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Skills and the labour market Research ideas Contributors: CEPII, CBS, University of Groningen, WIIW, WIFO, IVEI.

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Presentation on theme: "Skills and the labour market Research ideas Contributors: CEPII, CBS, University of Groningen, WIIW, WIFO, IVEI."— Presentation transcript:

1 Skills and the labour market Research ideas Contributors: CEPII, CBS, University of Groningen, WIIW, WIFO, IVEI

2 Potential research ideas capital-skill complementarity (CBS, University Groningen, WIIW) skill-biased technology change (e.g. through information technology capital and R&D capital) (NIESR, WIIW, University of Groningen, IVIE) –Skill premium and technological change –Relative skill/unskilled employment and technological change –Impact of ICT capital on highly skilled workers, low skilled workers and intermediate skill workers (job polarisation) changes in supply of skills and technological change (IVEI, University of Groningen) role of industry structure (WIIW) impact of trade and outsourcing (WIFO) demand of labour for specific types of workers (WIIW) –older and younger workers (age biased technological change) –specific occupations such as engineers and technicians

3 Potential research ideas relationship between output growth and employment evolution by skill level and age groups Impact of overeducation on wages (NIESR) Product market structure and labour market institutions (NIESR) –joint effects on productivity, employment and wages –Effects on returns to skills and investment in human capital. Other determinants of heterogeneous labour demand Determinants of labour demand at the industry level (ln H, = f(ln W/P, Y, K, R&D))

4 Potential research ideas Novelty: consistent and uniform data set covering up N=72 industries, T=30, J countries Econometric methods; issues to address –SUR –adjustment costs, dynamic panel data methods –T>N = > single equation: dynamic heterogeneous panel data method, Pesaran, Shin, Error correction model applied to panel data –N>>T =>dynamic panel data model (first difference GMM, system GMM) –Heterogeneity across industries –Endogeneity of wages

5 Role of industry structure in skill upgrading size of low skill industries is decreasing (=> between shifts effect) within industry shifts shift-share analysis; decomposition of the change in the employment share of highly skilled/unskilled workers : –contribution to the total change which results from employment shifts between sectors of different skill intensities. –contribution to the total change which results from the shift towards skilled workers within a sector Extension: occupational structure Aim: –within and between contributions change over time –differences between manufacturing and non-manufacturing –Between effect differ across EU countries

6 capital-skill complementarity Krusell et al. (2000): –capital-embodied technological change alone can account for most of the variations in the skill premium –use of quality-adjusted prices for a number of durable equipment categories such as office and computing equipment including peripheral equipment and accounting machinery (OCAM), communication equipment, general industrial equipment and transportation equipment. Relative employment equation, LS/LU=f(WS/WU, C, IT-C, R&D) System of labour demand equations Measurement: (i) CSC may differ across industries, (ii) structural breaks over time, (iii) robust to measurement of capital (fixed/variable factor, quality adjustment) and skills

7 Skill-biased technological change Berman, Bound and Griliches (1994): –change in the cost share of non-production workers is positively related to the industry's initial ratio of investment in computers to total investment. –one third of the change in the non-production wage bill share can be explained by the computer variable DSN: change in non-production wage bill share DlnK/Y: change in capital-output ratio R/Y: R&D intensity CI/I: ratio of investment in computers to total investment Pn/Pj relative wages between non-production and production workers

8 Impact of outsourcing and trade Measure of outsourcing: intermediate goods imports from the same industry) Impact of exports (exports generated by imported materials) on the demand for heterogeneous labour Combing trade statistics and EUKLEMS data

9 Product market structure and labour market institutions: effects on returns to skills and investment in human capital Link between PMC and wage inequality data

10 Previous literature Morrison-Paul Siegel (2001): US manufacturing, 73-89, four types of labour; results: high-tech CSC is significant (explains 78% of the increase of LH) OMahony, Robinson, Vecci (2004): US, UK, F, D, 1970-2000, three/four types of labour: IT is the major factor Chun (2000): US all industries, 1960-1995; IT CSC is significant, explains 25% of the increase of LH Fitzenberger (1999) Germany, non-manufacturing, measure: inolut coefficients of the computer/electrical industry; not significant Other evidence: Krusell et al. (2000), Machin and Van Reenen (1998), Green, Felstead and Gallie (2000), Riley and Young (1999), Green, Felstead and Gallie (2000), Hansson (2000), Mellander (2000) for Sweden, Lindquist and Skjerpen (2000) for Norway, Strauss-Kahn (2003), Goux and Maurin (2000) for France Theoretical lit: Caselli (1999) Summary. Studies agree on IT CSC but differ with respect to the magnitude of the impact

11 Empirical model I Labour demand model for each skill group –L nit : total annual hours of highly, medium and unskilled workers –Y it : value added in constant prices –WP nit : hourly wage deflated by the value added deflator –lnpit t : price index of information equipment and software –YRS:Average years of schooling in the working age population Estimation method: Fixed effects model

12 Empirical model II System of factor demand equations derived from a flexible cost function Generalised box-Cox cost function variable inputs: x nt = (x hnt, x snt, x unt, x mnt ) input prices as p nt = (p hnt, p snt, p unt, p mnt ) )

13 Empirical model II inputs –x hnt annual working hours of highly skilled workers (workers with a university degree/univ. entrance degree –x snt annual working hours of workers with apprenticeship training –x unt annual working hours of workers with compulsory school –x mnt total materials in constant prices Input prices (normalized to 1 in 1980) –p hnt hourly wages of highly skilled workers –p snt hourly wages of medium skilled workers –p umt hourly wages of low skilled workers –p hnt price index total material inputs fixed factors and total variable costs –y nt gross output in constant prices –k nt net capital stock in constant prices –ttime trend, alternatively: –(i) average years of schooling of the working age population –(ii) price index of information equipment and software csum of labour costs and total materials )

14 Empirical model II ) This system of four input demandsis derived by the application of Shepard's lemma. nt ezpxx /),,,( *

15 Empirical model II ) Estimation method: non-linear SUR with fixed effects. number of parameters: 24 due, two Box-Cox parameters, 4 x 21 industry dummies estimation problems –non-stationarity of the data –A second problem is the potential endogeneity of wages Elasticities of factor demand: –Two inputs are substitutes (complements) if the cross- price elasticity is significantly positive (negative).

16 Empirical model II ) impact of the net capital stock: Complementarity/substitutability : output elasticities: The time "elasticities'': elasticities of the different labour inputs with respect to the price of information processing equipment and software: The impact of the supply of skilled workers:

17 Hypotheses ) Hypothesis 1: Capital-skill complementarity is found if capital and skilled labour are complements while capital and unskilled labour are substitutes. Hypothesis 2: Technological change measured as the price index of information equipment and software favour higher skills and reduces the demand for low-skilled workers. Hypothesis 3: Own-wage elasticities in absolute values decrease with the skill level Hypothesis 4: Substitution possibilities between different labour inputs are higher than between labour and non-labour inputs. Hypothesis 5: Unskilled workers can be substituted more easily for materials than both medium-skilled workers and highly skilled workers.

18 Data and summary statistics ) annual two-digit industry data for Austrian industries for the period 1980-2003 data sources: National Accounts, calculations based on micro census, wage and salary statistics Annual hours worked: number of employees X actual hours per employee x 52 working weeks Capital stock: PIM NIPA: price index of Information Processing Equipment

19 Data and summary statistics ) Annual percentage changes in inputs, output, wages and prices, 1980-2003

20 summary statistics – evolution of quantities )

21 )

22 summary statistics – evolution of prices )

23 )

24 Empirical results II ) Results based on the factor demand system IPES investment reduce the demand for both unskilled workers and medium-skilled workers; with a large magnitude IPES have a positive but small impact on highly-skilled workers positive impact of net capital stock on all types of labour; highest impact on highly skilled workers demand for unskilled workers is quite elastic to changes in wages. zero substitutability relationship between skilled and unskilled workers substitutability relationship between unskilled workers and material inputs output elasticity of unskilled workers: 1.12 time elasticities: significantly positive for university graduates and significantly negative for unskilled labour years schooling: positive impact on highly-skilled labour, negative on medium and unskilled workers

25 Empirical results II )

26 ) Results based on the standard labour equations (estimated separately) Impact of IPES on the demand for unskilled workers is negative and highly significant; magnitude is large Robust when time trend and years of schooling are included Very small impact of IPES on medium and highly skilled workers Again: own-wage elasticities (absolute values) highest for unskilled workers, output elasticities increase the lower the skill level is Years of schooling also contribute the growing skill shares the industry level

27 Empirical results I ) Single equation estimates: fixed effects results, dep. Var: log (annual hours unskilled worker) Nobs: 504; N=21

28 Empirical results I ) Single equation estimates: fixed effects results, dep. Var: log (annual hours medium-skilled worker) Nobs: 504; N=21

29 Empirical results I ) Single equation estimates: fixed effects results, dep. Var: log (annual hours highly skilled worker) Nobs: 504; N=21

30 conclusions ) price decrease of IT equipment tends to reduce the demand for low-skilled workers; but little impact on the two upper skill level traditional CSC is the major factor explaining the demand for highly skilled workers All types of skills benefit from an increase in capital results are robust when supply side effects are taken into account demand for unskilled labour is more wage-elastic than the demand for medium-skilled labour. zero substitutability between different types of labour material inputs are a substitute for unskilled labour

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