“Measurement of trade policy is perhaps one of the toughest issues faced in the evaluation of trade policy, especially in cases where non-tariff barriers are the primary trade policy instrument … Even when trade restriction measures are available, as is the case with import tariffs, the available information comes at a highly disaggregate level. Economic analysis of these restrictions’ effects often requires the researcher to aggregate the information to a higher level (e.g., the industry, region or country) … economic analysis of the effect of these restrictions often requires the researcher to aggregate the information to a higher level (e.g., the industry, region, bilateral trade flow, or country) to map it to the level at which economic outcomes of interest are measured.” Goldberg and Pavcnik (2016)
Alesina, A., Furceri, D., Ostry, J., Papageorgiou, C., and Quinn, D., 2019. “Structural Reforms and Elections: Evidence from a World-Wide New Dataset,” National Bureau of Economic Research.
Auerbach, A., and Gorodnichenko, Y., 2013. “Output Spillovers from Fiscal Policy,” American Economic Review, vol.103(3), pp.141–46.
Caliendo, L., Feenstra, R., Romalis, J., and Taylor, A., 2017. “Theory and evidence for the last two decades of tariff reductions,” VoxEU.org, 26 April.
Chinn, M. and Ito, H., 2008. “A New Measure of Financial Openness,” Journal of Comparative Policy Analysis, vol.10–3, pp.309–322.
Corsetti, G., Meier, A., and Gernot Müller, G., 2012. “What Determines Government Spending Multipliers?,” CEPR Discussion Papers 9010.
Costinot, A., and Rodríguez-Clare, A., 2013. “Trade Theory with Numbers: Quantifying the Consequences of Globalization,” NBER Working Paper, No. 18,896.
Duval, R., and Furceri, D., 2018. “The Effects of Labor and Product Market Reforms: The Role of Macroeconomic Conditions and Policies,“ IMF Economic Review, Palgrave Macmillan; International Monetary Fund, vol.66(1), pp.31–69.
Ederington, J., and Ruta, M., 2016. “Non-Tariff Measures and the World Trading System,” World Bank Policy Research Working Paper, vol.7661.
Eichengreen, B. J., 1981. “A dynamic model of tariffs, output and employment under flexible exchange rates,” Journal of International Economics, vol.11, pp.341–359.
Eichengreen, B. J., Park, D., and Shin, K., 2012. “When Fast-Growing Economies Slow Down: International Evidence and Implications for China,” Asian Economic Papers.
Fratzscher, M., 2012. “Capital flows, push versus pull factors and the global financial crisis,” Journal of International Economics, vol.88, pp.341–356.
Furceri, D., and Loungani, P., 2018. “The distributional effects of capital account liberalization,” Journal of Development Economics, vol.130, pp.127–144.
Furceri, D., Loungani, P., and Ostry, J., 2019. “The Aggregate and Distributional Effects of Financial Globalization: Evidence from Macro and Sectoral Data,” Journal of Money, Credit and Banking, vol.51(S1), pp.163–198.
Furceri, D., Hannan, S., Ostry, J., and Rose, A., 2020. “Are tariffs bad for growth? Yes, say five decades of data from 150 countries,” Journal of Policy Modeling, vol.42, pp.850–859.
Furceri, D., Hannan, S., Ostry, J., and Rose, A., 2021. “The Macroeconomy After Tariffs,” The World Bank Economic Review, vol.0(0), pp.1–21.
Granger, C., and Terasvirta, T., 1993. “Modelling Non-Linear Economic Relationships,” OUP Catalogue, Oxford University Press, vol.9780198773207.
Hall, R., and Jones, C., 1999. “Why do Some Countries Produce So Much More Output per Worker than Others?,” Quarterly Journal of Economics, pp.83–116.
Henry, P. 2007. “Capital Account Liberalization: Theory, Evidence, and Speculation,” Journal of Economic Literature, vol.45(4), pp.887–935.
Jordà, O., 2005. “Estimation and Inference of Impulse Responses by Local Projections,” American Economic Review, vol.95(1), pp.161–18.
Kee, H. L., Nicita, A., and Olarreaga, M., 2009. “Estimating Trade Restrictiveness Indices,” Economic Journal, vol.119–534, pp.172–199.
Ostry, J., and Rose, A., 1992. “An empirical evaluation of the macroeconomic effects of tariffs,” Journal of International Money and Finance, vol.11, pp.63–79.
Rodríguez, F., and Rodrik, D., 2000. “Trade Policy and Economic Growth: A Skeptic’s Guide to the Cross-National Evidence,” NBER Macroeconomics Annual, pp.261–325.
Romer, C., and Romer, D., 2010. “The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks,” American Economic Review, vol.100(3), pp.763–801.
Romer, C., and Romer, D., 2018. “Why Some Times Are Different: Macroeconomic Policy and the Aftermath of Financial Crises,” Economica, vol.85(337): pp.1–40.
Rose, A., 2011. “Exchange Rate Regimes in the Modern Era: Fixed, Floating, and Flaky,” Journal of Economic Literature, vol.49–3, pp.652–672.
Sachs, J., and Warner, A., 1995. “Economic Reform and the Process of Global Integration,” Brookings Papers on Economic Activity, pp.1–118.
See Furceri et al. (2020) for a discussion on the output-effect of tariffs from earlier literature. For example, Eichengreen (1981) show that tariffs increase output and employment in the short run but could lead to decline in production in the long run. Ostry and Rose (1992) find no theoretical presumption about the effects of tariffs on output, with the impact depending on a host of factors.
The AREAER draws together information from a number of sources, including official IMF staff visits to its member. The individual country chapters include information related to restrictions on current international payments and transfers and multiple currency practices subject to the IMF’s jurisdiction, in accordance with Article VIII of the IMF’s Articles of Agreement, or maintained under Article XIV. The report also provides information on the structure and determination of exchange rates, monetary frameworks, arrangements for payments and receipts, procedures for resident and nonresident accounts, the operation of foreign exchange markets, controls on international trade and capital transactions, and measures implemented in the financial sector, including prudential measures. In addition, it lists exchange measures imposed by member countries for security reasons, including those reported to the IMF in compliance with IMF Executive Board decisions.
Each variable is, in principle, absolute, not relative; unity merely reflects the presence of a trade barrier (and zero its absence), not how the country*year observation compares with current best practice. In this, our measure differs from, e.g., Cerdeiro and Nam (2018).
The 1997 AREAER (p 1) states that the “Import and Import Payments” section of the data base describes the nature and extend of exchange and trade restrictions on imports.
Cerdeiro and Nam (2018) deplore the fact that measures of trade policy rarely extend far back in time.
MATR is also essentially unaffected by missing granular data since the latter can be filled in using AREAER entries on annual changes to fundamentals.
Figure A1.2 presents a histogram of the net changes in MATR between 1976 and 2016 for the 106 economies with data in both years. The histogram is clustered between zero and five, since MATR usually moves little on net even over 40 years.
That is, the mean weighted applied tariff rate for all products, available from the World Development Indicators.
Nevertheless, the visual impressions of Figure 4—and of other results elsewhere—stand up to more rigorous statistical inspection. This is clear from Table 1, which provides estimates when MATR is regressed on the variables of interest (such as the tariff rate or trade openness), controlling for year fixed effects as well as log size and income.
Since this is a three-way panel (countries, years, and fundamentals), we cannot use dynamic factor models. Dynamic factor analysis country-by-country does not seem sensible, since we only have 21 time-series observations. Factors and principal components extracted from the cross-section, year by year, yield basically the same factors as ours.
A different way to proceed is to use different sets of underlying AREAER fundamentals. Some of MATR’s components are more distant from the underlying objective of measuring trade restrictions; this suggests using only a more restricted set of fundamentals. But AREAER also provides indicators that we do not use, allowing for the more liberal use of fundamentals. We try both directions. Variant 1 is a restrictive version of MATR with only the sum of the eleven trade related variables (AREAER variables for both import restrictions [VII.A through VII.F], and export restrictions [VIII.A through VIII.E]). This is a relatively coarse variable, ranging in principle from 0–11. But we also create more inclusive measures than MATR. Variant 4 is the least restrictive and adds in 27 more fundamentals, using all the sub indicators of the main subcomponents (if there are any).Variants 3 and 4 are intermediate between Variant 1 and variant 4. In Figure A1.3, MATR is scattered against all six of these variants; it is highly correlated with each. This also shows up in more rigorous statistical analysis; Table 2 reports results when MATR and its variants are regressed against income, size, and year effects.
Novy’s trade costs and Quinn’s measure of current account and financial openness are available over long time spans.
Further details are available at https://www.unescap.org/resources/escap-world-bank-trade-cost-database.
http://reports.weforum.org/global-enabling-trade-report-2016/files/2016/11/GETR16_Global_FINAL_with-language-links.pdf; further details available at http://reports.weforum.org/global-enabling-trade-report-2016/downloads-page/. This measure is available for 2012, 2014, and 2016.
Further details are available at https://datacatalog.worldbank.org/dataset/overall-trade-restrictiveness-indices-and-import-demand-elasticities.
Further details are available at http://iresearch.worldbank.org/servicetrade/default.htm.
Rodríguez and Rodrik ask (p264) “… Do countries with lower policy-induced barriers to international trade grow faster, once other relevant country characteristics are controlled for? We take this to be the central question of policy relevance in this area… Note that this question differs from an alternative one we could have asked: Does international trade raise growth rates of income?” In his comment on the paper, Hsieh writes (p325) “Their main point is that the empirical evidence that purportedly shows a negative correlation between trade barriers and growth typically relies on measures that are either measures of macroeconomic imbalances or bad institutions and are not actually measures of trade barriers.”
Panel cointegration tests reject the null hypothesis that the estimated residual of equation (1) is non-stationary.
Equivalent to 0.82 changes in the index.
We have also reduced the sample in a number of ways, and again, present the results in Appendix 4 (Figure A4.2). In particular, we changed the sample through dropping: (i) series with gaps and less than 20 consecutive years of data; (ii) high inflation (>100%) episodes; (iii) small countries (population < one million); (iv) outliers (those with output residuals in the bottom and top percentiles of the distribution)22; (v) years before 1980; (vi) episodes with large changes in MATR change (corresponding to the 99th percentile of the distribution); (vii) observations from the Americas; and (viii) observations from Asia and Sub-Saharan Africa. Our results persist through all these perturbations. We also consider three perturbations to the methodology of (1). First, we expand the set of controls by including contemporaneous changes in the trade balance and the real exchange rate; this is equivalent to considering shocks to MATR that are orthogonal to contemporaneous shocks in these variables. Second, we restrict MATR to enter (1) only with a lag; that is, we exclude a contemporaneous effect of MATR on GDP. As discussed above, an important issue in estimating the causal economic effects of MATR is the contemporaneous relation between economic activity and MATR: our baseline specification (1) does not distinguish between changes in trade barriers that can be considered exogenous to economic activity in the short run, from those endogenous that are correlated with contemporaneous shocks to economic activity or that are motivated by short-term economic objectives. Another way to address endogeneity is to include a measure of expectations on contemporaneous growth as a control (Corsetti et al. 2012; Duval and Furceri 2018). We also implement this by including the IMF WEO GDP growth forecasts made in October of the same projecting year (e.g., the growth forecast for 2018 made in October of 2018). Happily our results remain robust to these alternative specifications. While these are only imperfect ways to address endogeneity, they provide some reassurance of the main findings. Finally, we re-estimate (1) but using the six different variants of MATR presented in Appendix 1(Figure A1.3); the results are presented again presented in Appendix 4 (Figure A4.3). Our key result – of a persistent, economically, and statistically significant decline in output after trade is restricted – does not depend on the precise measurement of MATR.
F(zit) = 0.5 is the cutoff between low and high z. The approach is similar to considering a dummy variable that takes value 1 when the z is below zero, or the underlying country characteristics (x) below average
These results are robust to alternative non-linear specifications, such as including in equation (1) either a) an interaction term between change in MATR and the level of tariff (GVC participation), or b) interactions between change in MATR and dummies that denote alternatively quartiles of distribution of the country’s characteristics.
Indeed, we run Granger causality tests between large episodes and growth, and do not find that past GDP growth helps to predict major changes in trade restrictions—the p-value for the test of the null hypothesis that GDP growth Granger cause large changes in MATR is about 0.76.
Forum for Research in Empirical International Trade.
Trade Law Center.
Foreign Trade Information Center. Organization of American States.
European Center for International Political Economy.