Understanding the rise in skilled labor demand in Brazil

1. Understanding the rise in skilled labor demand in Brazil Eduardo Pontual Ribeiro Universidade Federal do Rio de Janeiro, Brazil (IE/UFRJ) and Lehmann Visiting Scholar, UIUC eribeiro@ie.ufrj.br Paulo de Andrade Jacinto Universidade Federal de Alagoas, Brazil (UFAL)

2. Motivation

3. Motivation • Significant rise in skilled labor demand from mid 1990’s. • In the 1990’s, rise in skilled labor demand and decrease in skilled labor relative wages. • Different from other Latin American countries (increase in skilled relative wages andemployment share).

5. Motivation • From Gonzaga et al. (2006), JIE.

6. Motivation • Alternative explanations (data up to 2000) • Trade liberalization (Gonazaga, Menezes-Filho and Terra, 2006, JIE; Giovanetti and Menezes-Filho, 2007, Economia); • Skilled Biased Technical Change (Green, Dickerson and Arbache, 2001, WD; Pavcnik 2003, WB PRP).

7. Motivation • Other Alternative explanations • Capital deepening; • Non-neutral (heterothetic) technology; • Skill supply shift.

8. Goal • Compare the role of alternative explanations to the skill upgrade in manufacturing employment: skill biased technological change; non-neutral technology; relative wages; international trade. • Evaluate the role of non-neutral technology and supply changes.

9. Policy implications of the non-neutral technology hypothesis • If technology is non-neutral, economic growth suffices to reduce relative demand for less skilled workers. • Requires labor force training policies. • But let us not forget that it is not clear how to implement nor select effective training policies (future research).

10. Theoretical framework • Standard labor demand model, using a flexible technology functional form, such as translog. Cost minimizing firms (Baltagi and Rich 2005, Binsgwanger, 1974, Autor et al. 1994, et al.) • The underlying cost function depends on input wages, wi, output y, and input augmenting factors Ai. C=C(w1,..., wn, y, A1,...,An)

11. Theoretical framework • Technology types: • Homothetic technology (separability) and neutral technological progress (Hicks) C=C(w1,...,wn)k(y)a(A) • Homothetic technology (separability) and non-neutral technological progress (Hicks) C=C(A1w1,..., Anwn)k(y) • Heterothetic technology and non-neutral technological progress (Hicks) C= C(A1w1,..., Anwn, y)

12. Data Matched employer-employee data: • Employer data: • Annual Survey of Manufacturing (PIA): output and capital stock. • Technological Innovation Survey (PINTEC): R&D expenditures and innovation measures. • Trade Department data: firm export or import activity information. • Employee data: • Annual Social Information Report (RAIS): employment, educational level and wages.

13. Data • Three skill levels: • uskilled (us) – 0 to 7 years of schooling (incomplete fundamental education); • semi-skilled (ss) – 8 a 11 years of schooling (completed fundamental and incomplete high school); • skilled (sk) – 12 or more years (high school degree or more).

14. Initial data analysis: employment share growth decomposition. • Following Berman, et al. (1994), we evaluate whether the increase in skilled labor share was due to a composition effect (between firms) or a substitution effect (with firms). Ds=SiDsi êi + Si ŝiDei within between where s = skilled share of firm employment; e = firm share of total employment (ni/Sini); i=1,…,n firms.

15. Initial data analysis: employment share growth decomposition. • We further decompose employment share changes in output growing (G) and output decreasing (D) firms to have a first glance of possibly non-neutral technology. Ds=DsG+DsD = [SiGDsi êi + SiD ŝiDei] +[SiDDsi êi + SiD ŝiDei]

16. Skill Employment share growth decomposition

17. Firm mean wage growth decomposition

18. Empirical framework • System of labor demand functions Ssk=gsk+gsk,sk ln(wsk/wus)+gsk,ss ln(wss/wus)+ask,ylny +ask,KlnK +ask,zZ Sss=gss+gss,sk ln(wsk/wus)+gss,ss ln(wss/wus)+ass,yln y +ass,KlnK +ass,zZ Where Z stands for employment shifters.

19. Empirical framework Employment shifters Z • productor process innovation dummy; • (alternatively) R&D expenditures as share of value added. • Import activity over the year dummy; • Export activity over the year dummy. • Aggregate time shock dummies

20. Econometric estimates We estimate the following employment metrics: • Skill diferenced output elasticities; • Skill diferenced innovation effect; • Skill diferenced aggregate shocks effect; • Skill diferenced R&D employment elasticities; • Skill diferenced export and import activity effect.

21. Identification issues • First differences (FD) to control time fixed unobserved effects; • First differences and instrumental variables (FD-IV) to mitigate measurement error bias on wage, output and capital variables. • Instruments: two and three period lagged variables. • Panel: circa 10,000 firms per year, 1996-2003. Small firms (less than 30 employees) excluded.

22. Results: Elasticities

23. Results: Elasticities

24. Results: Non-homotheticity and aggregate shocks

25. Results: skill diferentiated effects

26. Specification tests

27. Main results • Inelastic wage elsticities; skilled unskilled labor may not be related; • Non-homethetic technology (for capital and output); • Aggregate shocks are differentiated by skill level; • Output elasticities are not larger for skilled workers than for usnkilled workers. • Non-neutral technological change; • Skill bised technological change. • Trade measures do not play a role.

28. Policy Implications • Increase in skill supply should sustain skilled demand over time. • Innovation and technological change are skill biased (productivity and income distribution trade off?). • Economic growth cannot be blamed for unskilled labor demand fall.

29. Limitations • International trade effects are not apropriately measured (imported imput usage; tariffs). • Cover manufacturing only (about 30% of total employment). • Time fixed R&D investment. • Skill measurement error (underestimate education for long tenured workers).