Does financial sector development affect the growth gains from trade openness?

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A sizeable literature suggests that financial sector development could be an important enabler of the growth benefits of trade openness. We provide a comprehensive analysis of how financial development can affect the relationship between trade openness and growth using a dynamic panel threshold model and an extensive dataset for a large sample of countries for the 1970–2015 period. We find that there is a financial development threshold in which trade openness has a positive and significant link with economic growth. We also find that when splitting the sample into industrialized and non-industrialized countries, the financial development threshold that enables the trade and growth association is higher in the former group of countries than in the latter. This finding is consistent with the fact that the export composition of industrialized countries is tilted towards more capital-intensive finance-constrained goods.

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Fig. 1


  1. 1.

    Nonetheless, the direction of causality between these two variables is not clear. For instance, Kim et al. (2010) find a negative association between trade openness and financial development in low-income countries reflecting their weaker institutions and higher vulnerability to economic shocks. Do and Levchenko (2004, 2007) find a positive association between trade openness and financial system development in advanced countries and a negative association in developing countries. Lastly, Law (2007) finds that trade and financial openness are more potent in promoting financial development in middle-income countries.

  2. 2.

    This factor, however, should outweight other benefits from a more developed financial sector—such as the mitigation of financial frictions and imperfections—noted earlier.

  3. 3.

    Note that, since we assume data are observed from \(t=0\), model (2) is defined for \(t=1,2,\ldots ,T\).

  4. 4.

    Model (7) is not well defined for \(t=0\) since \(\Delta y_{i0}\) and \(\Delta x_{i0}\) are missing; that is, values for \(t=-1\) are not available; for which, assumption on the initial period \(t=1\) is required to ensure consistent estimates under the ML approach.

  5. 5.

    For further details on the estimation see Ramírez-Rondán (2019).

  6. 6.

    These are the results when considering the full sample. The rejection of the null hypothesis also holds when considering other sub samples and different sets of control variables.

  7. 7.

    We also perform a test in which we allow two thresholds, but we find that the test is not statistically significant.

  8. 8.

    Note that the null hypothesis of a linear model is rejected in all cases.

  9. 9.

    See “Appendix” for the classification based on the World Economic Situation and Prospects 2019, United Nations. The main results are robust to the International Monetary Fund’s World Economic Outlook (October 2017) classification.

  10. 10.

    As such coefficients come from different independent samples and regressions, we use the following Z test, following the approach of Clogg et al. (1995): \(Z = (\widehat{\beta }_{2,Ind}-\widehat{\beta }_{2,Nonind})/(SE(\widehat{\beta }_{2,Ind})^2+SE(\widehat{\beta }_{2,Nonind}))^{0.5}\), where \(\widehat{\beta }_{2,Ind}\) and \(\widehat{\beta }_{2,Nonind}\) are the coefficients of trade openness once the threshold is met for the industrialized and non-industrialized countries, respectively; and SE stands for the standard error in Table 7; and the test follows a standard normal distribution under the null hypothesis of equality of the two coefficients.

  11. 11.

    At the same time, the shares of agricultural raw materials and ores and metals in merchandise exports are higher in the non-industrialized countries (3% and 9%, respectively versus 2% and 6% in industrialized countries). The capital to labor ratio is the quotient of total employment on the capital stock of each country, both provided by the Penn World Table. The trade composition of each country is provided by the World Development Indicators.

  12. 12.

    When we estimate a model with two thresholds, we find no evidence for a second threshold.

  13. 13.

    For instance, Levine (1998, 1999) use the legal system (creditor rights, enforces contracts and legal tradition) as instruments for financial development; these instruments were originally built by La Porta et al. (1997). Unfortunately, these instruments have two shortcomings in the context of our study: (1) they are not available at a panel data level and for a large number of countries (since the available data covers less than 50 countries), and (2) they are indicator variables (the threshold regression method requires that the threshold variable is obtained from a continuous distribution).

  14. 14.

    Similarly, Mauro (1995) finds that improving the control of corruption index in a standard deviation would make the annual growth rate rise by 1.3%.

  15. 15.

    Note that human capital and public infrastructure have negative effects in the industrialized countries; these unexpected results can be due to the few countries in the subsample, which makes the slope results quite sensitive.


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Correspondence to N. R. Ramírez-Rondán.

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We are grateful to Roberto Chang, Antonio Cusato, Diego Winkelried, Laura Alfaro and the two anonymous referees as well as the participants of the Ninth conference on Growth and Business Cycle in Theory and Practice (Manchester, United Kingdom); the 2018 Congress of the Peruvian Economic Association (Piura, Peru); the 2018 Annual Meeting of the Economics Society of Chile (Valparaiso, Chile); the Global Research on Emerging Economies Conference (Lima, Peru), and the Universidad del Pacífico Research Seminar for their valuable comments and suggestions. As usual, all remaining errors are ours.

Appendix: Classification of countries

Appendix: Classification of countries

See Table 14.

Table 14 Classification of countries.

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Ramírez-Rondán, N.R., Terrones, M.E. & Vilchez, A. Does financial sector development affect the growth gains from trade openness?. Rev World Econ (2019) doi:10.1007/s10290-019-00369-8

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  • Trade openness
  • Growth
  • Threshold model
  • Panel data

JEL Classification

  • F43
  • O41
  • C33