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The Impact of Handicaps on Employment: A Comparison Between Public and Private Sectors

Abstract

This study analyses the causal impact of long-term handicaps relative to short-term handicaps on

employment status in the short and medium run (1-5 years), by taking into account the effects of

activity sector (public or private), gender and handicap origin (accidents or diseases). We use the

Health and Professional Career survey (Santé et Itinéraires Professionnels - SIP), conducted in France

in 2006-2007, in order to evaluate the effect of long-term handicaps on individual’s career. In this

perspective, we build an individual/year panel and used the difference-in-differences with exact

matching method by distinguishing according to the sector of activity (public or private). We proceed

with matching including lagged effects of activity so as to control for the endogeneity between

employment and handicaps’ onset. Men and women are not affected in the same way by short-term

handicaps, men’s jobs being more threatened that those of women. Moreover, long-term handicaps

seem to have a greater effect on the transition towards both inactivity and long-term

unemployment, notably for low-skilled workers. Handicaps (linked to illness) thus drastically affect

careers. Although short-term handicaps more strongly affect low-skilled workers’ employment,

permanent handicaps seem more detrimental to the employment of the high-skilled workers. But,

above all, the public sector appears to completely protect individuals from losing their jobs in case of

short-term handicaps and greatly reduces the likelihood of losing their job following a long-term

handicap.

Résumé

Cet article étudie l’impact du handicap durable relativement au handicap transitoire sur la situation

d’emploi à court et moyen terme (1 à 5 ans), en tenant compte du secteur d’appartenance (public ou

privé), du genre et de l’origine du handicap. Nous utilisons l’enquête Santé et Itinéraires

Professionnels (Sip), conduite en France en 2006-2007, afin d’évaluer l’effet d’un handicap durable

sur la trajectoire professionnelle des individus. Dans cette optique, nous construisons un panel

individu/année et utilisons la méthode des doubles différences avec appariement exact en

distinguant selon le secteur d’activité (public ou privé). Nous effectuons également dans l’analyse un

appariement avec effet retard de l’activité afin de contrôler de l’endogénéité entre situation

d’emploi et handicap. Les hommes et les femmes ne sont pas affectés de manière semblable par un

handicap transitoire, l’emploi des hommes étant plus menacé que celui des femmes. Par ailleurs, le

handicap durable semble avoir un effet encore plus important sur le passage à l’inactivité et sur

l’accroissement du chômage de longue durée, notamment pour les travailleurs avec un faible niveau

de formation. Le handicap affecte donc de manière drastique la trajectoire professionnelle en

particulier quand il est du à une maladie. Si le handicap transitoire affecte plus fortement l’emploi

des travailleurs moins qualifiés, le handicap permanent semble lui davantage fragiliser l’emploi des

plus qualifiés. Mais surtout, le secteur public semble protéger totalement les individus contre la

perte d’emploi dans le cas d’un handicap transitoire et fortement diminuer la probabilité de perdre

son emploi suite à l’apparition d’un handicap durable.

Keywords: difference in differences, exact matching handicap, employment

JEL codes: C33, C52, I10, J20, J31

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I Introduction

Tackling handicap issue in the whole public policies is a principle reaffirmed in the circular of

4 September 20121. This principle is based on the United Nations’ Convention on the Rights

of Persons with Disabilities (UNCRPD) ratified by France on 18 February 2010 and on Law

2005-102 of 11 February 2005 for equal rights and opportunities, the participation and

citizenship for the disabled that completes Law 87-517 of 10 July 1987 on behalf of

employment of people with disabilities2, notably by converging the public and private

sectors3. Concretely, all bills must now include specific dispositions for persons with

handicaps, notably concerning occupational integration, accessibility and educational

integration.

Despite the harmonization of rules concerning the compulsory employment of disabled

workers measures (in French, obligation d’emploi des travailleurs handicapés - OETH)

between the private and public sectors as per the 2005 Handicaps Act, inequalities in

treatment between sectors persist. Indeed, when confronted with a handicap and

depending upon the nature and the origin thereof, employees in the public and private

sectors do not benefit from the same mechanisms for employment security and professional

career in the short and mid-term. Civil service, notably, presents certain specificities

(guaranteed employment, compulsory reclassification, long-term sick leave…) suggesting a

higher percentage of job retention than in the private sector. A differentiated effect of

handicaps on public sector agents exiting employment (notably from employment to

unemployment) as compared to employees in the private sector is also anticipated.

Nonetheless, coercive measures involving financial sanctions for companies not respecting

the OETH measures have also been implemented in the private sector and are expected to

protect the jobs of disabled people. The latter are thus just as much incentives for recruiting

or maintaining the employment of people with handicaps. Historically, structural differences

in the institutional framework between the public sector and the private sector concerning

the integration of disabled workers into the labour market could result in an asymmetry of

professional itineraries according to the type of employer, even if these discrepancies should

recede due to the relative convergence of the legal framework.

Moreover, although economic studies concerning the relationship between handicaps and

employment exists in some countries [CAI, 2009; LECHNER and VAZQUEZ-ALVAREZ, 2011; JONES,

2011], they do not take into account the specificities of the public sector and the private one

susceptible of altering this relationship. For this reason, we deemed it particularly relevant

1 Circular of 4 September 2012 on the integration of handicaps in legislative proposals (JORF n°0206 of 5 September 2012,

p. 14345). 2 Law 87-517 of 10 July 1987 made it compulsory for all employers (public and private) having over 20 paid workers,

assessed as full time equivalent workers, to employ disabled workers (OETH) representing at least 6% of the total

employees, under penalty of financial sanctions (with the exception of the public sector). Hiring disabled workers is

calculated according to the actual duration of their employment in the company over the calendar year regardless of the

duration of their job contract. 3 A specific fund was created for the integration of disabled persons in the civil service (Fonds pour l'insertion des personnes

handicapées dans la fonction publique, FIPHFP), while the Fund Management Organisation for the Professional Integration

of People with Disabilities (Association de gestion du fonds pour l’insertion professionnelle des personnes handicapées, AGEFIPH) handles the fund for the private sector employees. Like private employers, public employers are obliged to

contribute to the FIPHFP if they do not meet the legal quotas of 6% employment. Moreover, a hike in the contribution for

employers not respecting the OETH for four consecutive years was passed by the Law of 1 December 2008, which was

implemented on 1 January 2009 (1,500 times the hourly SMIC or minimum wage per missing unit, regardless of the size of

the company).

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to implement a study aiming at evaluating the impact of the occurrence of a handicap on

professional careers in a dynamic causal dimension.

This question calls for a clear definition of handicap. Two definitions could be used. In one

narrow approach focused on the individual (medical model), handicap is understood through

one’s physical and psychological deficiencies, while in a wider approach focused on the

interaction between individuals and their physical and institutional environment (social

model), handicap is understood more “positively” through the whole obstacles stemming

from social organisation to which the individual is confronted [RAVAUD et al., 2002]. We

adopt the first definition by considering handicaps mostly due to accidents and illnesses.

This study thus aims at measuring the impact of the occurrence of a handicap (transitory, i.e.

one year or less or long-term, i.e. more than one year) on professional careers in the short

and mid-terms according to sector of activity (public vs. private), all other things being equal.

It is important to distinguish between transitory and long-term handicap because it refers to

different causal hypotheses. Transitory handicaps can be regarded as health shocks with

immediate, acute effects but that will dim over time. On the contrary, long-term handicaps

can have more constant or even irreversible effects over time. We can thus assume that the

institutional or professional response to a permanent handicap will be different in terms of

workforce management (adapting work stations, replacement, reclassification…).

The data used is from the Health and Professional Career survey (Santé et itinéraire professionnel - SIP) carried out in 2006, which enables to build an individual/year panel

specifying, for each period, the professional and health career of people while taking

individual and temporal heterogeneity into account. Thanks to a retrospective calendar and

the rich data, it is possible to identify the date of handicap onset and its length, to know the

accurate type of employment (with notably the possibility of distinguishing public and

private sector employment) and to examine the evolution of the career following this

health-related event by rigorously constructing a counterfactual.

To do this, we use difference-in-differences method with exact matching for comparing the

professional trajectories of the population suffering from a long-term (or transitory)

handicap to that never having reported a health problem. This method enables removing the

effects of the different sources of heterogeneity, both observable and unobservable (at

individual and temporal level). Consequently, this methodology is particularly appropriate to

identify the independent causal effect of the occurrence of a handicap on people’s

professional career [MITRA, 2009; VALL-CASTELLO, 2012; STAUBLI, 2011; MARIE AND VALL CASTELLO,

2012; CAMPOLIETI and RIDDELL, 2012]. Indeed, notable differences exist, in terms of

distribution by gender, age, level of education and type of living conditions in childhood,

between the sample of individuals who experienced handicaps and those of individuals who

have had no health problems. Yet, these variables influence both the probability of coping

with a handicap and the employment situation. In addition, some unobservable variables

such as the parents’ health status [TRANNOY et al., 2010] can also bias the relationship

between handicap and professional trajectory. Finally, the temporal horizon can also disturb

the causal relationship between handicap and employment, on the one hand, because the

probability of experiencing a handicap and the compensatory mechanisms can vary over

time and on the other hand, because performances on labour market are highly dependent

upon the economic context.

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This study is organized as follows. Firstly, we insert our work into the existing literature by

highlighting its two main contributions (section 2). Then, we present the data used as well as

the methodology retained to deal with it (sections 3 and 4). Then, we expose our main

results (section 5) before concluding by proposing extensions (section 6).

II Handicaps and Professional Careers: A Brief Survey

Our research differs from the existing French and international literature for two main

reasons:

i) The comprehensiveness of the database enables a more accurate analysis of the

concept of handicap, notably because it makes possible to distinguish between

transitory and permanent handicaps, as well as the interactions between handicap

and professional trajectories understood throughout the career of individuals. As a

consequence, dynamic causal phenomena can be highlighted, which is novel in

France to our knowledge.

ii) The public/private distinction has never been the object of an advanced empirical

analysis in works studying handicap’s impact on professional trajectories.

II.1 A lack of French studies

In this field, International studies can be put into three categories: the treatment of

endogeneity biases, the efficacy of legislative and fiscal measures relative to handicaps and

the impact of handicaps on the professional trajectory.

A first series of studies tackles the issue of endogeneity between self-reported disability

measures and labour force participation [DWYER and MITCHELL, 1999; LINDEBOOM and KERKHOFS,

2002; BENITEZ-SILVA et al., 2004; CAI, 2009; GANNON, 2009; AKASHI-RONQUEST et al., 2011]. In

particular, a very large number of articles in this literature deal with the potential existence

of a justification hypothesis according to which self-reported disability would rely on

people’s labour market situation. More precisely, people would tend to justify their labour

force non-participation by over reporting a handicap (most frequently defined as a long-term

health event that limits the type and quantity of work that people can perform). Although

some studies such as that of LINDEBOOM and KERKHOFS [2002] and more recently GANNON

[2009] show, respectively on Dutch and Irish panel data, that the economic incentives tied to

the people’s labour market status urge people to misreport their disability status (even

when controlling for unobserved heterogeneity as in GANNON, 2009), other studies conclude

the opposite result. This is notably the case of CAI [2009] who was not able to demonstrate

this justification hypothesis using Australian panel data: indeed, he found that the

endogeneity between both variables is not statistically significant for men and that, although

the endogeneity is statistically significant for women, the sign of the coefficient does not

corroborate the expected effect.

More generally, DWYER AND MITCHELL [1999], BENITEZ-SILVA et al. [2004] AND AKASHI-RONQUEST et al. [2011] were interested in the problems posed by the use of self-reported disability

measures as explanatory variables for labour market non-participation (being understood

principally by the fact that people receive disability insurance benefits), i.e. both

endogeneity bias like the aforementioned justification hypothesis and also measurement

errors, notably sampling or non-response errors. DWYER AND MITCHELL [1999], through

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American data, concluded to an absence of a justification hypothesis, and measurement

errors disturbing the relationship between retiring and self-reported disability measures.

However, AKASHI-RONQUEST et al. [2011] demonstrated, also using American data (aggregated

on the county level4) that the relationship between the share of individuals self-reporting

disability and the labour force non-participation is mainly biased by a sampling error and not

an endogeneity problem. As for BENITEZ-SILVA et al. [2004], they identified, still using

American data, that reporting a handicap to the Social Security administration in view of

receiving benefits was not systematically biased upwards as compared to a report made

within the framework of an anonymous questionnaire, thus a priori without any specific

stakes.

A second category of studies deals with the efficacy of disability insurance benefits programs

(in term of eligibility criteria and generosity) or programs maintaining disabled persons’

employment [MITRA, 2009; STAUBLI, 2011; MARIE AND VALL CASTELLO, 2012; CAMPOLIETI AND

RIDDELL, 2012].

VALL-CASTELLO [2012] evaluated the effects of the policy promoting employment among

disabled women in Spain. The author studies the impact of reducing social security

contributions for employers who hired disabled women. The study uses a difference-in-

differences method with a recursive bivariate probit model to evaluate the existence of

changes in trends for the employment of females in relation to that of males due to the

policy, conditional on any pre-existing trends. It shows a significant effect of the deductions

of the Social Security employer contributions and estimates an average elasticity of

employment with respect to those deductions of 0.08 for women with long-term disabilities.

Other studies have also used the difference-in-differences method to examine the influence

of the disability insurance programs on individuals’ professional careers. CAMPOLIETI AND

RIDDELL [2012] analyses, through Canadian data, the impact of two public disability insurance

policies on the probability of employment for disability beneficiaries (but also on the

probability of entering or leaving the disability rolls) with moral hazard logic. They identify a

significant positive effect of the policy consisting in introducing a minimum earning

threshold, below which individuals can concurrently receive disability benefits, on labour

market participation for insured people with a long-term (i.e. lasting at least one year) and

severe disability. They also observed that the effect is stronger for women than for men.

Along the same lines, STAUBLI [2011] studies through, in Austrian case, the effect of

tightening up the eligibility criteria for disability insurance program on return to work and

disability enrolment and identified a statistically significant positive effect for this type of

measure on labour market participation (in the private sector) for disabled men aged 55 and

over (an increase of 1.6 to 3.4 points). The method is based on a study of differences over

time and according to a gender-age mix. MARIE AND VALL CASTELLO [2012] looks into the impact

of establishing a more generous disability insurance program on the rate of labour market

participation for claimants suffering from partial disability (who can combine disability

benefits and job income) for individuals aged 55 and over (the only ones eligible for such

benefits increase). Using Spanish data, the authors find that a 36% increase in the amount of

disability pensions reduces the probability of being employed by 8%. Finally, MITRA [2009]

demonstrate, through South African data, that relaxing disability screening of potential

beneficiaries increases the rate of labour market non-participation by 8.6% (in the broad

sense i.e. including “discouraged workers”) but only for men between 45 and 64 years-old.

4 Equivalent to departmental data in France.

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The third category of studies, to which the present study belongs, has to do with the impact

of disability on labour market outcomes including professional careers. JONES [2011]

estimates the influence of different characteristics of the disability (type, origin, duration,

severity) on the probability of being employed and on labour market earnings, using an ad hoc module of the 2002 UK Labour Force Survey. The study only includes people reporting

disabilities and estimates are made separately for men and women. On the methodological

level, selection effects due to only considering employees experiencing disabilities are taken

into account. The results reveal a positive influence of disability at birth on employment, but

only for men, as well as a positive influence of disability resulting from a traffic accident for

both men and women. On the other hand, mental illness negatively affects employment

both for men and women. The characteristics of the disability have far less influence on

earnings than it has on employment.

Furthermore, two other articles are based on a rather similar methodology, using matching

methods with temporal lag between the occurrence of the health shock leading to disability

and the evaluation of outcomes in terms of vocational integration.

LECHNER AND VAZQUEZ-ALVAREZ [2011] estimates the effect of becoming disabled (officially

recognized) on labour market outcomes: employment, unemployment, exiting the labour

market, net annual earnings and per capita household disposable income, and average

weekly hours worked. They use German panel data from 1984 to 2002 that they cut into

sequences of four successive years and implement propensity score matching. The

individuals belonging to the control group, the so-called “untreated” are individuals not

officially recognised as disabled over the four-year sequence while the “treated” are

individuals officially recognized as disabled between the first and second years of the

sequence, the authors insisting on the objectivity of the disability measure. The matching

was carried out on observable characteristics over the first year and matching variables

correspond to the characteristics susceptible of influencing the occurrence of a disability.

They then compare the different outcomes (unemployment, employment…) between the

treated and the untreated in t2, t3 and t4 and obtain the following results: disability does not

lead to a reduction in earnings nor in hours worked, at least during the three years following

the occurrence of a disability, when the disabled person remains employed; becoming

disabled reduces the probability of being employed by 9% and approximately 13% for those

with a high degree of disability three years after its onset. Nonetheless, this reduction in the

probability of being employed is not accompanied by an increase in the probability of being

unemployed, which could be interpreted in terms of “voluntary” employment exit.

More than disability in the strict sense of the term, GARCIA-GOMEZ [2011] studied the impact

of a health shock on labour market outcomes in nine European countries on the basis of the

European Community Household Panel. The author notably uses the propensity score

method with two consecutive health state measures enabling to estimate the occurrence of

a health shock (the perceived health deterioration and illness prevalence) and targets test

and control groups depending on the sequence during which the health shock occurred. He

thus studies the health shock over three years, considering in t1 a person in good perceived

health and in employment in t1 and t2. The treated group is that which is in poor health in t2

and t3, which implies that the shock lasts at least until t3 while individuals in this group were

in good health in t1. The control group is in good perceived health in t1, t2 and t3. The results,

obtained by applying matching methods, suggest that health shocks have a significant causal

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effect on the probability of employment: persons suffering from a health shock are much

more susceptible of leaving their job and transiting through disability.

Moreover, in France, DUGUET AND LE CLAINCHE [2012 a, b] developed a series of studies on the

relation between chronic illnesses and employment, some of which were done using the SIP

survey that shows a negative causal effect of a health shock on the probability of being

employed, in particular for low-skilled workers. Exploiting the 2006 survey wave and by

applying a differences-in-difference with exact matching method, they show that non-

professional health events penalise quite more low-skilled people. The latter, already

weakened on the labour market and transit more frequently from employment to

unemployment, while individuals possessing a secondary or higher level of education remain

employed more often.

The analysis that we are implementing follows this series of studies by explicitly targeting

handicaps, due to chronic illnesses or accidents, and by taking into account the sectoral

differences (public/private), which has been seldom studied in France and abroad. We also

tackle endogeneity bias related to the impact of being employed on handicap by using

lagged variables regarding labour market performances before handicap occurrence.

II.2 Measuring Handicap

Two approaches of handicap are traditionally distinguished: the medical model which is an

individualistic approach focused on the disabled person, its deficiencies and impairments,

and the obstacles derived from its handicap; the social model which is an “interactionist”

approach where the handicap is understood only by considering the environment the

disabled person faces (perception of their colleagues and immediate entourage, facility of

access to public places, ergonomics of the work station…), and where the accent is put on

the adaptation of the environment to the handicap and to the needs of people suffering

from handicaps. This second approach is more comprehensive but is also more difficult to

measure.

Apart from the perimeter retained (medical versus social model), and as with every health

indicators, the data collection mode that enables handicap measure is questionable.

Schematically, the handicap can be caught in two ways: objectively through administratively

recognised or diagnosed handicap or subjectively through self-reported measures in surveys.

In the first case, social benefits paid by the National Healthcare Insurance (for in accordance

to handicaps (or to partial or permanent impairment) or work condition adjustments

granted by employers5 enable estimating a more objective measure of the handicap [LECHNER

AND VAZQUEZ-ALVAREZ, 2011]. The nature of benefits paid by the National Healthcare Insurance

confers de facto a transitory or permanent nature on the handicap [VALL-CASTELLO, 2012]. In

the studies relative to work conditions adjustment, the measure of handicap is also made

objective either by a clinical evaluation or by an administrative recognition directly recorded

in the survey (for instance, CAMPOLIETTI [2004], with a study using data from the Survey of

Ontario Workers with Permanent Impairments).

In the second case, the reported handicap is often caught in a rather crude way through a

question referring to a disability over a year old that limits the number of hours worked

5 Under the title of laws on the accommodation of work conditions for disabled persons, once one (or more impairments) is

recognized.

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and/or financial resources [BENITEZ-SILVA ET AL., 2004; JONES, 2006; JONES, LATREILLE AND SLOANE,

2006; JONES AND SLOAN, 2010].

As far as we are concerned, we choose to adopt the medical model and measure handicap

through questions identifying diseases and accidents as the main origins of handicap, but

without precisely distinguishing between the different kinds of deficiencies.

II.3. A Public/Private Distinction Never Before Considered

On this second point, the comparison between sectors seemed all the more pertinent to us

as it falls within the scope of job access conditions for disabled persons and arrangements

implemented to protect disabled employees.

Although the employment rate for disabled persons is much lower than that of the general

population (35 % for people with administrative recognition of their handicap as compared

to 65 % for people between 15-64 years-old in 20076), we also notice differences according

to the sector and the associated type of human resources management. Disabled employees

are, first of all, more numerous in the public sector than in the private one. In France, the

share of disabled people in the public sector was 4.4 % in 2011 with discrepancies according

to the different categories of civil service (3.3 % in Civil Service of the State including public

French National Healthcare Insurance, consular organizations, and the French Post Office;

5.1 % in civil service in the hospital sector and 5.3 % in local government civil service) while

in the private sector the rate is 2.8%, with a higher rate in industry – 3.5% - than in the

tertiary service – 2.4% [AMROUS, 2012].

Job Access to handicapped people is regulated by specific measures applicable to the private

sector subsequent to the law of 1987, which became effective on 1 January 1988. Those

companies not respecting the OETH measures must pay an annual contribution to the Fund

Management Organisation for the Professional Integration of People with Disabilities

(association nationale de gestion du fonds pour l'insertion professionnelle des personnes handicapées, AGEFIPH), the amount of which increases with the size of the company and the

number of missing disabled employees7. This principle is extended to public employees by

the law of 2005, which became effective in 2006. This same law then reinforced the legal

framework favouring the professional integration of disabled workers by compelling public

and private employers to proceed with “reasonable”8 workplace accommodations

(transformation of the work station, adjusting hours), based on the recommendations of the

workplace health service, so as to enable disabled workers to access to employment or keep

their job. Moreover, it must be noted that, until 2005, recruiting disabled workers in the civil

service could be done through reserved jobs; this was abandoned on 1 January 2006 (except

for professional soldiers) so as to enable persons suffering from a handicap to access to all

civil service corps. We can add that some public employers are excluded from the OETH. All

these facts could question the relatively more protective side of the civil service with regard

6 Nonetheless, there is no difference in the rate of employment between the general population and disabled persons

taken in a broader sense i.e., “persons having administrative recognition or reporting a health problem lasting over 6

months and having great difficulty traveling or having had one or several accidents over the past year” (Source: DARES,

supplement to the 2007 Employment Survey). 7 A penalty of 400 times the hourly minimum wage (SMIC) for companies with 20 to 199 employees, 500 times the SMIC for

companies with 200 to 749 employees and 600 times the SMIC for companies with over 750 employees. 8 The reasonable character of workplace accommodations is evaluated regarding the potential aids employers can benefit

from the AGEFIPH and the FIPHFP, respectively, in order to carry out these accommodations.

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to disabled workers. There are currently two paths that disabled workers can follow to

integrate into the civil service: through the competitive exam process with the possibility of

accommodating the exams (note that the age limits do not apply to disabled workers) or by

having administrative recognition of a handicap that allows people to apply for all of the

administrations via a specific contractual agreement. Finally, subsidized contracts are

another way to favour the professional integration of disabled workers, notably the two

categories of Single Integration Contracts (contrat unique d’insertion, CUI) – Employment

Initiative Contracts (contrats initiative emploi, CUI-CIE) for employers in the market sector

and Employment Support Contract (contrats d’accompagnement dans l’emploi, CUI-CAE) for

employers in the non-market sector – which replace previous subsidised contracts since 1

January 2010 (“contrats d’avenir”, Employment Support Contract - contrat d'accompagnement dans l'emploi…).

When employees are affected by a handicap, in theory, the civil service protects them more

against the risk of losing their job (except perhaps for contractual employees who tend to be

more and more numerous in the civil service). Moreover, there exists an obligation for the

reclassification of workers whose health status, beyond handicap, does not allow them to

maintain their current positions. In the private sector, this results in a change of job position

or a transfer; in the civil service, it means proceeding with an assignment to another position

corresponding to their grade, then by a reclassification to another professional corps via a

provisional assignment. In contrast, civil servants can benefit more from early retirement

(notably women) than employees in the private sector, which could incite them to leave

their employment more precociously. Finally, in case of incapacity to exercise an activity and

when reclassification is impossible, people are put into early retirement due to handicap (at

full rate, subject to having a sufficient length of contribution), regardless of the sector, public

and private. Moreover, we can consider that the government employees with handicaps are

less likely to be evicted from their job by their employer. Indeed, besides the fact that civil

servants have job security (and that contractual persons with a handicap are most often

given tenure after a year), the civil service’s duty to conduct themselves in an exemplary

manner should lead the latter to take handicaps into greater consideration in the

management of their agents’ careers.

This public-private distinction regarding the professional integration of disabled workers is

supported by a certain number of studies. Within the framework of American studies,

BALDWIN et al. [1994] thus demonstrates that disabled women working in the public sector

are less penalized regarding their wages than those working in the private one and as

compared to non-disabled women of reference. DUGUET AND LE CLAINCHE [2012c] show that

women who benefited from the accommodation of work conditions subsequent to

developing cancer are more often employed in the public sector. According to COUTROT AND

ROUXEL [2011], seniors in the public sector are, on average, less often exposed to recurring

physical pain at work (working at night, repetitive work, physically demanding work,

exposure to toxic products) than seniors in the private sector: they are thus fewer among

the unemployed benefiting from administrative recognition of a health problem9 (10% of

seniors in the private sector as compared to 4% of seniors in the public sector). But, on the

contrary, they are more numerous among inactive people in general, notably the retired,

9 Persons inactive at the time of the survey who declared receiving disability benefits, an allowance or a capital for

workplace accidents or occupational illnesses, an allowance for disabled adults (allocation pour adultes handicapés, AAH), a

compensatory allowance for a third person or having benefited from a job for disabled workers or possessing a handicap

card.

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due to the more frequent use of early retirement measures in the civil service. Finally,

according to BAHU, MERMILLIOD AND VOLKOFF [2011], risk accumulation (i.e. having been

exposed to one of the four physical pains listed above for at least 15 years) concerns

employees in the private sector more than those in the public one.

This quick overview of differences in treatment between employees in the public and private

sectors does not, in theory, allow us to conclude concerning the sense of the relationship

between on one hand handicap and functional limitations tied to physical pain at work and

on the other hand labour market participation. Indeed, although certain factors (e.g. a

greater possibility of early retirement) theoretically imply for employees in the public sector

early exits from the labour market, towards inactivity, other elements (e.g. less exposure to

physical pain, greater possibility of reclassification…) plead on the contrary in favour of a

later exit.

III. Material and econometric strategy

III.1. Database

The Health and Professional Itinerary Survey was designed within the framework of a

partnership between the Drees and the Dares, with scientific support from the Center for

Employment Studies (Cee). The implementation thereof was carried out by the National

Institute of Statistics and Economic Studies (Insee). The first wave, in 2006, retrospectively

questioned 14,000 persons aged between 20 and 74 and living in ordinary households in

France on their life paths (family, professional, and health status) and provided a detailed

description of these different dimensions at the time of the survey. The SIP Survey mainly

aimed for two objectives:

1. to better understand health determinants, by defining health status in regard with

employment status and career path;

2. to measure the incidence of health status in the large sense on people’s career paths,

career risks and potential discriminations they may know.

We will now present more precisely the variables used in the analysis of the effect of

handicap on professional trajectories (by distinguishing long-term handicaps from transitory

ones).

III.2. Variables

III.2.1. Treatment Variables: Long-Term and Transitory Handicap

A series of questions enabled individuals to self-report a handicap and its potential link with

changes in their professional situation. These reports can be checked through the onset and

end date of the handicap (these questions generally do not exist in declaring surveys).

Moreover it is possible to obtain the origin of the handicap (illness, accident,...). Finally,

administrative or official10

recognition enables us to make the reported measure of the

handicap more objective, particularly if it is permanent.

10

Administrative recognition of a handicap (notably though the Recognition of the Quality of handicapped Workers

(Reconnaissance de la Qualité de Travailleur Handicapé, RQTH) is underestimated in relation to the needs of handicapped

persons insomuch as, for fear of being discriminated against, not all eligible individuals request it [LE CLAINCHE, 2006].

11

Our first basic sample consisted of 1,777 handicapped individuals having finished their initial

education. The SIP Survey enquired as to the origin of the handicap, which, through a

meticulous study of the clearly formulated responses, made possible to correct the raw data

encoded as “other”. We finally identify the causes of the disability for 98 % of the sample:

accidents explain the origin of 41 % of handicaps and a third of them are due to an illness or

health problem (see table 1 in appendix).

The estimated density of the first disability more closely identifies the most likely ages for

the occurrence of a handicap: handicap of birth, handicap due to accident at around 18

years, handicap caused by first maternity or again at 50 years (see figure 1 in appendix). We

can differentiate handicap and its consequences by using the age at the first handicap (see

table 2 in appendix). In our sample, 23 % of handicapped people experienced their first

handicap before 11 years-old. For the needs of our study, we chose to limit our sample by

excluding handicap occurring before 18 years old. Indeed, lifestyle choices, notably regarding

education investment and professional path, are made very early in the lifecycle for people

born with a handicap. We also remove handicaps beginning in 2006 because we cannot

estimate their impact on labour market status within an adequate time horizon. Finally, we

thus keep 1,008 handicapped individuals in our sample.

Then, the handicap measures used in our study are defined through the dates reported for

the beginning and the end of a handicap (the absence of an end date informs us as to the

permanent nature of the handicap). In order to define long-term handicap, we referred to

publications as well as our data. First, the publications show a highly probable permanence

beyond one year [GALARNEAU AND RADULESCU, 200911

], and a number of econometric studies

are based on using data where handicaps are understood through a health problem or a

disability that has affected an individual’s work for at least one year [BENITEZ-SILVA et al., 2004; DRYDAKIS, 2010, 2012; TENNANT, 2012; CAMPOLIETI AND RIDDELL, 2012

12]. Secondly, this one-

year reference is coherent with our data. Statistically, we estimated an absorbing state

Weibull model to analyse the duration of a handicap from the date at which any reversing of

the shock seems improbable. We find that about 50% of men and women reported a

permanent handicap (see table 3 in appendix). These figures are also almost corresponding

to the share of people declaring a handicap whose duration is superior to one year. We thus

distinguish handicaps lasting under one year (called “long-term handicap” or “permanent

handicap”) from handicaps lasting over one year (called “short-term handicap” or “transitory

handicap”, which ultimately leads to split the sample of 1008 individuals in two parts: on

one side, the 507 having experienced or experiencing a transitory handicap, and on the

other side the 501 experiencing a permanent handicap.

Finally, we kept three populations, whose labour market outcomes are compared. Two

control groups are defined: individuals having suffered from a long-term or transitory

handicap and a test group composed of healthy population (with no health problems at all,

i.e. no handicap whatever their origin and no psychological or physical diseases). We

11

These two authors demonstrate that there are no significant statistical differences in terms of labour supply (number of

hours worked) between individuals having experienced a disability for one year and those not having experienced a

disability. This is no longer the case when the duration of the disability is superior to one year. 12

CAMPOLIETI and RIDDELL’s study [2012] is based on the definition of disability retained by the American Social Security

Disability Insurance Benefits plan (SSDI): “The SSDI is a contribution-based program that defines disability as the inability to

engage in any substantial gainful activity (SGA) by reason of a medically determinable physical or mental impairment that is

expected to last for a continual period of one year or more.” [CAMPOLIETI and RIDDELL, 2012].

12

distinguished these groups regarding their employment status and their belonging to the

public or private sector.

People having experienced or currently experiencing a transitory handicap (up to one year)

are more likely to be men aged approximately 50 years of age having attended secondary

school and having had a difficult childhood, while people suffering from a long-term

handicap (lasting an average of 13.6 years) are more likely to be slightly older women (53.5

years of age), also having attended secondary school and having suffered from difficulties in

childhood (see appendix 1, table 4).

III.2.2. Performance Variables: Professional Trajectory According To Sector

Our performance variables are employment, unemployment and inactivity indicators

summarizing the professional trajectories of individuals on the labour market. The

decomposition between public and private employment is justified by the differences in the

context of professional integration of handicapped people (see section 2.2 above). We can,

in fact, believe that the public sector protects its employees better than does the private

sector. The average of these indicators for an individual gives the proportion of years spent

in employment, in unemployment and in inactivity. We thus obtain the individual’s

professional trajectory. More precisely, we seek to measure how the rates of employment

unemployment and of inactivity (in the public and private sectors) for handicapped persons

vary as compared to healthy individuals with similar characteristics.

III.2.3. Matching Variables: Individual and Lagged

We measure individual observable heterogeneity using explanatory variables available in the

database. We thus made an exact match between each individual suffering from a transitory

or long-term handicap and those individuals never having experienced a handicap or, more

generally, health problems, and whose observable characteristics are identical. In order to

take into account the endogeneity bias linked to the impact of employment on a disability

we tested this impact by using a lagged performance variable on the labour market before

the occurrence of a disability. We used lagged public/private sector employment,

unemployment and inactivity variables.

We thus compared two groups of individuals whose characteristics are identical according to

gender, age, education level, having been raised by their parents, having encountered

problems in childhood or not (trauma, war, and violence at school or in their

neighbourhoods, hard living conditions), having had problems affecting a relative during

childhood (family conflict, death of a family member, a relative with serious health

problems, long separation from a family member) or not, but also characteristics describing

the professional career before the handicap’s onset. The variables relative to the living

conditions in childhood are considered in literature as possibly affecting health status in

adulthood [CASE, FERTIG, PAXSON, 2005; TRANNOY et al., 2010; DUGUET, LE CLAINCHE, 2012 a,b].

Nevertheless, an unobservable individual heterogeneity could persist due to missing

variables. In theory, this heterogeneity could be correlated with explanatory variables and

having experienced a health status event. The difference-in-differences method enables to

eliminate the effect of these individual unobservable.

IV Estimation Method

IV.1. Principles

13

We use the difference-in-differences estimator with matching in order to remove the effect

of different sources of heterogeneity, both observable and unobservable. The observable

heterogeneity effect is eliminated by proceeding with an exact matching of the individuals

on the observable variables. The unobservable individual and time effects are eliminated by

difference in differences before and after the date of the handicap. We expand the earlier

work of DUGUET and LE CLAINCHE [2012a] by allowing for the inclusion of lagged endogenous

variables in the list of the matching variables. This should neutralize the double causalities

between long-term handicap and professional paths, but also the selection effects related to

the initial choice of the public or private sectors.

We match individuals according to their biographical data and their past situation in the

labour market (employment in the public or private sectors, unemployment or inactivity one

year before the occurrence of a transitory or long-term handicap), so as to neutralize the

effect of their participation in the labour market on the occurrence of the handicap.

Therefore, we compare people with the same characteristics and same initial situation in the

labour market before and after the handicap event. The double differencing removes the

effects of correlated unobserved individual variables and correlated temporal effects.

Eliminating the (correlated) time effects is important in our case since, on the long run, there

is both a variation of the probability of handicap occurring according to the prevention

programs that exist at a given time, and a variation of medical progress that can affect the

return to work.

We chose to take the distinction between the public and private sectors into account in the

labour market performance indicators and not by dividing the sample in two mainly because

the individuals can, over the working life cycle, alternate between being employed in the

public and private sectors from one year to the next.

IV.2 Implementation

We estimate the effect of the handicap by exact matching of qualitative variables and by

nearest neighbour matching with a calliper for age. We impose a maximum age gap of three

years between two twins so as to avoid having the nearest neighbour been, in fact, very

distant.

The basic estimate matching variables are as follows:

- Gender (women, man);

- Level of education (primary, secondary or higher education);

- Having been raised by both parents (dichotomous);

- Difficult living conditions during childhood (dichotomous);

- Difficulties for family members during childhood (dichotomous);

- Age;:

- Lagged performance variable, taken in τ-1.

The matching variables, with the exception of age, do not vary over time. They are

determined before the occurrence of the handicap. The age is calculated at the same date

for all individuals (as of 31/12/2006). The performance variables are taken in τ-1 and in τ+d,

with 1 ≤ d ≤ 5. We thus calculate the performance difference before and after the

occurrence of the handicap: ∆y = yτ+d – yτ-1.

14

For the treated population, Ti ∈ (respectively for the untreated individuals, Nj ∈ ), this

difference is noted: Ti

Tdi

Ti yyy 1,, −+ −=∆ ττ (respectively

Nj

Ndj

Nj yyy 1,, −+ −=∆ ττ for the untreated

population).

The effect of the handicap is estimated by:

{ }3,,11 ≤−==

∆−∆= ∑ ∑

∈ ∈

∧

jijiiTi Aj

Nj

i

Ti

T

aaXXjAyN

yN

i

γ

where X designates the vector of the qualitative matching variables, a represents age,

( )TcardNT = and ( )ii AcardN = . The matching is made with replacement; therefore we

need to use the non parametric bootstrap method in order to estimate the standard error of

our estimator. We took 500 repetitions.

V Results

V.1 Grid of reading

In order to correctly interpret the results (see appendix 3), it is necessary to consider the

following elements. We only comment on estimations where the lagged effects of activity

are taken into account (see tables 6 to 11) by distinguishing, when the individual is

employed, whether they are working in the private or public sectors. The ex-ante selection

bias (before the handicap intervenes in τ) and the endogeneity essentially linked to reverse

causality (the impact of employment on the handicap)13

are thus controlled for. We assume

that the justification bias is not significant for our handicap measure insofar as it is a self

reported handicap unrelated administrative recognition.

Matching on the sector in τ-1 enables taking into account the ability of the sector to protect

an employee suffering from a handicap in τ. Both definitions of handicap are considered:

transitory handicap and long-term handicap. The first type of handicap generally

corresponds to relatively minor accidents and the second to serious accidents or possible

consequences from chronic illnesses. Handicaps due to illnesses or health problems

represent one-third of the total, those explained by accidents around 40% (see appendix 1,

table 1). We expect a stronger effect from handicaps lasting over one year. The first

proportion given in the tables represents the average value of the performance variable on

the labour market in τ-1 in the absence of treatment (i.e. the appearance of a handicap). It

represents the base value that will be modified by the occurrence of a handicap at date τ.

This value can be used to calculate relative gaps. Then we indicate the effects on the treated

population with different margins between the performance value in τ-1 and the values from

τ+1 to τ+5.

For instance, in table 6, the first line presents the probability of being employed one year

before the occurrence of the handicap (in τ-1) for employees having experienced a handicap

in τ (the treated). 88.0 % of employees having experienced a transitory handicap during the

year τ were employed one year before; 16.4 % of those employees worked in the public

sector and 71.6 % in the private sector. Between τ-1 and τ+1, the probability of being

13

In table 5, the lagged effects of activity are not taken into account. There is no matching on the labour market situation

before the handicap (τ-1). Only the constant individual variables are matched in the observed times, beginning with τ+1.

15

employed decreases by 6.6 percentage points (pp) among people experiencing transitory

handicap (i.e., treated).

V.2 Main Results

V.2.1. Transitory handicap: Estimate with Lagged Endogenous Variable

The effect of a short-term handicap is clearly negative, and does increase over time. One

year after the occurrence of the handicap, the overall employment rate is reduced by 6.6 pp

and, after five years, it goes down further, to 10.6 pp. The overall decrease in the

employment rate following a short-term handicap is explained by the private sector’s

belonging. The public sector seems to play its protective role (see appendix 3, table 6).

Moreover, it is probable that the 2005 Law, which promotes employment support of

handicapped persons, notably when workers have only a temporary work contract at the

time of handicap’s occurrence, is not really being applied in the private sector since the data

only covers up to 2006.

This decrease in employment can translate into a long period of unemployment or into

inactivity (see appendix 3, table 7). We find that a huge majority of workers suffering from a

transitory handicap passes through a period of inactivity: effects on treated increase from

4.7% to 7.9% (appendix 3, table 7). These results seem quite similar to those of LECHNER and

VAZQUEZ-ALVAREZ [2011], who show that suffering from a disability diminishes one’s chances

of being employed, without increasing the probability of being unemployed and increases

the probability of exiting to the labour market. Two stratifications of these overall results are

thus possible: by gender (appendix 3, table 8) and by level of education (appendix 3, table

10).

Decreasing in employment in the private sector primarily concerns men on all horizons and

at increasing levels and women in τ+1 and three years after the onset of the handicap.

Hence, differences in the public and private sectors are not so much explained by the

existence of employee protection against unemployment in the public sector, as they are by

other mechanisms or causes. One might mention differences in the nature and severity of

handicaps affecting men and women: men tend to have more mobility disabilities while

women have more sensory and psychic ones [BALDWIN et al., 1994]. Mobility disabilities

might be more difficult to deal with within the scope of a career in the private sector.

Moreover, functional limitations have a stronger impact on men’s jobs, as opposed to

women’s, particularly after the age of 50 [BARNAY, 2010]. By differentiating according to the

origin of transitory handicap, we can obtain a measure of inability to work after a handicap

(table 12). Comparing to handicap linked to an accident, when the handicap is caused by

illness, the return to work is more difficult including into public sector (decreasing in

employment is significantly high in medium-term)14

.

The gender effect is doubled with an education level effect (appendix 3, table 10).

Employees in the public sector have, on average, a higher diploma level than those in the

private sector; especially for women. In the case of health capital decreasing, we can assume

that a diploma would partially compensate for the handicap in terms of one’s professional

career. Thus we may note that the scope of the effect of a transitory handicap in the private

14

Lack of statistical power does not allow to cross origin of the handicap and gender.

16

sector depends on the level of education. This latter effect is very significant over five years

following the onset of a handicap, especially for those with the lowest levels of education

(with high and increasing coefficients), while only occasionally so for other employees (at

one year for an intermediate level of education and five years for a higher level). The double

“gender/qualification” effect is clearly observed in the private sector for underskilled men

for whom the impact of a transitory handicap is major and increases over time. In light of

these results, it is interesting to analyse the impact of a permanent handicap. Does the

public sector still play its protective role for employee facing a permanent handicap?

V.2.2. Long-term Handicaps: an Estimate with Lagged Endogenous Variables

Populations which experience a long-term handicap seem to be selected in τ-1 since the

level of employment of treated patients is lower than that of the transitory handicapped

people one year before the onset of the handicap (81% versus 88%, respectively; see

appendix 3 table 6). This difference also refers to a population which, on average, is 53.5

years of age, as opposed to 50 years of age for people who have experienced a transitory

handicap. The impact of a long-term handicap appears to be far greater (-20 pp at the first

year) and concerns both the private and public sectors. Its impact within the private sector is

very strong and increases over time. The public sector no longer plays a protective role,

however the negative effects of permanent handicap remain controlled in duration (with a

decline of employment comprised between -11.2 pp and -14.0 pp for significant years). It is

probable that measures taken in terms of reclassification and workplace accommodation are

responsible for limiting the fall in employment rates within the public sector.

One must look at paths of exit from labour market, which concern both unemployment

lasting over a year and, above all, inactivity that prevails with advancing age. This evolution

among those leaving employment refers also to situations of anticipated cessation of

professional activity which could benefit the handicapped. We can quote job seeking

exemptions for those over 55, disablement or disability pensions, or early retirements due to

exposure to dangers, such as early retirement for asbestos workers, early termination of

some salaried workers [BARNAY, op.cit]. The gender effects are increased and they contribute

to explain sectoral effects. In T-1, the differences in employment levels are high; 71.4 % for

women and 94 % for men who would suffer from a long-term handicap one year later. In the

private sector, the immediate impact in T+1 of a long-term handicap among women is very

strong but the relative decline in the employment level is less rapid for women than men (-

31.4 to -41.0 pp in T+5, as opposed to -15.4 to -32.4 pp in T+5, respectively). In the public

sector, only handicapped women see their employment level significantly and definitively

altered. Two types of hypotheses, which remain to be tested, can be put forward: the first is

linked to the nature of permanent handicap, the second potentially suggests a discrimination

effect one can infer, to wit the “glass ceiling” phenomenon which discourages them from

pursuing job. Other studies also show, all else things being equal, that women theoretically

take more sick leaves than men, which would account for their employment exit more

frequently and for longer periods [CHAUPAIN-GUILLOT S. AND GUILLOT O., 2007]. Programs

specific to the public sector generally seem to contain, on average, the deleterious and

widespread effects of long-term handicaps upon the employment of salaried workers.

However, when we stratify our data according to workers’ level of education, we can

observe over the five years following treatment a decrease of employment resulting from a

long-term handicap which is greater among non-graduated workers, and, on the contrary, is

17

mitigated for those with higher diplomas. Thus we can corroborate the results of DUGUET and

Le CLAINCHE [2012 a,b], which show that the negative impact of accidents and chronic illness

on the employment of less-educated individuals is greater compared to those with more

education, as well as JONES, LATREILLE AND SLOAN [2006], which also highlights the “protective”

role of a diploma in the face of the negative effects of a disability on participation in the

labour market in Wales.

V Conclusions

These results indicate some striking facts that corroborate the pertinence of the acute

distinctions between the private and public sectors. The public sector clearly differs from the

private one because it guarantees job security to workers experiencing or having

experienced a transitory handicap due to accident. Moreover, their workforce is

characteristically older, more educated and made up of more women. The specifics of the

public sector can provide at least a partial explanation for why the onset of handicap would

be more detrimental for the employment of salaried workers. Indeed, at the end of 2010, 60

% of the employees in the public sector were women, compared to 44 % in the private

sector [DGAFP, 2012]. Consequently, while the appearance of a handicap has less impact

upon women’s employment than that of men, the differences between the public and

private sectors, which have been highlighted in the preceding section, could be explained by

this difference in the socio-demographic composition of the employees themselves. In

contrast with employees of the public sector, private sector workers who experienced a

handicap have a non-zero probability of becoming unemployed after the appearance of said

disability. The public sector is also differentiated from the private sector by having, on

average, a greater amount of employees with higher education. Moreover, several studies

have shown that the professional career of workers with higher education level were less

affected by the occurrence of a handicap than would be non-graduated workers [JONES,

LATREILLE AND SLOANE, 2006; DUGUET AND LE CLAINCHE, 2012a,b]. Therefore, it is possible that the

mitigated negative impact of handicap in the public sector can be explained by the presence

of employees with a higher level of education, who will generally remain longer in the labour

market, and not necessarily due to the fact that their handicap is financially supported.

Despite all this, it would seem that the public sector does, nevertheless, ensure a better level

of support for its handicapped workers, whether their condition is transitory or permanent.

Acknowledgements

This research benefited from the financial support of Drees (French Health Ministry) and

Dares (French Labour Ministry). We would like to thank Bénédicte Apouey (Pse) and Elise

Coudin (Crest) for their comments on a preliminary version of this study. We also thank the

participants to the 35th

French Health Economists Seminar.

18

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20

Appendix 1: Descriptive Statistics

Table 1: HANDICAP ORIGIN

Origin of the handicap Raw Data

Corrected Data

An illness or health problem 20.9% 33.8%

A malformation or an accident at birth 12.9% 14.5%

An accident 37.5% 41.1%

Ageing 4.0% 4.1%

Consequences of medical care or surgery 4.0% 4.6%

Other 20.8% 1.9%

Sample: All handicaps (N=2095) of the individuals that have completed their initial education

(N=1777).

Table 2: AGE DISTRIBUTION AT FIRST HANDICAP

Age %

0 to 10 years 23.0%

11 to 20 years 21.3%

21 to 40 years 29.6%

Over 40 years 26.1%


(N=1777).

21

Figure 1: AGE DENSITY AT FIRST HANDICAP

Parzen-Rosenblatt kernel density estimator, with a Gaussian kernel


(N=1777).

Density

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

Value

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

Variable age_hand

22

Table 3: DURATION OF THE FIRST HANDICAP (ABSORBING STATE WEIBULL MODEL)

Survival function : �� = � + �1 − �� exp�−ℎ��

�: Proportion of the population with a permanent handicap

All parameters are significant at conventional levels. The averages and medians apply to the

individuals that exit from handicaps 46.5 % of the handicap durations are right-censored. Maximum

likelihood estimates.

Sub-samples � H α

Women

Parameters’ estimates 0.503 0.273 0.880

Duration average 4.18

Duration median 2.89

Men




Origin: Accident




Origin: Illness




Origin: Birth




Origin: Other





(N=1777).

23

Table 4: INDIVIDUALS’ AVERAGE CHARACTERISTICS

No handicap

Transitory

Handicap :

one year or

less

Long-term

Handicap :

more than

one year

Matching variables:

Gender:

Women 56.3% 40.9% 56.8%

Men 43.7% 59.1% 43.2%

Age 44.5 50.0 53.5

Level of education:

Primary 15.2% 22.8% 31.4%

Secondary 36.6% 47.1% 39.3%

Higher education 48.2% 30.1% 29.4%

Childhood:

Raised by both parents living together 86.7% 85.2% 84.2%

Bad material conditions, bad treatment, war, violence 16.5% 33.7% 36.9%

Family conflicts, relatives with health problems, long

separation from family, death of a family member 37.3% 47.1% 54.2%

Conditioning variables:

Worked in the public sector 23.3% 25.1% 26.8%

Handicap average duration 0.0 1.0 13.6

Number of individuals 5,726 501 507

24

Appendix 2: Difference-in-differences estimation

We consider that our performance variable, for the individuals facing a handicap (� ∈ �), is

represented by:

�� = �� + �� + �� > �� + �� + ��

where ��is an individual correlated effect, ��a time correlated effect, �� the effect of the

handicap on the individual i‘s performance, ��the effect of observable explanatory

variables and �� a white noise. Likewise, the performance variable for the individuals who do

not have handicaps (� ∈ �), is given by:

� � = � + �� + � !� " + � �.

In order to measure the average effect of a handicap on handicapped persons (the average

effect of the “treatment” on the treated), � = E�$%|% ∈ '�, we eliminate the correlated

effects by differences, the observable variables effects by matching and the white noise

effects by averaging. More precisely, we distinguish two periods, before and after the

handicap. To simplify the exposition, we present the case with a lag of one year: �� − 1

and�� + 1. . Let us consider an individual confronted with a handicap in��, we will have:

��,�)*+ − ��,�),+ = �� + ��)*+ + �� + �� + ��,�)*+ − !�� + ��),+ + �� + ��,�),+"= �� + ��)*+ − ��),+ + ��,�)*+ − ��,�),+

Consider now an individual who will not have a handicap, and match this individual with the

individual i. We take all the matched individuals, in number ��, and compute their

performance differences between the two dates (thus we also match the dates). We obtain:

1�� -!� ,�)*+ − � ,�),+" ∈.) = 1�� - /� + ��)*+ + � �� + � ,�)*+ − !� + ��),+ + � �� + � ,�),+"0 ∈.)= ��)*+ − ��),+ + 1�� -!� ,�)*+ − � ,�),+" ∈.)

Therefore, the difference-in-differences for individual i is equal to:

25

�1� = ��,�)*+ − ��,�),+ − 2 1�� -!� ,�)*+ − � ,�),+" ∈.)3

= �� + ��)*+ − ��),+ + ��,�)*+ − ��,�),+− 2��)*+ − ��),+ + 1�� -!� ,�)*+ − � ,�),+" ∈.)

3= �� + ��,�)*+ − ��,�),+ + 1�� -!� ,�)*+ − � ,�),+" ∈.)

By averaging these differences on the disabled individuals, we obtain:

�1 = 1�4 - �1� =�∈561�4 - �� + 1�4 -!��,�)*+ − ��,�),+"�∈56

+ 1�4 - 1�� -!� ,�)*+ − � ,�),+" ∈.)�∈56�∈56

Assuming that the� are white noises with a constant expectation 78, we obtain:

E8��1� = 1�4 -��∈4

which is the empirical counterpart of � = E�$%|% ∈ '�. Our estimator is unbiased and

consistent.

26

Appendix 3: Econometric results

Table 5: Effect of a handicap on the employment status,

without matching on the lagged occupation status

Employment Including: public

employment

Including: private

employment

Matching

rate

Estimation Student Estimation Student Estimation Student

transitory handicap (N=501)

Level in T-1 (on the treated) 87.6% 17.6% 70.0%

Effect on the treated: T+1/T-1 -4.5% 2.82** 0.6% 0.78 -5.1% 3.09** 88.4%

Divided by the level in T-1 (in %) -5.1%

3.4%

-7.3%

Effect on the treated: T+2/T-1 -4.1% 2.13** 0.3% 0.27 -4.4% 2.30**


1.7%

-6.3%



0.0%

-7.1%

Effect on the treated: T+4/T-1 -6.5% 3.26** -0.5% 0.37 -6.0% 2.88**


-2.8%

-8.6%



-2.8%

-9.9% Long-term handicap (N=507)


Effect on the treated: T+1/T-1 -16.4% 8.19** -2.9% 3.23** -13.5% 6.98** 90.5%


-14.9%

-22.1%

Effect on the treated: T+2/T-1 -18.0% 8.40** -2.4% 2.07** -15.7% 7.58**


-12.4%

-25.7%

Effect on the treated: T+3/T-1 -19.0% 8.27** -2.2% 1.82* -16.8% 7.63**


-11.3%

-27.5%



-13.9%

-29.8%


Divided by the level in T-1 (in %) -24.9% -10.8% -29.3%

* significant at 10 %; **: significant at 5%.

27


with matching on the lagged occupation status


employment

Including: private

employment

Matching

rate


Short-term handicap (N=501)


Effect on the treated: T+1/T-1 -5.8% 3.97** 1.2% 1.66* -7.0% 4.74** 85.0%


7.3%

-9.8%



6.1%

-9.6%



4.9%

-11.0%



3.7%

-12.0%



3.7%

-13.8%

Long-term handicap (N=507)




-12.8%

-22.3%



-11.2%

-27.0%



-8.9%

-30.9%



-14.0%

-34.1%




28

Table 7: Effect of a handicap on unemployment and inactivity

The lagged occupation status has been included in the list of the matching variables.

Unemployment >=1

year

Inactivity Matching

rate

Estimation Student Estimation Student

transitory handicap (N=501)

Level in T-1 (on the treated) 1.6% 10.3%

Effect on the treated: T+1/T-1 1.1% 1.18 4.7% 3.67** 85.0%

Effect on the treated: T+2/T-1 0.9% 0.82 5.1% 3.05**




Long-term handicap (N=507)

Level in T-1 (on the treated) 1.8%

17.2%

Effect on the treated: T+1/T-1 3.9% 3.50** 12.9% 6.95** 86.0%

Effect on the treated: T+2/T-1 3.9% 3.17** 15.7% 7.48**





29

Table 8: Effect of a transitory handicap on employment,

by gender



employment

Including: private

employment

Matching

rate


Women (N=205)




2.5%

-9.7%

Effect on the treated: T+2/T-1 -2.0% 0.61 -0.7% 0.38 -1.4% 0.44


-2.9%

-2.8%

Effect on the treated: T+3/T-1 -5.7% 1.56 -1.4% 0.64 -4.3% 1.34


-5.8%

-8.5%



-6.2%

-15.0%



-10.0%

-24.1%

Men (N=296)




14.2%

-9.8%



18.6%

-12.2%

Effect on the treated: T+3/T-1 -7.9% 3.56** 2.2% 1.67* -10.1% 4.45**


19.5%

-11.8%



16.8%

-10.9%


Divided by the level in T-1 (in %) -6.2% 22.1% -9.9%


30

Table 9: Effect of a long-term handicap on employment,

by gender



employment

Including: private

employment

Matching

rate


Women (N=288)




-11.9%

-31.4%



-12.4%

-34.0%



-13.4%

-34.6%



-13.9%

-41.6%



-7.4%

-41.0%

Men (N=219)


Effect on the treated: T+1/T-1 -13.9% 5.21** -1.7% 1.49 -12.2% 4.68** 84.0%


-11.6%

-15.4%



-4.1%

-23.3%



0.0%

-29.5%



-13.6%

-29.6%




31

Table 10: Effect of a transitory handicap on employment,

by education level



employment

Including: private

employment

Matching

rate


Primary education (N=114)


9.7%

67.7%



35.1%

-16.8% Effect on the treated: T+2/T-1 -11.5% 2.56** 2.3% 0.74 -13.8% 3.59**


23.7%



12.4%



12.4%



13.4%

-30.0%

Secondary education (N=236)


10.7%

80.1%



9.3%

-10.0%

Effect on the treated: T+2/T-1 -4.8% 1.91* 1.6% 1.11 -6.4% 2.40**


15.0%

-8.0%



15.9%

-7.6%



11.2%

-7.4%

Effect on the treated: T+5/T-1 -3.9% 0.03 1.3% 0.71 -5.2% 1.79*


12.1%

-6.5%

Higher education (N=151)


30.7%

60.6%

Effect on the treated: T+1/T-1 -2.4% 1.29 0.0% 0.00 -2.4% 1.29 84.1%


0.0%

-4.0%

Effect on the treated: T+2/T-1 -3.5% 1.18 -0.9% 0.60 -2.6% 1.00 77.5%


-2.9%

-4.3%

Effect on the treated: T+3/T-1 -4.0% 1.18 -1.0% 0.44 -3.0% 1.02 69.5%


-3.3%

-5.0%

Effect on the treated: T+4/T-1 -6.3% 1.69* -1.0% 0.42 -5.2% 1.53 65.6%


-3.3%

-8.6%




32

Table 11: Effect of a long-term handicap on employment,

by education level



employment

Including: private

employment

Matching

rate


Primary education (N=159)


10.3%

58.8%



-14.6%

-23.0% Effect on the treated: T+2/T-1 -18.5% 4.47** -2.4% 1.66* -16.1% 3.76**


-23.3%

-27.4% Effect on the treated: T+3/T-1 -23.1% 4.85** -2.6% 1.64 -20.5% 4.40**


-25.2%



-35.0%



-27.2%

-42.5%

Secondary education (N=199)


15.3%

70.1%

Effect on the treated: T+1/T-1 -19.1% 5.79** -2.3% 1.65* -16.8% 5.62** 88.9%


-15.0%

-24.0%



-7.8%

-29.2%



-13.1%

-30.8%



-17.6%

-30.7%



-18.3%

-32.0%

Higher education (N=149)


30.1%

57.7%



-11.0%

-19.9%



-8.6%

-25.8%



-6.3%

-26.7%



-6.6%

-35.0%




33


by handicap origin



employment

Including: private

employment

Matching

rate


Handicap origin : Accident (N=552)




2.6%

-12.2%



6.9%

-13.6%



4.4%

-14.7%



4.6%

-16.2%



3.3%

-17.9%

Handicap origin : Illness (N=363)




-10.7%

-23.3%

Effect on the treated: T+2/T-1 -19.4% 7.32** -2.5% 2.23** -16.9% 6.46**


-13.2%

-28.0%



-10.2%

-31.0%



-14.8%

-35.4%




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