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Explore the economic theories behind labour market discrimination, including taste discrimination, statistical discrimination, and value discrimination. Understand the factors that contribute to wage disparities between ethnic groups and genders.
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Labour market discrimination(Borjasch. 9.) • Reading instructions: • You can skip ”The impact of statistical discrimination on wages”, section 9.10 and • Read 9.9 extensively to understand the kinds of factors that can play a part).
The perfect competition model assumes: • Wages are determined by the productivity of the worker. • All workers with the same productive characteristics and same work conditions receive the same wage. • Theories of wage discrimination attempt to explain why this is not always the case and why there are large and persistent labour market inequalitites between ethnic groups and between women and men.
Economic theories of labour market discrimination • Taste discrimination • By employers • By co-workers • By customers • Statistical discrimination • Value discrimination
Tastediscrimination • Theorybeginswith Chicago-economist Gary Becker (1957) • Assumetherearetwogroups in the population and thatoneofthem is discriminated. • Thesecan be white/black, men/women, nativeborn/immigrant, ethnicmajority/ethnicminority, heterosexual/homosexual. • As example, takethesegroupsto be women and men.
1. Employerdiscrimination • Assume that • employers care about the gender of their employees - not only their productivity. • Women’s wage is wf but from the point of view of employer i the cost of employing a woman is wf(1+di) di is the coefficient of discrimination(of employer i) If di > 0, the employer ”has a taste for discrimination” – is prejudiced - dislikes hiring female workers. There is discrimination against women. If di < 0 the employer prefers hiring women. Then there is nepotism in favour of women.
Assume (for simplicity) that men and women are perfect substitutes in the production of the firm (equally productive). • If di = 0the profit maximising employer i will hire the labour that is cheapest. • If wf<wm only women will be employed. • If wf>wm only men will be employed. • But if di0 the employer may not hire the cheapest labour. Then: • If wf(1+ di)<wm only women will be employed. • If wf(1+ di)>wm only men will be employed.
If there is employer discrimination against women and wages are equal, discriminating firms will employ only men. • If the wage of women is lower than that of men, some firms will hire only women (those who are not discriminatory or not discriminatory enough to outweigh the wage differential). • When there is employer taste discrimination, firms will be segregated.
Example • Assume that firm 1 has a discrimination coefficient of 0.3, • firm 2 has a discrimination coefficient of 0.2 • and firm 3 does not discriminate at all.
Employment in prejudicedfirms VMP VMP wm wf+d3 wf+d2 wf All male firm All female firm
The discriminating employer maximises utility but not profits. • If the all-male firm hired women it would • Hire workers at a lower price • Hire a larger number of workers • Profits would be larger for both these reasons. • The larger the discrimination coefficient the greater the loss in profits.
Employerdiscrimination segregation.Whataboutwages? • The smaller the female/male wage ratio, the larger the number of firms that hire women. If there are no non-discriminating firms, the demand curve intersects the vertical axis at a point R<1 S Wf/Wm 1 The more prejudice, the lower the equlibrium gender wage ratio D Ef
2. Employeediscrimination • To discriminating employers a female wage of w ”seems” or ”feels” lika a wage of w(1+d), d>0. • To discriminating workers who receive a wage w, the wage ”feels” like a wage of w(1-d), d>0, if they have to work alongside a woman. • An employer with a mixed work-force has to pay a ”compensating differential” to the male workers. A non-prejudiced employer will hire only female workers if the female wage rate is below the male (and v.v.) • With employee discrimination the work-force will be completely segregated and the wages of men and women will be equalised (if productivity is equal).
3. Customerdiscrimination • To discriminating employers a female wage of w ”seems” or ”feels” lika a wage of w(1+d), d>0, if they have a woman working for them. • To discriminating workers who receive a wage w, the wage ”feels” like a wage of w(1-d), d>0, if they have to work alongside a woman. • To discriminating customers a price p for the product of a firm ”feels” like a price of p(1+d), d>0, if they have to buy it from/deal with a woman when they buy it. • Firms where customer contact is important will tend not to employ women or not to employ them in ”visible” jobs.
Statisticaldiscrimination • Statistical discrimination means that an individual is judged on the basis of information about a group, not specifically about himself/herself. • Employers may believe (mistakenly) that one group is less productive than another always or on average. But there can be discrimination of individuals even when employers have correct information about groups and there is no preference discrimination. • Employers may know (correctly) that one group is less productive than another on average. • Employers may know that the dispersion of productive skills is larger in one group. In these cases and if it is impossible, troublesome or expensive to get information about the individual, it is economically rational for the employer to discriminate.
Example 1:Difference in group means • An employer can hire a young woman or a young man in an important position. Their CVs are equally good. Women on average take longer parental leave so the employer makes an ”educated guess” that this woman will take one or two years leave in the next few years but the man at most 2-3 months. • If employees who take parental leave are less profitable, the employer hires the man or hires the woman at a lower wage than he would have paid the man.
Example 2: Difference in dispersion • An employer can hire a person with a degree from Stockholm university (SU) or one with a degree from a US university. US universities vary in quality. This university may be better than SU – and it may be less good. A degree from SU gives enough skill. • If the advantage of hiring someone who is more qualified than necessary is smaller than the disadvantage of hiring someone who is not qualified enough, the employer will hire the person with a degree from SU.
Effects of statisticaldiscrimination. • For the individual: Some are treatedbetter and someworsethantheirindividualskills and abilitieswouldimply. • For the employer: Decreases the probability of getting ”the wrong person in the wrongplace or at the wrongwage”. Reduces risk. Butthere is a risk of usingincorrect information or underestimating the need to get individual information. • For society it is likely to decreaseefficiency. • Allocation and wage setting would be moreefficientifindividual information wasavailable. • Given the distribution of skills and the absence of individual information, it canimprovelabour market efficiency. • But the knowledge that there is statisticaldiscriminationmay make indivduals make less than optimal decisions in human capital investment and jobsearch.
Educationdifferencesbetweenthoseborn in Sweden and immigrants are on averagevery small butdifferaccording to country and gender. Women
Immigrants with high education have higher unemployment, lower earnings, smaller returns to education and less skilled jobs than natives with the same education. • But if the education is acquired in Sweden the difference is substantially smaller. (Statistical discrimination or ”non-transferability”?)
Source: http://www.ssd.scb.se/databaser/makro/Produkt.asp?produktid=UF0529&lang=1
Valuediscrimination • Economic theory usually assumes that employers have correct information about productivity and that workers expect compensation for skills they have acquired. • But in a society where one gender or some ethnic groups have traditionally had a subordinate position evaluations of the jobs and skills which are associated with them can be systematically undervalued. • Industries, firms and occupations with a large percentage of women or minorities usually pay less, even though workers have the same formal qualifications.
Comparative worth • In comparative worth-assessments, the skills required for different jobs, work conditions and demands of the job are compared across different occupations. Such studies nearly always find that female dominated jobs are lower paid relative to the job characteristics. • This can be because of supply and demand factors – and it can be because of value discrimination.
Can onemeasurediscrimination? The Oaxaca decomposition. In empirical research wage differentials between to groups are divided into an explained term and an unexplainedterm. This is always done on the basis of a chosen wage equation. • Starting idea: Differences in average wages can be either: • Because of different average productivity • Because one of them is discriminated.
A completely made-up example: • Suppose that the only difference between male and female workers we consider is that 15% of men have technical-engineering degrees and only 10% of women. • Assume also that male engineers receive 120 "monies" more than other men while female engineers get a only 80 monies extra for being engineers. • If we multiply the difference in number of engineers by the wage premium that male engineers receive we get 120*0.05=6 monies which is how much less men would earn on average if as few men as women were engineers. • The remainder of the wage gap is equal to the difference between the premia, 120-80 monies times the number of female engineers, that is to say 40*0.10=4 monies due to discrimination of female engineers. In other words, if women were paid like men they would be paid, on average 4 monies more.
The decomposition, more formally • Assume that wages depend on education. • For women • where s is years of education above compulsory school and ε a residual with zero mean. • For men
Here the gender wage differentials depends on three things: • Different average length of schooling • Different pay for women and men with only compulsory school (different intercepts) • Different returns to schooling 1. is a difference between workers 2 & 3 are differences in the wage equation (in the structure of rewards)
Average wages of women and men: • The gender wage gap:
The term shows how much larger/smaller men’s average wage would be if they had the same schooling as women. It is called the explained term. The term shows the part that is due to unequal rewards for women and men, the unexplained term. This is how much women’s average wage would increase if they were paid like men.
Returning to the example: • If more women became engineers so that the share of engineers was 15% among women too, women’s average wage would increase by 0.05*80 and the wage gap would be reduced by 4 monies. But since the 15% male engineers are paid 120-80=40 monies more there would still be an unexplained gender differential of 6 monies. • This shows that in our example we can calculate the explained and the unexplained term in two different ways!
The total differential of 10 monies depends both on differences in characteristics and on discrimination. How much is ascribed to which depends on whether we believe that female engineers are paid too little or male are paid too much.
Formally again: • We can decompose the gender wage gap like this:
The two different “explained terms” evaluate the difference in education according to the two different wage functions. Men’s earnings function The red and blue pieces show the explained terms with the two methods wm Women’s earnings function wf sf sm
The decomposition can be made for equations with more variables • If wages depend on n variables: • For a woman • For a man
The explained terms become Where Δ stands for difference between the means for men and women
Interpretation of the decomposition: • If there is discrimination, it will make up a part of the unexplained term. But not all of the unexplained term is due to discrimination. Variables that affect productivity may not be included in the equation. • Conversely, part of the “explained term” may be due to discrimination since discrimination, both inside and outsidethe labour market influence the choice of which skills to acquire and where to work.
Example: Experience/career breaks • Career breaks/parental leave lead to lower wages. (Less experience and loss of skills.) • But due to statistical and other discrimination women get lower wages even if they do not stay home with children. • Because women will get lower wages anyway, it will more often be mothers who stay home with a child. • In addition, men seem to be more “punished” in terms of wages if they take parental leave. (study by Albrecht, Björklund, Sundström & Vroman).
Parental allowance and temporary leave to care for a sick child are unequally shared between mothers and fathers. 2009 47 839 78 22 4 489 65 35 Sources: About women and men 2006 (SCB) and for 2009 www.forsakringskassan.se
Women work in sectors/industries/occupations that pay less • A traditional theory is “occupational crowding”: If women are concentrated in fewer areas, there will be large supply of labour there and that will depress wages. • The low wages in female dominated areas can also be because women are discriminated in hiring in high-paying ones or because they expect to be discriminated if they work in a male dominated area.
Women in the Swedish labour market • The gender wage ratio: • 1968 0.72 • 1981 0.82 • 1991 0.81 • 2000 0.82 • (Source: LLS)
An Oaxaca decomposition: • Le Grand (Acta Sociologica, 1991) decomposes the gender wage differential in Sweden 1981 • Human capital variables • Education, experience, seniority, immigrant, big cities Family • Married/cohabitating, housework, interrupted work career • Job segregation • Positional grade, union member, public sector, occupational segregation • Working conditions • Physically demanding, lack of autonomy, inconvenient work hours, monotonous, piece-work, commuting time, part time, hectic work
Percent of gender wage gap explained • Human capital 20.5 19.2 12.0 12.4 • Family 11.9 1.7 3.3 • Segregation 41.1 41.7 • Working conditions -0.5
Gender segregation • By sector (state/municipal/private) • Industry • Occupation
Summing up – possible reasons for gender wage differential • Differences in human capital • Occupational crowding • Gender stereotyping of jobs and activities • Discrimination in hiring and promotion • Unequal division of housework and childcare • Wage discrimination – taste d., statistical d. and value d.
