1
UNIVERSITY OF PISA
SANT'ANNA SCHOOL OF ADVANCED STUDIES
DEPARTMENT OF "ECONOMIA E MANAGEMENT"
Master of Science in Economics
LIFE SATISFACTION AMONG PEOPLE WITH AND WITHOUT
DISABILITIES: A COMPARATIVE STUDY
CANDIDATE
SUPERVISOR
Asya Bellia
Prof. Simone D'Alessandro
2
Abstract
People with disabilities are less satisfied with their life compared to non disabled people. The aim of the present work is to understand how much of this differential is explained by the actual impairments disabled people have and how much by the barriers they face to a full participation in society. The data is taken from the European Survey on Income and Living Conditions from 2013, which includes an ad-hoc module on well-being. My work is divided in four parts. In the first part I analyze the determinants of well-being for disabled and non disabled people. I run two sets of regressions: in the first two I regress life satisfaction on the same explanatory variables, restricting the sample first to people with disabilities, then to non disabled individuals. In the third regression I consider the entire sample, adding a disability dummy, while in the fourth I add an interaction term between disability and being employed. People with disability derive more satisfaction with their life from being employed, compared to non disabled people. In the second part of the present work, I restrict the sample to
employed people and analyze the effect of working part-time and having a contract of limited duration on the life satisfaction of disabled and non disabled individuals. Both working part-time and having a temporary job have a negative effect on subjective well-being, but this effect is greater, in absolute value, for disabled respondents. In the third part of my work, I compare three groups: the severely disabled, the mildly disabled and the non disabled. Average life satisfaction decreases with the degree of functional limitation, but the positive effect of having a job is greater for people with severe disabilities. In the fourth part, I conduct the same kind of analysis I did in the first, but divide the sample by age group: I compare the determinants of life satisfaction for people aged 16-40 to the correlates of subjective well-being for people over 40. People with disabilities derive a greater life satisfaction from working than non disabled people, regardless of age, but the impact of employment on subjective well-being is stronger among younger disabled respondents.
3
Acknowledgements
I would like to thank my advisor, Prof. D’Alessandro, for all the support and the help he gave me. My thanks go also to Prof. Parenti and Prof. Corsini, for helping me and offering their precious advise, even though they had no obligation to do so.
I am especially grateful to Prof. Manfredi, not only for the advice he gave me in realizing the present work, but also for meeting my needs as a disabled student for my entire academic carrier. My thanks go also to the Unit for the Inclusion of Disabled Students (USID).
In particular, I would like to thank the former Rector's delegate for Disability, Prof.
Mancarella (now Rector of the University of Pisa), as well as his successor, Prof. Fanucci. I am very grateful to the staff of the USID office: Federica Gorrasi, Barbara Testa, Roberto Pasquini and Alfonso Curreri, as well as to the people who chose to do their community service for the office.
I want to thank my friends and family, for the support they gave me. My thanks go to Milena, Marcello, Ilaria, Giulio, Tommaso, Andrea Caravaggio, Andrea Lari and so many others: without your patience, support and constant encouragement, I could not have made it.
4
CONTENTS
1. Introduction p. 5
2. Review of the literature p. 8
2.1. Correlates of life satisfaction p.8
2.1.1. Income p.8
2.1.2. Socio-demographic variables and social connections p. 9
2.1.3. Health and disability p. 11
2.2. Studies on people with disabilities p. 14
2.2.1. Income, wealth and the costs of disability p. 14
2.2.2. Socio-demographic characteristics and social connections p. 15
2.2.3. Health p. 17
3. Methodology p. 18
3.1. Part 1: The determinants of life satisfaction p. 33
3.2. Part 2: The determinants of life satisfaction for employed people p. 35 3.3. Part 3: The correlates of life satisfaction by degree of limitation p. 39
3.4.Part 4: Life satisfaction by age group p. 46
4. Robustness checks p. 50
4.1. Part 1: The determinants of life satisfaction p. 50
4.2. Part 2: The determinants of life satisfaction for employed people p. 53 4.3. Part 3: The correlates of life satisfaction by degree of limitation p. 55
4.4. Part 4: Life satisfaction by age group p. 57
5. Final remarks and suggestions for further research p.59
5
Chapter 1: Introduction
The aim of the present work is to investigate the determinants of life satisfaction for people with disabilities. In particular, I wonder if some correlates of life satisfaction are more important for people with disabilities than they are for non disabled people.
In the academy literature, "life satisfaction" is sometimes referred to as "well-being" of "happiness". According to neoclassical economics, individual life satisfaction is measured by the extent to which the individual's preferences are satisfied. Such preferences, which are revealed in the decisions the individual makes in his market behaviour, can be used to build her utility function. Individuals, then, aim at maximizing their utility functions within their budget constraint, that is, conditional to their income level.
However, there is a raising concern among economists in regards to the adequacy of revealed preferences as a measure of life satisfaction. As a result, since the 90's there have been economic studies on subjective well-being (SWB).
In psychology, subjective well-being indicates how people think and feel about their life. As such, it is based on self-assessment rather than observable behaviours (Conceição & Bandura, 2008).
There are a number of studies on the correlates of life satisfaction. In particular, a strand of literature is dedicated to the effect on life satisfaction of acquiring a disability.
Most economic scholars find that, after an individual becomes disabled, their life satisfaction drops, to increase some years later, though adaptation is not complete.
Psychologists study hedonic adaption as well, though they emphasis the role of personality features in responding to life-altering events, such as becoming disabled (Powdthavee, 2009).
The strand of literature investigating the relationship between life satisfaction and disability frames disability as a health problem pertaining exclusively to the individual and, when studying adaptation, the focus is on psychological adaptation. That is, the change in the values and believes of the individual some years after acquiring a disability.
Although personal resources are certainly fundamental in mediating the relationship between disability and life satisfaction, I think there is a gap in the literature.
6
Disability is complex phenomenon and this the reason why there is no official definition of disability. Rather, there are different models of disability. The first to emerge was the medical model.
According to the medical model each disability is caused by a specific medical condition and it is a personal problem, the individual's inability to function.
Following the disability civil rights movement, the social model of disability was born.
According to this model, people are disabled by society. That is, disability originates not from a medical condition, but from limitations imposed on people with physical and mental
impairments by society and by an unaccommodating physical environment.
In the social model of disability, a distinction is drawn between impairment and disability, whereas disability is a form of social oppression experienced by people with impairments (Shakespeare & Watson, 2002). Thus, disabled people are viewed as a minority group, just as black people or the LGBT community.
The World Health Organization integrates medical and social model of disability in the International Classification of Functioning, Disability and Health (ICF) or biopsychosocial model of disability.
Such classification is based on various domains. One is "body structure and function": body structures refers to anatomic parts of the body, while body functions identifies a very specific set of functions (i.e. bending one's leg or speaking clearly) in order to assess the degree of functioning of the individual.
Another domain is that of Activities, that is voluntary actions with specific goals, i.e. brushing one's teeth or combing one's hair.
The last domain is Participation, which refers to those activities that are fundamental for the full participation in society, such as going to school or holding a job.
Furthermore, environmental factors (i.e. architectural barriers) and personal factors, such as individual values and believes, also play a role in the ICF model of disability (Mont, 2007).
7
From Mont (2007)
Most of the studies on disability and life satisfaction focus on personal factors to study hedonic adaptation. In contrast, the present studies aims at comparing the determinants of life satisfaction for people with and without disabilities.
The issue I focus on is not how much an individual adapts to his or her own disability, but how to explain the differential in average subjective well-being between disabled and non disabled individuals.
This work is organized as follows: in the first chapter I introduce the topic, in the second I present a review of the literature, in the third chapter I discuss the methodology, Chapter 4 evaluates the robustness of my models, Chapter 5 contains my final remarks and some suggestions for further research.
8
Chapter 2: Review of the literature
This review is organized as follows. Firstly, I review the correlates of life satisfaction, which include personal income, as well as personal characteristics such as gender, educational level, (type of) employment, personal relationships and health.
The reason for including health is that, in the literature on happiness, subjective well-being or life satisfaction, disability is usually framed as an health condition.
In the second part on my review, I focus on papers and official statistics which shed light on the correlation between disability and employment, education, (type of) employment and self-perceived health.
2.1.Correlates of life satisfaction 2.1.1 Income
There is a positive correlation between income and life satisfaction. However, the marginal returns to income are decreasing (Easterlin, 1974). That is, as individual income gets higher, the additional life satisfaction associated with an increase in income reduces.
Furthermore, aggregate national happiness does not change with GDP. In other words, while at the individual level higher income is associated with greater happiness, at the aggregate level this is not the case. This finding is known as the "Easterlin Paradox".
Possible explanations for this phenomenon take into account the role of relative income (Dorn, Fischer, Kirchgassner & Sousa-Poza, 2007; Ferrer-i-Carbonell, 2005), hedonic adaptation (Gilbert, 2006), expectations (McDonald & Douthitt, 1992, Stutzer, 2005) and many other variables.
As regards relative income, one could explain the Easterlin Paradox if one supposes that individuals are more satisfied with their life if their income is higher than the income of their reference group (variously defined).
In such a framework, an increase in individual income will have no effect on subjective well-being if the income of other individuals belonging to the same reference group experience a similar increase as well.
9
Hedonic adaptation could also explain the decreasing marginal returns to income. Thus, an increase in income may lead to a temporary increase in subjective well-being, but in time the individual will go back to almost the same level of happiness as before.
In psychology, the set point theory postulates that the individual level of life satisfaction depends only upon personality and DNA, so that individuals adapt fully to any life event.
I will come back to the topic of adaptation later.
Individuals with relatively high aspirations experience lower levels of subjective well-being for any level of income (Stutzer, 2004; Di Tella, Haisken-De New & MacColluch, 2005). In other words, expectations have a negative impact on life satisfaction.
2.1.2. Socio-demographic variables and social connections
Life satisfaction seems to be U-shaped in age (Blanchflower and Oswald, 2008), at least in cross-sectional datasets.
According to Alesina et al. (2004), women are more satisfied with their life.
Blanchflower and Oswald (2004b) find that educational attainment has a positive impact on life satisfaction. According to Stutzer (2004), however, people with medium educational attainment are the more satisfied with their life.
This contrasting evidence suggests that in assessing the impact of education in life satisfaction, it is important to consider the correlation between education itself and other variables, such as employment, income, health and personal characteristics.
A number of studies investigated the correlation between well-being and unemployment. According to various authors (Di Tella et al., 2001; Frey and Stutzer; 2000, 2002) employed people are 5-15% more satisfied with their life than unemployed people. Thus, there is a strong negative correlation between unemployment and life satisfaction.
However, for reasons that will be clear later, I will examine the association between employment and life satisfaction.
Bardasi and Francesconi (2004) found that workers with a temporary contract, as well as part-time workers, are less satisfied with their life.
10
As regards self-employment, according to most studies being it has no impact on life satisfaction, but some authors (Blanchflower and Oswald, 1998) discovered a positive
correlation between life satisfaction and self-employment in British, International (ISSP) and US datasets. Such a positive effect of self-employment is significant only for the rich, once income is controlled for (Alesina et al., 2004).
I will not investigate the impact of self-employment on life satisfaction because, as I will show, very few people are employed, and the percentage of disabled people who are self-employed is negligible.
Many studies found that personal relationships are very important for subjective well-being. Helliwell (2003) found that married people the most satisfied with their life, while people who are separated are the least satisfied, even less than divorced and widowed individuals.
This result may have something to do with the fact that married people feel they are in a stable relationship. In fact, analyzing unmarried couples, Brown (2000) revealed that unmarried people with stable relationships are just as satisfied with their life as married individuals.
Blanchflower and Oswald (2004) found a similar result.
Personal relationships, however, are not limited to romantic relationships. They include friendship and social connections in general.
Using GSS data for the period 1975-2004, Bartolini, Bilancini and Pugno (2011) study the impact of various measures of social connections and confidence in institutions on life satisfaction.
These measures include marital status, social contacts and various trust measures, among which one finds both "Trust in others" and measures of trust in different institutions, for a total of 13 measures. The study concludes that all the indicators of social connections and confidence in institutions have a positive impact on life satisfaction at the individual level.
Furthermore, using SOEP data Bartolini, Bilancini and Sarracino (2012) find that social connections increase life satisfaction.
2.1.3 Health and Disability
11
One has to consider, though, that there could be a correlation between health and education, as people with higher education often hold better paid job and can thus take better care of themselves (Bukenya, Gebremedhin and Schaeffer, 2003).
Lundetrae and Gabrielsen (2007) investigated the association between literacy skills among Danish, Finnish, Norwegian and Swedish people aged 16-65, introducing sex, age and
educational level as control variables. The data comes from the International Survey of Adults Skills (PIAAC).
The study concludes that people at the lowest literacy level are 1.99-3.24 times more likely to report poor health than people with the highest literacy level.
Besides the role of education or literacy, since health is self-reported, there could be a
problem of reverse causality, so that people who are more satisfied with their life would tend to report a better health condition.
Furthermore, personality and mood are very likely to play a role in self-reported health.
As regards disability, empirical evidence suggests that being disabled reduces life satisfaction.
Most studies about the subjective well-being of people with disabilities investigate one of the following topics:
a. The determinants of life satisfaction for disabled people
b. How people who become disabled later in life adapt to their new condition
One of the most important papers about disability and life satisfaction (Oswald and Powdthavee, 2006) is precisely about this topic, that is, hedonic adaptation to disability.
According to the psychological theory known as "set point" theory, each individual has a set level of subjective well-being, which depends only on personal characteristics.
Therefore, a positive (negative) shock will cause a temporary increase (drop) in life
satisfaction, but the individual's subjective well-being will gradually decrease (increase) as he adapts to his new circumstances, going back to the pre-shock level. This theory implies that hedonic adaptation is complete.
Neoclassical economic theory, however, posits that there is no hedonic adaptation at all. Each individual has a set of ordered preferences which are described by a utility function. Since
12
such utility function does not vary trough time, any positive (negative) shock will cause a permanent increase (decrease) in life satisfaction.
Powdthavee (2009) aims at reconciling psychological and economic theory, measuring the degree of hedonic adaptation to disability.
Focusing on the negative shock of becoming disabled yields a unique opportunity because, while other negative shocks (such as becoming unemployed or losing a partner) are
potentially reversible, the transition from being non disabled to being disabled is not.
Powdthavee use waves 7-14 (i.e. from 1998 to 2004) of the British Household Panel Survey in order to analyze the longitudinal patterns of life satisfaction in people who acquired a disability. Thus, the respondents who reported being disabled in 1998 were not a focus of this study. However, they did report lower levels of life satisfaction compared to non disabled respondents.
As for people who acquired a disability, Powdthavee found that, in the year they became disabled, their life satisfaction decreased, only to increase year after year as they adapted to their new status. The degree of adaptation was estimated to be 30% for severely disabled people and 50% for moderately disabled people.
In order to identify a respondent as disabled, the authors use the following question from the BHPS: "What describes your current situation…long term sick or disabled?". The severity of the disability was assessed based on the following question: "Does your health in any way limit your daily activities compared to most people your age?".
In the authors' words, the phrase "daily activities" refers to housework, climbing stairs, dressing oneself and walking for at least 10 minutes.
The disabled people who are not limited in any of their daily activities are classified as Moderately Disabled, while those who are limited in at least one of these activities are classified as Severely Disabled. I will use similar questions from the EU-SILC to identify disabled people.
Although this study is very interesting and, indeed, one of the few studies about life satisfaction and disability based on longitudinal data, if one wants to investigate the determinants of subjective well-being for disabled people one has to search elsewhere.
13
Abbado, Sarti and Sciulli (2016) analyze data from the 2011 ISTAT survey on "Not Self Sufficient Individuals' Social Inclusion".
The survey is aimed at people who live in the household, i.e. not in residential care. The respondents are people aged 11-87 who declared to have functional limitations in a previous survey taken in the year 2004-2005.
Therefore, the people involved surely have a disability, as opposed to a temporary limitation. Furthermore, people who just became disabled are excluded from the sample.
The sample is built so as to be representative of the Italian population and the sample size is 9,000, of which 2,744 severely limited in their daily activities and 6,293 mildly limited.
Life satisfaction is not measured through a single index, but it is divided into four domains (Relatives relations, Friends relations, Economic conditions, Leisure Time).
The paper concludes that older disabled people appear to be more satisfied with their life, in particular with their economic situation.
Another factor with has a positive effect on the satisfaction with one's financial situation is the educational level.
Being a single mother, on the other hand, has a negative impact on the satisfaction with one's economic conditions.
More severely disabled people report lower satisfaction as regards economic conditions, interactions with friends and leisure time.
Van Campen and van Santvoort (2013) used the European General Social Survey: comparing 21 European countries, they found that, though disabled people in general report lower degrees of life satisfaction compared to non disabled ones, there some differences across countries.
In particular, the gap in life satisfaction between disabled and non disabled population seems to be smaller in Northern European countries, and noticeably wider in Eastern European countries.
14
As for the determinants of life satisfaction, van Campen and van Santvoort conclude that subjective well-being is mostly explained by personal resources, as opposed to the severity of the disability, socio-demographic characteristics or participation to social life.
Thus, their results are in line with the set point theory.
The same can be said about the results of the paper by Riis et al (2005): studying
hemodialysis patients, they find no difference between the level of life satisfaction of the patients and that of healthy people. They explain such findings claiming that hemodialysis patients adapt completely to their illness.
Uppal (2006) investigates the impact the age of disability onset, the kind of disability and its severity on the life satisfaction of 24,036 disabled people from Canada.
He finds a negative relationship between disability and life satisfaction. However, the kind of disability does not affect subjective well-being and neither does per capita family income, while unemployment has a negative impact. Furthermore, the respondents who are disabled from birth are likely to report higher levels of life satisfaction.
2.2.Studies on people with disabilities
2.2.1 Income, wealth and the costs of disability
McKnight (2014) found that people with disabilities are usually poorer than non disabled people.
In her analysis, she highlights how people who become disabled when they are 65 or older have the opportunity to accumulate wealth, while those who acquire a disability earlier or are born disabled suffer a wealth penalty because of it.
Such wealth penalty derives from two main factors. The first cause of the wealth penalty is that disabled people have lower levels of education compared to their non disabled peers, and thus lower paid jobs. The second obstacle to accumulating wealth for disabled people of working age is that they have more expenses than people without disabilities.
15
As Belli (2014) points out, the additional costs of disability increase with the severity of the disability itself.
Zaidi and Buchardt (2005) try to estimate the additional costs of disability using the "standard of living" approach. This approach is based on the premise that the standard of living depends on both income and needs.
They find that the costs of disability are high, especially for disabled people living alone and for people with severe functional limitations.
This implies that the standard of living of disabled people is lower than that of non disabled people with the same income.
2.2.2. Socio-demographic characteristics and social connections
Several studies point out how disabled people are less educated than their non disabled peers.
The issue of education is particularly relevant when it comes to disabled people, since the functional limitations associated with a disability may make it difficult, or
impossible, to perform certain jobs (Pagàn & Malo, 2009). Therefore, having a high level of education may be the only way, for some people with disabilities, to be able to access the labor market at all.
However, the educational level of disabled people is lower than average.
This phenomenon might partly be explained by the fact that disabled people are, on average, older than the rest of the population: while some children are disabled from birth and some people become disabled when their relatively young due to accidents, most people become disabled later in life.
As I will show in the present work, there is a negative correlation between age, so that older generations are less educated than younger ones.
However, even among young people, the disabled are less educated than their peers. In order to understand why that is, one has to explain what the educational options for disabled people are. The discussion that follows applies to young people with
disabilities.
In most countries, disabled people can either attend schools for people with special educational needs (special schools) or mainstream schools.
16
Disabled activists are opposed to special schools, that are seen as a form of segregation (Oliver & Barnes, 1998, Powell, 2003).
However, even in mainstream schools it is difficult to implement successful inclusive practices (Shah, 2008).
According to an official report by the Academic Network of European Disability experts (Ebersold et al., 2011) the challenges faced by disabled students include: - Lack of assistive equipment especially for students who are 16 or older
- Inaccessible educational facilities for students with mobility impairments - Unavailability of vocational courses for students with disabilities
- Insufficient human support especially for people with more severe disabilities, because usually mainstream teachers do not know how to support people with disabilities - No support beyond secondary school in some countries. In Italy, act no 17/99 requires
all universities to appoint a lecturer whose responsibility it is to ensure the integration of disabled students into the university system.
As regards employment, Jones et al. (2006b) analyze the British labor market.
They use data from the LFS and distinguish between non disabled people, people with a work limiting disability and people with a disability which is not work limiting. They also differentiate workers by gender.
The participation rate of disabled is considerably lower than that of non disabled people. They find little evidence of wage discrimination against the disabled. However, people with work-limiting disabilities suffer from a wage penalty due to their reduced productivity. Inexplicably, such penalty is higher for women than it is for men.
As regards the type of work, Jones and Sloane (2010) used the 2004 British Employment Relations Survey in order to estimate the level skill mismatch for workers with disabilities. They asked to people with work limiting disabilities the following question: "How well do the skills you personally have match the skills you need to do your present job?".
The possible answers were: "Much higher/a bit higher/about the same/a bit
lower/much lower". They found that the disabled workers who are limited in the type or the amount of work they can perform are 6 percentage points more likely to be over-skilled compared to non disabled workers, with negative implications in terms of earnings.
17
They conclude that giving disabled workers more opportunities to decide how to do their job could reduce the negative effect of over-skilling.
Flexibility seems to be particularly important for workers with disabilities. Pagàn and Malo (2009) use data on 13 countries from the European Community Household Panel 1995-2001.
They find that disabled people are more likely to be self-employed compared to non disabled people. Furthermore, the level of job satisfaction of disabled people who are self-employed is higher than that of disabled employees.
2.2.3 Health
Many studies found a strong association between disability and poor self-reported health.
In the field of medicine, some studies investigated the relationship between disability and self-reported health.
Jamoom et al (2008) used data from the 1998-2000 Behavioral Risk Factor
Surveillance System on 11,905 adults with disabilities. They found that people who became disabled at 21 or older were more likely to report poorer health compared to those disabled earlier, even after introducing socio-demographic controls.
This suggests that hedonic adaptation to disability may take considerably longer than 7 years, as suggested by Podthavee (2009).
Furthermore, personal resources are as important a determinant of self-reported health for disabled people as they are for non disabled people, especially mastery and self esteem (Cott et al., 1999).
Another factor that could explain such a strong association between disability and poor health is the fact that disabled people are, on average, less educated than non disabled people and people with lower levels of education are more likely to report poor health, as I explained above.
In addition, some disabled people may think their health is poor just because they have impairments (in accordance with the medical model of disability), that is, there may be a cultural factor associated with self-reported health.
18
Chapter 3: Methodology
The data comes from the European Survey of Income and Living Conditions (EU-SILC) for year 2013.
This is an annual survey including all European countries and three non European countries (Iceland, Serbia and Switzerland). However, I restrict the sample to the States that were part of Euro zone in 2012, so that the data on income is expressed in euro, ensuring homogeneity between countries.
The survey is addressed to individuals living in private households, so that collective households are excluded from the questionnaire. The dataset contains both longitudinal and cross-sectional data, but I chose data from 2013 because the survey that year included an ad-hoc module on well-being.
I built the variable "Age" as the difference between Year of the personal interview and Year of
birth, then I restricted the original dataset to people aged 16-64, as I am interested in the
active population. The sample size is 224,021.
In order to identify disabled people, I used two questions: Suffer from any chronic
(long-standing) illness or condition? and Limitation of activities because of health problems. The
first question was I yes or no question, while the second had 3 possible answers: Yes, strongly
limited; Yes, limited; No, not limited.
I classified as disabled only those respondents who declared they suffered from a chronic condition, which limited them in their daily activities, in accordance with the ICF model of disability.
Furthermore, the disabled respondents who were strongly limited in their daily activities were classified as severely disabled, those who were not as mildly disabled.
19
According to official EU statistics1, the disability prevalence among people aged 16-64 is 12.8%.
If one applies the ICF model to the EU-SILC dataset, however, the percentage of disabled people in the Euro zone equals 15.5%. Therefore, it would appear that my estimation of the disability prevalence is not in line with European statistics.
However, the difference between the two rates probably derives from two factors:
1. The official EU statistics refer to 2012, while the data I use are from 2013
2. They are based on the European Health and Social Integration Survey (EHSIS), a population survey which is specifically designed to provide information on the ICF model of disability2, while my estimations are based on EU-SILC data.
1 http://ec.europa.eu/eurostat/statistics-explained/index.php/Disability_statistics_-_prevalence_and_demographics 84.6% 11.0% 4.5%
20
Hence, even if I apply the same model as in the EU statistics, the prevalence rate of disability I obtain is different because I use a different dataset and consider a different year.
Nonetheless, it is interesting to compare the prevalence rates of disability, based on different models.
If one adopted the medical model of disability, one would classify as disabled everyone who suffers from a chronic illness or condition, and then distinguish between Severely disabled,
Mildly disabled and Non limited disabled.
2 http://ec.europa.eu/eurostat/statistics-explained/index.php/Disability_statistics_background_-_European_health_and_social_integration_survey 75.2% 9.5% 10.9% 4.5%
Figure 2: Disability prevalence (medical model)
Non disabled Non limited disabled Mildly disabled Severely disabled
21
This approach is common in the literature on labor market participation, since labor force surveys often include questions such as “Are you disabled?” and “Does your disability limit the kind or the amount of work you can do?” (Jones et. al. 2006a, 2006b, 2010).
As can be seen, the medical model would tend to overestimate the disability prevalence, mainly because some chronic conditions do not result in activity limitations.
Of course, if one was to apply the medical model properly, one would classify people
according to their medical diagnosis, not based on self-declared limitations in daily activities.
However, sometimes a diagnosis tells you little. If one has cerebral palsy, for example, one may or may not be able to walk. The medical diagnosis would be the same, regardless of a person’s abilities.
On the other hand, if one adopted the social model of disability, one would distinguish between those who are Severely limited in daily activities, those who are Limited and the people who are Not limited in daily activities.
22
As can be seen, this classification, surprisingly, overestimates the disability prevalence as well.
To sum up, the biopsychosocial model results in a prevalence rate of 15.5%, the medical model results in a prevalence rate of 25% and the classification by degree of limitation in a disability rate of 19.1%.
In order to understand the root of these differences, I present the following table.
80.8% 14.2%
4.9%
Figure 3: Limitations in daily activities
Non limited Limited Strongly limited
23
Table 1: Chronic condition and limitations in daily activities because of health problems (%)
Chronic condition Limitations in daily activities
Yes, strongly limited Yes limited No, not limited
Yes 4.5 11.0 9.5
No 0.5 3.7 70.9
One can notice that some of the respondents declared they were strongly limited in daily activities because of health problems, even though they do not suffer from any long-standing illness or condition (0.5%). A slightly higher percentage (3.7%) affirmed they were limited in their daily activities, but did not suffer from chronic conditions.
Of course, this is probably the result of measurement error. However, I would like to take this opportunity to explain what exactly is meant by “limitations in daily activities”. This phrase indicates a set of daily activities that is specified by the questions in Box 2 (Mont, 2007).
Box 2: Census Questions on Disability Designed by UN Washington Group on Disability Statistics
Because of a physical, mental, or emotional health condition… 1. Do you have difficulty seeing even if wearing glasses?
2. Do you have difficulty hearing even if using hearing aid/s or are you deaf? 3. Do you have difficulty walking or climbing stairs?
4. Do you have difficulty remembering or concentrating?
5. Do you have difficulty (with self-care such as) washing all over or dressing? 6. Do you have difficulty communicating (for example, understanding or being understood by others)?
Question response categories: No, Some, A lot, and Unable.
24
Life satisfaction is an ordered categorical variable, ranging from 0 (not at all satisfied) to 10 (completely satisfied).
I build "Employed", a dummy which takes value 1 if Self-defined economic status is between 1 and 4, 0 otherwise. Self-defined economic status is a categorical variable, taking values:
1. Employee working full-time
2. Employee working part-time
3. Self-employed working full-time (including family worker)
4. Self-employed working part-time (including family worker)
5. Unemployed
6. Pupil, student, further training, unpaid work experience
7. In retirement or in early retirement or as given up business
8. Permanently disabled and/or unfit to work
9. In compulsory military community or service
10. Fulfilling domestic tasks or care responsibilities
11. Other inactive person
Using the same variable, I can build a dichotomous variable, "Part-time".
The variable "Income" is an ordered categorical variable, ranging from 1 to 12 and increasing with total household gross income.
"Partner" is dummy variable, which equal 1 when "Consensual Union" takes values 1 (Yes, on legal basis) or 2 (Yes, without legal basis), 0 otherwise.
The variable "Education" refers to the Highest ISCED level attained. The latter takes the following values:
0. Pre-primary education: if one has not completed elementary school 1. Primary education: if one completes elementary school
25
3. (Upper) secondary education: if one graduates high-school
4. Post-secondary non tertiary education: if one graduates a vocational school 5. First stage of tertiary education (not leading directly to and advanced research
classification): if one has a Bachelor's or a Master's degree
For statistical purposes, one's level of education can be considered:
- Low: if one does not graduate from either high-school or a vocational school
- Medium: if one graduates from high-school or vocational school, but does not have a Bachelor's degree
- High: if one has a Bachelor's and/or a Master's degree
In my regressions I will use Highest ISCED level attained as a proxy for education, while in the descriptive statistics I will distinguish between low, medium, and high educational level.
The variable "Female" takes values 1 if the respondent is male, 2 if they are female. Country dummies will be added in all regressions, with Austria as the omitted category. I deliberately omitted the variable General health, due to its high correlation with the disability dummy. In fact, if one regresses life satisfaction on both the disability dummy and General Health (together with all the variables I mentioned above), the disability dummy is not significant.
Correlation matrix (entire sample)
Life sat. Disabled Age Female Education Employed Partner Life sat. Disabled -0.20 Age -0.11 0.23 Female 0.01 0.03 0.01 Education 0.18 -0.10 -0.07 0.02 Employed 0.16 -0.15 -0.04 -0.11 0.28 Partner 0.09 0.01 0.35 -0.01 0.04 0.17 Income 0.26 -0.09 0.01 -0.02 0.20 0.25 0.19
Life satisfaction is negatively correlated with disability and age, while subjective well-being increases with the level of education and income.
Women report higher levels of subjective well-being compared to men, people who have a job are happier than the rest and having a partner has a positive effect on life satisfaction.
26
Disabled people are less satisfied with their life compared to people without disabilities. Furthermore, they are older, less educated and more likely not to be employed.
As a result, they have lower incomes compared to the rest of the population. If one divides the sample according to disability status, one notices that the determinants of life satisfaction are of the same sign and of similar magnitude for both people with disabilities and people without, with three exceptions, Employed, Partner and Income.
Correlation matrix (disabled people)
Life sat. Age Female Education Employed Partner Life sat. Age -0.06 Female 0.02 -0.02 Education 0.19 -0.10 0.00 Employed 0.21 -0.15 -0.05 0.27 Partner 0.15 0.18 -0.04 0.01 0.09 Income 0.29 -0.05 -0.02 0.17 0.26 0.27
Correlation matrix (non disabled people)
Life sat. Age Female Education Employed Partner Life sat. Age -0.07 Female 0.02 0.01 Education 0.17 -0.04 0.03 Employed 0.12 -0.12 -0.12 0.27 Partner 0.08 0.39 -0.02 0.04 0.18 Income 0.23 0.04 -0.01 0.19 0.24 0.18
While the correlation coefficient between life satisfaction and having a job equals 0.12 for non disabled respondents, it is 0.21 among respondents with disabilities. The correlation coefficient between Partner and life satisfaction equals 0.15 for people with disabilities and 0.08 for non disabled people. Finally, the correlation between income and life satisfaction equals 0.23 for people with no disabilities, and 0.29 for disabled people.
How can one explain such differences?
The importance of having a job for people with disabilities can be explained by referring to the social model of disability. People with impairments who are employed participate to society more fully than those without a job, so that the disabling effect of their impairment is lessened.
27
The greater relevance of income for the subjective well-being of people with disabilities probably derives from the fact that they face additional costs compared to non disabled people, because of their limitations in daily activities.
The higher subjective well-being associated with being in a romantic relationship for disabled people is more difficult to explain. It could be related to the fact that it is more difficult for people with disabilities to find a partner compared to non disabled people (Livneh, 1982; Robillard, Fichten, 1983; Ulivieri et al., 2014). Another possible explanation is that the “Partner” dummy might actually be a proxy for social connections.
Regardless, the greatest difference between the disabled and non disabled people, in terms of the determinants of subjective well-being, seems to be in the importance attributed to being employed.
In Figure 4, I analyze the relationship between life satisfaction, disability status and employment.
28
As can be seen, not only having a job has a positive effect on average life satisfaction, regardless of disability status, but the gap between the average subjective well-being of disabled and non disabled workers is actually smaller than between people with and without disabilities who do not work.
In all the correlation matrices, there is a positive correlation between educational level and the employment dummy, which equals 0.27.
Therefore, in Table 2, I analyze the relationship between disability status and education.
No Yes
Disabled No Yes
Figure 4: Average life satisfaction by employment and disability status
Employed A v e ra g e l if e s a ti s fa c ti o n 0 2 4 6 8 10
29
Table 2: Education by disability status (%)
Disabled Educational level Total
Low Medium High
No 27.9 44.1 28.0 100
Yes 35.9 44.6 19.5 100
X2 2.5308
Df 2
P-value 0.2821
As expected, disabled people are less educated than non disabled people, although the hypothesis that there is no difference between the two groups cannot be rejected. One might argue that the gap in educational attainment people with disabilities and people without is mediated by age, that is, disabled respondents are less educated because they are older and there is a negative correlation between educational attainment and age (-0.07).
In Table 3, I analyze the relationship between disability and age .
Table 3: Disability by age
Disabled Age Total
16-40 41-64 No 48.0 52.0 100 Yes 21.2 78.8 100 X2 14.674 Df 1 P-value 0.0001278
As can be seen, disabled people are, indeed, significantly older than non disabled people. This is due to many factors:
1. Very few people are born disabled
2. Some disabilities are only acquired (for example, Spinal Cord Injury)
3. Most people with disabilities acquire them later in life, either following accidents, or because of long-standing conditions that result in limitations in daily activities only after a certain period of time.
As I pointed out before, the lower level of education disabled people have compared to the rest of the population might be linked to the fact that they are older than them. In order to verify if this is the case, I need an indicator of education which is independent by age, i.e. which keeps age fixed
30
In Table 4, I analyze the percentage of “Early leavers from education and training” by disability status. The latter are people aged 18-24 with at most lower secondary education, who are not attending further education or training programs.
Table 4: Early leaver from education or training by disability status (%)
Disabled Early lever Total
No Yes No 63.6 36.4 100 Yes 57.5 42.5 100 X2 0.54975 Df 1 P-value 0.4584
The percentage of early leavers from education and training is higher among the disabled (42.5%) than among people with no disabilities, although the hypothesis that there is no difference between the two groups cannot be rejected.
In Table 5, I analyze the employment rate by disability status.
Table 5: Employment rate by disability status (%)
Disabled Employed Total
No Yes No 36.7 63.3 100 Yes 55.3 44.7 100 X2 6.2521 Df 1 P-value 0.0124
As can be seen, the employment rate is significantly lower among people with disabilities. I wonder if education mitigates the negative relationship between disability and employment. In Table 6, I restrict the sample to people with a Bachelor’s or Master’s degree.
31
Table 6: Employment rate by disability status among university graduates (%)
Disabled Employed Total
No Yes No 20.1 79.9 100 Yes 35.2 64.8 100 X2 4.9965 Df 1 P-value 0.0254
The employment rate of college graduates is considerably higher than average for both disabled and non disabled people, but the gap between the two groups remains significant. Thus, a higher educational level increases the likelihood of being employed, but does not reduce the employment rate differential between disabled and non disabled people.
There could be two reasons why the employment rate is lower among people with disabilities:
1. Few disabled people enter the labour market: the limitations in daily activities, which might make workplace adaptations necessary, or the lack of vocational programs for individuals with disabilities may discourage them from looking for a job
2. The unemployment rate is higher among disabled people: potential employers may find it more cost-effective to employ non disabled people than people with disabilities, especially if workplace adaptations are necessary in order for them to be able to work. Furthermore, many employers tend to underestimate the work-skills of people with disabilities (Jones, 2008)
In Table 7, I analyze the relationship between activity rate and disability status. Active people are individuals aged 16-64 who are either working or unemployed.
Table 7: Activity rate by disability status (%)
Disabled Active Total
No Yes No 26.1 73.9 100 Yes 43.1 56.9 100 X2 5.6297 Df 1 P-value 0.01766
32
As can be seen, the activity rate of the disabled is significantly lower than that of people without disabilities.
In particular, almost half of the disabled respondents are inactive (43.1%), against about one quarter of the non disabled respondents (26.1%).
In Table 8, I compare the unemployment rate among the two groups.
Table 8: Unemployment rate by disability status
Disabled Unemployed Total
No Yes No 85.7 14.3 100 Yes 78.5 21.5 100 X2 1.302 Df 1 P-value 0.2538
There is a higher unemployment rate among individuals with disabilities compared to the non disabled, although the hypothesis that there is no difference among the two groups cannot be rejected.
The remainder of this chapter is structured as follows. In Part 1, I analyze the determinants of life satisfaction for disabled and non disabled respondents, fist separately, then all together. In Part 2, I consider only working people and analyze the effect of working part-time and having a temporary job on subjective well-being. In Part 3, I differentiate between severely disabled, mildly disabled and non disabled respondents, and repeat the same kind of analysis I undertook in the first part. In Part 4, I study the determinants of life satisfaction by age, running separate regressions for people aged 16-40 and 41-64.
All the regressions I run are ordered logistic regressions, in order to account for the fact that the response variable, i.e. life satisfaction, is an ordered categorical variable. In order to give a meaningful interpretation to my results, I report the odds ratio for each coefficient (I assume proportional odds). The odds ratio (or marginal effect) for regressor x is given by
𝑂. 𝐷.𝑥= 𝑒𝛽, 𝑤ℎ𝑒𝑟𝑒 𝛽 = 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝑜𝑓 𝑥
When O.D. > 1, an increase in regressor x is associated with a greater probability of reporting higher levels of life satisfaction, and vice versa.
33
3.1. Part 1: The determinants of life satisfaction
Table 9: Logistic regressions
Variable Odds ratio
Disabled Non disabled All (1) All (2)
Age 0.8387*** (0.0028) 0.8405*** (0.0021) 0.8384*** (0.0017) 0.8404*** (0.0018) Age squared 1.0019*** (0.0000) 1.0019*** (0.0000) 1.0019*** (0.0000) 1.0019*** (0.0000) Female 1.1772*** (0.0213) 1.1334*** (0.0098) 1.1463*** (0.0089) 1.1425*** (0.0089) Education 1.1383*** (0.0092) 1.1802*** (0.0043) 1.1722*** (0.0039) 1.1726*** (0.0039) Employed 1.7986*** (0.0246) 1.3959*** (0.0122) 1.4715*** (0.0108) 1.3984*** (0.0105) Partner 1.7409*** (0.0243) 1.7457*** (0.0113) 1.7459*** (0.0102) 1.7441*** (0.0102) Income 1.1482*** (0.0051) 1.1512*** (0.0028) 1.1525*** (0.0024) 1.1516*** (0.0024) Disabled 0.4385*** (0.0130) 0.3819*** (0.0095) Disabled*Employed 1.3074*** (0.0065)
Country dummies Yes*** Yes Yes*** Yes***
Residual deviance 98696.87 467975.14 567170.62 567059.67
AIC 98762.87 468041.14 567238.62 567129.67
Psuedo-R2 0.07212512 0.05049993 0.08233874 0.08251827
Heteroskelasticity robust standard errors
In the Table 9, I analyze the determinants of life satisfaction for people with disabilities (column 1), non disabled individuals (column 2) and the entire sample (columns 3 and 4). As can be seen, being disabled decreases the probability of being reporting high levels of life satisfaction.
For both disabled and non disabled people, life satisfaction is U-shaped in age. All other regressors have a positive impact on well-being.
Among disabled people, having a job makes it considerably more likely to report higher levels of life satisfaction, compared to non disabled people.
The implication of these findings is that the negative impact of disability on average life satisfaction reduces in magnitude if the disabled respondents are employed.
In particular, the odds ratio associated with being disabled is 0.4385 (column 3) and the corresponding regression coefficient is -0.8244.
34
This implies that disabled respondents are 56.15% less likely to report higher levels of life satisfaction.
In fact, the reduction in the likelihood of reporting higher levels of subjective well-being is computed as
∆ = 1 − 0.4385 % = 56.15 %
As for being employed, the odds ratio associated with the employment dummy equals 1.4715, which means that having a job increases the probability of reporting high levels of subjective well-being by
∆ = 1.4715 − 1 % = 47.15 %
The implicit assumption of the model in column 3 is that the marginal effect of having a job is the same for people with and without disabilities.
In column 4, I relax that assumption, introducing an interaction term between disability and employment.
In the fourth model, the marginal effect of being disabled is 0.3819, corresponding to a coefficient of 0.9626.
Thus, disability causes a reduction in the probability of reporting higher levels of life
satisfaction equal to 61.81%. The negative effect of being disabled on subjective well-being is greater (in absolute value) in model 4 than in model 5.
However, the odds ratio associated with being Employed equals 1.3984, corresponding to the coefficient 0.3353, and that of derived from being both disabled and employed is 1.3074, corresponding to the coefficient 0.2680. Therefore, the marginal effect of being disabled and employed equals
𝑀. 𝐸.𝐷,𝐸= 𝑒(−0.9626+0.3353 +0.2680 )= 0.6982
Thus, disabled people who are employed are considerably more satisfied with their life compared to those who are not.
35
3.2. Part 2: The determinants of life satisfaction for employed people
Now I restrict the sample to employed people, in order to understand if particular working conditions affect life satisfaction, and if there are differences in the preferences of disabled versus non disabled people.
In Table 10, I report the proportion of respondents by disability status and employment for the entire sample.
Table 10: Disability status and employment (%)
Disabled Employed
No Yes
No 31.0 53.5
Yes 8.5 6.9
Disabled workers constitute only 6.9% of the entire sample. Most disabled people (8.5% of the sample) do not work.
Nonetheless, I investigate the effect of working conditions on life satisfaction.
In particular, I use the "Part-time" dummy I built and the variable "Temporary job", as I renamed Type of contract. The latter equals 1 if the employee has a permanent job, 2 if she has a contract of limited duration.
Table 11 reports the percentage of employed people who work part-time, by disability status. Table 11: Disability by part-time job (%)
36
Disabled Part-time Total
No Yes No 83.6 16.4 100 Yes 77.5 22.5 100 X2 0.80966 Df 1 P-value 0.3682
As can be seen, there is a higher percentage of part-time workers among disabled than non disabled workers, though the hypothesis that there is no difference between the two groups cannot be rejected.
It is also possible that some people with disabilities cannot work full-time, precisely because of their health conditions.
In Table 12, I report the percentage of employed people who have a temporary contract, by disability status.
Table 12: Disability by temporary job (%)
Disabled Temporary job Total
No Yes No 87.3 12.7 100 Yes 89.2 10.8 100 X2 0.037091 Df 1 P-value 0.8473
As can be seen, there is a lower percentage of workers with temporary jobs among disabled than non disabled workers, though the difference is not statistically significant.
In the Table 13, I analyze the effect of working part-time and having a temporary contract on disabled and non disabled workers.
In particular, the first model considers only disabled workers, the second focuses on non disabled workers. The third and the fourth consider the entire sample, introducing a disability dummy (column 3) and interaction terms between work variables and disability (column 4).
37
Table 14: Logistic regressions
Variable Odds ratio (employed people)
Disabled Non disabled All (1) All (2)
Age 0.8936*** (0.0059) 0.9033*** (0.0034) 0.9021*** (0.0030) 0.9022*** (0.0030) Age squared 1.0011*** (0.0001) 1.0010*** (0.0000) 1.0010*** (0.0000) 1.0010*** (0.0000) Female 1.0360 (0.0367) 1.0592*** (0.0138) 1.0566*** (0.0129) 1.0566*** (0.0129) Education 1.1892*** (0.0155) 1.1873*** (0.0059) 1.1879*** (0.0055) 1.1879*** (0.0055) Partner 1.8001*** (0.0395) 1.7513*** (0.0154) 1.7551*** (0.0144) 1.7555*** (0.0144) Income 1.2098*** (0.0115) 1.1933*** (0.0053) 1.1966*** (0.0048) 1.1965*** (0.0048) Part-time 0.9455 (0.0405) 0.9642* (0.0170) 0.9640* (0.0159) 0.9612* (0.0156) Temp. job 0.7667*** (0.0537) 0.8022*** (0.0209) 0.7984*** (0.0194) 0.8048*** (0.0194) Disabled 0.5015*** (0.0202) 0.5412*** (0.0089) Disabled*Part-Time 1.0218*** (0.0034) Disabled*Temp. job 0.9291*** (0.0101)
Country dummies Yes*** Yes*** Yes*** Yes***
Residual deviance 35560.53 241876.80 277606.43 277605.07
AIC 35628.53 241944.80 277676.43 277679.07
Psuedo-R2 0.2106959 0.2119514 0.2312864 0.2312901
38
For disabled workers, working part-time has a negative impact on life satisfaction. However, the odds ratio is very close to 1 (0.9455) and not statistically significant. However, having a temporary contract decreases the probability of reporting high levels of life satisfaction by more than 20%.
As for non disabled workers, the negative impact of working part-time is significant at the 5% level, but the marginal effect is even closer to 1 than for disabled workers, being equal to 0.9642. Having a temporary contract has a negative impact on life satisfaction, although the odds ratio associated with a temporary contract is slightly higher for non disabled workers (0.8022), than for disabled workers (0.7667).
In the third column, I consider the entire sample, introducing a disability dummy. Among workers, the marginal effect of being disabled is 0.5015. Although disabled workers are still less likely to be very satisfied with their life compared to non disabled workers, the marginal effect of being a disabled worker is higher than that of being disabled, worker or not.
In fact, the odds ratio associated with the disability dummy in Part 1 (column 3) is 0.4385, which is lower than 0.5015.
In column four, I introduce the interaction terms between the disability dummies and the labor variables.
As can be seen, all the regressors turn highly significant. However, the interaction term between disability and working part-time is very close to 1. It equals1.0218, to be precise. The interaction between disability and temporary contract is 0.9291.
These result have a straightforward interpretation in light of the analysis I conducted until now.
Working part-time has a negative impact on life satisfaction for non disabled people, but is not significant for people with disabilities. There are two possible explanations for this differential:
1. It is so difficult for disabled people to actually find a job, that working part-time or full-time makes no difference to them
2. Some people with disabilities may be able to work only part-time, because of their chronic conditions.
39
As for having a temporary contract, the prospect of having to look for a new job one the contract expires might be more daunting for disabled workers, given the difficulties they face in finding a job in the first place.
In conclusion, having a part-time job has no significant impact on the life satisfaction of disabled people, while having a temporary contract decreases the likelihood of reporting high levels of life satisfaction.
Contracts of limited duration have the same effect on the subjective well-being of non
disabled workers, but of a smaller magnitude. On the other hand, workers with no disabilities seem to be more dissatisfied with working part-time than non disabled workers, although not by much.
3.3. Part 3: The correlates of life satisfaction by degree of limitation
In this section I consider the entire sample and distinguish between severely and mildly disabled people.
If one distinguishes between Severely disabled, Mildly disabled and Non disabled people, one discovers that life satisfaction decreases with the degree of limitation in daily
40
As I explained at the beginning of this chapter, I defined as “Severely disabled”, the respondents with a chronic illness or condition that resulted in strong limitations in daily activities. Those who do have a long-standing condition and are limited by it, but not strongly, I classified as mildly disabled.
As can be seen in Figure 1, severely disabled people constitute 4.5% of the sample, while 11% of the respondents are mildly disabled.
Among the disabled respondents, people with mild disabilities are by far the majority, as can be seen in Table 15.
Non disabled Mildly disabled Severely disabled
Figure 5: Average life satisfaction by degree of limitation
Degree of limitation A v e ra g e l if e s a ti s fa c ti o n 0 2 4 6 8 10
41
Table 15: Disabled people by degree of limitation (%)
Degree of limitation Percentage
Mild 71.9
Severe 28.9
Total 100
In the following tables, I compare highest educational level attained and employment rates by degree of limitation.
Table 16: Educational level by degree of limitation (%)
Degree of limitation
Educational level
Total
Low Medium High
Non disabled 27.9 44.1 28.0 100 Mildly disabled 33.8 44.6 21.6 100 Severely disabled 41.3 44.5 14.1 100 X2 7.1787 Df 4 P-value 0.1267
As can be seen, the educational level decreases with the degree of limitation.
While 44-45% of the respondents graduated high school and pursued no further education, regardless of limitations in daily activities, the percentage of respondents who did not
graduate high school is about 28% among non disabled people, 33.8% among mildly disabled people and 41.3% among severely disabled people.
Conversely, among severely disabled people university graduates are 14.1%, against 21.6% among people with mildly disabilities and 28% among non disabled respondents.
However, the P-value is greater than 0.05, indicating that the hypothesis that there is no difference between the three groups cannot be rejected.
As I pointed out in the first section, another indicator that I find interesting is the percentage of early leavers from education and training. As disabled people are, on average, considerably older than the rest of the population, they might have had fewer educational opportunities compared to younger cohorts.
If that was the case, the differential in the educational levels of people with and without disabilities would not depend upon physical, mental, or emotional impairment, but simply by age cohort.
42
Since the indicator “Early leavers from education and training” refers to people aged 18-24, it is not affected by age, contrary to educational level.
In Part 1, I discovered that there is a higher percentage of early leavers from education and training among the disabled than the rest of the population, although the differential is not significant.
Now, I divide disabled people by degree of limitation.
Table 17: Early leaver from education or training by degree of limitation (%)
Degree of limitation Early leaver Total
No Yes Non disabled 63.6 36.4 100 Mildly disabled 62.5 37.5 100 Severely disabled 44.1 55.9 100 X2 9.7701 Df 2 P-value 0.007559
As the degree of limitation in daily activities grows, so does the percentage of early leavers from education and training.
Among non disabled respondents, the people aged 18-24 who attained at most lower
secondary education that are not in education or training programs are 40.1%. Among mildly disabled individuals, the percentage rises to 45.1%, while most of the respondents with severe limitations in daily activities whose highest educational attainment is completing middle school (58%) are early leavers from education and training.
As can be seen, there is a huge differential between people with no disabilities or mild impairments, on one side, and people with severe limitations, on the other side. This gap in educational achievement is impossible to detect when one puts severely and mildly disabled individuals in one category, because people with severe disabilities are a minority, even among the disabled population.
I can think about two possible reasons why people with disabilities, and especially people with greater limitations, would have a lower level of education compared to non disabled people.
The first has to do with the difficulties of ensuring equal education opportunities for disabled people: as the ANED report on education pointed out, disabled students encounter a number of obstacles in accessing, sometimes physical, such as architectural barriers, sometimes
43
derived from the fact that teachers are not given adequate information on how to include disabled students.
These difficulties in accessing education are more severe for people with strong limitations in daily activities, who might need more adjustments.
The second reason why disabled people are less educated may be connect with difficulties in accessing the labor market.
People with high levels of education expect to get good jobs, however, as I discovered in Part 1, the employment rate of disabled people is considerably lower than that of non disabled, while their unemployment rate is higher.
This might discourage people with disabilities and their families from investing in education. In Table 18, I analyze the employment rate by degree of limitation, first for the entire sample, then for university graduates.
Table 18: Employment rate by degree of limitation (%)
Degree of limitation Employed Total
No Yes Non disabled 36.7 73.3 100 Mildly disabled 48.2 51.8 100 Severely disabled 72.7 27.3 100 X2 27.189 Df 2 P-value 0.000001247
The employment rate drops drastically as the degree of limitation increases: most of the non disabled respondents (72.3%) are employed, against a little over one half of the individuals with mild limitations in daily activities (51.8%) and over a quarter of the respondents with severe limitations (27.3%).
I wonder how much of these employment rate differentials depend on the degree of limitation, and how much on education: disabled people are less educated than non disabled people, and this is especially true for people with severe limitations.
In Table 19, I compare the employment rates of university graduates by degree of limitation.
44
Degree of limitation Employed Total
No Yes Non disabled 20.1 79.9 100 Mildly disabled 29.6 70.4 100 Severely disabled 56.9 43.1 100 X2 31.856 Df 2 P-value 0.000000121
As can be seen, the employment rate of university graduates is higher compared to the rest of the population.
However, the gap between people without impairment or with mild impairments, on the one side, and those with severe disabilities, on the other side, remains huge and statistically significant.
In Table 20, I repeat the analysis I did in Part 1, distinguishing disabled people by degree of limitation.
Table 20: Logistic regressions
Variable Odds ratio Severely Disabled Mildly disabled
Non disabled All (1) All (2)
Age 0.8610*** (0.0052) 0.8473*** (0.0034) 0.8405*** (0.0021) 0.8411*** (0.0017) 0.8420*** (0.0017) Age squared 1.0016*** (0.0001) 1.0018*** (0.0000) 1.0019*** (0.0000) 1.0019*** (0.0000) 1.0018*** (0.0000) Female 1.1096* (0.0406) 1.1596*** (0.0250) 1.1334*** (0.0098) 1.1389*** (0.0089) 1.1372*** (0.0089) Education 1.0835*** (0.0176) 1.1494*** (0.0109) 1.1802*** (0.0043) 1.1704*** (0.0039) 1.1707*** (0.0039) Employed 1.5972*** (0.0492) 1.5857*** (0.0292) 1.3959*** (0.0122) 1.4322*** (0.0107) 1.3961*** (0.0106)
