Trade and Income in the Long Run: Are There Really Gains, and Are They Widely Shared?



Diego A. Cerdeiro and Andras Komaromi


The virtues of trade – or lack thereof – for the level and distribution of income have recently come under close public scrutiny. General answers are hard to come by, as the effects of opening up to trade may depend on specific characteristics of a country or sector at a given point in time. Cross-country analysis, which exploits heterogeneity among economies with very different living standards, can however shed light on the following question: is trade good for income and equity in the long run? This paper aims to provide answers to this question by building and expanding on previous work that uses geography to extract exogenous variation in countries’ trading patterns.

On the face of it, we observe a strong positive correlation between trade and income, and a negative correlation between trade and inequality in the cross section of countries; countries with higher trade openness (exports plus imports as a share of GDP) tend to have higher living standards and lower income inequality. The gap between more open and less open economies in terms of their GDP per capita and income Gini coefficient is persistent, and, if anything, it has widened in the last two decades (Figure 1). However, inferring causality from this pattern is complicated. Trade openness is arguably endogenous in these simple bivariate relationships as many variables that affect income and inequality directly may also be correlated with trade itself. For example, countries that adopt open trade policies may also pursue other market-friendly domestic policies and conduct stable fiscal and monetary policies. Since these policies are likely to affect income and inequality, trade openness is likely to be correlated with important factors that are omitted from this na¨ıve approach.

One strand of the literature attempts to exploit countries’ exogenous geographic characteristics to achieve causal identification. The empirical success of the gravity model of trade demonstrates that geography is a powerful determinant of bilateral trade (e.g. Head and Mayer, 2014). The seminal paper by Frankel and Romer (1999, henceforth FR) showed that one can use this insight to construct an instrument for countries’ overall trade openness. In particular, they estimate a gravity equation that includes only geographical variables such as bilateral distance, area, and whether the countries are landlocked, and they aggregate the fitted values to obtain the predicted trade openness of each country. FR argue that the included geographic characteristics are unlikely to have important effects on countries’ income except through their impact on trade. Thus, the constructed trade openness can be used to obtain instrumental variables estimates for trade’s impact on income.

In this paper, we adopt the FR identification strategy to analyze the effect of trade on income and inequality, and investigate the robustness of the results over time and to alternative specifications. We extend the work of FR in four directions. First, in addition to real income per capita, we also estimate the impact of trade openness on various measures of within-country inequality. Second, instead of focusing on one cross-section of countries at a given point in time, we utilize more complete annual data from 1990 to 2015, and check whether the estimated effects are stable qualitatively and quantitatively over time. Third, as an improvement in the econometric methodology, we employ the Poisson pseudomaximum likelihood estimator to fit our gravity model of bilateral trade, which has a number advantages over simple OLS. Finally, we pay particular attention to establishing the validity and to testing the strength of our instrument. For example, in robustness checks we address a prominent early criticism of FR’s trade instrument (Rodriguez and Rodrik, 2001), and control for climate and institutions which may be correlated with geographical factors.

Our cross-country estimates for trade’s impact on real income are consistently positive and significant over time. The results indicate that a one percentage point increase in trade openness raises real income per capita by between 2 and 5 percent, with the lower estimates obtained for the period after the global financial crisis – a feature we relate to cyclical factors affecting the long-run estimate. At the same time, we find that, if anything, trade tends to reduce overall income inequality. The point estimates suggest that one-percentage-point higher openness causes the income of the bottom decile to increase by about 4 percent relative to the income of the top decile of the income distribution. When measuring income inequality with the Gini coefficient, most point estimates also suggest an inequality-reducing effect of trade. Although the estimated impact of trade on inequality is almost always negative, in many cases the coefficients appear insignificant according to standard asymptotic tests. Moreover, the weak instrument diagnostics signal lower reliability of the conventional IV inference in these specifications. Therefore, we adopt a cautious interpretation of the results, and emphasize the lack of statistical evidence for an inequality-inducing effect of trade in the long run.

Our results are qualitatively unaffected under various robustness checks. For example, in our baseline regressions we calculate openness using only merchandise trade, but we confirm that the instrument is also relevant for trade openness including services. We also control for the direct effect of geography through climate and the indirect effect through the historical development of institutions. We show that previous findings about the irrelevance of trade compared to institutions (the “primacy of institutions” result of Rodrik, Subramanian and Trebbi (2004)) can be attributed to important sample selection issues rather than the overall predominance of institutions. Our robustness checks support the qualitative message from our baseline regressions: trade improves living standards and there is no statistical evidence for any negative effect on aggregate income inequality. However, an important limitation is that the methodology can only provide suggestive evidence for the effects of trade policies. If geography-induced trade barriers have different impacts than policy-induced barriers, our results may not be directly informative for the makers of trade policy.

Related literature. Our work is closely related to cross-country studies that exploit geography to capture an exogenous component of trade, as in the pioneering work of Frankel and Romer (1999). In this context, Rodriguez and Rodrik (2001) have argued that geography can also affect income through its effect on public health, institutions and natural endowments (see also Rodrik, Subramanian and Trebbi, 2004). Dollar and Kraay (2003) point out that, due to the very high correlation between trade and measures of institutional quality, regressions including both variables tend to be uninformative. A critical survey of these discussions can be found in Hallak and Levinsohn (2004). More recently, Feyrer (2009b) circumvents the problem that physical distance can have an effect on income through non-trade channels by exploiting the effective shortening of some bilateral distances due to improvements in air transportation technology over time. Using a time-varying instrument based on geographic fundamentals, this paper also finds a positive effect of trade on income. Similar qualitative results are also found in Feyrer (2009a), who uses the 1967-1975 closing off of the Suez Canal as a natural experiment to investigate the trade-income causal relationship.

None of the studies mentioned above consider the effect of trade on income inequality, which is an integral part of our analysis. A growing empirical literature has used within-country micro data to look at the effect of trade shocks on income and wage inequality (for an overview, see for example Goldberg, 2015; Helpman, 2016).2 Worker-level data for a single country has the advantage that one can control for all macro-level shocks and characteristics. However, it has the drawback of potentially low external validity. Thus, our paper complements this literature by exploiting cross-country variation in different measures of income inequality, and providing answers about the long-run effects of trade on countries’ income distribution. A recent paper by Fajgelbaum and Khandelwal (2016) studies the distributional impact of relative price changes caused by international trade, considering that poor and rich households have different consumption baskets. They find that the price effects of trade typically favor the poor, which reinforces our results that are based only on the distribution of nominal income.

The rest of the paper is organized as follows. Section 2 describes the methodology. Section 3 presents the baseline results, an analysis of the validity and strength of the instrument and the robustness checks. Section 4 concludes.

2 Methodology and Data Sources

The FR conceptual framework

Let O denote a country-level outcome variable of interest such as real income per capita or a measure of inequality. For each year in which data are available, we want to obtain an estimate of the (long-run) effect of trade on this outcome variable. Consider, then, the reduced-form equation relating the outcome to a country’s international and internal trade,

Oi = α + βTi + γWi + εi, (1)

where Ti is country i’s international trade (for example, exports plus imports over GDP), Wi is within-country trade, and εi captures other determinants of Oi. Equation (1) is reduced form in the sense that it does not shed light on the specific channels through which trade’s effect on Oi operates. International and within-country trade are partly determined by geographical characteristics. Let Pi denote country i’s proximity to other countries, Si be a measure of the country’s size, and δi and νi be other factors influencing i’s international and within-country trade. We can then write

Ti = ψ + φPi + δi, (2)
Wi = η + φSi + νi. (3)

Given that estimation will rely solely on cross-sectional variation, we will need data for as many countries as possible. Since a coherent dataset of internal trade statistics is not available, we proceed as in Frankel and Romer (1999) and focus only on estimating the effects of international trade.4 To do so, substitute for Wi
in (1) using (3), and obtain

Oi = (α + γη) + βTi + γφSi + (γνi + εi). (4)

For almost any conceivable outcome variable Oi, residual εi will be correlated with residuals δi and νi . If O is real income, then for example behind-the-border regulatory barriers will affect both income and the level of internal and international trade. This implies that Ti will generally be endogenous, and OLS estimates of (4) deliver inconsistent estimates of β. However, the fact that proximity Pi affects Ti through equation (2) can be used for identification. Being a physical characteristic, clearly Oi cannot influence Pi. Key, then, for Pi to be uncorrelated with (γνi + εi), and thus be a valid instrument, is that proximity does not affect income through a channel different from international trade.

A geography-based instrument

The aim is to extract the cross-country variability of openness that is due to geographical determinants. Let tij denote trade between country i and country j in nominal terms. A log-linear model of the geographical determinants of this trade (expressed as fraction of i’s GDP, Yi, as in the openness measure) would be

ln(tij/Yi) = b0 + b1 ln Dij + b2 ln Ni + b3 ln Nj + b4 ln Ai + b5 ln Aj + b6(Li + Lj ) + b7Bij
+Bij [b8 ln Dij + b9 ln Ni + b10 ln Nj + b11 ln Ai + b12 ln Aj + b13(Li + Lj )]
+eij , (5)

where Dij is the distance between i and j, Ni is population size, Ai is area, Li is a dummy taking value 1 if the country is landlocked, and Bij a common-border dummy. Note, thus, that both area and population are included as measures of a country’s size.

Instead of estimating the log-linear equation (5) via OLS as in Frankel and Romer (1999), we use the Poisson pseudo-maximum likelihood (PPML) estimator popularized by Santos-Silva and Tenreyro (2006). PPML has several desirable properties for our application: (i) it admits zero bilateral trade flows which need to be dropped for OLS due to the necessary logarithmic transformation; (ii) it remains consistent even if the error term in the original nonlinear gravity relationship is heteroskedastic; and (iii) the fitted values directly yield an estimate of the level of bilateral trade, whereas OLS requires further assumptions to move from the estimated log-linear relationship to the predicted trade levels.

Equipped with these consistent estimates of E(tij/Yi|·), we can construct our instrument as

Tbi = Xjt\ij/Yi.

This instrument will be used to estimate our baseline specification

Oi = a + bTi + c1 ln Ni + c2 ln Ai + ui,

where, again, Oi is a measure of real income or inequality, Ti is trade openness, and Ni and Ai are population and area. This specification directly corresponds to equation (4) derived within the simple conceptual framework, noting that we include two measures of size. For Oi, we will consider the following outcome variables: log real GDP per capita (PPP-adjusted), log ratio of the average income of the top and bottom income deciles, and the market and net Gini cofficients. The market Gini is based on the distribution of household market (pretax, pre-transfer) income, while the net Gini is based on household disposable (post-tax, post-transfer) income.

Data sources

Our dataset covers the period 1990-2015. Data on bilateral merchandise exports come from the IMF’s Direction of Trade Statistics (DOTS). The advantage of this dataset is the extensive country and time coverage, with data available for more than 180 countries up to 2015. The downside is that it only reports trade in goods.6 Data on population, land area and real income per capita were downloaded from the World Bank’s World Development Indicators (WDI) database. Bilateral distance between countries, distance from the equator and the dummy variables for common border and being landlocked come from CEPII’s GeoDist database. For the bilateral distance variable, we use the population-weighted distance between major cities in the two countries.

We use the World Panel Income Distribution database by Lakner and Milanovic (2015) to construct countries’ ratio of top-to-bottom income per capita. Country-level income distribution data have heterogeneous periodicity and frequency across countries, and Lakner and Milanovic (2015) allocate each country’s vintage to the closest five-year intervals between 1988 and 2008. Since our gravity estimates start in 1990, the income ratio data will be used to obtain four different cross-section estimates of the effect of trade on the income ratio (1993, 1998, 2003, 2008). Gini data come from the Standardized World Income Inequality Database (SWIID, Solt, 2016). The SWIID relies extensively on imputed data across and within countries (see e.g. Jenkins, 2015). Imputation is particularly prevalent for countries in less developed regions. We use this dataset given its extensive cross-country coverage, but these issues naturally call for care in the interpretation of the results.

We use the Rule of Law index from the Worldwide Governance Indicators (WGI) project as a measure of institutional quality. Countries’ average temperature between 1961-1999 (a proxy for climate) is available from the World Bank Climate Change Knowledge Portal. The settler mortality rates used in Acemoglu, Johnson and Robinson (2001) were downloaded from Daron Acemoglu’s website.

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© Diego A. Cerdeiro and Andras Komaromi

The paper was originally posted here.