Labor market consequences of trade
openness and competition
in foreign markets:
the case of Mexico
Daniel Chiquiar
Enrique Covarrubias
Alejandrina Salcedo
November 2nd, 2012
The views and conclusions presented in this study are exclusively the responsibility of the authors and do not necessarily reflect those of Banco de Mexico.
Index
1. Introduction
2. Regional exposure
competition
to
trade
openness
and
3. Relationship between exposure measures and
Mexican labor market indicators
4. Econometric analysis
a) NAFTA
b) Chinese competition
5. Conclusions
2
1. Introduction
 This paper analyzes the labor market consequences of trade
liberalization and of competition in international markets, for the
Mexican case.
 In particular, we look at the consequences of:
o The introduction of NAFTA in 1994, which increased Mexican
exports to the US.
o The accession of China to the WTO in 2001, which increased
Chinese exports to the US, substituting Mexican products in this
market.
3
1. Introduction
Market Share in US Imports
Percentage
25%
Mexico
China
20%
15%
10%
5%
China's accession to WTO
NAFTA
0%
1993
1995
1997
1999
2001
2003
2005
2007
2009
Source: Comtrade database, United Nations.
4
1. Introduction
 Given its initial comparative advantages, Mexico responded to trade
integration through NAFTA mostly by specializing in unskilled laborintensive processes.
• NAFTA boosted the formation of regional production-sharing
arrangements between Mexico and the US.
• Maquiladoras are a clear example of such arrangements. Moreover, they
represent the increase in specialization of Mexican firms in unskilled labor
intensive assembly activities.
 The accession of China to the WTO increased competition for Mexican
exports in the US market.
• There is a large overlap in the kind of products that both Mexico and China
have specialized in, and therefore their export mixes are very similar.
• Consequently, the increase in Chinese exports had a negative effect on
Mexico’s market share in US imports.
 Mexican labor markets could have benefited from NAFTA, while
increased Chinese competition could have had a negative impact.
5
1. Introduction
 We follow Autor, Dorn and Hanson (2012), who estimate the
effect that the increase in US imports from China had on the US
labor market.
 To identify such effect, they exploit regional variation in the
exposure of local US labor markets to the increase in imports
from China.
• Regions whose activities were more concentrated on the
production of goods that experienced an important increase in
imports would have a greater exposure, and their labor markets
could have been more affected.
• They use an instrumental variables approach to identify a causal
effect.
6
1. Introduction
 Following their methodology, in this paper we estimate the effect
of trade openness (NAFTA) and of the increase in Chinese
competition in US markets on the Mexican labor market.
 With this purpose, we estimate two measures of exposure:
o Exposure to trade openness.
o Exposure to Chinese competition in US markets.
 Using variation at the regional level (metropolitan areas), we
estimate the impact of a higher exposure level on labor market
indicators in the last two decades.
 We implement an instrumental variables approach too.
7
1. Introduction
 We find significant effects of NAFTA and Chinese competition in
US markets on the Mexican labor market.
Effects on the Mexican Labor Market of NAFTA and Competition from China
NAFTA
China
(1993-2000)
(2000-2009)
Unemployment
Decrease
Increase
Employment
Some evidence of
an increase
Decrease
Wages
Increase
Decrease
8
Index
1. Introduction
2. Regional exposure to trade openness and
competition
3. Relationship between exposure measures and
Mexican labor market indicators
4. Econometric analysis
a) NAFTA
b) Chinese competition
5. Conclusions
9
2. Regional exposure to trade openness and competition
Measures of exposure
Trade openness due to NAFTA
Trade competition from China in US markets
(1993-2000)
(2000-2009)
∆𝑂𝑃𝑊𝑖
𝑈𝑆
=
𝑗
𝐸𝑖𝑗 ∆𝑋𝑗
𝐸𝑗 𝐸𝑖
𝑀𝑥𝑈𝑆
∆𝐼𝑃𝑊𝑖
𝑈𝑆
=
𝑗
𝐸𝑖𝑗 ∆𝑀𝑗
𝐸𝑗 𝐸𝑖
𝑈𝑆𝐶ℎ𝑖
where:
𝑀𝑥𝑈𝑆
•
∆𝑋𝑗
•
∆𝑀𝑗
•
𝐸𝑖𝑗 is the number of workers in sector j in region i in Mexico at baseline.
•
•
𝐸𝑖 is the number of workers in region i in Mexico at baseline.
𝐸𝑗 is the total number of workers in sector j in Mexico at baseline.
𝑈𝑆𝐶ℎ𝑖
is the change in Mexican exports to the US in sector j.
is the change in US imports from China in sector j.
10
2. Regional exposure to trade openness and competition
 We base the analysis on metropolitan areas.
• NAFTA effect: 37 metro areas that comprise 161 municipalities and represent
around 30 percent of the population.
• China effect: 56 metro areas that comprise 344 municipalities and
represent around 60 percent of the population.
 We distinguish between metropolitan areas in border and non
border states.
 The main data sources for the analysis are the employment
survey, the economic censuses and UN Comtrade.
11
2. Regional exposure to trade openness and competition
Nafta effect: Map of Metropolitan Areas
12
2. Regional exposure to trade openness and competition
Chinese competition effect: Map of Metropolitan Areas
13
2. Regional exposure to trade openness and competition
Exposure to Trade Openness (NAFTA)
∆𝐎𝐏𝐖𝒊 𝑼𝑺
70
60
Cities in border states
50
Cities in non border states
*
40
30
20
10
****
Cities specialized in the automobile
industry1/
***
*
Matamoros
Cd. Juarez
Tijuana
Chihuahua
Tampico
Saltillo
Toluca
Aguascalientes
Nuevo Laredo
Cuernavaca
Puebla
Hermosillo
Torreon
Monterrey
Guadalajara
Veracruz
Celaya
Queretaro
San Luis Potosi
Mexico City
Monclova
Merida
Culiacan
Coatzacoalcos
Orizaba
Leon
Durango
Morelia
Zacatecas
Colima
Tuxtla Gutierrez
Oaxaca
Campeche
Tepic
Villahermosa
Manzanillo
Acapulco
0
1/ The regions specialized in the automobile industry are those for which this industry represents at least 29% of its exposure to trade openness.
14
Tijuana
Juárez
Reynosa-Río Bravo
Mexicali
Matamoros
Nuevo Laredo
Guaymas
Tehuantepec
Guadalajara
Chihuahua
Tehuacán
Piedras Negras
Tlaxcala-Apizaco
Monterrey
Aguascalientes
Saltillo
Moroleón-Uriangato
La Laguna
Ocotlán
San Francisco del Rincón
Querétaro
San Luis Potosí-SGS
Toluca
Puebla-Tlaxcala
León
Pachuca
Monclova-Frontera
Valle de México
Cuernavaca
Mérida
Zamora-Jacona
Tulancingo
La Piedad-Pénjamo
Córdoba
Orizaba
Tecomán
Coatzacoalcos
Tampico
Minatitlán
Morelia
Tula
Zacatecas-Guadalupe
Veracruz
Cuautla
Xalapa
Oaxaca
Acapulco
Rioverde-Ciudad Fernández
Colima-Villa de Álvarez
Poza Rica
Villahermosa
Tepic
Tuxtla Gutiérrez
Acayucan
Cancún
Puerto Vallarta
2. Regional exposure to trade openness and competition
Exposure to Chinese Competition in US markets
∆𝑰𝑷𝑾𝒊 𝑼𝑺
90
80
70
Metropolitan areas in border state
60
Metropolitan areas in non border state
50
40
30
20
10
0
15
2. Regional exposure to trade openness and competition
Exposure to trade openness (NAFTA) (∆𝑂𝑃𝑊𝑖 𝑈𝑆 ) vs.
exposure to Chinese competition in US markets (∆𝐼𝑃𝑊𝑖 𝑈𝑆 )
70
Specialized in automobile industry
border
Other
60
Specialized in automobile industry
non-border
Other
∆𝑂𝑃𝑊𝑖 𝑈𝑆
50
40
30
20
10
0
0
10
20
30
40
50
60
70
80
90
∆𝐼𝑃𝑊𝑖 𝑈𝑆
16
2. Regional exposure to trade openness and competition
 In border cities, the 5 industries (at 3 digit SITC) that contribute the most
to the exposure measures fall in the following categories (at 2 digits):
3-digit SITC Industries that Contribute the Most to each Exposure Measure
Grouped in 2-digit SITC Categories
NAFTA 1/
China 2/
 Office machines and automatic
processing machines (75)
1/
2/
data-
 Office machines and automatic
processing machines (75)
data-
 Telecommunications and sound-recording
and reproducing apparatus and equipment
(76)
 Telecommunications and sound-recording
and reproducing apparatus and equipment
(76)
 Electrical machinery,
appliances (77)
 Electrical machinery,
appliances (77)
apparatus
and
apparatus
and
 Power-generating machinery and equipment
(71)
 General industrial machinery and equipment
(74)
 Road vehicles (78)
 Miscellaneous manufactured articles (89)
US
5 main sectors that contribute to ∆OPW i in 8 of the 9 cities in border states.
5 main sectors that contribute to ∆IPW i U S in 11 of the 12 metropolitan zones in border states.
17
2. Regional exposure to trade openness and competition
 The industries that allowed border regions to benefit from NAFTA
are the kind of sectors in which Mexico has lost comparative
advantage with respect to China, except for the automobile
industry.
 On the contrary, cities in non-border states do not show a clear
specialization pattern.
18
2. Regional exposure to trade openness and competition
Revealed Comparative Advantage (RCA) of China and Sectorial Specialization
Index (SSI) of Mexican Metropolitan Zones
RCA of China vs. SSI of Metropolitan Zones in
Border States
RCA of China vs. SSI of Metropolitan Zones in
Nonborder States
(1999, SITC 2 digits)
(1999, SITC 2 digits)
7
7
Spearman correlation
coeff. = 0.3263**
6
6
5
5
4
RCA China 1999
RCA China 1999
Spearman correlation
coeff. = -0.3263**
89 Miscellaneous
manufact.
3
2
76 Telecomm.
1
74 Industrial
mach. and equip.
75 Computers
77 Electrical
4
3
82
2
66
0
0.5
1
1.5
2
SSI Border MZ 1999
2.5
3
62
03
58
0
0
65
63
1
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
SSI Nonborder MZ 1999
Source: China RCA: Comtrade database, United Nations.
SSI index: Mexican Economic Census 1999, INEGI.
19
Index
1. Introduction
2. Regional exposure
competition
to
trade
openness
and
3. Relationship between exposure measures and
Mexican labor market indicators
4. Econometric analysis
a) NAFTA
b) Chinese competition
5. Conclusions
20
3. Relationship between exposure measures and Mexican labor market indicators
Unemployment in Mexico and exposure to NAFTA openness (∆𝑂𝑃𝑊𝑖 𝑈𝑆 )
Change in unemployed population as a
proportion of the labor force vs. exposure
0.02
1.5
Hermosillo
1
Nuevo Laredo
0.5
Tijuana
0
Saltillo
Matamoros
Chihuahua
-0.5
Cd. Juárez
Monterrey
-1
Monclova
correlation=-0.3691**
(Unemployed pop/Labor Force)2000.-(Unemployed pop./Labor force)1993
ln(Unemployed population2000)-ln(Unemployed population 1993)
Logarithmic differences in unemployed
population vs. exposure
Hermosillo
0.01
Nuevo Laredo
0
Tijuana
-0.01
Cd. Juárez
-0.02
Saltillo
Monterrey
-0.03
Matamoros
-0.04
-0.05
-0.06
Monclova
-1.5
0
20
40
60
ΔOPW US
80
Chihuahua
correlation= -0.4322***
-0.07
0
20
40
60
80
ΔOPW US
Source: ENEU (1993 and 2000), Economic Census (1994), and UN Comtrade.
21
3. Relationship between exposure measures and Mexican labor market indicators
Employment in Mexico and exposure to NAFTA openness (∆𝑂𝑃𝑊𝑖 𝑈𝑆 )
Logarithmic differences of employed population vs. exposure measure
All sectors
Manufacturing
0.6
Non-manufacturing
0.6
0.8
ln(Manufacturing employment2000)-ln(Manufacturing employment1993)
ln(Total employment2000)-ln(Total employment1993)
0.5
Hermosillo
0.4
Cd. Juárez
Tijuana
Saltillo
Chihuahua
0.3
0.2
Monterrey
0.1
correlation= 0.2980*
0
Monclova
Saltillo
0.7
Matamoros
Hermosillo
Chihuahua
0.6
Nuevo
Laredo
Cd. Juárez
0.5
Tijuana
0.4
Monterrey
0.3
0.2
Monclova
correlation=0.4182**
0.1
0
0
20
40
ΔOPW US
60
80
Nuevo
Laredo
0.5
Matamoros
0.4
Hermosillo
Tijuana
0.3
ChihuahuaCd. Juárez
Saltillo
0.2
0.1
Monterrey
0
correlation=0.0315
-0.1
Monclova
-0.2
-0.1
-0.1
ln(Non Manuf. employment2000)-ln(Non Manuf. employment1993)
Matamoros
Nuevo
Laredo
0
20
40
ΔOPW US
60
80
0
20
40
ΔOPW US
60
80
Source: ENEU (1993 and 2000), Economic Census (1994), and UN Comtrade.
22
3. Relationship between exposure measures and Mexican labor market indicators
Wages in Mexico and exposure to NAFTA openness (∆𝑂𝑃𝑊𝑖 𝑈𝑆 )
Logarithmic differences in wages vs. exposure measure
Manufacturing
All sectors
Non-manufacturing
0.4
0.3
0.3
correlation= 0.4649***
correlation= 0.3419**
ln(Wages all sectors 2000)-ln(Wages all sectors 1993)
0.2
Tijuana
0.1
Monclova
Monterrey
Saltillo
0
Nuevo
Laredo
Chihuahua
-0.1
Matamoros
-0.2
Cd. Juárez
-0.3
40
ΔOPW US
Nuevo
Laredo
Tijuana
0.1
Monterrey
0
Saltillo
Chihuahua
Monclova
-0.1
Matamoros
-0.2
Hermosillo
-0.3
Cd. Juárez
-0.4
Tijuana
0.2
Monclova
0.1
Monterrey
Saltillo
0
Nuevo
Laredo
-0.1
Chihuahua
Matamoros
-0.2
Cd. Juárez
-0.3
Hermosillo
-0.4
20
0.2
-0.5
Hermosillo
0
ln(Wages manufacturing2000)-ln(Wages manufacturing1993)
0.3
ln(Wages non manufacturing2000)-ln(Wages non manufacturing 1993)
correlation= 0.3675**
60
80
-0.4
-0.6
0
20
40
ΔOPW US
60
80
0
20
40
ΔOPW US
60
80
Source: ENEU (1993 and 2000), Economic Census (1994), and UN Comtrade.
23
3. Relationship between exposure measures and Mexican labor market indicators
Unemployment in Mexico and exposure to Chinese competition (∆𝐼𝑃𝑊𝑖 𝑈𝑆 )
Logarithmic differences in unemployed
population vs. index of exposure
Change in unemployed population as a
proportion of the labor force vs. index of
exposure
0.1
Unemp. pop./Labor Force 2009 - Unemp. pop./Labor force 2000
ln(Unemployed pop.2009) - ln (Unemployed pop. 2000)
4
correlation= 0.3076**
3
Juárez
Nuevo Laredo
2
Saltillo
MonclovaMonterrey
Frontera
Chihuahua
1
Matamoros
Tijuana
Mexicali
Reynosa-Río
Piedras Negras
0
Guaymas
-1
-2
0
20
40
60
80
100
ΔIPWUS
Juárez
Matamoros
0.08
Saltillo
Nuevo
MonclovaLaredo
Frontera
Piedras Negras
Chihuahua
0.06
0.04
Monterrey
Tijuana
Guaymas
Mexicali
0.02
Reynosa-Río Bravo
0
correlation= 0.5225***
-0.02
-0.04
0
20
40
60
80
100
ΔIPWUS
Source: ENE and ENOE (2000 and 2009), Economic Census (1994), and UN Comtrade.
24
3. Relationship between exposure measures and Mexican labor market indicators
Employment in Mexico and exposure to Chinese competition (∆𝐼𝑃𝑊𝑖 𝑈𝑆 )
Logarithmic differences of employed population vs. exposure measure
All sectors
2.5
correlation = -0.0743
1
Mexicali
0.5
Monclov a-Frontera
Monterrey
Rey nosaRío Brav o
Chihuahua
Nuev o Laredo
0
Juárez
Tijuana
Matamoros
-0.5
Piedras Negras
-1
Guay mas
ln(Manuf. employment2009) - ln (Manuf. employment2000)
1.5
0.5
Monclov a-Frontera
0
Saltillo
Chihuahua
Monterrey
Nuev o
Laredo
-0.5
Mexicali
Tijuana
Rey nosa-Río Brav o
Matamoros
Juárez
correlation = -0.2702**
-1
Piedras Negras
-1.5
Guay mas
-2
-1.5
0
20
40
60
80
100
0
20
40
ΔIPWUS
60
ΔIPWUS
80
100
ln(Non manuf. employment2009) - ln (Non manuf. employment.2000)
1
2
ln(Total employment2009) - ln (Total employment2000)
Non-manufacturing
Manufacturing
2
correlation= -0.0045
1.5
1
Monclov aFrontera
0.5
Rey nosa-Río Brav o
Mexicali
Nuev o Laredo
Juárez
Tijuana
Matamoros
0
-0.5
Piedras Negras
-1
Guay mas
-1.5
0
20
40
60
80
100
ΔIPWUS
Source: ENE and ENOE (2000 and 2009), Economic Census (1994), and UN Comtrade.
25
3. Relationship between exposure measures and Mexican labor market indicators
Wages in Mexico and exposure to Chinese competition (∆𝐼𝑃𝑊𝑖 𝑈𝑆 )
Logarithmic differences in wages vs. exposure measure
Manufacturing
All sectors
1.2
0.8
correlation= -0.4919***
0.6
0.4
0.2
Rey nosa-Río Brav o
0
Chihuahua
Matamoros
Saltillo
Piedras
Monterrey Negras
-0.2
Juárez
Nuev o Laredo
Monclov a
-Frontera
Mexicali
Guay mas
-0.4
Tijuana
-0.6
0
20
40
60
80
100
1
correlation= -0.1054
0.6
0.4
Rey nosa-Río Brav o
0.2
Piedras
Negras
Juárez
Monclov a-Frontera
0
Guay mas
Matamoros
Nuev o
Laredo
-0.2
Mexicali
Tijuana
-0.4
-0.6
ln(Wages non manuf.2009) - ln (Wages non manuf.2000)
0.8
ln (Wages manufacturing2009) - ln (Wages manufacturing2000)
ln(Wages all sectors 2009) - ln (Wages all sectors 2000)
1
Non-manufacturing
0.8
correlation= -0.5729***
0.6
0.4
0.2
Rey nosa-Río Brav o
0
Chihuahua
Piedras
Negras
-0.2
Monclov aFrontera
-0.4
Matamoros
Nuev o
Laredo
Juárez
Mexicali
Guay mas
-0.6
Tijuana
-0.8
-0.8
0
20
40
ΔIPW
US
60
ΔIPWUS
80
100
0
20
40
60
80
100
ΔIPWUS
Source: ENE and ENOE (2000 and 2009), Economic Census (1994), and UN Comtrade.
26
Index
1. Introduction
2. Regional exposure
competition
to
trade
openness
and
3. Relationship between exposure measures and
Mexican labor market indicators
4. Econometric analysis
a) NAFTA
b) Chinese competition
5. Conclusions
27
4. Econometric analysis
Estimation strategy to identify the effect of trade exposure
on Mexican labor market variables
NAFTA
Regression
equation
Δy i = α + βΔOPW i
Period
OC
other countries to the US
Δy i = α + βΔIPW i
US
+ γX i + e i
2000-2009
ΔIPW i
US demand to other countries
(ΔX
Additional
Controls X i
+ γX i + e i
1993-2000
ΔOPW i
Instrument
US
China
)
Proportion of working women
Proportion of the population with high school
education
OC
Export capacity of China
(ΔM other countries from China )
Proportion of working women
Proportion of the population with high school
education
28
Index
1. Introduction
2. Regional exposure
competition
to
trade
openness
and
3. Relationship between exposure measures and
Mexican labor market indicators
4. Econometric analysis
a) NAFTA
b) Chinese competition
5. Conclusions
29
4. Econometric analysis: NAFTA openness
Estimation of the effect of NAFTA openness exposure
on unemployment
Dependent variable:
unemployment
us
ΔOPW
NAFTA
s.e.
p-value
Additional controls
Observations
R-squared
us
β x (ΔOPW
NAFTA
Gap) x 100
Log differences
Change in variable
as a ratio of
working-age
population
Change in variable
as a ratio of labor
force
(1)
(2)
(3)
-0.0173**
(0.00656)
0.0127

-0.000401***
(0.000125)
0.00292

-0.000712***
(0.000214)
0.00213

37
0.264
37
0.301
37
0.286
-32.53%
-0.75 pp
-1.34 pp
All regressions are estimated by instrumental variables and in all cases the proportion of women who work and the proportion of
the population with high school education are included as controls .
Standard errors given in parenthesis.
*** p<0.01, ** p<0.05, * p<0.1, a p<0.15
30
4. Econometric analysis: NAFTA openness
Estimation of the effect of NAFTA openness exposure
on unemployment
Heterogeneous effects
Log differences
Change in variable
as a ratio of
working-age
population
Change in variable
as a ratio of labor
force
(1)
(2)
(3)
-0.0142**
(0.00553)
0.0148
-0.000330***
(0.000105)
0.00353
-0.000581***
(0.000179)
0.00273
-0.0305*
(0.0172)
0.0856
-0.000706**
(0.000326)
0.0377
-0.00127**
(0.000556)
0.0289



37
0.280
37
0.320
37
0.309
-28.94%
-0.67 pp
-1.18 pp
-23.86%
-0.55 pp
-0.99 pp
Dependent variable: unemployment
us
ΔOPW
NAFTA*dborderauto
s.e.
p-value
us
ΔOPW
NAFTA*drest
s.e.
p-value
Additional controls
Observations
R-squared
us
β borderauto x (ΔIPW
us
β rest x (ΔIPW
NAFTA
NAFTA
Gapborderauto) x 100
Gaprest) x 100
All regressions are estimated by instrumental variables and in all cases the proportion of women who work and the proportion of the population
with high school education are included as controls .
Standard errors given in parenthesis.
*** p<0.01, ** p<0.05, * p<0.1
31
4. Econometric analysis: NAFTA openness
Effect of exposure on unemployment rates
Mean unemployment rate
Metro areas in border states or specialized in auto industry
Other metro areas
Difference
1993
2000
Difference
4.07%
3.31%
2.37%
2.76%
-1.70 pp
-0.54 pp
-1.16 pp
Mean ΔOPW
Metro areas in border states or specialized in auto industry
Other metro areas
Difference (gap)
26.08
6.20
19.88
Unemployment explained by a greater exposure in metro areas in border states or specialized in auto industry
Coefficient
-0.000712
Explained effect (coefficient x gap)
-1.42 pp
32
4. Econometric analysis: NAFTA openness
Estimation of the effect of NAFTA openness exposure
on employment
Dependent variable: logarithmic differences of employed population
(1)
Manufacturing
employment
(2)
Non-manufacturing
employment
(3)
0.000987
(0.00169)
0.564
0.00193
(0.00289)
0.509
1.86%
3.63%
Total employment
ΔOPWus NAFTA
s.e.
p-value
β x (ΔOPWus NAFTA Gap) x 100
Skilled workers
Unskilled workers
(4)
(5)
6.59e-05
(0.00187)
0.972
-0.00259
(0.00255)
0.318
0.00328
(0.00207)
0.123
0.12%
-4.87%
6.17%
Note: Workers with an education level lower than high school are classified as unskilled.
Number of observations : 37 cities.
All regressions are estimated by instrumental variables and in all cases the proportion of women who work and the proportion of the
population with high school education are included as controls.
Standard errors given in parenthesis.
*** p<0.01, ** p<0.05, * p<0.1
33
4. Econometric analysis: NAFTA openness
Estimation of the effect of NAFTA openness exposure
on employment
Heterogeneous effects
Dependent variable: logarithmic differences of employed population
(1)
Manufacturing
employment
(2)
Non-manufacturing
employment
(3)
0.00174
(0.00140)
0.224
0.00423*
(0.00239)
0.0868
-0.00227
(0.00437)
0.607
Total employment
ΔOPWus NAFTA*dborderauto
s.e.
p-value
ΔOPWus NAFTA*drest
s.e.
p-value
βborderauto x (ΔOPWus NAFTA Gapborderauto) x 100
us
βrest x (ΔOPW
NAFTA
Gaprest) x 100
Skilled workers
Unskilled workers
(4)
(5)
0.000353
(0.00158)
0.824
-0.00134
(0.00211)
0.53
0.00370**
(0.00176)
0.0429
-0.00798
(0.00744)
0.292
-0.00117
(0.00491)
0.813
-0.00795
(0.00657)
0.235
0.00147
(0.00546)
0.789
3.55%
8.62%
0.72%
-2.73%
7.54%
-1.78%
-6.24%
-0.92%
-6.22%
1.15%
Note: Workers with an education level lower than high school are classified as unskilled.
Number of observations : 37 cities.
All regressions are estimated by instrumental variables and in all cases the proportion of women who work and the proportion of the
population with high school education are included as controls.
Standard errors given in parenthesis.
*** p<0.01, ** p<0.05, * p<0.1
34
4. Econometric analysis: NAFTA openness
Estimation of the effect of NAFTA openness exposure
on wages
Dependent variable: logarithmic differences of wages
(1)
Mean wage in
manufacturing
sector
(2)
Mean wage in nonmanufacturing
sector
(3)
0.00362**
(0.00165)
0.0354
0.00742***
(0.00257)
0.00672
6.81%
13.95%
Mean wage
us
ΔOPW
NAFTA
s.e.
p-value
β x (ΔOPWus NAFTA Gap) x 100
Mean wage of
skilled workers
Mean wage of
unskilled workers
(4)
(5)
0.00327*
(0.00167)
0.0578
0.00468**
(0.00208)
0.0314
0.00523***
(0.00172)
0.00449
6.15%
8.80%
9.84%
Note: Workers with an education level lower than high school are classified as unskilled.
Number of observations : 37 cities.
All regressions are estimated by instrumental variables and in all cases the proportion of women who work and the proportion of the
population with high school education are included as controls.
Standard errors given in parenthesis.
*** p<0.01, ** p<0.05, * p<0.1
35
4. Econometric analysis: NAFTA openness
Estimation of the effect of NAFTA openness exposure
on wages
Heterogeneous effects
Dependent variable: logarithmic differences of wages
(1)
Mean wage in
manufacturing
sector
(2)
Mean wage in nonmanufacturing
sector
(3)
0.00318**
(0.00139)
0.0285
0.00522**
(0.00205)
0.0159
0.00549
(0.00431)
0.213
Mean wage of
skilled workers
Mean wage of
unskilled workers
(4)
(5)
0.00321**
(0.00142)
0.0304
0.00389**
(0.00175)
0.0331
0.00491***
(0.00145)
0.00184
0.0169**
(0.00637)
0.0125
0.00355
(0.00440)
0.426
0.00806
(0.00543)
0.148
0.00662
(0.00450)
0.151
6.48%
10.64%
6.54%
7.93%
10.01%
4.29%
13.22%
2.78%
6.30%
5.18%
Mean wage
us
ΔOPW
NAFTA*dborderauto
s.e.
p-value
ΔOPWus NAFTA*drest
s.e.
p-value
us
β borderauto x (ΔOPW
us
β rest x (ΔOPW
NAFTA
NAFTA
Gapborderauto) x 100
Gaprest) x 100
Note: Workers with an education level lower than high school are classified as unskilled.
Number of observations : 37 cities.
All regressions are estimated by instrumental variables and in all cases the proportion of women who work and the proportion of the
population with high school education are included as controls.
Standard errors given in parenthesis.
*** p<0.01, ** p<0.05, * p<0.1
36
Index
1. Introduction
2. Regional exposure
competition
to
trade
openness
and
3. Relationship between exposure measures and
Mexican labor market indicators
4. Econometric analysis
a) NAFTA
b) Chinese competition
5. Conclusions
37
4. Econometric analysis: Chinese competition
Estimation of the effect of exposure to Chinese competition
on unemployment
Dependent variable:
unemployment
ΔIPWus
Additional controls
Observations
R-squared
us
β x (ΔIPW Gap) x 100
Log differences
Change in variable as a ratio Change in variable as a ratio
of working-age population
of labor force
(1)
(2)
(3)
0.0072a
(0.0049)
0.0003***
(0.0001)
0.0006***
(0.0001)



53
0.33
53
0.31
53
0.33
11.01%
0.51 pp
0.93 pp
All regressions are estimated by instrumental variables and in all cases the proportion of women who work and the proportion of the population
with high school education are included as controls .
Standard errors given in parenthesis.
*** p<0.01, ** p<0.05, * p<0.1, a p<0.15
38
4. Econometric analysis: Chinese competition
Effect of exposure on unemployment rates
Mean unemployment rate
Metro areas in border states
Metro areas in non border states
Difference
2000
2009
Difference
2.54%
2.70%
8.01%
4.92%
5.47 pp
2.22 pp
3.25 pp
Mean ΔIPW
Metro areas in border states
Metro areas in non border states
Difference (gap)
44.70
9.20
35.50
Unemployment explained by a greater exposure in metro areas located in border states
Coefficient
0.00061
Explained effect (coefficient x gap)
2.17 pp
39
4. Econometric analysis: Chinese competition
Estimation of the effect of exposure to Chinese competition
on employment
Dependent variable: logarithmic differences of employed population
(1)
Manufacturing
employment
(2)
Non-manufacturing
employment
(3)
-0.0038
(0.003)
-0.0083***
(0.003)
-5.7%
-12.7%
Total employment
ΔIPWus
β x (ΔIPWus Gap) x 100
Skilled workers
Unskilled workers
(4)
(5)
-0.0024
(0.003)
-0.0031
(0.003)
-0.0071*
(0.004)
-3.6%
-4.7%
-10.8%
Note: Workers with education levels lower than high school are classified as unskilled.
Number of observations : 53 metropolitan areas.
All regressions are estimated by instrumental variables and in all cases the proportion of women who work and the proportion of the
population with high school education are included as controls.
Standard errors given in parenthesis.
*** p<0.01, ** p<0.05, * p<0.1
40
4. Econometric analysis: Chinese competition
Estimation of the effect of exposure to Chinese competition
on employment
Dependent variable: logarithmic differences of wages
(1)
Mean wage in
manufacturing
sector
(2)
Mean wage in nonmanufacturing
sector
(3)
-0.005***
(0.001)
-0.0006
(0.002)
-7.6%
-0.9%
Mean wage
ΔIPWus
β x (ΔIPWus Gap) x 100
Mean wage of
skilled workers
Mean wage of
unskilled workers
(4)
(5)
-0.0065***
(0.001)
-0.0054***
(0.002)
-0.005***
(0.001)
-9.9%
-8.2%
-7.7%
Note: Workers with education levels lower than high school are classified as unskilled.
Number of observations : 53 metropolitan areas.
All regressions are estimated by instrumental variables and in all cases the proportion of women who work and the proportion of the
population with high school education are included as controls.
Standard errors given in parenthesis.
*** p<0.01, ** p<0.05, * p<0.1
41
Index
1. Introduction
2. Regional exposure
competition
to
trade
openness
and
3. Relationship between exposure measures and
Mexican labor market indicators
4. Econometric analysis
a) NAFTA
b) Chinese competition
5. Conclusions
42
5. Conclusions
 Based on the methodology proposed by Autor, Dorn and Hanson
(2012), we have exploited regional variation in Mexico to study
the effects of trade openness and trade competition on the
Mexican labor markets in the last twenty years.
• We found that NAFTA had a positive impact on labor market indicators
(unemployment, employment, and wages), while the increased
competition from China in the US market has had a negative effect.
• It is noticeable that metro zones in border states were able to benefit more
from NAFTA, but were also more vulnerable to Chinese competition.
 Those metro zones specializing in the auto industry could be avoiding the
negative effects of increased Chinese exports.
43