status. Gerontologist, 36(3), 311–321. Scholar
  18. Bradley, C. J., & Bednarek, H. L. (2002). Employment patterns of long-term cancer survivors. Psycho-Oncology, 11(3), 188–198. Scholar
  19. Bradley, C. J., Bednarek, H. L., & Neumark, D. (2002). Breast cancer survival, work, and earnings. Journal of Health Economics, 21(5), 757–779. Scholar
  20. Bradley, C. J., Given, C. W., & Roberts, C. (2002). Race, socioeconomic status, and breast cancer treatment and survival. Journal of the National Cancer Institute, 94(7), 490–496. Scholar
  21. Bradley, C. J., Neumark, D., Bednarek, H. L., & Schenk, M. (2005a). Short-term effects of breast cancer on labor market attachment: Results from a longitudinal study. Journal of Health Economics, 24(1), 137–160. Scholar
  22. Bradley, C. J., Neumark, D., Luo, Z., Bednarek, H., & Schenk, M. (2005b). Employment outcomes of men treated for prostate cancer. Journal of the National Cancer Institute, 97(13), 958–965. Scholar
  23. Bradley, C. J., Neumark, D., Luo, Z., & Schenk, M. (2007). Employment and cancer: Findings from a longitudinal study of breast and prostate cancer survivors. Cancer Investigation, 25(1), 47–54. Scholar
  24. Burdett, K. (1978). A theory of employee job search and quit rates. The American Economic Review, 68(1), 212–220.
  25. Cai, L., & Kalb, G. (2006). Health status and labour force participation: Evidence from Australia. Health Economics, 15(3), 241–261. Scholar
  26. Cai, L., & Liu, A. Y. (2011). Public–private sector wage gap in Australia: Variation along the distribution. British Journal of Industrial Relations, 49(2), 362–390. Scholar
  27. Cain, G. G., & Dooley, M. D. (1976). Estimation of a model of labor supply, fertility, and wages of married women. Journal of Political Economy, 84(4), S179–S199. Scholar
  28. Cameron, A. C., & Trivedi, P. K. (1990). Regression-based tests for overdispersion in the Poisson model. Journal of Econometrics, 46(3), 347–364. Scholar
  29. Campesino, M., Ruiz, E., Glover, J. U., & Koithan, M. (2009). Counternarratives of Mexican-origin women with breast cancer. Advances in Nursing Science, 32(2), E57.
  30. Card, D. (1999). The causal effect of education on earnings. Handbook of Labor Economics, 3, 1801–1863. Scholar
  31. Chen, W., Zheng, R., Baade, P. D., Zhang, S., Zeng, H., Bray, F., et al. (2016). Cancer statistics in China, 2015. CA: A Cancer Journal for Clinicians, 66(2), 115–132. Scholar
  32. Cherrier, M. M., Borghesani, P. R., Shelton, A. L., & Higano, C. S. (2010). Changes in neuronal activation patterns in response to androgen deprivation therapy: A pilot study. BMC Cancer, 10(1), 1. Scholar
  33. Chiappori, P.-A. (1997). Introducing household production in collective models of labor supply. Journal of Political Economy, 105(1), 191–209. Scholar
  34. Chiappori, P.-A., Fortin, B., & Lacroix, G. (2002). Marriage market, divorce legislation, and household labor supply. Journal of Political Economy, 110(1), 37–72. Scholar
  35. Connelly, R. (1992). The effect of child care costs on married women’s labor force participation. The Review of Economics and Statistics, 74, 83–90. Scholar
  36. Cotton, J. L., & Tuttle, J. M. (1986). Employee turnover: A meta-analysis and review with implications for research. Academy of Management Review, 11(1), 55–70. Scholar
  37. Cragg, J. G. (1971). Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica: Journal of the Econometric Society, 39, 829–844, Scholar
  38. Cubbin, C., Pedregon, V., Egerter, S., & Braveman, P. (2008). Where we live matters for our health: Neighborhoods and health. Princeton, NJ: Robert Wood Johnson Foundation Commission to Build a Healthier America.Google Scholar
  39. Currie, J., & Madrian, B. C. (1999). Health, health insurance and the labor market. Handbook of Labor Economics, 3, 3309–3416. Scholar
  40. Dahl, S., Cvancarova, M., Dahl, A. A., & Fossa, S. D. (2016). Work ability in prostate cancer survivors after radical prostatectomy. Scandinavian Journal of Urology, 50(2), 116–122. Scholar
  41. Darity, W. a. M., Patrick (1998). Evidence on Discrimination in Employment: Codes of color, codes of gender. Journal of Economic Perspectives, 12(2), 63–90. Scholar
  42. Engel, J., Kerr, J., Schlesinger-Raab, A., Sauer, H., & Holzel, D. (2003). Axilla surgery severely affects quality of life: Results of a 5-year prospective study in breast cancer patients. Breast Cancer Research and Treatment, 79(1), 47–57.Google Scholar
  43. Finkelstein, A., Luttmer, E. F., & Notowidigdo, M. J. (2013). What good is wealth without health? The effect of health on the marginal utility of consumption. Journal of the European Economic Association, 11(s1), 221–258. Scholar
  44. Freedman, R. A., Virgo, K. S., He, Y., Pavluck, A. L., Winer, E. P., Ward, E. M., et al. (2011). The association of race/ethnicity, insurance status, and socioeconomic factors with breast cancer care. Cancer, 117(1), 180–189. Scholar
  45. Gehrke, B., & Weber, E. (2018). Identifying asymmetric effects of labor market reforms. European Economic Review, 110, 18–40. Scholar
  46. Golden, L. (2008). Limited access: Disparities in flexible work schedules and work-at-home. Journal of Family and Economic Issues, 29(1), 86–109. Scholar
  47. Grogan, L., & Sadanand, A. (2013). Rural electrification and employment in poor countries: Evidence from Nicaragua. World Development, 43, 252–265. Scholar
  48. Grossbard, S. (2015). A theory of allocation of time in markets for labor and marriage: Macromodel. In Grossbard S. (Ed.), The marriage motive: A price theory of marriage (pp. 21–32). New York: Springer. Scholar
  49. Grossman, M. (1972). On the concept of health capital and the demand for health. Journal of Political Economy, 80(2), 223–255. Scholar
  50. Gruber, J. (2000). Health insurance and the labor market. Handbook of Health Economics, 1, 645–706. Scholar
  51. Grunfeld, E. A., Drudge-Coates, L., Rixon, L., Eaton, E., & Cooper, A. F. (2013). The only way I know how to live is to work”: A qualitative study of work following treatment for prostate cancer. Health Psychology, 32(1), 75.Google Scholar
  52. Gurmu, S. (1997). Semi-parametric estimation of hurdle regression models with an application to medicaid utilization. Journal of Applied Econometrics.;2-Y.Google Scholar
  53. Gurmu, S., & Trivedi, P. K. (1996). Excess zeros in count models for recreational trips. Journal of Business & Economic Statistics, 14(4), 469–477. Scholar
  54. Halpern, J., & Hausman, J. A. (1986). Choice under uncertainty: A model of applications for the social security disability insurance program. Journal of Public Economics, 31(2), 131–161. Scholar
  55. Hanoch, G. (1965). The “backward-bending” supply of labor. Journal of Political Economy, 73(6), 636–642. Scholar
  56. Hansen, J. A., Feuerstein, M., Calvio, L. C., & Olsen, C. H. (2008). Breast cancer survivors at work. Journal of Occupational and Environmental Medicine, 50(7), 777–784. Scholar
  57. Heckman, J. J. (1977). Sample selection bias as a specification error (with an application to the estimation of labor supply functions). Cambridge, MA: National Bureau of Economic Research. Scholar
  58. Heinesen, E., Imai, S., & Maruyama, S. (2018). Employment, job skills and occupational mobility of cancer survivors. Journal of Health Economics, 58, 151–175. Scholar
  59. Hellström, J. (2002). Count data modelling and tourism demand. Umeå: Umeå Economic Studies.Google Scholar
  60. Hofler, R. A., & Murphy, K. J. (1994). Estimating reservation wages of employed workers using a stochastic frontier. Southern Economic Journal, 60, 961–976.Google Scholar
  61. Holzbeierlein, J. M., McLaughlin, M. D., & Thrasher, J. B. (2004). Complications of androgen deprivation therapy for prostate cancer. Current Opinion in Urology, 14(3), 177–183. Scholar
  62. Hoynes, H., Simeonova, E., & Simonsen, M. (2016). Health and the labor market–New developments in the literature. Labour Economics, 43, 1–5. Scholar
  63. Jeon, S. H. (2017). The long-term effects of cancer on employment and earnings. Health Economics, 26(5), 671–684. Scholar
  64. Jones, A. M. (1992). A note on computation of the double-hurdle model with dependence with an application to tobacco expenditure. Bulletin of Economic Research, 44(1), 67–74. Scholar
  65. Kiasuwa Mbengia, R., Tiraboschi, M., Bouland, C., & de Brouwer, C. (2018). How do social security schemes and labor market policies support the return-to-work of cancer survivors? A review article on challenges and opportunities in the European Union. Journal of Cancer Policy. Scholar
  66. Killingsworth, M. R., & Heckman, J. J. (1986). Female labor supply: A survey. Handbook of Labor Economics, 1, 103–204. Scholar
  67. Kirschenbaum, A., & Weisberg, J. (2002). Employee’s turnover intentions and job destination choices. Journal of Organizational Behavior, 23(1), 109–125. Scholar
  68. Kline, P., & Moretti, E. (2013). Place based policies with unemployment. The American Economic Review, 103(3), 238–243. Scholar
  69. Koopman, C., Pelletier, K. R., Murray, J. F., Sharda, C. E., Berger, M. L., Turpin, R. S., et al. (2002). Stanford presenteeism scale: Health status and employee productivity. Journal of Occupational and Environmental Medicine, 44(1), 14–20.Google Scholar
  70. Lloyd, K. M., & Auld, C. J. (2002). The role of leisure in determining quality of life: Issues of content and measurement. Social Indicators Research, 57(1), 43–71. Scholar
  71. McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (Vol. 37). Boca Raton: CRC Press. Scholar
  72. Mehnert, A. (2011). Employment and work-related issues in cancer survivors. Critical Reviews in Oncology/Hematology, 77(2), 109–130. Scholar
  73. Melaku, Y. A., Appleton, S. L., Gill, T. K., Ogbo, F. A., Buckley, E., Shi, Z., et al. (2018). Incidence, prevalence, mortality, disability-adjusted life years and risk factors of cancer in Australia and comparison with OECD countries, 1990–2015: Findings from the Global Burden of Disease Study 2015. Cancer Epidemiology, 52, 43–54. Scholar
  74. Miller, K. D., Siegel, R. L., Lin, C. C., Mariotto, A. B., Kramer, J. L., Rowland, J. H., et al. (2016). Cancer treatment and survivorship statistics, 2016. CA: A Cancer Journal for Clinicians, 66(4), 271–289. Scholar
  75. Mincer, J. (1962). Labor force participation of married women: A study of labor supply. In Mincer, J. (Ed.), Aspects of labor economics (pp. 63–105). Princeton, NJ: Princeton University Press.Google Scholar
  76. Moses, K. A., Paciorek, A. T., Penson, D. F., Carroll, P. R., & Master, V. A. (2010). Impact of ethnicity on primary treatment choice and mortality in men with prostate cancer: Data from CaPSURE. Journal of Clinical Oncology, 28(6), 1069–1074. Scholar
  77. Ng, T. W., & Feldman, D. C. (2009). Age, work experience, and the psychological contract. Journal of Organizational Behavior, 30(8), 1053–1075. Scholar
  78. Okunade, A. A., Suraratdecha, C., & Benson, D. A. (2010). Determinants of Thailand household healthcare expenditure: The relevance of permanent resources and other correlates. Health Economics, 19(3), 365–376. Scholar
  79. Osmani, A. R., & Okunade, A. A. (2018). Cancer survivors in the labor market: Evidence from recent US micro-panel data. Economic Modelling. Scholar
  80. Pedersen, V. H., Armes, J., & Ream, E. (2012). Perceptions of prostate cancer in Black African and Black Caribbean men: A systematic review of the literature. Psycho-Oncology, 21(5), 457–468. Scholar
  81. Podor, M., & Halliday, T. J. (2012). Health status and the allocation of time. Health Economics, 21(5), 514–527. Scholar
  82. Portney, P. R., & Mullahy, J. (1986). Urban air quality and acute respiratory illness. Journal of Urban Economics, 20(1), 21–38. Scholar
  83. Pudney, S. (1989). Modelling individual choice. The econometrics of corners, kinks and holes. Oxford: Basil Blackwell.Google Scholar
  84. Ransom, M. R. (1987). An empirical model of discrete and continuous choice in family labor supply. The Review of Economics and Statistics, 69, 465–472. Scholar
  85. Salmon, C., & Tanguy, J. (2016). Rural electrification and household labor supply: Evidence from Nigeria. World Development, 82, 48–68. Scholar
  86. Schmidt, D. E., & Duenas, G. (2002). Incentives to encourage worker-friendly organizations. Public Personnel Management, 31(3), 293–308. Scholar
  87. Schneider, U., Trukeschitz, B., Mühlmann, R., & Ponocny, I. (2013). Do I stay or do I go?”—Job change and labor market exit intentions of employees providing informal care to older adults. Health Economics, 22(10), 1230–1249. Scholar
  88. Short, P. F., Vasey, J. J., & Moran, J. R. (2008). Long-term effects of cancer survivorship on the employment of older workers. Health Services Research, 43(1p1), 193–210. Scholar
  89. Siegel, R. L., Miller, K. D., & Jemal, A. (2018). Cancer statistics, 2018. CA: A Cancer Journal for Clinicians, 68(1), 7–30. Scholar
  90. Steiner, J. F., Cavender, T. A., Main, D. S., & Bradley, C. J. (2004). Assessing the impact of cancer on work outcomes: what are the research needs? Cancer: Interdisciplinary International Journal of the American Cancer Society, 101(8), 1703–1711. Scholar
  91. Storey, D. J., McLaren, D. B., Atkinson, M. A., Butcher, I., Liggatt, S., O’Dea, R., et al. (2012). Clinically relevant fatigue in recurrence-free prostate cancer survivors. Annals of Oncology, 23(1), 65–72. Scholar
  92. Strauss, J., & Thomas, D. (1998). Health, nutrition, and economic development. Journal of Economic Literature, 36(2), 766–817.Google Scholar
  93. Swanberg, J. E., Nichols, H. M., Ko, J., Tracy, J. K., & Vanderpool, R. C. (2017). Managing cancer and employment: Decisions and strategies used by breast cancer survivors employed in low-wage jobs. Journal of Psychosocial Oncology, 35(2), 180–201. Scholar
  94. Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica: Journal of the Econometric Society, 26, 24–36. Scholar
  95. Trevor, C. O. (2001). Interactions among actual ease-of-movement determinants and job satisfaction in the prediction of voluntary turnover. Academy of Management Journal, 44(4), 621–638. Scholar
  96. US Bureau of Labor Statistics. (2015). Labor force characteristics by race and ethnicity, 2014. Washington, DC: US Bureau of Labor Statistics.Google Scholar
  97. Van Breukelen, W., Van der Vlist, R., & Steensma, H. (2004). Voluntary employee turnover: Combining variables from the ‘traditional’ turnover literature with the theory of planned behavior. Journal of Organizational Behavior, 25(7), 893–914. Scholar
  98. Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica: Journal of the Econometric Society, 307–333,
  99. Ward, E., Jemal, A., Cokkinides, V., Singh, G. K., Cardinez, C., Ghafoor, A., et al. (2004). Cancer disparities by race/ethnicity and socioeconomic status. CA: A Cancer Journal for Clinicians, 54(2), 78–93. Scholar
  100. Welch, F. (1967). Labor-market discrimination: An interpretation of income differences in the rural South. Journal of Political Economy, 75(3), 225–240. Scholar
  101. White-Means, S., & Hersch, J. (2005). Health insurance disparities in traditional and contingent/alternative employment. International Journal of Health Care Finance and Economics, 5, 351–368. Scholar
  102. White-Means, S., Rice, M., Dapremont, J., Davis, B., & Martin, J. (2015). African American women: surviving breast cancer mortality against the highest odds. International Journal of Environmental Research and Public Health, 13(1), 6. Scholar
  103. Wooldridge, J. M. (2005). Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity. Journal of Applied Econometrics, 20(1), 39–54. Scholar
  104. Yen, S. T., Jensen, H. H., & WANG, O. (1996). Cholesterol information and egg consumption in the US: A nonnormal and heteroscedastic double-hurdle model. European Review of Agricultural Economics, 23(3), 343–356. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of Tennessee Health Science CenterMemphisUSA
  2. 2.University of MemphisMemphisUSA

Personalised recommendations