Post on 23-Feb-2016
description
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!