Wages, Human Capital, and the Allocation of Labor across Sectors

Transcription

1 Wages, Human Capital, and the Allocation of Labor across Sectors Berthold Herrendorf and Todd Schoellman Arizona State University June 30, 2014 Herrendorf and Schoellman

2 Motivation Structural Transformation Kuznets 1973 nobel price lecture: Structural transformation is one of the main features of modern economic growth In this paper, we focus on agriculture and the rest of the economy ( non agriculture ) Structural transformation then means: if an economy grows labor gets reallocated from ag to non ag Herrendorf and Schoellman 1

3 Structural Transformation in the U.S. Share in total employment 1.0 Non-agriculture Agriculture GDP per capita ranged from around $1, 000 to $30, 000 (in 1990 international prices) Herrendorf and Schoellman 2

4 Two views of structural transformation Efficient allocation View Assumes that at each moment labor is allocated efficiently between sectors Growth leads to structural transformation Mis allocation View Documents large gaps in labor productivity between non ag and ag Concludes that the allocation of labor must be inefficient there must be barriers which hinder the reallocation of labor their removal would lead to structural transformation and growth Opposite directions of causality and very different policy implications There is little evidence to distinguish between these views Herrendorf and Schoellman 3

5 Our contribution We collect wage data from 37 population censuses of 13 countries % of world population four of the five most populous countries (India, US, Indonesia, Brazil) We document that wage workers in non ag earn considerably higher wages than in ag human capital broadly constructed accounts for most of the wage gaps We conclude that this points to the efficient allocation view Herrendorf and Schoellman 4

6 Relation between our work and the second strand of the literature Literature: There are large labor productivity gaps P a Y a L a << P ny n L n where PY is value added and L are workers The inequality is about average product, but efficiency is about marginal product If factors are paid their marginal products and production functions are CRS, then average and marginal products are linked via the labor share θ: Y j L j = θ j Y j L j Herrendorf and Schoellman 5

7 If labor shares are equal, then Under these assumptions Y j L j = Y j L j P a Y a L a << P ny n L n P a Y a L a << P n Y n L n Caselli (Handbook,2005), Restuccia etal (JME,2008) there is too much labor in agriculture moving labor out of agriculture generates growth Our paper workers in ag have less human capital dividing earnings by human capital gives wages per efficiency unit that are similar P a Y a H a = P ny n H n Herrendorf and Schoellman 6

8 Outline Facts about the U.S. Model Evidence from Switchers in the U.S. Cross country Analysis Related literature Conclusion Herrendorf and Schoellman 7

9 Overview Facts About Wages and Human Capital in the U.S. Population census and CPS nationally representative required data on wages, schooling, age, and so on sufficient sample sizes in agriculture We use only workers who are years old work for wages and report positive wage income for the relevant period We assign workers to sectors according to their reported industry of employment Ag: crop and livestock farming Non ag: all other industries Herrendorf and Schoellman 8

10 We construct wages for Some key data missing before 1980, so we start in 1980 Census and March CPS: last year s income divided by product of hours usually worked in a week times weeks worked in the year Outgoing rotation groups of monthly CPS files: hourly wage or weekly earnings divided by hours worked for the prior week Herrendorf and Schoellman 9

11 Fact 1: Large Wage Gaps Estimate i indexes workers and j sectors d t and d j are time and sector dummies Z i jt are controls for state and gender ε i jt is an iid error with zero mean log(w i jt ) = β t d t + β j d j + β z Z i jt + ε i jt Choosing non ag as the omitted group, wage gap equals exp(β a ) Herrendorf and Schoellman 10

12 Table 1: Wage Gaps in the U.S U.S. Census March CPS Monthly CPS Average wage in non ag 75 91% larger than in ag This is the striking key fact that we want to understand in this paper Herrendorf and Schoellman 11

13 Fact 2: Large Differences in Observed Characteristics Table 2: Gaps in Years of Schooling (U.S ) U.S. Census March CPS Monthly CPS Years of Schooling Non ag workers have more than 3 more years of schooling on average Herrendorf and Schoellman 12

14 Evaluate years of schooling with standard Mincer returns from Hall Jones (QJE,1999) Table 3: Gaps in Wages and Human Capital (U.S ) U.S. Census March CPS Monthly CPS Wage gaps man capital gaps Residual wage gaps % residual wage gaps That is, even after standard control for human capital ag workers make 30% less Using Mincer returns estimated on our data gives similar results Herrendorf and Schoellman 13

15 Fact 3: Large Differences in Sector specific Mincer Returns Estimate log(w i jt ) = β t d t + β z Z i jt + (β j + β s j s i jt + β c j c i jt ) d j + ε i jt where s are total years of schooling and c are years of college Findings β sa << β sn and β ca β cn This can be illustrated by the estimated log wage function log(w i j (.)) Herrendorf and Schoellman 14

16 Estimated Log-Wage Fraction Estimated Log-Wage Fraction Years of Schooling Years of Schooling Non-Agriculture Agriculture Non-Agriculture Agriculture (a) U.S. Census (b) March CPS Estimated Log-Wage Fraction Years of Schooling Non-Agriculture Agriculture (c) Monthly CPS Figure 1: Returns to Schooling Vary by Sector Back Herrendorf and Schoellman 15

17 Table 4: Gaps with Sector specific Returns to Schooling (U.S ) U.S. Census March CPS Monthly CPS Wage gaps Human Capital Gaps Residual Wage Gaps Small residual wages gaps 0 12% Two candidate explanations Ag has higher benefits (in progress) Non ag has higher cost of living Farmers have 8% lower cost of living Herrendorf and Schoellman (2012) Herrendorf and Schoellman 16

18 What to Make of These Findings? Two interpretations of sector specific Mincer returns to schooling Sectoral hypothesis Schooling generates less human capital for workers who choose agriculture Differences in Mincer returns reflect differences in sectoral technologies Selection hypothesis Workers sort according to unobserved innate ability ( farmers are morons ) Differences in Mincer returns reflect differences in workers Two methods of telling the hypothesis apart Different theoretical interpretations Evidence from people who switch sector ( switchers ) Herrendorf and Schoellman 17

19 Model Environment One period Large number L of individuals Two goods Agricultural good y a Non agricultural good y n Utility α log(y a ) + (1 α) log(y n ) Herrendorf and Schoellman 18

20 Endowments One unit of time Innate ability x [0, x] Years of schooling s [0, s] Individual (x, s) has human capital h j (x, s) in sector j Notation Ω: set of individual characteristics L(x, s): number of individuals with characteristics (x, s) Herrendorf and Schoellman 19

21 Technology in sector j Y j = H j Barriers Tax τ 0 on wages in non agriculture that is lump sum rebated Herrendorf and Schoellman 20

22 Equilibrium definition A competitive equilibrium is goods prices (P a, P n ) rental prices (W a, W n ) a tax rate τ choices of consumption and sector (y a, y n, ν a, ν n )(x, s) for all (x, s) Ω output and labor in each sector Y j, H j such that: (y a, y n, ν a, ν n )(x, s) solve the individual problem: max α log(y a ) + (1 α) log(y n ) y a,y n,ν a,ν n s.t. P a y a + P n y n = W a h a (x, s)ν a + (1 τ)w n h n (x, s)ν n + T Herrendorf and Schoellman 21

23 (Y j, H j ) solve the firm problem in sector j: max Y,H P jy W j H s.t. Y = H Markets clear: Y j = y j (x, s)l(x, s) H j = (x,s) Ω (x,s) Ω h j (x, s)ν j (x, s)l(x, s) Government budget balanced: T L = τ W n h n (x, s)ν n (x, s)l(x, s) (x,s) Ω Herrendorf and Schoellman 22

24 Sorting Equilibria Assumptions h j (x, s) = exp(γ j xs + β c c) γ a < γ n c max{0, s 12} are college years Three key features h j (x, 0) = 1 Mincer return to schooling s depends on sector j and ability x Additional return to college c is independent of sector and ability Estimated Mincer Returns Herrendorf and Schoellman 23

25 Proposition 1: Competitive Equilibrium If γ a < γ n, there is a unique threshold χ (0, x s) such that: individuals with xs = χ are indifferent between the two sectors, i.e. individuals with xs < χ choose ag individuals with xs > χ choose non ag In equilibrium: W a exp(γ a χ) = (1 τ)w n exp(γ n χ) P a Y a L a < P ny n L n Herrendorf and Schoellman 24

26 Proposition 2. If W a = W n and τ = 0, then γ a = γ n. Follows from W a exp(γ a χ) = (1 τ)w n exp(γ n χ) Proposition 2 implies the selection hypothesis Herrendorf and Schoellman 25

27 Proposition 3. If γ a = γ n, then barriers are determined by the wage gaps: τ = W n W a W n Follows from W a exp(γ a χ) = (1 τ)w n exp(γ n χ) Proposition 3 will be useful for the cross country analysis Herrendorf and Schoellman 26

28 Evidence from Switchers in the U.S. Switchers are workers who change sector (and stay in the same house) Selection hypothesis: Mincer returns of switchers do not change Sectoral hypothesis: Mincer returns of switchers do change Herrendorf and Schoellman 27

29 Switchers in the CPS Households are in the CPS for 4 months, out for 8 months, in for 4 months We focus on the fourth month of each spell, when extra data are collected ( outgoing rotation groups ) These observations are separated by one year We study the changes in wages for workers who switch sectors in the intervening year Herrendorf and Schoellman 28

30 Wage changes relative to non agriculture ag to ag ag to non ag non ag to ag How to distinguish the two hypothesis Selection hypothesis: Mincer returns of switchers do not change Sectoral hypothesis: Mincer returns of switchers do change Herrendorf and Schoellman 29

31 4. Cross country Analysis 37 country year pairs with IPUMS data on wages and employment Brazil (1980,1991,2000) Canada (1971,1981,1991,2001) West Germany (1970) India (1983,1987,1993,1999,2004) Indonesia (1976,1995) Israel (1995) Jamaica (1982,1991,2001) Mexico (1990,2000,2010) Panama (1970,1990,2000) Puerto Rico (1990,2000) US (1970,1980,1990,2000,2005,2010) Uruguay (2006) Venezuela (1981,1990,2001) 30% of the world population in 2010 Four of the five most populous countries in 2010 (India, US, Indonesia, Brazil) Herrendorf and Schoellman 30

32 Development facts about these countries Cross country variation in GDP per capita around 20 Largest productivity gaps non ag. vs. ag. around 4 Largest employment share in agriculture around 2/3 Herrendorf and Schoellman 31

33 Cross country facts about wages and human capital Minimum Median Maximum Raw Wage Gap Controlling for Geography and Gender Human Capital with Hall Jones Returns Human Capital with Sector specific Returns Adjusted Wage Gap Raw Wage Gap Cumulative Adjustment for: Schooling Geography and Gender Sector-Specific Returns Herrendorf and Schoellman 32

34 Recall Proposition 3 If γ a = γ n then τ = W n W a W n Table 5: Implied values of τ Minimum Median Maximum Herrendorf and Schoellman 33

35 Comparison to the Literature Our τ s are an order of magnitude smaller than in Restuccia etal (JME,2008) Possible explanations other than barriers/wedges Difference in cost of living between rural and urban areas Difference in prevalence of the shadow economy Herrendorf and Schoellman 34

36 What About Productivity Gaps? Although adjusted wage gaps are small in our countries the maximum productivity gaps between non ag. and ag. are a factor 4 Possible reasons Proprietors mis allocated unlikely if they are like wage workers likely if they are different from wage workers Land mis allocated (Adamopoulos and Restuccia) Mis measurement (Herrendorf and Schoellman) Herrendorf and Schoellman 35

37 Related literature Young (QJE,2013) Micro data for poor and middle income countries Migration flows go in both directions One in five individuals born in rural area moves to urban area as an adult One in four individuals born in urban area moves to rural area as an adult Herrendorf and Schoellman 36

38 Gollin, Lagakos, Waugh (2012) Micro data for poor countries including African ones Construct human capital with aggregate Mincer returns from Hall and Jones Find large residual productivity gaps after controlling for human capital Herrendorf and Schoellman 37

39 5. Conclusion Implications for growth development literature Small scope for policy reforms that aim to generate growth by removing barriers Construction of human capital at the sector level Essential to take into account selection according to unobserved innate ability Sectoral Mincer returns deliver this, aggregate Mincer returns don t Herrendorf and Schoellman 38

40 Areas for future research Extend the US analysis to period before 1980 Extend the cross country analysis to more countries Provide evidence on sector differences with regard to Benefits Cost of living Shadow economy Herrendorf and Schoellman 39

41 Defensive Slides Herrendorf and Schoellman 40

42 Matching workers over time in the CPS The CPS samples dwellings based on address; the occupants may differ over time While the CPS records when households change dwelling, coding errors are common We match persons who share the same dwelling, household, and person identifier We check whether the matches are logically consistent in terms of age, sex, and race Herrendorf and Schoellman 41

43 Table 6: Gaps for Matched and Unmatched Samples in the U.S. Monthly CPS Matched CPS Gaps in Wages Gaps in Human Capital with Sector specific Returns Estimated Log-Wage Fraction Estimated Log-Wage Years of Schooling Years of Schooling Non-Agriculture Agriculture Non-Agriculture Agriculture (a) Monthly CPS (b) Matched CPS Figure 2: Sectoral Return to Schooling in Matched and Unmatched CPS Data Herrendorf and Schoellman 42

44 Wage changes for switchers log(w i j j t) = β t d t + β d j j d j j + ε i j j t. Table 7: Wage Changes for Stayers and Switchers in the U.S. a a a n n a n n Herrendorf and Schoellman 43

45 Selection of Switchers Estimated Log-Wage Years of Schooling Estimated Log-Wage Years of Schooling Non-Agriculture Agriculture Non-Agriculture Agriculture Non-Ag -> Ag Ag -> Non-Ag (a) Stayers (b) Stayers and Switchers (Before) Estimated Log-Wage Years of Schooling Non-Agriculture Non-Ag -> Ag Agriculture Ag -> Non-Ag (c) Stayers and Switchers (After) Figure 3: Sectoral Return to Schooling for Switchers and Stayers Herrendorf and Schoellman 44

46 Table 8: Change in Value of Education Selection View Sectoral View Actual Change a n n a Table gives the wage change due to changing value of schooling for switchers. Values are provided for two stylized theories as well as the actual change observed in the model. Herrendorf and Schoellman 45