Employment, Occupational Mobility and Job Skills of Cancer Survivors Eskil Heinesen, Rockwool Foundation Research Unit Susumu Imai, Hokkaido University Shiko Maruyama, University of Technology Sydney

Improvement in cancer screening and treatment Motivation Improvement in cancer screening and treatment  increased the number of people surviving cancer They live longer and many of them return to work Natural questions: How large is the “cancer effect” on later labour market outcomes? Who can return to work? Who cannot? To what job? (the same one or something different?) Important for cancer survivors and society Potentially more effective labour market policies for cancer survivors

Significant negative effects of cancer on labour market participation Previous Literature Significant negative effects of cancer on labour market participation E.g. Bradley et al. (2002, 2005), Steiner et al. (2004), Moran et al. (2011), Short et al. (2008), Datta Gupta et al. (2011), Heinesen and Kolodziejzcyk (2013) Significant educational gradient: negative effect much stronger for the low-educated E.g. Heinesen and Kolodziejzcyk (2013), Thielen et al. (2015) We contribute to this literature focusing on type of job E.g. physically demanding jobs vs cognitively demanding jobs occupation / job skills / skill requirement ...

Question 1: Does type of pre-diagnosis occupation matter? Hypothesis: Cancer affects employment negatively because cancer and its treatment have negative effects on ability to work (e.g. through fatigue/tiredness/loss of energy)  If cancer reduces ability mainly by reducing physical strength, the cancer effect is expected to be stronger for physically demanding jobs

Question 2 Question 2: If the cancer effect varies by type of job, does it explain the negative educational gradient? Relevance: The educational gradient implies that taxing impacts of cancer is “regressive”, the society may need a better social security system / labour market policy  Important to understand the mechanism behind the educational gradient of the cancer effect

Question 3 Question 3: If survivors return to work, do they return to the same occupation? Hypothesis: If cancer and its treatment reduce ability to work, cancer survivors may want to switch to less demanding occupations (typically lower physical requirements) However, it may be difficult to find a new job at another firm Barriers due to firm- or job-specific human capital  If too difficult, they may choose retire unwillingly

Does type of occupation matter for the cancer effect? 3 Questions to be Answered in This Paper Does type of occupation matter for the cancer effect? If yes, does it explain the educational gradient? If survivors return to work, do they return to the same occupation? We answer these questions using Danish register data Population data of working-age men and women 2001-2005 Follow them over 5 years

Empirical Framework Treatment Group Men and women aged 30-60 (year t) First cancer diagnosis (year t) Excluding skin cancer (previous studies found no effect) Employed at t-2 Survival to t+5 Control group No cancer diagnosis ever at t Compare their outcomes at t+4

Econometric Specification: (1) No interaction OLS regressions:    

Econometric Specification: (2) with Interaction OLS regressions:    

Control variables in estimation: Age (30 dummies), base year (5 dummies), education (3 dummies) county of residence (14 dummies), family type (2 dummies) the number of contacts to the primary health care sector t-2 (3 variables: GPs, specialists and dentists) hospitalisation t-5 – t-2 by type of diagnosis (16 dummies) consumption of selected categories of prescription drugs t-5 – t-2 by type of drug (20 dummies) job skill requirements t-2 (6 variables) industry of employment t-2 (9 dummies) not employed t-3 and t-5 (2 dummies), full-timer t-2 and t-5 (2 dummies) log earnings t-2 and t-5 some unemployment t-2 and t-5 (2 dummies), and degree of unemployment

Our Innovation: Detailed Job Characteristics Job characteristics from US O*NET (Occupation Information Network) data (and its earlier version, Dictionary of Occupational Titles, DOT) Not just traditional blue collar/white collar distinction, but very detailed For 1000 different occupations Over 200 aspects of skills and job characteristics

PCA: Example: Physical strength requirements

Our Innovation: Detailed Job Characteristics It has been used in different contexts: To analyse heterogeneity in returns to skills (Ingram and Neumann, 2006; Bacolod and Blum, 2010; Poletaev and Robinson, 2008; Yamaguchi, 2012) To compare skill characteristics of immigrants’ jobs with natives (e.g., Ottaviano et al., 2013; Imai et al. 2014; Foged and Peri, 2015) But not in the context of effects of health shocks (e.g. cancer)

Job Characteristics US O*NET database linked to Danish occupation codes (ISCO) using crosswalks between US SOC codes and Danish ISCO codes To summarize the O*NET data  6 job characteristics variables: (1) analytical skills, (2) interpersonal skills, (3) physical strength, (4) fine motor skills, (5) visual skills, (6) customer contact To create each of these, we apply factor analysis (PCA) for the Danish workforce

Very similar pattern for control group, and in t+4 Job characteristics for the cancer group in year t-2: Means by education and gender Very similar pattern for control group, and in t+4 In main analysis, we focus on Cognitive = (analytical + interpersonal)/2 Manual = (physical strength + fine motor skills)/2

Control variables, balancing properties Normalized differences small. The majority are below 0.03 numerically No common support problems: Propensity score distribution of cancer and control groups based on probit estimation of the risk of cancer

Data. Overlap in propensity score distributions Data. Overlap in propensity score distributions. Males (left) and females (right)

Robustness of Results Unconfoundedness assumption may be problematic, e.g. due to unobserved health and life-style variables affecting both the risk of cancer, the probability of surviving cancer and potential labour market outcomes However, such unobserved variables may be assumed to also affect lagged labour market outcomes and health indicators, which we control for If unconfoundedness does not hold, the sign of the bias is unclear: Individuals with “weak” unobserved characteristics presumably have a higher risk of getting cancer, but a lower chance of surviving cancer We address potential problems with the unconfoundedness assumption in 3 supplementary analyses (robustness checks)

Robustness of Results 1. Robustness check: we use an alternative control group of later cancer patients which may be assumed to be more similar to the cancer group in terms of unobserved life-style variables 2. Robustness check: DID analysis of cancer effects on earnings, income and job skills. (DID not relevant here for labour market status outcomes since all individuals in the analysis are employed at baseline)  

Robustness of Results  

Key Results: Employed t+4 + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001

Main Results Cancer has significant effects on outcomes in t+4: Employment (-7 %-points), educational gradient, job skill gradients Disability pension (+5-6 %-points), educational gradient, job skill gradients Much of the educational gradient explained by occupational gradient Similar results for earnings, gross income, and disposable income Cancer has no significant association with: Mobility w.r.t. plant / occupation / industry (conditional on t+4 employment) Job characteristics t+4 (conditional on t+4 employment) The hourly wage rate (conditional on t+4 employment) OLS and IPW produce similar estimates of cancer effects Results robust to alternative control group (later cancer patients) and DID Placebo test indicates no important bias

Conclusions Does type of occupation matter for the cancer effect? Yes. Cancer effect larger for more “manual” pre-diagnosis jobs If yes, does it explain the educational gradient? Yes to a great degree, if not 100% If survivors return to work, do they return to the same occupation? Yes. Didn’t find move to less demanding jobs

Cancer group by type of cancer, and survival