Chronic diseases and labour force participation in Australia

Journal of Health Economics 28 (2009) 91–108

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Chronic diseases and labour force participation in Australia Xiaohui Zhang a , Xueyan Zhao a,b,∗ , Anthony Harris b a b

Department of Econometrics and Business Statistics, Monash University, Australia Centre for Health Economics, Monash University, Australia

a r t i c l e

i n f o

Article history: Received 4 July 2007 Received in revised form 1 August 2008 Accepted 1 August 2008 Available online 19 August 2008 JEL classification: C3 I1 J2 Keywords: Labour force participation Chronic diseases Multivariate probit Endogeneity Treatment effect

a b s t r a c t We examine the impact of several chronic diseases on the probability of labour force participation using data from the Australian National Health Surveys. An endogenous multivariate probit model is used to account for the potential endogeneity of the incidence of chronic conditions such as diabetes, cardiovascular diseases and mental illnesses. The cross-equation correlations are significant, rejecting the exogeneity of the chronic illnesses. Marginal effects of exogenous socio-demographic and lifestyle variables are estimated through their direct effects on labour market participation and indirect effects via the chronic diseases. The treatment effects of chronic diseases on labour force participation are estimated via conditional probabilities using five-dimensional normal distributions. The estimated effects differ by gender and age groups. Although computationally more demanding, these treatment effects are compared with results from a univariate model treating the chronic conditions exogenous and the structural effects from the multivariate probit model; both significantly overestimate the effects. © 2008 Elsevier B.V. All rights reserved.

1. Introduction Chronic diseases are the major cause of death and disability worldwide, and increasingly affect people from the developing as well as the developed countries. This is a reflection of significant changes in dietary habits, physical activity levels, and tobacco use worldwide as a result of industrialization, urbanization, economic development and increasing food market globalization (WHO, 2005). Although many developed countries have been successful in reducing the mortality risk from chronic diseases in recent years, there remains a significant burden of illness, pain and disability, as well as the associated economic burden in terms of treatment costs and reduced productivity at work, unemployment and premature retirement. The prevalence of major chronic illnesses including diabetes, cardiovascular diseases, mental illness and asthma has been on the rise in the past decade in developed countries. For example, increases in asthma prevalence have been reported for the US, England, Canada, Israel, and Australia (Anderson et al., 1994; CDCP, 1995). Amos et al. (1997) estimated that in 1997, 124 million people worldwide had diabetes, 97% of these having non-insulin dependent diabetes. By the year 2010 the total number of people with diabetes was projected to reach 221 million. In the Australian National Health Surveys, 5.7% of the adult respondents (older than 18) reported they had diabetes at the time of the survey in 2004–2005, compared to 4.4% in 2001 and 2.8% in 1995. The trend is likely to continue due to an aging population and increasing risk factors associated with changing modern lifestyle. Advances in medical technologies have also meant that people are now living longer with more

∗ Corresponding author at: Department of Econometrics and Business Statistics, Menzies Building, Clayton Campus, Monash University, VIC 3800, Australia. Tel.: +61 3 99052415; fax: +61 3 99055474. E-mail address: [email protected] (X. Zhao). 0167-6296/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.jhealeco.2008.08.001

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health conditions. These chronic health problems not only place a huge burden on health care and welfare systems, but also impact negatively on the labour market performance. It is thus crucial to evaluate the economic and social consequences of a growing trend in conditions such as diabetes based on available data. Empirical studies linking lifestyle behaviour and other risk factors to chronic conditions will inform policies aimed at providing incentives to maximize productivity in the workforce. There is a large literature on the impacts of individuals’ health on labour force participation and labour market performance. The theoretical notion is that health, which deteriorates over time but is capable of enhancement as a result of household production, can be considered an endowment of human capital, like education, that is positively related to the ability to work and work productively (Becker, 1964, 1965; Lancaster, 1966; Grossman, 1972, 1999; Currie and Madrian, 1999). This implies higher returns from work for healthy workers and healthy people are more likely to work. Poor health may also impact directly on an individual’s preference via the relative utilities of work and leisure in the labour supply decision and reduce the total amount of time available to earn money. In addition, sickness in most Western countries gives an entitlement to income from welfare benefits conditional on not working (Grossman, 1999; Disney et al., 2006; Cai and Kalb, 2006). There are then a number of theoretical links between health status and work that suggest not only that better health improves labour outcomes but also that poorer health is likely to be associated with lower labour supply. Studies analysing the impact of health on labour market outcomes have either focussed on the impact of overall health status or the impact of specific chronic conditions such as diabetes or mental health. In both cases much attention has focussed on measurement issues surrounding the variables used to proxy health status. Most studies do not have an accurate measure of overall health status but rather rely on a global self-reported health measure as a proxy. There has been much discussion in the literature regarding the potential endogeneity associated with such global self-reported health measure, and the resulting biased estimates of health effect on labour market outcomes (Anderson and Burkhauser, 1984, 1985; Stern, 1989; Bound, 1991; Dwyer and Mitchell, 1999; Bound et al., 1999; Campolieti, 2002; Cai and Kalb, 2006). The potential endogeneity bias could arise for a number of reasons. It has been suggested that the respondents may have economic or psychological reasons to alter their self-reported health to rationalise their labour market decisions. For example, social security eligibility conditions may encourage those surveyed individuals who do not participate in the labour force to overstate their ill health as a justification for their non-participation, even though the empirical literature has not found strong evidence of this effect so far. The subjective nature of the self-reported health also means that the responses may not strictly be comparable across individuals and measurement errors are possible which may also render health measures endogenous. In addition, poor health may be associated with unobserved individual characteristics such as time or risk preference that also impact on labour market decisions. Thus, the implications for an empirical model of the relationship between health and labour outcomes is that the determinants of health and the determinants of labour outcomes need to be estimated in a system of equations that allows the unobserved factors to be accounted for. Other researchers have examined the effects on labour market performance of specific chronic health conditions such as diabetes (Brown et al., 2005; Bastida and Pagan, 2002; Kahn, 1998) or mental health problems (Butterworth et al., 2006). Although specific health conditions may also be self-reported when using data from surveys, relative to self-assessed global health status, they are less likely to be subject to reporting or measurement errors. The focus on chronic illnesses is particularly important in the context of population ageing, given the increasingly high prevalence of many chronic diseases among the older population and the link between health and the decision to retire. Among the studies on specific chronic health conditions and labour market performance, most have treated the incidence of chronic illness as exogenous (for example, Bastida and Pagan, 2002 and Kahn, 1998). However, incidences of chronic conditions such as diabetes, cardiovascular disease, osteoporosis and mental illness may not be exogenous. They are likely to be the results of lifestyle behaviour and other unobservable individual heterogeneity that also determine the result of labour market outcomes. Any degree of measurement errors due to the subjectivity of the self-reported medical conditions would also mean that such reported chronic diseases are potentially endogenous. Exceptions in this literature are Brown et al. (2005) and Norton and Han (2007). Norton and Han (2007) estimated a linear probability model for employment status, accounting for endogeneity of BMI using an instrumental variable approach. The use of a linear probability labour equation has made accounting for endogeneity of a regressor a lot easier. Brown et al. (2005) studied the impact of diabetes on employment using a recursive bivariate probit model. However, they did not take full advantage of the system equations approach in presenting all results of interest.1 Another feature of this empirical literature is that specific chronic conditions are studied in isolation. However, there is considerable co-morbidity among many chronic illnesses associated with common risk factors attributable to behaviour and lifestyle. For example, the risk of cardiovascular disease and diabetes are highly correlated not only because diet and exercise are risk factors for both but also because high blood pressure is a common complication of diabetes. Diabetes is associated with long-term conditions such as diseases of the circulatory system and eyes. In particular, people with diabetes are at an

1 In fact, due to computational complexity they reported structural effect of diabetes on employment probability but not the full treatment effect of an endogenous dummy regressor via conditional probabilities as does this paper. They also did not present marginal effects of exogenous factors such as having a diabetic mother on employment probability. They only reported the estimated coefficients. They stated: “In the bivariate probit, the marginal effects of the exogenous variables in the employment equation are more difficult to estimate because they have a direct effect on employment and an indirect effect via the diabetes equation.”

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increased risk of developing coronary disease, stroke and peripheral vascular disease. According to the 2004/2005 Australian National Health Survey, 68% of those reporting having diabetes also reported having some kind of heart or circulatory diseases, as compared to the 30% incidence among the general population. In this paper, we examine the impacts of four categories of chronic diseases (cardiovascular diseases, diabetes, mental disease and other chronic diseases including asthma, cancer and arthritis) on individuals’ labour force participation using an endogenous multivariate probit model with a recursive structure and unit record data from the Australian National Health Surveys. The endogenous multivariate approach with a full-information maximum likelihood (FIML) estimator allows for potential endogeneity of the incidences of chronic illnesses in the labour force participation decision. We also account for any intrinsic correlations across different chronic conditions via co-morbidity and unobservable personal heterogeneity by allowing for correlations across the error terms of the four chronic disease equations. We fully explore the advantages of the multivariate approach by estimating various joint, marginal and conditional probabilities via the multivariate distribution across five endogenous variables. These predicted probabilities for the various population groups provide valuable policy relevant information. In particular, we estimate the treatment effect on labour force participation of each chronic illness using information of both the structural coefficients and cross-equation correlations. We also estimate the marginal effects of exogenous variables via the sums of direct effects on labour market participation and indirect effects via all four chronic illnesses. Finally, the analysis is carried out for four groups separately for male and female and for prime aged and older workers. Many of the studies of health and work focus on older workers and retirement. Given the increase in chronic conditions among younger people, obesity related in particular, it is important to quantify the impact on prime aged workers as well. Section 2 presents the endogenous multivariate probit model that jointly determines the outcome of labour force participation and the incidences of chronic illnesses. Section 3 details the data. Section 4 presents the results in terms of marginal effects of exogenous variables and treatment effects of chronic illnesses on the probability of labour force participation for the four population groups. Section 5 summarises the paper. 2. A multivariate endogenous probit model 2.1. The model We specify a system of probit equations with a triangular endogenous structure to estimate the effects of chronic diseases on labour force participation where the chronic health conditions are allowed to be determined endogenously. Let L* (L* ∈ (−∞,∞)) be a latent variable that is proportional to the propensity of labour force participation. The decision of participation in the labour force is modelled via the latent equation: L∗ = XL ˇL + H CH + D CD + M CM + O CO + εL ,

(1)

where XL is a vector of exogenous covariates, CH , CD , CM and CO are dummy variables indicating the incidences of cardiovascular diseases, diabetes, mental health conditions and other chronic diseases, respectively, with Ch = 1 for having the disease and Ch = 0 otherwise (h = H, D, M and O), ˇL ,  H ,  D ,  M and  O are unknown coefficients, and εL is the error term. The latent variable L* is mapped to the observable binary indicator variable L via

 L=

if L∗ > 0 if L∗ ≤ 0,

1 0

(2)

with L = 1 indicating labour force participation and L = 0 otherwise. The incidences of chronic diseases are assumed endogenous in Eq. (1) and are determined by: ∗  = XH ˇH + εH , CH

(3)

CD∗ = XD ˇD + εD ,

(4)

∗ CM

(5)

=

 XM ˇM

+ εM ,

and CO∗ = XO ˇO + εO ,

(6)

where Ch∗ is the latent variable for chronic disease h, Xh is a vector of exogenous covariates, ˇh is coefficient vector, and εh is the error term (h = H, D, M and O). The unobservable latent variable Ch∗ is related to the observable Ch via the following mapping:

 Ch =

1 0

if Ch∗ > 0 if Ch∗ ≤ 0

(h = H, D, M and O).

(7)

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Assume that the error terms in Eqs. (1) and (3)–(6) are independent and identically distributed for all individuals2 and follow a multivariate normal distribution with mean zero and covariance matrix ˙. That is, (εL ,εH ,εD ,εM ,εO ) ∼ MVN(0, ˙), where

⎛1

⎜ LH ˙ = ⎜ LD ⎝ LM LO

LH 1 HD HM HO

LD HD 1 DM DO

LM HM DM 1 MO

⎞

LO HO ⎟ DO ⎟ . ⎠ MO 1

(8)

We assume Var(εL ) = Var(εh ) ≡ 1 (h = H, D, M and O) for identification purpose. Eqs. (1)–(8) define an endogenous multivariate probit model with a recursive simultaneous structure that jointly determines the labour force participation decision and the incidences of the chronic diseases.3 The multivariate probit specification allows for correlations across the disturbances of the five latent equations, which embody unobserved characteristics for the same individuals. A univariate approach ignoring the potentially non-zero off-diagonal elements in ˙ will produce inconsistent coefficient estimates when the correlation across the error terms exists (Maddala, 1983). Under the model specification in Eqs. (1)–(8), the probability of observing an individual with a particular set of values for the five binary dependent variables is given by:  P(L = l, CH = m, CD = j, CM = k, CO = s) = ˚5 ((2l − 1)(XL ˇ + mH + jD + kM + sC ), (2m − 1)XH ˇH , (2j − 1)XD ˇD ,  ˜ ˇM , (2s − 1)XO ˇO ; ˙) (2k − 1)XM

and

⎛

1 (2l − 1)(2m − 1)LH ⎜ ˜ ⎜ (2l − 1)(2j − 1)LD ˙= ⎝ (2l − 1)(2k − 1)LM (2l − 1)(2s − 1)LO

(2l − 1)(2m − 1)LH 1 (2m − 1)(2j − 1)HD (2m − 1)(2k − 1)HM (2m − 1)(2s − 1)HO

(9)

(2l − 1)(2j − 1)LD (2m − 1)(2j − 1)HD 1 (2j − 1)(2k − 1)DM (2j − 1)(2s − 1)DO

(2l − 1)(2k − 1)LM (2m − 1)(2k − 1)HM (2j − 1)(2k − 1)DM 1 (2k − 1)(2s − 1)MO

⎞

(2l − 1)(2s − 1)LO (2m − 1)(2s − 1)HO ⎟ (2j − 1)(2s − 1)DO ⎟ ⎠ (2k − 1)(2s − 1)MO 1

(l, m, j, k and s = 0, 1) ˜ is the five-dimensional standard Gaussian c.d.f. (cumulative distribution function) with a variance–covariance where ˚5 (·, ˙) ˜ matrix ˙. Given a random sample of (Li , CH,i , CD,i , CM,i , CO,i , XL,i , XH,i , XD,i , XM,i , XO,i ) of N individuals (i = 1, 2, . . ., N), the model can be consistently and efficiently estimated by maximising the following log-likelihood function: log L =

1 1 1 1 1 N

di,lmjks log P(Li = l, CH,i = m, CD,i = j, CM,i = k, CO,i = s|XL,i , XH,i , XD,i , XM,i , XO,i )

(10)

i=1 l=0 m=0 j=0 k=0 s=0

where the indicator function is given by di,lmjks = I(Li = l, CH,i = m, CD,i = j,CM,i = k, CO,i = s), I(A) = 1 if A is true and I(A) = 0 if A is false. 2.2. Marginal and conditional probabilities and “treatment effects” Conditional on observed exogenous covariates, marginal probabilities of less than five-dimensions and various conditional probabilities can be predicted for any individual based on the probability expression in Eq. (9) after the model is estimated. As demonstrated in Section 4, these probabilities provide a rich source of information for deriving policy relevant measures. One such measure is the effects of chronic health conditions on labour force participation. The “treatment effect” (Greene, 2004; Damrongplasit et al., 2007) of having a particular chronic health condition on the probability of labour force participation for an individual X can be estimated as the difference in the predicted conditional probabilities of labour force participation with and without a health condition: ˆ = 1|Ch = 1; X) − P(L ˆ = 1|Ch = 0; X), TEh = P(L

(11)

where h = H, D, M and O, and X = (XL , XH , XD , XM , XO ) are the observed exogenous covariates for the individual.

2

The subscript representing individuals is omitted in Eqs. (1)–(7) for simplicity. Maddala (1983) discussed conditions for logical-consistency and identification for the bivariate probit model. For a bivariate probit model with the endogenous variables appearing in dummy variable form on the right hand side, a recursive structure is required to satisfy the logical-consistency condition (p. 119). He also states that identification of the recursive model requires exclusion restriction (p. 122). However, as pointed out by others (Heckman, 1978; Wilde, 2000; Greene, 2002), due to non-linearity, the model is mathematically identified strictly even without exclusion restriction. Our empirical specification does satisfy exclusion restriction, which enhances the identification of the model. 3

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Similarly, the effect of “multiple treatments” of more than one chronic condition on labour force participation can also be estimated. For example, the effect of having both mental health conditions and diabetes as compared to those with neither condition can be estimated as: ˆ = 1|CD = CM = 0; X). ˆ = 1|CD = CM = 1; X) − P(L P(L

(12)

Additionally, information on the correlation across the three chronic conditions and the other diseases via unobservable heterogeneity after accounting for observable individual characteristics may also be of policy interests. For any particular socioeconomic and demographic group X, we can compute, for example, the “treatment effect” of diabetes on the incidence of cardiovascular diseases as ˆ H = 1|CD = 0; X). ˆ H = 1|CD = 1; X) − P(C P(C

(13)

3. Data We use unit record data from the Australian National Health Surveys (NHS). There have been four NHS surveys since 1989 with 5-year intervals that obtain information on individuals’ health status, health-related lifestyle behaviours, as well as standard socioeconomic and demographic characteristics. The surveys were conducted using a stratified multistage area sample of private dwellings throughout non-sparsely settled areas of Australia. The sample design ensured that within each State or Territory each person had an equal chance of selection. Data were collected by face-to-face interviews by trained interviewers. In each selected household, information was collected for one adult 18 years of age and older, all children aged 0–6 years, and one child aged 7–17 years. Pooled data for adults from the 2001 and 2004/2005 surveys involving over 37,000 Australian individuals are used in this study. Data for the binary labour force participation variable L are obtained from information on labour market status in the survey. Dummy variables for Ch (h = H, D, M and O) are compiled based on answers to questions on individuals’ long-term chronic health conditions. The chronic conditions of cancer, asthma, and arthritis are combined to one category of ‘other’. Data on mental wellbeing were collected via the Kessler Psychological Distress Scale-10 (K-10) questionnaire, including information on self-reported long-term mental and behavioural problems. Individuals are asked to answer 10 questions, and there are choices of five levels for each question. K-10 results are commonly grouped into four categories for levels of psychological distress: low (10–15), moderate (16–21), high (22–29), and very high (30–50). Individuals with K-10 scores of 22 or higher are considered to be requiring professional help (NHS, 2005). We have defined CM = 1 for those with scores of 22–50 as having a mental health condition. Definition of all variables used in the model is given in the Appendix A. Table 1 presents some conditional and unconditional probabilities showing the observed correlations across the four chronic health conditions for adult respondents, indicating potential correlation via unobservable individual heterogeneity and suggesting the need of estimating the four health equations with correlated errors. For instance, in the combine data set of the 2001 and 2004/2005 surveys, among those who currently have diabetes, 66% currently have cardiovascular diseases and 18.4% have poor mental health, much higher than the corresponding unconditional probabilities of 28.9% and 13.3% among the general population. For those who currently have both diabetes and poor mental health, the proportion of having cardiovascular diseases is as high as 71.8%, more than twice of the marginal probability of having cardiovascular diseases (28.9%) for the general population. These sample statistics indicate strong correlation across the four disease conditions. A system equation estimation with correlated errors across the four endogenous health conditions in this study will enable the estimation of conditional correlations after impacts of observable explanatory factors are controlled. To allow for the estimation of different relationships between chronic diseases and the decision of labour force participation by gender and by age, we estimate the model for four separated groups: males aged from 18 to 49, males aged from 50 to 64, females aged from 18 to 49 and females aged from 50 to 64. Table 2 presents the observed labour force participation

Table 1 Sample statistics on some probabilities across chronic diseases for adult respondents aged 18–64 (%) 2001

P(·) P(·|CH = 1) P(·|CD = 1) P(·|CM = 1) P(P(·|CO = 1) P(·|CH = 1, CD = 1) P(·|CH = 1, CM = 1) P(·|CD = 1, CM = 1)

2004/2005

CH

CD

CM

27.5 100 63.5 32.2 39.9 100 100 70.5

4.4 10.2 100 6.2 7.1 100 13.6 100

13.2 15.4 18.5 100 19 20.6 100 100

CO 23 34 38 34 100 43 42 47

Total

CH

CD

CM

CO

CH

CD

CM

CO

30.3 100 67.7 37.5 46.6 100 100 72.7

5.7 12.8 100 7.9 8.8 100 15.2 100

13.4 16.6 18.3 100 18.2 19.7 100 100

33.5 51.6 51.6 45.5 100 58.6 62.3 71.2

28.9 100 66 35 44 100 100 71.8

5.1 11.6 100 7.1 8.1 100 14.5 100

13.3 16 18.4 100 18.5 20 100 100

28.7 43.6 45.8 40 100 52.2 53.4 61.3

CH : cardiovascular diseases; CD : diabetes; CM : mental health; CO : other diseases. Source: pooled data from 2001 and 2004/2005 NHS.

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Table 2 Sample labour force participation rates by chronic illnesses—by demographic groups (%)

Male 18–64 (N = 14, 169) Male 18–49 (N = 10, 185) Male 50–64 (N = 3, 984) Female 18–64 (N = 16, 123) Female 18–49 (N = 11, 746) Female 50–64 (N = 4, 377)

CH = 1

CH = 0

CD = 1

CD = 0

CM = 1

CM = 0

CO = 1

CO = 0

73.6 88.5 62.4 58.8 72.6 45.6

88.4 91.7 75.9 71.9 75 59.3

63.4 83.8 53.1 45.1 62 32.6

86.4 91.5 72.3 69.6 74.9 54.8

64.2 74.3 40.3 58 64.6 36.7

88.2 93.5 74.7 71.1 76.8 56.3

75.8 87.5 60.8 61 71.8 46.3

88.1 92.1 75.3 71.7 75.4 58.4

CH : cardiovascular diseases; CD : diabetes; CM : mental health; CO : other diseases. Source: pooled data from the 2001 and 2004/2005 NHS. Table 3 Estimated correlation coefficients for the multivariate probit model

LH LD LM LO HD HM DM HO DO MO

Males 18–49 Coefficient (t-statistics)

Females 18–49 Coefficient (t-statistics)

Males 50–64 Coefficient (t-statistics)

Females 50–64 Coefficient (t-statistics)

0.29 (2.8)** 0.02 (0.1) 0.76 (16.0)** 0.49 (5.2)** 0.25 (4.9)** 0.23 (7.8)** 0.10 (1.6) 0.11 (4.2)** −0.01 (-0.2) 0.22 (8.1)**

0.40 (5.7)** 0.22 (2.3)** 0.74 (20.0)** 0.60 (10.4)** 0.20 (4.2)** 0.16 (6.7)** 0.04 (0.7) 0.19 (8.3)** 0.14 (3.0)** 0.18 (8.2)**

0.57 (7.3)** 0.32 (3.5)** 0.56 (8.6)** 0.77 (14.1)** 0.30 (8.1)** 0.21 (5.2)** 0.15 (2.8)** 0.12 (4.2)** 0.07 (1.8)* 0.25 (6.7)**

0.75 (17.2)** 0.40 (5.3)** 0.75 (17.5)** 0.18 (3.7)** 0.32 (7.2)** 0.17 (5.1)** 0.20 (3.9)** 0.15 (5.2)** 0.14 (3.1)** 0.17 (4.7)**

(**) and (*) indicate 5% and 10% significant, respectively.

probabilities against the four health indicators by age–gender groups.4 The figures indicate that the impacts of chronic health conditions on the labour force participation decision are very different between male and female and between prime aged and older individuals. For example, the magnitudes of such impacts for the elder groups are much higher relative to their younger counterparts. In the elder male and female group, for those currently having cardiovascular diseases, the proportion of being in labour force decreases by more than 13 percentage points compared to those without cardiovascular diseases, while this reduction in the probability of labour force participation for the prime age female and male group are only 2 and 3 percentage points. Note also that the effects of mental health on labour force participation are higher for males than females, and the influence for older male is larger than all other three groups.5 Again, it will be important to estimate these effects with our econometric model after other observable factors important to labour force participation are controlled. Details on exogenous variables for individual socioeconomic, demographic and lifestyle factors used in this study are also listed in the Appendix A. As detailed in Section 4, we have used standard individual variables in the labour market participation equation characterising socioeconomic and demographic status as well as other labour market endowments such as education, language abilities and health. For the health condition equations, we have also included health-related lifestyle variables6 in addition to standard demographic and socioeconomic variables. 4. Results 4.1. Estimated coefficients and cross-equation correlations The estimated parameters and t-statistics for the endogenous multivariate probit model in Eqs. (1)–(8) are given in Table 3 and Tables 4a and 4b. Looking first at the estimated pair-wise correlation coefficients between the errors of the five equations as presented in Table 3, those between labour force participation and chronic diseases are all statistically significant except

4 As pointed out by a referee, focusing on the probability of labour force participation rather than hours or part-time/full-time work may underestimate the full impact of health on work. Also, for older workers, there may be a range of options for labour market transition to part-time work, retirement, unemployment, or disability pension. A closer examination of such information would be more desirable (See Zucchelli et al., 2008), but it is beyond the scope of this study. 5 A referee pointed out that there may be potential under-reporting by males and over-reporting by females for mental distress. This may bias the estimated effects of mental problem on labour market participation in different directions by genders. 6 An anonymous referee pointed out the potential endogeneity of the lifestyle variables in the chronic conditions equations. Contoyannis and Jones (2004) used a multivariate probit model to study the effects of lifestyle factors on health allowing endogeneity of lifestyle variables. We have decided to treat lifestyle variables exogenous as our focus in the paper is to quantify the effects of chronic conditions on labour force participation. Treating lifestyle variables as well as the chronic conditions as endogenous will result in too large a model to be tractable. As the lifestyle variables only appear in the labour equation, we expect much of the correlation between lifestyle and labour variables via unobservable personal traits has been accounted for by the estimated correlation between chronic conditions (error terms in the chronic condition equations) and labour participation variables. Any potential bias in the estimated effect of chronic conditions and lifestyle factors on labour participation is likely to be small.

Table 4a Estimated coefficients for two prime aged groups (male 18–49 and female 18–49) Cardiovascular diseases Variables Male Coefficient (t-statistics)

Female Coefficient (t-statistics)

ONE

1.40** (15.5)

1.40** (24.1)

ONE

0.05 (0.2)

0.12 (0.6)

ONE

YEAR

0.00 (0.0)

0.03 (1.2)

YEAR

−0.05 (−1.2)

0.08** (2.3)

YEAR

AGE18

−0.13 (−1.2)

−0.07 (−0.7)

AGE18

−0.11 (−1.2)

AGE24

AGE29

0.03 (0.3)

AGE34

0.01 (0.2)

AGE39

−0.04 (−0.6)

AGE44

−0.02 (−0.3)

−0.31** (−5.0) −0.38** (−7.0) −0.35** (−6.8) −0.17** (−3.5) −0.03 (−0.7)

DUMRA2

−0.97** (−7.5) −0.88** (−11.7) −0.56** (−9.5) −0.55** (−10.0) −0.33** (−6.5) −0.24** (−4.7) −0.14** (−5.2) 0.02 (0.6)

AGE18

AGE24

−1.37** (−6.8) −0.84** (−9.6) −0.80** (−10.6) −0.59** (−9.5) −0.40** (−7.0) −0.22** (−4.0) −0.14** (−4.7) 0.01 (0.2)

DUMRA3

−0.04 (−0.7)

0.13** (2.6)

HIGHDEG 0.42** (5.7)

−0.41** (−5.2) −0.55** (−16.1) 0.43** (10.2)

−0.10** (−2.2) DUMEXREG 0.07 (1.6)

DIPLOMA 0.27** (5.5)

0.27** (9.2)

OVERW

YEAR12

0.07 (1.1)

0.13** (3.4)

OBESE

CH

−0.51** (−2.4) −0.16 (−0.4)

−0.61** (−4.4) −0.47** (−2.1) −1.34** (−16.3) −0.96** (−8.1)

MARRIED 0.35** (6.8) AUS

0.11** (2.3)

BADENG

−0.68** (−5.7) −0.03 (−0.6)

KID

CD CM CO

−1.94** (−15.5) −0.96** (−4.9)

−0.15** (−5.3) 0.18** (5.9)

AGE29 AGE34 AGE39 AGE44 LNINCOME

Diabetes Variables

Male Coefficient (t-statistics)

Female Coefficient (t-statistics)

Mental health problem Variables Male Coefficient (t-statistics)

Female Coefficient (t-statistics)

Other diseases Variables Male Coefficient (t-statistics)

Female Coefficient (t-statistics)

−1.15** (−2.9) 0.08 (1.0)

−0.36 (−0.9)

ONE

2.91** (13.7)

2.98** (16.4)

ONE

0.72 (3.6)**

0.74 (4.5)**

0.09 (1.2)

YEAR

−0.04 (−1.0)

0.07** (2.3)

AGE18

−0.18** (−5.3) 0.00 (0.0)

YEAR

−0.61** (−2.2) −0.59** (−3.6)

−0.10** (−2.5) −0.10 (−0.9)

AGE18

−0.03 (−0.4)

AGE24

AGE29

−0.31** (−3.6) −0.13* (−1.8)

0.00 (0.0)

AGE29

AGE34

−0.06 (−0.9)

0.00 (−0.1)

AGE34

AGE39

−0.03 (−0.5)

0.05 (0.9)

AGE39

AGE44

0.06 (0.9)

−0.01 (−0.1)

AGE44

MARRIED

−0.01 (−0.1)

−0.05 (−1.3)

LNINCOME

KID

−0.24** (−5.3) −0.04 (−0.7)

−0.23** (−6.2) −0.08* (−1.8)

DUMRA2

−0.24** (−2.4) −0.36** (−5.2) −0.37** (−5.9) −0.28** (−5.0) −0.34** (−6.1) −0.19** (−3.5) −0.23** (−8.3) 0.11** (2.7)

−0.32** −(3.3) −0.27** −(4.5) −0.24** −(4.5) −0.24** −(4.6) −0.29** −(5.8) −0.19** −(3.7) −0.24** −(10.1) 0.13** (3.5)

DUMRA3

0.01 (0.2)

0.05 (1.2)

−0.60** (−19.5) 0.29** (6.4)

−0.56** (−20.6) −0.27** (−3.3) −0.25** (−5.8) −0.16** (−4.5) 0.32** (9.0)

DUMCSM

0.05 (1.2)

0.14** (3.9)

DUMEXREG 0.02 (0.5)

0.06 (1.6)

OVERW

0.09** (2.4)

0.18** (6.0)

OBESE

0.31** (2.0)

0.47** (4.6)

EXH

0.14** (2.3)

−0.04 (−0.6)

0.08** (2.1)

EXM

−0.01 (−0.2)

0.05 (1.2)

0.25** (2.3)

EXL

−0.03 (−0.7)

−0.05 (−1.4)

AGE24 AGELT24

−1.26** (−4.0) −0.67** (−4.2) −0.62** (−4.6) −0.23** (−2.2) −0.19* (−1.9)

AGE24

DUMRA2

−0.12** (−2.0) −0.06 (−0.6)

−0.55** (−3.9) −0.46** (−3.8) −0.21** (−2.1) −0.26** (−2.5) −0.26** (−4.8) −0.08 (−0.9)

−0.04 (−0.9)

DUMRA3

−0.06 (−0.6)

−0.14 (−1.3)

LNINCOME

0.03 (0.6)

DUMCSM

−0.07 (−0.7)

−0.07 (−0.8)

DUMCSM

0.21** (5.3)

0.18** (5.5)

DUMEXREG −0.06 (−0.6)

0.01 (0.1)

DUMEXREG 0.06 (1.3)

0.80** (5.5)

0.45 (4.3)

OVERW

0.17** (2.0)

0.41** (5.5)

OBESE

−0.08 (−0.5)

OBESE

1.02** (5.3)

0.94** (6.0)

EXH

−0.34 (−4.2)

EXM

−0.15** (−2.8) −0.08* (−1.7)

DUMCSM

AGE29 AGE34 AGE39 AGE44 LNINCOME

HIGHDEG

EXL

X. Zhang et al. / Journal of Health Economics 28 (2009) 91–108

Labour force participation Variables Male Female Coefficient Coefficient (t-statistics) (t-statistics)

(**) and (*) indicate 5% and 10% significant, respectively.

97

98

Table 4b Estimated coefficients for two older groups (male 50–64 and female 50–64) Labour force participation

Cardiovascular diseases

Diabetes

Mental health problem

Other diseases

Male Coefficient (t-statistics)

Female Coefficient (t-statistics)

Variables

Male Coefficient (t-statistics)

Female Coefficient (t-statistics)

Variables

Male Coefficient (t-statistics)

Female Coefficient (t-statistics)

Variables

Male Coefficient (t-statistics)

Female Coefficient (t-statistics)

Variables

Male Coefficient (t-statistics)

Female Coefficient (t-statistics)

ONE YEAR

0.84** (11.2) 0.22** (4.8)

0.33** (4.2) 0.16** (3.3)

ONE YEAR

2.75** (10.8) 0.02 (0.4)

ONE YEAR

1.99** (9.1) 0.24** (5.2)

0.84** (3.6) 0.58** (12.5)

AGE54

AGE54

0.45** (5.4)

0.44** (6.3)

AGE54

AGE59

0.36** (6.5)

0.46** (8.0)

AGE59

−0.08 (−1.3)

AGE59

AGE59

0.31** (3.9)

0.20** (2.9)

AGE59

−0.16** (−2.7) −0.07 (−1.2)

MARRIED

0.08** (2.0)

LNINCOME

DUMCSM

−0.11* (−1.7) 0.08 (0.9)

DUMCSM

−0.41** (−12.4) 0.08 (1.4)

−0.25** (−4.1) −0.11* (−1.9) −0.22** (−6.2) 0.15** (2.3)

HIGHDEG

0.42** (7.0)

DUMEXREG

DUMEXREG

0.17** (2.2)

0.04 (0.4)

LNINCOME

DUMEXREG

0.10** (2.0)

0.16** (3.1)

DIPLOMA

0.10** (2.5)

0.29** (7.0)

BMI25

0.31** (6.4)

0.29** (6.9)

BMI25

0.37** (4.8)

0.54** (6.0)

EXHM

BMI25

0.12** (2.7)

0.17** (3.7)

YEAR12

−0.05 (−0.7) −0.86** (−6.3) −0.46** (−2.6) −1.19** (−8.6) −1.31** (−12.4)

0.02 (0.2)

EXL

−1.16** (−14.3) −0.45** (−3.0) −1.42** (−15.7) −0.20** (−2.6)

DUMCSM

−0.16** (−2.3) 0.22** (2.6)

−0.15** (−2.7) −0.15* (−1.7) −0.62** (−14.5) −0.32** (−4.8) −0.19** (−3.3) 0.29** (4.4)

LNINCOME

DUMCSM

−0.46** (−14.2) −0.14** (−2.5) 0.04 (0.9)

MARRIED

−0.02 (−0.6) 0.18** (2.9)

−0.28** (−8.6) −0.23** (−3.9) 0.07 (1.4)

LNINCOME

AUS

−0.29** (−7.1) 0.11** (2.8)

0.49 (1.2) −0.03 (−0.3) −0.32** (−3.3) −0.19** (−2.2) −0.36** (−5.7) 0.03 (0.3)

3.43** (10.9) −0.09 (−1.4)

0.75** (12.0)

0.45 (1.4) −0.02 (−0.4) −0.18** (−2.2) −0.05 (−0.7) −0.32** (−6.7) 0.01 (0.1)

ONE YEAR

0.59** (8.4)

2.65** (13.0) −0.03 (−0.7) −0.09 (−1.6)

ONE YEAR

AGE54

1.49** (6.8) −0.03 (−0.6) −−0.25** (-4.4) −0.06 (−1.1)

DUMEXREG

0.14* (1.8)

0.12* (1.9)

OBESE

0.11 (0.4)

0.39* (1.9)

CH CD CM CO

(**) and (*) indicate 5% and 10% significant, respectively.

AGE54

HIGHDEG

−0.76** (−14.9) −0.09 (−1.1)

X. Zhang et al. / Journal of Health Economics 28 (2009) 91–108

Variables

X. Zhang et al. / Journal of Health Economics 28 (2009) 91–108

99

for the one relating to diabetes for the younger male group. These indicate there still remain significant correlations between labour force participation and the chronic diseases via unobservable individual characteristics after controlling for observable exogenous factors and having these disease variables directly appearing in the labour force participation equation. These results also serve to reject the exogeneity hypothesis of the four health conditions, justifying joint estimation of labour force participation and chronic condition equations. The correlations between the four chronic condition indicators are also significant for almost all pairs except for a couple relating to diabetes for the two younger groups, which may be due to the low occurrence of diabetes in the younger groups. These suggest that joint estimation of the four disease equations is necessary to account for co-morbidity and unobservable individual risk factors such as lifestyle and genetic factors. Note that the correlations between the error terms for mental health and labour force participation are as high as 0.7 for the two younger groups. For the two older groups, correlations between labour force participation and both mental and cardiovascular diseases in the structural errors are also very high (between 0.56 and 0.75). For the correlations in the unobserved factors across the four health conditions, those between cardiovascular diseases and diabetes are the highest for all four groups. This is not surprising given the shared lifestyle risk factors for diabetes and cardiovascular disease and that vascular complications of diabetes are common. In addition, error term correlation between cardiovascular diseases and mental health is higher for males than females for both age groups, while that between diabetes and mental health is the highest for older females. The estimated coefficients for the covariates are shown in Tables 4a and 4b. Although the magnitudes of the coefficients in the probit equations are not directly meaningful in terms of marginal effects on the probability of labour force participation (which will be discussed in Section 4.3), they do indicate the directions of the direct effects and rankings of the impacts for unit changes in individual explanatory factors. Looking first at the structural coefficients of the four chronic conditions in the labour force participation equation, all are negative and statistically significant except for diabetes for the younger male group, indicating that suffering from these chronic illnesses directly decreases the probability of labour force participation. From Tables 4a and 4b, it can also be seen that the significant exogenous variables have the expected signs. Being married increases the chance of males being in labour force but decreases that of females. Being born in Australia, compared with those born overseas, has a significant positive influence on labour force participation for all groups except for old males. The ability to speak English is also an important indicator for labour force participation. In terms of educational level, as expected for all four groups, the higher the level is, the more likely the respondent is in labour force. As the magnitudes of the coefficients are not directly meaningful, the marginal effects of exogenous variables will be discussed in terms of direct and indirect impacts on the probability of labour force participation in Section 4.3. 4.2. Predicted probabilities and treatment effects of chronic illnesses on labour force participation As discussed in Section 2.2, using the estimated parameters from the model, five-dimensional joint probabilities, as well as various marginal and conditional probabilities can be predicted for any individual given the individual’s exogenous characteristics x. These probabilities provide important policy relevant information involving the five endogenous binary variables of labour force participation and chronic diseases. For example, we can calculate the predicted probabilities of labour force participation for an average individual in each of the four age and gender groups (P(L = 1)), as well as the chances of having one or more of these chronic diseases (e.g. P(CD = 1) or P(CD = 1 and CH = 1)). We can also predict the probabilities of labour force participation conditional on the status of the three diseases (e.g. P(L = 1|CD = CH = CM = 1) or P(L = 1|CD = 1)). Treatment effect of a particular disease can be obtained as the difference between the two conditional probabilities (e.g. TED = P(L = 1|CD = 1) − P(L = 1|CD = 0)). Note that the marginal probability of labour force participation cannot be obtained from univariate normal distributions based on Eq. (1) alone. It must be calculated via multivariate normal distributions using the estimated correlation coefficients.7 The same is true for other joint and conditional probabilities. In particular, the effect of a chronic disease Ch on labour force participation cannot be estimated as the ‘marginal effect’ of dummy variable Ch using Eq. (1) alone. It must be calculated via the conditional probabilities as described above because Ch is an endogenous variable. Table 5 presents some marginal and conditional probabilities for labour force participation predicted at the sample means of all exogenous variables for each of the four age–gender groups. The treatment effects (TE) of each chronic disease on labour force participation are then calculated, together with the associated standard errors obtained using Delta method and numerical derivatives. Looking first at the impact of mental health on prime aged males, the predicted probability of labour force participation for those with mental health problems is 90.87%, but this probability increases to 95.56% for people without such mental health problems. These conditional probabilities result in an average treatment effect of mental health conditions of 4.69% for males aged 18–49 years, indicating that on average those with mental health problems have 0.0469 lower probability of being in the labour force compared to the rest in the same group, other factors being controlled equal. In comparison, the

7

For

example,

k)/ m,j,k,s=0,1

P(L = 1) =

m,j,k,s=0,1

P(L = 1, CH = m, CD = j, CM = k, CO = s)

and

P(L = 1|CD = 1) =

m,j,k=0,1

P(L = s, CH = m, CD = 1, CM = j, CO = k), where the five-dimensional probabilities are given in Eq. (9).

P(L = 1, CH = m, CD = 1, CM = j, CO =

100

X. Zhang et al. / Journal of Health Economics 28 (2009) 91–108

Table 5 Selected predicted labour force participation probabilities (%)a Male 18–49

Female 18–49

Male 50–64

Female 50–64

Marginal Probabilities P(L = 1)

95.14 (0.31)

80.40 (0.55)

77.59 (0.94)

61.35 (3.83)

Some conditional probabilities P(L = 1|CH = 0) P(L = 1|CH = 1) P(L = 1|CD = 0) P(L = 1|CD = 1) P(L = 1|CM = 0) P(L = 1|CM = 1) P(L = 1|CO = 0) P(L = 1|CO = 1) P(L = 1|CD = 1, CM = 1) P(L = 1|CD = 0, CM = 0) P(L = 1|CH = 1, CM = 1) P(L = 1|CH = 0, CD = 0, CM = 0) P(L = 1|CH = 1, CD = 1, CM = 1, CO = 1) P(L = 1|CH = 0, CD = 0, CM = 0, CO = 0)

95.31 (0.30) 93.69 (0.93) 95.18 (0.31) 91.28 (2.72) 95.56 (0.29) 90.87 (1.47) 95.41 (0.29) 93.70 (0.83) 79.48 (6.60) 95.59 (0.29) 71.89 (8.05) 95.57 (0.30) 57.76 (9.86) 95.56 (0.30)

80.51 (0.57) 79.76 (1.35) 80.44 (0.56) 76.74 (3.64) 80.46 (0.57) 80.02 (1.33) 80.27 (0.56) 80.88 (1.23) 71.30 (5.83) 80.49 (0.57) 62.76 (6.67) 80.34 (0.58) 54.05 (6.91) 79.76 (0.61)

79.20 (1.07) 75.12 (1.83) 78.55 (1.04) 67.08 (3.49) 79.94 (0.90) 54.30 (4.14) 80.01 (1.01) 72.52 (1.74) 27.78 (8.92) 80.48 (0.94) 41.99 (7.22) 81.09 (1.03) 27.84 (4.47) 81.31 (1.09)

63.19 (3.63) 58.95 (4.81) 61.36 (3.71) 61.16 (8.72) 62.81 (3.25) 52.02 (8.97) 63.82 (3.54) 57.91 (4.69) 42.00 (9.35) 62.61 (3.10) 17.82 (14.43) 61.15 (1.34) 13.33 (14.21) 62.19 (1.50)

−1.62 (2.69) −3.91 (1.47)** −4.69 (0.83)** −1.71 (0.02)** −16.11 (6.63)** −23.68 (8.08)** −37.79 (9.91)**

−0.74 (1.36) −3.70 (3.63) −0.44 (1.36) 0.61 (1.23) −9.19 (5.87) −17.58 (6.74)** −25.71 (7.01)**

−4.08 (2.18)* −11.47 (3.85)** −25.64 (4.17)** −7.49 (1.90)** −52.70 (9.20)** −39.10 (7.44)** −53.47 (4.80)**

−4.24 (3.43) −0.20 (6.83) −10.79 (6.72) −5.92 (2.78)** −20.61 (8.23)** −43.32 (14.73)** −48.87 (14.49)**

Some treatment effects P(L = 1|CH = 1) − P(L = 1|CH = 0) P(L = 1|CD = 1) − P(L = 1|CD = 0) P(L = 1|CM = 1) − P(L = 1|CM = 0) P(L = 1|CO = 1) − P(L = 1|CO = 0) P(L = 1|CD = 1,CM = 1) − P(L = 1|CD = 0, CM = 0) P(L = 1|CH = 1, CD = 1, CM = 1) − P(L = 1|CH = 0, CD = 0, CM = 0) P(L = 1|CH = 1, CD = 1, CM = 1, CO = 1) − P(L = 1|CH = 0, CD = 0, CM = 0, CO = 0)

For the estimated treatment effects, (**) and (*) indicate 5% and 10% statistical significance, respectively. a All probabilities are predicted for the mean covariates, and standard errors are presented in brackets, all measured in percentage points. For example 95.14 (0.31) here indicates the probability of being in labour force participation for the younger male group is 95.14%, and the standard error of it is 0.31%.

treatment effect of mental health problems are 0.44%, 25.64% and 10.79%, respectively for prime aged females, older males and older females, respectively, although the results for the two female groups are not statistically significant. Note the high magnitude of the mental health effect for the older male group. In fact, mental health problems have the most severe impact among the four health conditions for both male groups. The treatment effects of diabetes are higher for older males (11.47%) than prime aged males (3.91%). Similarly, the treatment effects of heart conditions are also much higher for older people than the prime aged individuals, all exogenous factors being controlled. Generally speaking, for all diseases, the negative effects are more significant for males than females given the lower labour force participation for females relative to males. Notably, the impacts of mental health are significantly higher for males than females within the same age group. For both genders, the effects are higher for the older groups than their younger counterpart, as older people’s labour market decisions are more likely to be influenced by their health status.8 Finally, as expected, the labour market impacts are much more severe for people suffering from more than one disease. For example, the conditional probability of labour force participation for older males suffering from all four chronic conditions is predicted to be only 27.84%, comparing to 81.31% for those having none of these diseases. The difference between the two is as high as 53.47%, with a 95% confidence interval of 44.1–62.9%. Again, the effects are higher for older groups than younger groups for both genders. Table 6 presents some predicted probabilities for the chronic diseases which indicate strong correlations across different chronic conditions controlling for other factors. Look at the unconditional univariate probabilities first. While there is a dramatic increase in the probability of having each of the physical conditions for the older groups relative to the young, the probability of mental health problems is not obviously different across the two age groups. Generally speaking, other factors being equal, the predicted probabilities for the self-reported mental problems are lower for males than their female counterparts for the same age group. Interestingly, this contrasts with the significantly higher treatment effect of mental health on labour force participation for males compared to females as shown in Table 5. Moving to the cross disease results in Table 6, the probabilities of jointly having multiple diseases are higher than the products of the associate univariate probabilities, justifying the multivariate approach using cross illness correlations. For ˆ H = 1, CD = 1) = 5.20%, while P(C ˆ H = 1)P(C ˆ D = 1) = 3.32%. In fact, the ‘treatment example, for the older male group, P(C effects’ at the lower part of Table 6 show that the effect of having one disease on the incidence of having another can be

8 We have conducted sensitivity analysis as suggested by a referee, by separating asthma out of Co as a 5th condition and estimating a 6-equation MVP for the male 50–64 group. The estimated effects for the three chronic conditions seem robust to the grouping of several conditions into Co ; the treatment effects on labour force participation for diabetes, cardiovascular diseases and mental problem change from 4.08%, 11.47% and 25.64% to 4.58%, 13.58% and 25.73%, respectively.

X. Zhang et al. / Journal of Health Economics 28 (2009) 91–108

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Table 6 Selected predicted probabilities for chronic diseases (%)a Male 18–49

Female 18–49

Male 50–64

Female 50–64

10.53 (0.37) 1.14 (0.16) 9.05 (0.34) 15.88 (1.29)

14.82 (0.40) 1.21 (0.13) 15.44 (0.41) 20.19 (1.02)

39.48 (0.88) 8.42 (0.52) 9.16 (0.57) 32.39 (0.83)

43.38 (0.89) 4.97 (0.44) 13.56 (0.63) 41.77 (0.90)

0.31 (0.07)

0.36 (0.06)

5.20 (0.40)

3.46 (0.35)

Conditional probabilities P(CH = 1|CD = 1) P(CH = 1|CD = 0) P(CH = 1|CM = 1) P(CH = 1|CM = 0) P(CH = 1|CO = 1) P(CH = 1|CO = 0) P(CM = 1|CH = 1, CD = 1, CO = 1) P(CM = 1|CH = 0, CD = 0, CO = 0)

27.04 (4.38) 10.34 (0.37) 19.69 (4.29) 9.62 (0.35) 13.95 (3.55) 9.89 (2.84) 28.88 (5.40) 7.08 (0.33)

29.69 (4.27) 14.63 (0.39) 20.96 (3.94) 13.69 (0.33) 21.34 (3.77) 13.17 (3.06) 27.49 (4.60) 12.97 (0.43)

61.74 (2.97) 37.44 (0.91) 54.18 (3.03) 38.00 (0.91) 44.63 (1.54) 37.02 (1.05) 22.56 (3.12) 5.06 (0.54)

69.47 (3.66) 42.01 (0.91) 54.12 (2.35) 41.69 (0.94) 48.99 (1.42) 39.34 (1.16) 29.18 (3.53) 9.01 (0.77)

Treatment effect P(CH = 1|CD = 1) − P(CH = 1|CD = 0) P(CH = 1|CM = 1) − P(CH = 1|CM = 0) P(CH = 1|CO = 1) − P(CH = 1|CO = 0) P(CM = 1|CH = 1, CD = 1, CO = 1) − P(CM = 0|CH = 0, CD = 0, CO = 0)

16.70 (4.37) 10.07 (1.53) 4.07 (1.05) 21.79 (5.44)

15.06 (4.28) 7.27 (1.18) 8.17 (1.08) 14.52 (4.65)

24.31 (3.10) 16.18 (3.16) 7.61 (1.85) 17.50 (3.26)

27.46 (3.75) 12.43 (2.49) 9.65 (1.86) 20.17 (3.77)

Marginal probabilities P(CH = 1) P(CD = 1) P(CM = 1) P(CO = 1) Joint probabilities P(CH = 1, CD = 1)

a All probabilities and treatment effects are predicted for the mean covariates, and standard errors are presented in brackets, all measured in percentage points. For example 10.53 (0.37) here indicates the probability of currently having cardiovascular diseases for the younger male group is 10.53%, and the standard error of it is 0.37%. All the estimated treatment effects are significantly different from 0 at 5% level.

rather large. For example, other personal characteristics being equal, the onset of diabetes will increase the probability of having cardiovascular problems by 0.2746 for older females, a change from 0.4201 for those without diabetes to 0.6947 for those with diabetes. In other words, the chances of having cardiovascular diseases almost double if that person already has diabetes. Finally, having all three categories of physical conditions increases the probability of having mental problems by 14–22% relative to those having none of the three conditions. 4.3. Comparison with alternative models The multivariate approach in Eqs. (1)–(8) is less restrictive relative to a univariate approach but involves a significant increase in computing complexity. To compare our results with alternative but simpler approaches, we present in Table 7 three sets of estimates for the effects of chronic diseases on the probability of labour force participation. For each of the four age–gender sample groups, the first column repeats the treatment effects in Table 5 using the multivariate probit model in this paper (‘MVP-TE’). The second column reports the structural effect of chronic diseases using the estimated MVP structure coefficients in Eq. (1) alone via univariate probabilities based on Eq. (1) as if Ch were exogenous (‘MVP-SE’). Finally, a univariate Table 7 Effects of chronic diseases on the probability of labour force participation—results from three alternative models (%)a MVP-TE Male 18–49

MVP-SE

UVP-exogenous

MVP-TE Male 50–64

MVP-SE

UVP-exogenous

Cardiovascular diseases Diabetes Mental health Other diseases

−1.62 (2.69) −3.91 (1.47)** −4.69 (0.83) ** −1.71 (0.02) **

−7.36 (3.15) ** −2.35 (5.36) −28.22 (1.95) ** −13.95 (3.14) **

−1.85 (0.85) ** −4.45 (2.48) * −13.56 (1.31) ** −2.59 (0.76) **

−4.08 (2.18) * −11.47 (3.85) ** −25.64 (4.17) ** −7.49 (1.90) **

−30.59 (4.97) ** −16.39 (6.38) ** −42.41 (4.76) ** −46.57 (3.91) **

−6.16 (1.70) ** −10.64 (3.06) ** −33.55 (2.84) ** −8.31 (1.80) **

Cardiovascular diseases Diabetes Mental health Other diseases

−0.74 (1.36) −3.70 (3.63) −0.44 (1.36) 0.61 (1.23)

−46.05 (3.22) ** −17.87 (5.94) ** −56.57 (3.60) ** −8.00 (3.05) **

−7.44 (1.88) ** −9.77 (3.84) ** −21.23 (2.48) ** −8.63 (1.93) **

Female 18–49

Female 50–64 −20.20 (4.61) ** −15.62 (7.42) ** −44.45 (2.69) ** −31.70 (4.01) **

−3.11 (1.27) ** −12.26 (3.80) ** −9.63 (1.27) ** −2.77 (1.13) **

−4.24 (3.43) −0.20 (6.83) −10.79 (6.72) −5.92 (2.78) **

(**) and (*) indicate 5% and 10% statistical significance, respectively. MVP-TE: treatment effect estimated in our paper via five-dimensional multivariate conditional probabilities using MVP model. MVP-SE: structural effect estimated using the MVP structural coefficients in our paper for the labour force equation alone but univariate marginal effects as if chronic diseases were exogenous. UVP-exogenous: marginal effect estimated from single equation UVP coefficients treating all chronic diseases as exogenous variables. a All effects are predicted for the mean covariates, and standard errors are presented in brackets, all measured in percentage points (%). For example −1.62 (2.69) here indicates, according to the method of MVP-TE, those currently having cardiovascular diseases, compared to those not having cardiovascular diseases currently, will have 1.62% lower probability of being in labour force for the younger male group, and the associated standard error is 2.69%.

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model of Eq. (1) alone is estimated treating all Ch variables exogenous, and the resulted marginal effects for Ch are presented in the third column (‘UVP-exogenous’). The results in Table 7 show that the three models give very different estimates for the effects of chronic diseases on labour force participation. Overall, the negative effects from our MVP-TE results are much smaller in magnitude than those from the other two models. For example, for the older male group, the onset of mental health is estimated to reduce the chance of labour force participation by 25.6%, comparing to the estimated effect of 33.5% from the single equation UVPexogenous model and 42.4% from the MVP-SE model. As the MVP model nests the UVP model, a hypothesis test for the correlation coefficients being zero in Table 3 will reject the UVP, so a MVP model accounting for the endogeneity of chronic diseases is preferred to the UVP. Once a MVP model is preferred, the MVP-TE is the treatment effects of endogenous chronic illnesses conditional on unobservable characteristics associated with these endogenous conditions. The MVP-PE using the structural coefficients and univariate probabilities in this case produces a much larger effects of chronic illness on labour market participation.9 4.4. Marginal effects of exogenous variables—direct and indirect effects In addition to the relationships across the endogenous variables, we are also interested in the marginal contributions of the exogenous variables on the probability of labour force participation. Importantly, marginal effects need to be estimated in terms of effects on the probabilities rather than the latent variables. The endogenous multivariate probit model also means the marginal effect of an exogenous factor on labour force participation is computationally more complicated involving both the direct effect on labour force participation and indirect effects via the four illness indicators. These are estimated via numerical derivatives of the multivariate normal distribution functions with respect to the exogenous variables. The direct effect refers to the effect via coefficients ˇL in the probability expression, while the indirect effect on labour force participation via a condition Ch relates to the effect through  h and ˇh (Greene, 1998). When an exogenous variable appears more than once in XL and Xh (h = H, D, M, O), the total marginal effect is the sum of direct and all indirect effects. The standard errors of these marginal effects are estimated using DELTA method (Greene, 2004). In the following, we present in turn the marginal effects of exogenous variables on the incidences of the chronic illnesses and on the probability of labour force participation. 4.4.1. Marginal effects on the incidences of chronic illnesses Table 8 presents the marginal effects, evaluated at the means of all explanatory variables, as well as the t-statistics, on the univariate marginal probabilities of the four chronic disease indicators for all four demographic groups. While these refer to the unconditional probabilities, the marginal effects can also be calculated for any other joint, marginal and conditional probabilities and treatment effects. All the significant marginal effects have the expected signs. Looking at the age effects first, relative to the oldest age band for the two younger groups, the marginal effects of all age band dummies are significant on the incidences of the three physical chronic conditions. In particular, the incidence of cardiovascular diseases and diabetes are positively related to the age bands indicating greater probabilities as one gets older, other factors being equal. However, there are some interesting results regarding mental health. Within the 50–64 age groups for both genders, individuals in younger age bands are more likely to have mental illnesses than those in the oldest band of 60–64. For the prime aged groups, there is no significant age effect for female mental health, while for prime males, those aged 20–29 years have 2–5% lower probability of having mental conditions than the rest, other factors being controlled equal. Smoking status, a lifestyle risk factor, shows some interesting results. There is some evidence that being ex-regular smokers increases the probability of cardiovascular diseases and diabetes but the onset of these two diseases relates negatively to the current smoking status, indicating negative effects of past regular smoking and potential lifestyle changes after the onset of the illnesses. The effects are particularly significant for older male group; past regular smoking increases the probability of having diabetes and having other disease by 3% and 3.5%, respectively, but current smokers are 9% less likely to have cardiovascular diseases. On the other hand, both current and ex-smoking relate to higher probability of mental illness for all four groups, with marginal effects higher for females than males. For example, current smokers of 50–64 females are 6.2% more likely to have mental illness and ex-regular smokers are 2.6% more likely to have mental illness. Finally, for all four groups, overweight individuals have higher chances of having cardiovascular diseases and diabetes. With regard to mental health, we also find that the presence of dependent children in the income unit decreases the chance of the adult individual having poor mental health for the two prime aged groups. Having a higher degree reduces the probability of mental illness by 1.9–3.3% for the two female groups. Exercise, even at a low level, has significant positive relationship with good mental health status for all groups. Being obese also increases the probability of poor mental health for the female groups, but this relationship is not significant for male individuals. 4.4.2. Marginal effects on the probability of labour force participation Tables 9a and 9b summarises the direct and indirect effects of all exogenous variables on the probability of labour force participation. For example, looking at the impact of age on labour force participation for the older male group, compared

9

This measure has been used in the literature for similar models (Contoyannis and Jones, 2004; Li and Tobias, 2006).

Table 8 Marginal effects on the marginal probability of chronic diseases (%)a Cardiovascular disease Male

Older groups (50–64) YEAR −0.011 (−0.60) AGE54 −0.098** (−4.38) AGE59 −0.025 (−1.11) MARRIED HIGHDEG LNINCOME −0.107** (−8.57) DUMCSM −0.090** (−3.89) DUMEXREG 0.028 (1.44) BMI25 0.117** (6.44) OBESE EXHM EXL

0.018** (2.30) −0.225** (−7.59) −0.205** (−11.85)

Male 0.002 (0.99)

Mental wellbeing

Other diseases

Female

Male

Female

Male

Female

0.003 (1.15) −0.019** (−2.10) −0.018** (−3.55)

−0.016** (−2.48) −0.016 (−0.88) −0.050** (−3.58)

−0.042** (−5.26) 0.000 (0.01) −0.006 (−0.41)

−0.009 (−1.05) −0.058* * (−2.40) −0.088** (−5.09)

0.020** (2.33) −0.090** (−3.26) −0.077** (−4.48)

−0.021* (−1.79) −0.010 (−0.89) −0.005 (−0.50) 0.009 (0.89) −0.001 (−0.14) −0.039** (−5.29) −0.007 (−0.74) −0.098** (−18.54)

0.000 (0.02) −0.001 (−0.07) 0.013 (0.96) −0.001 (−0.09) −0.012 (−1.34) −0.055** (−6.15) −0.019* (−1.75) −0.134** (−20.29)

−0.089** (−5.70) −0.067** (−4.84) −0.082** (−5.85) −0.045** (−3.45)

−0.069** (−4.44) −0.067** (−4.57) −0.082** (−5.74) −0.052** (−3.64)

−0.055** (−4.16) −0.024** (−2.82) −0.012* (−1.67) 0.047** (6.35) 0.011 (1.27)

−0.065** (−3.29) −0.060** (−5.82) −0.038** (−4.54) 0.077** (8.99) 0.020** (2.09)

−0.057** (−7.62) 0.028** (2.64) 0.002 (0.18) 0.034** (3.38) −0.003 (−0.26) −0.007 (−0.84) 0.011 (0.30) 0.005 (0.36) 0.021* (1.83) 0.075** (4.97)

−0.067** (−9.62) 0.036** (3.48) 0.015 (1.21) −0.012 (−1.10) 0.013 (1.27) −0.014 (−1.60) 0.039 (1.31) 0.017 (0.80) 0.051** (4.39) 0.132** (11.72)

0.087** (5.24) −0.057** (−2.74) −0.026 (−1.24)

0.228** (12.51) −0.097** (−4.12) −0.044* (−1.88)

−0.146** (−12.34) 0.029 (1.39) 0.035** (2.01) 0.044** (2.71)

−0.087** −(6.17) 0.058** (2.28) 0.064** (3.10) 0.067** (3.66)

−0.130** (−9.52) −0.126** (−10.03) −0.076** (−6.45) −0.056** (−4.72)

−0.038** (−5.23) −0.020** (−4.25) −0.018** (−4.51) −0.007** (−2.10) −0.006* (−1.83)

−0.017** (−3.97) −0.014** (−3.67) −0.007** (−2.07) −0.008** (−2.38)

−0.031** (−5.15) 0.005 (0.57) 0.029** (2.61)

−0.003** (−2.05) −0.002 (−0.58) −0.002 (−0.54)

−0.008** (−4.71) −0.003 (−0.87) −0.004 (−1.24)

−0.009 (−0.92) 0.006 (0.64) 0.042** (5.50) 0.104** (4.15)

−0.002 (−0.76) −0.002 (−0.61) 0.005* (1.92) 0.030 (1.37)

−0.002 (−0.73) 0.000 (0.08) 0.013** (5.59) 0.030** (4.78)

−0.013 (−0.73) −0.037 (−1.62) −0.030 (−1.32)

−0.004 (−0.37) −0.028** (−2.24) −0.008 (−0.68)

−0.003 (−0.34) −0.033** (−3.38) −0.019** (−2.21)

−0.180** (−14.22) −0.055** (−2.52) 0.016 (0.88) 0.113** (6.90)

−0.049** (−6.65) 0.002 (0.14) 0.026** (2.25) 0.057** (4.93)

−0.037** (−5.59) 0.003 (0.26) 0.004 (0.42) 0.055** (6.60)

−0.013 (−0.39)

0.059** (2.28)

−0.015 (−1.45) 0.073** (5.33) 0.051** (3.90) −0.018* (−1.69) 0.014 (0.88) −0.126** (−14.85) 0.037** (2.62) 0.023* (1.84)

0.004 (0.36) 0.095** (6.22) 0.045** (2.93) −0.034** (−2.69) −0.033* (−1.74) −0.134** (−14.24) 0.062** (4.42) 0.026* (1.93)

0.018 (0.44) −0.014 (−1.14) −0.027** (−2.30)

0.086* (1.91) −0.070** (−4.88) −0.041** (−3.35)

X. Zhang et al. / Journal of Health Economics 28 (2009) 91–108

Prime aged groups (18–49) YEAR −0.009 (−1.25) AGE18 −0.250** (−7.04) AGE24 −0.154** (−9.73) AGELT24 AGE29 −0.145** (−10.71) AGE34 −0.108** (−9.57) AGE39 −0.073** (−6.98) AGE44 −0.039** (−3.99) MARRIED KID HIGHDEG LNINCOME −0.026** (−4.65) DUMRA2 0.002 (0.23) DUMRA3 −0.007 (−0.67) EXH EXM EXL DUMCSM −0.019** (−2.18) DUMEXREG 0.013 (1.58) OVERW 0.039** (5.29) OBESE 0.145** (5.10)

Diabetes Female

(**) and (*) indicate 5% and 10% significant, respectively. a t-Statistics of the estimated marginal effects are presented in parentheses ().

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Table 9a Direct and indirect effects on labour force participation probability (%)—prime aged groups (18–49) Direct effect

Indirect effect from the diseases

Total (t-statistics)

Cardiovascular diseases

Diabetes

Mental wellbeing

Other diseases

Males 18–49 YEAR AGE18 AGE24 AGELT24 AGE29 AGE34 AGE39 AGE44 MARRIED AUS BADENG KID HIGHDEG DIPLOMA YEAR12 LNINCOME DUMRA2 DUMRA3 EXH EXM EXL DUMCSM DUMEXREG OVERW OBESE

−0.00007 −0.01470 −0.01206 – 0.00286 0.00168 −0.00484 −0.00265 0.03974 0.01286 −0.07708 −0.00329 0.04718 0.03049 0.00786 – – – – – – – − – −

0.00047 0.01336 0.00820 – 0.00774 0.00576 0.00389 0.00210 – – – – – – – 0.00137 −0.00010 0.00036 – – – 0.00099 −0.00071 −0.00209 −0.00775

−0.00006 – – 0.00094 0.00050 0.00046 0.00017 0.00014 – – – – – – – 0.00009 0.00004 0.00005 – – – 0.00005 0.00004 −0.00012 −0.00076

0.00274 0.00270 0.00844 – 0.00357 0.00163 0.00091 −0.00156 0.00019 – – 0.00662 0.00117 – – 0.01660 – – 0.00933 0.00406 0.00211 −0.00798 −0.00178 0.00000 0.00226

0.00079 0.00515 0.00781 – 0.00788 0.00599 0.00732 0.00400 – – – – – – – 0.00503 −0.00245 −0.00020 −0.00303 0.00023 0.00064 −0.00102 −0.00047 −0.00186 −0.00666

0.00387 (0.80) 0.00651 (0.56) 0.01238 (1.29) 0.00094 (0.53) 0.02255 (2.50)** 0.01553 (1.87) * 0.00745 (0.97) 0.00203 (0.28) 0.03993 (6.95) ** 0.01286 (2.26) ** −0.07708 (–4.41) ** 0.00332 (0.63) 0.04835 (6.04) ** 0.03049 (5.55) ** −0.01616 (−1.78) * 0.02308 (9.15) ** − 0.00251 (−1.82) * 0.00021 (0.15) 0.00630 (2.41) ** 0.00430 (2.37) ** 0.00275 (1.77) * −0.00796 (−1.95) * −0.00292 (−1.44) −0.00408 (−0.18) −0.01291 (−1.05)

Females 18–49 YEAR AGE18 AGE24 AGE29 AGE34 AGE39 AGE44 MARRIED AUS BADENG KID HIGHDEG DIPLOMA YEAR12 LNINCOME DUMRA2 DUMRA3 EXH EXM EXL DUMCSM DUMEXREG OVERW OBESE

0.01149 −0.02293 −0.10843 −0.13342 −0.12031 −0.05909 −0.01184 −0.05248 0.06200 −0.14119 −0.18925 0.15056 0.09525 0.04586 – – – – – – – – – –

−0.00352 0.04337 0.03950 0.02505 0.02437 0.01477 0.01074 – – – – – – – 0.00604 −0.00104 −0.00560 – – – 0.00166 −0.00114 −0.00818 −0.02015

−0.00042 0.00298 0.00285 0.00267 0.00224 0.00102 0.00125 – – – – – – – 0.00129 0.00039 0.00069 – – – 0.00035 −0.00004 −0.00201 −0.00457

0.01515 −0.00010 0.00224 −0.00008 0.00033 −0.00456 0.00044 0.00428 – – 0.01970 0.00670 – – 0.04838 0.00000 0.00000 0.02330 0.02148 0.01371 −0.02764 −0.00713 0.00000 −0.02134

−0.00579 0.02556 0.02173 0.01945 0.01882 0.02330 0.01473 – – – – – – – 0.01894 −0.01003 −0.00433 0.00331 −0.00371 0.00383 −0.01097 −0.00480 −0.01442 −0.03726

0.01691 (1.95)** 0.04888 (1.59) −0.04211 (−2.27) ** −0.08633 (−5.35) ** −0.07455 (−5.00) ** −0.02456 (−1.71) * 0.01531 (1.06) ** −0.04820 (−5.07) ** 0.06200 (5.91) ** −0.14119 (−5.05) ** −0.16955 (−16.02) ** 0.15726 (11.34) ** 0.09525 (9.23) ** 0.04586 (3.42) ** 0.07465 (17.71) ** −0.01068 (−2.88) ** −0.00924 (−2.10) ** 0.02661 (3.34) ** 0.01777 (3.66) ** 0.01755 (4.50) ** −0.03660 (−3.73) ** −0.01310 (−1.77) * −0.02460 (−5.77) ** −0.08332 (−6.35) **

(**) and (*) indicate 5% and 10% significant, respectively.

with the reference group aged 60–64, males being aged 50–54 has a positive direct impact on labour force participation and positive indirect impacts via lower chances of having cardiovascular diseases, diabetes and other diseases. However, this youngest age band has the highest chances of having mental health problems and thus contributes negatively to the probability of labour force participation. The total impact of this dummy variable on labour force participation for the older male group is significantly positive. The total marginal effects on labour force participation of various demographic, socioeconomic, geographic and lifestyle factors, as well as the t-statistics, are presented in the table. Look first at the total effects of age on labour force participation, as these provide useful information for estimating the impact of ageing on labour market. For the prime male group, men aged 25–34 are around 2% more likely to be in labour force participation relative to the rest in the group. For the prime female group, there is a U-shaped age profile. Other factors equal, being aged 20–34 is less likely to be in labour force than the 45–49 base group (by 7–9% for the primary child-bearing ages of 25–34), while being under 20 or aged 40–44 is more likely to be

X. Zhang et al. / Journal of Health Economics 28 (2009) 91–108

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Table 9b Direct and indirect effects on labour force participation probability (%)—older groups (50–64) Direct effect

Indirect effect from the diseases

Total (t-statistics)

Cardiovascular diseases

Diabetes

Mental wellbeing

Other diseases

Males 50–64 YEAR AGE54 AGE59 MARRIED AUS HIGHDEG DIPLOMA YEAR12 LNINCOME DUMCSM DUMEXREG BMI25 OBESE EXHM EXL

0.09643 0.25721 0.15678 0.03509 −0.00932 0.07897 0.04432 −0.02349 – – – – – – –

0.00380 0.03494 0.00884 – – – – – 0.03831 0.03225 −0.00983 −0.04196 – – –

0.00090 0.00673 0.00199 – – – – – 0.01170 −0.00045 −0.00618 −0.01358 – – –

0.00809 −0.03996 −0.02801 0.00987 – −0.00747 – – 0.06842 −0.01992 −0.01235 – −0.00985 0.00773 0.01478

−0.04612 0.03030 0.01365 – – – – – 0.07717 −0.01511 −0.01840 −0.02344 – – –

0.06310 (3.86)** 0.28922 (13.71) ** 0.15325 (7.82) ** 0.04496 (2.69) ** −0.00932 (−0.56) 0.07150 (2.78) ** 0.04432 (2.52) ** −0.02349 (−0.72) 0.19559 (17.17) ** −0.00324 (−0.21) −0.04677 (−3.59) ** −0.07898 (−6.44) ** −0.00985 (−0.39) 0.00773 (1.15) 0.01478 (2.26) **

Females 50–64 YEAR AGE54 AGE59 MARRIED AUS HIGHDEG DIPLOMA YEAR12 LNINCOME DUMCSM DUMEXREG BMI25 OBESE EXHM EXL

0.10223 0.48598 0.29973 −0.18898 0.06870 0.27200 0.18751 0.01057 – – – – – – –

0.00908 0.02570 0.02062 – – – – – 0.12383 0.03795 −0.01127 −0.07756 – – –

0.00090 0.01105 0.00661 – – – – – 0.01249 −0.00095 −0.00123 −0.01875 – – –

−0.00305 −0.06817 −0.03177 0.02407 – 0.02337 – – 0.09583 −0.04450 −0.01830 – −0.06110 0.05013 0.02916

−0.03039 0.01291 0.00581 – – – – – 0.01161 −0.00766 −0.00857 −0.00894 – – –

0.07876 (3.33) ** 0.46747 (9.13) ** 0.30100 (8.04) ** −0.16492 (−5.53) ** 0.06870 (2.75) ** 0.29536 (7.75) ** 0.18751 (6.53) ** 0.01057 (0.23) 0.24375 (13.55) ** −0.01517 (−0.76) −0.03938 (−2.41) ** −0.10525 (−7.33) ** −0.06110 (−1.80) * 0.05013 (3.74) ** 0.02916 (2.93) **

(**) and (*) indicate 5% and 10% significant, respectively.

in labour force. Being married increases the labour force participation probability for men (by around 3.9–4.5%), while for women it reduces the chance of labour force participation (by 4.8% for the younger group and 16.5% for older females). The presence of children in the income unit decreases the chance of a prime aged female being in labour force by 17%. For the prime aged males, the effect of having children is not significant. Being born in Australia significantly increases the probability of labour force participation for the two prime aged groups and older female group, but it does not affect the probability of labour force participation for the older male group. Speaking poor English decreases the chance of being in labour force by 14.1% for prime females but only 7.8% for the prime male group. Turning to the impact of education, an important human capital attribute, it can be seen that compared to the reference level of less than 12 years education, the impacts of higher degree and diploma on labour force participation are significant for all four groups, while that of year 12 level is not significant except for the younger female group. We found that females’ labour force status is more likely to be influenced by education levels than males, and older persons’ labour force status is more prone to be influenced by education than younger persons. Finally, turning to the lifestyle factors, although lifestyle variables do not appear in the labour force participation equation, they still indirectly impact on the respondent’s labour force status through the endogenous health variables.10 As presented in Tables 9a and 9b, any level of exercise is shown to increase the probability of labour force participation for all four age–gender groups; the marginal effects are higher for females than males, with the highest increase of 5% in the probability of labour force participation for medium to high levels of exercise for older females relative to those doing no-exercise. The effect of smoking is also interesting. While both current and ex-regular smokers are significantly less likely to be in labour force for the two younger groups other factors being equal, for the two older groups, only ex-regular smoking significantly reduces labour force participation overall. The current smoking effect is cancelled out due to its negative correlation with current heart conditions and positive effect on the incidences of all three other chronic diseases. Being overweight or obese reduces labour force participation for all four groups, and the effects are more significant for females. The marginal effect for older females for being overweight or obese (BMI25) is the highest at 10.5%.

10 While variables such as ex-regular smoking can capture the lagged effect on health, due to the lack of longitudinal information in the cross-sectional data used here, we have used current lifestyle variables to proxy past lifestyle behaviour.

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It is interesting to compare the effects of two important lifestyle factors: smoking and exercise. For all three groups other than the older females, the negative effect of smoking is larger than the positive effects of exercise, especially for the older males. For example, for the male 50–64 age group, being an ex-regular smoker reduces the participation probability by 4.7% while any level of exercise increases labour participation by 1.4% at most. 5. Conclusions We have used an endogenous multivariate probit model with a recursive structure to estimate the impact of chronic illnesses on labour force participation for working age individuals using unit record data from an Australian national representative survey. The modelling specification takes account of possible endogeneity of the incidences of chronic illnesses and correlations across the chronic illnesses from co-morbidity and unobservable personal heterogeneity. We used a fullinformation maximum likelihood estimator that is consistent and efficient. The estimated correlations across the error terms of the probit equations are statistically significant, justifying the need of a system equations approach. They also suggest that, even if self-reported chronic conditions are arguably less subjective and involve less reporting errors compared to global selfassessed health measures, they may still be endogenous being jointly determined by the same unobservable characteristics that also impact on labour market behaviour. We have fully explored the advantages of the systems approach and calculated various marginal and conditional probabilities in five-dimensional normal distribution space. These allow proper estimation of average treatment effects of chronic illnesses on the probability of labour force participation. We show that treating the chronic conditions as exogenous or computing the structural effect in a multivariate probit model will both overestimate the effects. The model is estimated separately for males aged 18–49, males aged 50–65, females aged 18–49 and females aged 50–65. Increasing prevalence of chronic illnesses such as diabetes, cardiovascular diseases and mental health problems is likely to pose significant negative impacts on the participation and performance of the labour force in the developed countries. Understanding the effects of age and the incidences of inter-related chronic illnesses on the labour force participation decisions of older individuals is particularly important in the context of population aging as older workers are encouraged to remain in the workforce longer. We have shown that the impacts of chronic conditions on the probability of labour force participation vary significantly by gender and by age group. Older workers above 50 years of age for both sexes have much higher effects than their younger counterparts, indicating that they are more likely to respond to the onset of chronic illnesses by dropping out of the labour force. For example, having diabetes will reduce the probability of labour force participation by 3.91% for prime aged males, while this effect is 11.47% for the older males. Another finding is that the effects of mental illnesses are much higher for males than females for both age groups. Alarmingly, the highest effect of mental health on labour force participation is found for the group of 50–65 year old males; the probability of labour force participation drops from 79.94% to 54.30% with the onset of mental health problems. Strong correlations are also found across the four chronic illness categories after controlling for observable explanatory factors. This suggests intrinsic relationships across these health conditions, co-morbidity that result from unobservable personal characteristics such as genetics and lifestyle factors that are common risk factors across the chronic conditions. The highest correlation is found between diabetes and cardiovascular conditions for all four demographic groups. Such correlations are also shown to differ by gender and by age group. We have also estimated the marginal effects of individual demographic, socioeconomic, and lifestyle factors on labour force participation decision via both the direct effect and the indirect effect via the four chronic conditions. Although some of these factors do not appear in the equation of labour force participation directly, they still influence the labour force status through the health conditions. Healthier lifestyle, exercise, not having been smoking regularly in the past and keeping healthy body mass will increase the probability of labour force participation, especially for older individuals. For example, ex-regular smoking is shown to significantly reduce the probability of labour participation for both older groups via the chronic conditions. The approach used here has a number of advantages in the analysis of the impact of diseases on health and labour market performance. In contrast to the use of a global health measure the use of specific disease measures reduces the potential measurement error. At least in the case of physical diseases it also reduces the potential endogeneity bias attributable to self-justification of employment behaviour and the possible reverse causality of labour market outcomes on health status. Unlike studies of a single disease however, the approach here allows for the possibility of common risk factors across chronic diseases in the determination of labour market participation. For example being overweight increases the probability of being out of the labour force by 0.11 for women aged 50–64 due to an increased risk of diabetes (0.02) cardiovascular disease (0.08) and other chronic diseases (0.01). The multivariate probit approach adjusts for both the direct effect on labour force participation of factors such as age and the indirect effect through each chronic disease. In this case it results in estimates of the total effects that differ from those from the simpler single equation approach. Finally, co-morbidity is often an important determinant of health service use and patient outcomes, and the multivariate probit approach developed here has wide potential application beyond labour market outcomes to the analysis of individual health and health service use. Acknowledgement We thank Bill Griffiths, Brett Inder, Don Poskitt and Frank Vella for helpful discussions.

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Appendix A See sample statistics and definition of all variables used in the model (Table A1). Table A1 Variable definition and sample statistics Variable

Definition

L CH CD CM CO YEAR AGE18 AGE24 AGELT24 AGE29 AGE34 AGE39 AGE44 AGE49 AGE54 AGE59 AGE64 MARRIED AUS BADENG KID HIGHDEG DIPLOMA YEAR12 LESS12

1 if in labour force, 0 otherwise 1 if currently have cardiovascular diseasesa , 0 otherwise 1 if currently have diabetesb , 0 otherwise 1 if currently have mental health problem, 0 otherwise 1 if currently have cancer, asthma or arthritis, 0 otherwise 1 if individual in 2004–2005 survey, 0 otherwise 1 if age between 18 and 19, 0 otherwise 1 if age between 20 and 24, 0 otherwise 1 if age between 18 and 24, 0 otherwise 1 if age between 25 and 29, 0 otherwise 1 if age between 30 and 34, 0 otherwise 1 if age between 35 and 39, 0 otherwise 1 if age between 40 and 44, 0 otherwise 1 if age between 45 and 49, 0 otherwise (referenced group) 1 if age between 50 and 54, 0 otherwise 1 if age between 55 and 59, 0 otherwise 1 if age between 60 and 64, 0 otherwise (referenced group) 1 if married, 0 otherwise 1 if born in Australia, 0 otherwise 1 if speak poor English, 0 otherwise 1 if have dependant kid in income unit, 0 otherwise 1 if have higher education level, 0 otherwise 1 if have diploma, 0 otherwise 1 if have year 12 education, 0 otherwise 1 if have not finish year 12 education, 0 otherwise (referenced group) Logarithm of household income 1 if live in the major cities of Australia, 0 otherwise (referenced group) 1 if live in the inner part of Australia, 0 otherwise 1 if not live in major cities or inner part of Australia, 0 otherwise 1 if have high level exercise, 0 otherwise 1 if have medium level exercise, 0 otherwise 1 if have low level exercise, 0 otherwise 1 if have no exercise, 0 otherwise (referenced group) 1 if have either high level or medium level exercise, 0 otherwise 1 if smoke currently, 0 otherwise 1 if smoke regularly before, 0 otherwise 1 if never smoke regularly, 0 otherwise (referenced group) 1 if overweight, i.e. BMI is between 25 to 40, 0 otherwise 1 if obese, i.e. BMI is more than 40, 0 otherwise 1 if either overweight or obese, i.e. BMI more than 25, 0 otherwise

LNINCOME DUMRA1c DUMRA2 DUMRA3 EXH EXM EXL EXN EXHM DUMCSM DUMEXREG DUMNSM OVERW OBESE BMI25

Male 18–49

Female 18–49

Male 50–64

Female 50–64

Mean

S.D.

Mean

S.D.

Mean

S.D.

Mean

S.D.

0.91 0.11 0.02 0.11 0.16 0.51 0.05 0.11 0.16 0.14 0.17 0.18 0.19 0.17

0.281 0.319 0.129 0.312 0.369 0.500 0.210 0.316 0.366 0.345 0.375 0.384 0.391 0.372

0.75 0.15 0.02 0.17 0.21 0.50 0.04 0.11

0.435 0.360 0.131 0.380 0.405 0.500 0.198 0.318

0.71 0.39 0.09 0.12 0.33 0.55

0.455 0.487 0.281 0.322 0.469 0.498

0.53 0.44 0.06 0.15 0.42 0.55

0.499 0.496 0.243 0.358 0.494 0.497

0.15 0.17 0.19 0.18 0.16

0.352 0.377 0.388 0.386 0.366 0.38 0.33 0.29 0.67 0.66

0.486 0.471 0.452 0.472 0.474

0.37 0.33 0.29 0.59 0.68

0.482 0.472 0.456 0.492 0.468

0.17 0.42 0.07 0.34

0.379 0.493 0.257 0.473

0.15 0.31 0.07 0.47

0.361 0.464 0.251 0.499

6.48

0.703

6.31

0.692

0.34 0.36 0.31

0.473 0.479 0.461

0.40 0.30 0.30

0.490 0.456 0.457

0.24 0.42 0.34

0.428 0.493 0.473

0.18 0.28 0.54

0.383 0.450 0.499

0.22 0.68

0.411 0.466

0.02 0.56

0.126 0.496

0.51 0.77 0.01 0.43 0.20 0.41 0.16 0.23

0.500 0.420 0.119 0.496 0.399 0.491 0.371 0.421

0.53 0.77 0.02 0.59 0.21 0.35 0.17 0.27

0.499 0.418 0.137 0.492 0.408 0.477 0.373 0.444

6.65 0.66

0.658 0.473

6.52 0.67

0.673 0.472

0.20 0.14

0.398 0.346

0.21 0.13

0.404 0.335

0.11 0.25 0.35 0.29

0.316 0.434 0.475 0.454

0.05 0.23 0.44 0.29

0.217 0.419 0.496 0.453

0.33 0.24 0.43 0.58 0.17

0.471 0.428 0.421 0.494 0.376

0.28 0.23 0.49 0.38 0.16

0.450 0.423 0.500 0.486 0.364

a In the NHS data, cardiovascular diseases include rheumatic heart disease, heart attack, stroke (including after effects), angina, high cholesterol, high blood pressure/hypertension, low blood pressure/hypertension, hardening of the arteries/atherosclerosis/arteriosclerosis, etc., fluid problems/fluid retention/oedema, haemorrhoids, varicose veins, rapid or irregular heart beats/tachycardia/palpitations, heart murmur, and heart value disorder. b Diabetes here include Type I (insulin dependent diabetes mellitus/juvenile onset diabetes/type A), Type II (non-insulin dependent diabetes mellitus/adult onset diabetes/type B), gestational (pregnancy), and high sugar level (HSL). c This is according to the ASGC (Australian Standard Geographical Classification) Remoteness classification, which is based in the plus version of Accessibility/remoteness Index of Australia (ARIA+) mapped to CDs from the 2001 Census of Population and Housing, and classified to the following categories in the output of the NHS 2001 and 2004–2005: Major cities of Australia (index from 0 to 0.2); inner regional Australia (index from 0.2 to 2.4); outer regional/remote/very remote Australia (index greater than 2.4).

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