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1. (a) Rio de Janeiro, 1994–2010 0 .02 .04 .06 .08 .1 .12 .14 .16 .18 .2 .22 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Age Within firm wage growth Between firm wage growth (b) Veneto, 1984–2001. 0 .02 .04 .06 .08 .1 .12 .14 .16 .18 .2 .22 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Age Within firm wage growth Between firm wage growth Notes: Average annual change in log wages, separately for firm stayers (within) and firm switchers (between). A3 Figure A4: Mobility across firm classes: Transition matrix.
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2. (a) Rio de Janeiro, 1994–2010. -.02 0 .02 .04 .06 .08 .1 1 2 3 4 5 6 7 8 9 10 Firm class where experience was acquired Baseline Results Alternative Residualization of Wage Growth - Flexible Age Alternative Residualization of Wage Growth - No Education (b) Veneto, 1984–2001. -.02 0 .02 .04 .06 .08 .1 1 2 3 4 5 6 7 8 9 10 Firm class where experience was acquired Baseline Results Alternative Residualization of Wage Growth - Flexible Age Notes: Estimates of returns to experiences acquired in different firm classes, using different ways of residualizing unexplained wage growth. Black dots: baseline estimates from Figure 1. Yellow diamonds: fully flexible specification of age effects; in Rio de Janeiro, the fully flexible age profiles are further education-specific. Orange crosses in Rio de Janeiro only: same as baseline approach but without netting out education effects (i.e., fully comparable to Veneto baseline).
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3. (a) Rio de Janeiro, 1994–2010. 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 between firm-class var. / between firm var. 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 number of firm classes (b) Veneto, 1984–2001. 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 between firm-class var. / between firm var. 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 number of firm classes Notes: Ratio between i) between firm-class variance of unexplained wage growth, over ii) between-firm variance of unexplained wage growth, as a function of the number of firm classes (2–30). The logic of decomposing the variance into a within and between components comes from the law of total variance: V ary(Y ) = Ex[V ary(Y |X)] | {z } “within” + V arx[Ey(Y |X)] | {z } “between” . Denoting unexplained earnings growth by g, Figure B1 plots: V ark[Eg(g|firm-class=k)] V arj [Eg(g|firm=j)] . Figure B2: Density of unexplained earnings growth, by firm class.
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4. (a) Rio de Janeiro, 1994–2010. 0 5 10 15 -.4 -.2 0 .2 .4 .6 k=1 k=2,...,9 k=10 overall (b) Veneto, 1984–2001. 0 5 10 15 -.4 -.2 0 .2 .4 .6 k=1 k=2,...,9 k=10 overall Notes: Densities of unexplained earnings growth across firm classes. Classes ordered according to mean unexplained earnings growth. Dashed blue line marks the density of the overall distribution. A23 Table B1: Firm-class distributions of unexplained earnings growth.
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5. A25 are typically found in high-skill professional occupations (Ghosh and Waldman, 2010), whereas our findings hold across the skill distribution. Moreover, such type of contractual arrangements could be positively correlated with on-the-job learning—i.e., they could be one of the “mechanisms” underlying firm heterogeneity in learning opportunities since these contracts may be implemented precisely to incentivize workers’ human capital investments and effort (Lazear and Rosen, 1981; Waldman, 1990; Zabojnik and Bernhardt, 2001).
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6. A32 F Firm Characteristics and Learning Classes F.1 How well do observables jointly predict firm class? Random forest classification Using the data at the firm level (firm is the unit of observation, with characteristics averaged across years), we use half of the sample to train and validate a random forest classification algorithm (Athey and Imbens, 2019). In the other half of the data, we use the algorithm to predict firm class and compare it against its actual classification. We feed the random forest a variety of firm characteristics, but no variables related to employees’ wage growth as this is the input our clustering methodology described in Section 3.2 uses to classify firms. Table F1: Predicting firm class using observables: Random forest classification results.
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43. Firm classes 1–10 represent our firm categorization based on unexplained earnings growth distributions. NC are small firms not categorized by the clustering algorithm. PS is the public sector in each data set. Other is experience acquired outside the state of Rio de Janeiro or outside the region of Veneto. All specifications include year fixed effects and control for age with six age-category indicators. We allow for returns to experiences acquired in different firm classes to differ across workers’ unobserved skills recovered through the iterative method proposed by De La Roca and Puga (2017), as documented in Section 5.2. We present the estimates of the main effects, γm, and the interaction effects, δm, in equation (17). Standard errors clustered at the person level. *p<0.10; **p<0.05; ***p<0.01.
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44. Firm size. At the firm level, and keeping constant other covariates, we see that larger firms are less likely to belong to class 1 in Rio de Janeiro (Figure F2), and less likely to belong to class 1 and to class 10 in Veneto (Figure F3). Despite the lack of a clear-cut relationship between firm size and class, some facts are consistent with previous work suggesting greater learning opportunities for young workers in large firms (Arellano-Bover, 2020, 2022): in both Rio de Janeiro and Veneto, large firms are less likely to belong to class 1, and somewhat more likely to belong to class 9—i.e., the second-ranked category in terms of learning opportunities.
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52. Homogeneous experience return -.02 0 .02 .04 .06 .08 .1 1 2 3 4 5 6 7 8 9 10 Firm class where experience was acquired Age category dummies Flat age profile at 35 No age control (b) Veneto, 1984–2001. Homogeneous experience return -.02 0 .02 .04 .06 .08 .1 1 2 3 4 5 6 7 8 9 10 Firm class where experience was acquired Age category dummies Flat age profile at 35 No age control Notes: Estimates of returns to experiences acquired in different firm classes, using different ways of controlling for age effects. Black dots: baseline estimates from Figure 1, controlling for six age-category fixed effects. Blue diamonds: control for an age polynomial restricting the age profile to be flat at 35. Green squares: no age controls. Flat lines: returns to homogeneous experience for each respective age controls. Figure A6: Robustness by alternative residualization of unexplained wage growth: returns to experiences acquired in different firm classes.
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25. Worker flows and establishment wage differentials : a breakdown of the relationship. (2012). Petit, Héloïse ; GILLES, Fabrice ; Duhautois, Richard.
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26. Professional Network and Career Coevolution. (2011). Seabright, Paul ; Berardi, Nicoletta.
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27. Are older workers overpaid? A literature review. (2011). van Vuuren, Daniel ; de Hek, Paul.
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28. Professional Network and Career Coevolution. (2011). Seabright, Paul ; Berardi, Nicoletta.
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29. Worker flows, job flows and establishment wage differentials : analyzing the case of France. (2011). Gilles, Fabrice ; Duhautois, Richard ; Petit, Heloise.
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30. Professional Network and Career Coevolution. (2011). Seabright, Paul ; Berardi, Nicoletta.
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31. Unobserved Individual and Firm Heterogeneity in Wage and Job‐Duration Functions: Evidence from German Linked Employer–Employee Data. (2011). Hübler, Olaf ; Cornelissen, Thomas ; Hubler, Olaf ; Cornelien, Thomas .
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32. Labour Market Entry Conditions, Wages and Job Mobility. (2010). Bachmann, Ronald ; Bauer, Thomas ; David, Peggy .
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33. Centering and reference groups for estimates of fixed effects: Modifications to felsdvreg. (2010). Sass, Tim ; Mihaly, Kata ; McCaffrey, Daniel F. ; Lockwood, J. R..
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34. Labor Market Entry Conditions, Wages and Job Mobility. (2010). Bechara (née David), Peggy ; Bachmann, Ronald ; Bauer, Thomas.
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35. Credit within the Firm. (2010). Schivardi, Fabiano ; Pistaferri, Luigi ; Guiso, Luigi.
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36. An empirical assessment of assortative matching in the labor market. (2010). van den Berg, Gerard ; Mendes, Rute ; Lindeboom, Maarten.
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37. Empirical evidence on occupation and industry specific human capital. (2010). Sullivan, Paul.
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38. Credit within the Firm. (2010). Schivardi, Fabiano ; Pistaferri, Luigi ; Guiso, Luigi.
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39. DOES RAIDING EXPLAIN THE NEGATIVE RETURNS TO FACULTY SENIORITY?. (2010). Bratsberg, Bernt ; Ragan, James F. ; Warren, John T..
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40. The importance of two-sided heterogeneity for the cyclicality of labour market dynamics. (2009). Bechara (née David), Peggy ; Bachmann, Ronald.
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41. Managers and wage policies. (2009). Monteiro, Natália ; Bastos, Paulo.
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42. The Importance of Two-Sided Heterogeneity for the Cyclicality of Labour Market Dynamics. (2009). Bechara (née David), Peggy ; Bachmann, Ronald.
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43. Interindustry Wage Differences: An Empirical Review. (2009). Ricaurte, Miguel.
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44. Dots to boxes: Do the size and shape of spatial units jeopardize economic geography estimations?. (2008). Combes, Pierre-Philippe ; Briant, Anthony.
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45. DOTS TO BOXES: DO THE SIZE AND SHAPE OF SPATIAL UNITS JEOPARDIZE ECONOMIC GEOGRAPHY ESTIMATIONS?. (2008). Combes, Pierre-Philippe ; Briant, Anthony.
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46. Dots to Boxes: Do the Size and Shape of Spatial Units Jeopardize Economic Geography Estimations?. (2008). Lafourcade, Miren ; Combes, Pierre-Philippe ; Briant, Anthony.
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47. Seniority and Job Stability: A Quantile Regression Approach Using Matched Employer-Employee Data. (2007). Steffes, Susanne ; Boockmann, Bernhard.
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48. Job mobility in Portugal: a Bayesian study with matched worker-firm data. (2007). van den Berg, Gerard ; Mendes, Rute ; Horny, Guillaume.
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49. Unobserved Individual and Firm Heterogeneity in Wage and Tenure Functions: Evidence from German Linked Employer-Employee Data. (2007). Hübler, Olaf ; Cornelissen, Thomas ; Hubler, Olaf ; Cornelien, Thomas .
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50. International Competition, Returns to Skill and Labor Market Adjustment. (). Silva, Joana ; Greenaway, Sir David ; Falvey, Rodney.
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