72 Alonso Alfaro Ureña
Revista de Ciencias Económicas 37-N°2: julio-diciembre 2019 / 71-90 / ISSN: 0252-9521/ ISSN: 2215-3489
los resultados de la metodología de CRC utili zando esta versión de la W IOD para calcula r
las ganancia s del comercio para la economía cost arricense. A dicionalmente se presenta n
ejercicios contrafactuale s en los que se compara la situación vigente c on autarquía y otros
niveles de tarifa s promedio utilizando diferentes est ructuras produc tivas y esquema s de
competencia. Los result ados pueden constituir información valiosa sobre cu ánto puede ganar
una economía pequeña y a bierta como la costar ricense gracias a l comercio internacional, y
en qué se diferencia de otros países en u na situación similar.
PALABR AS CLAVE: GANA NCIAS DEL COMERCIO, COSTA RICA , INSUMO-PRODUCTO
CLASIFICACIÓN JEL: F10, I30, D57.
One of the oldest and most interesting questions in the economic literature is how
to quantify the ga ins from trade2. Recent work by Costinot & Rodríguez-Clare (2014) (CRC)
described how the results of a wide array of tra de models developed in the last two decade s can
provide parsimonious measures of the gains from tr ade. Those include, for example, one sector
models, multiple sector models, and models with intermediate goods. Different structures for
how competition works in those markets are also con sidered, such as perfect, Bertrand, a nd
The results presented in CRC4 are useful for evaluating t he effects of globalization and t he
differences that arise for different countries depending on t he level of integration to the rest of the
world. The authors use the World Input Output Database (WIOD) constr ucted by Dietzenbacher, Los,
Stehrer, Timmer & de Vries (2013) for computing the gains from trade. However, this database does
not include Costa Rica as an individual c ountry, it is included as part of the “Rest of t he World”.
For a small, open economy such as Costa Rica it is of particula r interest to quantify how
much does the country gain f rom having its economy open to trade with the rest of t he world. The
two main contributions of thi s paper are that, first, it updates t he results from CRC to the 2011
data, which changed quantit atively after the trade collapse t hat followed the Great Recession and,
second, it computes the gains from trade for Costa R ica using this version of the WIOD.
The results are, in general, consistent with the gains from trade f rom similar small open
economies. The gains from the current situation are a bove the average of the rest of the world, while
increasing dramatica lly when the assumptions allow for multiple sectors in perfect competition.
II. THE NEW DATABASE
Costa Rica did not update its own Input Output Matri x (IOM) for many decades. Leiva &
Vargas (2014) mention that before 2014 there had been only two matrices in the history of the
country, one from 1969 (Modelo Insumo-Producto para Cost a Rica - 1969: Un ensayo de Economía
Inter-industrial), and the 2011 version developed by the Banco Central de Costa Rica (BCCR). There
have been other approximations in between, such as t he matrix from 1991, which had been the
most widely used before the new publication. Even though in February 2 016 the newest version of
2 The positive implications of opening to trade have been well established theoretically for decades, see Samuelson (1939).
3 In all of these models simple resulting expressions summarize how much real consumption could increase when the country
opens to trade.
4 The work presented in the current paper focuses exclusively on the computation of the gains from trade from the gravity
models presented at the beginning of CRC. There are further theoretical and empirical discussions in their work which are not
discussed here, but should be of interest of any reader who wants to understand other modeling options, and the discussion of
the costs of using parsimonious models such as the ones discussed in this paper. These costs are usually related to the restricting
assumptions regarding functional forms that must be used, which may not be a good t in all dimensions of the data.