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Department of Economics, Harvard University and Institute of Economics, Hungarian Academy of Sciences. The first version of this paper was written while I was a summer intern in the Research Department of the International Monetary Fund. I am particularly grateful to my supervisor, Guy Meredith for useful discussions. I also thank John Campbell, Richard Cooper, Domenico Giannone, Elhanan Helpman, Borja Larrain, Marc Melitz, Kenneth Rogoff, Ádám Szeidl, Silvana Tenreyro, Oved Yosha, and seminar participants at Harvard and the Centre for Economic Policy Research workshop on “Macroeconomics and Economic Geography” for comments. Any remaining errors are mine.
See the estimates for the α coefficients in Evenett and Keller (2002). These have to be multiplied by two because we are considering exports plus imports, not just imports.
See Anderson and van Wincoop (2001) for a theoretically sound estimation of the gravity equation with trade barriers.
Formally speaking, because production is linear, the equilibrium allocation will almost always be a corner solution and not a smooth function of exogenous variables.
Note that this does not require a complete set of Arrow-Debreu securities, only that securities perfectly correlated with each of the shocks can be traded.
The difference between income (GNP) and output (GDP) in this model is that the former includes payoffs from financial transactions.
This assumes that there is no intra-industry trade. In a neoclassical model where products are homogeneous, no intra-industry trade would occur in the presence of arbitrarily small trade costs.
I use capital as the factor of production for expositional purposes only. The portfolio choice problem is more naturally interpreted this way.
At a more formal level, we can think of country risk as a pure background risk. In the constant absolute risk aversion framework background risks have no effect on portfolio choice.
With normal shocks, returns can be arbitrarily low, making wealth zero (or even negative) with positive probability. A power-utility investor would never take on such a risk, no matter how generously it is rewarded.
That is, the CARA and the CRRA functions have the same first and second derivatives at the point Yj.
First, since there is a riskless asset, forward contracts can be easily created from spot contracts. Second, if an asset is in positive net supply (such as the share of an industry), that supply may be incorporated into the set of non-financial assets. (In fact, the question of net supply seems to be an important distinction between financial and non-financial assets.) Third, because I am interested in the risksharing arrangements among regions, I have constructed assets that are pure bets. That is, assets have a zero expected payoff. If any of the assets had a nonzero expected payoff, it could be divided into a riskless asset delivering the expected payoff plus a pure bet. Fourth, the assets having an identity covariance matrix is not restrictive because the vector space of zero-mean assets always has an orthonormal basis; I pick that basis as the set of assets.
Here it is an important distinction that the shocks are just industry-specific and not region-specific.
This special case is not included in equation (17), which is a first-order condition for equilibrium. Complete specialization is attained as a corner solution, where the first-order condition is not binding.
In particular, countries with the same level of financial integration may be trading in different markets, aiming to insure different industries. Hence the sign of the cross derivative, f12, would be ambiguous depending on whether financial openness is aimed at more or less risky industries. Assuming, however, that the amount of international insurance in each of the sectors is identical within a country (or, at least, less than proportionately biased towards high-risk sectors), we can establish f12>0.
The main reason is that there is no widely accepted empirical framework using the Ricardian trade theory. (See discussion in the Introduction.) Some early, albeit atheoretical tests are found in Balassa (1963).
I thank Abdul Abiad for providing this data.
The “world” average is calculated from the 22 biggest economies which have data on all the industries.
As is common in cross-sectoral estimations, we have to scale the dependent variable to make sure that the estimations are not sensitive to the overall size of the sector. An industry with a broader classification will inevitable trade more than a narrowly defined industry. Unlike in most such empirical exercises, the scaling variable is theoretically pinned down by equation (22).
This may be a much higher percentage of exports because net exports tend to be small relative to domestic consumption.