The sources of the urban wage premium by worker skills: Spatial sorting or agglomeration economies?

Authors


  • We are grateful for constructive comments from three anonymous reviewers which improved the paper, as well as from seminar participants at the Max Planck Institute in Jena, CIRCLE at Lund University and the Jönköping International Business school. We also wish to thank Katarina Nilsson Hakkala (Aalto University), Fredrik Heyman (IFN) and Fredrik Sjöholm (Lund University) for sharing their data on non-routine job tasks by occupations. Martin Andersson acknowledges financial support from the Swedish research council FORMAS (Dnr 2011-80), as well as the Swedish Research Council (Linnaeus Grant No. 349200680) and the Swedish Governmental Agency for Innovation Systems (Grant agreement 2010-07370).

Abstract

We estimate the respective importance of spatial sorting and agglomeration economies in explaining the urban wage premium for workers with different sets of skills. Sorting is the main source of the wage premium. Agglomeration economies are in general small, but are larger for workers with skills associated with non-routine job tasks. They also appear to involve human capital accumulation, as evidenced by the change in the wage of workers moving away from denser regions. For workers with routine jobs, agglomeration economies are virtually non-existent. Our results provide further evidence of spatial density bringing about productivity advantages primarily in contexts when problem-solving and interaction with others are important.

Resumen

En este artículo estimamos la importancia respectiva de las economías de clasificación y de aglomeración espacial en cuanto a explicar la prima salarial de los trabajadores urbanos con diferentes conjuntos de habilidades. La clasificación es la fuente principal de la prima salarial. Las economías de aglomeración son pequeñas en general, pero son mayores para los trabajadores competentes en tareas laborales no rutinarias. También parecen estar relacionadas con la acumulación de capital humano, como lo demuestra el cambio en el salario de los trabajadores que se desplazan fuera de las regiones con mayor densidad. Para los trabajadores con labores rutinarias, las economías de aglomeración son prácticamente inexistentes. Nuestros resultados proporcionan pruebas adicionales de que la densidad espacial proporciona ventajas productivas, principalmente en contextos en los que importa la resolución de problemas y la interacción con otros.

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1 Introduction

Workers in urban areas of high spatial economic density earn higher wages than their counterparts in rural and more sparsely populated regions. Glaeser and Maré (2001) report that wages of urban workers in the United States are about 33 per cent higher than their non-urban counterparts. Combes et al. (2008) report that average wages in Paris are about 15 per cent higher compared to other large French cities, 35 per cent higher than in mid-sized cities and as much as 60 per cent higher than in the rural areas of France. The empirical regularities of this kind are generally referred to as the urban wage premium (UWP).

While the UWP is established as a general phenomenon, less is known about its sources and particularly whether it differs across workers with different sets of skills. This paper deals directly with these issues. We quantify the UWP for workers with different degrees of non-routine skills, respectively, and estimate the relative importance of spatial sorting and agglomeration economies in explaining the spatial wage disparities for each type of worker. The analyses in the paper provide empirical evidence on which type of skills are rewarded by density, and bear on the broader question of the contexts in which agglomeration is important.

1.1 Background and motivation

Recent research on the UWP has focused on two main lines of inquiry. One puts the issue of untangling the sources of the density wage premium at centre stage, where a main question regards the respective importance of non-random spatial sorting of workers and agglomeration economies (Glaeser and Maré 2001; Wheeler 2006; Yankow 2006; Gould 2007; Combes et al. 2008, 2011; Melo et al. 2009; Puga 2010; Baum-Snow and Pavan 2012). Spatial sorting refers to selection and explains the gap by more productive workers being more prone to locate in denser regions. This explanation involves no causal effect of spatial density on worker productivity. The existence of agglomeration economies, on the other hand, implies that density boosts worker productivity, for example through more efficient matching or faster human capital accumulation due to knowledge spillover phenomena (cf. Duranton and Puga 2004).1 A general finding in this literature is that spatial sorting of workers is the main source of the UWP (Combes et al. 2008).

The other line of inquiry focuses on differences in the magnitude of the UWP across workers with different sets of skills. Bacolod et al. (2009) show that the UWP is not uniform across workers, but depends on workers' skills. They maintain that the empirical literature on spatial wage differentials tends to equate skills with education levels, which does not capture horizontal differentiation of skills, such as cognitive, people and motor skills. The horizontal dimension of skills is important, they argue, as it may condition the ability to learn from the environment as well as the extent to which one benefits from matching and interaction with others, that is, how much one benefits from agglomeration. Consistent with this, they show that it is primarily workers with jobs in which cognitive and people skills are important that enjoy a UWP.2

Skills that make workers better apt to benefit from agglomeration should not only be reflected in workers having a higher UWP, but also with regard to the importance of agglomeration economies as a source of the wage premium. Though a higher UWP among workers with certain skills could in principle be due to stronger self-selection of the more able ones towards urban regions compared to workers with other types of skills, the argument that learning and matching effects are stronger for workers with skills related to problem-solving and interaction with others is indeed not about spatial sorting. It is instead an argument emphasizing interactions between workers and their local environment that lead to productivity gains, namely, agglomeration economies. The implication is that for workers with problem-solving and interaction skills, agglomeration economies should quantitatively be a more important source of the density wage premium. We test this prediction, thus bridging the two lines of inquiry on the UWP.

Available evidence on the magnitude and sources of the UWP by worker skills is limited. Bacolod et al. (2009) employ data on a sample of US workers and estimate the effect of agglomeration on the hedonic price of cognitive, people and motor skills, respectively. While the question of the sources of the UWP for the various sets of skills is not spelled out explicitly in their paper, they isolate agglomeration economies by controlling for measures of worker ability as well as unobserved worker heterogeneity. Our approach is different in terms of both the measure of skills and identification strategy.

1.2 Measuring non-routine skills

We employ a longitudinal matched employer-employee dataset covering the full population of Swedish private sector workers over a seven-year period (2002–2008). These data do not include any direct information on worker skills, but do inform about the occupation according to the ISCO-88 classification scheme. To differentiate between skills we make use of a job-task classification scheme developed by Becker et al. (2009), which reports the fraction of non-routine job tasks associated by each ISCO-88 occupation. Their original classification is based on a German work survey, which reports answers to 81 questions regarding workplace tool use by occupation. Tools are codified according to whether or not the use of a tool indicates non-routine tasks. Becker's et al. (2009) classification is similar to that of Autor et al. (2003) and Spitz-Oener (2006) in that occupations are linked to the involved share of routine vs. non-routine tasks. We thus measure workers' non-routine skills by the extent of non-routine job tasks involved with the occupation of that worker.3

Autor et al. (2003) define non-routine job tasks as tasks that cannot be performed by computers. In Becker et al. (2009) non-routine tasks are defined as tasks characterized by non-repetitive work methods.4 Such non-routine job tasks typically involve problem-solving and a lack of deductive rules and codifiable information (Hakkala et al. 2008; Becker et al. 2009). This corresponds to the way in which Autor et al. (2003) conceptualize non-routine tasks. In relation to Bacolod's, et al. (2009) types of skills, cognitive and people skills are surely more important for non-routine job tasks, and we expect that workers with skills associated with non-routine job tasks should benefit more from density. Such skills are for example likely to imply that workers are more able to learn more from interaction with others in the local environment. Workers with non-routine skills may also be more specialized, implying that matching is more important thus increasing the benefits of thicker local labour markets (cf. Bacolod et al. 2009).

1.3 Identification strategy: spatial sorting and agglomeration economies

As we seek to quantify the sources of the wage premium of different workers, a key issue in our analysis is identification of spatial sorting and agglomeration economies, respectively. Recent work by, for example, Combes et al. (2008) and Mion and Naticchioni (2009) illustrates that quantification of spatial sorting of workers depends crucially on the ability to account for worker heterogeneity, and that spatial sorting on unobservable skills account for a large fraction of spatial wage disparities. We quantify the importance of spatial sorting as a source of wage disparities by first estimating raw wage-density elasticities and then study their sensitivity to the inclusion of observable and unobservable (time-invariant) worker characteristics. Our data allow us to assess the role of several observable worker, employer and regional characteristics, as well as permanent worker heterogeneity. Agglomeration economies are indirectly quantified as a residual wage gap after accounting for spatial sorting. A significant wage-density elasticity that remains after controlling for spatial sorting of workers on observable and unobservable skills should in principle capture agglomeration economies (cf. Combes et al. 2008).

The empirical strategy is straightforward: if sorting is important, we should observe that the (raw) wage premium drops significantly as we account for worker heterogeneity. The importance of agglomeration economies is instead reflected by the magnitude of the remainder wage-density elasticity. By undertaking these analyses for workers with skills associated with high and low fractions of non-routine job tasks, respectively, we empirically assess the magnitude and sources of the UWP for workers with different degrees of non-routine skills.

To further probe our analysis of the sources of the UWP, we follow Glaeser and Maré (2001) and identify workers that move from urban to rural regions. The idea behind this is that agglomeration economies capture different effects, such as matching and learning. Learning implies that workers in cities may enjoy faster human capital accumulation, for instance through knowledge spillover phenomena (Glaeser 1999; Rauch 1993). Because accumulated human capital stays with the worker, the advantages of having worked in a larger dense region should remain while moving away. Static agglomeration economies, on the other hand, should be lost upon moving away from the agglomeration (cf. De La Roca and Puga 2012).5 To test the argument that workers with non-routine skills are more apt to learn from their environment, we identify routine as well as non-routine workers who move away from dense agglomerations and test, for each category of worker, if their wage drops or remains the same upon moving. This is a straightforward and simple test of whether learning by workers depends on the skills, and the hypothesis is that non-routine workers show stronger learning.6

The paper includes some additional features further separating it from previous studies. Many of the analyses of the UWP separate urban from rural regions with a dichotomous variable or employ continuous measures of regional density that only account for the internal density of regions. The analysis in this paper recognizes the message emphasized by Irwin et al. (2010), in other words, there is interdependence across regions that produces a continuum from dense urban regions to more remote rural ones. Our measure of density is access to economic ‘mass’, as measured by each region's exponentially travel time-distance-weighed access to total wage earnings inside the region as well as to all other regions. The total density of a location is decomposed into three spatially distance-weighed components: (i) municipal; (ii) regional; and (iii) extra-regional. This decomposition allows us to obtain a parameter estimate for each aggregation level, making it possible to assess the importance of each component, such as the relative importance of the municipal and the regional density. With these measures the total density of a region is not only dependent on its internal characteristics, but also on the characteristics of surrounding regions and its travel time-distance to those regions. This captures interdependence between regions. Moreover, most of the existing analyses have been conducted on countries hosting large metropolitan areas, such as the US (Glaeser and Maré 2001; Gould 2007), Germany (Möller and Haas 2003) and France (Combes et al. 2008). Sweden is a small and generally sparsely populated country (around 9 million inhabitants on a total land area of about 410,000 km2). Most cities and urban areas in the country are small in an international context, and only three cities may, with a generous standard, be labelled metropolitan.7 An analysis of Sweden thus constitutes a conspicuous contrast to existing analyses on countries with big urban areas such as New York and Paris.

1.4 Main findings

We find sharp differences between workers with non-routine and routine skills in terms of the magnitude of the spatial wage disparities as well as their sources. Workers with skills associated with non-routine job tasks enjoy an unadjusted wage-density elasticity of about three per cent. For these workers, agglomeration economies are significant, though quantitatively of much smaller importance than spatial sorting. After controlling for observed and unobserved worker heterogeneity we find that a doubling of either municipal or regional density yields a wage increase in the order of 0.5 per cent. Non-routine workers also appear to be better apt to accumulate human capital, as evidenced by that workers that move away from denser regions keep (or increase) their wage upon moving. For workers with skills associated routine job tasks on the other hand, agglomeration economies appear to be non-existent.

1.5 Outline

The rest of the paper is organized as follows: Section 2 presents the data, defines variables and also provides the big picture regarding wages, education levels and skills in the economic geography. Section 3 describes our empirical strategy, focusing on how we empirically assess the relative importance of spatial sorting and agglomeration economies as sources of the UWP. Section 4 presents the results and Section 5 concludes.

2 Data, variables and descriptives

2.1 Data

We use a matched employer-employee audited register dataset, maintained by Statistics Sweden. The data comprise all employees in Sweden during the period 2002 to 2008. By construction of the data, employees are assigned to their work establishment (and thus sector, occupation and location) in the month of November each year. Though the data span all sectors of the economy, we exclude all public sector employees and workers in the agricultural and mining industries. This isolates workers whose wage formation is determined by market outcomes and workers in sectors whose locations are not directly linked to natural resources. As we are interested in labour income, we also exclude workers whose primary income comes from self-employment. Workers in our data are in the age interval 20–64.

This leaves us with a panel containing about 2.4 million employees with a mean population size of just short of 2 million yearly observations. The discrepancy between the number of individuals and the number of observations per year is an effect of the cut-off values created by the age interval and to a lesser extent by increased labour force participation in later stages of the reporting period. The data inform about several characteristics of each employee and their employer. For employees we have information such as education (length and specialization), sex, age, wage income and immigrant status. Employee characteristics include basic observables such as sector and employment size.

2.2 Variables and classification of non-routine job tasks

2.2.1 Density

Our variable of main interest is spatial economic density. Many studies of the UWP distinguish urban dense areas from rural ones by a dichotomous indicator variable based on some threshold value of, for example, population size. Alternatively, they consider a continuous indicator measuring the internal density of each region, commonly employment per square kilometre. The density measure employed in this paper is different.

We define density in a way akin to Harris's (1954) classic measure of market potential. The basic spatial unit in our analysis is the municipality of which there are 290 in Sweden. Specifically, the data inform about in which municipality each worker's employer is situated. These spatial units are in general of limited size and there is significant commuting and other types of interaction across municipal borders. Many of the spillover effects alluded to in the literature on agglomeration economies and human capital spillovers are thus likely to transcend municipal borders, especially as they may be mediated by labour market mobility (cf. Andersson and Thulin 2013). The same applies from the viewpoint of spatial sorting. When workers choose where to operate in space, they most likely consider characteristics of an integrated labour market, which in general comprises more than one municipality. We may thus expect interdependencies between municipalities, such that it is not only the internal density of municipalities that matter, but also the surroundings. On these grounds we employ an accessibility approach. One can think of the total density of a municipality r as the sum of municipal, regional and extra-regional accessibility to total wage-earnings, W:

display math(1)
  • inline image, municipal accessibility to total wage earnings of municipality r;
  • inline image, regional accessibility to total wage earnings of municipality r;
  • inline image, extra-regional accessibility to total wage earnings of municipality r.

Total wage earnings reflect the magnitude of economic activities (or economic mass) and accessibility to economic activity is our measure of spatial economic density. Municipal density is simply each municipality's total wage earnings weighed exponentially with travel time-distances by car between zones within the municipality. Regional accessibility is defined in a similar way but here we sum the municipality's access to every other municipality belonging to the same local labour market region.8 Extra-regional accessibility is the sum of its accessibility to all municipalities outside the region. The distance-decay parameter λ takes on three different values for municipal, regional and extra-regional accessibility, respectively. These parameter values are based on observed commuting behaviour of workers, and are estimated for Swedish municipalities by Johansson et al. (2003) using doubly constrained gravity models.

The accessibility approach recognizes that the density of a municipality is built up through a geographic continuum where the contribution of other places' economic activities falls as travel-time distances increase.9 Thereby, the measure is consistent with Tobler's (1970) ‘1st law of geography’: everything is related, but near things are more related than distant things. Because of the nature of the exponential distance-decay function, the contribution of municipalities far away is small but remains positive. In terms of an urban-rural dichotomy, the accessibility formulation recognizes interdependence across places where there is a continuum from dense urban regions to more remote rural ones (cf. Irwin et al. 2010).

In the empirical analysis we include inline image, inline image and inline image as three distinct independent variables. This allows us to assess which type of density that matters. In general we expect density effects to primarily pertain to the local labour market region in which the workers work, namely, inline imageand inline image.

2.2.2 Controls: observable characteristics

We control for several characteristics of workers and employers that may influence a worker's wage. The observable characteristics that we include in the analysis are presented and defined in Table 1. Experience and its squared value are standard control variables and in accordance with previous literature we expect that wages increase with experience but at a diminishing rate. Years of schooling is assumed to have a positive influence on a worker's wage.

Table 1. Variables, definitions and expected sign
VariableDefinitionExpected sign
  1. Notes: a The individuals included are workers who are primarily wage labourers, but like most other studies using audited full population register data where wage incomes are drawn from tax declarations, we lack information on the number of hours worked. While this represents the best information available, we recognize that using yearly wages may be a source of bias in an OLS setting under the assumption that workers in dense areas systematically work longer hours than workers in sparse areas and consequently make higher yearly wages. In a fixed effects setting, this is a smaller problem. The reason is that a bias can in this case only arise if workers in dense areas systematically work increasingly longer hours, relative to workers in sparse areas during the reporting period, or that workers moving to more dense regions increase their working hours by moving. In the empirical analyses that follow, every model specification further includes region-year effects, which means that any systematic region-specific trends by which workers in certain regions increase working hours over time is picked up. All variables are based on audited register data maintained by Statistics Sweden. Accessibility calculations based on travel time distances by car between municipalities. Travel time distances by car are obtained from the Swedish Road Administration.
WageThe total wage earnings of a worker during a yearan.a
ExperienceThe employee's age minus years of schooling minus 6. This definition follows Rauch (1993).+
Experience squaredSame as above but squared.
SchoolingTheoretical years of schooling.+
Education specializationDummies for different education specializations, defined according to the 1-digit SUN2000 classification, which is based on ISCED 1997.n.a
ImmigrantA dummy which is 1 if the worker is a first generation immigrant, 0 otherwise.
SexA dummy which is one if the worker is male, 0 otherwise.+
TenureThe number of years the worker has been employed at her current workplace. Max tenure is the observational year minus 2001, as we have no information prior to 2001.+
Number of prior employersThe number of different employers the worker has had since 2001.
Job changeA dummy which is 1 if the worker changed occupation between year t and t − 1.
Log of number of employeesThe natural logarithm of the total number of employees at the workplace at which the employer is employed.+
Sector affiliationDummies for different sectors at the level of 2-digit NACE sectors.n.a
Municipal densityExponentially distance-weighed accessibility to wage sums in the municipality the worker works in.+
Regional densityExponentially distance-weighed accessibility to wage sums to all municipalities in the local labour market region the municipality belongs to.+
Extra-regional densityExponentially distance-weighed accessibility to wage sums to all municipalities in Sweden except those belonging to the municipality's local labour market region.+

We also include a set of dummy variables reflecting the educational specialization of the worker. These are defined at the 1-digit SUN2000 classification system in Sweden, which corresponds to the 1997 International Standard Classification of Education (ISCED). This leaves us with nine dummy variables reflecting the educational specialization of each worker. We have a priori no clear idea of how different educational specializations may influence a worker's wage, but we acknowledge that they reflect potentially relevant characteristics of the workers. The analysis further includes immigrant and sex dummies. The former is 1 if the worker is a first generation immigrant and the latter is 1 if the worker is male. The general finding in the literature is that immigrants have lower average wages whereas males have higher average wages than females.

Tenure is an important variable in labour market analyses and is assumed to reflect the quality of the match between the worker and her workplace (Farber 1994). On these grounds, we expect that tenure is positively associated with a worker's wage. We define tenure as the number of years the worker has stayed with her current workplace. Due to data availability reasons, max tenure is the observation year minus 2001 because we have no information prior to 2001. In addition to tenure we also include the number of prior employers and a dummy for whether the worker switched jobs between year t and t-1. Both these variables may reflect workers in search of a good match in the labour market, why we expect them to be negatively associated with wages.

The employment size of the establishment at which the workers are employed is another important determinant of wages. Ample studies in labour market economics show that larger firms pay higher wages (Oi and Idson 1999).10 We expect that establishment size has a positive influence on wages. Furthermore, we include dummy variables to account for the possibility that wages may depend on the sector in which a worker is employed. The analysis includes one sector dummy for each 2-digit sector among NACE sectors 15–74.11 The sector of a worker is determined by the sector affiliation of the establishment he or she is employed by.

2.2.3 Measuring non-routine job tasks

The data on the fraction of non-routine job tasks by occupation originate from Becker et al. (2009) and details on the construction of the data as well as their various robustness checks are documented therein.12 They classify answers in a German qualification and career survey for 1998/1999, undertaken by the German Federal Institute for Vocational Training and the research institute of the German Federal Labour Agency. It tracks the usage of 81 different tools in a multitude of occupations. Becker et al. (2009) classify different tools according to their relation to non-routine tasks (non-repetitive work methods). The different tasks are then mapped to ISCO-88 standardized occupations. For each 2-digit occupation, the degree of non-routine tasks is then computed as the ratio between the average number of non-routine tasks in the occupation and the maximum number in any occupation, and the numbers are then standardized so that the fraction of non-routine tasks in an occupation varies between 0 and 1.

In Table 2 we follow Hakkala et al. (2008) and present the fraction on non-routine job tasks for each occupation at the 2-digit ISCO-88.13 The general picture is that science-based, engineering and corporate management occupations have the highest fraction of non-routine tasks. A low degree of non-routine job tasks are found in occupations related to agriculture, fishing, extraction sectors and simpler transport services. The patterns reported in the table confirm that non-routine job tasks typically involve problem-solving with a general lack of deductive rules and codifiable information (Hakkala et al. 2008). The occupations with high fractions of non-routine tasks are also jobs in which cognitive and people skills should be important (cf. Bacolod et al. 2009).

Table 2. The fraction of non-routine tasks in different 2-digit occupations according to ISCO-88
Occupation titleFraction non-routine tasks (%)
  1. Note: Based on Hakkala et al. (2008) using task data developed by Becker et al. (2009).
Physical, mathematical and engineering science professionals100.0
Life science and health professionals90.4
Physical and engineering science associate professionals79.7
Corporate managers78.4
Other professionals63.0
Teaching professionals61.2
Life science and health associate professionals56.3
Legislators and senior officials54.4
Other associate professionals52.7
Office clerks52.1
General managers46.6
Stationary-plant and related operators43.6
Metal, machinery and related trades workers41.6
Precision, handicraft, printing and related trades workers39.8
Teaching associate professionals36.1
Personal and protective services workers32.0
Customer services clerks27.1
Extraction and building trades workers21.4
Machine operators and assemblers18.8
Other craft and related trades workers17.7
Market-oriented skilled agricultural and fishery workers10.8
Models, salespersons and demonstrators8.1
Drivers and mobile-plant operators6.3
Labourers in mining, construction, manufacturing and transport2.5
Agricultural, fishery and related labourers0.9

2.3 Wages, education levels and skills in the Swedish economic geography

Table 3 presents the mean wage, fraction of graduates, mean experience and the fraction of workers working in any of the three largest regions in Sweden for all workers as well as for occupations with high and low fractions of non-routine job tasks, respectively.14 About one third of all workers in the population work in the three largest regions and about 15 per cent are university graduates. Workers with jobs requiring more non-routine tasks are much better educated and are the ones most prone to work in a metropolitan area. Roughly 36 per cent of all workers with non-routine jobs work in a metropolitan area compared to 19 per cent for workers with less non-routine tasks in their job. The mean wage of workers with jobs associated with high fractions of non-routine tasks is also higher than for other types of jobs.

Table 3. Key figures divided by fraction of non-routine work tasks
Job typeMean wage (EUR)Graduate share (%)Mean experienceMetropolitan share (%)
  1. Notes: Graduate share is the fraction of workers with a university education of at least three years. Metropolitan share is the fraction of workers that work in three biggest labour market regions: Stockholm, Gothenburg and Malmo. Wages converted to EUR using the 2008 exchange rate between SEK and EUR of 9.68. High (low) fraction non-routine jobs are those with fraction non-routine tasks above (below) the mean fraction across all occupations (see Table 2).
All types of professions29,698152227
High fraction non-routine tasks36,683282336
Low fraction non-routine tasks23,08832119

The unadjusted wage differential between metropolitan and non-metropolitan workers overall and for jobs with high and low fractions of non-routine tasks is presented in Table 4. For the private sector as a whole, the raw wage differential between metropolitan and non-metropolitan workers amounts to just over 20 per cent.

Table 4. Mean wages (2008) and unadjusted wage gap between metropolitan and non-metropolitan workers
Job typeMetropolitan wage (EUR)Non metropolitan wage (EUR)Wage differential (%)
  1. Notes: The metropolitan areas are defined as the three biggest labour market regions: Stockholm, Gothenburg and Malmo. Wages converted to EUR using the 2008 exchange rate between SEK and EUR of 9.68. High (low) fraction non-routine jobs are those with fraction non-routine tasks above (below) the mean fraction across all occupations (see Table 2).
All types of professions34,41727,92623
High fraction non-routine tasks41,02434,24520
Low fraction non-routine tasks22,63423,195−2

The urban-rural wage gap appears to depend crucially on the type of job. The difference is substantially larger for occupations with high fraction non-routine tasks (20 per cent) whereas the same ‘raw’ wage differential is negative but small for occupations with low fractions of non-routine tasks. These patterns are broadly consistent with the recent literature (e.g., Gould 2007; Bacolod et al. 2009), and suggest that spatial sorting with regard to type of jobs is one reason for the (unadjusted) overall UWP.

One reason for the described wage differences between workers in metropolitan and non-metropolitan regions may of course be that better educated workers are more inclined to move to bigger cities. Indeed, highly educated individuals tend to agglomerate in cities, for instance since specialized workers are better matched with employers where markets are thick (Strange 2009) and since highly educated individuals may self-select to cities where consumption amenities are abundant (Lee 2010).15 Workers with higher education levels indeed have higher wages, and the graduate share in the metropolitan areas was 28 per cent in 2008, while it was 13 per cent in other areas.

The subsequent empirical analysis focuses on the relationship between density as measured by accessibility to total wage earnings and workers' wages. Figure 1 plots the logarithmic relationship between mean wages in our population and our (summed up) density measure. It is clearly the case that workers in denser municipalities have higher average wage.

Figure 1.

The relationship between mean wages (log) and accessibility to total wage earnings (log) across Swedish municipalities in 2008

A simple ordinary least squares (OLS) estimation of the log of density on the log of average wages across municipalities in Sweden using the data in Figure 1 yields the following results (t-values beneath parameter estimates, N = 290):

display math(2a)
display math(2b)

where the estimation in (2b) separates between the three components of the total density of municipalities (see equation (1)). The estimates in (2a) show that 10 per cent higher density is associated with about 0.5 per cent higher wages. The decomposition of the total density in (2b) shows that the municipal density is responsible for the bulk of this relationship with an estimated coefficient of 0.04. The density of the local labour market region and extra-regional density contribute with a significantly smaller share amounting to about 0.01 each.

3 Empirical strategy

The baseline empirical model is as follows:

display math(3)

where wirt is the wage earnings of individual i at time t working in municipality r. inline image, inline image and inline image represent municipal, regional and extra-regional accessibility to wage earnings, respectively. The baseline model always includes year dummies (Dt), dummies for local labour market regions (DR) as well as time dummies interacted with dummies for local labour market regions (Dt × DR). Year dummies are intended to account for general business cycle effects, and region dummies are included to capture region-specific effects. The region-year effects account for any region-specific time-varying shocks shared by all workers in the same local labour market region.16 Previous work, for example, Moretti (2004) emphasizes the importance of accounting for both region and region-year effects. Z is a matrix of control variables. εirt is an error term. Our main interest is in the β parameters.

To quantify the importance of spatial sorting we start by estimating ‘raw’ wage-density elasticities, indicating how wages of private sector workers correlate overall with our three density measures. We obtain these raw elasticities by estimating the model in (3) using pooled OLS without any controls besides year, region and region-year dummies. We then estimate four additional models, while keeping the raw wage-density elasticities as points of reference.

In the first estimation we add standard Mincerian observable worker characteristics in the form of years of schooling, experience, sex, immigrant status as well as dummies reflecting different education specializations (Mincer 1974). The second estimation adds labour market information of each worker, that is, tenure, number of prior employers, a dummy for whether the worker's current occupation is new for the worker and employer size. This second specification also includes two-digit NACE industry dummies to capture differences in general wage levels across industries. As the reference estimation, these two specifications are estimated with pooled OLS. This means that identification of the wage-density elasticities is based on differences across workers in municipalities of varying densities, while controlling for observable worker and employer characteristics as well as time, industry and region-year effects.

The two additional specifications exploit the panel structure of the data and add worker fixed effects (FE). These worker FE fully absorb any permanent heterogeneity at the worker, employer, industry or municipality level. Due to the within transformation of the FE estimator, identification of the wage-density elasticities is now based on changes over time in the three density measures. As the within variation of each respective density measure is limited, the parameters of the density variables are primarily identified based on workers who over years move between municipalities of varying densities.17 The first FE model is the basic model in Equation (3) augmented with worker FE but excluding any other controls besides year and region-year dummies. The second one adds time-varying worker and employer characteristics, including industry dummies. The inclusion of FE worker effects means that these observables are also identified from changes over time.18

This empirical set-up allows us to quantify how sensitive the estimated wage-density elasticities are to spatial sorting on observable and unobservable worker characteristics. In view of previous research such as Combes et al. (2008), we expect that the wage-density elasticities are significantly reduced when accounting for worker characteristics, especially unobservable permanent worker heterogeneity. Any remainder significant wage-density elasticities should reflect agglomeration economies.

We further isolate workers who move from high to low density regions. In our empirical context we accomplish this in a straightforward manner by identifying workers who move from any of Sweden's three metropolitan regions (Stockholm, Gothenburg and Malmo) to any other place in Sweden. We then estimate whether they reduce or keep their wage upon leaving a metropolitan region, using both pooled OLS and FE models. The idea behind this is to test for learning effects in the form of human capital accumulation effects (Glaeser and Maré 2001; De La Roca and Puga 2012): if workers gain human capital in cities, the advantages of having worked in a larger and denser city should remain while moving away.

We systematically apply the empirical strategy described above for workers with occupations associated with high and low fractions of non-routine job tasks, respectively. We thus split the sample of workers in two groups; one with workers having occupations with a fraction of non-routine tasks above the mean fraction for all occupations, and one with a non-routine job task fraction below the mean (see Table 2). This allows us to identify differences in the importance of spatial sorting and agglomeration economies between the two groups in a straightforward way.19

4 Results

Table 5 presents results for all private sector workers in Sweden. Starting from the left, the first three specifications are pooled OLS estimations and the last two are panel estimations with worker fixed effects. The municipal and regional densities are significant and positive in all specifications. Workers earn more in denser regions. It is thus not only the density of the municipality that matter, but also the density of the wider local labour market region in which the worker operates. This is in line with expectations as labour market regions represent integrated labour markets and consist of municipalities between which there is intense interaction. The extra-regional density is negative and significant, indicating that if the surroundings of a labour market region grow it has a negative impact on wages in the region, all else equal. This may be understood as an effect from lagging behind the surroundings.

Table 5. The relationship between spatial economic density and wages, all private sector workers
 Raw OLSMincerian OLSFull OLSRaw with worker FEFull with worker FE
  1. Notes: The table reports estimates of wage-density elasticities for private sector workers in Sweden 2002–2008. Raw refers to the wage equation in Equation (3) without any further controls. The Mincerian model adds years of schooling, experience and its squared value as well as dummies for immigrants, males and education specialization. The full specification further adds variables reflecting labour market status and employer characteristics of each worker. OLS refers to the pooled OLS estimator and FE to a panel estimator with worker fixed effects. All variables are defined in Table 1. The full FE model excludes immigrant and sex dummies as these reflect time-invariant worker characteristics. All models include year and region dummies as well as region-year dummies, where the latter account for any region-specific time-varying shocks shared by all workers in the same local labour market region. The dependent variable is the natural logarithm of wage earnings. Robust standard errors are presented in brackets. *** p < 0.01, * p < 0.1.
Municipal density (log)0.0326***0.0218***0.0205***0.00773***0.00538***
(0.00322)(0.00224)(0.00123)(0.000242)(0.000242)
Regional density (log)0.0335***0.0218***0.0195***0.00790***0.00522***
(0.00777)(0.00425)(0.00641)(0.000518)(0.000514)
Extra-regional density (log)−0.0323*−0.0221−0.0248***−0.0127***−0.00797***
(0.0185)(0.0139)(0.00788)(0.000679)(0.000674)
Years of schooling 0.0930***0.0823*** 0.117***
 (0.00468)(0.00300) (0.0190)
Experience 0.0503***0.0408*** 0.0587***
 (0.00326)(0.00279) (0.0190)
Experience2 −0.000781***−0.000635*** −0.000745***
 (5.42e-05)(4.64e-05) (2.84e-06)
Immigrant (dummy) −0.136***−0.108***  
 (0.00737)(0.00409)  
Male (dummy) 0.351***0.330***  
 (0.00947)(0.00425)  
Tenure  0.0176*** −0.0109***
  (0.000458) (0.000127)
Number of prior employees  −0.0120*** −0.0189***
  (0.00286) (0.000176)
New occupation (dummy)  −0.0930*** −0.0268***
  (0.00222) (0.000366)
Employer size (log)  0.0257*** 0.0183***
  (0.00225) (0.000175)
Year dummiesYesYesYesYesYes
Region dummiesYesYesYesYesYes
Region × year effectsYesYesYesYesYes
Education type dummiesNoYesYesNoYes
Industry dummiesNoNoYesNoYes
Observations12,367,70012,367,70012,367,70012,367,70012,367,700
Individuals2,681,1642,681,1642,681,1642,681,1642,681,164
R-squared0.0310.2480.2880.0590.078

The raw unadjusted wage-density elasticity is about 0.03 for municipal and regional density, respectively. Taken together, they correspond broadly with the estimates reported by Ciccone and Hall (1996), who find that a doubling of density is associated with about six per cent higher productivity. The wage-density elasticities are also rather insensitive to observable worker characteristics. In the Mincerian model which adds years of schooling, experience as well as dummies for sex, immigrants and education specialization, the estimated wage-density elasticities for municipal and regional density only falls marginally – from 0.03 to about 0.02.

The estimated parameters change only slightly from adding indicators for labour market status and employer characteristics (full OLS model). These patterns suggest that spatial sorting of workers on basic observable worker and employer characteristics is not a quantitatively important source of the raw wage-density relationship.

The picture changes as we control for permanent worker heterogeneity with worker fixed effects. The second column from the right shows the results with the raw specification with worker fixed effects, namely, excluding any other controls besides year and region-year dummies. A comparison of the wage-density elasticities in this specification with the ones obtained with ‘raw OLS’ (second column from the left) illustrates what worker fixed effects means for the magnitude of the estimated wage-density elasticities.

As is evident from Table 5, the inclusion of worker fixed effects induces the wage-density elasticities to drop sharply. Both the municipal and the regional densities drop from about 0.03 to 0.008. The raw OLS estimates are thus almost four times as a high as the estimates obtained with worker fixed effects. The estimates show that after accounting for worker fixed effects, a doubling of either municipal regional density is associated with about 0.8 per cent higher wages. This result suggests that spatial sorting on unobservable worker characteristics is indeed an important source of the wage-density relationship. After controlling for sorting there remains a small but significantly positive wage-density elasticity, indicating the existence of a small agglomeration effect. Sorting effects dominate, and these patterns are broadly in line with the findings by Combes et al. (2008) on worker-level data for France.

Turning to the control variables we find that the estimated influence of years of schooling is positive throughout, and the magnitude of the estimates are roughly in accordance with results reported in previous studies (cf. OECD 1998). Moreover, more experienced workers earn in general better though the positive effect falls off as experience rises. Immigrants earn less on average whereas male workers earn more than females. In the OLS specifications, tenure and the number of prior employees are positive, though the latter estimate is statistically insignificant. That longer tenure is positive is in line with the hypothesis that tenure signals match quality (cf. Farber 1994). In the fixed effects specification, however, tenure is negative and significant. This may be explained in two ways. First, those with long tenure represent a select group which may have lower career aspirations. Second, the fixed effects model may capture those that switch employer and make a career move after a number of years of accumulation of experience with the same employer.20 Employer size is positive and significant throughout which is an established result in the literature (Oi and Idson 1999).

The main aim of this paper is to test whether the magnitude and sources of the wage density premium vary across workers with different sets of skills. We split the population of workers in two groups: one with jobs with high fraction non-routine tasks and one with jobs with low fraction non-routine tasks. Table 6 reports results obtained for the first group – workers with jobs associated with high fraction non-routine tasks.

Table 6. The relationship between spatial economic density and wages for workers with occupations associated with high fractions of non-routine job tasks
 Raw OLSMincerian OLSFull OLSRaw with worker FEFull with worker FE
  1. Notes: The table reports estimates of wage-density elasticities for private sector workers in Sweden 2002–2008 with occupations associated with high fractions of non-routine job tasks (see Table 2). Raw refers to the wage equation in equation (3) without any further controls. The Mincerian model adds years of schooling, experience and its squared value as well as dummies for immigrants, males and education specialization. The full specification further adds variables reflecting labour market status and employer characteristics of each worker. OLS refers to the pooled OLS estimator and FE to a panel estimator with worker fixed effects. All variables are defined in Table 1. The full FE model excludes immigrant and sex dummies as these reflect time-invariant worker characteristics. All models include year and region dummies as well as region-year dummies, where the latter account for any region-specific time-varying shocks shared by all workers in the same local labour market region. The dependent variable is the natural logarithm of wage earnings. Robust standard errors are presented in brackets. *** p < 0.01, ** p < 0.05, * p < 0.1.
Municipal density (log)0.0317***0.0253***0.0250***0.00810***0.00655***
(0.00206)(0.00260)(0.00141)(0.000345)(0.000346)
Regional density (log)0.0364***0.0263***0.0240***0.00868***0.00618***
(0.00734)(0.00523)(0.00829)(0.000772)(0.000769)
Extra-regional density (log)−0.0271−0.0231−0.0253**−0.0124***−0.00834***
(0.0232)(0.0183)(0.0114)(0.00105)(0.00105)
Years of schooling 0.0797***0.0766*** 0.133*
 (0.00312)(0.00289) (0.0691)
Experience 0.0556***0.0513*** 0.0926
 (0.00204)(0.00185) (0.0691)
Experience2 −0.000862***−0.000794*** −0.000737***
 (3.95e-05)(3.54e-05) (4.05e-06)
Immigrant (dummy) −0.0419***−0.0371***  
 (0.00285)(0.00260)  
Male (dummy) 0.353***0.351***  
 (0.00319)(0.00306)  
Tenure  0.0105*** −0.00644***
  (0.000400) (0.000172)
Number of prior employees  0.000754 −0.00741***
  (0.000741) (0.000237)
New occupation (dummy)  −0.0703*** −0.0151***
  (0.00209) (0.000502)
Employer size (log)  0.0259*** 0.0149***
  (0.00314) (0.000236)
Year dummiesYesYesYesYesYes
Region dummiesYesYesYesYesYes
Region × year effectsYesYesYesYesYes
Education type dummiesNoYesYesNoYes
Industry dummiesNoNoYesNoYes
Observations5,986,4545,986,4545,986,4545,986,4545,986,454
Individuals0.0380.2580.2800.0610.074
R-squared1,388,1661,388,1661,388,1661,388,1661,388,166

The results are similar as those reported in Table 5. The raw OLS estimates are around 0.03 for municipal and regional density, while the extra-regional density is negative (though not significant). Controlling for observable worker and employer characteristics reduces the estimates for municipal and regional density to about 0.02. Also for workers with non-routine jobs, spatial sorting effects dominate. Including worker fixed effects in the raw model reduces the estimated wage-density elasticities substantially. For municipal and regional density the difference between the raw OLS and the raw model with worker fixed effects amounts to a factor of almost four. There is a general tendency that the estimates with worker fixed effects are larger for workers with jobs in which non-routine tasks are important, but the differences to Table 5 are still marginal.

Results for workers with jobs associated with low fractions of non-routine tasks are presented in Table 7. The first apparent result is that the raw OLS estimates suggest a non-existent or negative wage-density relationship for jobs with low fraction non-routine tasks. The estimated coefficient for municipal density is negative and significant, whereas the remaining densities are positive but insignificant. There are thus no clear patterns that workers with these jobs earn more in denser areas. Instead, it appears that workers with routine jobs in denser municipalities earn less than their more rural counterparts.

Table 7. The relationship between spatial economic density and wages for workers with occupations associated with low fractions of non-routine job tasks
 Raw OLSMincerian OLSFull OLSRaw with worker FEFull with worker FE
  1. Notes: The table reports estimates of wage-density elasticities for private sector workers in Sweden 2002–2008 with occupations associated with low fractions of non-routine job tasks (see Table 2). Raw refers to the wage equation in equation (3) without any further controls. The Mincerian model adds years of schooling, experience and its squared value as well as dummies for immigrants, males and education specialization. The full specification further adds variables reflecting labour market status and employer characteristics of each worker. OLS refers to the pooled OLS estimator and FE to a panel estimator with worker fixed effects. All variables are defined in Table 1. The full FE model excludes immigrant and sex dummies as these reflect time-invariant worker characteristics. All models include year and region dummies as well as region-year dummies, where the latter account for any region-specific time-varying shocks shared by all workers in the same local labour market region. The dependent variable is the natural logarithm of wage earnings. Robust standard errors are presented in brackets. *** p < 0.01, ** p < 0.05.
Municipal density (log)−0.0124***−8.97e-050.00714***0.00311***0.00215***
(0.00339)(0.00217)(0.000639)(0.000363)(0.000363)
Regional density (log)0.009170.004830.00623***0.00358***0.00176**
(0.00651)(0.00445)(0.00224)(0.000741)(0.000736)
Extra-regional density (log)0.006730.00244−0.00807**−0.00555***−0.00318***
(0.0111)(0.00944)(0.00365)(0.000950)(0.000944)
Years of schooling 0.0212***0.0208*** 0.0627***
 (0.00324)(0.00252) (0.0206)
Experience 0.0351***0.0252*** 0.0479**
 (0.00152)(0.00145) (0.0206)
Experience2 −0.000583***−0.000431*** −0.000614***
 (2.02e-05)(2.10e-05) (4.16e-06)
Immigrant (dummy) −0.128***−0.0968***  
 (0.00870)(0.00505)  
Male (dummy) 0.366***0.319***  
 (0.0146)(0.00914)  
Tenure  0.0247*** −0.0124***
  (0.000979) (0.000195)
Number of prior employees  −0.0220*** −0.0265***
  (0.00320) (0.000262)
New occupation (dummy)  −0.114*** −0.0521***
  (0.00302) (0.000558)
Employer size (log)  0.0202*** 0.0208***
  (0.00146) (0.000282)
Year dummiesYesYesYesYesYes
Region dummiesYesYesYesYesYes
Region × year effectsYesYesYesYesYes
Education type dummiesNoYesYesNoYes
Industry dummiesNoNoYesNoYes
Observations6,565,6706,565,6706,565,6706,565,6706,565,670
Individuals0.0180.1770.2230.0430.061
R-squared1,659,8861,659,8861,659,8861,659,8861,659,886

In the Mincerian specification which adds education, experience and other basic worker characteristics, none of the density variables is statistically significant. Only in the full OLS model, which further adds employer characteristics and indicators of the labour market status of the workers, the wage-density elasticities become statistically significant and have the same sign as in previous tables. They are substantially smaller than in Tables 5 and 6. The estimated parameters for municipal and regional density are about 0.007 and 0.006, respectively, and the same estimates for jobs with high-fraction non-routine tasks are in the order of 0.02, that is, a difference of a factor of more than three. This pattern does not change from the inclusion of worker fixed effects. The last two columns in the table show that adding worker fixed effects reduces the estimated elasticities for municipal and regional density to about 0.002. Again, the same elasticities for non-routine jobs (Table 6) are about three times as large.

We draw three main conclusions from these patterns. First, the wage-density relationship is much weaker for jobs with low fractions of non-routine tasks. The wage premium from operating in denser regions is much smaller for these jobs – a pattern that is robust across specifications. Second, the effects from spatial sorting on basic observable worker characteristics go in the opposite direction for these jobs as compared with non-routine jobs. Conditioning on Mincerian variables as well as indicators of workers' labour market status increases rather the decreases the estimated wage-density elasticities. Yet, spatial sorting on permanent worker heterogeneity go in the same direction as for jobs with high fraction of non-routine tasks. Third, the wage-density relationship attributable to agglomeration economies is significantly smaller for jobs with low fraction of non-routine tasks. The remainder wage-density elasticities after controlling for observable and unobservable worker heterogeneity is about three times larger for jobs with high fraction non-routine tasks, 0.006 compared to 0.002. In economic terms these effects are nevertheless small, as they imply that a doubling of density yields about 0.6 and 0.2 per cent higher wages, respectively.

Our estimates thus imply that operating in dense regions primarily generates benefits for workers with skills associated with non-routine job tasks, and that these types of jobs are also more likely to be found in denser regions in the first place (see Table 3). The wage-density elasticities found for the full sample of Swedish workers (Table 5) are primarily driven by workers with skills associated with non-routine jobs. These results correspond to the analyses by Bacolod et al. (2009) who find that an urban wage premium predominantly applies to jobs in which cognitive and people skills are important.

The results in Tables 5-7 do show evidence of agglomeration economies in the sense that significant wage-density elasticities remain after controlling for worker characteristics, observable as well as unobservable. But the mere existence of remainder wage-density elasticities reported in Tables 5-7 does not inform about the type of agglomeration economy. As explained in the previous sections, to further probe the results, and to cautiously get at learning effects, we also estimate wage premiums for workers that move away from a dense metropolitan area. If density fosters human capital accumulation, this means that benefits remain with the worker upon moving away from dense agglomerated regions. Such learning should primarily pertain to workers with non-routine skills who are more apt to learn from the environment.

Table 8 presents the estimated coefficient of a dummy variable which identifies workers that move from any of Sweden's three main metropolitan labour market regions (Stockholm, Gothenburg and Malmo) to anywhere else in the country. These estimations include the full set of variables as the ‘full worker fixed effects’ estimations in Tables 5-7. Due to the inclusion of worker fixed effects, the coefficient estimate shows whether the wage of a worker remains unaffected, increase or decrease upon moving away from a metropolitan region. An insignificant or positive parameter estimate lends support for human capital, as it means that the worker at least retains his or her wage upon moving away from a larger agglomeration. We present results obtained for all workers as well as for workers with jobs associated with high and low fraction non-routine tasks, respectively.

Table 8. Wage premium for workers moving away from a metropolitan region to the rest of the country, by fraction of non-routine job tasks
 All private sector workersHigh fraction non-routine tasksLow fraction non-routine tasks
  1. Notes: The table reports the coefficient estimate of a dummy variable reflecting a move from any of Sweden's three metropolitan labour market regions (Stockholm, Gothenburg and Malmo) to anywhere else in Sweden. The underlying model is a panel estimator with worker fix effects including the full set of additional control variables reported in the ‘Full with worker FE’ specification in Tables 67. Complete estimation results are obtained from the authors upon request. The dependent variable is the natural logarithm of wage earnings. Robust standard errors are presented in brackets. *** p < 0.01.
Dummy for moving away from metropolitan region

0.00286 ***

(0.0014)

0.01085***

(0.0020)

−0.00055

(0.0021)

ModelFull with worker fixed effectsFull with worker fixed effectsFull with worker fixed effects

We find a small significant premium for those workers that move away from a metropolitan region among the full sample of Swedish private sector workers. For workers with jobs associated with high fraction non-routine tasks the premium is positive, statistically significant and substantially larger compared to the full sample of workers. For workers with jobs associated with low fraction of non-routine tasks, however, the estimated coefficient is negative albeit insignificant. These patterns are consistent with density fostering human capital accumulation that remains with the workers upon moving away from the agglomerations. Moreover, these effects appear to be particularly strong for workers with jobs with non-routine tasks. These are jobs requiring problem-solving and more interaction with others implying not only that learning is more important, but also greater opportunities for learning.

One way to appreciate the positive parameter estimate of the dummy for moving away is that workers' may value the greater variety of consumption-based amenities and the greater thickness of the local labour markets in the metropolitan regions, and thus want to be compensated when moving away from these regions (cf. Roback 1982). Yet, such compensation can only be motivated if the workers bring human capital that is valued by the employers. From this perspective, one may argue that it is only workers with jobs associated with high fraction non-routine tasks that ‘learn enough’ in the city to motivate such compensation. A caveat should be noted: among other possible sources of endogeneity, the workers that move may be a self-selected minority. For instance, Gould (2007) emphasizes that the decision to move may be endogenous as the change in the wage associated with moving may be correlated with changes in the quality of the opportunities in the different locations. As we do not fully account for such potential endogeneity here, the results in Table 8 should be interpreted as somewhat restrained empirical support for the learning proposition.

5 Conclusions

The main conclusion from this paper is that the benefits from agglomeration are not uniform across workers and activities. Agglomeration yields productivity gains primarily in contexts in which problem solving and interaction with others are important. This conclusion is derived from an analysis of how the magnitude and sources of the wage density premium differs across workers with different degrees of skills associated with non-routine job tasks. Non-routine job tasks typically involve problem solving, lack of deductive rules and codifiable information, as well as interaction with others.

The analyses in the paper demonstrate that the relationship between wages and spatial economic density is significantly stronger for workers with skills pertaining to non-routine tasks, and such workers are more concentrated to denser regions in the first place. A main finding is that agglomeration economies, that is, productivity gains from interactions between workers and their local environment, are quantitatively a more important source of the density wage premium for non-routine workers. Skills associated with non-routine job tasks are better rewarded in denser regions. Agglomeration economies appear to be virtually non-existent for workers with routine job tasks.

In a broad sense, these results reinforce the idea of large city regions as ‘innovation environments’, fostering and rewarding activities related to face-to-face interaction, knowledge, ideas and development of new products, designs, organizational routines and technology blueprints. Innovation is indeed a prominent example of a context in which skills associated with non-routine tasks are imperative. The literature on 'geography of innovation' has for a long time made the argument that cities matter more for innovation, but the kind of micro-based evidence presented in this paper, where the question of which sets of skills and job tasks are better rewarded in agglomerations is directly addressed, provides an improved understanding of these issues. After all, innovation processes are essentially linked to workers' skill sets and the nature of their jobs and tasks.

As regards the source of spatial wage disparities in general, our analyses line-up with the growing evidence suggesting that who you are is more important than where you live in explaining spatial wage disparities. The main reason why workers in denser regions earn more is simply that they are different from the workers in more rural regions. Spatial sorting on permanent unobserved worker heterogeneity is the main source of the density wage premium.

Further work on these issues may take a variety of directions. One is to untangle the various sources of agglomeration economies. For instance, to what extent are the stronger agglomeration economies of non-routine workers driven by matching, learning and sharing mechanisms, respectively? Another route is to focus on the location processes of workers with different skills and abilities. This applies to both theoretical and empirical work. Since a large part of the density wage premium is due to spatial sorting (even for non-routine workers), the question of why denser areas are more attractive places for workers with different skill sets and their migration patterns appear as a particularly relevant line of inquiry.

Appendix: Appendix

Table A1.  
VariableMeanMedianSt. dev
  1. Note: N = 12,367,700.
Yearly wage (log)7.807.880.660
Municipal density (log)22.9622.871.668
Regional density (log)21.7922.514.734
Extra-regional density (log)21.1821.181.093
Years of schooling12.05122.079
Experience21.962111.88
Immigrant (dummy)0.1300.3343
Male (dummy)0.650410.4768
Tenure3.2532.022
Number of prior employees0.4400.933
New occupation (dummy)0.1700.3753
Employer size (log)4.063.911.868

Footnotes

  1. 1

    Duranton and Puga (2004) discuss three families of micro-foundations of agglomeration economies – sharing, matching and learning.

  2. 2

    Gould (2007) as well as Möller and Haas (2003) also find that the UWP is significantly larger for better educated workers, and Baum-Snow and Pavan (2012) show that large cities foster human capital accumulation, especially for more highly skilled workers. None of these studies consider the horizontal dimension of skills emphasized by Bacolod et al (2009).

  3. 3

    This should reflect worker skills in the sense that workers with a job requiring a large fraction on non-routine tasks should have skills associated with non-routine work.

  4. 4

    Details of the classification as well as the correspondence between this and the job task classification in Spitz-Ooener (2006) can be found in Becker et al. (2009).

  5. 5

    Models of matching effects in thick markets suggest that the average quality of each match is higher in agglomerations (cf. Hesley and Strange 1990; Kim 1990). Such an agglomeration economy surely does not follow workers.

  6. 6

    We are cautious in drawing strong conclusions from the analyses of the wages of movers as we are not able to fully account for the endogeneity issue raised by Gould (2007); changes in wages form moving may be correlated with changes in the quality of opportunities in different regions.

  7. 7

    If we for instance apply the ‘big city’ classification in Yankow (2006), only one metropolitan area in Sweden (Stockholm) would barely pass the bar.

  8. 8

    Local labour market regions comprise a number of municipalities forming an integrated labour market, and are delineated based on the intensity of inter-municipality commuting flows.

  9. 9

    This also alleviates potential problems with spatial autocorrelation (Andersson and Gråsjö 2009).

  10. 10

    This is often explained by larger firms being better equipped than smaller firms in terms of resources and productivity, as well as by behavioural arguments. The latter includes that larger firms may be more apt to adopt discretionary wage policies and paying efficiency wages to deter shirking.

  11. 11

    In the analyses presented in the sequel, we have also tested if the results depend on the level at which the sector dummies are defined. Our results are robust to using sector dummies at the 2, 3, 4 or 5 digit level.

  12. 12

    They also classify jobs according to the extent it involves interaction. There is considerable overlap between the two classifications, where non-routine tasks tend to involve interaction tasks. In all analyses presented in the sequel, we have also tested this classification and results are robust. We choose the non-routine classification as it emphasize jobs in which cognitive and people skills should be important.

  13. 13

    Hakkala et al. (2008) use the task data mapped to ISCO-88 occupations developed by Becker et al. (2009) in the analysis of how multinational activities influence demand for different job tasks.

  14. 14

    High fraction non-routine jobs are those occupations with fraction non-routine tasks above the mean fraction across all occupations. Low fraction non-routine jobs are those whose fraction of non-routine tasks is below the mean.

  15. 15

    There is a large literature on the extent to which the location of educated workers is driven by amenities or productivity (e.g., Moretti 2008) but this issue is not the main focus and beyond the scope of this paper.

  16. 16

    To be precise, the region-year dummies account for shocks over time that are common for all employees working in municipalities belonging to the same local labour market region R. We choose the local labour market region as aggregation level for the region-specific shocks as the labour market regions represent integrated local labour markets and comprise several municipalities connected through intense commuting flows. There are 81 local labour market regions in Sweden.

  17. 17

    For each of density variable, the within variation is substantially smaller than the between variation. For municipal, regional and extra-regional density the between variation is about 2.7, 3.7and 2.9 times larger than the within variance, respectively.

  18. 18

    In a similar way, the regions-specific effects (DR) are identified from workers that move between local labour market regions over time.

  19. 19

    An alternative strategy would be to include the fraction of non-routine job tasks as a separate independent variable. The pooled OLS estimations would then identify its effect through differences across workers, whereas identification with the FE estimator would be based on workers that shift occupations over time (the fraction of non-routine job tasks of an occupation is time-invariant). We have considered this strategy as well and the findings reported in the sequel are robust to this alternative approach.

  20. 20

    Such effects are more likely to be captured when the estimates are based on within variance, as workers are here followed over years.

Ancillary