Regional Input-Output Matrices and an Application to Analyze a Manufacturing Export Shock in Mexico

AutorJorge A. Alvarado/Miroslava Quiroga/Leonardo E. Torre/Daniel I. Chiquiar
Ensayos Revista de Economía, 38(2), 227-258, November 2019
ISSN Electrónico: 2448-8402 | ISSN Impreso: 1870-221X | ©2019 Los autores
Citar así: Alvarado, Jorge, Miroslava Quiroga, Leonardo Torre y Daniel Chiquiar (2019). Regional Input-Output Matrices and an Application to
Analyze a Manufacturing Export Shock in Mexico. Ensayos Revista de Economía, 38(2), 227-258,
Regional Input-Output Matrices and an
Application to Analyze a Manufacturing
Export Shock in Mexico
Matrices insumo producto regionales y una aplicación para
analizar impactos de las exportaciones manufactureras en
Jorge Alvarado
Miroslava Quiroga*
Leonardo Torre*
Daniel Chiquiar*
Article information
11 november 2017
4 october 2019
Based on the national Input-Output Matrix (IOM) 2012
calculated by INEGI, we estimate with the Flegg approach
four regional Input-Output Matrices (RIOMs) using Banco
de México’s regionalization (Northern, North-Central,
Central and Southern). These RIOMs are employed to
evaluate the impact on regional gross output, value added
and employment from a 10,000 million dollar shock on
Mexican manufacturing exports. The results show that the
effects on the absolute values of gross output, value added
and employment in the North are clearly larger than those
estimated for the other regions. Another finding is that the
total effects of the regional shocks tend to concentrate in
the manufacturin g sector, w ith the high est concentration
observed in the North, and the lowest in the South. It is
also shown that the North is, b y far, the region
experiencing the greatest change in its value added relative
to GDP, followed by the North Central, the Central and the
South. The results suggest a strong linkage between the
manufacturing sector and tertiary activities, particularly
commerce and services in the central regions, as well as
between manufacturing and oil and gas extraction in the
JEL Classification:
O14; R11; R12; R15
Input-Output Model;
Regional Analysis;
Multiplier Effects;
§ Email:
* Banco de México.
Facultad de
Alvarado et al. / Ensayos Revista de Economía, 38(2), 227-258
Información del
11 noviembre 2017
4 octubre 2019
Clasificación JEL:
R11; R12; R15
Palabras clave:
Modelo insumo
producto; Análisis
regional; Efecto
Agradecimientos: Los autores agradecen los comentarios de Joana Chapa, Juan Carlos
Chávez, Alejandrina Salcedo, Daniel Sámano, dos dictaminadores anónimos y a los
participantes del 4º Congreso Anual de Economía y Políticas Públicas en la Universidad
Iberoamericana en la Ciudad de México. Los puntos de vista y conclusiones en este trabajo
son responsabilidad exclusiva de los autores y no necesariamente reflejan los del Banco de
México. Los errores restantes son responsabilidad de los autores.
This paper estimates the direct and indirect effects that an exogenous shock to
the manufacturing exporting sector can have on other sectors of economic
activity at the regional level in Mexico. Positive shocks that originate in a
particular manufacturing sector can have spill-over effects on ot her
manufacturing sectors an d on other activities—-such as services or
construction—- via input-output linkages. To identify these effects, this paper
extends traditional input-output matrix (IOM) analysis to obtain regional
input-output matrices (RIOMs), which can be useful tools to characterize the
regional heterogeneity in the organization of economic activity within a
country. An IOM summarizes information regarding the economy’s
productive structure useful to evaluate the aggregate impact on the entire
economic system produced by exogenous shocks that initially originate within
a particular activity. The estimation of IOMs at the regional level allows for a
richer characterization of the aggregate effects from the exogenous shocks as
Alvarado et al. / Ensayos Revista de Economía, 38(2), 227-258
we may identify the differential spill-over effects that these shocks may have
across regions within the same country.
Previous work has found that the regional impact of trade liberalization may
be very heterogeneous. For example, Chiquiar (2005) and Cosar and
Fajgelbaum (2016) study the regional impact of external economic integration
and find that specialization patterns (i. e. sectoral composition) can lead to
uneven effects of international trade. Similarly, authors, Dorn and Hanson
(2013) find differential effects of import competition from China in local labor
markets in the U.S. In a related paper, Chiquiar et al. (2014) also find
heterogenous effects of trade shockssuch as the enactment of NAFTA or the
entry of China into the WTO-—on Mexican labor markets. This paper focuses
on a particular channel that can exacerbate or dampen the differential responses
to an exogenous export shock at the regional level, and that can be relevant to
explain heterogeneous regional effects of external shocks. In particular, even
if the first order effect of an exogenous shock on exports for a particular region
depends on its export orientation, regions in which sectors are more
interconnected will benefit greater from the same shock relative to those with
weaker sectoral links. This implies that heterogeneous effects can arise not
only from a region’s export capability, but also from its underlying
microeconomic structure in terms of how economic activity is organized.
Indeed, Acemoglu et al. (2012) and Foerster et al. (2011) have emphasized the
role of intersectoral linkages as an amplification mechanism that accounts for
a substantial amount of aggregate fluctuations. Moreover, Caliendo et al.
(2016) argue that intersectoral and interregional linkages are keys to
understanding the response of the aggregate economy to micro-level shocks.
Methodological advances, the availability of new and reliable data, and the
development of more powerful and easy-to operate computational tools have
made IOM analysis and its extension to RIOMs a tool that can be effectively
implemented to further our understanding of the organization of economic
activity and its consequences for aggregate outcomes. This paper uses the
methodology in Alvarado et al. (2016) in order to estimate RIOMs for Mexico.
In particular, RIOMs are estimated for the regionalization of the Mexican
economy used in the Reporte Sobre las Economías Regionales of Banco de
México, which divides Mexico into four economic regions: North, North-
Center, Center, and South.1
1 We recognize that it is not a possible to determine the optimal regionalization of a country.
In fact, in the case of Mexico other researchers have already estimated RIOMs defining the
regions differently to ours. See, for instance, Callicó et al. (2000) for the Western region
(Colima, Jalisco, Michoac án y Nayarit); Ayala y Chapa (2007) for the North-East (Nuevo
León, Coahuila y Tamaulipas); and vila (2015), who compiles estimations of RIOMs for
seven regions obtained by different authors. There are also some estimations of RIOMs at

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