Skill match and urban wage premia

Dans le document Essays on location choice: agglomeration, amenities and housing (Page 85-94)

Getting a first job: quality of the labor matching in French cities

2.6 Skill match and urban wage premia

Empirical evidence of Section 2.5 reveals that the expected quality of skill match improves with density. This result is consistent with the existence of agglomeration economies in-duced by labor market pooling (A. Marshall, 1890) and better worker-firm match (Du-ranton and Puga, 2004). This better job match may capitalize into higher wages if it significantly enhances productivity. This section first seeks to measure the effect of skill match on individual earnings. In addition, large empirical findings show that wages are higher in big cities. Controlling for worker and city characteristics, wage elasticities with respect to density traditionally range between 0.04 and 0.10 reflecting the existence of an urban wage premium20. Figure 2.5 provides a graphical representation of this pre-mium using the sample of French school leavers. This section also intends to identify the contribution of skill match in explaining this urban wage premium.

Note: The average monthly wage obtained by French school leavers in their first position is plotted against the density of the labor market. The wage elasticity (without controls) is about 0.04.

Figure 2.5: Average wage by employment area

I follow Andini et al. (2013) and Abel and Deitz (2012) by comparing estimates from the following wage regression using ordinary least squares:

ln W agesi =β01M atchingi02Xi03Hc(i)4Zedu(i),c(i)5Socc(i),c(i)edu(i)occ(i)+i where ln W agesi is the logarithm of the wage of individual i during her first month of work and M atchingi is the matching index of individual i. The remaining variables are defined in Section 2.4. The sample is restricted to workers occupied in full-time positions

20Extensive research explores the magnitude and determinants of this wage gain. Revisiting estimates of wage elasticities to account for endogeneity and sorting, Combes et al. (2010) find an estimate of 0.02. De La Roca and Puga (2012) show that workers in big cities gain from a static and a dynamic advantage through accumulation of experience. D’Costa and Overman (2013) find that workers moving to cities experience a jump in wages but do not face faster wage growth once located in cities. Yankow (2006) finds that US workers face a wage growth premium during the first years after moving to large cities. Wheeler (2006) finds similar wage growth premium for a sample of young male workers and shows that this growth is mainly explained by changes in occupations rather than faster wage growth within occupations – consistent with the matching hypothesis. Glaeser and Mar´e (2001) also show that a large proportion of the urban wage premium (which is about 33% compared to nonurban workers) accrues to workers through time and remained capitalized when they leave metropolitan areas.

and all standard errors are clustered at the level of the local labor market. The results of this wage regression will shed light on the extent of (1) the skill match premium, (2) the urban wage premium, and (3) the influence of the first in explaining the second.

Estimation results are provided in Table 2.12.

Table 2.12: Wage regressions

First, column (1) reveals that the coefficient associated with skill matching is positive and significant. A unit increase in the matching index is associated with a 1.14% rise in wage. This finding shows that a better skill match translates into higher wages and higher returns from schooling. This is consistent with previous evidence from the labor economics literature, such as Chevalier (2003) or Robst (2007).

Second, the positive coefficient on employment density mirrors the urban wage pre-mium. Everything else equal, an increase in employment density in a local labor market is associated with higher wages. The coefficient shows that, after controlling for

educa-tional (both majors and degrees), occupaeduca-tional, individual and area characteristics, a 10%

increase in the number of workers per square kilometers is associated with a 0.14% rise in wages. This corresponds to adding 88 workers per square kilometers in the area. This coefficient is highly significant but lower than traditional estimates. The main explanation is that gains from agglomeration are on average smaller for young workers than for older or experienced individuals, consistent with Glaeser and Mar´e (2001), Wheeler (2006), De La Roca and Puga (2012) and Combes et al. (2012)21. The rest of the coefficients reveals that most covariates play a significant role in explaining wages. Older men and natives enjoy higher wages. The unemployment duration between initial training and work is associated with a wage penalty. Finally, the coefficient associated with the Herfindahl index shows that workers in specialized cities experience higher wages. This result is even stronger when the city is specialized in the activity in which the individual is occupied (as shown by the coefficient associated with Socc(i),c(i)). This is consistent with the existence of localization economies.

Third, I evaluate the contribution of skill match on the urban wage premium. I illus-trate how the wage elasticity with respect to urban density varies when skill match is not taken into account in the wage regression. An increase in the coefficient on employment density implies that part of the urban wage premium is explained by the omission of the skill match variable (Abel and Deitz, 2012; Andini et al., 2013). Results are provided in column (2). Strikingly, the coefficient on employment density is not affected when exclud-ing the matchexclud-ing index from the vector of explanatory variables. It remains stable at its initial value. This result implies that the effect of urban density on wages is not driven by the better job match observed in dense cities. Indeed, the elasticity of individual earnings with respect to employment density remains at 0.0137, irrespective of the quality of the match. This shows that urban density plays a significant role in determining wages, and that this premium mainly operates through channels that are not related to skill match.

Standard errors have the same magnitude across the two specifications: both coefficients are precisely estimated. Besides, estimations are performed on the same sample of work-ers which can rule out the hypothesis that this result is explained by a sample selection problem.

Conversely, column (3) only includes the matching index. The coefficient on this variable remains significantly positive. A test on the coefficient shows that this value differs from the estimate of column (1) only at a 10% significance level. This is consistent with the idea that the effect of skill match does not interact with the effect of density. In column (4), I include an interaction term between matching and density as an explanatory variable. The coefficient on the interaction term is not statistically significant. The monetary reward from being well matched does not vary with the density of the labor market. The coefficient on skill match turns out to be insignificant but the overall effect of skill match at the mean remains positive when taking into account both the direct effect and the interaction term. The coefficient on employment density remains positive and highly significant.

Figure 2.6 illustrates the marginal effect of density on wages for different values of skill match. It also reports the 95 percent confidence intervals. The graph confirms that urban density has a positive impact on wages and that this effect is not affected by the quality of a worker’s match. Indeed, the wage elasticity with respect to density is not statistically different between mismatched and matched workers. In turns, Figure 2.7 shows that an increase in skill match only affect wages around the average density (the red line in the

21Conditioning out worker effects, they find that the relationship is reversed.

graph). The impact of skill match on wages is statistically above zero in labor markets with a density between 55 and 1,480 workers per square kilometers. However, there is no statistical difference in the size of the skill match premium between small and big cities.

Figure 2.6: Marginal effect of employment density (in log) on wages, conditional on skill match.

Figure 2.7: Marginal effect of skill match on wages, conditional on employment density.

The wage regressions reveal that skill match and urban density both play a significant role in determining a worker’s wage. These two wage premia do not statistically interact such that each premium comes on top of the other. Regarding the positive relationship established in Section 2.5, this result is puzzling. Several explanations can account for this result

First, Andini et al. (2013) find similar patterns when studying the influence of several labor market pooling variables, including employment turnover, training and matching.

The latter is measured by self-assessed evaluations of appropriate experience and training.

Even if these variables were positively correlated with population density, the coefficient on density is unaffected – or at most marginally affected – by the inclusion of the labor market variables in their wage regressions. Second, it is very likely that the monetary

reward form being well matched in a big city materializes over time. Glaeser and Mar´e (2001), Wheeler (2006) and De La Roca and Puga (2012) show that the urban premium does not instantaneously accrue to workers but strengthens with tenures, job changes or occupational changes. Therefore, the wage elasticity with respect to density might be affected by a worker’s skill match only over her lifespan. This may be a relevant explanation as the urban wage premium is already lower for the sample of school leavers than in traditional estimations. Third, minimum wages and wage setting institutions are particularly strong in France. This is even more striking for young workers without experience who are more likely to be paid at the minimum price. In the absence of wage flexibility, wages cannot directly react to changes in productivity. The relationship between density and matching might affect productivity but not wages because of these rigidities. Andini et al. (2013) find consistent results with other indicators of labor market pooling in Italy – another country with high wage rigidities. Unfortunately, it is not feasible to retrieve firms’ identity in the dataset, and consequently to get information on productivity. In the U.S., wages are more flexible than in France. Accordingly, we can expect any change in productivity to capitalize into wage differentials more easily.

This seems to be the case empirically as Abel and Deitz (2012) find that skill matching accounts for 5 to 8% of the urban wage premium that accrues to college graduates in American cities.

In the rest of the paper, I try to evaluate whether this finding is robust when correcting for standard limitations of wage regressions. Table 2.13 summarizes the main findings.

For sake of brevity, I only report the coefficient on matching and density.

Natural amenities may affect city attractiveness and therefore labor supply and wages (e.g. Albouy, 2008; Combes et al., 2010; Glaeser et al., 2001; Rappaport, 2007, 2008;

Roback, 1982). I control for local amenities by including access to mountains, lakes and sea shores in the area. The results are provided in columns (1.a) to (1.c). The two coefficients of interest remain positive and significant. A comparison across columns confirms the previous findings. The coefficient on employment density remains very stable, while the coefficient on matching is only marginally affected by the exclusion of density.

The results reveal that these two determinants matter for explaining individual earnings and they do not statistically interact22.

Besides, the sorting of talented people across space also plays a role in explaining the urban wage premium (Andersson et al., 2013; Behrens et al., 2014; Combes et al., 2008;

Mion and Naticchioni, 2009) and the distribution of income (e.g. Andersson et al., 2013;

Combes et al.,2008,2012; Matano and Naticchioni, 2012; Mion and Naticchioni,2009). I replicate the strategy described in Section 2.5.4 to condition out the effect of workers’ self-selection. Columns (2.a) to (2.c) then include two observable workers characteristics used in the previous section: dummies for parents’ occupational categories and dummies for the place where students grew up. Estimations still control for the role of natural amenities.

The comparison between estimates yields similar results: both variables contribute to explaining wages and the coefficients remain strikingly stable across estimations. This shows that the better job match observed in big cities does not significantly explain the effect of urban density on earnings, and vice versa.

22I also control for the role of consumption amenities by including the total number of cinemas and theater seats in each city as explanatory variables. This strategy does not affect any of the results and the coefficient on employment density remains very stable. As we may suspect consumption amenities to be endogenously determined, the estimates are less reliable and are therefore not reported in the table.

Table 2.13: Wage regressions - Robustness checks

(1.a) (1.b) (1.c) (2.a) (2.b) (2.c)

Natural amenities (1) + Ability sorting

Matching 0.0116** 0.0121** 0.0153*** 0.0158***

(0.005) (0.005) (0.006) (0.006)

Density 0.0131*** 0.0131*** 0.0125*** 0.0126***

(0.002) (0.002) (0.002) (0.002)

Fixed effects Y Y Y Y Y Y

Controls Y Y Y Y Y Y

Observations 27,809 27,809 27,809 26,165 26,165 26,165

R-squared 0.450 0.450 0.448 0.458 0.457 0.456

(3.a) (3.b) (3.c) (4.a) (4.b) (4.c)

(2) + Industry FE (3) + Firm size

Matching 0.0147*** 0.0151*** 0.0147** 0.0150**

(0.006) (0.006) (0.006) (0.006)

Density 0.0131*** 0.0132*** 0.0104*** 0.0105***

(0.002) (0.002) (0.002) (0.002)

Fixed effects Y Y Y Y Y Y

Controls Y Y Y Y Y Y

Observations 26,165 26,165 26,165 21,069 21,069 21,069

R-squared 0.466 0.466 0.464 0.481 0.481 0.480

(5.a) (5.b) (5.c)

(4) + Non-movers

Matching 0.0157* 0.0157*

(0.009) (0.009)

Density 0.0128*** 0.0128***

(0.003) (0.003)

Fixed effects Y Y Y

Controls Y Y Y

Observations 10,495 10,495 10,495

R-squared 0.517 0.517 0.515

Note: Robust-clustered standard errors in parentheses. All estimations include fixed effects for the degree, the degree field and the occupation.

In columns (3.a) to (3.c), I push the data further. As stressed in Helpman et al. (2012), wage variations within industries and occupations account for the major fraction of wage inequalities. Besides, workers with different abilities may sort differently across industries (Grossman, 2013). Therefore, I include in Table 2.13 industry fixed effects. Estimations lead to the same results.

Grossman (2013) argues that large and productive firms may strongly differ from small enterprises. For instance, big firms have more human resources managers who make them more productive in identifying the right workers. In order to control for the effect of firms size on wages and labor matching, I include in columns (4.a) to (4.c) the size of the firm as a control variable, measured by the total number of employees. The coefficient on employment density is slightly lower in these estimations. The effect of labor matching on wages remains unaffected. Similarly, all coefficients of interest remain comparable across the three estimations.

Finally, I replicate the analysis of Section 2.5.4 by focusing onnon-movers in columns (5.a) to (5.c). As we expect more capable or talented workers to sort into large cities, this strategy aims at reducing the influence of spatial sorting on wages. These estimations control for the level of amenities, social origins, the city of childhood, the industry and the size of the firm in addition to the other standard fixed effects and controls. Even if the number of observations falls drastically, the results remain robust. Holding everything else equal, matching and density are important determinants that capitalize into higher wages. In addition, the labor market variable does not statistically contribute to explain the influence of urban density on individual earnings.

To finish, I consider in Table 2.14 the simultaneity between wages and location. As people are attracted by areas with high wages, I correct for this reverse causality using instrumental variables. The top of the table replicates the baseline wage regression.

Columns (1.a) and (1.b) use the historical remoteness as an instrument for employment density while it is combined with population densities in 1901 and 196 in the rest of the table. In addition, columns (3.a) and (3.b) condition out the influence of sorting and natural amenities while columns (4.a) and (4.b) also include the industry and firm size fixed effects. All IV estimations lead to the same result. Reported statistics reveal that the instruments are strong and relevant. The estimates show that both wage premia matter: well match workers enjoy higher wages as well as individuals located in dense employment areas. A comparison between IV regressions confirms the general findings.

The coefficient on urban density remains unaffected when including or excluding the matching index. This implies that the positive influence of urban density on wages is not mainly driven by the better skill match experienced in dense cities.

Table 2.14: Wage regressions - Instrumental variables

(1.a) (1.b) (2.a) (2.b)

Baseline wage regression

Instruments: Historical remoteness Three instruments

Matching 0.0112** 0.0114**

(0.005) (0.005)

Density 0.0171*** 0.0171*** 0.0135*** 0.0136***

(0.003) (0.003) (0.002) (0.002)

Fixed effects Y Y Y Y

Controls Y Y Y Y

Observations 27,809 27,809 27,809 27,809

R-squared 0.087 0.08 0.087 0.088

Kleibergen-Paap Weak Instr. 61.92 61.98 1273 1275

Underidentification KP-LM 0.000165 0.000163 0 0

F statistics 78.71 77.42 1304 1291

(3.a) (3.b) (4.a) (4.b)

Amenities and sorting (3) + Industry and firm size Instruments Three instruments Three instruments

Matching 0.0153*** 0.0147**

(0.006) (0.006)

Density 0.0123*** 0.0124*** 0.0104*** 0.0105***

(0.002) (0.002) (0.003) (0.003)

Fixed effects Y Y Y Y

Controls Y Y Y Y

Observations 26,165 26,165 21,069 21,069

R-squared 0.085 0.085 0.130 0.130

Kleibergen-Paap Weak Instr. 1280 1281 1391 1391

Underidentification KP-LM 4.43e-10 4.44e-10 4.04e-10 4.03e-10

F statistics 2602 2619 3024 3011

Note: Robust-clustered standard errors in parentheses. All estimations include fixed effects for the degree, the degree field and the occupation.

2.7 Conclusion

This paper evaluates the impact of urban density on a specific source of agglomeration economies: the quality of match between a worker’s field of education and her first occu-pation. A. Marshall (1890), Duranton and Puga (2004) and more specifically Helsley and Strange (1990) have shown that because search costs are lower and the variety of jobs and skills is greater in a thick and dense labor market, it must be easier for a worker to find a job that corresponds to her skills and qualifications. Likewise, it must be faster for a firm to fill a job vacancy with an appropriate candidate. As a result, we expect the quality of match between skills and jobs to increase with the density of the labor market.

Using a large dataset describing the entire French active population, I develop an original and continuous measure of matching based on observed realized matches and labor market outcomes. I then exploit original survey data of young individuals entering the French labor market in 2004 and use this measure to evaluate the relatedness between students’ field of study and the first position they found on the labor market. Results show that the quality of skill match increases with density. Controlling for individual, regional, occupational and educational characteristics, the probability for students to find a job unrelated to their qualifications – or for firms to hire workers with unfitted skills – decreases in the thickness of the local labor market. This result is consistent with the predictions of the extant theoretical literature and robust when I take into account the simultaneous determination of matching and agglomeration and the influence of spatial sorting. Everything else being equal, the difference in the expected matching index between a city at the 95% percentile of the city size distribution (cities like Marseille or Lille) and the densest employment area (the capital city of Paris) is about 0.18. At the average of the sample, this broadly corresponds to an increase in the quality of match by 40%.

Finally, the analysis is extended to identify the contribution of skill match on individual earnings. After conditioning out the influence of natural amenities, sorting or problems of reverse-causality, empirical findings show that skill match positively affects the wage of matched workers. This match premium comes on top of the urban wage premium.

Appendix: Matching Index – illustration and

Dans le document Essays on location choice: agglomeration, amenities and housing (Page 85-94)