Differential labour market impacts from disability onset.

HEALTH ECONOMICS Health Econ. 24: 302–317 (2015) Published online 18 December 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/hec.3017

DIFFERENTIAL LABOUR MARKET IMPACTS FROM DISABILITY ONSET CAIN POLIDANOa,* and HA VUb a

Melbourne Institute of Applied Economic and Social Research, University of Melbourne, Parkville, Victoria, Australia b Graduate School of Business, Deakin University, Burwood, Victoria, Australia

ABSTRACT We estimate the causal labour market impacts of disability onset by gender, age and education levels up to 4 years after onset using longitudinal data from the Household Income and Labour Dynamics Australia survey and difference-in-difference propensity score matching techniques. We find lasting negative impacts on employment, especially full-time employment, which is due more to reduced movement into full-time employment than downshifting from full-time to part-time work following onset. Those without post-school education qualifications are particularly vulnerable to the impacts of onset and are more likely to be out of work and on income support than those with qualifications up to 4 years after onset, due in part because they have greater difficulty adjusting. Copyright © 2013 John Wiley & Sons, Ltd. Received 13 February 2013; Revised 30 September 2013; Accepted 6 November 2013 KEY WORDS:

disability; employment; transitions

1. INTRODUCTION Low employment rates among people with disability place a significant financial burden on Organisation for Economic Co-operation and Development (OECD) countries, with expenditure on disability and sickness benefits in 2007 at around 2% of GDP, which was more than twice the expenditure on unemployment benefits (OECD, 2010). The disadvantage of people with disability is evidenced not just by low employment rates but also by inferior working conditions. In particular, workers with a disability are more likely to be in low paid and non-traditional jobs (DeLeire, 2000; Kidd et al., 2000; Wilkins, 2004). As a result, people with disability are more likely to experience material hardship (She and Livermore, 2007), which is only partly ameliorated by access to social security (Burchardt, 2003a; Jenkins and Rigg, 2004). To design policy measures to deal with the labour market and material disadvantage of people with disability, it is imperative to understand the causal impacts of disability onset and how they vary across the working-age population. An understanding of how impacts vary across the population enables policy makers to target prevention and rehabilitation measures at groups that need them most. However, to properly evaluate heterogeneous impacts, it is important to account for not only differences in the initial impacts but also differences in the ability of people to adjust over time. Some groups may find adjustment more difficult than others; for example, people with low levels of education may be disadvantaged because employers may be less willing to accommodate their condition following onset and a lack of credentials may make it hard for them to find alternative employment. *Correspondence to: Melbourne Institute of Applied Economic and Social Research, University of Melbourne, Parkville, Victoria 3010, Australia. E-mail: [email protected]

Copyright © 2013 John Wiley & Sons, Ltd.

DIFFERENTIAL LABOUR MARKET IMPACTS FROM DISABILITY ONSET

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This study makes an important contribution to the understanding of the heterogeneous labour market impacts of disability onset (across education, age and gender) up to 3–4 years after onset. To date, much of the literature on the differential impacts of disability has employed cross-sectional analysis (e.g., DeLeire, 2000; Kidd et al., 2000; Wilkins, 2004; Jones, 2007). In addition to being unable to control for disadvantage pre-dating onset, cross-sectional studies are unable to examine how the impacts of onset change over time and whether some individuals are better able to adapt to onset than others. To our knowledge, previous longitudinal studies of disability onset or health shocks either do not examine heterogeneous impacts (Burkhauser and Daly, 1998; Burchardt, 2003a; Gannon, 2005; Gannon and Nolan, 2007; Oguzoglu, 2010; García-Gómez, 2011 and Lechner and Vazquez-Alvarez, 2011) or have estimated short-term heterogeneous impacts in the year of onset (Burchardt, 2003b; Pelkowski and Berger, 2004) or up to 1 year following onset (Jenkins and Rigg, 2004). An exception is Charles (2003), who estimated longer-term heterogeneous impacts on earnings, total hours worked and hourly earnings from disability onset experienced between 1968 and 1993. We build on the Charles (2003) paper in a number of ways. First, by estimating impacts among people who experienced disability onset between 2003 and 2006, we provide more up-to-date estimates of longer-term heterogeneous impacts. Second, we estimate impacts across different types of post-school education qualifications – those who attain no post-school qualifications, vocational education and training (VET) qualifications and higher education qualifications – rather than just comparing outcomes between those who do and do not undertake any post-secondary education.1 This distinction is important because higher education, which is a more general form of training than VET, may better equip people to return to employment longer-term. Third, we examine additional outcomes such as reliance on income support, the use of part-time work and employment rates. A second contribution of this study to the literature is in explaining the reason for the high prevalence of part-time employment among workers with disability. Part-time work is often viewed as a way for workers to manage their condition while maintaining/re-joining employment following disability onset (Schur, 2003 and Jones, 2007), by either ‘down-shifting’ from full to part-time work or by ‘easing-back’ to work after time out from work. In practice, whether workers have freedom to move to part-time employment following disability onset is untested. Previous longitudinal studies to date have estimated the impacts of onset on overall employment, but not on part-time work (Gannon and Nolan, 2007; García-Gómez, 2011; Lechner and Vazquez-Alvarez, 2011; Burkhauser and Daly, 1998; Burchardt, 2003a, 2003b; Jenkins and Rigg, 2004). In this paper, we examine transitions into part-time employment from all employment states prior to onset – part-time work, full-time work and out of work. The contribution to the literature on the use of part-time work among people with disability is enhanced by the use of Australian panel data from the Household, Income and Labour Dynamics in Australia (HILDA) survey. Australia has high flows into and out of employment and has the second highest rates of part-time employment in the OECD (Abhayaratna et al., 2008), partly because of flexible labour laws.2 For people who experience onset, income support arrangements in Australia also encourage part-time employment by allowing people to work up to 15 h per week without their eligibility being affected. Strong economic growth and tight labour markets during much of the period of analysis (2003–2009) also provided incentives for employers to accommodate people with disability. The environment in Australia during the period of analysis provides an insight into the possible role of labour market conditions in supporting part-time work for people with disability. Impacts of disability onset are estimated in this study using a difference-in-differences (DID) propensity score matching (PSM-DID) approach. PSM-DID has been used in the related health shock literature to examine longer-term impacts of road injuries (Dano, 2005) and hospitalisations (García-Gómez et al., 2013), as well to examine impacts of disability onset (García-Gómez, 2011). The main motivation for using PSM-DID is in controlling for observable and unobservable factors in selection into disability onset. The use of HILDA is ideal because it contains detailed health information, including a short-form 36-item health questionnaire and 1 2

Defined by whether or not at least 13 years of education was completed. In 2013, 7% of those out of work in Australia in May were employed in June (ABS, 2013), which is comparable with employment flow rates in the USA (Gomes, 2012).


Health Econ. 24: 302–317 (2015) DOI: 10.1002/hec

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C. POLIDANO AND H. VU

information on types of health conditions, which have been exploited in a number of health-related studies (e.g., Cai and Kalb, 2006; Cai, 2010; Oguzoglu, 2010). We find that disability onset negatively impacts employment rates, especially full-time employment rates, increases the chance of being on income support and elevates the risk of belonging to a low-income household up to 3–4 years after onset. Importantly, we find that the negative effects are greatest for people without postschool qualifications. For people without qualifications, impacts on employment, income support reliance and the risk of being low-income are more than twice as great as for those who have VET qualifications, 3–4 years after onset. The relatively large impact for those without qualifications is not only because of differences in initial impacts but also because people without qualifications are slower to return to work. Although the effect is less pronounced than for education, we find that the impacts of disability onset are greater for the young and prime age (15–44 years). Estimated results by pre-onset employment status suggest that the large impacts on full-time employment, relative to part-time employment, are related more to a reduction in the movement into full-time employment following onset rather than due to down-shifting or easing-back to part-time employment. This paper is structured as follows: Section 2 is an overview of key disability-related policies in Australia, Section 3 is a description of the data, Section 4 is an outline of the modelling approach, Section 5 is a discussion of the results, and Section 6 is the conclusion.

2. DISABILITY POLICY IN AUSTRALIA Central to interpreting results presented in this study is an appreciation of the Australian policy context that may affect labour market participation and hours of work following disability onset. Flexible labour laws, including deregulated shopping hours and liberal workplace relations arrangements that allow employers to hire staff on a casual and part-time basis, mean that the rate of part-time employment in Australia is second among OECD countries behind the Netherlands (Abhayaratna et al., 2008). The flexibility of movement to part-time work in Australia is seen as important in maintaining participation in the workforce. In each of the economic downturns in the last three decades in Australia, there has been a marked fall in full-time employment and a corresponding rise in part-time work (Australian Bureau of Statistics (ABS), 2010). For people with a disability, the move to part-time employment is also supported by income support arrangements (mainly the Disability Support Pension (DSP)) that allow recipients to work part-time. A person is eligible for DSP if, subject to income and asset tests, they are assessed by a medical professional to be unable to work a minimum of 15 h per week at or above the minimum wage or be re-skilled for such work for at least the next 2 years because of disability or injury. Being unable to work a minimum of 15 h ‘at or above the minimum wage’ means that the assessment also takes into account the impact of the condition on a person’s labour productivity and their resulting ability to find at least 15 h of work per week at or above the minimum wage. However, although DSP may support part-time work, the incentive to work part-time while on DSP may be dulled by means tests that reduce the amount of benefit when income exceeds a certain threshold. Like in the USA and the UK, employment of people with disability in Australia is supported by antidiscrimination legislation – the Disability Discrimination Act 1992. The Disability Discrimination Act imposes obligations on employers to make ‘reasonable adjustments’ to the workplace for employees with disability. Adjustments may come in many forms, including time off from work to recuperate, shifts from full-time to part-time work and workplace modifications/additions to plant and equipment. When deciding whether an adjustment is reasonable, employers are entitled to weigh up the costs against potential benefits. This means that employers may be less willing to make accommodations for people with low skill levels and people who may leave employment before the cost of the accommodation is recouped, such as women of child-bearing age and older-aged workers. Copyright © 2013 John Wiley & Sons, Ltd.



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3. DATA AND KEY DEFINITIONS Our analysis uses data from the nine waves of HILDA, covering the period 2001–2009, that were available at the time of analysis.3 We limit the sample to those of working age: 15–64 years for men and 15–62 years for women.

3.1. Disability Disability in HILDA is self-identified as the presence of a specific health condition that restricts everyday functioning. Respondents, after being shown a list of long-term health conditions, are asked: ‘do you have a long-term health condition, impairment or disability (such as these) that restricts you in everyday activities and has lasted or is likely to last, for 6 months or more?’ By using this measure, our definition of disability is consistent with ‘functional disability’ that was adopted by Burchardt (2003b), Wilkins (2004), Pelkowski and Berger (2004), and Gannon and Nolan (2007) among others. We choose to use functional disability rather than work-limiting disability mainly to mitigate potential issues with justification bias (Bound, 1991). The functional disability measure should be less prone to justification bias because it asks people to report specific health conditions rather than just asking them whether they have a ‘work-limiting condition’ (Bound, 1991; Cai, 2010). 3.2. Labour market outcomes The focus of this study is on estimating the longer-term impacts of disability onset. Key labour market outcomes include the following: full-time employment, part-time employment, unemployment and not in the labour force (NLF). Individuals are employed if they report working in paid employment in the last 7 days and the distinction between part-time and full-time work depends on whether respondents work more than or less than or equal to 35 h per week on average in all their jobs. To examine whether those who cease employment retain their job, we distinguish between NLF who have a job to return to and those who do not. Of secondary interest are the flow-on effects of labour market impacts on income support receipt and the chances of becoming low-income. Income support receipt is measured as a binary variable of whether or not an individual is in receipt of a DSP or another form of income support including unemployment benefits, child or disability carer payments and military service benefits. Following Jenkins and Rigg (2004), low-income is defined as having a household disposable income in the bottom quintile of all working-age households in HILDA in a given year.4 Household income is a better measure of the impacts on material deprivation than personal income because it takes into account the spillover effects that disability may have on the working decision of other household members. Any extra living costs associated with having a disability are not taken into consideration in this measure. Table I shows descriptive statistics on people with disability in HILDA. On a pooled sample of nine waves, the number of observations of working-age people with and without disability is 19 986 and 76 742, respectively, which means around 20% of the working-age population report having a long-term health condition. Consistent with the literature, Table I shows people with disability are at a disadvantage in the labour market. In particular, people with a disability have lower employment rates and are more likely to be in part-time work (among those in work). The employment disadvantage for those with disability appears to be greater for those with low levels of education, for workers older than 44 years and for men.

3 4

For more information on HILDA, see http://www.melbourneinstitute.com/hilda/default.html. To adjust for differences in household size, we use the OECD square root scale (OECD, 2008) and divide real household income by the square root of the number of people living in the household.



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Table I. Key labour market outcomes among working-age population, by disability status (HILDA, 2001–2009) Employment rate (%)

Male Female Post-school qualification Higher education VET education No qualifications Age 15–-24 25–-34 35–-44 45–-54 55–-64 All No. of observations

Full-time employment rate (%)

Income support rate (%)

Has a disability

No disability

Has a disability

No disability

Has a disability

No disability

59 47

91 73

80 50

90 56

39 44

6 15

76 60 46

90 87 78

70 72 63

76 79 72

16 36 48

3 8 14

58 65 63 59 35 53

79 83 85 89 65 82

64 73 70 67 58 67

74 80 72 76 67 74

41 33 34 36 55 42

15 10 9 7 19 11

19 986

76 742

19 986

76 742

19 986

76 742

Note: Employment rate is the proportion of the working-age population who are employed. Full-time employment rate is the full-time employed as a proportion of those in the working-age population who are employed. Income support rate is the proportion of the workingage population on income support.

3.3. Disability onset Consistent with Burkhauser and Daly (1998), Burchardt (2003a), and Jenkins and Rigg (2004), we define disability onset as the commencement of a disability that lasts for at least two consecutive periods and was preceded by at least two consecutive periods without disability. This definition is chosen to reduce the possibility of including temporary health conditions. To avoid the problem of multiple spells within HILDA, we limit our analysis to the first onset observed in HILDA (onset group). However, it should be kept in mind that only around one-third of the disability onset group report that the onset is the first time they had experienced their condition. Impacts estimated on this small subset of the onset group who experience their condition for the first time in HILDA are generally consistent with impacts for the entire onset group, but the magnitude of the impacts for the former group is greater.5 To isolate the impacts of disability onset, we compare the outcomes of those from the onset group to the outcomes of a ‘matched’ control group who do not experience onset. Given the definition of onset used in this study, the first onset group is identified in 2003. To estimate impacts up to 4 years after onset, we limit the last onset group to 2006 (and not 2008) because, at the time of analysis, it was the last period in which 4 years of post-onset outcomes could be observed (with data to 2009). As a result, our onset sample used for estimation consists of those who incurred disability onset from 2003 to 2006. Although we could estimate longer-term impacts by restricting the analysis to those who experienced onset prior to 2006, this would substantially reduce the sample size. On the basis of data produced in Figures 1 and 2, we measure the impacts of disability onset from a reference period of 2 years prior to the onset year (2 on the x-axis). Figures 1 and 2 measure health and employment outcomes of the onset group and the (unmatched) control group, respectively, before and after onset. They show that although the largest divergence in health and employment trends occurs in the year onset is recorded (between 1 and 0 on the x-axis), the divergence appears to commence in the year prior (between 2 and 1 on the x-axis). The commencement of health and employment impacts in the year prior to onset being observed is likely to be related to the fact that disability onset is often a slow process rather than a sudden change in status (Jenkins and Rigg, 2004). Figures 1 and 2 also bring to light two other important issues. First, prior to onset, there are clear differences between health measures and employment rates between the onset group and the unmatched control group, 5

Results are available from the corresponding author upon request.




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Figure 1. SF-36 health measures

Figure 2. Employment rates

which underlines the importance of using PSM-DID to control for observed and unobserved heterogeneity. Second, we do not observe any major divergence in employment trends just prior to the assumed commencement of impacts (between 3 and 2 on the x-axis) between the onset and unmatched control groups. Divergence in employment trends may suggest that the disability onset group is reporting a disability to justify job loss (justification bias) and that the common trend assumption, that underpins the use of PSM-DID, is violated. Copyright © 2013 John Wiley & Sons, Ltd.


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Table II. Disability onset sample in HILDA, 2001–2009 2001

2002

2003

2004

2005

2006

2007

2008

2009

Total

9494 2250 – –

8999 1909 – –

8236 2369 169 3721

8123 2165 135 3610

8203 2378 124 3598

8457 2197 122 3700

8310 2207 – –

8427 2059 – –

8493 2452 – –

76 742 19 986 550 14 629

Onset group composition by Gender Males Females

– –

– –

74 95

66 69

50 74

50 72

. –

. –

– –

240 310

Initial employment status Full-time employed Part-time employed Out of work Post-school qualification Higher education VET education No qualifications

– – – – – – –

– – – – – – –

94 32 43

78 33 24

65 31 28

70 24 28

307 120 123

31 38 66

33 39 52

30 42 50

– – – – – – –

– – –

39 44 86

– – – – – – –

– – –

133 163 254

Age (years) 15–34 35–44 45–54 55+

– – – –

– – – –

42 41 56 30

30 46 37 22

39 36 31 18

37 33 34 18

– – – – –

– – – – –

– – – –

148 156 158 88

All

–

–

169

135

124

122

–

–

–

550

No disability Has a disability Onset group Control group

Note: – not observed due to the definition of onset and sample selection. VET, vocational education and training.

More formally, results from a placebo test show no statistically significant divergence in any outcomes between the onset and matched control group from 3 to 2 years prior to onset. The results from the placebo test hold for every sub-group considered.6 When analysing impacts by pre-onset employment, education and age, we restrict the choice of control group individuals to those who have identical reference period employment, education and age categories (respectively) as members of the onset group. Reference period education status is the highest qualification attained and is categorised as either no post-school qualification, VET qualification (International Standard Classification of Education (ISCED) 1997 levels 2C, 3C, 4B or 5B) or higher education qualification (ISCED 1997 levels 5A and 6). Reference period employment categories are employed full-time, employed part-time or not employed. Age is broken-up into 15–34, 35–44, 45–54 and 55+ categories. On the basis of our definition of onset, we identify 675 individuals who experience disability onset during the period 2003–2006 (Table II). Of those, we exclude 59 for whom we do not observe post-onset outcomes for at least 4 years because of attrition and another 56 who did not return their self-completion SF-36 health questionnaire. Thus, our final onset sample consists of 550 individuals for whom we observe at least 4 years of post-onset outcomes. Results generated with attriters included in the sample were much the same as results with them excluded; suggesting attrition does not substantially affect our estimates.7 Mean health characteristics of those observed to experience onset are presented in Table III. Overall, around half of those who experience onset still report the presence of a disability 3–4 years after onset. By education level, there are only minor differences in the types of conditions, but those from VET and no qualifications are more likely to experience multiple conditions, which may be associated with greater severity of disability (Wilkins, 2004). Those with VET and no qualifications are also more likely to have their condition persist until 3–4 years after onset, 6 7

Results are available upon request. Results are available upon request.




309

Table III. Mean health characteristics of disability onset sample Higher education

VET

No qualification

All

Disability type in year of onset (%) Sensory condition Physical condition Other medical condition Mental health condition Multiple conditions

10.5 31.6 31.6 7.5 18.0

7.4 25.2 33.1 3.7 30.1

9.8 22.8 32.7 9.8 24.4

9.3 25.6 32.5 7.5 24.5

Disability duration (%) Disability persists 2–3 years after onset Disability persists 3–4 years after onset Work-limiting condition (%)b

55.6 44.4 55.8

61.3 54.0 52.6

64.2 54.7 53.4

61.3 52.0 59.1

SF-36 general health (0–100)c Reference period Year of onset 3–4 years after onset

68.0 59.8 62.2

66.5 59.2 61.0

67.5 57.0 59.1

67.3 58.3 60.4

a

VET, vocational education and training. Sensory: sight, hearing and speech problems. Physical: limited use of legs, feet, arms or legs; difficulty gripping; disfigurement or deformity; other conditions that affect physical work (e.g. back pain). Other medical: shortness of breath; fits; chronic pain; other long-term conditions requiring treatment. Mental: Learning disorder; intellectual disability; mental illness; emotional or nervous disorder and acquired brain damage. b A condition that is reported to limit the type or amount of work the respondent can do. c Higher values reflect better health. a

compared with those with higher education, but there are only minor differences in their SF-36 general health measures by education. Taken together, these results suggest that differences in the type/severity of disability may not explain differences in labour market impacts between those with VET and no qualifications. 4. ECONOMETRIC METHODOLOGY Inference about the causal effects from disability onset is based on evaluating the outcomes of those who experience onset relative to the outcomes if onset had not occurred (counterfactual outcomes). Formally, let Dit ∈ {0,1} be an indicator of whether individual i experiences disability onset in period t, y1itþs be the post-onset outcome at time t + s, s ≥ 0, and y0itþs be the counterfactual outcome at time t + s. In this study, we are interested in estimating the average causal effects of disability onset among those who experience onset, which can be written as follows: E fτ is jDit ¼ 1g ¼ E y1itþs jDit ¼ 1 E y0itþs jDit ¼ 1

(1)

where causal inference relies on the estimation of the counterfactual outcome (last term). This counterfactual outcome is approximated using outcomes of a ‘matched’ control group generated using a propensity score function (a probit model in this study). Let pi denote the predicted probability of onset for individual i from the onset group (denoted as O) and pj denote the predicted probability for individual j in the control group (denoted as C), a matching estimator of the causal effect of disability onset can be written as follows: X 1X ^τ s ¼ yitþs g pi ; pj yjtþs O i∈O i∈O

! (2)

where g() is a function that assigns weights to control group member j in the construction of the counterfactual for onset group member i. Copyright © 2013 John Wiley & Sons, Ltd.


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Different algorithms can be used to generate the weights (Smith and Todd, 2005). Nearest Neighbour Matching (NNM), for each treated observation, assigns a weight of 1 for the counterfactual with the most similar propensity score and 0 otherwise. Kernel Matching (KM) and Local Linear Matching (LLM) use a weighted average of many control group members for each treated observation. Specifically, KM weights are given by G pi pj =an (3) g pi ; pj ¼ ∑j∈C G pi pj =an where G() is a kernel function and an is a bandwidth parameter. LLM weights are given by Gij ∑k∈C Gik ð pk pi Þ2 Gij pj pi ∑k∈C Gik ð pk pi Þ g pi ; pj ¼ 2 ∑j∈C Gij ∑k∈C Gik ð pk pi Þ2 ∑k∈C Gik ð pk pi Þ

(4)

where Gij = G((pi pj)/an ). LLM differs from KM in that it includes a linear term in the propensity score of the treated individual and this inclusion is advantageous whenever control groups are distributed asymmetrically around the treated observations.8 Asymptotically different estimators yield similar results; however, in small samples, there is a trade-off between bias and efficiency (Smith and Todd, 2005). NNM estimators are less biased, whereas nonparametric estimators are more efficient. We use LLM as our main estimator, with a Gaussian kernel and a bandwidth of 0.06.9 We choose LLM over KM because it is less sensitive to propensity score distributions. For sensitivity analysis, we also estimate results using NNM. Propensity score matching assumes that after conditioning on a set of observable characteristics, outcomes are mean independent of treatment status. This conditional independence assumption (CIA) is plausible when all covariates that affect both selection and outcomes are observed. Thus, the richness of the data is crucial to the performance of the matching methods. A further assumption of PSM is the common support or overlap condition. Although the violation of the common support is less of a concern because of the large number of control group members, we still impose the common support condition in all estimations using the minima and maxima approach (Dehejia and Wahba, 1999). To fully exploit the longitudinal nature of our data, we combine the LLM and NNM matching estimators with the DID approach, by estimating impacts on before and after onset changes in labour market outcomes. The use of DID matching estimator (Heckman et al., 1997) is motivated by the fact that it has the additional advantage of eliminating unobserved time-invariant differences. In their analysis of different matching estimators, Smith and Todd (2005) conclude that DID matching estimators perform much better than cross-sectional matching methods. 4.1. Specification of the propensity score function The estimation method rests on the assumption of common trends between the onset and matched groups. To ensure common trends, we exact match on periods. We also utilise the rich health, education, labour market and personal information in HILDA to ensure that the characteristics of those in the matched control group closely resemble those in the onset group (measured prior to onset). With respect to variable selection, we follow the matching literature (Heckman et al., 1997; Heckman et al., 1998) and include variables that are likely to affect the chances of onset and the labour market outcomes considered in this study (the estimated coefficients for the probit model are in Table A.1 of online supporting information). An attractive feature of HILDA is its short-form questionnaire of 36 health-related items (SF-36), which has been proven to produce reliable measures of mental and physical health (Ware et al., 1993). Controlling for the poorer prior health of those who experience onset relative to those who do not (Figure 1) is important because it 8 9

For more discussion on the advantages of LLM, see Fan (1992a, 1992b). Results are robust to the use of alternative bandwidths, and these results are available upon request.



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Table IV. Estimated effects of disability onset, LLM-DID Year of onset (less than 1 year since time of onset)

Employed Full-time employed Part-time employed Unemployed Labour market participation NLF has a job NLF does not have a job Low-income household Income support

1st year after (1–2 years after)

2nd year after (2–3 years after)

3rd year after (3–4 years after)

ATET

s.e.

ATET

s.e.

ATET

s.e.

ATET

s.e.

0.090*** 0.053*** 0.036** 0.010 0.100*** 0.025* 0.074*** 0.034* 0.036**

0.015 0.015 0.015 0.010 0.015 0.014 0.015 0.018 0.017

0.106*** 0.071*** 0.034** 0.002 0.103*** 0.036** 0.067*** 0.043** 0.072***

0.017 0.018 0.017 0.012 0.018 0.014 0.016 0.019 0.018

0.114*** 0.072*** 0.041** 0.008 0.105*** 0.029* 0.076*** 0.064*** 0.054***

0.017 0.018 0.017 0.012 0.017 0.015 0.016 0.020 0.017

0.106*** 0.119*** 0.013 0.010 0.097*** 0.036** 0.060*** 0.071*** 0.067***

0.018 0.019 0.020 0.011 0.018 0.015 0.017 0.019 0.018

ATET, average treatment effects on the treated; NLF, not in the labour force; s.e., standard error. ***is significant at 1%; **is significant at 5%; *is significant at 10%.

is likely to explain some of the differences in labour market outcomes between the two groups. In the matching, we include reference period information on each of the SF-36 health measures (physical functioning, physical role, bodily pain, mental health, emotional health, general health, social functioning and vitality), which are measured from 0 to 100, where higher values represent better health. People who experience disability onset are disadvantaged in the labour market prior to onset (Jenkins and Rigg, 2004), which is evident in Figure 2. To deal with selection by prior labour market disadvantage, we control for a range of labour market variables from the reference period, including industry of employment, labour market status, real labour earnings ($’000 s, 2009 prices) and percentage of time in employment since first left full-time education. For people out of work in the reference period, we include a variable that measures time since last job to control for differences in recent work experience. To control for the effect of labour market conditions, we include regional unemployment rates from the year of onset. Other labour market variables such as job satisfaction, types of employment contract, years worked in the same occupation, firm size and years worked with the same employer were also trialled, but were found to be insignificant and were omitted. Personal information used in the matching includes gender, marital status, the presence of dependent children, immigrant status, age, state and region of residence (urban, rural, remote), equivalised household income excluding own earnings ($’000 s, 2009 prices) and highest education level. To control for time effects, we include wave dummies. Other personal variables including age of dependent children, socio-economic status of region of residence and homeownership were also trialled, but were omitted because they were insignificant. To ensure the CIA is met, post-match balancing tests are used, as proposed by Rosenbaum and Rubin (1985). The balancing tests check for differences (using t-tests) in the variable means of the treatment and matched control groups. Significant differences suggest the CIA is violated. In all of our estimations, there were no significant differences in mean characteristics between the onset and matched control group (Table A.2 of online supporting information for balancing test results).10 5. RESULTS Results from the LLM with DID (LLM-DID) estimator are presented in Table IV. The estimated impacts are measured as average treatment effects on the treated (ATET) and their standard errors are from a 500-draw bootstrap procedure. Results for NNM with DID (NNM-DID) (Table A.3 of online supporting information) are generally consistent with those from the LLM-DID estimator, suggesting that results are robust to the choice of matching algorithms. 10

Post-onset balancing tests were used to help derive the best cut-off points for continuous variables age and proportion of time in employment since first left full-time education, which were treated as dummy variables for greater flexibility in estimation.



312


5.1. Overall impacts Disability onset is estimated to reduce employment by 9 percentage-points in the year of onset (Table IV), with no significant increase in unemployment. It is important to note however; of the 9 percentage-point reduction in employment in the year of onset, only 2.5 percentage-points is due to people stopping work and not looking for work because they still have a job (NLF and has a job). This result suggests that people who stop working generally do not have a job kept open for them. Employment impacts persist for at least 3–4 years after the time of onset. Longer-term employment impacts translate into significant increases in the chances of receiving income support and becoming a low-income household. Importantly, we find that the high prevalence of part-time work among people with disability can be explained by greater impacts on full-time employment relative to part-time employment following onset. A key question is whether the greater reduction in the rate of full-time employment relative to part-time employment is due to down-shifting from full-time to part-time work. 5.2. Impacts by employment status prior to onset Table V presents estimated impacts conditional on initial employment status. These results suggest the large impact on full-time employment presented earlier is due more to large reductions in transitions into full-time work (among those who in the reference period were out of work and in part-time employment) than due to down-shifting from full-time to part-time employment. For those initially in part-time work and out of work, disability onset is estimated to reduce transitions to full-time employment by 12.8 percentage-points and 14.3 percentage-points, respectively, 3–4 years after onset. There is some evidence that those full-time employed prior to onset move to part-time work, but not until 3–4 years after onset. We also find no evidence that those out of work who experience disability onset are more likely to choose part-time work as a way of easing back, with no positive impact on transitions to part-time employment found. Part-time work also does not seem to buffer against the impacts of disability onset, with those employed parttime prior to onset at least as likely to exit employment as those who were employed full-time. There may be several reasons for the limited use of part-time employment in response to disability onset despite the tight labour conditions and flexible labour laws in Australia. First, employers may be less willing to shoulder additional costs associated with workplace modifications to accommodate people with disability if they are only able to work part-time. Second, it may be difficult to create part-time schedules in many jobs Table V. Estimated employment effects of disability onset, conditional on employment status in the reference period, LLM-DID Employment status in the reference period

Year of onset (less than 1 year since time of onset)

1st year after (12 years after)

2nd year after (23 years after)

3rd year after (34 years after)

ATET

ATET

s.e.

ATET

s.e.

ATET

s.e.

s.e.

Full-time Part-time Out of work

0.057*** 0.058 0.169***

0.017 0.039 0.043

Impact on employment 0.085*** 0.019 0.063*** 0.081** 0.040 0.107*** 0.161*** 0.044 0.247***

0.018 0.039 0.046

0.056*** 0.103** 0.218***

0.019 0.041 0.050


0.045* 0.068* 0.088***

0.023 0.041 0.026

Impact on full-time employment 0.079*** 0.024 0.055** 0.056 0.045 0.097** 0.087*** 0.031 0.102***

0.024 0.045 0.032

0.109*** 0.128*** 0.143***

0.025 0.044 0.034


0.012 0.011 0.082**

0.016 0.052 0.039

Impact on part-time employment 0.006 0.017 0.007 0.025 0.051 0.010 0.074* 0.041 0.146***

0.019 0.049 0.039

0.053** 0.025 0.075

0.021 0.048 0.047

***is significant at 1%; **is significant at 5%; *is significant at 10%. Copyright © 2013 John Wiley & Sons, Ltd.


313


including those involving highly specialised skills for operating expensive capital equipment. Third, the nature of a longer-term disability may severely restrict the capacity to work, even in a part-time capacity. Finally, it is possible that many people with disability find part-time work unattractive because of potentially lower welfare benefits from higher earnings and the fixed costs involved in working. For those out of work, disability onset is estimated to reduce the chances of being in employment by 21.8 percentage-points after 3–4 years. This compares to a 5.6 percentage-point and a 10.3 percentage-point reduction in employment rates for those previously employed full-time and part-time, respectively. Those out of work prior to onset may face a number of factors that pre-dispose them to labour market disadvantage following onset, such as a lack of employer contacts and work-relevant general skills, such as an ability to communicate with others and work as part of a team work, self-confidence and time management. However, we cannot rule out the possibility that the large impacts on those out of work is due to justification bias. 5.3. Impacts by education, age and gender Unlike Jenkins and Rigg (2004) who find no significant difference in employment impacts by education, we find marked differences in initial employment impacts by education (Table VI). In the year of onset, we estimate a 4.9 percentage-point, a 6.4 percentage-point and an 11.5 percentage-point reduction in employment probabilities for those with higher education qualifications, VET qualifications and no qualifications, respectively. A possible explanation for the discrepancy in findings is that Jenkins and Rigg (2004) limit their analysis to those employed prior to onset. From Table V, the employment impacts of onset are estimated to be larger for those out of work. Because a high proportion of people out of work have no post-school qualifications, the estimates from Jenkins and Rigg (2004) may underestimate the overall impacts on those without qualifications. Table VI. Estimated effects from disability onset, conditional on education status in the reference period, LLM-DID Education status




3rd year after (3–4 years after)

ATET

s.e.

ATET

s.e.

ATET

s.e.

ATET

s.e.

Higher education VET No qualifications

0.049* 0.064** 0.115***

0.029 0.031 0.025

0.089*** 0.076** 0.112***

0.048 0.072** 0.160***

0.033 0.034 0.027

0.044 0.064* 0.150***

0.031 0.035 0.030


0.051* 0.071** 0.056**

0.027 0.034 0.025

Effects on full-time employment 0.054 0.037 0.024 0.072* 0.039 0.053 0.088*** 0.028 0.116***

0.038 0.039 0.026

0.042 0.150*** 0.140***

0.036 0.041 0.027


0.002 0.007 0.059**

0.029 0.031 0.027

Effects on part-time employment 0.035 0.033 0.024 0.004 0.033 0.019 0.024 0.029 0.044

0.036 0.034 0.027

0.002 0.086** 0.010

0.036 0.038 0.031


0.000 0.055* 0.044*

0.023 0.032 0.026

Effects on income support 0.032 0.024 0.021 0.065* 0.034 0.061* 0.101*** 0.029 0.072**

0.020 0.032 0.030

0.013 0.038 0.119***

0.017 0.035 0.031


0.025 0.025 0.050

0.024 0.035 0.033

Effect on low-income household 0.019 0.028 0.051* 0.064* 0.038 0.063 0.059* 0.032 0.082**

0.031 0.040 0.032

0.064* 0.051 0.095***

0.033 0.034 0.029

Effects on employment 0.032 0.033 0.027

***is significant at 1%; **is significant at 5%; *is significant at 10%. Copyright © 2013 John Wiley & Sons, Ltd.


314


Table VII. Estimated effects of disability onset by gender, LLM-DID Year of onset (less than 1 year since time of onset)

1st year after (1–2 years since)

2nd year after (2–3 years since)

3rd year after (3–4 years since)

ATET

s.e.

ATET

s.e.

ATET

s.e.

ATET

s.e.

Male Female

0.076*** 0.109***

0.021 0.024

0.128*** 0.097***

0.114*** 0.117***

0.023 0.025

0.103*** 0.118***

0.024 0.025

Male Female

0.084*** 0.047**

0.024 0.022

Effect on full-time employment 0.110*** 0.028 0.103*** 0.066*** 0.025 0.066***

0.028 0.025

0.130*** 0.126***

0.027 0.026

Male Female

0.008 0.062**

0.018 0.025

0.018 0.031

Effect on employment 0.026 0.024

Effect on part-time employment 0.019 0.025

0.011 0.051**

0.02 0.026

0.026 0.008

0.023 0.030

0.022 0.024

0.070*** 0.070***

0.023 0.027

0.028 0.028

0.045* 0.093***

0.027 0.027

Effect on income support Male Female

0.018 0.054**

0.021 0.025

Male Female

0.022 0.041

0.025 0.026

0.070*** 0.079***

0.023 0.026

0.058*** 0.058**

Effect on low-income household 0.036 0.051*

0.024 0.027

0.088*** 0.048*

***is significant at 1%; **is significant at 5%; *is significant at 10%.

Differences in initial employment impacts by education appear to grow over time. For those without qualifications, impacts 3–4 years after onset are greater than initial impacts, whereas for those with post-school qualifications, the impacts are on par or have declined relative to their initial levels. There are also clearly differences in income support reliance. In particular, 3–4 years after onset, there is an estimated 11.9 percentage-point increase in income support receipt among people without qualifications, compared with 3.8 and 1.9 percentage point increases among people with VET and higher qualifications, respectively. Access to income support does not prevent an increase in the likelihood of becoming low-income 3–4 years after onset. These results are consistent with differences in estimated impacts on earnings and hours worked between those with at least 13 years of education those with less than 13 years of education estimated in Charles (2003). Differential impacts by education, including differential impacts by education over time, may be due to a number of reasons. First, for those without qualifications who leave/lose their job, a lack of credential may make it harder for them to find suitable alternative employment, partly because employers may be less willing to make accommodations. Second, those without education qualifications may work in more physically demanding jobs where it is more difficult for employers to make workplace adjustments. Third, because income support is means-tested, it is more likely to be accessed by people without qualifications, which may dampen their incentive to work after disability onset. Fourth, the results may be driven by differences in ability to work due to differences in the nature, duration and severity of health conditions. However, data presented in Table III suggests that health differences are unlikely to explain differences in impacts between those with VET qualifications and those with no qualifications. Also, results generated using alternative measures of disability onset that better control for differences in disability severity and duration produce similar results.11 Compared with differences in impacts by education, differences in the effects of disability onset by gender and by age are less pronounced (Tables VII and VIII). The absence of clear differences in impacts by gender

11

Alternative results were generated using the report of two consecutive periods with a work-limiting disability, preceded by two periods without any disability and three consecutive periods with a disability preceded by two periods without. Results were also generated for disability onset that lasts at least three periods, instead of at least two. Results are available upon request.



315


Table VIII. Estimated effects of disability onset by age group, LLM-DID Age in reference period (years)


rd



3 year after (3–4 years after)

ATET

s.e.

ATET

s.e.

0.140*** 0.101*** 0.094*** 0.088

0.036 0.031 0.035 0.062

0.134*** 0.113*** 0.077** 0.083

0.041 0.033 0.034 0.062

0.036 0.032 0.038 0.068

0.140*** 0.076** 0.132*** 0.138**

0.038 0.033 0.043 0.065

0.032 0.034 0.04 0.067

0.006 0.037 0.054 0.055

0.04 0.036 0.041 0.065

ATET

s.e.

ATET

15–34 35–44 45–54 55+

0.070* 0.069** 0.120*** 0.073

0.036 0.027 0.032 0.054

0.099** 0.087*** 0.103*** 0.127**

15–34 35–44 45–54 55+

0.044 0.036 0.081** 0.073

0.033 0.029 0.036 0.062

15–34 35–44 45–54 55+

0.026 0.033 0.040 0.000

0.037 0.028 0.036 0.064

15–34 35–44 45–54 55+

0.025 0.026 0.044 0.022

0.037 0.029 0.03 0.051

15–34 35–44 45–54 55+

0.005 0.032 0.066* 0.061

0.038 0.032 0.036 0.054

s.e.

Effects on employment 0.04 0.028 0.034 0.061

Effects on full-time employment 0.063 0.051* 0.116*** 0.061

0.039 0.031 0.041 0.067

0.093*** 0.074** 0.039 0.068

Effects on part-time employment 0.036 0.036 0.013 0.066

0.035 0.032 0.037 0.067

0.047 0.028 0.056 0.021

Effects on income support 0.080** 0.039 0.019 0.043 0.03 0.054* 0.088** 0.035 0.094*** 0.053 0.052 0.041

0.038 0.029 0.032 0.045

0.078* 0.058** 0.052 0.079

0.043 0.029 0.034 0.052

0.037 0.036 0.041 0.064

0.100** 0.092*** 0.058 0.046

0.042 0.032 0.036 0.058

Effects on low-income household 0.038 0.033 0.062* 0.086

0.039 0.03 0.036 0.063

0.040 0.046 0.114*** 0.093

***is significant at 1%; **is significant at 5%; *is significant at 10%.

(Table VII) is consistent with findings from Jenkins and Rigg (2004). However, a finding of note is the apparent larger negative effect on part-time employment for women up to 2–3 years after onset. This difference could be due to gender differences in initial employment status, with females being more likely to work part-time and out of labour initially than their male counterparts. Results across age categories should be interpreted with caution because of the relative small number of observations for the oldest age onset cohort (Table II). That said, contrary to findings from Wilkins (2004), who conducted cross-sectional analysis using Australian data, we found no evidence to suggest that disability onset later in life has a greater impact on labour market participation. If anything, results presented in Table VIII suggest that employment impacts 3–4 years after onset are less for those 45 years and older than for those less than 45 years. Our results are consistent with those of previous longitudinal studies by Pelkowski and Berger (2004) and Jenkins and Rigg (2004) that found employment impacts to be most pronounced during prime age. One possible reason for the smaller employment impacts after 45 is that disability later in life may be associated more with a slow deterioration in health rather than a sharp health shock (Jenkins and Rigg, 2004). When disability onset occurs at the end of a slow deterioration in health, many of the labour market adjustments, such as early retirement or plans for early retirement, are made prior to onset. In this study, labour market adjustments from deteriorating health prior to the reference period are controlled for in the matching process using SF-36 health measures and employment status from the reference period. Copyright © 2013 John Wiley & Sons, Ltd.


316


6. CONCLUSIONS This paper makes two main contributions to the literature. The first contribution is highlighting the potential difficulties that people without post-school qualifications face in adjusting to disability onset. Although we found some differences in impacts by age and gender, these are relatively small compared with differences in impacts by education. We show that not only are people without qualifications impacted more in the short term but also their initial disadvantage grows over time. Following onset, people without education qualifications may take more time to return to work because of a lack of education credentials, differences in the nature of jobs performed and/or lower incentives to return to work because of greater access to means-tested income support. However, we cannot rule out the possibility that the divergent adjustment paths are due to differences in severity and/or duration of disability, although tests of robustness suggest that this is unlikely to explain differences between those with no qualifications and those with VET qualifications. Importantly, we found that the type of post-school education qualification does not appear to matter much, with similar impacts estimated for those with VET and higher education qualifications. The divergence in adjustment paths across educational levels underlines the importance of examining longer-term impacts of onset, rather than just estimating initial impacts. From a policy perspective, estimates of longer-term adjustment paths help government to assess whether intervention is needed and where it should be targeted. However, to ensure interventions are well designed, more research is needed to better understand why those without education qualifications have greater difficulty adapting. If it can be established that differential impacts by education are linked to qualifications, then this would support the need for policies aimed at encouraging greater participation in education among those without post-school qualifications. The second contribution of this paper is in explaining the high prevalence of part-time work among people with disability and to put into context the use of part-time work as a possible accommodation for disability onset. Although previous studies have shown that people with disability prefer part-time work (Schur, 2003; Jones, 2007), our results suggest that in practice, part-time work is not widely used to accommodate disability onset. In particular, we find little evidence that people who were full-time employed down-shift to part-time work to stay connected with work or that people who were not employed at the time of onset choose part-time employment to ease-back to work. These findings question the notion that supportive arrangements for part-time work, as found in Australia, can help people return to or maintain employment, following disability onset. We raise a number of possible reasons for the limited use of part-time work following onset, including both demand-side and supply-side factors. Before policies can be designed to encourage part-time work as an employer accommodation, there is a need for future research into why it is not a common post-onset pathway under current arrangements.

ACKNOWLEDGEMENTS

We use the confidentialised unit record file from the HILDA survey. The HILDA Project was initiated and is funded by the Commonwealth Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (MIAESR). The findings and views reported are those of the authors and should not be attributed to DSS or MIAESR. We thank Richard Burkhauser, John Haisken-DeNew, Tue Gorgens, Duncan McVicar, Yi-Ping Tseng and two anonymous reviewers for their comments on earlier drafts and Brendan Houng for his research assistance.

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SUPPORTING INFORMATION Additional supporting information may be found in the online version of this article at the publisher’s web-site. Copyright © 2013 John Wiley & Sons, Ltd.


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