Heterogeneity in valuation of human capital

In the previous chapter it was shown that human capital has a wide range of market and non-market returns. Therefore, individuals and society benefit from human capital investments in various ways. Although the heterogeneity in valuation of human capital is likely to be present in the case of all these returns, in this chapter only two types of private market returns are analysed. These are wages and fringe benefits, which are probably the most important private returns on human capital. Furthermore, these returns are directly related to valuation of human capital on the labour market, as they are the direct benefits that employees will receive in compensation for their labour.

Different employees receive different wages and fringe benefits from the labour market. In the case of perfect competition on the labour market there are generally two types of explanations for why some employees receive higher wages and fringe benefits than others. First, in the case of equilibrium, wages are equal to the marginal product of labour. Therefore, wage differences reflect differences in productivity. Second, according to the hedonic theory of wages presented by Rosen (1974) wages will reflect the working conditions on the job.

Employees, who have worse working conditions will receive more compensation for that. In Rosen’s original theory this compensation occurred in the form of higher wages, but it is also possible that fringe benefits are offered as compensation for bad or risky working conditions (van Ommeren et al.

2002). Similarly, the division of labour compensation between wages and fringe benefits can be different across firms and employees, as according to Eberts and Stone (1985), employees may be compensated for lower wages by a higher level of fringe benefits or vice versa. So, it could be concluded that under

by differences in productivity, fringe benefits and/or working conditions. In the same way, differences in fringe benefits between two employees may be caused by differences in productivity, wages and/or working conditions. As productivity is affected by human capital, then in perfectly competitive labour markets and under similar working conditions human capital should be valued at the same rate for all employees in terms of labour compensation. Therefore, under perfect competition, there may exist heterogeneity in valuation of human capital in the sense that employees with equal productivity will receive higher levels of some components of labour compensation than others. But there will not exist heterogeneity in valuation of human capital in the sense that equally productive employees will get different levels of overall labour compensation.

If imperfect competition exists on the labour market, then it is possible that the human capital of different employees is valued at different rates. This means that equally productive employees may receive unequal compensation for their labour. The main sources of imperfect competition, which allow that kind of heterogeneity in human capital valuation, are differences in the bargaining power between employers and employees and the presence of discrimination on the labour market.

Under the conditions of perfect competition there is an unlimited number of employers and employees on the labour market, but in reality this in not the case for many labour markets. In some labour markets there are only a few or just one employer. This will result in high bargaining power for the employers and it will result in the lower wage level in comparison to a more competitive labour market. The situation could be the other way around if there are only a few employees or if the employees are covered by trade unions and they act collectively in wage bargaining. In such a case, the employees will have high bargaining power and this will result in higher wage levels compared to a more competitive labour market.

Heterogeneity in valuation of human capital can also be caused by discrimination in the labour market. Labour market discrimination can be defined as a situation in which equally productive employees are treated unequally in the labour market in a way that is related to an observable characteristic such as gender or ethnicity (Altonji and Blank 1999). In terms of valuation of human capital, unequal treatment means offering unequal compensation for labour. In most empirical studies this means offering unequal wages to equally productive employees. Two types of labour market discrimi-nation – taste and statistical discrimidiscrimi-nation are distinguished in the literature.

Taste discrimination can occur in the form of employer, employee or consumer discrimination. According to Becker (1971), employer discrimination is a situation in which some employers are prejudiced against some employees that belong to a certain group (for example, an ethnic minority). In such a situation, prejudiced employers will prefer to employ members of some particular groups to members of another group. This will lead to a situation in which equally productive employees from different groups receive different wages. Employee

discrimination means that there are some prejudiced employees, who do not like to work together with members of another group. In the case of consumer discrimination prejudiced consumers will obtain less utility from buying a similar good from members of particular groups. Therefore, they will do so only if the price of the good is lower. As with employer discrimination, employee discrimination will lead to a situation where the employees that belong to the discriminated group, receive lower wages.

Statistical discrimination may also occur when employers do not have perfect information about the productivity of employees. In such a case, employers may use some observable characteristics, such as race or gender as proxies for productivity if they are correlated with productivity (Phelps, 1972).

Therefore, employers may prefer to hire members of certain groups, and this leads to wage differences between different groups as taste discrimination.

Besides discrimination on the labour market, pre-market discrimination could also exist. This will occur if some individuals are discriminated against in schooling or other forms of human capital acquisition. Pre-market discrimi-nation will result in differences of human capital between members of different groups, but not in differences in valuation of human capital (Aigner and Cain, 1977). So pre-market discrimination is not relevant in valuation of human capital in the labour market, and this issue is not considered in the following analysis

In reality, remarkable differences exist in the wage rates of observably identical employees. Identical employees may also have different access to fringe benefits. It also happens to be the case that certain groups of employees tend to have different wage rates than other groups. Investigations into the size and causes of wage and to a lesser extent fringe benefits, differences between different groups of individuals has been an extensively researched area in labour economics during the last four decades. The four most important kinds of wage and fringe benefits differences, which are often called wage and fringe benefit gaps, are:

1. Gender wage and fringe benefit gap 2. Ethnic wage and fringe benefit gap 3. Union wage and fringe benefit gap

4. Public-private sector wage and fringe benefit gap

In the next subsections, we will take a closer look at each of these gaps, focusing on both the theoretical and empirical implications emerging from them.

1.2.2.1. Gender wage and fringe benefit gap

Women everywhere have traditionally earned lower wages in comparison to men, and although these differences have decreased over recent decades, still substantial gender wage gaps exist. In 2004, the median gender wage gap averaged 18% across 21 OECD countries. This gap ranges from 6% in New Zealand to 40% in Korea.

To some extent gender wage gaps could be explained by differences in human capital between men and women. In the case of formal education, for younger people in developed countries there no longer exists a significant difference in the attained educational level, but older women tend to have a relatively lower education level. For the US, this has been documented by Blau (1998). The quality of education for men and women is usually equal because in developed countries, and in the overwhelming majority of cases, men and women attend the same schools. Some authors argue that aptitude and achievement test scores will reflect the quality of education. There exist some systematic differences on the basis of gender in these kinds of test results.

Brown and Corcoran (1997) show that among high school graduates in the US, males score better on mathematics achievement tests, while females have better results for reading and vocabulary tests. But as women tend to score better on one group of tests and men on the other types of tests, then on the average it is likely that there does not exist large gender differences in the quality of schooling.

Differences in job market experience are much more important determinants of gender wage gaps than differences in formal education. Men have more labour market experience than women in terms of the total duration of employment. In addition to less time spent on the labour market, women are also more likely to be employed in part-time jobs. The importance of job market experience as a determinant of the gender wage gap is highlighted by Blau and Kahn (1997), who have shown that increases in women’s employment has narrowed the gender wage gap in the US.

Women also have lower tenure and therefore they have lower levels of firm-specific human capital. Women have a higher job turnover than men. There also exist gender differences in terms of quitting jobs (Sicherman 1996). Women tend to leave more for non-market reasons, such as pregnancy, changes of residence and health. As they are more likely to quit jobs, then this could lead to the fact that women are offered less on-the-job training. According to Gronau (1988), women are hired for jobs that offer lower employer financed investment in human capital. Therefore, the gender wage gap is age dependent, women get less employer paid training over their career and gender differences in accumulated human capital and wages increase during the working career (Barron, Black and Loewenstein, 1993).

Besides differences in human capital, job characteristics are often presented as an explanation for the gender wage gap. Men and women tend to be

employed in different occupations. Women are employed in occupations with lower wage levels. Several empirical studies have found evidence that the share of women in a certain occupation has a negative impact on the average wage for that occupation (Blau and Beller, 1988; Lewis, 1996). The question is how these differences of occupations reflect differences in working conditions. Hersch (1991) investigates the effect of working conditions on the gender wage gap and finds that men’s wage advantage is partly caused by the fact that they work in more hazardous jobs.

Gender differences in working conditions could be caused by different preferences. For example, women tend to have a higher risk-aversion than men.

Several studies, for example, Sunden and Surette (1998) and Lehman and Warning (2001), have shown that women tend to take less risks in their savings and investment behaviour. Furthermore, these differences in risk aversion can lead to different preferences in terms of the form of pay. According to Chauvin and Ash (1994), women are more employed in jobs with a relatively high level of base wage in comparison to the share of contingent pay. As there exists a positive relationship between the share of contingent pay and the size of the wage then this relationship causes women’s wages to be lower.

The previously listed characteristics usually do not fully explain gender wage gaps. For example, Altonji and Blank (1999) show that only about 25% of the gender wage gap in the US is caused by the gender differences in human capital, personal and job characteristics. Therefore, it could be argued that the unexplained part of the gender wage gap may reflect the discrimination of women in the labour market in the form of receiving lower wages for similar work on a similar job. But it is also possible that human capital is not correctly or fully accounted for in the analysis of the gender wage gap. Alternatively, job characteristics such as occupation and industry are not measured precisely enough in many studies, and this may bias the results. Marini (1989) has pointed out that unexplained gender wage gaps become smaller when more detailed occupational control variables are used. Therefore, it may be the case that gender wage differences are largely caused by the fact that women are employed in industries and occupations with lower wages. However, it is difficult to assess if that kind of occupational and industrial segregation is caused by gender differences in productivity, discrimination against women in the hiring process or differences in preferences for job characteristics between men and women. In order to solve that puzzle some authors have tried to compare the gender differences in productivity and wages in order to test for the presence of discrimination. These studies have given mixed results in different countries. For example, Hellerstein et al. (1999) have found for the US that wage differences are greater than productivity differences, but Crepon et al.

(2002) have found that in France the wage and productivity differences between men and women are equal.

A few studies have been conducted about the gender gap in fringe benefits.

pension plans. Their results show that women receive lower pension funds in comparison to men. Pesando et al. (1991) have investigated the same issue, but found the opposite result. Solberg and Laughlin (1995) have investigated the effect of fringe benefits on the gender wage gap. They use data about a wide range of fringe benefits and include them together with wages as part of the total compensation. Their results show that in the US the gender wage gap is larger than the corresponding gap in fringe benefits. This result suggests that women are compensated for lower wages with higher level of fringe benefits.

1.2.2.2. Ethnic wage and fringe benefit gap

Substantial ethnic wage gaps exist in most countries that are not ethnically entirely homogenous. In the vast majority cases ethnic majorities have higher wages, and there are only very few exceptions to that rule; for example, whites as an ethnic minority have higher wages in South Africa (Allanson et al., 2002;

Leibbrandt et al., 2005). Ethnic wage gaps are most extensively studied in the US and Western-European countries. In the US the main topics are black-white and Hispanic-white wage gaps and in European countries there are different ethnic minorities in different countries. According to US Current Population Survey in 2004, wages for black males were 25.5% lower than wages for white males. Hispanic males receive 36.8% lower wages than white males. For females the corresponding wage gaps are 10.9% and 10.0%. In the UK, according to Blackaby et al (1998), ethnic minorities had on the average 17%

lower wages than natives in the 1990s, while for some minorities the wage gap was up to 31%. Kee (1995) documents that different ethnic minority groups have 12–43% lower wages than the ethnic majority in the Netherlands.

Ethnic wage gaps are to a relatively large extent explained by differences in human capital. First, ethnic minorities usually have a lower level of formal education. For example, in the US in 1996 according to the Current Population Survey, 28.2% of white males, 10.9% of black males and 8.6% of Hispanic males have acquired a college level education or higher. For females these shares are respectively 24.9%, 13.8% and 7.5% (Altonji and Blank 1999).

Several studies have tried to estimate what the ethnic wage gap would be if the different ethnic categories had similar levels of formal education. For example, according to O’Neill (1990), the black-white wage gap in the US for males will decrease from 17 percentage points to 12 percentage points if both ethnic groups had a similar level of education measured by the years of schooling.

Besides the quantity of education, the quality of education can also be a determinant of ethnic wage gaps. Ethnic minorities in many cases attend schools of relatively poor quality. Card and Cruger (1992) have shown that black-white differences in the quality of education measured according to schooling inputs are important determinants of the black-white wage gap in the US. Johnson and Neal (1996) have shown that the black-white wage gap could be explained by

the racial differences in the Armed Forces Qualification Test (AFQT) scores, which could be interpreted as a proxy for ability or skills or quality of schooling. According to Maxwell (1994), the differences in quality of education are a more important determinant of the black-white wage gap than differences in the quantity of education.

In many cases ethnic minorities consist of immigrants and in these cases besides the differences in formal education, the lack of location specific human capital could be a reason for the lower wages for migrants. As over time immigrants acquire location-specific human capital then their earnings depend on the time spent in the new country. Those kinds of earnings patterns are described by the theories of assimilation (Chiswick 1978). For many countries, there exists evidence that for immigrants an education attained abroad has lower rates of return than the education attained in the new country. For example, McManus et al. (1983) have found these kinds of results for the US, Chiswick and Miller (1985) for Australia and Kee (1995) for the Netherlands. Several other studies have documented the fact that language skills are important determinants of the earnings of immigrants. For example, Daneshavry et al.

(1992) and Carliner (1996) have found English language wage premiums for immigrants in the US. According to Bratsberg and Terrel (2002), lower rates of return on education for immigrants could be caused by differences in the schooling quality between their homeland and the destination country.

Differences in labour market experience are also important for the existence of the ethnic wage gap. Ethnic minorities tend to have less job market experience. In many cases, especially in European countries, they have less chance of finding jobs and therefore they accumulate less job market experience over their life-cycle (Blackaby et al 1998). Antecol and Bedard (2004) show on the basis of US data that ethnic differences in actual labour market experience explain about half of the black-white and Hispanic-white wage gaps in the US.

Furthermore, it was noticed already by Flanagan (1974) that ethnic minorities are less likely to be offered on-the-job training by their employers. So, even if ethnic minorities had an equal level of labour market experience they will accumulate less human capital as they receive less on-the-job training.

Differences in working conditions are not likely to be a determinant of ethnic wage gaps. Ethnic minorities tend to have worse working conditions in comparison to ethnic majority. For example, Richardson et al. (2004) studied fatal occupational injury rates in the US and they found that these rates are

Differences in working conditions are not likely to be a determinant of ethnic wage gaps. Ethnic minorities tend to have worse working conditions in comparison to ethnic majority. For example, Richardson et al. (2004) studied fatal occupational injury rates in the US and they found that these rates are