Education, experience and dynamic urban wage premium *

Transcription

1 1 Education, experience and dynamic urban premium * Fredrik Carlsen, Jørn Rattsø and Hildegunn E. Stokke Department of Economics, Norwegian University of Science and Technology fredrik.carlsen@svt.ntnu.no; jorn.rattso@svt.ntnu.no; hildegunnes@svt.ntnu.no Abstract The individual economic advantage of living in a city depends on education level. We investigate how work experience and firm specific seniority affects the agglomeration effect across education groups. The data are based on administrative registers covering all full time workers in the private sector of Norway during , about 4.7 million worker year observations, including work experience by region and firm since Accounting for unobservable abilities with identification based on movers, we show that both the static agglomeration effect and the dynamic experience effect in cities increases in education level. Concerning firm specific seniority, we find that low educated gain from firm specific tenure in cities while high educated gain from shifting between firms. JEL codes: J24, J31, J61, R12, R23 Key words: Agglomeration economies, sorting, education, worker experience, firm specific seniority Date: October 13, 2014 * We appreciate discussions at the 2012 European Meeting of the Urban Economics Association, the 2012 North American Meeting of the Regional Science Association International, the 2013 Meeting of the Western Regional Science Association, the 2013 Congress of the European Economic Association, the Urban and Regional Economics seminar at LSE, and at the Labour Economics/Empirical Microeconomics workshop at the Norwegian School of Economics, and comments from Ragnhild Balsvik, Nate Baum Snow, Paul Cheshire, Jorge De la Roca, Steve Gibbons, Christian Hilber, Ana I. Moreno Monroy, Jarle Møen, Henry Overman, Kjell Gunnar Salvanes and Olmo Silva. We are grateful for the cooperation of Statistics Norway. An earlier version of this paper was titled Urban premium and the role of education: Identification of agglomeration effects for Norway.

2 2 1. Introduction Highly educated individuals have higher productivity and tend to live in cities. The observation has led to a literature dealing with skilled cities or smart cities (Glaeser and Saiz, 2004, Shapiro, 2006, and Winters, 2011). The urban concentration of highly educated explains a large part of the observed urban rural gap. In addition to education, also place and type of experience matters for productivity and may imply a dynamic agglomeration effect. Our contribution is to investigate the role of work experience and firm specific seniority in cities across education groups. The bottom line is that the learning advantage of living in cities is increasing in the level of education and is largest for highly educated. Also, the highly educated take advantage of shifting firm in cities, while the low educated benefit from long firm specific seniority. Recent studies of agglomeration effects separating between education groups apply individual level census data. Hedonic regressions clarify how the s reflect characteristics of the workers and allow for the estimation of an agglomeration effect that controls for sorting on observables. Most authors conclude that static agglomeration effects are higher for those with the highest education level. Wheeler (2001) shows how the effect is increasing with the level of education. Rosenthal and Strange (2008) find that the urban premium for workers with college degree is higher than the rest. Bacolod et al. (2009) concludes that the effect of population size increases with education level, but that the effect is equal for workers with college and high school degrees. Some contrarian evidence is published by Lee (2010) involving health care workers. He concludes that the urban premium decreases as the skill level rises. The interpretation is that high skilled prefer to live in cities and smaller cities must pay more to recruit them. Glaeser and Mare (2001) innovated the handling of heterogeneity and sorting using individual panel data and individual fixed effects for the US. They conclude that larger local markets have higher s and productivity even when heterogeneity is taken into account. Combes et al. (2008) argue that sorting is more important, but they do not have observation of education level and their individual fixed effect consequently represents a mixed bag of education, skill, experience and ability. Recent contributions have added an investigation of

3 3 experience and where experience is made. Andersson et al. (2014), Baum Snow and Pavan (2012) and Gould (2007) find that level effects and return to experience are the most important factors explaining the premium and that moving to cities promote human capital accumulation. Baum Snow and Pavan and the analysis of Gould work with structural models and smaller datasets. De la Roca and Puga (2012) have rich register data of Spain and estimate a model with identification based on movers and including the individual history of experience. They find that working in a larger city gives an immediate premium that is expanded over time when living in a large city. De la Roca (2011) expands the analysis by looking at both initial and return migration. D Costa and Overman (2014) show that workers with experience in cities experience higher growth. In recent analyses addressing the spatial distribution of college graduates, Wang (2013) confirms that unobserved abilities matter for the sorting and Ahlin et al. (2013) elaborate on the individual characteristics of importance. We take advantage of the empirical literature on the returns to experience and seniority. An early influential article by Topel (1991) shows large returns to firm specific seniority in the United States. In this case it is important to account for worker experience by firm in the estimation of agglomeration effect. The issue is controversial, but worth pursuing. Altonji and Shakotko (1987) innovated the handling of the endogeneity of labor market experience with instrumentation. Altonji and Williams (2005) challenge the Topel results and find little effect of seniority. Recent studies, notably Buchinsky et al. (2010) and Dustmann and Meghir (2005), find similar returns to experience as the older literature, but also high returns to seniority. Firm specific seniority may affect the urban premium, and in our context it is of importance to study if this effect varies across education groups. We use administrative register data for the whole working population of Norway during 2003 to We exclude part time workers and workers in the public and primary sectors and apply a dataset with about 4.7 million worker year observations. The rural urban dimension is included by separating between 89 labor market regions characterized by population size. The agglomeration effect is analyzed as a city premium where we separate out the 7 largest city labor market regions with more than inhabitants. The role of industrial composition and occupation choice is represented by 54 industrial sectors

4 4 and 350 occupations, respectively. The key worker observables are education level and work experience. In the analysis we separate between primary, secondary and tertiary education. The experience is distinguished between cities versus smaller regions. We start out with a raw agglomeration effect in cities of 14.1%. When considering the premium for observationally equivalent workers, the city effect drops to 7.5%. The reduction of the agglomeration effect when observables are accounted for is consistent with the literature. Introducing individual fixed effects and identification based on movers (5% of the workers move at least once during ) reduces the effect to 5.1%. The estimates in the existing literature vary and will be discussed below, but taking away about 2/3 of the raw differences when controlling for observable worker characteristics and unobserved abilities is in accordance with earlier work. Studies accounting for observed individual characteristics show that the static agglomeration effect increases with the level of education. The Norwegian data are consistent with this and comparable to Wheeler (2001), but the differences across education groups are much smaller when we correct for unobserved abilities. The city effect is 3.2% for primary educated, 4% for secondary educated, and 6% for those with higher education. It follows that the ability selection is more important for the higher education groups. Glaeser and Resseger (2010) introduce worker experience in the understanding of the premium, and their results suggest that learning effects are stronger in more skilled metropolitan areas. De la Roca and Puga (2012) relate work experience and residence to investigate how urbanization matters for learning and to calculate the dynamic premium. Following their approach we analyze learning across education groups by separating between experience made in cities (7 city regions with more than inhabitants) and the rest. Work experience and firm specific seniority are taken into account and occupation fixed effects are included. De la Roca and Puga find that workers in cities accumulate more valuable experience. We show that this learning effect depends on both education level and firm seniority. The initial premium is not affected by the inclusion of worker experience history, but the experience effect adds to the medium term premium because of the city advantage in experience. The dynamic premium differs according to education, both because the effect of city experience increases with the

5 5 level of education and because individuals with higher education on average have longer experience in cities. The experience effect increases the difference between primary and secondary educated on the one hand and the tertiary educated on the other. The mediumterm premium is about 11% for highly educated compared to 5% for primary educated. The estimates show that experience in the cities is more valuable for the highly educated. We have investigated in more detail the role of firm specific seniority in the city size effect. Firm specific seniority is expected to increase the city learning. The results indicate that it is not an advantage to have long tenure in the firm. Firm specific seniority has a negative effect. And interestingly, the interaction of the city effect and experience in the same firm is positive for primary and secondary educated and negative for the higher educated. Those with higher education have a higher urban premium when the experience is outside the present firm. Section 2 discusses our econometric strategy and data. The estimates of static agglomeration effects across education groups are presented in section 3. Section 4 moves on to dynamic agglomeration effects based on experience effects dependent on education and sector. The robustness of the results is investigated in section 5. Section 6 summarizes our conclusions and indicates future research. 2. Econometric strategy and data The analysis deals with the economic importance of urbanization and we concentrate on the agglomeration effect of cities. We follow the approach of separating out the city regions and estimate a city premium. Norwegian cities are small by international comparison, and most regions have large unpopulated areas. Based on information about commuting flows between municipalities, Statistics Norway has divided Norway into 89 travel to work areas, denoted economic regions. The economic regions conform to NUTS 4 regions, as defined by the European Union standard of regional levels. This level of aggregation captures functional regions understood as common labor markets. We define the city group as labor market regions with more than inhabitants in The group consists of 7 regions that basically cover the 4 largest cities in Norway: Oslo, Stavanger, Bergen and Trondheim.

6 6 Our dataset is computed from three administrative registers: the employment, tax and education registers. The employment register links workers and firms, and gives information on work contracts for all employees during It includes the number of days worked, the type of contract 1, and from 2001 onwards it also has information on the exact number of hours worked per week. Based on this we calculate the number of hours worked per year, which is combined with data on annual income from the tax register to give a measure of hourly s for all employees. 2 We concentrate on workers with full time contracts (at least 30 hours per week). Workers with more than two contracts during a year, as well as workers with one full time and one part time contract are excluded. Workers with two full time contracts are excluded if the number of days worked that year exceeds 455 days. This means that we allow for a maximum of 3 months overlap between the two contracts. To avoid extreme observations, we exclude individuals working less than 50 hours or more than 3500 hours per year. Similar, workers with hourly below 70 NOK or above 1250 NOK are also excluded. Finally, we focus on workers between 25 and 65 years old. Information about the work contracts back to 1993 is used to calculate a measure of work experience for each worker, both overall experience, experience by type of region (cities vs. the rest) and experience in the worker s present firm (firm specific seniority). The dataset links workers and firms and includes about distinct firms. The workers are allocated to 60 employment sectors. Since the productivity of resource based sectors are unrelated to urbanization, we exclude the primary sectors agriculture, fishing and forestry. In the public sector s are determined by national regulation and public sector workers are excluded (sectors public administration, education and health care). We are left with 54 sectors and the largest are construction, domestic trade, retail sales and business services. The education register covers the whole adult population and gives information about the highest completed education level in the beginning of October 1 The employment register separates between three contract types: Full time contracts with at least 30 hours work per week, part time contracts with hours work per week, and part time contracts with less than 20 hours work per week. 2 Self employed workers are not included.

7 7 each year. We also have information on the age, gender, immigration status and home region of all individuals. Finally, information on occupation group is available from 2003 onwards at the 4 digit level and includes 350 occupations. The final dataset includes almost workers every year during , giving a total of about 4.7 million worker year observations. Workers can enter and leave the dataset during the 8 year period, and in total about 1 million different workers are included in the dataset. We estimate the urban premium for the whole sample of workers, as well as for three subgroups of workers according to the level of education: tertiary (workers that have completed at least one year at college/university), secondary (workers that have completed at least one year of secondary education) and primary (workers with not more than compulsory schooling). Workers with secondary education account for the largest share with about 2.5 million observations. The subgroups of workers with primary and tertiary education contain 0.9 and 1.3 million observations, respectively. As noticed in the introduction, the heterogeneity of the population represents an important challenge in the estimation of agglomeration effects. Geographical sorting of workers may create correlations between urban scale/density and observable and unobservable worker characteristics, such as education, experience and ability. Sorting may therefore introduce measurement errors in the estimation. We start out with a hedonic regression of hourly s that controls for observable worker characteristics, as well as sector, year and occupation fixed effects: ln w isot city it s o t X it isot (1) where wisot is the hourly income for worker i in sector s and occupation o in year t (for the period ) and are represented by, s city it is the city dummy. Sector, occupation and year fixed effects o and, respectively. is a vector of parameters and t isot is an error term. The vector of observable worker characteristics ( X it ) includes dummies for age (5 year intervals), gender, immigration status and education level (primary, secondary, tertiary), as well as aggregate work experience since 1993 (calculated in days and expressed

8 8 in years). When estimates are done for subgroups according to the level of education, the education dummies are not included in X it. We exploit the panel dimension of the data, and use movements between cities and the rest of the country to control for unobservable worker characteristics. In our dataset, 5% of the workers (about workers) move at least once during Worker fixed effects ( i ) are added to the hedonic equation above: ln w isot city it s o t i X it isot (2) The estimated static agglomeration effect ( ) controls for sorting of workers based on both observable characteristics like education level and experience and unobservable characteristics like abilities. 3 Sector, year and occupation fixed effects are still taken into account. The agglomeration effects estimated in equations (1) and (2) only capture static urban premiums, while working in urban areas are likely to affect s over time through learning effects. We follow the approach of De la Roca and Puga (2012) and capture the dynamic effect on s by allowing the value of experience to vary between cities and smaller regions. As an extension of previous research, years of experience in the worker s present firm are taken into account to check whether the learning effect varies with firm specific seniority. Our third, and preferable, estimation of agglomeration effects is therefore based on the following hedonic equation: ln w city X exp_ c sen sen city (3) isot it s o t i it 1 it 2 it 3 it it isot where exp_ c it is work experience in cities acquired by worker i up until time t and represents years of experience in the worker s present firm (seniority). 4 The specification separates between the static and the dynamic urban premium, and also allows the sen it 3 Observable worker characteristics that are time invariant (gender and immigration status) are not included in this regression. 4 The regression also includes quadratic experience terms.

9 9 dynamic learning effect to vary with worker seniority. The immediate static premium is still given by the estimated coefficient on the city dummy (α). The dynamic premium follows from the estimated coefficients on experience in cities and firm specific seniority in cities ( 1 and 3, respectively), and is calculated based on average experience values. The medium term premium adds the effect of city experience to the static premium. Table 1 presents descriptive statistics for the main variables used in the individual level regressions. As shown in panel a, the average worker in our dataset has an hourly of NOK, is about 42 years old, and has about 8.5 years of work experience. On average the firm specific seniority is 4.3 years, while the experience made in a city region is 3.9 years. Panel b offers mean values of hourly s, age and work experience for the three education groups. Tertiary educated workers have higher s, are somewhat younger, have less overall work experience and lower firm specific seniority, but more experience in cities. According to panel c, about 19% of the workers have only primary education, while workers with secondary and tertiary education account for about 53% and 28.5% of the sample, respectively. About 72% of the workers are male and 12% are immigrants. The allocation of workers between city regions and the rest is roughly equal, but cities have higher share of tertiary educated workers (38% vs. 20%), lower share of male workers (67% vs. 75%) and more immigrants (15% vs. 9%). Table 1 about here 3. Static agglomeration effects across education groups We start out presenting the raw agglomeration effect of cities of 14.1% in column 1 of Table 2. Interestingly, this is exactly the same raw effect as in the dataset for the UK by D Costa and Overman (2014, Table 2). Their definition of city region is inhabitants in a travel to work area, somewhat lower than the cutoff chosen here. Although city size definition varies according to country setting, the urban gap is typically in this order of magnitude. Gould (2007) find a rural urban gap of 17.5% for white collars workers and Glaeser and Mare (2001) 25% overall. Bütikofer et al. (2014) apply a longer time series for Norway and estimate a gap of 16%.

10 10 To control for observed heterogeneity we run an individual level regression over the whole sample including all individual characteristics, as well as year, sector and occupation fixed effects, as described by equation (1) in section 2. The city effect is reduced to 7.5% in column 2. The education gap is about 5% from primary to secondary education and 19% from primary to tertiary education. The male advantage is 13.6%. Non western immigrants have lower s on average. Experience matters, and the effect is non linear. Wages increase with experience for the first 25 years, and on average one extra year of experience adds 1% to s. The importance of unobserved characteristics has been a source of concern and only a few studies have been able to follow movers between regions to identify the ability factor. The regression in column 3 includes worker fixed effects (as described by equation (2) in section 2). The city effect is 5.1% when observable and unobservable individual characteristics are taken into account. It follows that about 2/3 of the city gap is accounted for by observable and unobservable individual characteristics. The result is similar to Bütikofer et al. (2014) using a different dataset for Norway. The comparable city effect of D Costa and Overman (2014) controlling for sorting is 2.3%, but their controls are different and they do not include experience variables. Gould (2007) finds less reduction of the premium for white collar workers, but much larger for blue collar. Glaeser and Mare (2001) produced worker fixed effect estimates that were about 1/3 of the initial gap. The inference of the effect of education now is based on change of situation for the individuals. The effect of a year of experience increases to almost 8% on average indicating a negative correlation between ability and experience. Table 2 about here Our main interest is the separate city effects for each education group. The raw agglomeration effect is distinctly different for the three education groups, increasing from 6.1% for primary educated workers to 12.4% for workers with tertiary education. Cities offer larger premium the higher the education level of the individual. When controlling for observable characteristics of workers and their workplace, we reproduce the differences of

11 11 agglomeration effects across education groups obtained by Wheeler (2001). The results are shown in the first three columns of Table 3. The city effects are quite similar to the raw differences and vary from 4.6% for primary educated to 11.1% for individuals with higher education. The difference between the estimated effect for the lower and higher education group is significantly different from zero at the 1% level. Wheeler did not account for worker fixed effects, but as shown in the last three columns of Table 3, his conclusion still holds when worker fixed effects are included. However, taking away the ability factor reduces the city effect overall and also reduces the differences among education groups. The city effect now varies from 3.2% for primary educated to 6% for those with tertiary education. Unobserved abilities are more important for tertiary educated, and explain about half the urban premium for this education group. The estimates also show that overall experience is more valuable for highly educated individuals. On average, one extra year of experience increases s of primary and secondary educated by about 7%, while the highly educated get a increase of 9.3%. Table 3 about here Previous studies conclude that high education groups have higher static agglomeration effect. We have shown that the overall city effect and the differences between education groups are strongly reduced when we control for unobservable individual characteristics. The size of the static agglomeration effect of cities is about 3 4% among primary and secondary educated and about 6% for those with higher education. All education groups gain from staying in larger cities. 4. Dynamic agglomeration effects experience by type of region and firm The dynamics of agglomeration are related to the accumulation of experience. We separate between experience in cities versus smaller regions. Table 4 presents regressions with worker fixed effects and with experience disaggregated based on where it is accumulated. The static city effect now is 5.2%, about the same as without control for city experience (see column 3, Table 2). The effect of having experience from cities is of economic importance, and given the average city experience of 3.9 years in the data, the dynamic learning effect

12 12 adds about 5% to the urban premium. The total agglomeration effect consequently is more than 10%. The dynamic premium reflects the accumulation of experience over time, and in particular accounts for where the experience is accumulated. In our dataset half the gain from working in a city comes from the static effect and the other half is the dynamic learning effect. Table 4 about here We have shown in section 3 that the static agglomeration effect is large for all education groups and with about 3% point difference between primary educated and the higher educated. The result is reproduced when experience is disaggregated by type of region and the estimated effects are reported in columns 2 4 of Table 4. The initial city premium controlling for observables and unobservables varies from 3.3% for the primary educated to 6.1% for those with higher education. The dynamic premium differs between education groups, both because the effect of city experience increases with the level of education and because individuals with higher education on average have longer experience in cities. The experience effect increases the difference between primary and secondary educated on the one hand and the tertiary educated on the other. The result is displayed in Figure 1 where the primary educated are compared with the tertiary educated. The static city effect is shown in year 0, and the premium is increasing over time. The increase in the premium is slightly decreasing over time. Figure 1 about here The medium term premium is calculated based on average years of experience in cities for the education groups. The average city experience is 4.5 years for the highly educated and the full city premium is 11.1%. The primary educated have on average 3.3 years of experience and the city premium is 5.1%. The difference is statistically significant at the 1% level. The urban premium is compared across models in Table 7, and this model is reported in the first row. The estimates show that experience in cities is more valuable for the highly educated. The combination of the static effect and the experience effect implies

13 13 that the highest educated gain more from living in cities. The combined static and dynamic effect from working in cities increases with education. The introduction of firm specific seniority adds to the understanding of the combined effects of education and experience, but does not change the urban premium much. The analysis is extended to include experience in the same firm and interaction between city size effect and firm seniority. The hypothesis is that experience in the same firm in cities increases the city agglomeration effect. The results for the three education groups are shown in Table 5. Broadly it seems not to be an advantage to have experience from your present firm. Firm specific seniority has a negative effect. And interestingly, the interaction of the city effect and experience in the same firm is positive for primary and secondary educated and negative for the higher educated. Those with higher education have a higher urban premium when the experience is outside the present firm. The combined static and dynamic agglomeration effects are shown in row 2 in Table 7. Taking into account firm specific seniority does not affect the agglomeration effects much and the differences across education groups are reproduced. Table 5 about here The full sample analyzed above makes use of data about worker experience since It follows that they will not give a full history of worker experience for all workers. The analysis is repeated for a sample of workers where we have full history of experience, that is workers born later than The results reported in Table 6 would have differed from Table 5 if worker experience early in life was important. But it is not. The estimates are very similar, both concerning experience effects and agglomeration effects taking into account experience. Table 6 about here 5. Robustness check

14 14 Given the volume of data involved here, there are many ways of specifying the models estimated. We have studied the heterogeneity of the population with respect to gender and race. The results are robust when we study men only and when we exclude foreign immigrants. The importance of other robustness checks are presented in Table 7, and the first three rows summarize the urban premiums in the models above. Table 7 about here We have chosen to investigate the city agglomeration effect in the 7 largest city regions in Norway (above inhabitants). The robustness of the cutoff has been investigated. In row 4 of Table 7 (and in Appendix Table 1) we introduce a separation between cities (above inhabitants) and small cities (between and inhabitants) and analyze the importance of experience and agglomeration effect in the two types of cities. The agglomeration effect of the cities is somewhat higher, but is negligible in the smaller cities. The literature has been concerned with the endogeneity of population size and density since the contribution of Ciccone and Hall (1996) handling the endogeneity with instrumentation based on historical population numbers. We do not think that the instrumentation based on historical data is convincing, and have chosen to study a cutoff separating between the cities and the rest. We have reported results with instrumentation of a continuous population size variable in an early working paper version (Carlsen, Rattsø and Stokke, 2013), and the results are broadly in accordance with the analysis presented here. An interesting alternative approach to endogenous migration is offered by Bütikofer et al. (2014). They compare brothers with different migration histories. The challenge with the cutoff procedure is that the endogenous migration may affect what regions are above the cutoff. To investigate this we have thrown out observations from smaller cities close to the cutoff. The result is reported in row 5 of Table 7 (and Appendix Table 2). The city agglomeration effect estimated is hardly affected by this exclusion compared to the situation where a small city effect is separated out. As mentioned, the agglomeration effect is somewhat higher compared to the basic model of Table 5. Our

15 15 interpretation is that the city effect now captures movers from periphery regions to cities and that this is larger than for those moving from small cities to cities. Finally, a major concern in the estimation of city size effects is the role of amenities motivating migration. Four types of amenities have been shown to be important in the literature school quality, cultural services, crime, and climate. We have checked the robustness of the results with respect to a set of amenity variables. The measure of school quality is based on Borge and Naper (2006). They have estimated municipal fixed effects based on individual data of student achievement in English and with other relevant controls. The weighted municipal effects are aggregated to regional school quality. Cultural amenities are measured as net per capita regional spending on museums in the year Public safety is measured by number of violence related crimes per 1000 inhabitant and as an average over the period Finally, climate is represented by the average winter temperature during Descriptive statistics of the amenity variables are given in Table 1 in section 2. The city agglomeration effect including the amenity variables is shown in the last row of Table 7 and documented in Appendix Table 3. The city effect is not much affected and the differences between education groups remain. 6. Concluding remarks We have used register data for all full time workers in the private sector in Norway (about 4.7 million worker year observations) to study the city premium. The individual panel data include observations of education levels and occupations, as well as labor market, employment sector and firm level affiliation, and also allow for identification of unobserved individual effects based on migration between regions. The main focus is the analysis of differences in city effects for the s across education groups taking into account firmspecific seniority. The analysis takes into account the location and firm specific work experience of the individuals. The experience is distinguished between cities (more than inhabitants) and smaller regions. The initial static premium is not affected by the inclusion of worker experience history, but the experience effect adds to the medium term effect

16 16 since experience in large regions is found to be more valuable. The experience effect differs with respect to education, and in particular the highly educated gain from agglomeration. Firm specific seniority is found to be to the advantage of primary and secondary educated, but interestingly we find that tertiary educated gain from shift of firm in cities. Future work will look in more detail at the individual choices of firm and sector affiliation. References Ahlin, L., M. Andersson and P. Thulin (2013), Market thickness and the early labor market career of university graduates: An urban advantage? CIRCLE Working Paper No. 2013/2, Lund University. Altonji, J. and R. Shakotko (1987), Do s rise with job seniority? Review of Economic Studies 54, Altonji, J. and N. Williams (2005), Do s rise with job seniority? A reassessment, Industrial and Labor Relations Review 58, Andersson, M., J. Klaesson and J.P. Larsson (2014), The sources of urban premium by worker skills: Spatial sorting or agglomeration economies? Papers in Regional Science, forthcoming. Bacolod, M., B. Blum and W. Strange (2009), Skills in the City, Journal of Urban Economics 65, Baum Snow, N. and R. Pavan (2012), Understanding the city size gap, Review of Economic Studies 79, 1, Borge, L. and L. Naper (2006), Efficiency potential and efficiency variation in Norwegian lower secondary schools, FinanzArchiv 62, Buchinsky, M., D. Fougere, F. Kramarz and R. Tchernis (2010), Interfirm mobility, s and the returns to seniority and experience in the United States, Review of Economic Studies 77, Bütikofer, A., D. Polovkova and K.G. Salvanes (2014), What is the city but the people? Changes in urban premium and migrant selection since 1967, mimeo, Norwegian School of Economics. Carlsen, F., J. Rattsø and H.E. Stokke (2013), Education, experience and dynamic urban premium, NTNU Working Paper 14/2013. Ciccone A. and R. Hall (1996), Productivity and the density of economic activity, American Economic Review 86, 1,

17 17 Combes P P., G. Duranton and L. Gobillon (2008), Spatial disparities: Sorting matters! Journal of Urban Economics 63, D Costa, S. and H. Overman (2014), The urban growth premium: Sorting or learning? Regional Science and Urban Economics 48, De la Roca, J. (2011), Selection in initial and return migration: Evidence from moves across Spanish cities, IMDEA Working Paper No De la Roca, J. and D. Puga (2012), Learning by working in big cities, CEPR Discussion Paper No. DP9243. Dustmann, C. and C. Meghir (2005), Wages, experience and seniority, Review of Economic Studies 72, Glaeser, E. and D. Mare (2001), Cities and skills, Journal of Labor Economics 19, 2, Glaeser, E. and M. Resseger (2010), The complementarity between cities and skills, Journal of Regional Science 50, 1, Glaeser E.L. and A, Saiz (2004), The rise of the skilled city, Brookings Wharton Papers on Urban Affairs 5, Gould, E.D. (2007), Cities, workers, and s: A structural analysis of the urban premium, Review of Economic Studies 74, Lee, S. (2010), Ability sorting and consumer city, Journal of Urban Economics 68, Rosenthal, S. and W. Strange (2008), The attenuation of human capital spillovers, Journal of Urban Economics 64, Shapiro, J. (2006), Smart cities quality of life, productivity, and the growth effects of human capital, Review of Economics and Statistics 88, Topel, R.H. (1991), Specific capital, mobility and s: Wages rise with seniority, Journal of Political Economy 99, Wang, Z. (2013), Smart city: Learning effects and labor force entry, mimeo, Department of Economics, Brown University. Wheeler, C. (2001), Search, sorting and urban agglomeration, Journal of Labor Economics 19, 4, Winters J. (2011), Why are smart cities growing? Who moves and who stays, Journal of Regional Science 51, 2,

18 18 Figure 1: Urban premium for primary and tertiary educated workers years after move to city Urban premium primary and tertiary educated Years after move to city primary tertiary

19 19 Table 1: Descriptive statistics Panel a Mean St dev Min Max Hourly (in NOK) Age Total work experience (in years) Work experience in cities Work experience in same firm Work experience small cities Average winter temperature (Celsius) School quality (English grade) Crime (violence incidents per 1000 inhabitants) Public expenditure museums (in NOK) Panel b Mean values Primary educated Secondary educated Tertiary educated Hourly (in NOK) Age Total work experience (in years) Work experience in cities Work experience in same firm Work experience small cities Panel c Share of observations Full sample Cities Rest of country All workers Primary education Secondary education Tertiary education Male Immigrant Immigrant, western Immigrant, non western Notes: Work experience is calculated in days from 1993 onwards, and expressed in years. The city group is defined as regions with more than inhabitants in 2010 (7 regions), while small cities are defined as regions with 2010 population in the range (13 regions). Secondary education corresponds to workers that have completed at least one year of secondary education, while tertiary education includes workers with at least one year at university/college. Western immigrants are defined as immigrants from Europe, Japan, North America, Australia or New Zealand. The average winter temperature is given in Celsius degrees and is the average during The measure of school quality is based on student performance in English adjusted for student and family characteristics (estimated by Borge and Naper, 2006), and is given on a scale from 1 to 6 with 6 as the best. The number of violence related crimes per 1000 inhabitants is measured as an average during , while net per capita public expenditures on museums is from the year 2010.

20 20 Table 2: Estimation of static urban premium (not including dynamic learning effects) (1) Dependent variable City 0.141*** (0.0004) (2) 0.075*** (0.0003) (3) 0.051*** (0.0008) 0.092*** (0.0004) *** 0.016*** Experience 0.015*** (0.0001) (Experience) *** Secondary education 0.052*** (0.0004) (0.002) Tertiary education 0.186*** 0.098*** (0.0005) (0.0034) Immigrant, western 0.008*** (0.0005) Immigrant, non western 0.027*** (0.0009) Male 0.136*** (0.0004) Year fixed effects Yes Yes Yes Sector fixed effects No Yes Yes Occupation fixed effects No Yes Yes Age controls No Yes Yes Worker fixed effects No No Yes Observations R Notes: The regressions are based on yearly data for all full time workers in the private sector during The dependent variable is log hourly s. The city group is defined as regions with more than inhabitants in 2010, which includes 7 out of 89 regions. Work experience is calculated in days from 1993 onwards, and expressed in years. Sector fixed effects are at the 2 digit level and include 54 sectors. Occupation fixed effects are at the 4 digit level and include 350 occupations. The age controls are given as 5 year intervals. The dummies for education and immigrant status are defined in the notes to Table 1. Standard errors are given in parenthesis. *** indicates significance at the 1 percent level. All regressions include a constant term.

21 21 Table 3: Estimation of static urban premium (not including dynamic learning effects) by education groups Dependent variable (1) (2) (3) (4) (5) (6) Education group Primary Secondary Tertiary Primary Secondary Tertiary City 0.046*** (0.0007) 0.066*** (0.0004) 0.111*** (0.0006) 0.032*** (0.0024) 0.04*** (0.0013) 0.06*** (0.0013) Experience 0.007*** (0.0003) 0.006*** (0.0002) 0.02*** (0.0003) 0.079*** (0.0008) 0.082*** (0.0005) 0.112*** (0.0007) (Experience) *** *** *** *** *** Immigrant, western 0.006*** (0.0012) 0.007*** (0.0007) 0.005*** (0.0009) Immigrant, non western 0.007*** (0.0015) 0.024*** (0.0014) 0.06*** (0.0017) Male 0.124*** (0.0009) 0.142*** (0.0006) 0.123*** (0.0007) Year fixed effects Yes Yes Yes Yes Yes Yes Sector fixed effects Yes Yes Yes Yes Yes Yes Occupation fixed effects Yes Yes Yes Yes Yes Yes Age controls Yes Yes Yes Yes Yes Yes Worker fixed effects No No No Yes Yes Yes Observations R Notes: The regressions are based on yearly data during for three subgroups of full time workers in the private sector according to their level of education (primary, secondary, tertiary). The dependent variable is log hourly s. Explanatory variables are defined in the notes to Table 2. Standard errors are given in parenthesis. *** indicates significance at the 1 percent level. All regressions include a constant term.

22 22 Table 4: Estimation of urban premium, including experience by type of region Dependent variable (1) (2) (3) (4) Education group All Primary Secondary Tertiary City 0.052*** (0.0008) 0.033*** (0.0024) 0.041*** (0.0013) 0.061*** (0.0013) Experience 0.085*** (0.0004) 0.076*** (0.0009) 0.078*** (0.0005) 0.104*** (0.0008) (Experience) *** *** *** *** Experience cities 0.015*** (0.0003) 0.006*** (0.0007) 0.008*** (0.0004) 0.013*** (0.0005) (Experience cities) *** *** *** *** Year fixed effects Yes Yes Yes Yes Sector fixed effects Yes Yes Yes Yes Occupation fixed effects Yes Yes Yes Yes Age controls Yes Yes Yes Yes Worker fixed effects Yes Yes Yes Yes Observations R Notes: The regression in column (1) is based on yearly data for all full time workers in the private sector during , while columns (2) (4) consider subgroups according to workers level of education. The dependent variable is log hourly s. Experience in cities refers to work experience accumulated in the city group, defined as regions with more than inhabitants in Other explanatory variables are defined in the notes to Table 2. Standard errors are given in parenthesis. *** indicates significance at the 1 percent level. All regressions include a constant term.

23 23 Table 5: Estimation of urban premium, including experience by type of region and present firm Dependent variable (1) (2) (3) Education group Primary Secondary Tertiary City 0.032*** (0.0025) 0.039*** (0.0013) 0.064*** (0.0014) Experience 0.086*** (0.0009) 0.086*** (0.0005) 0.109*** (0.0008) (Experience) *** *** 0.001*** Experience cities 0.005*** (0.0007) 0.007*** (0.0004) 0.013*** (0.0005) (Experience cities) *** *** *** Experience same firm 0.006*** (0.0003) 0.005*** (0.0001) 0.003*** (0.0002) (Experience same firm) *** *** *** Experience same firm x City 0.001** (0.0003) 0.001*** (0.0001) 0.001*** (0.0002) Year fixed effects Yes Yes Yes Sector fixed effects Yes Yes Yes Occupation fixed effects Yes Yes Yes Age controls Yes Yes Yes Worker fixed effects Yes Yes Yes Observations R Notes: The regressions are based on yearly data during for three subgroups of full time workers in the private sector according to their level of education (primary, secondary, tertiary). The dependent variable is log hourly s. Experience in cities refers to work experience accumulated in the city group, defined as regions with more than inhabitants in Experience in same firm is a measure of firm specific seniority, defined as years of experience in the worker s present firm. Other explanatory variables are defined in the notes to Table 2. Standard errors are given in parenthesis. ***, ** and * indicate significance at the 1, 5 and 10 percent level, respectively. All regressions include a constant term.

24 24 Table 6: Estimation of urban premium young workers Dependent variable (1) (2) (3) Education group Primary Secondary Tertiary City 0.031*** (0.0038) 0.042*** (0.0018) 0.068*** (0.0017) Experience 0.109*** (0.0016) 0.098*** (0.0008) 0.13*** (0.0012) (Experience) *** (0.0001) *** *** Experience cities 0.005*** (0.0013) 0.008*** (0.0006) 0.014*** (0.0007) (Experience cities) ** (0.0001) *** *** Experience same firm 0.011*** (0.0006) 0.006*** (0.0003) 0.004*** (0.0005) (Experience same firm) *** (0.0001) *** * Experience same firm x City 0.002*** (0.0006) (0.0002) 0.003*** (0.0004) Year fixed effects Yes Yes Yes Sector fixed effects Yes Yes Yes Occupation fixed effects Yes Yes Yes Age controls Yes Yes Yes Worker fixed effects Yes Yes Yes Observations R Notes: The regressions are based on yearly data during for young workers, born after 1967, separated based on their level of education (primary, secondary, tertiary). The dependent variable is log hourly s. Explanatory variables are defined in the notes to Table 5. Standard errors are given in parenthesis. ***, ** and * indicate significance at the 1, 5 and 10 percent level, respectively. All regressions include a constant term.

25 25 Table 7: Urban premium in different regressions (including dynamic learning effects) Primary Secondary Tertiary Experience in city Experience in city and firm Experience in city and firm young workers Experience in city, firm and small city Experience in city and firm excluding small city workers Experience in city and firm controlling for amenities Notes: The table shows the urban premium for the three education groups in different regressions. All estimates include both static and dynamic agglomeration effects.

26 26 App Table 1: Estimation of urban premium controlling for experience in small cities Dependent variable (1) (2) (3) Education group Primary Secondary Tertiary City 0.037*** (0.0027) 0.046*** (0.0015) 0.074*** (0.0017) Small city 0.016*** (0.0031) 0.016*** (0.0017) 0.019*** (0.002) Experience 0.086*** (0.001) 0.085*** (0.0006) 0.107*** (0.0008) (Experience) *** *** *** Experience cities 0.006*** (0.0008) 0.008*** (0.0004) 0.015*** (0.0006) (Experience cities) *** *** *** Experience small cities 0.002** (0.0008) 0.002*** (0.0004) 0.004*** (0.0006) (Experience small cities) *** *** *** Experience same firm 0.006*** (0.0003) 0.005*** (0.0001) 0.003*** (0.0002) (Experience same firm) *** *** *** Experience same firm x City 0.001** (0.0003) 0.001*** (0.0001) 0.001*** (0.0002) Year fixed effects Yes Yes Yes Sector fixed effects Yes Yes Yes Occupation fixed effects Yes Yes Yes Age controls Yes Yes Yes Worker fixed effects Yes Yes Yes Observations R Notes: The regressions are based on yearly data during for three subgroups of full time workers in the private sector according to their level of education (primary, secondary, tertiary). The dependent variable is log hourly s. The city group is defined as regions with more than inhabitants in 2010, while small cities are regions with population in the range in Experience in cities and small cities refer to work experience accumulated in the respective city groups. Experience in same firm is a measure of firm specific seniority, defined as years of experience in the worker s present firm. Other explanatory variables are defined in the notes to Table 2. Standard errors are given in parenthesis. ***, ** and * indicate significance at the 1, 5 and 10 percent level, respectively. All regressions include a constant term.