Volatility of Employment in the Mexican Offshoring Industry

Myriam Alejandra Gómez CárdenasAdvisor: Lionel Fontagné

Introduction Motivation:

The Maquiladoras are seen as a channel by which countries export to Mexico a portion of its employment fluctuations over the business cycle.

Contribution: Comparison of the adjustments at the extensive and intensive margins for

different subsets of Maquila sectors over the period 1990-2006.

Benchmark Paper: Paul R. Bergin, Robert C. Feenstra & Gordon H. Hanson, 2009. "Offshoring

and Volatility: Evidence from Mexico's Maquiladora Industry," American Economic Review, American Economic Association, vol. 99(4), pages 1664-71, September.

Empirical model Extensive margin:

Intensive margin:

Number of plants

(Average) Employment per plant

Maquila share of manufacturing employment

Maquila share of manufacturing employment

Total Mexican manufacturing employment

Total Mexican manufacturing employment

Logic of least squares:

Database construction Dependent Variables:

Extensive margin: Number of plantsln P = ln Esh + ln Et

ln(plants) ln(mxemp_omfg)

ln(obreros) – ln(mxemp_omfg)Dataset A

Dataset B

Sources: Mexican National Institute of Statistics (INEGI) Mexican Central Bank U.S. Bureau of Labor Statistics

Database construction Dependent Variables:

Intensive margin: (Ave) Employment per plantln E = ln Esh + ln Et

Sources: Mexican National Institute of Statistics (INEGI) Mexican Central Bank U.S. Bureau of Labor Statistics

ln(obreros) – ln(plants) ln(mxemp_omfg)

ln(obreros) – ln(mxemp_omfg)Dataset A

Dataset B

Statistical summariesEstablishments and employment at State-level

United States of America

Pacific Ocean

Guatemala

ChihuahuaSonora

Coahuila

Nuevo LeonTamaulipas

Baja California

Jalisco

Guanajuato

Puebla

Mexico

Source: Mexican National Institute of Statistics (December 2006)

Source: Mexican National Institute of Statistics (December, 2006)

Food2%

Apparel23%

Footwear1%

Furniture15%

Chemicals9%

Transport15%Machinery

4%

E&E assembly8%

E&E compo-nents21%

Toys2%

Maquila Establishments

Food1%

Apparel18% Footwear

1%Furniture

6%

Chemicals4%Transport

28%

Ma-chinery

2%

E&E assembly13%

E&E components26%

Toys1%

Maquila Employment, Production Workers

Sector-level

Statistical summariesEstablishments and employment at sector-level

Source: P. Bergin, R. Feenstra and G. Hanson (2009)

Statistical summariesEmployment for production workers

First regression results Benchmark

(1) (2) (3) (4) (5) (6) (7) (8)VARIABLES lnP lnE lnP lnE lnP lnE lnP lnE lnEsh 0.779*** 0.221 0.713*** 0.287** 0.697* 0.303 0.733*** 0.267**

(0.107) (0.107) (0.0910) (0.0910) (0.240) (0.240) (0.104) (0.104)lnEt 1.044 -0.0441 1.249** -0.249 2.147 -1.147 1.614*** -0.614

(0.631) (0.631) (0.448) (0.448) (1.021) (1.021) (0.459) (0.459)Constant -4.530 4.530 -5.542* 5.542* -9.030 9.030 -7.345*** 7.345***

(3.220) (3.220) (2.277) (2.277) (4.287) (4.287) (2.253) (2.253)

Industry FE Yes Yes Yes Yes Yes Yes Yes Yes

Observations 480 480 720 720 480 480 1,200 1,200R-squared 0.978 0.946 0.981 0.983 0.715 0.640 0.984 0.956Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Notes: All variables are in logs. The sample contains data at a monthly frequency from 1996:1 to 2005:12. Regressions include controls for industry fixed effects. Standard errors are (clustered by industry) are in parentheses.

More rep. sectors (6)

Less rep. sectors (4) All sectors (10)

Authors’ sample (4)

First regression results Extension

(1) (2) (3) (4) (5) (6) (7) (8)VARIABLES lnP lnE lnP lnE lnP lnE lnP lnE lnEsh 0.630*** 0.370** 0.612*** 0.388*** 0.554** 0.446** 0.599*** 0.401***

(0.0774) (0.0774) (0.0683) (0.0683) (0.128) (0.128) (0.0719) (0.0719)lnEt 1.135 -0.135 1.248** -0.248 1.337 -0.337 1.283*** -0.283

(0.485) (0.485) (0.373) (0.373) (0.576) (0.576) (0.341) (0.341)Constant -3.749 3.749 -4.801* 4.801* -4.797 4.797 -4.860** 4.860**

(2.649) (2.649) (2.107) (2.107) (3.098) (3.098) (2.003) (2.003)

Industry FE Yes Yes Yes Yes Yes Yes Yes Yes

Observations 816 816 1,224 1,224 816 816 2,040 2,040R-squared 0.973 0.955 0.975 0.981 0.535 0.574 0.977 0.947Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Notes: All variables are in logs. The sample contains data at a monthly frequency from 1990:1 to 2006:12. Regressions include controls for industry fixed effects. Standard errors are (clustered by industry) are in parentheses.

More rep. sectors (6)

Less rep. sectors (4) All sectors (10)Authors’ sample (4)

Further research Granular origins of aggregate fluctuations (X. Gabaix, 2011):

Theory: Shocks experienced by large firms have the potential to generate aggregate shocks in a country.

Main motivation: The Mexican offshoring industry represents an important share of the

exports and GDP in the country. Therefore, shocks in a few firms belonging to the Mexican maquila industry

may explain the aggregate fluctuations on the volume of exports and GDP growth.

Q & A

Thank you!