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Trade policies and growth in emerging economies: policy experiments

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Abstract

This paper analyzes a dynamic endogenous growth model to quantify the channels through which international trade affects longer-term growth of an emerging economy, with an emphasis on the role played by trade policies. In the model, trade can promote (i) labor migration from agricultural to non-agricultural sectors and (ii) the inflow of foreign knowledge to enhance productivity in non-agricultural sector. The model is estimated to match with the data from selected advanced and emerging economies. Policy experiments suggest that openness to trade and elimination of trade barriers would raise annual real GDP growth by up to three percentage points for decades.

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Notes

  1. See, for example, Sachs and Warner (1995), Frankel and Romer (1999), Alcalá and Ciccone (2004), Keller (2004), and Felbermayr and Gröschl (2013).

  2. See Thompson (2010) for empirical survey on learning by doing.

  3. As documented by Grossman and Helpman (2015), opening to international trade can promote growth by facilitating knowledge transfer between economies.

  4. We choose Korea because it has the features of the economies on which we intend to study the role of international trade policies. We have also calibrated the small economy to the data of Thailand, China, and Poland, among others. The implications for the role of trade policies based on the calibration using those countries’ data are similar with the results when we use the data of Korea.

  5. The mechanism proposed in this paper should be viewed as a complement rather than a substitute to arguments provided in the literature regarding the relation between trade and growth. See, Melitz and Trefler (2012), for a review of other channels.

  6. Teignier (2018) argues that Korea’s gain in structural transformation and real GDP growth arising from opening could have been larger with less protections.

  7. Similar specification is adopted by Dolores Guillema, Papageorgiou and Pérez-Sebastián (2011).

  8. We disregard the government surpluses or deficits caused by such policy.

  9. The distortions include price-distorting trade measures (such as tariffs) and domestic price-supporting measures. The data provide a relative rate of assistance as \(RRA_{t}=(1+NRA_{A}^{t})/(1+NRA_{N}^{t})-1\), where \(NRA_{A}^{t}\) and \(NRA_{N}^{t}\) are nominal rate of assistance to agriculture and non-agriculture, respectively. We apply \((1+\sigma _{t}^{N})/(1+\sigma _{t} ^{A})=1/(RRA_{t}+1)\). The average of \((1+\sigma _{t}^{N})/(1+\sigma _{t}^{A})\) between 1991 and 2011 is 0.39 for Korea. For other economies, the averages of \((1+\sigma _{t}^{N})/(1+\sigma _{t}^{A})\) are 0.45 (Japan), 0.75 (Germany), 0.79 (France), 0.73 (United Kingdom), 0.91 (United States), and 1.01 (Australia).

  10. Using other values of A makes little difference on the model’s implications on the growth rate or the speed of structural transformation. In most of the equations in the model, A and \(N_{t}\) enter equations together as \(AN_{t}^{\theta }\) and the value of \(N_{t}\) will be adjusted when A takes a different value.

  11. Given the estimated model, we can also investigate how domestic frictions in goods and labor market affect output growth under a small open environment. See Appendix I for details.

  12. Note that the value of \(\gamma\) (calibrated from the “North”) from the benchmark is not applicable, as Korea’s agricultural production was lower than \(\gamma\) in 1968.

  13. We found there is a (different) range of A under which the simulated growth rate is consistent with data. Specifically, A cannot be lower than certain values and these lower bound values are different for different economies, whereas there is no upper bound limit for A. For each economy, without loss of generality, we use the lower bound value of A for the calibration and simulation exercises, as reported in Table 2. Other values in the range generate same simulation results.

  14. Alternatively, we also fix A of these countries to be the same as Korea and recalibrate other parameters. See Appendix II for details. The simulation results based on these alternatives are similar with those in Sect. 5.

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Authors and Affiliations

  1. International Monetary Fund, 1900 Pennsylvania Avenue Northwest, Washington, DC, 20006, USA

    Seung Mo Choi

  2. Department of Finance Mays Business School, Texas A&M University, 360p Wehner Building, 4218 TAMU, College Station, TX, 77843, USA

    Hwagyun Kim

  3. Department of Economics, Texas Tech University, Lubbock, TX, 79409, USA

    Xiaohan Ma

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  1. Seung Mo Choi
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  2. Hwagyun Kim
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Correspondence to Xiaohan Ma.

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S. M. Choi: The views expressed in this paper are those of the authors and do not necessarily represent the view of the International Monetary Fund, its management, or its Executive Board.

Appendices

Appendix 1

Given the estimated model, we can also investigate how domestic frictions in goods and labor market affect output growth under a small open environment.

1.1 Agricultural catch-up

Figure 11 shows the simulation result when there is no knowledge diffusion from non-agricultural to agricultural sector, i.e., θ = 0. Then, the marginal return of labor in non-agricultural sector becomes higher compared to the benchmark economy. As a result, structural transformation accelerates, which in turn leads to higher growth of the economy-wide productivity and thus higher real GDP growth.

Fig. 11
figure 11

Simulation without Cross-Sector Knowledge Diffusion. Note Dotted lines indicate the simulated time series of real GDP growth, employment share in non agricultural sector, productivity growth in non-agricultural sector, and consumer utility in the counter factual scenario that Korea does not have knowledge spillover from non-agricultural sector to agricultural sector. Solid lines show the simulated dynamics of the benchmark scenario. Circles represent the data on real GDP growth and employment share in non-agricultural sector. The simulated projection spans the period of 1968–2040 and is calculated based on the calibrated model

Full size image

1.2 Labor market friction

We consider an extreme assumption that lt does not change over time so that there is no structural transformation, i.e., we assume ξ = ∞. As shown in Fig. 12, labor no longer migrates from non-agricultural to agricultural sector, but remains at a constant level. This negatively affects the growth of productivity through the learning by doing channel, which in turn lowers the real GDP growth and the welfare.

Fig. 12
figure 12

Simulation without Structural Transformation. Note Dotted lines indicate the simulated time series of real GDP growth, employment share in non agricultural sector, productivity growth in non-agricultural sector, and consumer utility in the counter factual scenario that Korea does not have structural transformation due to labor market friction. Solid lines show the simulated dynamics of the benchmark scenario. Circles represent the data on real GDP growth and employment share in non-agricultural sector. The simulated projection spans the period of 1968–2040 and is calculated based on the calibrated model

Full size image

Appendix 2

As mentioned in Section V, for each economy, there is a lower bound value of A, and the corresponding values of N0 and δ shown in Table 2 such that the simulated growth rate fits data. In order to make comparison between economies while keeping the calibration sound, we can alternatively set the maximum minimal values of A = 1.16, the same as we use for Korea, for these economies and then recalibrate N0 and δ for each economy. The results are shown in Table 3. When we use these parameter values to run simulation exercises as those in the paper, we obtain similar conclusions with the benchmark model.

Table 3 Calibration results given A = 1.16
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Choi, S.M., Kim, H. & Ma, X. Trade policies and growth in emerging economies: policy experiments. Rev World Econ 157, 603–629 (2021). https://doi.org/10.1007/s10290-021-00413-6

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