Chapter 6 HUMAN CAPITAL. Modified for EC 375 by Bob Murphy. Copyright 2013 Pearson Education, Inc. Publishing as Addison-Wesley

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1 Chapter 6 HUMAN CAPITAL Modified for EC 375 by Bob Murphy

2 Human Capital So far have assumed labor is same across countries and over 5me. But quality can vary. Denote this as human capital: Produc5ve. Produced. Earns a return- - but goes only to worker who owns it and only while working (unlike physical capital). Depreciates. 2

3 Health as Human Capital Height as indicator of nutri5on. Caloric intake. Malnutri5on: less healthy, lower height, lower ability. Two Channels: Improved nutri5on brings more workers into workforce. Allows people who are working to work harder. 3

4 Health as Human Capital Fogel s research: In 1780, 20% of adults in UK were too malnourished to work. By 1980, completely eliminated. Accounts for 25% increase in output per adult. Also, increase in caloric intake over this period leads to 56% gain in output per worker. Total effect: 1.25*1.56 = 1.95 or 95% gain. Spread over 200 years gives, 0.33% per year. Actual growth rate of 1.15% per year. Nutri5on accounts for nearly 1/3! 4

5 Figure 6.1 Nutrition versus GDP per Capita Sources: FAOSTAT database, Heston, Summers, and Aten (2011). 5

6 Figure 6.2 Life Expectancy versus GDP per Capita Sources: Heston, Summers, and Aten (2011), World Development Indicators database. 6

7 Health as Human Capital Differences across countries in health are large and can contribute to differences in income. But where do these differences in health come from? Does income cause health? Becer nutri5on leads to higher income, But, higher income leads to becer nutri5on. True for health more generally. 7

8 Figure 6.3 How Health Interacts with Income 8

9 Figure 6.5 Effect of an Exogenous Shift in Income 9

10 Figure 6.4 Health and Income per Capita: Two Views 10

11 Education as Human Capital Large differences in educa5onal acainment across countries. Large increase in # years of schooling. Percent gains in developing countries very large. Cost of educa5on includes not only actual money spent but also opportunity cost: During 2010, U.S. spent $675 billion (govt.) and $236 (private), for a total of 6.2% of GDP. But opportunity cost es5mated at about same, so total is 12.4%, the same share as physical investment in that year. 11

12 Table 6.1 Changes in the Level of Education,

13 Education as Human Capital Measuring return to human capital is difficult: Can t separate educa5on from the person. No separate market for it. Infer return using data on wages. Return to educa5on is the increase in wages a worker would receive if he/she has one more year of schooling. Using interna5onal data: 13.4% for grades % for grades % for grades above 8 13

14 Figure 6.6 Effect of Education on Wages 14

15 Return to Education An example: A worker with 5 years of schooling would earn: Return = = So, if zero years gives wage of $1.00, then $0.82 is the amount of wage due to education. Fraction of wage due to human capital would be 0.82/1.82 = 45%. 15

16 Table 6.2 Breakdown of the Population by Schooling and Wages 16

17 The Return to Education and Human Capital s Share of Income Compute human capital s share of income: Rela5onship between educa5on and wages allows us to break this out. Look at gain in wage one gets from educa5on and use the distribu5on of popula5on across educa5on levels to calculate total gain. Compare this to the total wage to get share of wage due to human capital. 17

18 Figure 6.9 Share of Human Capital in Wages in Developing Countries 18

19 Figure 6.10 Share of Human Capital in Wages in Advanced Countries 19

20 The Return to Education and Human Capital s Share of Income Next, use the share of wage that is due to human capital to find the overall human capital share in income: Developing countries: 0.59 x 2/3 = Developed countries: 0.68 x 2/3 = Workers are in part capitalists due to educa5on. Assuming a share for physical capital of about 1/3, this implies that the share of total capital is a bit larger than 2/3. So, in the Solow model with just capital and labor it seems reasonable to assume capital share of at least 2/3. 20

21 The College Premium Return to educa5on actually varies across countries and within a country over 5me. Return is higher in poor countries than rich, reflec5ng scarcity of skilled workers. In U.S., decline in share of labor input supplied by less- educated workers should have reduced the college premium. 21

22 Figure 6.7 Share of Hours Worked by Education Level, Sources: Autor, Katz, and Krueger (1998), Autor, Katz, and Kearney (2008), Acemoglu and Autor (forthcoming). 22

23 Figure 6.8 Ratio of College Wages to High-School Wages Sources: Autor, Katz, and Krueger (1998), Autor, Katz, and Kearney (2008), Acemoglu and Autor (2010). 23

24 The College Premium Ship in demand for skilled workers must have offset the ship in supply: Increased openness to interna5onal trade: U.S. skilled workers become effec5vely more scarce on a global scale. Technological change is skill- biased: Technology has made educated workers rela5vely more produc5ve (e.g., computers increase the produc5vity of educated workers greatly but do licle for less- educated workers, and may actually replace them). 24

25 Explaining Variation in Income Due to Education Schooling is posi5vely correlated with income per capita. Doesn t imply causality: rich might be able to afford more educa5on. Use Solow model to assess how important differences in educa5on across countries are for explaining differences in GDP per capita. Leaving aside role of health in human capital as hard to measure. 25

26 Figure 6.11 Average Years of Schooling versus GDP per Capita Sources: Barro and Lee (2010), Heston, Summers, and Aten (2011). 26

27 Human Capital in the Solow Model Assume Cobb-Douglas production function: Y = AK α [ hl] 1 α where h = labor input per worker. Y = h 1 α AK α [ L] 1 α Same as before in Chapter 4 except that A Ch4 = h 1 α A. 27

28 Human Capital in the Solow Model Since we know that the steady state solution was: ss 1/1 α γ y Ch4 = A Ch4 n + δ the one here must be: α /1 α y ss = h 1 α 1/1 α γ A n + δ γ = h A 1/1 α n + δ α /1 α α /1 α 28

29 Human Capital in the Solow Model Compare two countries with same γ, n, and A, but different h: y ss γ i = h i A 1/1 α n + δ y i ss y j ss = h i h j α /1 α, y ss γ j = h j A 1/1 α n + δ α /1 α Need to understand relation between h and schooling. 29

30 Human Capital in the Solow Model Use data on return to educa5on: Interpret rela5ve earnings as capturing rela5ve labor inputs and that each unit of labor is paid a fixed amount. Use average level of schooling for each country to measure h rela5ve to a country with no schooling. 30

31 Human Capital in the Solow Model Example: Country i has average schooling of 12 years and country j has average schooling of 2 years. h i = h 0 = 3.16h 0 h j = h 0 = 1.29h 0 Plug into earlier equation: y i ss y j ss = h i h j = 3.16h h 0 = 2.47 Repeat for a large set of countries. Predict ratio of income per worker in each relative to income per worker in U.S.: 31

32 Figure 6.12 Predicted versus Actual GDP per Worker (Differences in Human Capital) 32

33 Figure 3.7 Predicted versus Actual GDP per Worker (Differences in Investment Rates) Source: Author s calculations using data from Heston, Summers, and Aten (2011). 33

34 Human Capital in the Solow Model Two Poten5al Issues: Analysis assumes quality of schooling is same across countries. Data on inputs and outputs suggest otherwise. Analysis uses private return to educa5on, i.e., it assumes the return is captured completely by the individual worker. But educa5on may have externali5es. One reason why governments get involved. 34

35 Figure 6.13 Student Test Scores versus GDP per Capita Source: PISA (2009). 35