Skip to main content

Advertisement

SpringerLink
Log in

Does inflation targeting matter for international trade? A synthetic control analysis

  • Published:
Empirical Economics Aims and scope Submit manuscript

Abstract

The literature highlights that inflation targeting (IT) economies enjoy a sizeable share of global trade, suggesting IT is a comparative advantage exclusive to such countries. Using a set of developed and developing countries—coupled with synthetic control methods—we estimate the causal effect of IT on the volume of international trade. We compare the post-IT path of each treated country to its synthetic counterpart. We find that IT adoption had sizeable positive (negative) long-run effects on Mexico’s (Brazil’s) trade and short-run effects, as large as 11 percentage points, on Uganda’s trade. For treated countries in Asia, Thailand recorded increases in trade, whereas the Korea Republic experienced nonlinear IT-induced trade effects. Norway registered negative IT effects, and Sweden, Canada, New Zealand and the UK experienced nonlinear effects in the post-IT period. IT induced negative and positive import and export volume and price changes in additional targeters. These heterogeneous IT-induced changes suggest that IT is not a source of comparative advantage in trade for all targeters and all post-adoption years.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Notes

  1. Greenspan (1994) defines price stability as the growth rate of inflation being sufficiently low such that households and firms do not consider it when making decisions.

  2. See Svensson (2010) for a discussion on the history, macroeconomic effects, theory, practice, and future of inflation targeting.

  3. Broadly speaking, we use Anderson and van Wincoop’s (2004) definition of trade costs, which includes all costs incurred in moving a good from producer to final user.

  4. In the 2015 joint OECD-WTO Aid for Trade monitoring survey, 87.0% of the 62 developing and least developed country respondents indicate that trade costs are very important for their export competitiveness, whereas 91.9% are of the view that trade costs are important or very important for access to imports.

  5. Allowing the control group to include suitable countries across all regions increases the size and power of the test Billmeier and Nannicini (2013).

  6. In terms of bias and variance, Ferman and Pinto (2019) show that the synthetic control estimator fares better over its DID counterpart in settings where treatment assignment is correlated with stationary common factors, non-stationary trends are present in the data, an imperfect pre-treatment fit exists, and the number of pre-treatment periods is not large.

  7. Additionally, the SCM limits study design bias in that researchers can construct a model unaware of how their design would influence the outcome of their study Abadie et al. (2010).

  8. Nevertheless, if the IT framework improves a country’s competitiveness in global trade, given that imports and exports are elastic to price changes, there may be a decrease in imports and an increase in exports; total trade may, hence, change based on the greater magnitude of these opposing effects.

  9. For example, Finland, Spain and the Slovak Republic adopted IT but eventually abandoned the regime.

  10. We thank two anonymous referees for highlighting these points.

  11. Our work also sits with the applied strand of SCM that investigates treatment effects in different macroeconomic contexts such as the evaluation of the impact of terrorism (Abadie and Gardeazabal 2003), economic and trade liberalization (Billmeier and Nannicini 2013), natural disasters (Cavallo et al. 2013). Ferman et al. (2018) document uses of the SCM in comparative case studies of civil wars and political risk, natural resources, international finance, education and research policy, health policy, political reforms, labour, taxation, crime, social connections, and local development.

  12. Abadie and Gardeazabal (2003) indicate that the restrictions on the weights in \(\tau \) prevents extrapolation bias thus restricting extrapolation within the support of our trade covariates for the control group. The vector of covariates for the treated unit will only be perfectly fitted if it lies in the convex hull of the trade covariates for the control group.

  13. Moreover, given the prevailing pattern in the adoption of IT regime by some countries, prima facie evidence to strongly suggest that the policy of IT adoption is contagious across countries is lacking.

  14. Note that an abundance of missing observations precludes us from starting prior to 1984.

  15. The length of such period is strictly left to our discretion as no reading thus far has indicated a prescribed length.

  16. Although the GDP deflator and the CPI are measures of inflation, note that an IT regime is predicated on the CPI measure of inflation and thus cannot be included in the model specification. Also, our SCM results with and without the GDP deflator are quantitatively and qualitatively indistinguishable.

  17. Furthermore, Gardeazabal and Vega-Bayo (2017) show that the SCM is less sensitive to changes in the control group relative to the panel data method put forward by Hsiao et al. (2012).

  18. In the sensitivity analyses, we perturb the core with additional covariates such as regional and oil exporter indicators, population, arable land, general government consumption and official exchange rate, which were also retrieved from The World Bank (2019). We also control for institutional quality, which is proxied by an average of variables from the International Country Risk Guide Database.

  19. In practice, two main data conditions must be satisfied for the SCM to work. Firstly, no observation must be missing for the outcome variable. Secondly, none of the covariates should have missing observations for the entire pre-treatment span of the treated unit under observation. Due to these two conditions, many countries were excluded from the sample. Following Abadie et al. (2010), we also exclude from the donor pool all countries that adopted IT but eventually abandoned the regime; these countries include Finland, Spain and the Slovak Republic. This was done because the counterfactual outcome is meant to reproduce what the outcome would be in the absence of IT.

  20. It is quite common in SC applications to have the number of control units exceeding the number of pre-treatment periods. Furthermore, Ferman (2019) shows that the synthetic control estimator can be asymptotically unbiased in settings where the number of control units exceeds the number of pre-treatment periods.

  21. All excluded information can be furnished upon request

  22. We utilize Abadie et al. (2011) for estimation in STATA and the augsynth package in R courtesy of Ben-Michael et al. (2021a).

  23. All control units contributed to the development of Canada, New Zealand and the UK synthetic trade.

  24. For some targeters, there seems to be a divergence between actual and synthetic trade in the post-IT short run; however, these trajectories signal some level of convergence in the long run.

  25. For example, for Korea Republic, the IT effect ranges from 26.18 to 51.34 percentage points from 2008 to 2017 but was \(-\) 14.60 (\(-\) 14.82) percentage points in 2002 (2003).

  26. Having implemented the policy in 2000 and 2001, respectively, both countries’ pre-IT counterfactual trade fit the observed relatively well, with South Africa boasting an RMSPE and \({\bar{R}}^2\) of 0.81 and 1, respectively, relative to Uganda’s values of 1.98 and 0.96. Synthetic South Africa is principally made up of Argentina (42.2%), Mauritania (10.3%) and Togo (12.4%). Synthetic Uganda’s primary donors are Bangladesh (65.1%) and Sudan (13.6%).

  27. The pattern of long-run convergence between actual and counterfactual trade is also evident among these targeters.

  28. For Colombia, the significant IT effect on trade ranges from \(-\) 26.56 to \(-\) 16.51 percentage points from 2009 to 2014; for Brazil, the IT effect ranges from \(-\) 20.10 to \(-\) 12.69 percentage points from 2005 to 2014; and for Mexico, the IT effect ranges from 17.93 to 39.06 percentage points from 2013 to 2017. Many of these effects are beyond the 5-year horizon, suggesting that medium- to long-run IT effects on trade may be more sensitive to pre-treatment fit.

  29. Stockman (1985) indicates that fully anticipated inflation influences the cost of holding money, which may have real effects on the exchange rate and international trade flow.

  30. Anecdotes suggest that the length of the transitional period differs across countries and depends primarily on the time taken to integrate the key elements. Albania, for example, adopted an IT lite regime in 2004 and later shifted to a full-fledged version in 2007, seeing a transitional period of three years. Additionally, the works of Ltaifa et al. (2012) and Pruski (2004) highlight that Brazil, Chile, Czech Republic, Ghana, Hungary, Israel, South Africa and Turkey experienced a transitional period of six months, ten, one, five, eight, two and four years, respectively. Thus, we choose the average transitional period to be four years.

  31. In reconciling these results with those of the synth wrapper in Table 8, we note that the synth wrapper results are adjusted for pre-treatment fit. However, the p-values we report for the null hypothesis of no IT effect on trade do not enjoy such adjustment.

  32. Thailand’s sizeable estimated effects, coupled with its in-time p-value and its trade dynamics with China during the post-IT period, warrant further investigation. Thus, we re-estimate Thailand’s SCM results by setting the timing to 4 years after IT implementation. We find that Thailand experiences material, positive and statistically significant IT effects on its trade in its post-IT period. However, these effects are smaller in magnitude than their counterparts implied by the in-space placebo. We thank a reviewer for pointing this out.

  33. Galiani and Quistorff (2017) divide all SCM effects by the corresponding pre-treatment match quality, as measured by RMSPE, to get “pseudo t-statistic” measures and “pseudo p-values”.

  34. IT countries omitted from this table register insignificant effects in the synth-wrapper.

  35. These estimates are from the synth wrapper that reports significance for more than 50% of the post-IT period-specific SCM estimates.

  36. We do not simultaneously control for all covariates because this would render our effective sample size useless.

  37. With the introduction of the logarithm of arable land in the augmented model, Sudan was ejected from the control group due to missing observations in the pre-treatment period for all targeters.

  38. In some instances, its inclusion marginally increases the RMSPE in the pre-treatment period for targeters in which Sudan had previously contributed to their synthetic outcomes; these countries include Albania, Brazil, India, Korea Republic, Mexico, South Africa, Sweden, Uganda, Uruguay.

  39. See Auboin and Ruta (2013) for a recent review.

  40. The addition of the official exchange rate to the augmented model resulted in Austria, Puerto Rico, France, Honduras, Malta, Mongolia and the Netherlands exiting the control group.

  41. This sample excludes China.

  42. This variable is also used to account for Gourdon and Messent (2017)’s finding that accession to WTO’s Government Procurement Agreement is effective in reducing this domestic bias in government procurement; see also Chen and Whalley (2011).

  43. With the addition of government consumption, 8 countries exited the control group.

  44. Also, there were marginally increases in the pre-IT RMSPE for Chile, Colombia, Brazil, Ghana, Guatemala, India, Indonesia, Korea Republic, Mexico, Norway, South Africa, Sweden, Thailand, Turkey, Uganda, Uruguay.

  45. The literature has examined the effect of various aspects of institutional quality on trade. See, for example, Anderson and van Wincoop (2003); Melitz (2003); Dunlevy (2006); Berkowitz et al. (2006); Aviat and Coeurdacier (2007); Nunn (2007); Levchenko (2007); Head et al. (2010); Amiti and Weinstein (2011); Levchenko (2012); Briggs (2013); Ferguson and Formai (2013); Sequeira and Djankov (2014); Araujo et al. (2016).

  46. The addition of the index to the augmented trade model resulted in 16 countries exiting the door pool.

  47. The latter manifests when the characteristics of the experimental unit are incorrectly parallelized by merging idiosyncratic differences in sizeable group of untreated units.

  48. We thank a reviewer for suggesting this idea.

  49. We thank the reviewers for highlighting this point.

  50. We use the synth wrapper to generate period-specific SCM estimates. The words in parentheses indicate the direction of the estimated IT effect.

  51. These import and export price data are from the Penn World Tables.

  52. For only a few post-IT years, Norway, Dominica Republic and Uganda experienced changes in their export prices, whereas Paraguay, Uruguay, Turkey and South Africa realized changes in their import prices.

References

  • Abadie A, Diamond A, Hainmueller J (2010) Synthetic control methods for comparative case studies: estimating the effect of california’s tobacco control program. J Am Stat Assoc 105(490):493–505

    Article  Google Scholar 

  • Abadie A, Diamond A, Hainmueller J (2011) Synth: an r package for synthetic control methods in comparative case studies. J Am Stat Assoc 42(13):1–17

    Google Scholar 

  • Abadie A, Diamond A, Hainmueller J (2015) Comparative politics and the synthetic control method. Am J Political Sci 59(2):495–510

    Article  Google Scholar 

  • Abadie A, Gardeazabal J (2003) The economic costs of conflict: a case study of the basque country. Am Econ Rev 93(1):113–132

    Article  Google Scholar 

  • Ahmed S, Appendino M, Ruta M (2017) Global value chains and the exchange rate elasticity of exports. The B.E. J Macroecon 17(1):1–24

    Google Scholar 

  • Amiti M, Weinstein DE (2011) Exports and financial shocks. Q J Econ 126(4):1841–1877

    Article  Google Scholar 

  • Andersen TB, Malchow-Møller N, Nordvig J (2014) Inflation-targeting, flexible exchange rates and macroeconomic performance since the great recession. CEPS Work Doc 394:10089

    Google Scholar 

  • Anderson J, Marcouiller D (2002) Insecurity and the pattern of trade: an empirical investigation. Rev Econ Stat 84:342–352

    Article  Google Scholar 

  • Anderson JE, van Wincoop E (2003) Gravity with gravitas: a solution to the border puzzle. Am Econ Rev 93(1):170–192

    Article  Google Scholar 

  • Anderson JE, van Wincoop E (2004) Trade costs. J Econ Lit 42(3):691–751

    Article  Google Scholar 

  • Anderson K, Josling T, Schmitz A, Tangermann S (2010) Understanding international trade in agricultural products: one hundred years of contributions by agricultural economists. Am J Agr Econ 92:424–446

    Article  Google Scholar 

  • Araujo L, Mion G, Ornelas E (2016) Institutions and export dynamics. J Int Econ 98:2–20

    Article  Google Scholar 

  • Athey S, Imbens GW (2017) The state of applied econometrics: causality and policy evaluation. J Econ Perspect 31(2):3–32

    Article  Google Scholar 

  • Auboin M, Ruta M (2013) The relationship between exchange rates and international trade: a literature review. World Trade Rev 12(3):577–605

    Article  Google Scholar 

  • Aviat A, Coeurdacier N (2007) The geography of trade in goods and asset holdings. J Int Econ 71(1):22–51

    Article  Google Scholar 

  • Bacchetta P, van Wincoop E (2000) Does exchange-rate stability increase trade and welfare? Am Econ Rev 90(5):1093–1109

    Article  Google Scholar 

  • Ben-Michael E, Feller A, Rothstein J (2021) The augmented synthetic control method. J Am Stat Assoc 5:1–34

    Google Scholar 

  • Ben-Michael E, Feller A, Rothstein J (2021b) Synthetic controls with staggered adoption. J R Stat Soc Ser B, Forthcoming

  • Berkowitz D, Moenius J, Pistor K (2006) Trade, law, and product complexity. Rev Econ Stat 88(2):363–373

    Article  Google Scholar 

  • Billmeier A, Nannicini T (2013) Assessing economic liberalization episodes: a synthetic control approach. Rev Econ Stat 95(3):983–1001

    Article  Google Scholar 

  • Briggs K (2013) Institutional quality as a barrier to trade. Appl Econ Lett 20(16):1453–1458

    Article  Google Scholar 

  • Broll U, Mukherjee S (2017) International trade and firms’ attitude towards risk. Econ Model 64:69–73

    Article  Google Scholar 

  • Brülhart M, Trionfetti F (2004) Public expenditure, international specialisation and agglomeration. Eur Econ Rev 48(4):851–881

    Article  Google Scholar 

  • Cavallo E, Galiani S, Noy I, Pantano J (2013) Catastrophic natural disasters and economic growth. Rev Econ Stat 95(5):1549–1561

    Article  Google Scholar 

  • Chang J-J, Chang W-Y, Tsai H-H, Wang P (2019) Inflation targeting, pattern of trade, and economic dynamics. Macroecon Dyn 23(7):2748–2786

    Article  Google Scholar 

  • Chen H, Whalley J (2011) The WTO government procurement agreement and its impacts on trade (Working Paper No. 17365). National Bureau of Economic Research

  • Chuang S-F, Huo T-M, Lin P-S (2005) Inflation and pattern of trade in a dynamic specific-factors model with money. Macroecon Dyn 9(3):358–371

    Article  Google Scholar 

  • Clark PB (1973) Uncertainty, exchange risk, and the level of international trade. Econ Inq 11(3):302–313

    Article  Google Scholar 

  • De Fiore F, Liu Z (2005) Does trade openness matter for aggregate instability? J Econ Dyn Control 29(7):1165–1192

    Article  Google Scholar 

  • de Mendonça HF, de Guimarães e Souza GJ (2012) Is inflation targeting a good remedy to control inflation? Journal of Development Economics 98(2):178–191

  • Dunlevy JA (2006) The influence of corruption and language on the protrade effect of immigrants: evidence from the American states. Rev Econ Stat 88(1):182–186

    Article  Google Scholar 

  • Ferguson S, Formai S (2013) Institution-driven comparative advantage and organizational choice. J Int Econ 90(1):193–200

    Article  Google Scholar 

  • Ferman B (2019) On the properties of the synthetic control estimator with many periods and many controls. Working Paper

  • Ferman B, Pinto C (2019) Synthetic controls with imperfect pre-treatment fit. Working Paper

  • Ferman B, Pinto C, Possebom V (2018) Cherry picking with synthetic controls. J Policy Anal Manag, Forthcoming

  • Firpo S, Possebom V (2018) Synthetic control method: inference, sensitivity analysis and confidence sets. J Causal Inference 6(2):1–26

    Article  Google Scholar 

  • Fratzscher M, Grosse-Steffen C, Rieth M (2020) Inflation targeting as a shock absorber. J Int Econ 123:103308

    Article  Google Scholar 

  • Galiani S, Quistorff B (2017) The synth_runner package: utilities to automate synthetic control estimation using synth. Stand Genomic Sci 17(4):834–849

    Google Scholar 

  • Gardeazabal J, Vega-Bayo A (2017) An empirical comparison between the synthetic control method and hsiao et al.’s panel data approach to program evaluation. J Appl Economet 32(5):983–1002

    Article  Google Scholar 

  • Ghironi F, Melitz MJ (2007) Trade flow dynamics with heterogeneous firms. Am Econ Rev 97(2):356–361

    Article  Google Scholar 

  • Gonçalves CES, Salles J, a. M. (2008) Inflation targeting in emerging economies: What do the data say? J Dev Econ 85(1):312–318

  • Gourdon J, Messent J (2017) How government procurement measures can affect trade. In OECD Trade Policy Papers. OECD Publishing, Paris

    Google Scholar 

  • Greenspan A (1994) Statement before the subcommittee on economic growth and credit formulation of the committee on banking, finance and urban affairs, U.S. house of representatives (Technical Report)

  • Head K, Mayer T, Ries J (2010) The erosion of colonial trade linkages after independence. J Int Econ 81(1):1–14

    Article  Google Scholar 

  • Hooper P, Kohlhagen SW (1978) The effect of exchange rate uncertainty on the prices and volume of international trade. J Int Econ 8(4):483–511

    Article  Google Scholar 

  • Hsiao C, Steve Ching H, Ki Wan S (2012) A panel data approach for program evaluation: measuring the benefits of political and economic integration of Hong Kong with Mainland China. J Appl Economet 27(5):705–740

    Article  Google Scholar 

  • Huchet-Bourdon M, Korinek J (2011) To what extent do exchange rates and their volatility affect trade? (OECD Trade Policy Papers No. 119). OECD Publishing

  • Hummels D, Klenow PJ (2005) The variety and quality of a nation’s exports. Am Econ Rev 95(3):704–723

    Article  Google Scholar 

  • IMF (2019) Annual report on exchange arrangements and exchange restrictions. International Monetary Fund

  • Kawai M, Zilcha I (1986) International trade with forward-futures markets under exchange rate and price uncertainty. J Int Econ 20(1–2):83–98

    Article  Google Scholar 

  • Krugman P (1980) Scale economies, product differentiation, and the pattern of trade. Am Econ Rev 70(5):950–959

    Google Scholar 

  • Krugman P (1996) Urban concentration: the role of increasing returns and transport costs. Int Reg Sci Rev 19(1–2):5–30

    Article  Google Scholar 

  • Lee W-S (2011) Comparative case studies of the effects of inflation targeting in emerging economies. Oxf Econ Pap 63(2):375–397

    Article  Google Scholar 

  • Leigh D, Lian W, Ribeiro MP, Szymanski R, Tsyrennikov V, Yang H (2017) Exchange rates and trade: a disconnect? (IMF Working Papers No. 17/58). International Monetary Fund

  • Levchenko AA (2007) Institutional quality and international trade. Rev Econ Stud 74(3):791–819

    Article  Google Scholar 

  • Levchenko AA (2012) International trade and institutional change. J Law Econ Organ 29(5):1145–1181

    Article  Google Scholar 

  • Lin S, Ye H (2007) Does inflation targeting really make a difference? evaluating the treatment effect of inflation targeting in seven industrial countries. J Monet Econ 54(8):2521–2533

    Article  Google Scholar 

  • Lin S, Ye H (2009) Does inflation targeting make a difference in developing countries? J Dev Econ 89(1):118–123

    Article  Google Scholar 

  • Ltaifa NB, Opoku-Afari M, Jafarov E (2012) Review of relevant country experiences in transitioning from monetary to inflation targeting. IMF Note

  • Melitz MJ (2003) The impact of trade on intra-industry reallocations and aggregate industry productivity. Econometrica 71(6):1695–1725

    Article  Google Scholar 

  • Melitz MJ, Ottaviano GI (2008) Market size, trade, and productivity. Rev Econ Stud 75(1):295–316

    Article  Google Scholar 

  • Melitz MJ, Redding SJ (2014a) Heterogeneous firms and trade. In: Handbook of international economics, vol 4, pp 1–54. Elsevier

  • Melitz MJ, Redding SJ (2014) Missing gains from trade? Am Econ Rev 104(5):317–21

    Article  Google Scholar 

  • Melitz MJ, Redding SJ (2015) New trade models, new welfare implications. Am Econ Rev 105(3):1105–46

    Article  Google Scholar 

  • Melitz MJ, Trefler D (2012) Gains from trade when firms matter. J Econ Perspect 26(2):91–118

    Article  Google Scholar 

  • Minea A, Tapsoba R (2014) Does inflation targeting improve fiscal discipline? J Int Money Financ 40:185–203

    Article  Google Scholar 

  • Nunn N (2007) Relationship-specificity, incomplete contracts, and the pattern of trade. Q J Econ 122(2):569–600

    Article  Google Scholar 

  • Pruski J (2004) Poland as an Example of Successful Transition From Inflation Targeting Lite to Full-Fledged Inflation Targeting. In: Schaechter A, Ugolini P, Stone M (eds) Challenges to central banking from globalized financial systems. International Monetary Fund, pp 142–147

  • Roldos JE (1992) A dynamic specific-factors model with money. Can J Econ Revue Canadienne d’Economique 25(3):729–742

    Article  Google Scholar 

  • Rose AK (2004) Do we really know that the WTO increases trade? Am Econ Rev 94(1):98–114

    Article  Google Scholar 

  • Samuelson PA (1954) The transfer problem and transport costs, II: analysis of effects of trade impediments. Econ J 64(254):264–289

    Article  Google Scholar 

  • Sandmo A (1971) On the theory of the competitive firm under price uncertainty. Am Econ Rev 61(1):65–73

    Google Scholar 

  • Sequeira S, Djankov S (2014) Corruption and firm behavior: evidence from African ports. J Int Econ 94(2):277–294

    Article  Google Scholar 

  • Shingal A (2015) Econometric analyses of home bias in government procurement. Rev Int Econ 23(1):188–219

    Article  Google Scholar 

  • Stockman AC (1985) Effects of inflation on the pattern of international trade. Can J Econ 18(3):587–601

    Article  Google Scholar 

  • Svensson L (2010) Inflation targeting. In: Durlauf S, Blume L (eds) Monetary economics. The New Palgrave Economics Collection, Palgrave Macmillan, London, pp 127–131

  • The World Bank (2019) World Development Indicators

  • United Nations (2019) World economic situation and prospects 2019

  • Viaene J-M, Zilcha I (1998) The behavior of competitive exporting firms under multiple uncertainty. Int Econ Rev 39(3):591–609

    Article  Google Scholar 

  • Wheeler G (2015) Reflections on 25 years of inflation targeting opening remarks. Int J Central Banking 11(S1):10059

    Google Scholar 

  • Wong K-M, Chong TT-L (2016) Does monetary policy matter for trade? Int Econ 147:107–125

    Article  Google Scholar 

  • Wong KP (2003) Export flexibility and currency hedging. Int Econ Rev 44(4):1295–1312

    Article  Google Scholar 

  • World Integrated Trade Solution (2017) Major import and export trading partners. https://wits.worldbank.org/CountryProfile/en/Country/CAN/Year/2017/TradeFlow/EXPIMP

  • WTO (2011) World Trade Report 2011. World Trade Organization, Geneva

  • WTO (2013) World Trade Report 2013. World Trade Organization, Geneva

  • WTO (2015) Why trade costs matter for inclusive, sustainable growth. Aid for trade at a glance 2015: reducing trade costs for inclusive, sustainable growth. WTO & OECD, Geneva, pp 34–60

Download references

Author information

Authors and Affiliations

  1. Department of Economics, The University of the West Indies at Mona, Kingston, Jamaica

    Nadine McCloud

  2. Research and Economic Programming Division, Bank of Jamaica, Kingston, Jamaica

    Ajornie Taylor

Authors
  1. Nadine McCloud
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ajornie Taylor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nadine McCloud.

Ethics declarations

Funding

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Organization Support

The authors did not receive support from any organization for the submitted work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

We are extremely grateful to the Editor, an Associate Editor and two anonymous referees for valuable comments that helped to significantly improve our paper’s quality. Also, we thank seminar participants in the Department of Economics at The University of the West Indies at Mona for their comments. All errors are our own The views expressed are those of the authors and do not necessarily reflect those of their institutions.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

McCloud, N., Taylor, A. Does inflation targeting matter for international trade? A synthetic control analysis. Empir Econ 63, 2427–2478 (2022). https://doi.org/10.1007/s00181-022-02221-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00181-022-02221-9

Keywords

JEL Classification

Search

Navigation