The Effects of Trade Openness on Assortative Matching of Workers and Firms Carl Davidson Michigan State University Fredrik Heyman Research Institute of Industrial Economics, Stockholm Steven Matusz Michigan State University Fredrik Sjöholm Research Institute of Industrial Economics, Stockholm, and Örebro University Susan Chun Zhu Michigan State University
Background Large literature on firm s adjustment to globalization (Bernard and Jensen, 1999; Roberts and Tybout, 1997). Less is known about labor market adjustment to globalization but some work has been done on wages (Heyman et al. 2007 on FDI; Munch and Skaksen, 2008 on export). But how does globalization affect matching of workers and firms?
Globalization and Assortative Matching Export oriented industries: falling trade costs could make good firms able to expand production Widening gap in revenues between good and bad firms Increased positive assortative matching. Positive assortative matching implies a positive correlation between individual and firm effects in a wage regression. Import competing industries: increased competition Falling prices Small positive assortative matching
Theoretical framework (Davidson et al., 2007) High and low skill workers search for jobs Ex ante identical firms choose what type of technology to adopt In equilibrium, firms are different Low-tech firms pay low wages (can use both type of workers) High-tech firms use high-skill workers and pay high wages
Theoretical predictions Openness affects the degree of assortative matching Increased openness makes positive assortative matching more likely in export-oriented markets but less likely in import-competing markets
Empirical Setup Assortative matching measured according to Abowd et al. (1999). Based on the following individual wage regression: η θ λ φ ν ln w = x + + Z + + ht ht h j ( h, t ) j ( h, t ) ht Wages is a function of observed and unobserved firm and worker characteristics.
Empirical Setup (cont.) Dependent variable is measured as full time equivalent wage. Worker characteristics: experience, experience squared, and a dummy variable for blue-collar occupations. Firm characteristics: firm size, share of high-skill workers, capital intensity, profits per employee and the share of females in the workforce. Year dummy variables are also included.
Empirical Setup (cont.) Matching is measured as: - the correlation between firm (observed and unobserved) and worker characteristics (observed and unobserved). - the correlation between unobserved firm and worker characteristics. => NB: Several papers have found that there is a negative correlation between the unobserved worker and firm components of wages, implying negative assortative matching.
Empirical Setup (cont.) The degree of assortative matching (correlation) is regressed on different industry measures on openness Trade shares ((X+M)/M) Share of MNEs Tariffs Offshoring
Data: Three register-based data sets from Statistics Sweden. Matched employer-employee data for the period 1990-2005. Firm level data Detailed information on all Swedish firms with at least 20 employees. Establishment level data Data on all establishments. Add information on the composition of the labor force with respect to educational level and demographics.) Individual wage data Detailed information on a very large representative sample of employed individuals. 2 million observations per year.
Data (cont.) Firm- and industry-level data on export and import. Data on Swedish and EU tariffs at the product level (HS). Source WTO
Estimation issues Using matched employer-employee data to estimate the above equation allows us to take into account both individual and firm heterogeneity. One estimation method is to use the least-square dummy variable approach (LSDV) by including dummy variables for the included individuals and firms. BUT, this method is difficult to implement when there are millions of individuals and thousands of firms in the data.
Estimation issues (cont.) An alternative approach is to include firm dummies and sweep out the worker effects by the within transformation. This method, called FEiLSDVj by Andrews et al. (2006), gives the same solution as the LSDV estimator (see Andrews et al., 2006 and Cornelissen, 2006). The firm effects are identified from workers that change firms over the period. The worker effects are estimated from repeated observations per worker. => This implies that the data must include a sufficient number of both multiple observations of workers and movers of workers across firms.
Table 1. Number of observations per person. 100.00 2,572,463 Total 22.78 37.97 48.24 56.51 63.55 69.46 74.47 78.84 82.85 86.17 89.35 92.14 94.64 96.70 98.64 100.00 22.78 15.20 10.27 8.27 7.04 5.91 5.01 4.38 4.01 3.32 3.18 2.79 2.50 2.06 1.94 1.36 585,320 390,540 263,793 212,592 180,833 151,879 128,671 112,439 102,945 85,384 81,635 71,684 64,297 52,880 49,912 35,013 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Cum. Percent Frequency Obs. per worker
Table 2. Number of movers per firm. Movers per firm Freq. Percent Cum. 0 1-5 6-10 11-20 21-30 31-50 51-100 >100 465 2,112 1,186 1,654 1,077 1,380 1,715 4,440 3.31 15.05 8.45 11.79 7.68 9.84 12.22 31.65 3.31 18.37 26.82 38.61 46.29 56.13 68.35 100.00 Total 14,044 100.00
Table 3. The effect of worker and firm characteristics on wages. Variables Coeff. Standard Errors t-value Experience.0026217.0000759 34.56 Exp.-sq -.0003603 4.53e-07-795.52 Blue-collar -.0458415.0002311-198.39 Log(size).0055015.0000793 69.39 high-skill.1184865.0010287 115.18 Log(Capital) -.0021357.0000722-29.58 Profits/ empl.0020644.0000611 33.79 Share female.0810815.0012493 64.90
Table 4. Correlation of firm and worker characteristics. Sample Corr. of firm and worker unobs. effects Corr. of firm and workers total effects Whole sample Workers observed at least 2 periods Workers observed at least 3 periods Firms with at least 2 movers Firms with at least 5 movers Workers with at least 3 observations and firms with at least 5 movers Preferred sample: Workers with at least 2 observations and firms with at least 5 movers 0.0772 0.0648 0.0529 0.0775 0.0784 0.0532 0.0653 0.1290 0.1244 0.1217 0.1295 0.1308 0.1221 0.1252
Table 5. Correlation of firm and worker characteristics. Different sub-periods. Period Correlations Correlations Mean Export Share of of observed Export share MNEs in unobserved and share (industry total sales effects unobserved effects level) (industry level) 1990-1995 -.0369.0085.3812473 1996-2000.0041.0612.4488021 2001-2005.0858.0948.5033073 Offshoring (industry level) 1990-1993 -.0688 -.0487.3656031 1994-1997.0354.0244.4250296 1998-2001 -.0237.0533.4649343 2002-2005.0613.0465.5057906 1997-2000.00974506.04620481.2426378.201881.4591525.1277053 1999-2002.0160591.0793098.242476.2067038.4792288.1309403 2001-2004.0722315.0486339.2436952.2058247.4999375.1338463
(Very) Preliminary regression results Openness increase assortative matching. The effect of openness on assortative matching is strong in OLS estimations and less strong in FE estimations. The effect of openness does not seem to differ between import competing and export oriented sectors.