DP2024-09

D i s t i n g u i s h i n g t h e U r b a n Wa g e
Premium from Human Capital
Externalities: Evidence from
MexicoK e i s u k e KO N D O

Revised April 11, 2024

Distinguishing the Urban Wage Premium from Human Capital
Externalities: Evidence from Mexico
Keisuke Kondo †RIETI & RIEB, Kobe University
Version: April 10, 2024

Abstract
This study bridges the gap between the urban wage premium and human capital externalities in Mexico.
High-skilled workers tend to be concentrated in large cities, leading to higher wages in large cities.
Merging worker-level microdata with geographical data in Mexico and employing the two-step
approach of the Mincer wage equation, this study identifies whether urban wage premium and humancapital externalities explain local wage premium, focusing on worker heterogeneity. This study ﬁnds
that the spatial sorting of workers and ﬁrms and human capital externalities entirely explain the urban
wage premium in Mexico. An interesting finding is the heterogeneous eﬀect of human capital
externalities on the wages of high- and low-skilled workers. Low-skilled workers benefit from humancapital externalities, whereas high-skilled workers do not. However, compared with low-skilledworkers, high-skilled workers get more than twice the private return to education anywhere they work.
This study provides evidence that locations where high-skilled workers are concentrated can pay
higher wages to low-skilled workers due to human capital externalities, compensating for their lower
private return to education.
JEL classification: J31, R12, R23
Keywords: Urban wage premium, Human capital externalities, Spatial sorting, Private returns to
education, Social returns to education† Corresponding Author. Research Institute of Economy, Trade and Industry. 1-3-1 Kasumigaseki, Chiyoda-ku, Tokyo, 100-

8901, Japan. (e-mail: kondo-keisuke@rieti.go.jp).
The views expressed in this study are exclusively those of the author and do not represent those of the author’s affiliatedorganizations, the Research Institute of Economy, Trade and Industry (RIETI) and the Research Institute for Economics andBusiness Administration (RIEB), Kobe University.

1. Introduction
Why do workers earn higher wages in large cities? Recent studies on urban economics have attempted toanswer the question of the urban wage premium. Although it is challenging to uncover the channels andsources of the urban wage premium, there have been advanced studies in recent years. As studied by Combes
et al. (2008), higher-skilled workers tend to concentrate in large cities, resulting in higher wages. This is
known as the spatial sorting of skilled workers. Combes et al. (2010) demonstrated that spatial sorting
explains about half of the urban wage premium obtained by the standard method. According to de la Rocaand Puga (2017), the dynamic learning process of working in large cities is another source of the urban wage
premium. Workers in large cities gradually increase their productivity, resulting in higher wages. Gould(2007) also revealed that urban work experience increases wages, especially white-collar workers
continuously earn higher wages after leaving cities. 1
This study bridges the gap between two types of literature—urban wage premiums and human capital
externalities (Glaeser and Maré, 2001; Heuermann et al., 2010). The literature on human capital externalities
has found that the concentration of high-skilled workers generates positive externalities to wages andproductivity (Rauch, 1993, Acemoglu and Angrist, 2001, Moretti, 2004a, b). For example, Acemoglu andAngrist (2001) estimated private and external rates of return to education in the US, and using the ordinary
least squares (OLS) estimation, they found that the external rate of return to education was 0.073 in 1960–
1980. Moretti (2004a) also investigated the social return to education using the proportion of college
graduates instead of average years of schooling. 2 However, some studies found no evidence of human capital
externalities. For example, Acemoglu and Angrist (2001) pointed out that the external return to education is
statistically insigniﬁcant and its magnitude is considerably small when the exogenous variation in averageschooling is employed. Ciccone and Peri (2006) proposed a new approach based on the change in average
education and found no evidence of human capital externalities for wages. Heuermann et al. (2010) conducted
a literature review by connecting the urban wage premium with human capital externalities because highskilled workers tend to concentrate in large cities.
1

See the studies by Matano and Naticchioni (2012), Combes et al. (2012), Kondo (2015), Matano and Naticchioni (2016),

Silva and Azzoni (2022), Dauth et al. (2022), Belloc et al. (2023), and Butts et al. (2023) for additional studies on the urban
wage premium and agglomeration economies. See the studies by Glaeser (1999), Glaeser and Maré (2001), Gould (2007),
Glaeser and Resseger (2010), Baum-Snow and Pavan (2012), Kondo (2017a), and de la Roca and Puga (2017) for dynamic
learning in larger cities.
2

The interaction between agglomeration economies and human capital externalities is also an interesting topic. For example,

Ciccone and Hall (1996) and Maré and Graham (2013) investigated whether employment density increases productivity.
Moretti (2004c) investigated how human capital externalities increases total factor productivity using the U.S. plant-level data.

2

Merging worker-level microdata with municipal data in Mexico, this study aims to distinguish between
urban wage premiums and human capital externalities. Figure 1 depicts a positive relationship between the
average years of schooling and population density at the municipal level using the 2005 and 2010 Populationand Housing Censuses (Conteo de Población y Vivienda 2005 and Censo de Población y Vivienda 2010) in
Mexico, revealing that high-skilled workers tend to work in large cities. However, the high correlation
between both variables leads to an econometric issue in identifying the variables that affect wages. Therefore,
an exogenous event is generally required to distinguish between urban agglomeration and the concentrationof high-skilled workers (Moretti, 2004b).
An advantage of the Mexican data is that there is a significant geographic variation between urban andhuman capital factors. High-skilled workers tend to move to border states, whereas the traditional
metropolitan area is Mexico City (Krugman and Elizondo, 1996). Figure 2 depicts the geographicaldistributions of the average years of schooling and population density. High-skilled workers are also
concentrated in the border states of Mexico, which are less dense than the Mexico City metropolitan area.
This variation allows us to identify the important urban wage premium and human capital externalities.
[Figures 1 and 2]
This study contributes to the literature by demonstrating that the spatial sorting of workers and ﬁrms andhuman capital externalities entirely explain the urban wage premium in Mexico. More importantly, it ﬁnds
heterogeneous human capital externalities between high- and low-skilled workers. Low-skilled workers
beneﬁt more from external returns to education in local labor markets than high-skilled workers. However,this study did not ﬁnd human capital externalities for high-skilled workers, implying that high-skilled workers
are concentrated in better locations that attract ﬁrms paying higher wages because they can get a higher
private rate of return to education anywhere they work. This finding is strongly related to Frías et al. (2024),
who show that exports have a significant positive effect on wage premiums in Mexico. It is suggested thathighly skilled workers earn higher wages when they work for exporting firms. Referring to Moretti (2012),
this study shows that locations where high-skilled workers are concentrated can pay higher wages to lowskilled workers because of human capital externalities, compensating for their lower private return toeducation.
The remainder of this paper is organized as follows. Section 2 explains the empirical framework. Section
3 presents the worker-level microdata in Mexico. Section 4 discusses the estimation results. Finally, Section
5 concludes.

32. Empirical Framework
2.1. Two-Step Approach for Factor Decomposition
The empirical strategy embodies two steps based on the studies by Combes et al. (2010), de la Roca andPuga (2017), and Kondo (2017b). First, we estimate a Mincer wage equation with municipality dummy
variables 𝜂𝜂𝑎𝑎 to identify a municipal wage premium. Thus, the regression model takes the following form:
log(𝑤𝑤𝑖𝑖𝑖𝑖𝑖𝑖 ) = 𝑿𝑿Individual
𝜷𝜷 + 𝑿𝑿Firm
𝑖𝑖𝑖𝑖 𝜸𝜸 + 𝜂𝜂𝑎𝑎 + 𝜏𝜏𝑡𝑡 + 𝑒𝑒𝑖𝑖𝑖𝑖𝑖𝑖 ,
𝑖𝑖𝑡𝑡

(1)

where 𝑤𝑤𝑖𝑖𝑖𝑖𝑖𝑖 is the hourly wage of worker 𝑖𝑖 residing in municipality 𝑎𝑎 at time t; 𝑿𝑿Individual
is a vector of𝑖𝑖𝑡𝑡

individual control variables (years of schooling, years of experience, gender dummy, marriage dummy,
is a vector of ﬁrm control variables
occupation dummies, and sector dummies) of worker 𝑖𝑖 at time 𝑡𝑡; 𝑿𝑿Firm𝑖𝑖𝑖𝑖
of worker 𝑖𝑖 at time 𝑡𝑡 (ﬁrm size dummies); 𝜷𝜷 and 𝜸𝜸 are vectors of the corresponding parameters; 𝜏𝜏𝑡𝑡 isa quarter dummy; and 𝑒𝑒𝑖𝑖𝑖𝑖𝑖𝑖 is an error term. The estimates of municipal dummies 𝜂𝜂̂ 𝑎𝑎 are interpreted as

municipal wage premiums. This approach does not control for unobservable individual skills but observableones, such as education and experience. 3
This study calculates three types of municipal wage ﬁxed eﬀects based on Equation (1). The ﬁrst

calculates the simple average municipal wage among workers. The regression includes no controls as follows:log(𝑤𝑤𝑖𝑖𝑖𝑖𝑖𝑖 ) = 𝜂𝜂𝑎𝑎 + 𝜏𝜏𝑡𝑡 + 𝑒𝑒𝑖𝑖𝑖𝑖𝑖𝑖 ,(2)log(𝑤𝑤
�𝑎𝑎𝑁𝑁 ) = 𝜂𝜂̂ 𝑎𝑎 − 𝜂𝜂̂ ̅𝑎𝑎 ,where log(wiat ) still includes spatial sorting factors. For example, this average municipal wage becomes

higher in large areas if more skilled workers are concentrated in large areas.

The second controls for spatial sorting of workers’ skills. The regression includes individual controls) as follows:(𝑿𝑿Individual
𝑖𝑖𝑡𝑡

3

Combes et al. (2008), Combes et al. (2011), and de la Roca and Puga (2017) controlled for unobservable individual ﬁxed

eﬀects in their estimations. However, municipal dummies can only be identiﬁed if workers migrated across municipalities
during the research period. This study does not control for unobservable individual ﬁxed eﬀects due to data limitation. The
relationship between municipal wage ﬁxed eﬀects and workers’ location choices might generate endogeneity bias in the second
step, known as spatial sorting (Combes et al., 2008).

4

log(𝑤𝑤𝑖𝑖𝑖𝑖𝑖𝑖 ) = 𝑿𝑿Individual
𝜷𝜷 + 𝜂𝜂𝑎𝑎 + 𝜏𝜏𝑡𝑡 + 𝑒𝑒𝑖𝑖𝑖𝑖𝑖𝑖 ,
𝑖𝑖𝑡𝑡

(3)

log(𝑤𝑤
�𝑎𝑎I ) = 𝜂𝜂̂ 𝑎𝑎 − 𝜂𝜂̂ ̅𝑎𝑎 ,where log�𝑤𝑤�𝑎𝑎I � still includes ﬁrm factors. For example, the average municipal wage is higher in large cities

if large ﬁrms are concentrated in large cities.

The third controls for spatial sorting of workers’ skills and ﬁrms because large ﬁrms pay higher wages(Brown and Medoﬀ, 1989). The regression includes individual and ﬁrm controls (𝑿𝑿Individual
and 𝑿𝑿Firm
𝑖𝑖𝑖𝑖 ,
𝑖𝑖𝑡𝑡

respectively) as follows:

𝜷𝜷 + 𝑿𝑿Firmlog(𝑤𝑤𝑖𝑖𝑖𝑖𝑖𝑖 ) = 𝑿𝑿Individual
𝑖𝑖𝑖𝑖 𝜸𝜸 + 𝜂𝜂𝑎𝑎 + 𝜏𝜏𝑡𝑡 + 𝑒𝑒𝑖𝑖𝑖𝑖𝑖𝑖 ,
𝑖𝑖𝑡𝑡

(4)

log(𝑤𝑤
�𝑎𝑎IF ) = 𝜂𝜂̂ 𝑎𝑎 − 𝜂𝜂̂ ̅𝑎𝑎 ,where log(𝑤𝑤�𝑎𝑎IF ) strongly captures local factors of municipal wage premium in municipality 𝑎𝑎.

This study considers the heterogeneous aspects of municipal wage ﬁxed eﬀects. It divides workers into

two groups (high- and low-skilled workers) and estimates Equations (2)–(4) to obtain municipal wage ﬁxed
eﬀects 𝜂𝜂𝑎𝑎 .

2.2. Urban Wage Premium and Human Capital Externalities
To distinguish the urban wage premium from human capital externalities, we regress the three types of
estimated municipal ﬁxed eﬀects on population density and average years of schooling in the second step.
Based on the study by Glaeser and Resseger (2010), the regression takes the following form:Typewhere log�𝑤𝑤�𝑎𝑎log(𝑤𝑤𝑖𝑖𝑖𝑖𝑖𝑖 ) = Const + 𝜆𝜆 log(Dens𝑎𝑎 ) + 𝜙𝜙Educ𝑎𝑎 + 𝜀𝜀𝑎𝑎 ,(5)�𝑎𝑎N ) , log(𝑤𝑤�𝑎𝑎F ) , log(𝑤𝑤�𝑎𝑎IF )} is the wage premium of municipality a; Const is a� ∈ {log(𝑤𝑤constant term; Densa is the population density of municipality a; Educ𝑎𝑎 is the average years of schoolingof municipality 𝑎𝑎; and 𝜀𝜀𝑎𝑎 is an error term. 4

Our interests are in two parameters 𝜆𝜆 and 𝜙𝜙, which measure the urban wage premium and external rateof return to education, respectively. First, this study estimates Equation (5) in terms of urban wage premiumand human capital externalities separately. Next, it considers the simultaneous inclusion of both variables.
An econometric issue is an endogeneity concern. Following Rauch (1993), this study considers an omitted

4This study additionaly considers the population potential (Stewart, 1947). See Section B in Supplementary Information.

5

variable bias. The location choice of ﬁrms also affects the wage level. Rauch (1993) found higher rents inlarge cities with higher education levels. The areas with better residence and business conditions attract moreproductive and proﬁtable ﬁrms that pay higher wages. As a result, high-skilled workers concentrate in better
locations, generating a positive relationship between local wages and average years of schooling. As arobustness check, this study introduces the number of housing units with home electrical appliances to control
residence and business conditions.

3. DataThis study uses the microdata on Mexican workers from the National Occupation and Employment
Survey (Encuesta Nacional de Ocupación y Empleo, ENOE). The survey has been conducted quarterly by
the National Institute of Statistics and Geography (Instituto Nacional de Estadística y Geografía, INEGI)
since 2005; ENOE compiles workers’ characteristics (e.g., age, gender, place of residence, and marriage),hourly wage, ﬁrm size, occupation, and sector. This study merges ENOE microdata from 2005:Q1 to 2010:Q4
with the municipal data of the 2005 and 2010 Population and Housing Censuses.
Table 1 presents the descriptive statistics for the ﬁrst-step regression. The dependent variable, hourly
wage, is adjusted using the consumer price index published by the INEGI. The base period is 2005:Q1.
Spatial diﬀerences in cost of living are adjusted using area diﬀerences in minimum wages. As of 2005, there
were three categories of minimum wage in Mexico depending on the degree of urbanization. The base areais the lowest minimum wage of the three categories. In line with the literature on the Mincer wage equation,
we calculate years of experience by subtracting six years and years of schooling from age. 5 The regression
models include control variables on gender, marriage status, occupation, and ﬁrm size. We create occupation
dummies at the two-digit level based on the occupation code. Firm-size dummies are created for 1–10, 11–
20, 21–50, 51–100, 101–250, 251–500, and 501 workers and over (the reference is Category 1–10 workers).
[Table 1]
The second-step regression includes municipal variables. This study considers the population density andaverage years of schooling at the municipal level. There are 2,454 municipalities in the 2005 census.
Population density is calculated from population and area data obtained from the 2005 and 2010 censuses. 6
The average years of schooling for people aged 15 and above are also available at the municipal level in the5

When years of experience take on a negative value, we drop the observations from the sample.

6

The National System of Municipal Information (Sistema Nacional de Información municipal, SNIM) provided summarizedmunicipal data, indicating the area, latitude, and longitude. Currently, the municipal data are available on the INEGI website,
https://www.inegi.org.mx/programas/ccpv/2005/ (accessed on March 22, 2024)

6

2005 and 2010 Population and Housing Censuses.
This study uses spatial smoothing based on the study by de la Roca and Puga (2017) to avoid bias arisingfrom administrative border discontinuity. Population density is calculated as population divided by area (inkm2) within a radius of 10 km from the administrative center of each municipality (cabecera municipal). The
average years of schooling are also smoothed by the sum of average years of schooling divided by the numberof municipalities within a radius of 10 km from the administrative center of each municipality. Based on thelatitude and longitude, this study calculates the great circle distances between any two municipalities using
Vincety’s formula (Vincenty, 1975). Finally, municipal wage ﬁxed eﬀects obtained from the ﬁrst-stepestimation of the Mincer wage equation are matched with population density and average years of schooling
via municipal code.
Table 2 presents the descriptive statistics for the second-step regression. The number of municipalities
becomes 653 for total workers in the second-step regression. As a robustness check, this study includes thenumber of housing units with home electrical appliances in each municipality, capturing the local residence
and business conditions. This variable is also available from the 2005 and 2010 Population and Housing
Censuses.
[Table 2]

4. Estimation Results and Discussions
4.1. First-Step Estimation Results
Table 3 presents the ﬁrst-step estimation results of the Mincer wage equation with municipality dummies
by worker skill. Total, high-skilled, and low-skilled workers correspond to Columns (1)–(3), Columns (4)–(5), and Columns (6)–(9) of Table 3, respectively. The wages in Equations (2)–(4) correspond to each columnof Table 3. The estimated municipal ﬁxed eﬀects are used in the second-step regressions.
To determine whether our estimation results are reliable, we compare them in Column (3) of Table 3 withthose obtained by Chiquiar (2008). He estimated a Mincer wage equation using data from the 1990 and 2000
censuses, obtaining 0.051 for years of education; 0.018 for years of experience; −0.023 for years ofexperience squared divided by 100; and 0.085 for a marriage dummy using the 2000 census data after
controlling for observable workers’ characteristics, region, position, occupation, sector, site-speciﬁc feature,and globalization. The results are similar to the estimates obtained in Table 3. Although we use microdata
from ENOE, parameters in our regression model seem to be correctly estimated.
This study ﬁnds heterogeneous private rates of return to education between high- and low-skilled workers.

7

While it is 8.1% for high-skilled workers, it is 3.2% for low-skilled workers, implying that a college degree
is a key factor for wage determination in Mexico. A ﬁrm-size dummy also indicates the heterogeneous effects
on wages of high- and low-skilled workers. Large ﬁrms pay higher salaries for both workers, and high-skilledworkers earn higher wages in large ﬁrms than low-skilled ﬁrms.
[Table 3]

4.2. Second-Step Estimation Results
Figure 3 compares the distributions of municipal wage ﬁxed eﬀects between municipalities with abovemedian (below-median) population density by worker skill. In Panels (a) to (c) of Figure 3, both distributions
overlap closely by controlling for spatial sorting of worker and ﬁrm factors. Visually, we hardly ﬁnd evidenceof urban wage premium after controlling for spatial sorting, which is statistically tested later.
[Figure 3]
Figure 4 compares the distributions of municipal wage ﬁxed eﬀects between municipalities with aboveand below-median average years of schooling by worker skill. In Panels (a) to (c) of Figure 4, the gap between
both distributions becomes smaller by controlling for spatial sorting of worker and ﬁrm factors. Both
distributions closely overlap for high-skilled workers in Panel (f) of Figure 4, whereas both distributions do
not overlap for low-skilled workers, implying that the concentration of high-skilled workers increases
individual wages of low-skilled workers at the regional level.
[Figure 4]
Figure 5 illustrates urban wage premium and the external return to education after controlling for spatialsorting. Panels (a) and (b) of Figure 5 correspond to the urban wage premiums for high- and low-skilledworkers. Panels (c) and (d) of Figure 5 correspond to human capital externalities for high- and low-skilledworkers. Compared with the urban wage premium, this study ﬁnds a clear positive relationship between theestimated municipal wage ﬁxed eﬀect and average years of schooling, implying that human capital
externalities are important, especially for low-skilled workers.
[Figure 5]
Table 4 presents the estimation results of the second-step regression by worker skill. Columns (1)–(3),(4)–(6), and (7)–(9) of Table 4 correspond to total, high-skilled, and low-skilled workers, respectively. Table
3 includes two regression models, where population density and average years of schooling are included
separately.

8

[Table 4]
The urban wage premium is conﬁrmed in the upper part of Table 4. When the spatial sorting is notcontrolled, the elasticity of population density to wage premium is 0.063 in Column (1) of Table 4. When thespatial sorting of workers and ﬁrms is controlled, the elasticity of population density to wage premium is0.031 in Column (3) of Table 4, which is less than half of that of Column (1) of Table 4. Furthermore, theelasticity of population density to wage premium is 0.018 for high-skilled workers and 0.033 for low-skilledworkers, implying that the urban wage premium is heterogeneous across workers.
The magnitude of the urban wage premium is close to that of previous studies. For example, Combes etal. (2008) obtained an elasticity of 0.030 in France, and similarly, Combes et al. (2010) obtained an elasticity
of 0.027 in France. Furthermore, de la Roca and Puga (2017) obtained an elasticity of 0.025 in Spain; D’Costa
and Overman (2014) obtained an elasticity of 0.022 in the UK; and Kondo (2017b) obtained an elasticity of
0.13 in Japan after controlling for ﬁrm factors. Using Italian microdata, Mion and Naticchioni (2009)
obtained an elasticity of 0.007 after controlling for individual ﬁxed eﬀects. Applying a quantile regression
approach to Italian microdata, Matano and Naticchioni (2012) obtained a range of 0.01–0.02 for the industrial
and service sectors.
The lower part of Table 4 conﬁrms the human capital externalities. When spatial sorting is not controlled,
the elasticity of average years of schooling to wage premium is 0.103 in Column (1) of Table 4. When thespatial sorting of workers and ﬁrms is controlled, the elasticity of average years of schooling to wage
premium is 0.047 in Column (3) of Table 4, which is less than half in Column (1) of Table 4. The magnitude
is close to that of Acemoglu and Angrist (2001), indicating that the external rate of return to education ranges
from 0.061 to 0.073 using the OLS estimation. After controlling for spatial sorting, this study ﬁnds
heterogeneous human capital externalities as found earlier. The external return to education is 0.030 for highskilled workers (Column (6) of Table 3) and 0.051 for low-skilled workers (Column (9) of Table 3).
Table 5 presents the estimation results of the second-step regression by worker skill by distinguishing theurban wage premium from human capital externalities. Columns (1)–(3), (4)–(6), and (7)–(9) of Table 5
correspond to total, high-skilled, and low-skilled workers, respectively. The estimation results indicate thatthe population density is not statistically significant when the average years of schooling are included in the
regression, implying that the concentration of high-skilled workers further leads to positive externalities inlocal labor markets. An entire part of the urban wage premium is explained by the external return to education.
As mentioned earlier, this study ﬁnds heterogeneous human capital externalities between high- and lowskilled workers. The magnitude of human capital externalities slightly decreases in each column comparedwith those in Table 4.

9[Table 5]
The summary of the OLS estimation is as follows. This study uncovers two sources of urban wage
premiums. Spatial sorting is the ﬁrst source of the urban wage premium, which leads to higher wages in large
cities. Furthermore, the concentration of high-skilled workers generates positive externalities in local labormarkets, which is the second source of the urban wage premium. This study uncovers that spatial sorting ofworkers and ﬁrms and human capital externalities entirely explain the urban wage premium in Mexico. Itemphasizes the heterogeneous eﬀects of human capital externalities on wages. Low-skilled workers enjoy
more beneﬁts of external return to education, whereas the external rate of return to education of high-skilledworkers is relatively small and less than half of that of low-skilled workers. Considering endogeneity issues,
this study further examines the magnitude of human capital externalities for high- and low-skilled workers.

4.3. Robustness Check
Table 6 presents the robustness check of the estimation results. Following Rauch (1993), this study
considers that the omitted variable generates a bias for urban wage premium and human capital externalities.
Table 6 includes an additional control variable—the number of housing units with home electricalappliances—denoting local residence and business conditions (See also Section A in Supplementary
Information).
This study ﬁnds that the variable on local residence and business conditions has a statistically significant
eﬀect on average municipal wages. The magnitude does not change after controlling for spatial sorting ofindividual worker skills, implying that this variable captures localized factors. For example, better locations
attract productive ﬁrms paying higher wages, and, as a result, high-skilled workers tend to concentrate there.
The robustness check conﬁrms this mechanism, implying that human capital externalities are overestimated.
This study ﬁnds heterogeneous human capital externalities between high- and low-skilled workers. The
external return to education is statistically insigniﬁcant and 0.8% for high-skilled workers in Column (6) of
Table 6, implying that high-skilled workers do not benefit from the external return to education. This isconsistent with previous studies, such as those by Acemoglu and Angrist (2001) and Ciccone and Peri (2006).
Our estimation results reveal that high-skilled workers are concentrated in better locations that attract ﬁrms
paying higher wages because their private return to education is suﬃciently high. Thus, they can increase
their wages through individual human capital accumulation.
This study ﬁnds that only low-skilled workers beneﬁt from the external return to education. For them, theexternal rate of return to education is statistically significant and is 2.7% after controlling for spatial sortingand ﬁrm location factors. This rate is suﬃciently high because their private return to education is 3.1% in10Column (9) of Table 3. This ﬁnding implies that locations where high-skilled workers are concentrated attract
low-skilled workers because the local average wage rate rises at a suﬃciently high rate. As discussed by
Moretti (2012), this study provides evidence of static human capital externalities for low-skilled workers.
[Table 6]

5. Conclusion
This study has investigated sources of the urban wage premium, focusing on human capital externalities.
Merging worker-level microdata with the geographical data of the censuses in Mexico and employing thetwo-step approach of the Mincer wage equation, it distinguishes between urban wage premium and humancapital externalities because high-skilled workers tend to concentrate in large cities. A novel feature of thisstudy is to uncover how a worker’s skill level affects the magnitude of the urban wage premium and humancapital externalities.
This study found that the spatial sorting of workers and ﬁrms and human capital externalities entirely
explain the urban wage premium in Mexico. An interesting ﬁnding is the heterogeneous eﬀect of humancapital externalities between high- and low-skilled workers, which is consistent with the ﬁndings of Moretti(2004a). Only low-skilled workers benefit from external returns to education in local labor markets. The
external rate of return to education for low-skilled workers is estimated as 5.0% with the OLS estimation and2.7% after controlling for local residence and business conditions. For high-skilled workers, the external rateof return to education is estimated as 3.1% with the OLS estimation and 0.3% after controlling for local
residence and business conditions. This study found that, despite where they work, the private rate of returnto education for high-skilled workers is more than twice that of low-skilled workers, implying that highskilled workers do not care about the external return to education because they increase their wages throughindividual human capital investment. These ﬁndings are also consistent with the studies by Acemoglu andAngrist (2001) and Ciccone and Peri (2006), who found no evidence of human capital externalities.
This study has several limitations. First, it did not control for unobservable individual ﬁxed eﬀects owing
to data limitation. Spatial sorting requires a rich panel dataset with migration information. Second, this study
did not focus on the dynamic process of learning by working in large cities. The estimation results imply thathigh-skilled workers search for locations for individual human capital investment, whereas low-skilledworkers search for locations with higher external returns to education. Third, this study did not directly
control for spatial sorting of ﬁrms owing to data limitation. Employer–employee matched data are required
to consider how ﬁrm characteristics and location factors affect wages (Abowd and Kramarz, 1999, Abowd etal., 1999, Dauth et al., 2022, Frías et al, 2024). Although this study did not find external return to education
11

for high-skilled workers at the regional level, it is also possible to examine external return to education at thefirm level. Although the estimation results are valid, resolving these issues will uncover the detailed
mechanisms of the urban wage premium and human capital externalities.

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14

Author Contributions
Keisuke Kondo: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation,
Resources, Data curation, Writing – Original Draft, Writing – Review & Editing, Visualization, Funding
acquisition.

Declaration of Competing Interest
The author declares no competing interests.

Funding
This study was supported by JSPS KAKENHI Grant Numbers 21K01497.

Acknowledgments
I thank Kensuke Teshima for his helpful comments and suggestions. All the remaining errors are naturally
my own. The original study was conducted under a scholarship granted by the Government of Mexico
through the Ministry of Foreign Aﬀairs of Mexico in 2012–2013. I am grateful for the beneﬁts received
during my stay at the Instituto Tecnológico Autónomo de México in 2012–2013.

Supplementary Information
The supplementary information is available.

15

Table 1 Descriptive Statistics of the First-Step Regression
Variable
Hourly Wage (Pesos)
Years of SchoolingYears of Experience
Years of Experience in Firm
Dummy (1=Female)
Dummy (1=Married)
Firm-Size Dummy (1–10 workers)
Firm-Size Dummy (11–50 workers)
Firm-Size Dummy (51–250 workers)
Firm-Size Dummy (251–500 workers)
Firm-Size Dummy (501 workers or more)
Occupation 1 Dummy
Occupation 2 Dummy
Occupation 3 Dummy
Occupation 4 Dummy
Occupation 5 Dummy
Occupation 6 Dummy
Occupation 7 Dummy
Occupation 8 Dummy
Occupation 9 Dummy
Occupation 10 Dummy
Occupation 11 Dummy
Sector 1 Dummy
Sector 2 Dummy
Sector 3 Dummy
Sector 4 Dummy
Sector 5 Dummy
Sector 6 Dummy
Sector 7 Dummy
Sector 8 Dummy
Sector 9 Dummy
Sector 10 Dummy
Sector 11 Dummy

Mean

S.D.

25.9579.577
19.154
6.817
0.384
0.451
0.478
0.236
0.070
0.046
0.170
0.084
0.048
0.016
0.137
0.279
0.115
0.058
0.168
0.032
0.064
0.000
0.069
0.015
0.169
0.092
0.153
0.066
0.056
0.057
0.105
0.128
0.089

20.8354.247
13.724
7.638
0.486
0.498
0.500
0.425
0.255
0.210
0.375
0.277
0.213
0.124
0.344
0.448
0.319
0.233
0.374
0.175
0.245
0.003
0.254
0.121
0.375
0.290
0.360
0.247
0.230
0.233
0.307
0.334
0.285

Min
1.695
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

Median19.3809.000
17.250
3.750
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

Max
213.333
17.00065.75045.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000

Notes: The number of observations is 1,973,517 from the 2005Q1–2010Q4 ENOE survey. Hourly wage is de ß ated by thequarterly average monthly consumer price index (2010=100) and the minimum wages of the municipalities where workers live.
Occupation 1: Professionals, Engineers, and Artists; Occupation 2: Education workers; Occupation 3: Financiers and Directors;
Occupation 4: O ß ce workers; Occupation 5: Blue collar workers, Craft workers, and helpers; Occupation 6: Sellers; Occupation
7: Transport operators; Occupation 8: Personal service workers; Occupation 9: Security o ß cers; Occupation 10: Farmers;
Occupation 11: Not speci ß ed. Sector 1: Agriculture, livestock, silviculture, hunting, and ß shing; Sector 2: Mining andelectricity; Sector 3: Manufacture; Sector 4: Manufacture; Sector 5: Manufacture; Sector 6: Restaurant and lodging; Sector 7:
Transport, communication, postal services, and warehouse; Sector 8: Professional, ß nancial, and corporate services; Sector 9:
Social services; Sector 10: Various services; Sector 11: Government and International Organizations.

16

Table 2 Descriptive Statistics for Second-Step Regression
Variables
Total Workers
Municipal Wage FE (No Control)
Municipal Wage FE (Individual Controls)
Municipal Wage FE (Individual & Firm Controls)
High-Skilled Workers
Municipal Wage FE (No Control)
Municipal Wage FE (Individual Controls)
Municipal Wage FE (Individual & Firm Controls)
Low-Skilled Workers
Municipal Wage FE (No Control)
Municipal Wage FE (Individual Controls)
Municipal Wage FE (Individual & Firm Controls)
Area Variables
Average Years of Schooling within 10km
log(Population Density within 10km)
log(Population Potential; delta = 2)
Log(Housing with H.E.A. within 10km)

Obs.

Mean

S.D.

Min

Median

Max

527
527
527

−0.192−0.117−0.106

0.257
0.186
0.178

−1.349−1.025−0.994−0.182−0.111−0.096

1.033
0.562
0.507

481
481
481

−0.088−0.105−0.081

0.320
0.226
0.217

−1.617−1.021−1.041−0.054−0.098−0.075

1.095
1.226
1.156

527
527
527

−0.145−0.119−0.109

0.230
0.195
0.188

−1.227−1.038−1.010−0.129−0.105−0.095

0.867
0.671
0.616

527
527
527
527

7.254
4.044
7.888
7.579

1.396
1.792
1.481
1.747

3.400
−1.452
4.680
2.635

7.158
3.996
7.781
7.499

10.9809.087
13.35612.101

Notes: The unit of observation is the municipality. H.E.A. stands for home electrical appliances. High-skilled workers are defined
as workers with at least 12 years of schooling in educational institutions, such as vocational schools, colleges, and graduate schools,
and working experience. Low-skilled workers are defined as workers with less than 12 years of schooling and less than a highschool diploma.

17

18

1,973,517
1,207
0.1006

No
No
Yes
Yes

(1)

1,973,517
1,2070.4400

Yes
Yes
Yes
Yes

0.0477***(0.0009)
0.0184***(0.0003)−0.0226***(0.0004)−0.1000***(0.0030)
0.0754***(0.0019)(2)

1,973,517
1,207
0.44690.0452***(0.0009)0.0175***(0.0002)−0.0213***(0.0004)−0.0969***(0.0033)
0.0714***(0.0019)0.0909***(0.0031)0.1158***(0.0059)
0.1383***(0.0072)
0.1975***(0.0111)
Yes
Yes
Yes
Yes

(3)

349,304
1,068
0.0538

No
No
Yes
Yes

(4)

349,304
1,068
0.3335

Yes
Yes
Yes
Yes

0.0800***(0.0027)
0.0263***(0.0008)−0.0439***(0.0020)−0.0614***(0.0046)
0.0924***(0.0038)(5)

349,304
1,068
0.34680.0757***(0.0026)
0.0248***(0.0008)−0.0411***(0.0018)−0.0524***(0.0043)0.0888***(0.0037)
0.1317***(0.0062)0.2017***(0.0074)
0.2349***(0.0093)0.2658***(0.0123)
Yes
Yes
Yes
Yes

(6)

1,624,213
1,207
0.1143

No
No
Yes
Yes

(7)

1,624,213
1,207
0.3455

Yes
Yes
Yes
Yes

0.0338***(0.0008)
0.0181***(0.0004)−0.0241***(0.0006)−0.1063***(0.0044)0.0706***(0.0024)(8)(9)

1,624,213
1,207
0.35280.0314***(0.0007)0.0173***(0.0004)−0.0230***(0.0006)−0.1041***(0.0048)0.0667***(0.0024)0.0873***(0.0052)
0.0992***(0.0105)
0.1186***(0.0130)0.1838***(0.0189)
Yes
Yes
Yes
Yes

Low-Skilled Workers

Notes: Heteroskedasticity-consistent standard errors clustered at the municipal level are in parenthesis. High-skilled workers are defined as workers with at least 12 years of schooling in educational
institutions, such as vocational schools, colleges, and graduate schools, and working experience. Low-skilled workers are defined as workers with less than 12 years of schooling and less than ahigh school diploma.

Number of Observations
Number of Municipalities
Adjusted R2

Occupation Dummy
Sector Dummy
Quarterly Dummy
Municipality Fixed-Eﬀect (η̂a )Firm-Size Dummy (501 workers or more)

Firm-Size Dummy (251–500 workers)

Firm-Size Dummy (51–250 workers)

Firm-Size Dummy (11–50 workers)

Dummy (1=Married)

Dummy (1=Female)Years of Experience Squared (× 1/100)

Years of Experience

Years of Schooling

Explanatory Variables

High-Skilled Workers

Dependent Variable: Hourly Wage, log(wiat )First-Step Estimation Results of the Mincer Wage Equation

Total Workers

Table 319527
0.45170.1034***(0.0105)Yes

527
0.5796

Number of Municipalities
Adjusted R2

Average Years of SchoolingNumber of Municipalities
Adjusted R2

527
0.71930.0531***(0.0068)
Yes

527
0.6686

0.0347***(0.0062)
Yes(2)

Individual
Controls

527
0.70570.0474***(0.0067)Yes

527
0.66040.0310***(0.0061)
Yes(3)

Individual &Firm Controls(5)

Individual
Controls(6)

Individual &Firm Controls

481
0.42790.0267***(0.0050)Yes
481
0.38990.0183***(0.0049)Yes

481
0.20810.0237**(0.0100)
Yes
481
0.45850.0425***(0.0078)Yes

481
0.40870.0299***(0.0074)
Yes

Regression Model 2: Human Capital Externalities

481
0.19900.0135**(0.0057)
Yes

Regression Model 1: Urban Wage Premium(4)

No controls

High-Skilled Workers

527
0.65190.0778***(0.0083)Yes

527
0.56130.0480***(0.0080)Yes(7)

No controls

527
0.74440.0555***(0.0064)
Yes

527
0.69210.0360***(0.0064)
Yes(8)

Individual
Controls

Low-Skilled Workers

527
0.73480.0506***(0.0064)
Yes

527
0.68650.0327***(0.0063)
Yes(9)

Individual &Firm Controls

Notes: Heteroskedasticity-consistent standard errors clustered at the municipal level are in parenthesis. The unit of observation is the municipality. This study uses the weight of the number ofpeople aged 15 years and above in each municipality to consider diﬀerent population sizes across municipalities. High-skilled workers are defined as workers with at least 12 years of schooling in
educational institutions, such as vocational schools, colleges, and graduate schools, and working experience. Low-skilled workers are defined as workers with less than 12 years of schooling andless than a high school diploma.

State Fixed-Eﬀect

State Fixed-Eﬀect

0.0632***(0.0109)
Yes(1)

Log(Population Density)

Explanatory Variables

No controls

Total Workers


Dependent Variable: Municipal Wage Fixed-Eﬀect, log w̄Typea

Table 4 Second-Step Estimation Results of Urban Wage Premium and Human Capital Externalities by Workers’ Skill

20

527
0.5788

Number of Municipalities
Adjusted R2

527
0.71980.0057(0.0088)0.0481***(0.0103)
Yes(2)

Individual
Controls

527
0.70600.0051(0.0085)0.0430***(0.0097)
Yes(3)

Individual &Firm Controls

481
0.45780.0040(0.0062)0.0390***(0.0098)
Yes−0.0007(0.0075)
0.0243*(0.0137)Yes
481
0.2063(5)

Individual
Controls(4)

No controls

High-Skilled Workers

481
0.40750.0017(0.0062)0.0285***(0.0091)
Yes(6)

Individual &Firm Controls

527
0.65130.0024(0.0108)0.0756***(0.0128)Yes(7)

No controls

527
0.74480.0054(0.0095)
0.0508***(0.0103)
Yes(8)

Individual
Controls

Low-Skilled Workers

527
0.73500.0047(0.0092)
0.0465***(0.0098)
Yes(9)

Individual &Firm Controls

Notes: Heteroskedasticity-consistent standard errors clustered at the municipal level are in parenthesis. The unit of observation is the municipality. This study uses the weight of the number ofpeople aged 15 years and above in each municipality to consider diﬀerent population sizes across municipalities. High-skilled workers are defined as workers with at least 12 years of schooling in
educational institutions, such as vocational schools, colleges, and graduate schools, and working experience. Low-skilled workers are defined as workers with less than 12 years of schooling andless than a high school diploma.

State Fixed-Eﬀect

Average Years of Schooling(1)0.0018(0.0101)
0.1018***(0.0138)
Yes

Log(Population Density)

Explanatory Variables

No controls

Total Workers


Dependent Variable: Municipal Wage Fixed-Eﬀect, log w̄Typea

Table 5 Second-Step Estimation Results of Distinguishing Urban Wage Premium from Human Capital Externalities by Workers’ Skills

21527
0.7315−0.0033(0.0104)
0.0244***(0.0079)
0.0279***(0.0074)
Yes−0.0079(0.0126)
0.0765***(0.0116)0.0299***(0.0113)Yes

527
0.5857(2)(1)

Individual
Controls

527
0.7161−0.0029(0.0100)
0.0222***(0.0076)
0.0245***(0.0078)
Yes(3)

Individual &Firm Controls

481
0.2211−0.0131(0.0100)−0.0087(0.0141)0.0388***(0.0133)
Yes(4)

No controls

481
0.4692−0.0054(0.0082)
0.0141(0.0088)0.0293***(0.0087)
Yes(5)

Individual
Controls

High-Skilled Workers

481
0.4158−0.0060(0.0081)0.0081(0.0084)
0.0240***(0.0088)
Yes(6)

Individual &Firm Controls

527
0.6573−0.0055(0.0133)
0.0550***(0.0087)
0.0244***(0.0092)
Yes(7)

No controls

527
0.7547−0.0033(0.0112)0.0282***(0.0079)
0.0267***(0.0078)
Yes(8)

Individual
Controls

Low-Skilled Workers

527
0.7434−0.0029(0.0109)
0.0266***(0.0076)
0.0235***(0.0081)
Yes(9)

Individual &Firm Controls

Notes: Heteroskedasticity-consistent standard errors clustered at the municipal level are in parenthesis. The unit of observation is the municipality. This study uses the weight of the number ofpeople aged 15 years and above in each municipality to consider diﬀerent population sizes across municipalities. High-skilled workers are defined as workers with at least 12 years of schooling in
educational institutions, such as vocational schools, colleges, and graduate schools, and working experience. Low-skilled workers are defined as workers with less than 12 years of schooling andless than a high school diploma.

Number of Municipalities
Adjusted R2

State Fixed-Eﬀect

Log(Housing with H.E.A)

Average Years of Schooling

Log(Population Density)

Explanatory Variables

No controls

Total Workers


Dependent Variable: Municipal Wage Fixed-Eﬀect, log w̄Typea

Table 6 Robustness Check of the Second-Step Estimation ResultsAverage Years of Schooling

12

10

8

6

4

2
−2

0

2

4

6

8

10

Log(Population Density)

Figure 1 Spatial Sorting of Individuals’ Skills
Note: Author’s own creation using the 2005 and 2010 Population and Housing Censuses. The average
years of schooling are measured for people aged 15 years and above in each municipality. Population
density is measured as the total number of people aged 15 years and above divided by the total municipal
area (kilometer squared) within a 10 km radius from the centroid of each municipality. The bubble size
represents the number of people aged 15 years and above in each municipality. All variables are the averageof the 2005 and 2010 Population and Housing Censuses.

22(8.33,10.98] (271)(7.40,8.33] (273)(6.84,7.40] (272)(6.42,6.84] (272)(6.02,6.42] (271)(5.63,6.02] (275)(5.23,5.63] (273)(4.69,5.23] (272)[1.90,4.69] (273)
No data (2)(a) Average Years of Schooling

Figure 2(5.33,9.28] (272)(4.53,5.33] (271)(4.13,4.53] (274)(3.71,4.13] (271)(3.28,3.71] (274)(2.89,3.28] (271)(2.44,2.89] (274)(1.58,2.44] (272)[−2.32,1.58] (273)
No data (2)(b) Population Density

Geographic Distributions of Skilled Individuals

Note: Author’s own creation using the 2005 and 2010 Population and Housing Censuses. The average yearsof schooling are measured for people aged 15 years and above in each municipality. Population density ismeasured as the total number of people aged 15 years and above divided by the total municipal area (km2)
within a 10 km radius from the centroid of each municipality. All variables are the average of the 2005 and2010 Population and Housing Censuses.

23

Total Workers
2.5
2.5
2.5

1.5
1
.5

Density Estimate

Density Estimate

Density Estimate

2
2
1.5
1
.5

0
−1.5

−1

−.5

0

.5

1

1.5
1
.5

0
−1.5

1.5

2

Municipal Wage Fixed Effect

−1

−.5

0

.5

1

0
−1.5

1.5

Municipal Wage Fixed Effect(a) No Controls

−1

−.5

0

.5

1

1.5

Municipal Wage Fixed Effect(b) Individual Controls(c) Individual and Firm Controls

2.5

2

2

1.5
1
.5

2.5

Density Estimate

2.5

Density Estimate

Density Estimate

High-Skilled Workers

1.5
1
.5

0
−1.5

−1

−.5

0

.5

1

1.5
1
.5

0
−1.5

1.5

2

Municipal Wage Fixed Effect

−1

−.5

0

.5

1

0
−1.5

1.5

Municipal Wage Fixed Effect(d) No Controls

−1

−.5

0

.5

1

1.5

Municipal Wage Fixed Effect(e) Individual Controls(f) Individual and Firm Controls

Low-Skilled Workers
2.5

2.5
2.5

1.5
1
.5
0
−1.5

2

Density Estimate

Density Estimate

Density Estimate

2

1.5
1
.5

−1

−.5

0

.5

1

1.5

Municipal Wage Fixed Effect(g) No Controls

0
−1.5

1.5
1
.5

−1

−.5

0

.5

1

1.5

0
−1.5

Municipal Wage Fixed Effect(h) Individual Controls

Figure 3

2

−1

−.5

0

.5

1

1.5

Municipal Wage Fixed Effect(i) Individual and Firm Controls

Urban Wage Premium and Spatial Sorting

Notes: The red-solid and blue-dashed lines indicate the municipal wage fixed eﬀect distributions of municipalities with above- and below-median population densities, respectively. High-skilled workers are defined
as workers with at least 12 years of schooling in educational institutions, such as vocational schools, colleges, and graduate schools, and experience. Low-skilled workers are defined as workers with less than 12
years of schooling and less than a high school diploma.

24

Total Workers
2.5
2.5
2.5

1.5
1
.5

2

Density Estimate

Density Estimate

Density Estimate

2

1.5
1
.5

0
−1.5

−1

−.5

0

.5

1

1
.5

0
−1.5

1.5

2
1.5

Municipal Wage Fixed Effect

−1

−.5

0

.5

1

0
−1.5

1.5

Municipal Wage Fixed Effect(a) No Controls

−1

−.5

0

.5

1

1.5

Municipal Wage Fixed Effect(b) Individual Controls(c) Individual and Firm Controls

2.5

2.5

2

2

2

1.5
1
.5

Density Estimate

2.5

Density Estimate

Density Estimate

High-Skilled Workers

1.5
1
.5

0
−1.5

−1

−.5

0

.5

1

1
.5

0
−1.5

1.5

1.5

Municipal Wage Fixed Effect

−1

−.5

0

.5

1

0
−1.5

1.5

Municipal Wage Fixed Effect(d) No Controls

−1

−.5

0

.5

1

1.5

Municipal Wage Fixed Effect(e) Individual Controls(f) Individual and Firm Controls

Low-Skilled Workers
2.5
2.5

2.5

1.5
1

2
1.5
1

.5

.5

0
−1.5

0
−1.5

−1

−.5

0

.5

Municipal Wage Fixed Effect(g) No Controls

1

1.5

Density Estimate

Density Estimate

Density Estimate

2

2
1.5
1
.5

−1

−.5

0

.5

Municipal Wage Fixed Effect(h) Individual Controls

1

1.5

0
−1.5

−1

−.5

0

.5

1

1.5

Municipal Wage Fixed Effect(i) Individual and Firm Controls

Figure 4 Human Capital Externalities and Spatial Sorting
Notes: The red-solid and blue-dashed lines indicate the municipal wage fixed eﬀect distributions of municipalities with above- and below-median average years of schooling, respectively. High-skilled workers aredefined as workers with at least 12 years of schooling in educational institutions, such as vocational schools,
colleges, and graduate schools, and experience. Low-skilled workers are defined as workers with less than
12 years of schooling and less than a high school diploma.

25

Urban Wage Premium
1.5
Municipal Wage Fixed EffectMunicipal Wage Fixed Effect

1.5
1
.5
0
−.5
−1
−1.5

1
.5
0
−.5
−1
−1.5

−2

0

2

4

6

8

10

−2

0

2

Log(Population Density)

4

6

8

10

10

12

Log(Population Density)(a) High-Skilled Workers(b) Low-Skilled Workers

Human Capital Externalities
1.5
Municipal Wage Fixed EffectMunicipal Wage Fixed Effect

1.5
1
.5
0
−.5
−1
−1.5

1
.5
0
−.5
−1
−1.5

2

4

6

8

10

12

2

Average Years of Schooling

4

6

8

Average Years of Schooling(c) High-Skilled Workers(d) Low-Skilled Workers

Figure 5 Urban Wage Premium and Human Capital Externalities
Note: Author’s own creation using the municipal wage fixed eﬀect estimated in Columns (6) and (9) of
Table 3. The bubble size represents the number of people aged 15 years and above in each municipality.
High-skilled workers are defined as workers with at least 12 years of schooling in educational institutions,such as vocational schools, colleges, and graduate schools, and experience. Low-skilled workers are defined
as workers with less than 12 years of schooling and less than a high school diploma.

26

Supplementary Information
Distinguishing the Urban Wage Premium from Human Capital
Externalities: Evidence from Mexico
Keisuke Kondo∗
RIETI & RIEB, Kobe University

Version:April 11, 2024

Contents- Appendix A.
Controlling for Local Residence and Business Conditions ······················································· 2- Appendix B.
Population Potential ················································································································ 5∗Corresponding Author. Research Institute of Economy, Trade and Industry (RIETI). 1-3-1 Kasumigaseki,
Chiyoda-ku, Tokyo, 100–8901, Japan. (e-mail: kondo-keisuke@rieti.go.jp).

Supplementary Information

Appendix A.

Controlling for Local Residence and Business Conditions

As a robustness check, this study introduces the number of housing units with home electricalappliances to control residence and business conditions. This section provides additional ﬁgures
for the number of housing units with home electrical appliances.
Figure A.1 compares the distributions of municipal wage ﬁxed eﬀects between municipalities
with above-median (below-median) number of housing units with home electrical appliances byworker skill. In Panels (a) to (c) of Figure A1, both distributions do not overlap by controllingfor spatial sorting of worker and ß rm factors, implying that higher wages are paid to workers inbetter residence and business conditions.
Figure A.2 illustrates local wage premium for high- and low-skilled workers after controllingfor spatial sorting. For both workers, this study ﬁnds a clear positive relationship between theestimated municipal wage ﬁxed eﬀect and the number of housing units with home electrical appliances, implying that better residence and business locations matter when we consider urban wage
premium.
[Figures A.1 and A.2]

2

Supplementary Information

Total Workers
2.5
2.5
2.5

1.5
1

Density Estimate

Density Estimate

Density Estimate

2
2
1.5
1

.5

.5

0
−1.5

0
−1.5

−1

−.5

0

.5

1

1.5

2
1.5
1
.5

Municipal Wage Fixed Effect

−1

−.5

0

.5

1

0
−1.5

1.5

Municipal Wage Fixed Effect(a) No Controls

−1

−.5

0

.5

1

1.5

Municipal Wage Fixed Effect(b) Individual Controls(c) Individual and Firm Controls

2.5

2

2

1.5
1
.5

2.5
Density Estimate

2.5

Density Estimate

Density Estimate

High-Skilled Workers

1.5
1
.5

0
−1.5

−1

−.5

0

.5

1

1.5
1
.5

0
−1.5

1.5

2

Municipal Wage Fixed Effect

−1

−.5

0

.5

1

0
−1.5

1.5

Municipal Wage Fixed Effect(d) No Controls

−1

−.5

0

.5

1

1.5

Municipal Wage Fixed Effect(e) Individual Controls(f ) Individual and Firm Controls

Low-Skilled Workers
2.5
2.5

2.5

1.5
1

2

Density Estimate

Density Estimate

Density Estimate

2

1.5
1

.5

.5

0
−1.5

0
−1.5

−1

−.5

0

.5

Municipal Wage Fixed Effect(g) No Controls

1

1.5

2
1.5
1
.5

−1

−.5

0

.5

1

1.5

0
−1.5

Municipal Wage Fixed Effect(h) Individual Controls

−1

−.5

0

.5

1

1.5

Municipal Wage Fixed Effect(i) Individual and Firm Controls

Figure A.1 Location and Spatial Sorting
Notes: The red-solid and blue-dashed lines indicate the municipal wage ﬁxed eﬀect distributionsof municipalities with above- and below-median number of housing units with home electricalappliances, respectively. High-skilled workers are deﬁned as workers with at least 12 years ofschooling in educational institutions, such as vocational schools, colleges, and graduate schools,
and experience. Low-skilled workers are deﬁned as workers with less than 12 years of schoolingand less than a high school diploma.

3Supplementary Information

1.5
Municipal Wage Fixed EffectMunicipal Wage Fixed Effect

1.5
1
.5
0
−.5
−1
−1.5

1
.5
0
−.5
−1
−1.5

2

4

6

8

10

12

2

Log(Housing with HEA)

4

6

8

10

12

Log(Housing with HEA)(a) Low-Skilled Workers(b) High-Skilled Workers

Figure A.2 Wage Premium and Local Residence and Business Condition
Note: Author’s own creation using the municipal wage ﬁxed eﬀect estimated in Columns (6)and (9) of Table 3. The bubble size represents the number of people aged 15 years and above ineach municipality. High-skilled workers are deﬁned as workers with at least 12 years of schooling in
educational institutions, such as vocational schools, colleges, and graduate schools, and experience.
Low-skilled workers are deﬁned as workers with less than 12 years of schooling and less than a highschool diploma.

4Supplementary Information

Appendix B.

Population Potential

Although earlier studies uses population/employment density or population size, these variables
measured at the administrative unit ignores commuting ﬂows. People cross municipal borders towork and shop. Economic activities are not exclusively conducted within deﬁned administrative
areas. Therefore, This study additionally considers population size including the surrounding
municipalities.
In the literature of economic geography, Stewart (1947a,b) proposed the notion of “potentialof population”, inspired by a concept in physics.1 Following Stewart (1947a,b), the general formof the population potential for municipality c is

PPa =

R

b=1

−δ
Pb Dab,(B.1)

where Pa is the population of municipality a, Dab is the great-circle distance between municipalities
a and b, δ is the distance-decay parameter, and R is the number of municipalities. This variable
can capture a potential size of population considering the surrounding municipalities. This study
uses δ = 2 for the distance decay parameter as a benchmark in the regression analysis.
The regression takes the following form:
log(w̄aType ) = Const + λ log(PPa ) + φEduca + εa ,

(B.2)



where log(w̄aType ) ∈ log(w̄aN ), log(w̄aI ), log(w̄aIF ) is the wage premium in municipality a, Const is
a constant term, Densc is the population density of municipality a, Educc is the average years ofschooling of municipality a, and εa is an error term.
Figure B.1 depicts the geographical distributions of the population potential. The Mexico City
metropolitan area shows a higher value of population potential.
Figure B.2 compares the distributions of municipal wage ﬁxed eﬀects between municipalities
with above-median (below-median) population potential by worker skill
Figure B.3 illustrates urban wage premium based on the population potential after controllingfor spatial sorting.
1

Stewart (1941) deﬁnes “potential of population” as the population divided by distance. The total potentials
of population are deﬁned as the summed potentials of population for each region. We call them as “population
potential” in this paper.

5Supplementary Information[Figures B.1–B.3]
Tables B.1–B.3 present the estimation results of the second-step regression by worker skill.
Instead of the population density, the population potential is used. We can ﬁnd that the estimation
results of the population potential are almost the same as those of the population density.
[Tables B.1–B.3]

6

7

527
0.3971

0.0644***(0.0119)
Yes

(1)

527
0.63760.0350***(0.0076)
Yes(2)

Individual
Controls

527
0.63290.0313***(0.0073)
Yes(3)

Individual& FirmControls

481
0.19480.0120*(0.0063)
Yes(4)

No controls

481
0.40840.0254***(0.0054)
Yes(5)

Individual
Controls

481
0.37880.0169***(0.0050)
Yes(6)

Individual& FirmControls

527
0.51750.0478***(0.0088)
Yes(7)

No controls

527
0.65970.0357***(0.0079)
Yes(8)

Individual
Controls

527
0.65700.0325***(0.0075)
Yes(9)

Individual& FirmControls

Low-Skilled Workers

Notes: Heteroskedasticity-consistent standard errors clustered at the municipal level are in the parenthesis. The unit of observation is the municipality. This study uses theweight as the number of people aged 15 years and above in each municipality to consider diﬀerent population sizes across municipalities. High-skilled workers are deﬁned asworkers with at least 12 years of schooling and more experiences in educational institutions, such as vocational schools, colleges, and graduate schools. Low-skilled workers
are deﬁned as workers with less than 12 years of schooling and less than a high school diploma.

Number of Municipalities
Adjusted R2

State Fixed-Eﬀect

Log(Population Potential, δ = 2)

Explanatory Variables

No controls

High-Skilled Workers


Dependent Variable: Municipal Wage Fixed-Eﬀect, log w̄aType

Second-Step Estimation Results of Wage Premium by Worker Skill

Total Workers

Table B.1

Supplementary Information

8527
0.57900.0040(0.0092)
0.1012***(0.0116)
Yes

(1)

527
0.71940.0049(0.0093)0.0505***(0.0086)
Yes(2)

Individual
Controls

527
0.70570.0044(0.0088)0.0450***(0.0082)
Yes(3)

Individual& FirmControls

481
0.2064−0.0021(0.0052)
0.0248**(0.0110)Yes(4)

No controls

481
0.45740.0018(0.0055)
0.0415***(0.0088)
Yes(5)

Individual
Controls

481
0.4074−0.0002(0.0055)
0.0300***(0.0082)
Yes(6)

Individual& FirmControls

527
0.65120.0021(0.0104)
0.0766***(0.0104)
Yes(7)

No controls

527
0.74430.0038(0.0101)
0.0534***(0.0084)
Yes(8)

Individual
Controls

527
0.73460.0034(0.0095)
0.0488***(0.0081)
Yes(9)

Individual& FirmControls

Low-Skilled Workers

Notes: Heteroskedasticity-consistent standard errors clustered at the municipal level are in the parenthesis. The unit of observation is the municipality. This study uses theweight as the number of people aged 15 years and above in each municipality to consider diﬀerent population sizes across municipalities. High-skilled workers are deﬁned asworkers with at least 12 years of schooling and more experiences in educational institutions, such as vocational schools, colleges, and graduate schools. Low-skilled workers
are deﬁned as workers with less than 12 years of schooling and less than a high school diploma.

Number of Municipalities
Adjusted R2

State Fixed-Eﬀect

Average Years of Schooling

Log(Population Potential, δ = 2)

Explanatory Variables

No controls

High-Skilled Workers


Dependent Variable: Municipal Wage Fixed-Eﬀect, log w̄aType

Second-Step Estimation Results of Wage Premium by Worker Skill

Total Workers

Table B.2

Supplementary Information

9527
0.58480.0015(0.0093)0.0739***(0.0106)
0.0253***(0.0089)
Yes

(1)

527
0.73140.0024(0.0090)0.0227***(0.0076)
0.0257***(0.0062)
Yes(2)

Individual
Controls

527
0.71600.0022(0.0086)0.0207***(0.0072)
0.0226***(0.0066)
Yes(3)

Individual& FirmControls

481
0.2184−0.0052(0.0050)−0.0101(0.0135)
0.0323***(0.0107)
Yes(4)

No controls

481
0.4685−0.0007(0.0055)
0.0130(0.0087)
0.0264***(0.0076)
Yes(5)

Individual
Controls

481
0.4149−0.0021(0.0057)
0.0074(0.0082)
0.0210***(0.0078)
Yes(6)

Individual& FirmControls

High-Skilled Workers

527
0.65670.0000(0.0103)
0.0536***(0.0081)0.0214***(0.0066)
Yes(7)

No controls

527
0.75440.0014(0.0097)
0.0268***(0.0077)0.0247***(0.0064)
Yes(8)

Individual
Controls

527
0.74320.0013(0.0093)0.0254***(0.0073)
0.0217***(0.0067)
Yes(9)

Individual& FirmControls

Low-Skilled Workers

Notes: Heteroskedasticity-consistent standard errors clustered at the municipal level are in the parenthesis. The unit of observation is the municipality. This study uses theweight as the number of people aged 15 years and above in each municipality to consider diﬀerent population sizes across municipalities. H.E.A. stands for Home Electrical
Appliances. High-skilled workers are deﬁned as workers with at least 12 years of schooling and more experiences in educational institutions, such as vocational schools, colleges,
and graduate schools. Low-skilled workers are deﬁned as workers with less than 12 years of schooling and less than a high school diploma.

Number of Municipalities
Adjusted R2

State Fixed-Eﬀect

Log(Housing with H.E.A)

Average Years of Schooling

Log(Population Potential, δ = 2)

Explanatory Variables

No controls

Total Workers


Dependent Variable: Municipal Wage Fixed-Eﬀect, log w̄aType

Table B.3 Robustness Check of Second-Step Estimation Results

Supplementary Information

Supplementary Information(12.84,14.41] (272)(12.58,12.84] (272)(12.41,12.58] (273)(12.28,12.41] (272)(12.15,12.28] (273)(12.03,12.15] (272)(11.88,12.03] (273)(11.54,11.88] (272)[10.88,11.54] (273)
No data (2)(a) Population Potential (δ = 1)
(9.19,13.36] (272)(8.37,9.19] (272)(7.99,8.37] (273)(7.64,7.99] (272)(7.34,7.64] (273)(7.10,7.34] (272)(6.81,7.10] (273)(6.28,6.81] (272)[4.45,6.28] (273)
No data (2)(b) Population Density (δ = 2)
(7.34,13.10] (272)(6.05,7.34] (272)(5.35,6.05] (273)(4.85,5.35] (272)(4.43,4.85] (273)(3.92,4.43] (272)(3.34,3.92] (273)(2.27,3.34] (272)[−1.17,2.27] (273)
No data (2)(c) Population Density (δ = 3)

Figure B.1 Geographic Distributions of Population Potential
Note: Author’s creation using the 2005 and 2010 Population and Housing Censuses. Population
density is measured the total number of people aged 15 years and above divided by the total
municipal area (km2 ) within a 10 km radius from the centroid of each municipality. All variables
are averaged between the 2005 and 2010 Population and Housing Censuses.

10Supplementary Information

Total Workers
2.5
2.5

2.5

1.5
1

2

Density Estimate

Density Estimate

Density Estimate

2

1.5
1

.5

.5

0
−1.5

0
−1.5

−1

−.5

0

.5

1

1.5

2
1.5
1
.5

Municipal Wage Fixed Effect

−1

−.5

0

.5

1

0
−1.5

1.5

Municipal Wage Fixed Effect(a) No Controls

−1

−.5

0

.5

1

1.5

Municipal Wage Fixed Effect(b) Individual Controls(c) Individual and Firm Controls

2.5

2.5

2

2

2

1.5
1
.5

Density Estimate

2.5

Density Estimate

Density Estimate

High-Skilled Workers

1.5
1
.5

0
−1.5

−1

−.5

0

.5

1

1
.5

0
−1.5

1.5

1.5

Municipal Wage Fixed Effect

−1

−.5

0

.5

1

0
−1.5

1.5

Municipal Wage Fixed Effect(d) No Controls

−1

−.5

0

.5

1

1.5

Municipal Wage Fixed Effect(e) Individual Controls(f ) Individual and Firm Controls

Low-Skilled Workers
2.5

2.5

1.5
1
.5
0
−1.5

2.5

2

Density Estimate

Density Estimate

Density Estimate

2

1.5
1
.5

−1

−.5

0

.5

1

1.5

Municipal Wage Fixed Effect(g) No Controls

Figure B.2

2
1.5
1
.5

0
−1.5

−1

−.5

0

.5

Municipal Wage Fixed Effect(h) Individual Controls

1

1.5

0
−1.5

−1

−.5

0

.5

1

1.5

Municipal Wage Fixed Effect(i) Individual and Firm Controls

Population Potential and Spatial Sorting

Notes: The red-solid and blue-dashed lines indicate the municipal wage premium distributionsof municipalities with above-median and below-median population density, respectively. Highskilled workers are deﬁned as workers with at least 12 years of schooling and more experiences in
educational institutions, such as vocational schools, colleges, and graduate schools. Low-skilledworkers are deﬁned as workers with less than 12 years of schooling and less than a high school
diploma.

11

Supplementary Information

1.5
Municipal Wage Fixed EffectMunicipal Wage Fixed Effect

1.5
1
.5
0
−.5
−1
−1.5

1
.5
0
−.5
−1
−1.5

4

6

8

10

12

14

4

6

8

10

12

Log(Population Potential, delta=2)

Log(Population Potential, delta=2)(a) Low-Skilled Workers(b) High-Skilled Workers

Figure B.3

14

Wage Premium and Population Potential

Note: Author’s creation using the municipal wage premium estimated in Column (6) and (9)of Table 3. The bubble size represents the number of people aged 15 years and above in eachmunicipality. High-skilled workers are deﬁned as workers with at least 12 years of schooling andmore experiences in educational institutions, such as vocational schools, colleges, and graduate
schools. Low-skilled workers are deﬁned as workers with less than 12 years of schooling and lessthan a high school diploma.

12Supplementary Information

References
Stewart JQ (1941) An inverse distance variation for certain social inﬂuences. Science, New Series
93(2404):89–90. https://doi.org/10.1126/science.93.2404.89
Stewart JQ (1947a) Empirical mathematical rules concerning the distribution and equilibrium of
population. Geographical Review 37(3):461–475. https://doi.org/10.2307/211132
Stewart JQ (1947b) Suggested principles of ‘social physics’. Science, New Series 106(1748):179–180.
https://doi.org/10.1126/science.106.2748.179

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