Globalization, inequality and the labor market

1. Globalization, inequality and the labor market Hartmut Lehmann TEP

2. Preliminaries • Human capital • The labor market • Labor demand • Labor supply • The market for educated labor • Distribution of pay (income) • Functional distribution of income • Size distribution of income • Pay differentials

3. Human Capital: demand for education as investment decision

4. Human Capital: demand for education as investment decision • Assumptions of model underlying previous figure: • Full employment, i.e. after schooling everyone finds a job; • Those with high school continue to work w/o interruptions from age 18 until age 65; • Those with university continue to work w/o interruptions from age 22 until age 65; • Human capital is only formed off the job, i.e. education is a full-time endeavor.

5. Human Capital: demand for education as investment decision • Investment decision: • Compare costs of investment with benefits of undertaking 4 years of education, where: • costs = direct costs plus opportunity costs of foregone earnings between 18 and 22 years of age; • benefits = difference between WT and WS (appropriately discounted) that educated person is paid every year between ages 22 and 65. • If benefits > costs, then undertake investment, in reverse case do not undertake investment. • In our figure the rectangle “benefits“ > the 2 rectangles “direct costs“ + “indirect costs/opportunity costs “  investment should be undertaken.

6. Human Capital: demand for education as investment decision • We can also look at the private rate of return (rr) to education; a rough approximation is: Difference 7301 6599 Depending on what is included in net total costs, we get a rr for males between 11.3 and 18.1%, and for females between 12.5 and 23.4% - Source: Sapsford and Tzannatos (1993).

7. Human Capital: demand for education as investment decision • We can also show the demand for education using a simple mathematical model that arrives at the “Mincer equation.“ • The following graph of life time working helps us to derive this equation: • A worker decided to get education or not (a 1-0 decision) with earnings for T years if s/he is not educated and with earnings of T-S years if educated (S here = years of schooling); • Labor market is assumed to be competitive; • Assume r= discount rate, the same for everybody; • Quite a few other assumptions, which we don‘t state.

8. Human Capital: demand for education as investment decision • The present value of earnings of an uneducated worker is the accumulation of his/her wage over T years discounted up to year T; • The present value of earnings of an educated worker is the accumulation of his/her wage over T-S years discounted up to year T-S:

9. Human Capital: demand for education as investment decision

10. Human Capital: demand for education as investment decision • Let‘s look at the foremost right expression of first equation: it basically tells us that a worker with an infinite working life has as the present value of earnings = present value of an annuity paid for ever (Wu / r), where Wu is the annuity. Since worker does not work forever we have a correction factor for the fact that working life finishes at T (1-e-rT); • Let‘s look at the foremost right expression of second equation: it has the same interpretation as the first equation (the annuity is however We) with the additional point that there is a second correction factor (e-rS) that takes account of the fact that earnings start S years later.

11. Human Capital: demand for education as investment decision • In a perfectly competitive labor market, for the marginal worker it must be true that PVu= PVe, i.e. under this condition the labor market is in equilibrium; so • This is the „“Mincer equation“ in its most basic form; it says: the log of earnings for educated workers is equal to the log of earnings of uneducated workers plus an amount that depends on the years of schooling (S) and the rate of return to schooling (r).

12. Human Capital: demand for education as investment decision • To take account of the fact that experience also determines wages and that the observed wage-esperience profile for many workers looks like:

13. Human Capital: demand for education as investment decision • Mincer estimated with cross section data from the 1950‘s the following equation: • The estimated regression was, using data for males and annual earnings, as follows (Mincer, 1974):

14. Human Capital: demand for education as investment decision • Interpretation of the regression results: • The coefficient on S =0.107, which says that the return to one year of education is about 11%; roughly equal to what rates to commercial investment were in the 1950‘s. • The coefficient on exp=0.081, which says that the first year of experience increases earnings by 8.1%, the negative coefficient on exp2 tells us that this increase is decreasing with years of experience (so the figure that shows a negative quadratic relationship between experience and earnings is borne out by the cross section results. • The variable exp measures potential experience as indicated above – this is ok since we are looking at male earnings and in the 1950‘s most males has an uninterrupted work history. • Finally note that the R2 tells us that nearly 30% of the variation in earnings is explained by the two variables S and exp, there ar obviously many other factors that have an impact on earnings, but this simple model is nevertheless powerful. • Since Mincer there have been many methodological improvements over this simple OLS cross section regression  Card (1999).

15. Human Capital: Mincer-type equation from Dohmen, Lehmann and Zaiceva (2008)

16. The competitive labor market

17. Labor demand If f(n) is a well-behaved concave short-run production function (i.e. f′(n) > 0 and f″(n) < 0 ); p is exogenous price; w wage and n employment.  Profits are π = p f(n) – wn (7) If firm is a profit maximiser it is indifferent between combinations of w and n that leave p f(n) – wn constant. The locus of all combinations of w and n that keep profits the same is called an isoprofit curve. The labour demand curve is locus of all points where for each wage the slope of the isoprofit curves is zero.

18. Labor Supply • Labour Force Participation • The entire civilian non-institutionalised population aged 15 to 65, Working Age Population, WAP, is decomposed into employed, E, unemployed, UN, and out of the labour force, O, where •  WAP=E+UN+O • The labour force, LF, is limited to those who work or are unemployed, i.e. • LF=E+UN • Participation Rate=LF/WAP

19. Short-Run Labor Supply – A graphical approach

20. Derivation of labor supply curve

21. The market for educated labor

22. The market for educated labor • RD=demand for educated labor by firms,it is NOT the demand for education by workers; • RS=supply of educated labor, i.e. the demand for education by individual workers. • Why is RD downward sloping? • If educated and uneducated labor are substitutes, then as RW , firms will substitute the cheap factor of production (uneducated labor) for the more expensive one (educated labor); • Differences in ability: with more educated labor,  length of schooling, if Ed=ed(length of schooling), i.e. if length of schooling enters education production function, then if Q=f(L,K,M,Ed), we get:

23. The market for educated labor • RS is upward sloping because: • Psychic and pecuniary costs rise as more people become educated because: • Supply of education services is positively sloped; • People have different financial endowments, time preference and different tastes of learning  at a low relative wage only a certain number of people wants to be educated, while as RW  more people will find it worthwhile. • Differences in ability: people with less ability find it mentally more costly to get an education, need to be compensated through higher wages to stay in education – a bit week as an argument.

24. The market for educated labor: comparative statics

25. The market for educated labor: comparative statics • RD shifts up because of technology and preference changes that require more educated labor; • RD also can shift up because of  imports from LDC‘s of goods that require a lot of uneducated labor as inputs  relative demand of educated labor in imports receiving country rises; • RS shifts down as more people are able or willing to undertake education (e.g., with a rise in per capita income and with more participation of women in labor market we will get such an increase in RS). • Decreases in RD and RS are empirically of little relevance. • In the last 30 years, the historically relevant comparative statics in developed countries is given by the shift from RS to RS‘‘ and from RD to RD‘: main results even though there has been a large increase in the supply of educated labor, RW has risen because the increase in the demand for educated labor has outpaced the increase in its supply.

26. Functional distribution of income • Definition: Functional distribution of income means how national income is divided between factors of production, in particular how national income is divided between income from employment and income from all other sources.

27. Functional distribution of income • Assumptions: production function exhibits Constant Returns to Scale (CRS) and economy is perfectly competitive. Under these assumptions Euler‘s Theorem applies: Total output is in its entirety consumed by the factors of production, since in the case of two inputs, K and L: • In the graph we have total income =OAEB; income going to labor =OwEB; income going to capital =wAE. In most developed countries these shares have been relatively stable until the nineties, with roughly two thirds going to labor and one third going to capital.

28. Size distributions of earnings/income

29. f(X) Log-normal distribution Pareto Tail 10th Median Mean 90th X Size distributions of earnings/income

30. Size distributions of earnings/income • Size distribution means that all workers are lined up according to the size of their earnings/income – starting with the lowest one. • The log-normal distribution reflects relatively well (apart from the highest earnings/incomes) how actual earnings are distributed. • This distribution is called log-normal because if we take the logs of the values we get normally distributed values. • The log-normal distribution has a mean > median, i.e. it is positively skewed, which implies that too many people are paid little while a minority that is substantial gets paid a lot. • “Pareto tail“ refers to the fact that there is a long fat tail at the higher end of the distribution; • So, log-normal distribution with “Pareto tail“ pretty much describes the size distribution of earnings/income across developed market economies and across time.

31. Size distributions of earnings/income • If we assume that abilities of persons are normally distributed, then why are earnings roughly log-normal? • One explanation: Gibrat‘s process – shown by example:

32. Size distributions of earnings/income

33. Size distributions of earnings/income • The above table shows how Gibrat’s process works. We have 10 tries of throwing a dice for 7 cases. The sums of the realized points are pretty close to each other, while the products are very different! • Also, when people have normally distributed characteristics, the sum of these characteristics is also normal, while the product of these characteristics is log-normal – by central limit theorem. • So, when adding up, e.g., human capital, family background, motivation, ability and social connectedness, we get normally distributed sums, while when we interact these characteristics, we no longer get this normal distribution.

34. Size distributions of earnings/income • Two problems with this type of reasoning: • Assumes that characteristics are independent from each other (hardly so, since human capital, family background, ability and social connectedness are interdependent); • Reasoning has no economic content (only desribes a statistical data generation process. • While log-normality (with Pareto tail) holds across countries and time, no really convincing explanation given in the literature.

35. Pay differentials • Higher wages are a function of: • Unionisation; • Firm size; • Human capital; • Regional effects; • Industry concentration. • One dynamic aspect of pay differentials: Some industries whose product demand  want to attract more labor  wages .