Private sector valuation of public sector experience: The role of education and geography *

Transcription

1 1 Private sector valuation of public sector experience: The role of education and geography * Jørn Rattsø and Hildegunn E. Stokke Department of Economics, Norwegian University of Science and Technology jornr@svt.ntnu.no, hildegunnes@svt.ntnu.no Abstract The labor and urban economics literature has shown that variation in returns to experience is important for the wage distribution of workers. The return to public sector experience is a neglected aspect. Public wages reflect other factors than marginal productivity what is the market valuation of public experience? We use rich administrative data for Norway and study the returns to experience in the private and the public sector. To capture the private sector valuation of public sector experience we separate between stayers in the private sector, stayers in the public sector, and shifters from the public to the private sector, notably from public administration. The return to public experience in the public sector is about 60% of the return to private experience in the private sector. Based on the shifters from the public to the private sector we find that the private sector pays much less for public compared to private experience. On average, the return to public experience for shifters is about the same as for the stayers in the public sector. Low-educated shifters to the private sector lose about half of the return to their public experience compared to the stayers, while high-educated shifters gain some. Low-educated women shifting to the private sector are less negatively affected than men. Geography plays a role, and high-educated shifters with experience from Oslo gain about 2%-point extra return. Further work will investigate the heterogeneity of shifters who they are and why they shift. Preliminary draft (January 31, 2017), comments welcome * First draft presented at the 12 th winter school on inequality and social welfare theory, Canazei, Italy, and we appreciate comments from David Albouy, Nate Baum-Snow, and Paolo Naticchioni.

2 2 1. Introduction Variation in the return to experience is important for the wage distribution of workers. The return to public sector experience is a neglected aspect. Studies of private-public wage gaps show that the return to education and experience is different in the public sector, although the sign and size of the gaps vary across countries. But public wages do not reflect marginal productivity what is the market valuation of public experience? We use rich administrative data for Norway and study the returns of shifters from the public to the private sector, notably from public administration. The data cover the wage performance during with about 2.7 million worker-year observations (about 600,000 workers, men and women). The history of work experience is registered since 1993 by region and sector. Shifters from public administration to the private sector represent about 170,000 worker-year observations (about 39,000 workers). We start out with a panel model with region, industry, occupation, and worker fixed effects. Consistent with the labor literature, we find increasing and concave experience premium curves. The public sector offers about 90% return to experience compared to the private sector. The private-public gap in experience premium is driven by high-educated men. The estimates show positive sorting to the public sector based on both unobserved ability and education. The positive selection is consistent with findings by Dickson et al. (2014) for European countries. Their private-public gap in return to experience varies between countries, and Spain has a similar small positive gap as in the Norwegian data. Our interpretation is that the gap is compensating other aspects of public sector jobs such as lower earnings volatility and job loss risk. The ambition of the paper is to study the accumulation of human capital in the public sector and compare the return to this accumulation with the private sector. The analysis of shifters to the private sector represents serious identification issues since change of employment sector is a choice and estimates comparing shifters with stayers will have selection bias. Given our dataset, we can control for selection on important observables, but selection on unobservables is a challenge. Our strategy is to compare the wage performance of workers

3 3 in the public sector and workers shifting to the private sector that have the same characteristic and history of experience and exploit differences in business cycle situations affecting the recruitment to the public sector across cohorts. The identification strategy is inspired by recent labor market analyses of Card et al. (2013), Dauth et al. (2016), and Kong et al. (2016). At this stage, we present a descriptive panel analysis of the wage conditions of stayers and shifters. The preliminary analysis of the wage performance of the shifters shows that the private sector pays about the same return to public experience as the public sector on average. But low-educated men and young workers lose about half of their return to experience by shifting to the private sector. The high educated gain some by shifting. Geography plays a role, and high-educated shifters with experience from Oslo gain about 2%-point extra return. Further work will investigate the heterogeneity of shifters who they are and why they shift. The main characteristic of the public sector across countries is relative wage compression. Comparing private and public wage distributions across individuals, the bottom of the distribution is lifted in the public sector and the top is cut off. Another common observation is large raw wage differences between the sectors that to a large extent are explained by the composition of the workforce. Introducing observable individual characteristics such as education and experience, take away most of the wage gap. Postel-Vinay and Turon (2007) summarize the literature and extend the evaluation of public sector wages. The evidence about the private-public gap is basically based on cross-section analysis, including Katz and Kruger (1991), Dustmann and van Soest (1998), Borjas (2002), and Schanzenbach (2015). Katz and Kreuger (1991) address the regional aspect and find that federal government regional pay levels in the US are unaffected by local economic conditions. Central government wage structures are typically uniform across regions, while local government wages may differ regionally, as shown by Gittleman and Pierce (2012). Dustmann and van Soest (1998) deal with non-random selection of education, labor market experience, and hours of work using instruments based on parents social and economic status. Cappellari (2002) and Postel-Vinay and Tyron (2007) introduce the analysis of earnings dynamics of the public and private sectors, further developed by Dickson et al. (2014). Postel-Vinay and

4 4 Tyron show that the public sector is different also with lower income mobility, income volatility, and job loss risk. Analyses of the return to experience in the public sector look at public sector valuation of public sector experience. Postel-Vinay and Turon (2007) find that the return to experience is slightly smaller in the public sector. Since the wage structure in the public sector is determined by institutional and political factors, public sector wages cannot be assumed to represent individual productivity. Our contribution is the analysis of private market valuation of the experience generated in the public sector. In our dataset, we identify shifters from the public to the private sector and analyze their private sector return to the public experience. Other authors have studied the interaction between private and public wages, notably Afonso and Gomes (2014) and Gomes (2015), but they do not address the valuation of public experience. Wage differences are related to education and occupation, but also work experience. Returns to experience can be substantial, and the literature has investigated the heterogeneity. Dustmann and Meghir (2005) show that returns are large in the first years of experience and then declining. The return to experience is higher for the highly educated and lasts longer. The more recent literature addresses the dynamics of the return to experience, and Jeong et al. (2015) argue that it is basically driven by demographic factors. It follows that cohort analysis is helpful in sorting out wage differences. The return to experience has also been analyzed in the context of the urban wage premium. Experience in cities is shown to give higher return by Glaeser and Mare (2001), Gould (2007), Baum-Snow and Pavan (2012), and Matano and Naticchioni (2016). Experience as a dynamic agglomeration effect is estimated by De la Roca and Puga (2017) and Carlsen et al. (2016). The econometric approach of this preliminary analysis and the dataset are described in section 2, and section 3 presents the results. Section 4 offers some suggestions for further work.

5 5 2. Econometric approach and data To estimate the returns to experience in the private and the public sector, we use data on hourly wages and worker characteristics from , with information on work experience dating back to The dataset is computed from three administrative registers: employment, tax, and education. The employment register links workers and firms and gives information on work contracts for all employees. It includes the length of the contract, the type of contract, and the number of hours worked per week. This is used to calculate the number of hours worked per year, which is combined with data on annual wage income from the tax register to give a measure of hourly wages. Information on work contracts back to 1993 is used to calculate actual full-time experience for each worker, using overall experience, experience by type of region (Oslo, other large cities, and the rest of the country), and experience by type of sector (private and public). The experience variables are calculated in days and expressed in years. The education register covers the whole adult population and gives information about the highest completed education level in the beginning of October each year. We also have information on the age, gender, immigrant status, industry affiliation, occupation group, firm affiliation, and home region of all individuals. The original dataset consists of about 16.4 million worker-year observations and 2.8 million workers. We exclude workers below 20 years of age, and focus on workers with complete history of work experience. Since data on work contracts are unavailable prior to 1993, the analysis is restricted to workers born in 1968 or later. This reduces the dataset by almost 9.2 million worker-year observations. Workers within the education and health care industries represent an unclear mix of private and public sector workers, and are therefore excluded from the analysis (almost 1.9 million observations). This implies that public sector workers refer to workers in public administration. We further exclude workers in the primary sectors of agriculture, fishing, and forestry (totaling 78,000 observations), and due to an incomplete history of work experience, foreign immigrants are also excluded (about 1.1 million observations). We concentrate on workers with full-time contracts (at least 30 hours per week). Workers with more than four contracts during a year, as well as workers with fewer than 31 working days during a year are excluded. These restrictions reduce the dataset by

6 6 about 0.7 million worker-year observations. Missing data on hours worked, level of education, or occupation group further excludes approximately 0.7 million observations. Finally, to avoid extreme observations, we exclude the top and bottom 1% of the wage distribution. The final dataset includes about 2.7 million worker-year observations during Workers can enter and leave the labor market during the eight-year period, and in total about 600,000 different workers are included. The workers are allocated to 54 industrial sectors, 350 occupation groups, 89 labor market regions, and about 125,000 firms. To capture the private sector return to public experience, we separate between stayers in the private sector, stayers in the public sector, and shifters between sectors (with focus on shifters from the public to the private sector). As seen from the descriptive statistics in Table 1, private sector stayers account for 2.35 million observations. Workers currently in the public sectors represent 215,000 observations (including shifters from the private to the public sector), while we have about 170,000 observations of shifters from the public to the private sector. Female workers account for 42% of the observations in the public sector, 30% in the private sector, and 25% among shifters. We separate between low-educated workers (primary and secondary school) and high-educated workers (college and graduate school). Compared to private sector workers, shifters and public sector workers have higher wages, are older and more educated, and are more likely to live in cities. While 22% of male workers in the private sector are high-educated, the same figures for male public sector workers and male shifters are 61% and 50%, respectively. Total work experience varies from 0 to 17 years and equals about 7 years on average for male private stayers and male public sector workers and almost 8 years for male shifters from the public to the private sector. Independent of sector, female workers have lower hourly wages, shorter work experience, higher education, and are more likely to live in cities compared to men. Table 1 about here The first econometric specification focuses on the own sector valuation of work experience: the return to private sector experience in the private sector and the return to public sector experience in the public sector. Individual hourly wages are regressed on years of private

7 7 and public work experience, observable worker characteristics ( X it ), as well as regional ( r ), industry ( s), year ( t ), occupation ( o), and worker fixed effects ( i ): 1, 2, (1) ln w irsot j e ijt j e ijt fem i X it r s t o i irsot j j where wirsot is the hourly wage income for worker i in region r, industry s, and occupation o at time t, fem i is a dummy that equals 1 if the worker is female, and e ijt represents years of work experience acquired by worker i in sector j (j = private, public) up until time t. 1 The coefficients 1, j give the return to (private and public) experience for male workers, while the corresponding returns to experience for female workers are given by 1, j 2, j. The estimated experience effects controls for sorting of workers based on both time-varying observable characteristics and unobservable characteristics (abilities). The error term is given by irsot and is a vector of parameters. The regression is estimated both aggregate and separately for low- and high-educated workers. To capture the private sector valuation of public sector experience, we separate out shifters from the public to the private sector and allow the return to public sector experience to vary between sectors. The full specification includes shifters from the private to the public sector, but the focus of the analysis is on public-private shifters. Since workers shifting sector before 2003 have constant years of public experience during , worker fixed effects are not included in this part of the analysis. where 1, 2, 3, ln w irsot j e ijt j e ijt fem i j e ijt shifter i j j j 4, jeijt femi shifteri X (2) it r s t o irsot j shifter i is a dummy that equals 1 if the worker has shifted between the private and the public sector. The other variables are defined in relation to equation (1). Worker characteristics include age, gender, and level of education. 1 The regressions include quadratic experience terms.

8 8 3. Results The analysis covers the career development for workers aged years and concentrates on the individual wage performance during based on observations of experience since The first model studies the return to experience separating between private and public sectors. The analysis shows the return to private experience in the private sector and the return to public experience in the public sector. The Mincer equation includes private and public experience (including quadratic terms), interaction terms for women, age (5-year groups), and region, industry, occupation, year, and worker fixed effects. The model is run for all workers and separately for low-educated (primary and secondary school) and higheducated (college and graduate school) workers. Column 1 of Table 2 reports the effects for all workers. First-year private sector return to experience for men is 13.1%, while first-year public sector return to experience is 11.9%. The public sector offers about 90% of the return to experience in the private sector on average. Consistent with the literature on experience, the experience premium curves are positive and concave. Women have lower return to experience in the private sector, about 2%- points, but not in the public sector. Public sector wage policy is designed to avoid discrimination of women and the policy seems to work in practice. Table 2 about here The return to experience for low-educated and high-educated workers is documented in columns 2 and 3 of Table 2. The low educated receive the same return to experience in the public sector as in the private sector. High-educated men have higher returns in the private than in the public sector, while females with high education are offered the same returns to experience in both sectors (although lower than the return offered to men in the private sector).

9 9 Our main interest is the private sector return to public experience how is the public sector experience valued at private markets? Compared to the analysis above, we now separate between stayers in the private sector, shifters from the private to the public sector, stayers in the public sector, and shifters from the public to the private sector. We use data for the historical work experience since 1993, and consequently a large part of the public experience of the shifters from the public to the private sector are given for the wage effects studied (during ). It follows that we cannot use worker fixed effect when studying the shifters. The model now includes worker characteristics (age, gender, and level of education) and region, industry, occupation, and year fixed effects. The model formulation implies a comparison of workers of the same age and gender, with the same level of education, living in the same type of region, working in the same industry, and having the same occupation. They vary in their historical work experience most of the workers have no experience from the public sector, but we have about 170,000 worker-year observations (39,000 workers) of public experience for shifters from the public sector to the private sector after 1, 2, 3 years or more. They have had different treatments in the public sector in the form of work experience and we estimate how this experience is rewarded at private markets. The estimates presented in Table 3 cover both stayers in the two sectors and shifters to the private sector. When worker fixed effects are excluded, the estimated return to experience is much lower, in accordance with other studies (such as De la Roca and Puga, 2017). The return to public experience in the public sector now is about 60% of the return to private experience in the private sector the first-year returns for men are 2.4% and 4.1%, respectively. 2 The larger gap without worker fixed effects is a first indication that public workers overall are positively selected. The private sector return to experience is similar for low-educated and high-educated stayers. Women have lower return to experience in the private sector, and the loss is larger for the high educated than the low educated. Loweducated stayers in the public sector have about the same return to experience as loweducated stayers in the private sector. High-educated stayers in the public sector have much 2 Figures 1 and 2 show how the aggregate marginal return to experience evolves with years of experience for male and female workers, respectively. The calculations are based on estimated returns in column 1 of Table 3, and separate between stayers in the private sector, stayers in the public sector, and shifters from the public to the private sector.

10 10 lower return to experience than those in the private sector. These findings hold for both men and women. The shifters to the private sector can inform us about the private sector return to public sector experience, and we find that the private sector pays much less for public compared to private experience. On average, the return to public experience for shifters from the public to the private sector is about the same as for the stayers in the public sector. In columns 2 and 3 we see that low-educated shifters to the private sector lose about 2/3 of the return to their public experience compared to the stayers, while high-educated shifters gain some. Low-educated women shifting to the private sector are less negatively affected than men. This pattern of returns to public experience comparing shifters and stayers is the starting point for future analysis of the heterogeneity of public-private gaps. Table 3 about here We investigate the heterogeneity of workers by alternative specifications of a model for all male stayers. Column 1 of Table 4 shows the raw estimates of return to experience with no control variables. In this case, the first-year private return to private experience is 6.2% and the public return to public sector experience is 6.8%. Controlling for worker characteristics (age and level of education) in the second column reduces the return to experience. The first-year return to private experience now is 4.6% and for public experience 2.8%. We conclude that the data imply positive sorting to work experience with respect to observable individual characteristics. The final column of Table 4 includes regional, industry, and occupation fixed effects and is consistent with the result in Table 3, here only for men. Firstyear return to private experience is further reduced to 4.1%. We do not report decomposition for education groups and separating out women, but the same reductions in the estimates with increased controls apply. The results indicate positive sorting to experience with respect to region, industry, and occupation in the private sector. Table 4 about here

11 11 The further analysis concentrates on the shifters from the public sector and their return to experience compared to stayers in the private sector. The labor literature has shown systematic differences across cohorts. We separate between three age groups: workers aged 20 29, 30 36, and years, and the findings are documented in Table 5. The main cohort effect shown is the much higher return to experience for the youngest group, both in the private and the public sector. The aggregate first-year return to private experience for male workers is 7.1% for the 20 29, compared to 2.6% for the and 1.5% for the Women have lower return to private experience, in particular the youngest cohort. The pattern is the same for stayers in the public sector, with highest return for the youngest age group and about zero return for the oldest age group. Young male workers loose about half their return to experience by shifting from the public to the private sector. Young female shifters are less negatively affected than men. We conclude that a life cycle analysis adds important information about the returns to both private and public sector experience. Table 5 about here The last aspect of public experience investigated here is the geographic dimension. Does it matter whether the public experience is accumulated in a large city? The analysis separates between the capital Oslo, large city regions with more than 100,000 inhabitants, and the rest. The results show distinct geographic differences in the return to experience. The separate findings for male and female workers are documented in Tables 6 and 7, respectively. Table 6 and 7 about here The aggregate analysis for male workers in the first column of Table 6 implies that the firstyear return to private experience in Oslo is 2.3%-points above the rest of the country. The other large city regions also offer higher return, about 0.7%-point. As seen from columns 2 and 3, the higher return to experience in cities increases with the level of education. The low-educated private sector stayers gain less from having experience in large cities compared to the high educated. The return to public experience in the public sector is about the same in cities as in the rest of the country, and equals the return to private sector

12 12 experience outside cities. Low-educated shifters to the private sector have much lower return to their public experience, especially when the experience is accumulated in Oslo. High-educated shifters receive the same return to their experience as in the public sector, but when the public experience is accumulated in Oslo, the return in the private sector increases by almost 2%-points. As seen from Table 7, the pattern is similar for female workers. Women have lower return to private experience than men, but the extra return from experience in Oslo is still around 2%- points (and is higher for high-educated than low-educated women). Shifters from the public to the private sector receive the same return to their experience as in the public sector, but high-educated shifters with experience from Oslo gain 2%-points extra return. 4. Concluding remarks A large labor and urban economics literature has shown that variation in returns to experience is important for the wage distribution of workers. The return to public sector experience is a neglected aspect. Relative wage compression is observed in the public sector the bottom of the distribution is lifted and the top is cut off. Return to education and experience in the public sector is influenced by wage policy. Public wages reflect other factors than marginal productivity what is the market valuation of public experience? We use rich administrative data for Norway and study the returns to experience in the private and the public sector and in particular the shifters from the public to the private sector, notably from public administration. We start out showing that the public sector offers about 90% return to experience compared to the private sector in a panel model with region, industry, occupation, and worker fixed effects. The private-public gap in experience premium is driven by high-educated male workers. The estimates show positive sorting to the public sector based on both unobserved ability and education. In a preliminary descriptive panel analysis of the shifters to the private sector, we find that the private sector pays about the same return to public experience as the public sector on average. But loweducated men and young workers lose about half of their return to experience by shifting to the private sector. The high educated gain some by shifting. Geography plays a role, and

13 13 high-educated shifters with experience from Oslo gain about 2%-points extra return. Future research will develop and implement an identification strategy to address remaining challenges with this analysis. The heterogeneity of the shifters must be investigated further who they are and why they shift. The ambition is to estimate a comparison of groups of workers with the same background in the public sector and where some shift to the private sector. The comparison must take into account different motivations to stay in and shift out of the public sector to avoid selection bias. References Afonso, A. and P. Gomes (2014). Interactions between private and public sector wages. Journal of Macroeconomics 39, Baum-Snow N. and R. Pavan (2012). Understanding the city size wage gap. Review of Economic Studies 79, 1, Borjas, G. (2002). The wage structure and the sorting of workers into the public sector. NBER Working Paper No Capellari, L. (2002). Earnings dynamics and uncertainty in Italy: how do they differ between the private and the public sectors? Labour Economics 9, Card, D., J. Heining and P. Kline (2013). Workplace heterogeneity and the rise of West German wage inequality. Quarterly Journal of Economics 128, 3, Carlsen, F., J. Rattsø and H. Stokke (2016). Education, experience and urban wage premium. Regional Science and Urban Economics 60, 1, Dauth, W., S. Findeisen, E. Moretti and J. Suedekum (2016). Spatial wage disparities Workers, firms, and assortative matching. Mimeo, UC Berkeley. De la Roca, J. and D. Puga (2017). Learning by working in big cities. Review of Economic Studies 84, 1, Dickson, M., F. Postel-Vinay, and H. Turon (2014). The lifetime earnings premium in the public sector: The view from Europe. Labour Economics 31, Dustmann, C. and C. Meghir (2005). Wages, experience and seniority. Review of Economic Studies 72,

14 14 Dustman, C. and A. van Soest (1998). Public and private sector wages of male workers in Germany. European Economic Review 42, Gittleman, M. and B. Pierce (2012). Compensation for state and local government workers. Journal of Economic Perspectives 26, 1, Glaeser, E. and D. Mare (2001). Cities and skills. Journal of Labor Economics 19, 2, Gomes, P. (2015). Optimal public sector wages. Economic Journal 125, 587, Gould, E.D. (2007). Cities, workers, and wages: A structural analysis of the urban wage premium. Review of Economic Studies 74, Jeong, H., Y. Kim and I. Manovskii (2015). The price of experience. American Economic Review 105, Katz, L. and A. Kruger (1991). Changes in the structure of wages in the public and private sectors. NBER Working Paper No Kong, Y.C., B. Ravikumar, and G. Vandenbroucke (2016), Explaining cross-cohort differences in life cycle earnings. Mimeo, Federal Reserve Bank of St. Louis. Matano, A. and P. Naticchioni (2016). What drives the urban wage premium? Evidence along the wage distribution. Journal of Regional Science 56, 2, Postel-Vinay, F. and H. Turon (2007). The public pay gap in Britain: Small differences that (don t?) matter. Economic Journal 117, Schanzenbach, M. (2015). Explaining the public-sector pay gap: The role of skill and college major. Journal of Human Capital 9, 1, 1-44.

15 15 Table 1: Descriptive statistics Private sector workers Public sector workers Shifters public private Male Female Male Female Male Female Hourly wage (log) Low-educated High-educated Age Age Age Oslo resident Other city resident Private experience Public experience Observations 1,642, , ,879 90, ,871 42,244

16 16 Table 2: Stayers, worker fixed effects (own sector valuation of experience) (1) (2) (3) Aggregate Low-educated High-educated Private experience 0.131*** (0.0011) 0.123*** (0.0013) 0.155*** (0.0022) (Private experience) *** *** *** Private exp. x Female *** (0.0008) *** (0.001) -0.03*** (0.0013) (Private exp.) 2 x Female *** *** *** Public experience 0.119*** (0.0015) 0.121*** (0.0025) 0.127*** (0.0024) (Public experience) *** *** *** Public exp. x Female ** (0.0019) *** (0.0036) (0.0022) (Public exp.) 2 x Female *** 0.001*** ** Observations 2,741,289 1,885, ,011 Notes: The regressions are based on yearly data for all full-time workers in the private and the public sector during , born in 1968 or later. All regressions include worker characteristics, as well as regional, year, industry, occupation, and worker fixed effects. Work experience is calculated based on actual days worked from 1993 onwards, expressed in years. Regional fixed effects include 89 labor market regions. Industry fixed effects are at the two-digit level and include 54 industries. Occupation fixed effects are at the four-digit level and include 350 occupations. Robust standard errors (clustered by workers) are given in parenthesis. All regressions include a constant term. *** and ** indicate significance at the 1% and 5% level, respectively.

17 17 Table 3: Stayers and shifters, private valuation of public experience (1) (2) (3) Aggregate Low-educated High-educated Private experience 0.041*** (0.0003) 0.034*** (0.0004) 0.036*** (0.0007) (Private experience) *** *** *** Private exp. x Female *** (0.0005) *** (0.0006) *** (0.0009) (Private exp.) 2 x Female *** *** *** Public experience 0.024*** (0.0008) 0.038*** (0.0014) 0.013*** (0.0009) (Public experience) *** *** Public exp. x Female *** (0.001) *** (0.002) -0.01*** (0.0013) (Public exp.) 2 x Female *** *** Public exp. x Shifter *** (0.0013) *** (0.0022) 0.006*** (0.0016) (Public exp.) 2 x Shifter *** 0.002*** Public exp. x Female x Shifter (0.0022) 0.014*** (0.0037) (0.0028) (Public exp.) 2 x Female x Shifter ** (0.0004) (0.0003) Observations 2,741,289 1,885, ,011 Notes: The regressions are based on yearly data for all full-time workers in the private and the public sector during , born in 1968 or later. All regressions include worker characteristics, as well as regional, year, industry, and occupation fixed effects. Work experience is calculated based on actual days worked from 1993 onwards, expressed in years. Regional fixed effects include 89 labor market regions. Industry fixed effects are at the two-digit level and include 54 industries. Occupation fixed effects are at the four-digit level and include 350 occupations. Robust standard errors (clustered by workers) are given in parenthesis. All regressions include a constant term. *** and ** indicate significance at the 1% and 5% level, respectively.

18 18 Table 4: Heterogeneity, decomposition, aggregate and men only Dependent variable (1) (2) (3) Private experience 0.062*** (0.0003) 0.046*** (0.0004) 0.041*** (0.0004) (Private experience) *** *** *** Public experience 0.068*** (0.0007) 0.028*** (0.0007) 0.024*** (0.0007) (Public experience) *** *** *** Year fixed effects Yes Yes Yes Education controls No Yes Yes Age controls No Yes Yes Regional fixed effects No No Yes Industry fixed effects No No Yes Occupation fixed effects No No Yes Observations 1,895,993 1,895,993 1,895,993 Workers 385, , ,895 R Notes: The regressions are based on yearly data for all male full-time workers in the private and the public sector during , born in 1968 or later. Work experience is calculated based on actual days worked from 1993 onwards, expressed in years. The education controls separate between four levels of education: primary, secondary, short tertiary (1-3 years at college/university), and long tertiary (at least 4 years at college/university). The age controls are given as five-year intervals. Regional fixed effects include 89 labor market regions. Industry fixed effects are at the two-digit level and include 54 industries. Occupation fixed effects are at the four-digit level and include 350 occupations. Robust standard errors (clustered by workers) are given in parenthesis. All regressions include a constant term. *** indicates significance at the 1% level.

19 19 Table 5: Cohort analysis, life-cycle effects (1) (2) (3) Age Age Age Private experience 0.071*** (0.0007) 0.026*** (0.0007) 0.015*** (0.001) (Private experience) *** *** Private exp. x Female *** (0.0012) *** (0.001) -0.01*** (0.0014) (Private exp.) 2 x Female *** *** *** Public experience 0.061*** (0.0023) 0.008*** (0.0013) *** (0.0016) (Public experience) *** (0.0003) *** *** Public exp. x Female *** (0.004) 0.004** (0.0018) 0.006*** (0.0019) (Public exp.) 2 x Female ** (0.0007) *** *** Public exp. x Shifter *** (0.0038) 0.007*** (0.0019) 0.015*** (0.0023) (Public exp.) 2 x Shifter *** (0.0008) ** *** Public exp. x Female x Shifter 0.025*** (0.0082) *** (0.0032) * (0.0035) (Public exp.) 2 x Female x Shifter * (0.002) *** (0.0004) (0.0003) Observations 1,128,735 1,124, ,206 Notes: The regressions are based on yearly data for all full-time workers in the private and the public sector during , born in 1968 or later. All regressions include worker characteristics, as well as regional, year, industry, and occupation fixed effects. Work experience is calculated based on actual days worked from 1993 onwards, expressed in years. Regional fixed effects include 89 labor market regions. Industry fixed effects are at the two-digit level and include 54 industries. Occupation fixed effects are at the four-digit level and include 350 occupations. Robust standard errors (clustered by workers) are given in parenthesis. All regressions include a constant term. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.

20 20 Table 6: Experience and geography, men only (1) (2) (3) Aggregate Low-educated High-educated Private experience 0.034*** (0.0004) 0.032*** (0.0005) 0.018*** (0.0008) (Private experience) *** *** Private experience in Oslo 0.023*** (0.0007) 0.015*** (0.0009) 0.027*** (0.001) (Private experience in Oslo) *** *** *** Private exp. in other cities 0.007*** (0.0004) 0.005*** (0.0005) 0.01*** (0.0008) (Private exp. in other cities) *** *** *** Public experience 0.032*** (0.001) 0.038*** (0.0018) 0.02*** (0.0012) (Public experience) *** *** *** Public experience in Oslo * (0.0016) 0.009** (0.0035) -0.01*** (0.0018) (Public experience in Oslo) *** (0.0003) *** Public exp. in other cities 0.003** (0.0012) (0.0023) (0.0014) (Public exp. in other cities) *** ** ** Public exp. x Shifter *** (0.0017) *** (0.0028) (0.0022) (Public exp.) 2 x Shifter *** *** (0.0003) Public exp. in Oslo x Shifter 0.008** (0.0037) *** (0.0073) 0.014*** (0.0044) (Public exp. in Oslo) 2 x Shifter (0.0005) ** (0.0009) * (0.0006) Public exp. in other cities x Shifter (0.0025) (0.0041) (0.0032) (Public exp. in other cities) 2 x Shifter (0.0003) (0.0005) (0.0003) Observations 1,895,993 1,387, ,750 Notes: The regressions are based on yearly data for all male full-time workers in the private and the public sector during , born in 1968 or later. All regressions include worker characteristics, as well as regional, year, industry, and occupation fixed effects. Work experience is calculated based on actual days worked from 1993 onwards, expressed in years. Regional fixed effects include 89 labor market regions. Industry fixed effects are at the two-digit level and include 54 industries. Occupation fixed effects are at the four-digit level and include 350 occupations. Robust standard errors (clustered by workers) are given in parenthesis. All regressions include a constant term. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.

21 21 Table 7: Experience and geography, women only (1) (2) (3) Aggregate Low-educated High-educated Private experience 0.009*** (0.0006) 0.011*** (0.0008) (0.001) (Private experience) *** *** Private experience in Oslo 0.02*** (0.0008) 0.014*** (0.0012) 0.021*** (0.0013) (Private experience in Oslo) *** *** *** Private exp. in other cities 0.008*** (0.0007) 0.007*** (0.0009) 0.008*** (0.0011) (Private exp. in other cities) *** *** ** Public experience 0.005*** (0.0012) 0.007*** (0.0021) 0.003* (0.0015) (Public experience) ** Public experience in Oslo 0.004** (0.0017) 0.008** (0.0031) (0.0021) (Public experience in Oslo) *** (0.0003) *** Public exp. in other cities 0.004*** (0.0014) (0.0025) (0.0018) (Public exp. in other cities) ** Public exp. x Shifter (0.0027) (0.0043) (0.0035) (Public exp.) 2 x Shifter (0.0003) (0.0004) (0.0004) Public exp. in Oslo x Shifter 0.01** (0.0046) (0.0079) 0.017*** (0.0056) (Public exp. in Oslo) 2 x Shifter ** (0.0006) (0.001) ** (0.0008) Public exp. in other cities x Shifter (0.0038) (0.006) (0.0048) (Public exp. in other cities) 2 x Shifter (0.0004) (0.0006) (0.0005) Observations 845, , ,261 Notes: The regressions are based on yearly data for all female full-time workers in the private and the public sector during , born in 1968 or later. All regressions include worker characteristics, as well as regional, year, industry, and occupation fixed effects. Work experience is calculated based on actual days worked from 1993 onwards, expressed in years. Regional fixed effects include 89 labor market regions. Industry fixed effects are at the two-digit level and include 54 industries. Occupation fixed effects are at the four-digit level and include 350 occupations. Robust standard errors (clustered by workers) are given in parenthesis. All regressions include a constant term. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.

22 22 Figure 1: Marginal return to experience, male stayers and shifters Marginal return to experience: Male workers Years of experience Private Public Shifters Figure 2: Marginal return to experience, female stayers and shifters Marginal return to experience: Female workers Years of experience Private Public Shifters