november 2nd, 2012

Labor market consequences of trade openness and competition

in foreign markets: the case of Mexico

November 2nd, 2012

Daniel ChiquiarEnrique CovarrubiasAlejandrina Salcedo

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.

2

Index

1. Introduction

2. Regional exposure to trade openness and competition

3. Relationship between exposure measures and Mexican labor market indicators

4. Econometric analysisa) NAFTAb) Chinese competition

5. Conclusions

3

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.

4

1. IntroductionMarket Share in US Imports

Percentage

0%

5%

10%

15%

20%

25%

1993 1995 1997 1999 2001 2003 2005 2007 2009

Mexico

China

China's accession to WTONAFTA

Source: Comtrade database, United Nations.

5

1. Introduction Given its initial comparative advantages, Mexico responded to trade integration

through NAFTA mostly by specializing in unskilled labor-intensive 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.

6

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.

7

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.

8

1. Introduction We find significant effects of NAFTA and Chinese competition in

US markets on the Mexican labor market.

NAFTA(1993-2000)

China(2000-2009)

Unemployment Decrease Increase

Employment Some evidence of an increase

Decrease

Wages Increase Decrease

Effects on the Mexican Labor Market of NAFTA and Competition from China

9

Index

1. Introduction


3. Relationship between exposure measures and

Mexican labor market indicators

4. Econometric analysis a) NAFTA b) Chinese competition

5. Conclusions


10

Measures of exposureTrade openness due to NAFTA

(1993-2000)

Trade competition from China in US markets

(2000-2009)

where:• is the change in Mexican exports to the US in sector j.• is the change in US imports from China in sector j.• is the number of workers in sector j in region i in Mexico at baseline.• is the number of workers in region i in Mexico at baseline.• is the total number of workers in sector j in Mexico at baseline.

11

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.


12

Nafta effect: Map of Metropolitan Areas


13

2. Regional exposure to trade openness and competitionChinese competition effect: Map of Metropolitan Areas

14

Exposure to Trade Openness (NAFTA)

0

10

20

30

40

50

60

70M

atam

oros

Cd.

Jua

rez

Tiju

ana

Chi

huah

uaTa

mpi

coSa

ltillo

Tolu

caAg

uasc

alie

ntes

Nue

vo L

ared

oC

uern

avac

aPu

ebla

Her

mos

illo

Torre

onM

onte

rrey

Gua

dala

jara

Vera

cruz

Cel

aya

Que

reta

roSa

n Lu

is P

otos

iM

exic

o C

ityM

oncl

ova

Mer

ida

Cul

iaca

nC

oatz

acoa

lcos

Oriz

aba

Leon

Dur

ango

Mor

elia

Zaca

teca

sC

olim

aTu

xtla

Gut

ierre

zO

axac

aC

ampe

che

Tepi

cVi

llahe

rmos

aM

anza

nillo

Acap

ulco

********

Cities in border states

Cities in non border states

Cities specialized in the automobile industry1/*

1/ The regions specialized in the automobile industry are those for which this industry represents at least 29% of its exposure to trade openness.


15

Exposure to Chinese Competition in US markets

0

10

20

30

40

50

60

70

80

90

Tiju

ana

Juár

ezRe

ynos

a-Rí

o Br

avo

Mex

ical

iM

atam

oros

Nue

vo L

ared

oGu

aym

asTe

huan

tepe

cGu

adal

ajar

aCh

ihua

hua

Tehu

acán

Pied

ras N

egra

sTl

axca

la-A

piza

coM

onte

rrey

Agua

scal

ient

esSa

ltillo

Mor

oleó

n-Ur

iang

ato

La L

agun

aO

cotlá

nSa

n Fr

ancis

co d

el R

incó

nQ

ueré

taro

San

Luis

Poto

sí-SG

STo

luca

Pueb

la-T

laxc

ala

León

Pach

uca

Mon

clov

a-Fr

onte

raVa

lle d

e M

éxic

oCu

erna

vaca

Mér

ida

Zam

ora-

Jaco

naTu

lanc

ingo

La P

ieda

d-Pé

njam

oCó

rdob

aO

rizab

aTe

com

ánCo

atza

coal

cos

Tam

pico

Min

atitlá

nM

orel

iaTu

laZa

cate

cas-

Guad

alup

eVe

racr

uzCu

autla

Xala

paO

axac

aAc

apul

coRi

over

de-C

iuda

d Fe

rnán

dez

Colim

a-Vi

lla d

e Ál

vare

zPo

za R

ica

Villa

herm

osa

Tepi

cTu

xtla

Guti

érre

zAc

ayuc

anCa

ncún

Puer

to V

alla

rta

Metropolitan areas in border state

Metropolitan areas in non border state


16

Exposure to trade openness (NAFTA) () vs. exposure to Chinese competition in US markets ()

∆ 𝐼𝑃𝑊 𝑖𝑈𝑆

∆𝑂𝑃𝑊

𝑖𝑈𝑆2. Regional exposure to trade openness and competition

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70 80 90

Specialized in automobile industryOtherSpecialized in automobile industryOther non-border

border

3-digit SITC Industries that Contribute the Most to each Exposure Measure Grouped in 2-digit SITC Categories

17


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):

2/ 5 main sectors that contribute to ∆IPW iU S in 11 of the 12 metropolitan zones in border states.

NAFTA 1/ China 2/

Office machines and automatic data-processing machines (75)

Office machines and automatic data-processing machines (75)

Telecommunications and sound-recordingand reproducing apparatus and equipment(76)

Power-generating machinery and equipment(71)

Road vehicles (78) Miscellaneous manufactured articles (89)

General industrial machinery and equipment(74)

Telecommunications and sound-recordingand reproducing apparatus and equipment(76)

Electrical machinery, apparatus andappliances (77)

Electrical machinery, apparatus andappliances (77)

1/ 5 main sectors that contribute to ∆OPW iU S in 8 of the 9 cities in border states.

18

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.


19

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

(1999, SITC 2 digits)

RCA of China vs. SSI of Metropolitan Zones in Nonborder States

(1999, SITC 2 digits)

Source: China RCA: Comtrade database, United Nations. SSI index: Mexican Economic Census 1999, INEGI.

74 Industrial mach. and equip.

75 Computers

76 Telecomm.

77 Electrical

89 Miscel laneous manufact.

0

1

2

3

4

5

6

7

0 0.5 1 1.5 2 2.5 3

RCA

Chin

a 19

99

SSI Border MZ 1999

Spearman correlation coeff. = 0.3263**

0358

6263 65

66

82

0

1

2

3

4

5

6

7

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

RCA

Chin

a 19

99

SSI Nonborder MZ 1999

Spearman correlationcoeff. = -0.3263**


Index

20

1. Introduction




5. Conclusions

21

Unemployment in Mexico and exposure to NAFTA openness ()

Logarithmic differences in unemployed population vs. exposure

Change in unemployed population as a proportion of the labor force vs. exposure

Source: ENEU (1993 and 2000), Economic Census (1994), and UN Comtrade.


Monterrey

ChihuahuaSaltillo

Cd. Juárez

Tijuana

Matamoros

Nuevo Laredo

Hermosillo

Monclova

-1.5

-1

-0.5

0

0.5

1

1.5

0 20 40 60 80

ln(U

nem

ploy

ed p

opul

atio

n 200

0)-ln

(Une

mpl

oyed

pop

ulat

ion 1

993)

ΔOPWUS

correlation=-0.3691**

Cd. Juárez

Tijuana

Nuevo Laredo

Monterrey Chihuahua

Saltillo

Matamoros

Hermosillo

Monclova

-0.07

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0 20 40 60 80

(Une

mpl

oyed

pop

/Lab

or F

orce

) 200

0.-(

Unem

ploy

ed p

op./L

abor

forc

e)19

93

ΔOPWUS

correlation= -0.4322***

22

Employment in Mexico and exposure to NAFTA openness ()Logarithmic differences of employed population vs. exposure measure


All sectors Manufacturing Non-manufacturing

Monterrey

Chihuahua

Saltillo Cd. JuárezTijuana

MatamorosNuevo Laredo

Hermosillo

Monclova

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0 20 40 60 80

ln(T

otal

em

ploy

men

t 200

0)-ln

(Tot

al e

mpl

oym

ent 1

993)

ΔOPWUS

correlation= 0.2980*

Monterrey

Chihuahua

Saltillo

Cd. Juárez

Tijuana

Matamoros

Nuevo Laredo

Hermosillo

Monclova

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 20 40 60 80

ln(M

anuf

actu

ring

empl

oym

ent 2

000)

-ln(M

anuf

actu

ring

empl

oym

ent 1

993)

ΔOPWUS

correlation=0.4182**

Monterrey

ChihuahuaSaltillo

Cd. Juárez

Tijuana

Matamoros

Nuevo Laredo

Hermosillo

Monclova

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0 20 40 60 80ln

(Non

Man

uf.

empl

oym

ent 2

000)

-ln(N

on M

anuf

. em

ploy

men

t 199

3)ΔOPWUS

correlation=0.0315


23

Wages in Mexico and exposure to NAFTA openness ()Logarithmic differences in wages vs. exposure measure



Monterrey

Chihuahua

Saltillo

Cd. Juárez

Tijuana

Matamoros

Nuevo Laredo

Hermosillo

Monclova

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0 20 40 60 80

ln(W

ages

all

sect

ors 2

000)-

ln(W

ages

all s

ecto

rs19

93)

ΔOPWUS

correlation= 0.3675**

Monterrey

ChihuahuaSaltillo

Cd. Juárez

Tijuana

Matamoros

Nuevo Laredo

Hermosillo

Monclova

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0 20 40 60 80

ln(W

ages

man

ufac

turin

g 200

0)-ln

(Wag

es m

anuf

actu

ring 1

993)

ΔOPWUS

correlation= 0.4649***

Monterrey

Chihuahua

Saltillo

Cd. Juárez

Tijuana

MatamorosNuevo Laredo

Hermosillo

Monclova

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0 20 40 60 80

ln(W

ages

non

man

ufac

turin

g 200

0)-ln

(Wag

es n

on m

anuf

actu

ring

1993

)ΔOPWUS



24

Unemployment in Mexico and exposure to Chinese competition ()

Logarithmic differences in unemployed population vs. index of exposure

Change in unemployed population as a proportion of the labor force vs. index of

exposure


-2

-1

0

1

2

3

4

0 20 40 60 80 100

ln(U

nem

ploy

ed p

op. 20

09) -

ln (U

nem

ploy

ed p

op. 2

000)

ΔIPWUS

Tijuana

Juárez

Reynosa-Río

Mexicali

Matamoros

Guaymas

Nuevo LaredoSaltillo

ChihuahuaMonterrey

Piedras Negras

Monclova-Frontera


-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0 20 40 60 80 100U

nem

p. p

op./L

abor

For

ce20

09-U

nem

p. p

op./L

abor

forc

e20

00

ΔIPWUS

Juárez

Tijuana

Matamoros

Reynosa-Río Bravo

Mexicali

Nuevo Laredo

Guaymas

ChihuahuaPiedras Negras

SaltilloMonclova-Frontera

Monterrey

correlation= 0.5225***

Source: ENE and ENOE (2000 and 2009), Economic Census (1994), and UN Comtrade.

25

Employment in Mexico and exposure to Chinese competition ()Logarithmic differences of employed population vs. exposure measure



-1.5

-1

-0.5

0

0.5

1

1.5

2

0 20 40 60 80 100

ln(T

otal

em

ploy

men

t 2009

) -ln

(Tot

al e

mpl

oym

ent 20

00)

ΔIPWUS

Tijuana

Mexicali

Rey nosa-Río Brav o

Juárez

Matamoros

Guay mas

Nuev o LaredoChihuahuaMonterrey

Piedras Negras

Monclov a-Frontera

correlation = -0.0743

-2

-1.5

-1

-0.5

0

0.5

1

0 20 40 60 80 100

ln(M

anuf

. em

ploy

men

t 2009

) -ln

(Man

uf. e

mpl

oym

ent 20

00)

ΔIPWUS

Tijuana

Juárez


Mexicali

MatamorosNuev o Laredo

Guay mas

ChihuahuaMonterrey

Saltillo

Monclov a-Frontera

Piedras Negras

correlation = -0.2702**

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

0 20 40 60 80 100

ln(N

on m

anuf

. em

ploy

men

t 2009

) -ln

(Non

man

uf. e

mpl

oym

ent. 2

000)

ΔIPWUS

TijuanaMatamoros

Guay mas

Juárez


MexicaliNuev o Laredo

Monclov a-Frontera

Piedras Negras

correlation= -0.0045


26

Wages in Mexico and exposure to Chinese competition ()Logarithmic differences in wages vs. exposure measure



-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100

ln(W

ages

all

sect

ors 2

009)

-ln

(Wag

es a

ll se

ctor

s 200

0)

ΔIPWUS


JuárezMatamoros

Mexicali

Tijuana

Guay mas

Nuev o Laredo

Chihuahua

Piedras Negras

Monclov a-Frontera

SaltilloMonterrey


-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

0 20 40 60 80 100

ln (W

ages

man

ufac

turin

g 200

9) -

ln (W

ages

man

ufac

turin

g 200

0)

ΔIPWUS

Tijuana


Juárez

Mexicali

Matamoros

Nuev oLaredo

Guay mas

Piedras Negras

Monclov a-Frontera

correlation= -0.1054

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 20 40 60 80 100

ln(W

ages

non

man

uf. 20

09) -

ln (W

ages

non

man

uf. 20

00)

ΔIPWUS

Tijuana

Mexicali

Juárez


Matamoros

Guay mas

Nuev o Laredo

Chihuahua

Monclov a-Frontera

Piedras Negras



1. Introduction




5. Conclusions

Index

27

28

4. Econometric analysisEstimation strategy to identify the effect of trade exposure

on Mexican labor market variables

Regression equation yΔ i = α + OPWβΔ i

US + Xγ i + e i yΔ i = α + IPWβΔ iUS + Xγ i + e i

Period 1993-2000 2000-2009

OPWΔ iOC IPWΔ i

OC

US demand to other countries Export capacity of China

( XΔ other countries to the US ) ( MΔ other countries from China )

Proportion of working women Proportion of working womenProportion of the population with high school education

Proportion of the population with high school education

NAFTA China

Additional Controls X i

Instrument

1. Introduction




5. Conclusions

Index

29

30

Estimation of the effect of NAFTA openness exposure on unemployment

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

Log differences

Change in variable as a ratio of working-age population

Change in variable as a ratio of labor

force

(1) (2) (3)

ΔOPWusNAFTA -0.0173** -0.000401*** -0.000712***

s.e. (0.00656) (0.000125) (0.000214)p-value 0.0127 0.00292 0.00213Additional controls

Observations 37 37 37R-squared 0.264 0.301 0.286

β x (ΔOPWusNAFTA Gap) x 100 -32.53% -0.75 pp -1.34 pp

Dependent variable: unemployment

4. Econometric analysis: NAFTA openness

31

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


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)

ΔOPWusNAFTA*dborderauto -0.0142** -0.000330*** -0.000581***

s.e. (0.00553) (0.000105) (0.000179)p-value 0.0148 0.00353 0.00273

ΔOPWusNAFTA*drest -0.0305* -0.000706** -0.00127**

s.e. (0.0172) (0.000326) (0.000556)p-value 0.0856 0.0377 0.0289

Additional controls


βborderauto x (ΔIPWusNAFTA Gapborderauto) x 100 -28.94% -0.67 pp -1.18 pp

βrest x (ΔIPWusNAFTA Gaprest) x 100 -23.86% -0.55 pp -0.99 pp


32

Effect of exposure on unemployment rates


1993 2000 Difference

Mean unemployment rateMetro areas in border states or specialized in auto industry 4.07% 2.37% -1.70 ppOther metro areas 3.31% 2.76% -0.54 ppDifference -1.16 pp

Mean ΔOPW Metro areas in border states or specialized in auto industry 26.08Other metro areas 6.20Difference (gap) 19.88

Unemployment explained by a greater exposure in metro areas in border states or specialized in auto industryCoeffi cient -0.000712Explained effect (coeffi cient x gap) -1.42 pp

33

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

Estimation of the effect of NAFTA openness exposure on employment


Dependent variable: logarithmic differences of employed population

Total employment Manufacturing employment

Non-manufacturing employment

Skilled workers Unskilled workers

(1) (2) (3) (4) (5)

ΔOPWusNAFTA 0.000987 0.00193 6.59e-05 -0.00259 0.00328

s.e. (0.00169) (0.00289) (0.00187) (0.00255) (0.00207)p-value 0.564 0.509 0.972 0.318 0.123

β x (ΔOPWusNAFTA Gap) x 100 1.86% 3.63% 0.12% -4.87% 6.17%

34



Estimation of the effect of NAFTA openness exposure on employment

Heterogeneous effects

Dependent variable: logarithmic differences of employed populationTotal employment

Manufacturing employment

Non-manufacturing employment Skilled workers Unskilled workers

(1) (2) (3) (4) (5)

ΔOPWusNAFTA*dborderauto 0.00174 0.00423* 0.000353 -0.00134 0.00370**

s.e. (0.00140) (0.00239) (0.00158) (0.00211) (0.00176)p-value 0.224 0.0868 0.824 0.53 0.0429

ΔOPWusNAFTA*drest -0.00227 -0.00798 -0.00117 -0.00795 0.00147

s.e. (0.00437) (0.00744) (0.00491) (0.00657) (0.00546)p-value 0.607 0.292 0.813 0.235 0.789

βborderauto x (ΔOPWusNAFTA Gapborderauto) x 100 3.55% 8.62% 0.72% -2.73% 7.54%

βrest x (ΔOPWusNAFTA Gaprest) x 100 -1.78% -6.24% -0.92% -6.22% 1.15%

35

Mean wageMean wage in manufacturing

sector

Mean wage in non-manufacturing

sector

Mean wage of skilled workers

Mean wage of unskilled workers

(1) (2) (3) (4) (5)

ΔOPWusNAFTA 0.00362** 0.00742*** 0.00327* 0.00468** 0.00523***

s.e. (0.00165) (0.00257) (0.00167) (0.00208) (0.00172)p-value 0.0354 0.00672 0.0578 0.0314 0.00449

β x (ΔOPWusNAFTA Gap) x 100 6.81% 13.95% 6.15% 8.80% 9.84%

4. Econometric analysis: NAFTA opennessEstimation of the effect of NAFTA openness exposure

on wages

Dependent variable: logarithmic differences of wages


36

4. Econometric analysis: NAFTA opennessEstimation of the effect of NAFTA openness exposure

on wagesHeterogeneous effects




sector


sector



(1) (2) (3) (4) (5)

ΔOPWusNAFTA*dborderauto 0.00318** 0.00522** 0.00321** 0.00389** 0.00491***

s.e. (0.00139) (0.00205) (0.00142) (0.00175) (0.00145)p-value 0.0285 0.0159 0.0304 0.0331 0.00184

ΔOPWusNAFTA*drest 0.00549 0.0169** 0.00355 0.00806 0.00662

s.e. (0.00431) (0.00637) (0.00440) (0.00543) (0.00450)p-value 0.213 0.0125 0.426 0.148 0.151

βborderauto x (ΔOPWusNAFTA Gapborderauto) x 100 6.48% 10.64% 6.54% 7.93% 10.01%

βrest x (ΔOPWusNAFTA Gaprest) x 100 4.29% 13.22% 2.78% 6.30% 5.18%

1. Introduction




5. Conclusions

Index

37

38

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

Log differences Change in variable as a ratio of working-age population

Change in variable as a ratio of labor force

(1) (2) (3)

ΔIPWus 0.0072a 0.0003*** 0.0006***(0.0049) (0.0001) (0.0001)

Additional controls


β x (ΔIPWus Gap) x 100 11.01% 0.51 pp 0.93 pp


4. Econometric analysis: Chinese competition

Estimation of the effect of exposure to Chinese competition on unemployment

39

Effect of exposure on unemployment rates


2000 2009 Difference

Mean unemployment rateMetro areas in border states 2.54% 8.01% 5.47 ppMetro areas in non border states 2.70% 4.92% 2.22 ppDifference 3.25 pp

Mean ΔIPW Metro areas in border states 44.70Metro areas in non border states 9.20Difference (gap) 35.50

Unemployment explained by a greater exposure in metro areas located in border statesCoeffi cient 0.00061Explained effect (coeffi cient x gap) 2.17 pp

40

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

Total employment Manufacturing employment

Non-manufacturing employment

Skilled workers Unskilled workers

(1) (2) (3) (4) (5)

ΔIPWus -0.0038 -0.0083*** -0.0024 -0.0031 -0.0071*(0.003) (0.003) (0.003) (0.003) (0.004)

β x (ΔIPWus Gap) x 100 -5.7% -12.7% -3.6% -4.7% -10.8%


Estimation of the effect of exposure to Chinese competition on employment

Dependent variable: logarithmic differences of employed population

41


sector


sector



(1) (2) (3) (4) (5)

ΔIPWus -0.005*** -0.0006 -0.0065*** -0.0054*** -0.005***(0.001) (0.002) (0.001) (0.002) (0.001)

β x (ΔIPWus Gap) x 100 -7.6% -0.9% -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

Estimation of the effect of exposure to Chinese competition on employment


1. Introduction




5. Conclusions

Index

42

43

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.

5. Conclusions

november 2nd, 2012

Documents

us markets

mexican labor markets

mexican exports

effect of trade openness

mexican case

competition relationship

exposure measures

measures of exposure