Quantifying the gains from trade

Giammario Impullitti, Omar Licandro 29 April 2018

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Recent research has shown that international trade can lead to job losses in some sectors and areas within a country and gains in others (Autor et al. 2013, Feenstra and Sasahara 2017), and it can also affect the country-wide level of wage inequality across workers (Helpman et al. 2017, Felbermayr et al. 2018). These side effects of globalisation have fuelled populist responses in Europe and the US, with political leaders promising to stop global market forces and re-empower the nation state (Rodrik 2017). If the gains from trade are small, is it worth facing the distributional consequences of globalisation and the political backlash associated with them?

The new quantitative trade theory

Assessing the size and identifying the sources of gains from trade is a long-standing challenge for economists. Theoretical and quantitative studies have mostly focused on static economies where changes in international market integration have only one-off effects on the levels of income and consumption but do not affect the long-run dynamics of these key economic variables (Costinot and Rodriguez-Clare, 2014). Since innovation and technological change are key drivers of income growth in the long-run (Aghion and Howitt, 2009, Akcigit et al. 2017), in a recent paper (Impullitti and Licandro 2018) we explore the sources and assess the size of the gains from trade in an economy where growth spurs from technological progress. 

Building a quantitative model for policy analysis requires a theory sufficiently grounded on a (large enough) set of relevant empirical facts. We focus on the following facts, each corresponding to a particular channel of gains from trade. 

  • First, there is strong evidence documenting competition effects of trade (Feenstra and Weinstein 2017). Increasing foreign competition is often found to reduce firm prices thereby shrinking their profit margins. Lower prices benefit consumer by increasing their purchasing power, this is the so-called pro-competitive effect of trade. 
  • Second, the reduction in profits forces some of the less-profitable and less-productive firms out of the market, thereby reallocating market shares toward the most productive firms (Bernard et al. 2012). This selection effect generates an additional channel of gains from trade, as more productive firms charge lower prices. 
  • Finally, foreign competitive pressure and selection, along with access to foreign markets induce firms to increase their investment in innovation to improve productivity and stay ahead of competitors (Bloom et al. 2015, Aghion et al. 2017). This innovation effect leads to dynamic gains from trade, as higher productivity growth produces not just one-off price reductions but a sequence of reductions across time, thereby increasingly benefiting present and future consumers.

We construct a model embedding all three key channels through which trade can potentially increase average income and consumption and calibrate it to replicate key aggregate and firm level trade and innovation statistics of the US economy. Frontier quantitative trade models embed only the first two channels in static economies where the effects of a policy change take place through timeless reallocations of market shares across firms and sectors but each firm's productivity is kept constant. The selection and competition effects of trade reallocates resources toward the most productive firms, thereby increasing the average level of productivity and reducing prices. In our dynamic economy, trade-induced reallocations increase the size of the most productive firms and raise their incentives to innovate, thereby pushing up the growth rate of productivity. Hence, the model is able to separately measure the static gains form competition and selection and the dynamic gains produced by the interaction of these forces with innovation-driven productivity growth.

Competition, selection and innovation: Quantitative assessment

In our main quantitative experiment, we explore a hypothetical scenario in which the US economy completely shuts down trade with foreign countries and compare some key economic outcomes with the current scenario where the economy shows an 8.6% import-to-GDP ratio. Focusing exclusively on the long-run (steady-state) equilibrium of our economy, we find that under the current trade level the US economy records an average profit rate that is about 30% lower than in autarky. Firms’ survival probability at entry is 8.6% under autarky and decreases to 2.7% under trade. The average growth rate, which in our economy corresponds to the growth rate of productivity, is 0.74% in autarky and 1.24% under trade. Due to the combination of these competition, selection and innovation responses to trade, the present value of long-run per-capita consumption (our measure of welfare) under trade is 50% higher than in autarky, a spectacular gain. As shown in Figure 1, about half of this consumption gain is accounted for by the effect of lowering the cost of trade on firms' incentives to innovate, the key source of growth in modern economies. Figure 1 computes the gains not only at the benchmark trade cost, correspondent to the observed trade level, but at all values of trade costs from the prohibitive cost to zero. It suggests that the dynamic gains become more relevant for larger liberalisation scenarios, which also yield stronger effect of trade on growth. 

Figure 1 Growth and welfare gains from trade

The key message of this research is that policy evaluation with static trade models is likely to underestimate the aggregate gains from globalisation. This result is relevant for the current debate on the economic effects of big trade agreements such as Trans-Pacific Partnership and the Transatlantic Trade and Investment Partnership, and episodes of economic disintegration such as Brexit. One of the key assessmentsof the trade consequences of Brexit (Dhingra et al. 2017) found that the worse-case scenario of no deal with the EU would imply an increase in trade barriers generating a loss equal to 2.7% of GDP per year, corresponding to £1,700 per family per year. This hypothetical scenario of no deal and reversal to trading under WTO rules is evaluated using a static model. The authors speculate that this is a conservative estimate, as considering dynamic effects would likely increase the size of the loss substantially. Once adapted to the specific exercise, our model could provide a framework for a more comprehensive assessment of the long-run impact of Brexit and other large-scale trade reforms.

Conclusion

Taking stock, our findings suggestthat policy evaluation with static trade models is likely to largely underestimate the gains from globalisation. We have shown that innovation can be a key driver of dynamic gains from trade. Other recent papers have suggested that dynamic gains from trade can also come via technology diffusion (Sampson, 2016, Perla and Tonetti, 2016), and also stressed the importance of quantifying both the short and long-run effects of trade by exploring the full transitional dynamics generated by trade shocks (Akcigit, et al. 2018). This new class of macro-trade models have laid the basis for future quantitative frameworks to measure the effects of globalisation on per-capita income and consumption. 

References

Aghion, P and P Howitt (2009), The Economics of Growth, MIT press.

Akcigit, U, S Ates, and G Impullitti (2018), "Innovation and Trade Policy in a Globalizing World", NBER Working Paper No. 24543.

Akcigit, U, J Grigsby and T Nicholas (2017), “The Rise of American Ingenuity: Innovation and Inventors of the Golden Age,” NBER Working Paper No. 23047.

Autor, D H, D Dorn, and G H Hanson (2013), “The China Syndrome: Local Labor Market Effects of Import Competition in the United States,” American Economic Review 103 (6): 2121-68.

Bernard, A B, B Jensen and P Schott (2012), “The Empirics of Firm Heterogeneity and International Trade,” Annual Review of Economics 4: 283-313.

Bloom, N, Draca and J Van Reenen (2016), “Trade Induced Technical Change: The Impact of Chinese Imports on Innovation, Diffusion and Productivity”, Review of Economic Studies 83(1): 87-117.

Costinot, A, and A Rodriguez-Clare (2014), “Trade Theory with Numbers: Quantifying the Consequences of Globalizatio,” in G Gopinath, E Helpman, and K Rogoff (eds), Handbook of International Economics. Vol. 4. 

Dhingra, S, H Huang, G Ottaviano, J P Pessoa and J Van Reenen (2017), “The Costs and Benefits of Leaving the EU: Trade Effects,” Economic Policy 32: 651-705.

Feenstra, R and A Sasahara (2017), “The ‘China Shock’, Exports and U.S. Employment: A Global Input-Output Analysis,” NBER Working Paper No. 24022.

Feenstra, R and D Weinstein (2017), “Globalization, Competition, and U.S. Welfare,” Journal of Political Economy 125(4): 1041-1074.

Felbermary, G, G Impullitti and J Prat (2018), “Firm Dynamics and Residual Inequality in Open Economies,” Journal of the European Economic Association, forthcoming.

Helpman, E, O Itskhoki, M-A Muendler and S J Redding (2017), “Trade and Inequality: From Theory to Estimation,” Review of Economics Studies 84(1): 357-405.

Impullitti, G, and O Licandro (2018). “Trade, Firm Selection, and Innovation: the Competition Channel,” Economic Journal 128(608): 189-229.

Perla, J, C Tonetti and M Waugh (2015). “Equilibrium Technology Diffusion, Trade, and Growth,” Working Paper, Stanford University.

Rodrik, D (2017). Straight Talks on Trade, Princeton University Press.

Sampson, T (2016). “Dynamic Selection: An Idea Flows Theory of Entry, Trade, and Growth,” Quarterly Journal of Economics 131(1): 315-380.

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Topics:  International trade

Tags:  gains from trade, innovation, globalisation

Associate Professor, School of Economics, University of Nottingham

Professor of Macroeconomics, University of Nottingham

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