On the basis of disability, we have observed clear differences in employment outcomes, earnings, and quality of life (Acemoglu and Angrist, 2001 and Deleire, 2000). Access to employment opportunities and income are two notable indicators of a person's quality of life. There is no single, consistently used, definition or method for classifying the disabled, and physical disability is only one form of disability. A school of thought posits that people are disabled when functional limitations impede on people's ability to perform activities necessary to maintain or improve their quality of life (instead of solely experiencing permanent or transitory physical or mental limitations). Explaining disability in terms of how functional limitations restrict the ability to perform activities provides the basis for this analytical work to be conducted.
The relevance of confronting disability for poverty reduction and development has long been neglected by development actors and only marginally addressed at the policy and implementation levels (Fritz et. al, 2009). The implementation of the UN Convention on the Rights of Persons with Disabilities (UNCRPD) on 3 May 2008 made disability, now framed as a human rights issue, an important part of the mainstream development agenda.
The subsequent section sets reviews the empirical evidence on the topic. This is followed by the section that establishes the econometric methods, the dataset for the analysis, and the variables in focus. Third comes the regression and decomposition sections, exploring employment first and earnings next. We conclude with a rehash and discussion of the outcomes alongside the literature, and point out areas of possible policy intervention.
Though disability exists in a continuum, societies around the world interact with disability largely through several layers of dichotomized frameworks. The society tends to see people as either disabled or not, and the ramification of this dichotomy is pervasive in the labor market where the impression of people's productivity tends to also be translated into a dichotomy. This dichotomized view of disability is also present in the way we study disability. Most of the rigorous empirical studies on how physical disabilities affect labor market outcomes have focused on high-income countries, whereas majority of the world's disabled live in the developing world. Disabilities could also be viewed from the prism of rural vs. urban. A World Bank disabilities and poverty survey found that there are higher proportions of people with disabilities in rural (and poorer) areas (Bickenbach, J., 2011).
Literatures show that employment, wage are affected by one's education (Huang, 1999), gender (Oaxaca, 1973), disabilities, experience, and urban/rural (Phimster, 2005). The human capital framework provides an explanation for the labor market wage and employment differentials, which is underscored by the assumption that there is a significant productivity gap beyond disabled and non-disabled. It predicts that the least educated workers, who by presumption possess fewer formally developed skills of cognitive and technical adaptability, tend to experience the greatest disability induced reduction in wages.
Taste based discrimination (Becker 1971), which is underpinned by prejudice, and statistical discrimination are the dichotomized frameworks for understanding discrimination in the labor market. Statistical discrimination, in essence, results when the actual or assumed statistical properties of a group are applied to anyone belonging to that group. Contrary to the above more traditional explanation of statistical discrimination, it has been posited that statistical discrimination could come about as result of factors beyond the average outcomes of one's group. These factors include noisier productivity signals (Aigner & Cain, 1977), differential screening or communication costs (Cornell and Welch 1996; Lang 1986).
Studies consistently identify employment effect of disability. The presence of wage discrimination forces some individuals to exit the labor market (Baldwin and Johnson, 1994), and explain some of the observed differences in employment rates. This disincentive effects of wage discrimination account for only two of the twenty-nine-percentage points and less than one percentage point, of the 26% gap in employment rate for disabled men and disabled women respectively (Baldwin and Johnson, 1994 & 1995). Disabilities affect the type of employment undertaken. Disabled people are twice as likely to be self-employed (Blanck et al., 2000).
There are two main explanations as to why people with disability are more likely to be self-employed. Firstly, employer discrimination reduces the relative wages of disabled employees, making self-employment more attractive. Secondly, the disabled may gain greater freedom and flexibility to accommodate their disability through self-employment. Evidence suggests that flexibility is a dominant reason, and that these forms of employment enable individuals who are unable to undertake in standard types of employment to work (Jones, 2008). Flexibility turns out to be a social amenity that comes with a cost. Not only are disabled people more likely to be self-employed, they are concentrated in non-standard forms of employment that have lower wages and fewer benefits on average (Schur, 2003). Even after controlling for personal characteristics, disabled people are significantly more likely to be in temporary and part-time employment.
Approximately 10% of the observed wage differential for men, and 20% for women, are potentially attributed to discrimination - with job demands and interactions included in the wage model (Baldwin & Chung, 2014). Studies that have sought to decompose the gap in employment probabilities find that about half of the difference in employment probability is explained by the differences in characteristics (Kidd et. al., 2000). This increases to over 70% when productivity and selection issues are controlled for (Madden, 2004).
The growing understanding of disabilities as a global phenomenon is compromised by the scarcity of quality research with focusing on the developing regions. The impact of disability depends on the environment in which an individual is situated (Silversetein et al. 2005). The power of a wider drawn geographical conclusions from an econometric finding become more substantial with external validation. This paper investigates the labor market outcomes of disability in the East African country of Tanzania.
Data and Econometric Methods
Characteristic of the Study Participants
This thesis uses the 2010/2011 Tanzania National Panel Survey (TZNPS), a cross-sectional dataset that was conducted as part of the LSMS Integrated Surveys on Agriculture project. (2) There are 16,000 individuals, in 3,280 households in this TZNPS program. The exclusion criteria for this project which comprises of those aged below 17 and those aged above 65, and individuals who are missing key demographic variables. This produces a sample of people in their prime working age. The ultimate dataset comprises of 4,739 individuals (about 300 are classified as disabled).
Employment & Earnings
This econometric methodology is in line with previous analysis of the impact of disability on employment and earnings (Kidd et al. 2000, Madden, 2004 and Jones, 2008). We use the probit regression technique for employment.
Binomial Equation for the probit regression is represented: (3)
[mathematical expression not reproducible] (1)
[P.sub.i] = the probability that the indicator variable [D.sub.i] = 1
[Employed.sub.i] = [[PHI].sup.-1](p) = [B.sub.0] + [B.sub.1] [Disability.sub.i] + [B.sub.2] [Rural.sub.i] + [B.sub.3] [Gender.sub.i] + [B.sub.04] [Education.sub.i] +[B.sub.5]Marital [Status.sub.1] + [B.sub.6][Region.sub.1i]
s = a standardized normal variable
The derivative of the equation shows the marginal effect of one-unit change in x on the probability that y = 1.
We modify the Mincerian wage (Mincer, 1974) regression specification by including a disability dummy:
ln(w(s, x, disability, region)) = [a.sub.0] + [[rho].sub.s].S + [B.sub.0].X + [B.sub.1].[X.sup.2] + [B.sub.1].Disability + [B.sub.1].region... (2)
We use the Blinder-Oaxaca model to decompose earnings. The Blinder-Oaxaca decomposition technique quantifies the separate...