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The research described in this paper was part of the work undertaken for the author’s Ph.D. dissertation at M.I.T. The author would like to thank Daron Acemoglu, Olivier Blanchard, Simon Johnson, and Jaume Ventura for invaluable guidance and support, Michael Alexeev, Shawn Cole, Andrei Shleifer, Petia Topalova, two anonymous referees, as well as numerous seminar participants for helpful suggestions, and Kevin Cowan and Claudio Raddatz for generously providing their data. Financial support from the National Science Foundation is gratefully acknowledged.
One possible classification of countries into North and South, based on PPP-adjusted per capita income, is offered in Table A4.
For example, institutions may influence firms’ choices of production process, e.g. Cowan and Neut (2002).
Indeed, there is both macro-level (e.g., Blanchard and Kremer, 1997; Claessens and Laeven, 2003), and micro-level evidence (e.g., McMillan and Woodruff, 1999; Johnson, McMillan, and Woodruff, 2002a, 2002b) that institutional arrangements do influence agents’ behavior in important ways.
The underlying mechanism, which is that a reallocation of industries between countries resulting from trade will affect welfare through reallocation of rents, is more general. It could also be modeled within the efficiency wage dual labor markets framework of Bulow and Summers (1986), or in a two-sector matching model of Acemoglu (2001). In the context of the interaction between globalization and European labor market institutions, a similar argument has been made by Allais (1994).
This paper is not the first to suggest that when a developed and a developing country open to trade, the North ends up with more desirable sectors. In the Young (1991) model, the South may lose because of decreased learning-by-doing. Galor and Mountford (2003) argue that the 19th century trade opening delayed demographic transition in developing countries, further increasing the South’s relative abundance in unskilled labor.
Generally, specificity is relevant for L as well. That is, fractions ϕLof L and ϕK of K become specific to the production unit. All that matters for the results, however, is the net effective specificity which in our case would be ϕKrx – ϕLw (see more on this in Caballero and Hammour 1998). All the results in this paper hold except for the knife-edge case in which the parameter values are such that the net effective specificity is zero. Thus, we sacrificed ϕL for expositional simplicity, and set ϕK= ϕ.
Though the approach to solving the model is similar to Case I, note that thinking of institutions in the contract incompleteness sense requires relaxing a different assumption in the standard Heckscher-Ohlin paradigm. In this case, we keep the common technology assumption, and focus instead on contracting problems. In particular, we must abandon the perfect competition in the factor markets assumption.
This expression relies on the implicit assumption that even though workers are strictly better off in the M-sector, they do not expend real resources competing for these jobs. Allowing for this possibility does not qualitatively alter the results in this section, provided that the M-sector rents are not dissipated completely. Complete rent dissipation occurs when the total expenditure by competing agents is equal to the total size of the M-sector rents. It can be ruled out by some relatively innocuous assumptions. For example, rents are not completely dissipated when agents are risk averse, or when agents differ in how much they value being in the M-sector. The latter could occur, for instance, if joining the M-sector is associated with dislocation (moving to the city), and agents differ in their disutility from it. For a detailed discussion of conditions under which complete rent dissipation breaks down, see Hillman (1989, pp. 58–72).
Note that while the aggregate welfare is at the first-best level, L may still lose from opening to trade, as it can no longer earn rents in the M-sector.
It is important to note that this is a direct consequence of assuming that contract incompleteness matters for capital and not for labor (ϕk> 0, ϕL= 0). Naturally, results are reversed, and more in line with the standard theory if one makes the opposite assumption. We hold the view that the assumption we made is more relevant empirically.
Empirical work (e.g. Acemoglu, Johnson, and Robinson, 2001) provides evidence that institutions are quite slow to change. Thus, this section should be interpreted as modeling the long-run effects.
Strictly speaking, of course, only labor employed in the M-sector earns rents, thus in some sense it would be most natural to take only this subset of the labor force to be the interest group. The problem with this choice is that the fraction of the labor force employed in the M-sector is itself a function of institutions in our model, so the boundaries of the interest group change with the policy choice. To avoid this problem, we assume that the interest group represents the entire labor force, and choose to ignore disagreements between its different subsets.
As discussed above, this is a direct consequence of the fact that the participation constraint for K in the M-sector must hold. In the presence of the holdup problem, the constraint is satisfied in part by pushing capital into the K-sector, thereby reducing its opportunity cost r. The higher the value of ϕ, the lower r must be to satisfy the constraint.
Alternatively, we could assume that the labor intensity in the M-sector production is very low.
A log-transformation cannot be used because many of the import shares are 0. Dropping all observations in which import shares are zero and estimating a specification with log(rel_shareic) as the dependent variable improves both the fit of the regression and the significance of the coefficient of interest.
We use this and other measures intermediate input use concentration following the work of Cowan and Neut (2002). We are grateful to Kevin Cowan for sharing the Stata code that generates these measures.
A measure of unskilled labor intensity is not included in the regression because by construction it is spanned by the constant term, capint3, and skint3.
Once again, the fourth factor, unskilled labor intensity, is implicit.
The number of firms available in each 4-digit SIC sector is generally small, often just 1 or 2 firms. To create meaningful averages, we compute them at 3-digit SIC level. We then drop all observations which were created by averaging less than 10 firms. We are very grateful to Claudio Raddatz for providing us with the necessary firm-level data and helpful advice.
Virtually the same results are obtained if we drop the 20 most institutionally intensive sectors, as well as the 10 or 20 least institutionally intensive sectors.
The exercise is complicated by the fact that per capita incomes are also highly correlated with the other country characteristics we use as controls. Indeed, the correlations between per capita incomes and capital and skill abundance are 0.90 and 0.83, respectively, higher than with institutional quality. We tried to allow per capita incomes to explain import shares through all the channels available to us, that is, we included interactions of per capita incomes with the other factors for which we have data. When we do this, the direct effect of institutional quality increases in magnitude, though still falls short of becoming statistically significant. By contrast, the point estimate on the interaction term of per capita GDP and institutional intensity becomes lower in magnitude, and remains insignificant.