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DIPARTIMENTO DI SCIENZE ECONOMICHE E SOCIALI
Direct and indirect effects of disabilityon employment probabilities:
a comparative analysis
Chiara MussidaDario Sciulli
Quaderno n. 107/maggio 2015
COP Mussida-Sciulli_107_2015.qxd:_ 14/05/15 14:13 Page 1
Università Cattolica del Sacro Cuore
DIPARTIMENTO DI SCIENZE ECONOMICHE E SOCIALI
Direct and indirect effects of disability on employment probabilities:
a comparative analysis
Chiara MussidaDario Sciulli
Quaderno n. 107/maggio 2015
Chiara Mussida, Dipartimento di Scienze Economiche e Sociali, UniversitàCattolica del Sacro Cuore, Piacenza
Dario Sciulli, Department of Economic Studies, Università “G. d’Annunzio”di Chieti-Pescara, viale Pindaro, 42 - 65127 Pescara (corresponding author)
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© 2015 Chiara Mussida, Dario Sciulli ISBN 978-88-343-2996-2
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Direct and Indirect Effects of Disability on Employment Probabilities:
A Comparative Analysis Chiara Mussida� and Dario Sciulli�
Abstract The interest for the effects of disability on employment probabilities has increased in the last decade. The socio-economic debate also stressed the need of empowering people with disabilities. Whilst the existing literature primarily examine the direct effects of disability, we extend the focus by analyzing also the indirect effects of disability i.e. the way employment probability of an individual is affected by the presence of disabled member(s) in own household. We perform a comparative analysis among four major Western European Countries, i.e., Italy, Spain, France and the UK, by using the EU SILC panel data for the period 2007-2010. We find negative direct effects of disability on employment probabilities in all the countries. We also find some evidence of significant indirect effects, especially for females, with mixed signs and magnitude across countries. Different institutions, policies and household behaviour contribute to explain cross-country differences.
Keywords: Disability, employment probabilities, caring activities, state dependence, initial conditions, country differences.
� Assistant Professor, Department of Economic and Social Sciences, Università Cattolica del Sacro Cuore, e-mail: [email protected]. � Corresponding author. Assistant Professor, Department of Economic Studies, Università “G. d’Annunzio” di Chieti-Pescara, Viale Pindaro, 42, 65127, Pescara, tel.: +39 085 453 7977, e-mail: [email protected].
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1. Introduction
The socio-economic debate on disability has increased strongly in the last decade. The European Disability Strategy 2010-2020 (European Commission 2010), stressed that at the begin of this century, one in six people in the EU has a disability, and this rate almost doubles when focusing on elderly people, as the ageing process involving developed societies is not always accompanied by healthy life. The European Strategy, with the aim of empowering people with disabilities, so that they can participate fully in society on an equal basis with others focuses on eliminating barriers, including those concerning employment.1 This would be essential as, for example, the employment rate, and more in general the labour market participation rate, of individuals reporting disabling conditions is generally low, beyond the effects of the economic crisis. Furthermore, little is known about the employment conditions of disabled people. In addition, OECD (2010) stressed that favoring the labour market integration of disabled people is a “win-win” policy, as it potentially increases social inclusion and incomes and provides for a more effective labour supply and positive effects for economic output in the long-term. Given these premises, it is not surprising that an increasing number of studies were investigating the relationship between having a disability and employment (or labour force participation). The great majority of the literature (see Jones 2008 for a review) focused on the direct effect of disability, i.e. the way individual employment probability is affected by own disability, leaving quite 1 The strategy focuses on eliminating barriers for disabled people in eight areas, i.e., accessibility, participation, equality, employment, education and training, social protection, health, and external action, for which specific key actions are identified.
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unexplored the indirect effect of disability, i.e. the way employment probability of an individual is affected by the presence of disabled member(s) in own household. We contribute to the existing literature in various ways. First, we extend the focus to indirect effects of disability, beyond the standard direct effects. This could be a relevant issue, especially when policies and institutions supporting disabled people are inadequate, and the consequences of living with disabled people relapse on working-age household members through caring activities. In addition, quite innovating with respect to country-specific studies, we perform a comparative analysis among four major Western European countries, i.e., Italy, Spain, France and the UK2. Cross-country differences include welfare system (Huys 2013), and several legislative3 and policy aspects (Eichhorst et al. 2010), that possibly affect differently the relationship between employment and disability. Relevant differences also emerge at household behavioral level (CESifo 2010) and in social norms. This can be particularly important as the sign, the significance and the magnitude of the net (both direct and indirect) effects of disability would depend by the relevance, for each country, of combined specific factors affecting employment through disability. Another contribution of this paper is that by using panel data, we have the opportunity to analyse the relationship between disability and employment over a certain period of time and this is quite relevant at least for two kinds of reasons. Firstly, given that current employment may also be influenced by past employment, we can incorporate this state dependence effect by introducing employment in the previous year. Second, the panel data allows to analyse both short and long-
2 All these countries ratified (by the 2010) the 2006 United Nations “Convention on the Right of Persons with Disabilities”, recognizing the right of persons with disabilities to work, on an equal basis with others. 3 The degree of centralization of legislation regulating disability allowances and personal assistance, the age limits concerning the granting of long-term benefits, the regulation of benefits for families with disabled children and long-term care allowances also differ by countries (e.g. Council of Europe, 2003).
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term effects of disability by including current and lagged disability status. Furthermore, with the aim of highlighting differences in household behaviour, our analyses separate males and females, which may differ for household time allocation and caring activities. Our study defines disability in the spirit of the social model, for which disability, whatever its origin, may be seen as a reduced form of the interrelations among impairment, technical help and environment, leading to activity limitations (Mitra 2008). In that direction, our empirical analysis is based on the 2007-2010 longitudinal component of the EU-SILC data (Eurostat 2010), that collect cross-country homogenous information on limitations in daily activities of surveyed individuals and therefore on disability. In order to avoid to get mixed up with early retirement and education enrolment issues, we limit our analysis to individuals aged between 25 and 64 years old. Following the recent literature focusing on the relationship between disability and employment (or labour force participation), our empirical strategy consists in estimating a dynamic random effects probit model accounting for endogenous initial conditions (Heckman 1981), that allows us to obtain consistent estimates of state dependence and relevant explanatory variables. In addition, we explicitly distinguish both short and long-term effects of disability in case of direct (see Gannon 2005, and Oguzoglu 2010), and indirect effects of disability. Estimates suggest, consistently with previous literature, the existence of negative direct effects of disability on employment probabilities. We also find some evidence of significant indirect effects, especially for females, with mixed signs and magnitude across countries. Section 2 discusses the underlying mechanisms connecting disability and employment; Section 3 provides the econometric approach used; Section 4 describes the data and the samples; Section 5 discusses the results and particularly the direct and indirect effects of disability; Section 6 concludes.
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2. The Background
There are a number of underlying mechanisms possibly explaining why disability may affect the employment probability, both through direct and indirect effects. In both cases, the observed outcome corresponds to the net effect of single factors acting, in turn, positively and negatively on employment. According to the predictions of a standard labour-leisure choice model, disability may affect the budget’s constraint, as it is likely to be associated to special/additional consumption requirements (She and Livermore, 2007). Furthermore, special consumption needs may increase the marginal utility of consumption reshaping individual preferences. In both cases, we could expect a positive effect of own disability on labour supply. In addition, from a demand side perspective, a positive impact on employability of disabled people may derive from active labour market policies (e.g., public employment services, training schemes and employment subsidies) aimed at favouring the integration of disabled people into the labour market (e.g., Eichhorst et al. 2010). On the other hand, a number of relevant factors may negatively affect the employment probabilities of disabled individual. For example, labour supply may decrease because of the income effect related to the reception of disability benefits, or because the substitution effect deriving from higher opportunity costs of working associated to disability (e.g., because of higher mobility costs).4 Furthermore, an increase of marginal utility of leisure (then a decrease in labour supply) may derive from the special time requirements for self-care or rehabilitation activities associated to disability (Mizunoya and Mitra 2013). In addition, according to the job-search model framework, the higher mobility costs faced by disabled people may decrease their job-search intensity and, then, reduce their employment perspective.
4 Promoting barrier free environment (including the accessibility to the transport system), is essential to reduce mobility costs associated to some disability types (United Nations 2004).
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Finally, from a demand side view, employers would be less prone to hire disabled people, as disability is likely to be associated to lower productivity and extra-costs for adjusting workplaces to disability requirements. In addition, prejudice and/or discrimination by employers may negatively affect the employment perspective of disabled people.5 This theoretical puzzle has been disentangled by a number of empirical works focusing on developed (Kidd et al. 2000 focused on British males, Gannon 2005 studied the Irish situation, Oguzoglu 2010 studied Australia, while Addabbo et al. 2014 and Agovino et al. 2014 focused on Italy) and developing countries (see Mizunoya and Mitra 2013 for a study on 15 developing countries). Evidence emerged pointed, with very few exception, in the direction of a negative impact of disability on employment (or labour force participation), suggesting the prevalence of both supply and demand side negative factors, including limited effectiveness of labour policies.6 However, while the direct negative effects of disability on own employment probability is supported by quite well established results, labor and health economists have provided less attention to indirect effects of disability (see Parodi and Sciulli 2008, for a study based on cross-sectional data). This aspect becomes more relevant once considered that the ageing process involving Western societies is not always accompanied by healthy life, resulting in increasing rates of disability among elderly people. In lack of effective public/private support policies for disabled people, caring activities are transferred to household members with possible detrimental effects for their labour supply.
5 Equality and therefore eliminating barriers to equality is one of the main areas of action of the European Disability Strategy. 6 Other studies focusing on labour market outcomes of disabled people highlighted their disadvantage in terms of wage (e.g., Jones et al. 2007) or re-employment probabilities (e.g. Sciulli et al. 2012). In addition, an increasing number of studies stressed the negative impact of poor health conditions on employment or participation rates (e.g., Cai et al. 2014).
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Similarly to above, extending the model predictions to allow for interactions among household members (e.g., Killingsworth and Heckman 1986), special/additional consumption needs of disabled household members determine an increase of household consumptions, possibly inducing a positive indirect effects on employment probabilities of non-disabled members. On the other hand, monetary transfers associated to disability or caring activities to support disabled household members, possibly reduce labour supply, especially in lack of adequate public services supporting disabled people. In addition, from a demand side perspective, it cannot be excluded that individuals living in households with disabled members are possibly subjected to lower hiring rates, in case employers know the presence of disabled members in worker’s household and suspect this may imply lower productivity and/or higher absence rate from work. 7 The sign of the indirect effect of disability would be positive or negative depending on the prevalence of factors increasing or decreasing the employment rate of individuals living in households with disabled members. Disentangling this puzzle requires an empirical investigation, which may provide information on the sign, the significance and the magnitude of the investigated impacts. In this context, carrying out cross-country analysis and separating by gender, can be particularly important to highlight the relevance of institutions, policies, and household behaviour due to societal constraints and social norms sometimes appointing to females dominant role in child and family care (e.g., Hersch and Stratton 2002).
7 For example, Italian legislation provides for special paid leaves in favor of workers which a household member is affected by strong disability (Law 104/92).
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3. The Econometric Model
The probability of an individual i being employed at time t is estimated by applying a random effects dynamic probit model on a balanced sample. The inclusion, among covariates, of the previous employment status allows us to disentangle the contribution to employment probabilities of unobserved heterogeneity and past employment (state dependence), and allows us to interpret our model as a first-order Markov process. The latent variable of the estimated model is specified as follows:
� �01 *
1'
1*
��
������
itit
itiititititit
yy
uDISDISxyy �� � (1)
with i = 1,…, N indicating the individual and t = 2…T the time periods. The dependent variable, y, takes value one if an individual i is employed at time t. xit is a vector of control variables, � is a vector of unknown parameters to be estimated, �i is the individual specific and time invariant random component and uit is the idiosyncratic error term. We assume that both �i and uit are normally distributed and independent of xit and that there is no serial correlation in uit. In addition, we include a vector of disability dummy variables DISit indicating, in turn, the own disability (D), the own strong disability (SD), the other household member disability (OHMD) and the other household member strong disability (OHMSD). Those dummy variables allow us to measure the direct and the indirect impact of (different levels of ) disability on individual employment probability. Furthermore, we also include lagged variables of own and other member(s) disability (DISit-1) into our model and this allows us to disentangle short and long-term effects of disability on employment probabilities. Finally � and � indicate two vectors of unknown parameters to be estimated related, respectively, to current and past disability dummy variables.
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Equation (1) assumes exogenous initial conditions and therefore independence between �i and yit-1. However, since it is most likely that the initial employment status is not randomly assigned to the individual, estimates obtained from equation (1) would be inconsistent. With the aim of providing consistent estimates, we follow the method proposed by Heckman (1981)8 which explicitly account for the initial conditions problem by approximating the unknown initial conditions with a static equation using information from the first wave available in the data. The Heckman estimator requires a simultaneous two-stage procedure. In the first stage a reduced form equation, approximating the conditional distribution of the initial conditions, takes the following form:
� �01 1'11 ��� iii zy �� (2)
where zi1 is a vector of exogenous variables that can include xi1 control variables, DIS1 disability dummy variables, an additional instrument, and where:
ii ��� �� 11 (3) with �i1 correlated with �i but uncorrelated with �i for t > 1. To obtain an estimate of the extent of both state dependence and the impact of own and other household members disability, and more in general to present the results as percentage effects, we need to calculate the average partial effect (APE) of the lagged dependent variable 1ity on � �1�ityP by following the method suggested by Stewart (2007). The method used here is based on estimates of
8 The so called initial conditions problem arises when the start of the observation period does not coincide with the start of the stochastic process. Wooldridge (2005) also proposed an estimator to account for initial conditions problem in non-linear dynamic random effects models. However, the literature (e.g., Akay, 2012) showed that the Heckman’s estimator performs better for short panel and, then, we rely on it in our paper.
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counterfactual outcome probabilities taking 1ity as fixed at 0 and
fixed at 1, and evaluated at xxit � (the mean):
� �� ��� �
��
��� �����
N
ijj DISDISx
Np
1
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1'ˆ'ˆˆˆ'1ˆ ���� , (4)
� �� ��� �
��
��� ����
N
iDISDISx
Np
1
21
0 1'ˆ'ˆˆ'1ˆ ��� (5)
The APE are given by: 0ˆˆ ppAPE j �
4. Data and Sample
Our data are from the EU-SILC panel. It is a rotating panel survey based on harmonised methodology and definitions across most members of the European Union (Eurostat, 2010). The topics covered by the survey are living conditions, income, social exclusion, housing, work, demography, and education. The survey is conducted in each country by its National Institute of Statistics and the sampling designs and operational details adopted are similar (Commission of the European Communities, 2008). We select data for Italy, Spain, France and the UK for the time window 2007-2010. As far as we are concerned, the rotating scheme of the survey implies that each sampled household remains in the sample for four years; the overlap between year t and t+1 is 75 per cent if there is no attrition, between year t and t+2 is 50 per cent, and between year t and t+3 is 25 per cent.9 Sampling units (households) to be added each year, and the
9 The rotation scheme of the EU SILC panel reduces/eliminate the phenomenon of attrition, i.e., unit non-response of eligible persons or households that occurs after the first wave of panel (Rendtel, 2002). As suggested by Eurostat (2010) we checked for the presence of attrition by examining the variable RB110 which gives information on the membership
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whole sample in the first wave of the survey, are selected according to two-stage stratified sampling designs. The primary sample units, municipalities, are stratified by region and demographic size, whereas the secondary sample units, households, are drawn from the population register of sampled municipalities. We focus on the population interviewed in the period 2007-2010, aged between 25 and 64. The models are estimated by gender and separately by country. The effective (balanced) sample sizes for men and women are 9,000 and 9,373 in Italy, 6,896 and 7,571 in Spain, 11,172 and 12,592 in France, and 2,601 and 3,018 in the UK. We are interested in the estimation of the direct and indirect effects of disability on the employment probabilities of both genders. Disability, indeed, affects the employment rates and there is a gender gap in those effects. Table 1 displays the employment rates computed on our samples by country and gender for each level/degree of activity limitation of the individual (direct effects) and of his/her household members (indirect effects).10 The rates are higher for men not disabled and also without disabled household members, especially in the UK (90.33% for men without disability and 85.07% for men without disabled people in the household). Severe disability (activity limitations) strongly affect the employment rates of both genders in all the countries examined. Interestingly, this is especially the case of
status. People are asked whether they were in the same household in previous waves (current household members) or not (not current household members) and whether and why they moved into/out the household since previous/last wave. By combining those information with those obtained from variable RB120 or “to where did the person move” we can reasonably exclude that there is attrition on our data. 10 The definitions of employment and non-employment do not match the ILO definition. In the EU-SILC questionnaire, the respondents are indeed asked to self-define the main economic status in the current year. The variables RB170 and RB210 contains information on the main activity status during the income reference period. People are asked whether they are working, unemployed, in retirement or early retirement or has given up business or other inactive person (Eurostat, 2010).
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Italian females and British males which show similar employment rates (25.22% for Italian females and 25.81% for men in the UK). Again, in the UK strong disability more strongly affect the employment rates of males than females, whilst the opposite is true in the other countries. Table 2 reports summary statistics by country and gender for the variables used in the econometric analysis. The dependent variable is the employment rate/probability. There is a gender gap in employment rates, measured as the difference between the male employment rate and the female employment rate, and female are disadvantaged (lower employment probabilities) in all the countries examined even though with a different extent. Italy and Spain show the highest values of the gender gaps, of 28 p.p. and 21.4 p.p., respectively, whilst the gaps in France and the UK is remarkably lower (10.9 p.p. in the UK and 10.1 p.p. in France). As said in the introduction, our analysis of direct and indirect effects of disability is based on individuals’ self-reported limitation in activities because of health problems at the moment of the interview (PH030 in the official coding of EU-SILC variables, Eurostat, 2010). We use dummy variables for own (and strong own) disability and for disability (and strong) of the other household members. Past employment accounts for state dependence, whereas lagged own and other household members’ disability allows us to disentangle short and long-term effects of disability on employment probabilities. However, because disability is self-reported, and a self-reporting bias problem may arise (see Kerkhofs and Lindeboom 1995, for similar problems with self-reported health), we test (Tables A1-A3) the robustness of cut point shifts with respect to a number of relevant explanatory variables (e.g., Contoyannis et al. 2004).11 We find that 11 Given that he dynamics of disability, as noted in the empirical literature, may be influenced by age, educational status, and income we investigate the issue of self-reporting bias by splitting the sample of males and females in all the countries examined into subsamples based on age (< 45 and > 45), highest attained educational qualification, and income quartiles. For each subsample we estimated dynamic random effects probit models controlling
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our measure of disability is robust to alternative specifications and, more in general, to self-reporting bias. Another concern with our analysis is related to the possibility that unobserved individual or household factors, simultaneously drive employment and disability variables. We test this circumstance estimating the correlation between the error terms in a two-equation framework, where employment and disability equations are estimated jointly using, respectively, a dynamic pooled probit and a dynamic pooled ordered probit model12. Estimates, reported in Table A4, are quite reassuring about the goodness of our estimation results. We find significant, but moderated, correlation between error terms just for French and British females when estimating direct effects, and for French females when estimating indirect effects. In addition, estimation results are consistent with those obtained using our benchmark model. Looking at the prevalence of disability, on average (Table 2), more than 10% of male and females in our samples reports activity limitations (disability), with the partial exception of British males (around 8,9% of the sample). The opposite is true for strong disability. Whilst the relative percentages (by country and gender) range on average between 3.5% and 5.5%, the values in the UK are slightly higher (7.2% for males and 6.5% for females). For more than 75% of our samples there is absence of disabled people in the household. The presence of disabled/individuals/household members with some activity limitations in the household is on average higher in Italy and France (around 15%), whilst the percentage of household members with strong disability is higher and similar in Italy and Spain (around
for initial conditions and correlated effects. Our findings (Tables A1-A3) confirm that the impact of disability as measured by the magnitude and sign of the APE does not change significantly once we split our samples of males and females by age, education, and income quintile in each country examined. Our measure of disability is therefore robust to alternative subsamples’ estimates and across countries. 12 We rely on pooled models, without directly accounting for unobserved heterogeneity to avoid computational burden.
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7% of the samples), and slightly lower and (again) similar in France and the UK (around 5.7%)13. We distinguish between four age groups (25-34; 35-44; 45-54, and 55-64) and three educational variables defined according to UNESCO’s International Standard Classification of Education (ISCED). The EU-SILC distinguishes between education completed in the lower secondary stage (ISCED 0-2), upper secondary education (ISCED 3), and post-secondary or tertiary education (ISCED 5-7). Dummy indicators for marital status and the presence and number of children by age in the household, i.e. 0-3 years old and 4-15 years old, help assessing the effect of the marital status and of the household variables, namely the presence of dependent children) on the probability of successful labour force participation. Again, as per the household, we include and control for one-single household/single as by definition there are no cross-effects for this category, and for the equivalised household income deflated at 2007 prices.14 This variable is an attempt at determining whether income affects individual employment probabilities in the presence of disability and/or disabled people in the household. The business-cycle effect is controlled for by introducing the regional unemployment rates.15
13 Institutionalisation of disabled people is more common in the UK, possibly contributing to explain lower percentage of household members with strong disability. 14 The equivalised household income is computed starting from the total disposable household income, variable HY020, and using the within-household non-response inflation factor, HY025, and the equivalised household size, hhsize. The income is computed in thousands as follows: eqhhincome = (HY020*HY025)/(hhsize*1000). It is also deflated by using the Consumer Price Index (CPI), gathered by ISTAT. 15 These figures are available in the Internet at http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=lfst_r_lfu3rt&lang=en.
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5. Results
We present two sets of estimations of random effects dynamic probit models on balanced samples for the relationship between disability and employment:16 one where we only control for state dependence (lagged employment) and current and lagged disability both own and of other household members’ (base model) and one where we add controls for individual characteristics (e.g., age, educational level, marital status), household variables (number and presence of children aged [0,3] and/or [4,15] in the household, equivalized household income) and regional unemployment rates (complete model). We estimate male and female subjects separately in all the countries examined for the period 2007-2010. Each table contains both base and complete models by gender for all the countries examined and the following relevant variables are displayed:17
- State dependence, i.e. lag employment probability (Table 3); - Direct effects of own and other household members’ disability
(Table 4); - Indirect effects of own and other household members’
disability (Table 5). To obtain an estimate of the extent of both state dependence and the impact of own and other household member disability, and more in general to present the results as percentage effects, we have calculated
16 We also estimated dynamic pooled probit models with robust standard errors. Nonetheless, given that the main findings were in line with our benchmark model, and also given that dynamic pooled probit models do not allow to explicitly account for the initial conditions problem, as explained in Section 3, we decided to use the model proposed by Heckman (1981). 17 As explained above, the complete models include a set of control variables, other than lagged employment and disability. The full set of variables is described in Table 2. For the sake of brevity here we only report and comment the estimates of the variables of interest. The complete results are nonetheless available upon request.
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the APE.18 In general, the overall estimation results accord with intuition and -despite the gender gap in employment probability, the results are remarkably robust to the inclusion and exclusion of the different combinations of disability measures in the estimations. In Table 3, we see that the APE of state dependence (lagged employment) suggest that previous employment (participation) has a significant positive effect on current employment for both genders and in all the countries examined. This suggests, that even after controlling for observed and unobserved differences among individuals, participation in the previous year is associated with a higher probability of participation in the current year. This effect is similar for men and women. In addition, the magnitude of the APE is also similar (in addition to the similarity by gender) in the base model specification in all countries. The impact of previous participation reduces when we estimate complete model, likely because the control variables absorb part of the impact of state dependence, for all the gender-country combinations with the exception of (especially) English males (the effect of previous employment increases from 53.2% in the base model to 58.8% in the complete model) and French females (slight increase from 51.8% in the base model to 52.2% in the complete model). As far as the direct effects of disability are concerned (Table 4), we see that strong ability limitations significantly reduce the employment probabilities of both genders in all countries. The impact is higher for men than women, especially in Italy and Spain. Once we add all the control variables (complete model), the direct effects of (strong) disability reduces in Italy, Spain and France, whilst the opposite is true in the UK. In this country, indeed, the impact of disability gets stronger especially for males. In general, and in line with expectations, the lagged direct effects of disability are lower, particularly in France and the UK.
18 The STATA module redprob does not allow computing the APE. We therefore computed the APE manually by following the method suggested by Stewart (2007). The procedure is explained in Section 3.
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Finally, we find mixed evidence concerning indirect effects of disability (Table 5). First, when estimating the complete model, a number of parameters loss their significance compared to those obtained with the base model. This is suggesting that the relationship between employment probabilities and disability of other household member(s) is importantly intermediated by individual, household and job-related factors. Second, we find evidence of a relevant negative impact against British males and a negligible one against Spanish males. Indirect effects against females are mixed, as we find a negative effect against Italian and Spanish females and a positive one in favor of both French and British women. Again, in the UK there is an increase in the negative impact of (indirect) disability once the complete model is estimated. The interpretations of our findings for direct and indirect effects are offered into the next subsections.
5.1. Direct Effects
We estimate random effects probit models to capture the direct effect of disability on participation (employment) by gender and separately by country. The main variables of interests are disability, i.e. we distinguish between some and severe (strong) activity limitations, and past employment (state dependence), but we also control for other factors, as mentioned earlier. In addition, it is likely that past disability has a direct effect in current employment, so we include lagged variables for the two types of disability (some and severe limitations, respectively). We therefore measure both the short and long-term effects of disability on employment. Estimates of the direct effects of disability suggest (Table 4), consistently with previous literature, the existence of negative direct effects of disability on employment probabilities, with mixed magnitude across countries. The effects of current disability are quite high for both genders in Spain, France and the UK, reducing the probability of current employment significantly. In those countries,
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disability has a greater negative effect on the employment probability of men compared to women. The impact of severe activity limitations is stronger and higher in all the countries examined compared to some activity limitations and the sign of the gender gap varies across countries. Male have a lower reduction in employment probabilities compared to females in Spain, France and the UK, whilst the opposite is true in Italy (the reductions in employment probabilities are -23.2% for males and -15.6% for females). In general, the negative effects of disability is reduced when the control variables are taken into account in Italy, Spain and France. In the UK, instead, the negative impact of disability increases and there is also a not negligible increase of/in the magnitude of the APE for both genders, especially for males when complete models are estimated (the negative impact of strong disability is of around -33.1%). Those effects, i.e. higher impacts of disability and subsequently higher APE, may be due to different distributions/behaviours of both initial conditions and neglected heterogeneity in the UK and also to the fact that the presence of unobservables may be more crucial here with respect to the other countries examined,19 especially to Italy (the APE for strong activity limitations are lower compared to the other countries, i.e. -9.2% for males and -8.5% for females). In addition, as explained above (Section 4), males with severe activity limitations in the UK are more strongly penalized than women in terms of employment opportunities. Past disability, in the previous year, also has a negative effect on current employment, even if it is lower than the effect of current disability. Again, males show higher disadvantage in terms of employment probabilities with respect to females. Our findings therefore confirm previous evidence, e.g., Kidd et al. 2000 and Gannon 2005 for British and Irish situations; Addabbo et al. 2014 and Agovino et al. 2014 for Italy; Mizunoya and Mitra 2013 for developing countries, which pointed in the direction of a negative impact of disability on employment.
19 The Heckman model, as explained in Section 3, explicitly accounts for initial condition problem and unobserved heterogeneity.
21
We now speculate on the possible explanations behind the negative direct effects of disability on employment across countries. There is a number of relevant factors, as explained above (Section 2), which may negatively affect the employment probabilities of disabled individuals both from a supply and demand side view. Labour supply, for instance, may decrease because of the income effect related to the reception of disability benefits/allowances, or because the substitution effect deriving from higher opportunity costs of working associated to disability (e.g., because of higher mobility costs). The welfare systems, as said in the Introduction, are however different across the countries examined and therefore the magnitude of those possible contributing causes to direct effects of disability is mixed across countries. Huys (2013), for instance, identifies four different welfare systems across Europe, for which the UK (and Nordic countries) would be characterized by the prevalence of direct payments, in France (and Western countries) would prevail collectively organized assistance through private charities, while in Southern countries, e.g., Italy and Spain, direct payments and/or organized assistance are less widespread. In addition, empirical evidence show that labor policies are sometimes not effective in promoting employment of disabled people, especially in Southern countries (e.g. Malo and Pagan 2014, Agovino and Rapposelli 2013). In addition, according to the job-search model framework, the higher mobility costs faced by disabled people may decrease their job-search intensity and, then, reduce their employment perspectives. From a demand side view, employers would be less prone to hire disabled people, as disability is likely to be associated to lower productivity and extra-costs for adjusting workplaces to disability requirements, i.e. prejudice and/or discrimination by employers towards disabled people. Discrimination on grounds of disability and/or age, as highlighted in the European Disability Strategy, is widespread in the EU. The elimination of barriers to equality is indeed one of the main areas for actions of the EU Strategy. Our results for the direct effects of disability suggest, therefore, the prevalence of supply and demand
22
side negative factors, including limited effectiveness of labour policies.
5.2. Indirect Effects
Focusing on indirect effects, the profile emerging reveals the impact on employment probabilities of disability of other household member(s) is mixed, according to the various dimensions analyzed here. With some exceptions, the main messages emerging from our estimation results suggest the indirect effects are more likely to affect females, that signs diverge across countries (namely, negative in Italy and Spain and positive in France and UK) and, finally, they more frequently act in the long-term. In particular, it suggests that promoting early interventions, aimed at anticipating the arise of negative effects of disability, would be effective in preventing employment and, then, income losses at household level in the long-run. In addition, it would be noted that the magnitude of indirect effects diverge across countries (Table 5), and they are generally smaller when compared to those concerning the direct effects of disability (Table 4). Looking at the males, we find evidence of a relevant negative effect (-13%) for the British ones because of current strong disability of other household member(s), and a quite negligible significant effect (-0.6%), because of past disability of other household member(s), for Spanish males. It seems particularly interesting to stress that indirect effects in the UK determine an asymmetric impact at gender level, as the employment probabilities of females increase in case of strong disability of other household member(s). In any case, it would be noted that the negative impact on British males is limited to the short run. This suggests a rapid reaction to disability problems in own household by British males, who would decrease their labor supply in the short term and, then, increase it in the medium term. This explanation would be compatible with a dynamic labor market, like
23
the British one, which would make easier the re-employment of those leaving temporarily the labor market to look after family members. In addition, this finding is consistent with previous evidence, showing a greater negative impact against British males providing caring activities in comparison with European males and British females (OECD 2011). When focusing on females, a mixed country-profile emerges. The presence of strong disabled members reduce the current probability of being employed of Italian females by 4.9%,20 while the Spanish ones are subjected to a detrimental impact (-2.9%) because of long-term effects of disability of household member(s). These findings suggest, albeit with proper country differences, the prevalence of negative transmission mechanisms, from disability to the employment of another household member, in Italy and Spain. This is possibly because the combination of household behavioral effects, assigning to females the role of providing care in the household, and consistently with social norms still being in force in Southern European countries, and inadequate policies supporting disability, for example, through the public provision of care to the disabled. In addition, even though disability benefits are usually not particularly abundant and relegated to individuals with severe disabilities, we cannot discard the hypothesis that a household income effect, related to the reception of monetary transfers, is at work diminishing the female labor supply. Conversely, French and British females are positively affected by the presence of long-run disability problems (respectively, strong disability and disability) in the own household. The positive impact is equal to 2.4% in France and 6.8% in the UK. Explanations to these findings include the need of increasing amount of income to meet the special/additional consumption requirements in presence of disabled
20 Even though we do not find explicit evidence of indirect effects of disability affecting Italian females in the long run, it cannot be excluded that part of it has been intercepted by state dependence (see Gannon 2005 for similar considerations).
24
members in the household. This explanation would be more robust if combined with positive effects deriving by effective support policies for disabled individuals, releasing females from the provision of caring activities into the own household. These policies would include the provision through special disability centers (public or private, including associations and cooperatives) accommodating disabled individuals during the working hours of relatives, and/or the provision of cheaper social services looking after disabled individuals at home.21 In addition, in the Anglo-Saxon countries, i.e., UK, indeed, governments implemented programs to increase the supply of part-time jobs’ opportunities (better reconciling work and family responsibilities), as well as private services, which have contributed to support women’s work (Boeri et al. 2005 ;Del Boca et al. 2005). These explanations are possibly not exhaustive, and they possibly work in association with specific household behavior and social norms characterizing each county analyzed here. Nevertheless, these findings highlight the existence of cross-country differences that would be, at least, partly explained by the interaction of different institutions and individual/household preferences, working in different countries. The cross-country heterogeneity emerged, in terms of both magnitude and, in particular, signs of indirect effects of disability, allows for further considerations. The adoption of best practices and policies, are possibly effective in avoiding the transmission of detrimental effects of disability to other household members, then preventing the risk of a general impoverishment of households characterized by the presence of disabled members, beyond those determined by the disability per se.
21 For example, the UK government provides support to disabled people who wish to stay in their home, through the disabled facilities grant, home improvement agencies and local handyperson services.
25
6. Conclusions
This study analyzes the direct and indirect effects of disability on employment comparing four major Western European countries, i.e., Italy, Spain, France and the UK, characterized by different institutions, behavior and social norms. By using the 2007-2010 longitudinal component of the EU-SILC data, the comparison benefits of the homogeneity in disability definition, based on information on daily activities limitations. Evidence concerning the direct effect of disability, i.e. the impact of being disabled on own employment probabilities, is negative and consistent with previous findings. The impact is particularly strong in the UK, when compared to Italy, Spain and especially France, and its magnitude is increasing with the seriousness of disability. We find evidence of long-term effect of disability, especially in the UK and Spain. Gender differences have also emerged. While the body of literature concerning direct effects of disability has strongly increased in the last decade, poor evidence exists for indirect effects, i.e. the impact on own employment because of the presence of disabled member(s) in own household. Accounting for indirect effects of disability, allows to look from a different perspective the relationship between employment and disability, including the role of household. At this stage, country and gender differences would be particularly accentuated because of differences in institutions, policies and household behavior characterizing the analyzed countries. We find significant and negative indirect impact against British males and a negligible one against Spanish males. The impact is more mixed against females. In those countries prevailing the traditional family care system (Italy and Spain), the presence of disabled member(s) negatively affect the employment of females. In France and UK, where different welfare systems are in force, and social norms allow for a different allocation of caring activities, the net indirect impact on females is positive in the long-term. More in general, as a result of those different care and welfare
26
systems, Italian and Spanish female still work less than women in France and the UK. Our estimation results also prove the existence of state dependence and endogenous initial conditions. In addition, empirical evidence are robust to reporting bias and endogeneity tests, quite reassuring us about the goodness of our speculations. Finally, even though we have tried to interpret our estimation results from a multi-faced perspective, it cannot be excluded that there might be other forces at work which could explain our findings. In addition, it would be stressed that we estimated the net direct/indirect effects of disability, meaning we cannot exclude that many of the underlying mechanisms possibly affecting employment probabilities, are contemporarily at work. It follows, that what has led to the (significant and not significant) estimated effect is their intensity, which may vary across gender and countries. However, an analysis being able to single out the contribution of each specific factor goes beyond the purpose of this study, and it will form part of future research.
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Kidd M. P., Sloane P. J. and Ferko I. (2000), ‘Disability and the Labour Market: An Analysis of British Males’, Journal of Health Economics, 19: 961-981. Killingsworth M.R. and Heckman JJ (1986), ‘Female labour supply: a survey’, in Handbook of Labor Economics, Vol. 1(Eds.) Ashenfelter O. and Layard R., North-Holland, Amsterdam. Malo M. Á. and Pagan R. (2014), ‘Hiring Workers with Disabilities when a Quota Requirement Exists: The Relevance of Firm’s Size’, in: Malo M.Á. and Sciulli D. (Eds.), Disadvantaged Workers, AIEL Series in Labour Economics, chapter 4: 49-63. Mitra, S. (2008), ‘The recent decline in the employment of persons with Disabilities in South Africa, 1998–2006’, South African Journal of Economics, 76(3), 480–492. Mizunoya S. and Mitra S. (2013), ‘Is There a Disability Gap in Employment Rates in Developing Countries?’, World Development, 42: 28-43. OECD (2011), ‘The impact of caring on family carers, Help Wanted? Providing and Paying for Long-Term Care’, Chapter 3. Oguzoglu U. (2010), ‘Dynamics of Work Limitation And Work In Australia’, Health Economics, 19: 656-669. Parodi G., Sciulli D. (2008), ‘Disability in Italian households: income poverty and labour market participation’, Applied Economics, 40(20): 2615–2630. Rendtel U. (2002), ‘Attrition in Household Panels: A Survey’, CHINTEX Working Paper No. 4, www.destatis.de/chintex/download/paper4.pdf.
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Sapir, A. (2006), “Globalization and the reforms of European social models”, Journal of Common Market Studies, 44(2): 369-390. Sciulli D., Menezes A., and Vieira J. (2012), ‘Unemployment Duration and Disability: Evidence from Portugal’, Journal of Labor Research, 33(1): 21-48. She, P. and Livermore G. (2007), ‘Material hardship, poverty, and disability among working-age adults’, Social Science Quarterly, 88(4): 970-989. Stewart M. B. (2007), ‘The Interrelated Dynamics of Unemployment and Low-Wage Employment’, Journal of Applied Econometrics, 22: 511-531. Torrisi, G. (2011), ‘Redistributive policies and recipients: an empirical analysis’, Journal of Academic Research in Economics, 2(1): 109-124. United Nations (2004), ‘Accessibility for the disabled’, http://www.un.org/. Wooldridge J (2005), ‘Simple solution to the initial condition problem in dynamic, non-linear panel data models with unobserved heterogeneity’, Journal of Applied Econometrics
31
TA
BL
ES
Tabl
e 1:
Em
ploy
men
t Rat
es b
y C
ount
ry a
nd G
ende
r, 20
07-2
010
IT
ALY
SP
AIN
O
wn
Oth
er h
ouse
hold
mem
ber
Ow
n O
ther
hou
seho
ld m
embe
r
M
ale
Fem
ale
Mal
e
Fem
ale
Mal
e Fe
mal
e M
ale
Fe
mal
e
No
disa
bilit
y 84
.45%
56
.24%
84
.12%
56
.06%
84
.33%
63
.00%
82
.97%
60
.20%
Dis
abili
ty
71.4
4%
43.2
5%
73.5
6%
46.1
6%
61.6
9%
41.1
9%
73.2
6%
55.0
1%
Stro
ng d
isab
ility
40
.87%
25
.22%
69
.10%
39
.70%
42
.60%
29
.93%
66
.41%
47
.61%
U
K
FRA
NC
E
O
wn
Oth
er h
ouse
hold
mem
ber
Ow
n O
ther
hou
seho
ld m
embe
r
M
ale
Fem
ale
Mal
e
Fem
ale
Mal
e Fe
mal
e M
ale
Fe
mal
e
No
disa
bilit
y 90
.33%
78
.53%
85
.07%
73
.92%
84
.32%
73
.51%
81
.63%
71
.16%
Dis
abili
ty
68.9
7%
58.0
5%
83.3
3%
73.9
3%
62.8
0%
57.3
7%
70.5
5%
62.5
9%
Stro
ng d
isab
ility
25
.81%
28
.93%
64
.75%
57
.06%
43
.23%
39
.74%
68
.03%
59
.13%
Not
e: E
mpl
oym
ent r
ates
com
pute
d on
bal
ance
d sa
mpl
es
Sour
ce: o
ur e
labo
ratio
ns o
f EU
SIL
C d
ata
32
Tabl
e 2:
Des
crip
tive
Stat
istic
s Rat
es b
y C
ount
ry a
nd G
ende
r, 20
07-2
010
ITA
LY
SPA
IN
FRA
NC
E U
K
M
ales
Fe
mal
es
Mal
es
Fem
ales
M
ales
Fe
mal
es
Mal
es
Fem
ales
Empl
oym
ent
0.81
4 0.
534
0.79
7 0.
583
0.79
6 0.
695
0.83
8 0.
729
Empl
oym
ent t
ime
1 0.
819
0.53
4 0.
811
0.59
0 0.
809
0.69
9 0.
847
0.73
2
Dis
abili
ty
No
disa
bilit
y 0.
859
0.83
0 0.
831
0.80
4 0.
830
0.81
1 0.
839
0.81
9
Dis
abili
ty
0.10
2 0.
133
0.12
9 0.
158
0.11
4 0.
135
0.08
9 0.
115
Stro
ng d
isab
ility
0.
038
0.03
7 0.
040
0.03
8 0.
055
0.05
5 0.
072
0.06
5
Hou
seho
ld m
embe
r no
disa
bilit
y 0.
775
0.77
4 0.
720
0.74
0 0.
828
0.82
6 0.
850
0.86
4
Hou
seho
ld m
embe
r dis
abili
ty
0.15
8 0.
154
0.20
4 0.
188
0.11
8 0.
115
0.09
7 0.
078
Hou
seho
ld m
embe
r stro
ng d
isab
ility
0.
067
0.07
1 0.
076
0.07
2 0.
055
0.05
9 0.
053
0.05
9
Age
Age
[25,
34]
0.18
0 0.
183
0.19
6 0.
185
0.16
1 0.
171
0.14
0 0.
149
Age
[35,
44]
0.29
0 0.
314
0.28
5 0.
295
0.29
3 0.
283
0.28
1 0.
301
Age
[45,
54]
0.31
7 0.
280
0.30
5 0.
300
0.28
8 0.
294
0.29
3 0.
276
Age
[55,
64]
0.21
4 0.
222
0.21
4 0.
219
0.25
9 0.
252
0.28
6 0.
274
(follo
w)
33
ITA
LY
SPA
IN
FRA
NC
E U
K
M
ales
Fe
mal
es
Mal
es
Fem
ales
M
ales
Fe
mal
es
Mal
es
Fem
ales
Educ
atio
n N
one,
ele
men
tary
, or l
ower
seco
ndar
y
0.47
3 0.
442
0.48
4 0.
492
0.22
1 0.
279
0.12
9 0.
128
Upp
er se
cond
ary
0.35
0 0.
357
0.23
6 0.
203
0.50
1 0.
411
0.41
7 0.
442
Post
seco
ndar
y or
terti
ary
0.17
7 0.
201
0.27
9 0.
303
0.27
8 0.
309
0.38
7 0.
398
Mar
ried
0.67
2 0.
718
0.69
4 0.
689
0.63
4 0.
622
0.67
0 0.
632
Sing
le
0.10
6 0.
063
0.04
6 0.
051
0.12
0 0.
109
0.11
8 0.
101
Num
ber o
f kid
s 0-3
0.
087
0.08
5 0.
080
0.07
5 0.
089
0.08
2 0.
103
0.09
4 0.
297
0.29
2 0.
292
0.28
4 0.
300
0.28
8 0.
323
0.30
8 N
umbe
r of k
ids 4
-15
0.47
3 0.
489
0.54
4 0.
542
0.65
3 0.
651
0.60
0 0.
624
0.76
4 0.
768
0.81
3 0.
811
0.95
8 0.
945
0.93
6 0.
933
Loca
l une
mpl
oym
ent r
ate
7.28
3 7.
437
13.2
73
13.2
47
8.31
2 8.
351
5.59
3 5.
532
3.44
4 3.
494
5.84
8 5.
872
2.47
4 2.
531
1.47
5 1.
478
Equi
valis
ed H
ouse
hold
Inco
me
19.0
17
18.4
05
15.0
07
14.6
60
22.9
64
22.3
27
25.0
58
24.1
26
12.3
71
12.9
84
9.32
5 9.
385
15.9
89
15.7
34
22.0
89
20.6
76
Del
ta u
nem
ploy
men
t rat
e 20
06-2
007
-10.
199
-10.
256
-3.8
95
-3.9
74
-9.3
55
-9.4
10
2.81
2 2.
730
4.
596
4.53
8 7.
874
7.47
3 8.
237
8.12
5 12
.915
12
.949
Obs
erva
tions
90
00
9372
68
96
7572
11
172
1259
2 26
00
3020
Not
es: S
tand
ard
devi
atio
ns in
par
enth
eses
for c
ontin
uous
var
iabl
es.
34
Tabl
e 3:
APE
for S
tate
Dep
ende
nce
Rat
es b
y C
ount
ry a
nd G
ende
r, 20
07-2
010,
bal
ance
d sa
mpl
es
M
ALE
S FE
MA
LES
B
ase
mod
el
Com
plet
e m
odel
B
ase
mod
el
Com
plet
e m
odel
ITA
LY
0.48
2 **
* 0.
404
***
0.48
7 **
* 0.
446
***
SPA
IN
0.47
0 **
* 0.
422
***
0.42
5 **
* 0.
367
***
FRA
NC
E 0.
542
***
0.50
2 **
* 0.
518
***
0.53
4 **
*
UK
0.
532
***
0.58
8 **
* 0.
475
***
0.39
0 **
*
* Si
gnifi
cant
at t
he 1
0% le
vel;
** si
gnifi
cant
at t
he 5
% le
vel;
***
sign
ifica
nt a
t the
1%
leve
l. So
urce
: our
ela
bora
tions
of E
U S
ILC
dat
a
35
Tabl
e 4:
APE
for D
irect
Eff
ects
of D
isab
ility
Rat
es b
y C
ount
ry a
nd G
ende
r. 20
07-2
010.
bal
ance
d sa
mpl
es
MA
LES
D
isab
ility
St
rong
dis
abili
ty
Dis
abili
ty t-
1 St
rong
dis
abili
ty t-
1
B
ase
mod
el
Com
plet
e m
odel
B
ase
mod
el
Com
plet
e m
odel
B
ase
mod
el
Com
plet
e m
odel
B
ase
mod
el
Com
plet
e m
odel
ITA
LY
-0.0
43
**
-0.0
06
-0.2
32
***
-0.0
92
***
-0.0
63**
*-0
.009
-0
.131
**
* -0
.011
SPA
IN
-0.0
91
***
-0.0
78
***
-0.1
74
***
-0.1
41
***
-0.0
56**
*-0
.015
**
-0
.136
**
* -0
.025
**
* FR
AN
CE
-0.0
34
***
-0.0
22
***
-0.0
37
***
-0.0
27
**
-0.0
26**
*-0
.006
**
-0
.072
**
* 0.
004
***
UK
-0
.056
**
* -0
.137
**
* -0
.138
**
* -0
.331
**
* 0.
005
0.
005
-0
.059
**
-0
.163
**
FEM
ALE
S
Dis
abili
ty
Stro
ng d
isab
ility
D
isab
ility
t-1
Stro
ng d
isab
ility
t-1
B
ase
mod
el
Com
plet
e m
odel
B
ase
mod
el
Com
plet
e m
odel
B
ase
mod
el
Com
plet
e m
odel
B
ase
mod
el
Com
plet
e m
odel
ITA
LY
-0.0
35
-0.0
07
-0.1
56
***
-0.0
85
**
-0.0
74**
*-0
.023
-0
.117
**
-0
.053
SPA
IN
-0.1
34
***
-0.0
75
***
-0.2
04
***
-0.0
98
**
-0.0
55**
-0
.009
-0
.188
**
* -0
.015
**
* FR
AN
CE
-0.0
50
***
-0.0
16
* -0
.105
**
* -0
.051
**
* -0
.057
***
-0.0
13
* -0
.171
**
* -0
.005
**
* U
K
-0.0
55
**
-0.0
77
**
-0.1
88
***
-0.2
15
***
-0.0
47*
0.00
4
-0.1
46
***
-0.0
25
***
36
Tabl
e 5:
APE
for I
ndire
ct E
ffec
ts o
f Dis
abili
ty R
ates
by
Cou
ntry
and
Gen
der.
2007
-201
0, b
alan
ced
sam
ples
MA
LES
OH
MD
O
HM
DS
OH
MD
O
HM
DS
B
ase
mod
el
Com
plet
e m
odel
B
ase
mod
el
Com
plet
e m
odel
Bas
e m
odel
C
ompl
ete
mod
elB
ase
mod
el
Com
plet
e m
odel
ITA
LY
-0.0
24
-0.0
09
-0.0
26
-0.0
25
-0.0
42
**
-0.0
12
-0.0
42
0.00
1
SPA
IN
-0.0
32
**
-0.0
35
-0.0
53
* -0
.138
-0
.020
*
-0.0
01
-0.0
06
0.00
2 FR
AN
CE
-0.0
16
**
-0.0
16
-0.0
05
-0.0
47
-0.0
20
**
-0.0
07
-0.0
22
* -0
.007
UK
-0
.004
0.02
0
-0.0
81
***
-0.1
30
**
0.01
0
0.03
8
0.00
1
0.02
6
FEM
ALE
S
OH
MD
O
HM
DS
OH
MD
O
HM
DS
B
ase
mod
el
Com
plet
e m
odel
B
ase
mod
el
Com
plet
e m
odel
Bas
e m
odel
C
ompl
ete
mod
elB
ase
mod
el
Com
plet
e m
odel
ITA
LY
-0.0
54
**
-0.0
28
-0.0
93
**
-0.0
49
**
-0.0
37
-0.0
14
-0.0
32
-0.0
13
SPA
IN
-0.0
22
-0.0
12
-0.0
35
-0.1
18
-0.0
61
**
-0.0
29
* -0
.014
-0
.002
FRA
NC
E -0
.025
*
-0.0
17
-0.0
35
* -0
.076
-0
.001
0.
010
0.00
3 0.
023
* U
K
0.01
1
-0.0
50
-0
.045
-0.1
66
0.
059
**0.
068
*0.
001
0.
005
* Si
gnifi
cant
at t
he 1
0% le
vel;
** si
gnifi
cant
at t
he 5
% le
vel;
***
sign
ifica
nt a
t the
1%
leve
l. So
urce
: our
ela
bora
tions
of E
U S
ILC
dat
a
37
APP
EN
DIX
Tabl
e A
1: A
vera
ge p
artia
l eff
ects
on
prob
abili
ty o
f rep
ortin
g ac
tivity
lim
itatio
ns fo
r dyn
amic
rand
om
effe
cts o
rder
ed p
robi
t by
age
grou
p, u
nbal
ance
d sa
mpl
es
Italy
M
EN
W
OM
EN
Age
� 4
5 A
ge >
45
A
ge �
45
Age
> 4
5
A
PE
s.e.
A
PE
s.e.
APE
s.e
.
APE
s.e
.
Lag
Dis
abili
ty
0.20
0 0.
007
***
0.32
3 0.
006
***
0.21
0 0.
007
***
0.34
2 0.
006
***
Educ
atio
n: R
efer
ence
- Pr
imar
y
Med
ium
edu
catio
n -0
.021
0.
007
***
-0.0
43
0.00
9 **
*-0
.019
0.
007
***
-0.0
56
0.01
1 **
*
Hig
h ed
ucat
ion
-0.0
31
0.00
9 **
*-0
.056
0.
013
***
-0.0
20
0.00
9 **
-0
.052
0.
015
***
Qua
ntile
of t
he in
com
e di
stri
butio
n: R
efer
ence
- 1s
t qua
ntile
qu2
-0
.010
0.
008
-0
.011
0.
011
-0.0
04
0.00
8 -0
.034
0.
013
***
qu3
0.
007
0.00
8
-0.0
22
0.01
2 *
-0.0
03
0.00
9 -0
.031
0.
013
**
qu4
-0
.014
0.
009
-0
.028
0.
012
* -0
.007
0.
009
-0.0
58
0.01
3 **
*
38
Spai
n
M
EN
WO
MEN
A
ge �
45
Age
> 4
5 A
ge �
45
Age
> 4
5
A
PE
s.e.
A
PE
s.e.
APE
s.e
. A
PE
s.e.
La
g D
isab
ility
0.
229
0.00
9 **
* 0.
329
0.00
9 **
*0.
222
0.00
9 **
*0.
344
0.00
8 **
*
Educ
atio
n: R
efer
ence
- Pr
imar
y
Med
ium
edu
catio
n -0
.018
0.
011
* -0
.057
0.
014
***
-0.0
21
0.01
1 **
*-0
.065
0.
015
***
Hig
h ed
ucat
ion
-0.0
38
0.01
1 **
* -0
.083
0.
015
***
-0.0
44
0.01
1 **
-0
.085
0.
016
***
Qua
ntile
of t
he in
com
e di
stri
butio
n: R
efer
ence
- 1s
t qua
ntile
qu2
-0
.015
0.
012
0.
003
0.01
4 0.
019
0.01
1 -0
.029
0.
015
**
qu3
-0
.030
0.
012
**
-0.0
34
0.01
5 **
-0
.006
0.
012
-0.0
57
0.01
5 **
*
qu4
-0
.028
0.
013
**
-0.0
27
0.01
6 **
-0
.003
0.
014
-0.0
67
0.01
6 **
*
39
Fran
ce
M
EN
WO
MEN
A
ge �
45
Age
> 4
5 A
ge �
45
Age
> 4
5
A
PE
s.e.
A
PE
s.e.
APE
s.e
. A
PE
s.e.
La
g D
isab
ility
0.
231
0.00
9 **
* 0.
370
0.00
6 **
*0.
236
0.01
7 **
*0.
377
0.00
6 **
*
Educ
atio
n: R
efer
ence
- Pr
imar
y
Med
ium
edu
catio
n -0
.025
0.
010
* -0
.032
0.
010
***
-0.0
29
0.01
0 **
*-0
.034
0.
010
***
Hig
h ed
ucat
ion
-0.0
50
0.01
2 **
* -0
.044
0.
014
***
-0.0
42
0.01
2 **
*-0
.046
0.
014
***
Qua
ntile
of t
he in
com
e di
stri
butio
n: R
efer
ence
- 1s
t qua
ntile
qu2
-0
.017
0.
010
* -0
.016
0.
013
-0.0
08
0.01
0 -0
.055
0.
013
***
qu3
-0
.027
0.
011
* -0
.027
0.
013
* -0
.031
0.
011
***
-0.0
41
0.01
3 **
*
qu4
-0
.047
0.
013
***
-0.0
41
0.01
3 **
*-0
.025
0.
013
**
-0.0
77
0.01
4 **
*
40
UK
M
EN
WO
MEN
A
ge �
45
Age
> 4
5 A
ge �
45
Age
> 4
5
A
PE
s.e.
A
PE
s.e.
APE
s.e
. A
PE
s.e.
La
g D
isab
ility
0.
231
0.01
4 **
* 0.
378
0.00
9 **
* 0.
264
0.01
2 **
* 0.
359
0.00
9 **
*
Educ
atio
n: R
efer
ence
- Pr
imar
y
Med
ium
edu
catio
n 0.
000
0.01
7
-0.0
05
0.01
7 0.
000
0.01
9 -0
.031
0.
017
**
Hig
h ed
ucat
ion
-0.0
13
0.01
8 *
-0.0
41
0.01
8 *
-0.0
38
0.02
1 *
-0.0
26
0.01
8
Qua
ntile
of t
he in
com
e di
stri
butio
n: R
efer
ence
- 1s
t qua
ntile
qu2
-0
.011
0.
015
-0
.049
0.
019
***
-0.0
25
0.01
6 -0
.008
0.
017
qu
3
-0.0
39
0.01
7 **
-0
.039
0.
019
**
-0.0
58
0.01
8 **
* -0
.076
0.
019
***
qu4
-0
.052
0.
020
***
-0.0
48
0.02
0 **
-0
.060
0.
020
***
-0.1
26
0.02
2 **
*
* Si
gnifi
cant
at t
he 1
0% le
vel;
** si
gnifi
cant
at t
he 5
% le
vel;
***
sign
ifica
nt a
t the
1%
leve
l. So
urce
: our
ela
bora
tions
of E
U S
ILC
dat
a
41
Tabl
e A
2: A
vera
ge p
artia
l eff
ects
on
prob
abili
ty o
f rep
ortin
g ac
tivity
lim
itatio
ns fo
r dyn
amic
rand
om e
ffec
ts o
rder
ed
prob
it by
edu
catio
nal a
ttain
men
t, un
bala
nced
sam
ples
Ita
ly
M
EN
WO
MEN
Pr
imar
y Se
cond
ary
Terti
ary
Prim
ary
Seco
ndar
y Te
rtiar
y
A
PE
s.e.
APE
s.e
. A
PE
s.e.
APE
s.e
. A
PE
s.e.
A
PE
s.e.
Lag
Dis
abili
ty
0.31
5 0.
006
***
0.21
4 0.
008
***
0.19
1 0.
011
***
0.32
9 0.
007
***
0.22
7 0.
008
***
0.21
3 0.
011
***
Age
0.
004
0.00
4 -0
.003
0.
003
-0.0
06
0.00
4 0.
011
0.00
5 *
0.00
5 0.
004
0.
004
0.00
5
Age
squa
re
0.00
0 0.
000
0.00
0 0.
000
0.00
0 0.
000
* 0.
000
0.00
0 0.
000
0.00
0
0.00
0 0.
000
Qua
ntile
of t
he in
com
e di
stri
butio
n: R
efer
ence
- 1s
t qua
ntile
qu2
-0
.009
0.
011
* -0
.014
0.
012
-0
.025
0.
019
-0
.022
0.
011
* -0
.014
0.
012
-0
.007
0.
018
*
qu3
-0
.007
0.
011
-0.0
12
0.01
1 *
-0.0
01
0.01
6 *
-0.0
19
0.01
3 *
-0.0
18
0.01
2 *
-0.0
04
0.01
7
qu4
-0
.017
0.
013
-0.0
25
0.01
1 *
-0.0
31
0.01
5 *
-0.0
44
0.01
5 -0
.027
0.
012
* -0
.024
0.
016
42
Spai
n
M
EN
WO
MEN
Pr
imar
y Se
cond
ary
Terti
ary
Prim
ary
Seco
ndar
y Te
rtiar
y
A
PE
s.e.
APE
s.e
.
APE
s.e
. A
PE
s.e.
APE
s.e
.
APE
s.e
.
Lag
Dis
abili
ty
0.32
6 0.
009
***
0.25
5 0.
013
***
0.20
3 0.
012
***
0.33
9 0.
009
***
0.23
6 0.
015
***
0.21
1 0.
011
***
Age
-0
.003
0.
005
-0.0
01
0.00
6
-0.0
010.
005
0.00
5 0.
005
0.01
3 0.
007
* 0.
001
0.00
4
Age
squa
re
0.00
0 0.
000
0.00
0 0.
000
0.
000
0.00
0 0.
000
0.00
0 0.
000
0.00
0
0.00
0 0.
000
Qua
ntile
of t
he in
com
e di
stri
butio
n: R
efer
ence
- 1s
t qua
ntile
qu2
-0
.007
0.
013
0.00
8 0.
021
-0
.023
0.01
9 -0
.011
0.
013
0.01
7 0.
020
-0
.010
0.
019
*
qu3
-0
.050
0.
015
***
0.00
5 0.
020
-0
.041
0.01
8 *
-0.0
33
0.01
4 **
-0
.030
0.
021
-0
.024
0.
018
qu4
-0
.041
0.
019
**
-0.0
07
0.02
0 *
-0.0
350.
016
* -0
.067
0.
020
***
-0.0
20
0.02
2 *
-0.0
24
0.01
6
43
Fran
ce
MEN
W
OM
EN
Pr
imar
y Se
cond
ary
Terti
ary
Prim
ary
Seco
ndar
y Te
rtiar
y
A
PE
s.e.
APE
s.e
.
APE
s.e
. A
PE
s.e.
APE
s.e
.
APE
s.e
.
Lag
Dis
abili
ty
0.38
9 0.
009
***
0.30
2 0.
008
***
0.23
8 0.
011
***
0.39
9 0.
008
***
0.29
9 0.
008
***
0.24
3 0.
010
***
Age
0.
005
0.00
7 0.
008
0.00
4 *
0.00
7 0.
004
0.00
7 0.
007
0.01
2 0.
004
***
0.00
0 0.
004
Age
squa
re
0.00
0 0.
000
0.00
0 0.
000
0.
000
0.00
0 0.
000
0.00
0 0.
000
0.00
0 **
0.
000
0.00
0
Qua
ntile
of t
he in
com
e di
stri
butio
n: R
efer
ence
- 1s
t qua
ntile
qu2
-0
.038
0.
018
* -0
.006
0.
012
-0
.016
0.01
8 -0
.066
0.
016
***
-0.0
23
0.01
2 **
0.
005
0.01
8
qu3
-0
.059
0.
020
***
-0.0
19
0.01
2
-0.0
150.
016
-0.0
53
0.01
8 **
* -0
.025
0.
013
**
-0.0
230.
017
qu4
-0
.063
0.
023
***
-0.0
55
0.01
4 **
* -0
.019
0.01
5 -0
.078
0.
022
***
-0.0
71
0.01
5 **
* -0
.022
0.01
6 *
44
UK
M
EN
WO
MEN
Pr
imar
y Se
cond
ary
Terti
ary
Prim
ary
Seco
ndar
y Te
rtiar
y
A
PE
s.e.
APE
s.e
. A
PE
s.e.
APE
s.e
. A
PE
s.e.
A
PE
s.e.
Lag
Dis
abili
ty
0.40
0 0.
016
***
0.33
3 0.
012
***
0.26
2 0.
014
***
0.40
6 0.
015
***
0.33
7 0.
011
***
0.25
5 0.
013
***
Age
-0
.002
0.
015
0.00
0 0.
006
0.00
6 0.
007
-0.0
13
0.01
3 *
0.00
2 0.
006
0.
003
0.00
6
Age
squa
re
0.00
0 0.
000
0.00
0 0.
000
0.00
0 0.
000
* 0.
000
0.00
0 0.
000
0.00
0
0.00
0 0.
000
Qua
ntile
of t
he in
com
e di
stri
butio
n: R
efer
ence
- 1s
t qua
ntile
qu2
-0
.059
0.
036
* -0
.049
0.
019
**
-0.0
140.
022
-0.0
33
0.03
1 *
-0.0
12
0.01
8
0.00
9 0.
020
qu3
-0
.001
0.
038
-0.0
74
0.02
1 **
* -0
.012
0.02
0 -0
.126
0.
045
* -0
.082
0.
020
***
-0.0
20
0.02
0
qu4
-0
.081
0.
057
-0.0
61
0.02
4 **
* -0
.026
0.02
0 *
-0.2
19
0.07
3 -0
.100
0.
026
***
-0.0
51
0.02
0*
* Si
gnifi
cant
at t
he 1
0% le
vel;
** si
gnifi
cant
at t
he 5
% le
vel;
***
sign
ifica
nt a
t the
1%
leve
l. So
urce
: our
ela
bora
tions
of E
U S
ILC
dat
a
45
Tabl
e A
3: A
vera
ge p
artia
l eff
ects
on
prob
abili
ty o
f rep
ortin
g ac
tivity
lim
itatio
ns fo
r dyn
amic
rand
om e
ffec
ts o
rder
ed
prob
it by
inco
me
quar
tile,
unb
alan
ced
sam
ples
Ita
ly
Men
1s
t qua
rtile
2n
d qu
artil
e 3r
d qu
artil
e 4t
h qu
artil
e A
PE
s.e.
APE
s.e
. A
PE
s.e.
APE
s.e
. La
g D
isab
ility
0.
280
0.00
9 **
* 0.
260
0.00
9 **
* 0.
268
0.00
9 **
* 0.
219
0.00
9 **
* A
ge
0.00
2 0.
005
* 0.
004
0.00
5 -0
.003
0.
004
-0.0
05
0.00
4 A
ge sq
uare
0.
000
0.00
0 0.
000
0.00
0 0.
000
0.00
0 0.
000
0.00
0 *
Educ
atio
n: R
efer
ence
- Pr
imar
y M
ediu
m e
duca
tion
-0.0
19
0.01
3 -0
.029
0.
012
**
-0.0
32
0.01
1 **
* -0
.029
0.
010
**
Hig
h ed
ucat
ion
-0.0
25
0.02
2 -0
.052
0.
020
**
-0.0
26
0.01
6 **
-0
.045
0.
012
**
Wom
en
1st q
uarti
le
2nd
quar
tile
3rd
quar
tile
4th
quar
tile
A
PE
s.e.
APE
s.e
. A
PE
s.e.
APE
s.e
. La
g D
isab
ility
0.
288
0.00
9 **
* 0.
275
0.00
9 **
* 0.
268
0.00
9 **
* 0.
240
0.01
0 **
* A
ge
0.00
9 0.
005
**
0.00
3 0.
005
0.00
5 0.
005
0.00
6 0.
005
Age
squa
re
0.00
0 0.
000
0.00
0 0.
000
0.00
0 0.
000
0.00
0 0.
000
Educ
atio
n: R
efer
ence
- Pr
imar
y M
ediu
m e
duca
tion
-0.0
22
0.01
4 *
-0.0
29
0.01
3 *
-0.0
31
0.01
2 *
-0.0
30
0.01
2 **
H
igh
educ
atio
n -0
.024
0.
021
-0.0
26
0.01
9 -0
.018
0.
015
-0.0
33
0.01
3 **
46
Spai
n
Men
1st q
uarti
le
2nd
quar
tile
3rd
quar
tile
4th
quar
tile
A
PE
s.e.
APE
s.e
. A
PE
s.e.
APE
s.e
.
Lag
Dis
abili
ty
0.32
6 0.
012
***
0.29
8 0.
012
***
0.25
7 0.
012
***
0.22
0 0.
013
***
Age
-0
.015
0.
007
**
0.00
9 0.
007
-0.0
06
0.00
6 0.
002
0.00
5 A
ge sq
uare
0.
000
0.00
0 **
* 0.
000
0.00
0 0.
000
0.00
0 *
0.00
0 0.
000
Educ
atio
n: R
efer
ence
- Pr
imar
y M
ediu
m e
duca
tion
-0.0
61
0.02
2 **
* -0
.043
0.
018
**
-0.0
04
0.01
5 -0
.025
0.
017
Hig
h ed
ucat
ion
-0.0
44
0.02
4 **
-0
.068
0.
021
***
-0.0
44
0.01
6 **
* -0
.046
0.
015
***
W
omen
1st q
uarti
le
2nd
quar
tile
3rd
quar
tile
4th
quar
tile
A
PE
s.e.
APE
s.e
. A
PE
s.e.
APE
s.e
.
Lag
Dis
abili
ty
0.31
1 0.
012
***
0.31
6 0.
012
***
0.25
8 0.
013
***
0.22
5 0.
013
***
Age
0.
012
0.00
7 **
0.
006
0.00
6 -0
.005
0.
006
0.00
4 0.
006
Age
squa
re
0.00
0 0.
000
0.00
0 0.
000
0.00
0 0.
000
0.00
0 0.
000
Educ
atio
n: R
efer
ence
- Pr
imar
y M
ediu
m e
duca
tion
-0.0
41
0.02
0 *
-0.0
24
0.01
8 -0
.048
0.
018
***
-0.0
17
0.01
9 *
Hig
h ed
ucat
ion
-0.0
51
0.02
4 *
-0.0
70
0.02
2 **
* -0
.059
0.
017
***
-0.0
35
0.01
6 **
47
Fran
ce
Men
1st q
uarti
le
2nd
quar
tile
3rd
quar
tile
4th
quar
tile
A
PE
s.e.
APE
s.e
. A
PE
s.e.
APE
s.e
.
Lag
Dis
abili
ty
0.36
7 0.
010
***
0.30
7 0.
011
***
0.28
8 0.
010
***
0.26
1 0.
011
***
Age
0.
012
0.00
7 0.
005
0.00
6 0.
010
0.00
5 0.
004
0.00
5 A
ge sq
uare
0.
000
0.00
0 0.
000
0.00
0 0.
000
0.00
0 0.
000
0.00
0 Ed
ucat
ion:
Ref
eren
ce -
Prim
ary
Med
ium
edu
catio
n -0
.050
0.
015
-0.0
13
0.01
4 -0
.010
0.
015
-0.0
30
0.01
5 *
Hig
h ed
ucat
ion
-0.0
74
0.02
5 -0
.056
0.
022
* -0
.037
0.
018
**
-0.0
24
0.01
5
Wom
en
1st q
uarti
le
2nd
quar
tile
3rd
quar
tile
4th
quar
tile
A
PE
s.e.
APE
s.e
. A
PE
s.e.
APE
s.e
.
Lag
Dis
abili
ty
0.35
6 0.
010
***
0.31
5 0.
010
***
0.28
8 0.
011
***
0.28
4 0.
011
***
Age
0.
007
0.00
6 0.
003
0.00
6 0.
011
0.00
5 *
0.00
6 0.
005
Age
squa
re
0.00
0 0.
000
0.00
0 0.
000
0.00
0 0.
000
0.00
0 0.
000
Educ
atio
n: R
efer
ence
- Pr
imar
y M
ediu
m e
duca
tion
-0.0
43
0.01
4 **
* -0
.015
0.
014
-0.0
16
0.01
4 -0
.037
0.
016
**
Hig
h ed
ucat
ion
-0.0
71
0.02
4 **
* -0
.016
0.
020
-0.0
41
0.01
7 *
-0.0
27
0.01
5 **
48
UK
Men
1s
t qua
rtile
2n
d qu
artil
e 3r
d qu
artil
e 4t
h qu
artil
e
APE
s.e
. A
PE
s.e.
APE
s.e
. A
PE
s.e.
Lag
Dis
abili
ty
0.41
0 0.
011
***
0.32
2 0.
016
***
0.26
5 0.
018
***
0.23
5 0.
020
***
Age
-0
.004
0.
010
-0.0
02
0.00
7 0.
007
0.00
9 0.
014
0.01
0 A
ge sq
uare
0.
000
0.00
0 0.
000
0.00
0 0.
000
0.00
0 0.
000
0.00
0 Ed
ucat
ion:
Ref
eren
ce -
Prim
ary
Med
ium
edu
catio
n 0.
027
0.02
4 -0
.017
0.
023
-0.0
39
0.02
4 0.
022
0.02
9 H
igh
educ
atio
n -0
.040
0.
029
-0.0
41
0.02
6 -0
.041
0.
024
**
0.00
3 0.
028
**
Wom
en
1st q
uarti
le
2nd
quar
tile
3rd
quar
tile
4th
quar
tile
A
PE
s.e.
APE
s.e
. A
PE
s.e.
APE
s.e
. La
g D
isab
ility
0.
402
0.01
1 **
* 0.
366
0.01
4 **
* 0.
264
0.01
6 **
* 0.
184
0.01
9 **
* A
ge
0.01
0 0.
009
-0.0
13
0.00
8 0.
000
0.00
8 -0
.001
0.
008
Age
squa
re
0.00
0 0.
000
0.00
0 0.
000
* 0.
000
0.00
0 0.
000
0.00
0 Ed
ucat
ion:
Ref
eren
ce -
Prim
ary
Med
ium
edu
catio
n -0
.043
0.
023
**
0.01
2 0.
025
* -0
.010
0.
027
0.02
0 0.
031
**
Hig
h ed
ucat
ion
-0.0
99
0.03
1 **
* -0
.013
0.
027
-0.0
04
0.02
7 0.
012
0.03
0 **
* Si
gnifi
cant
at t
he 1
0% le
vel;
** si
gnifi
cant
at t
he 5
% le
vel;
***
sign
ifica
nt a
t the
1%
leve
l. So
urce
: our
ela
bora
tions
of E
U S
ILC
dat
a
49
Tabl
e A
4. E
ndog
enei
ty te
st: c
orre
latio
n be
twee
n er
ror t
erm
s of e
mpl
oym
ent a
nd d
isab
ility
equ
atio
ns
D
isab
ility
O
HM
Dis
abili
ty
Para
met
er
s.e.
Para
met
er
s.e.
ITA
LY
Mal
e -0
.042
0.
166
-0.1
84
0.12
6
Fem
ale
-0.2
65
0.20
3 -0
.013
0.
125
SPA
IN
Mal
e -0
.418
0.
233
0.04
3 0.
191
Fem
ale
0.21
1 0.
197
0.14
8 0.
216
FRA
NC
E M
ale
0.28
4 0.
164
0.11
8 0.
167
Fem
ale
-0.3
99
0.10
9 **
* 0.
289
0.16
3 *
UK
M
ale
-0.1
56
0.31
8 0.
199
0.28
5
Fem
ale
0.46
7 0.
160
**
0.18
5 0.
241
* Si
gnifi
cant
at t
he 1
0% le
vel;
** si
gnifi
cant
at t
he 5
% le
vel;
***
sign
ifica
nt a
t the
1%
leve
l. So
urce
: our
ela
bora
tions
of E
U S
ILC
dat
a
50
Printed by Gi&Gi srl - Triuggio (MB)
May 2015
DIPARTIMENTO DI SCIENZE ECONOMICHE E SOCIALI
Direct and indirect effects of disabilityon employment probabilities:
a comparative analysis
Chiara MussidaDario Sciulli
Quaderno n. 107/maggio 2015
COP Mussida-Sciulli_107_2015.qxd:_ 14/05/15 14:13 Page 1