Inflation targeting and exchange rate regimes in emerging markets

Christian Ebeke; Armand Fouejieu

doi:10.1515/bejm-2017-0146

Article

Inflation targeting and exchange rate regimes in emerging markets

Christian Ebeke and Armand Fouejieu

Published/Copyright: July 11, 2018

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information Explore this Subject

From the journal The B.E. Journal of Macroeconomics Volume 18 Issue 2

Abstract

This paper investigates the effects of the adoption of inflation targeting (IT) on the choice of exchange rate regime in emerging markets (EMs), conditional on certain macroeconomic conditions. Using a large sample of EMs and after dampening the endogeneity of the adoption of IT using a selection on observables, we find that IT countries on average have a relatively more flexible exchange rate regime than other EMs. However, the flexibility of the exchange rate regime shows strong heterogeneity among IT countries. IT countries with low trade and financial openness and with a large share of external debt exhibit a lower exchange rate flexibility than others. Moreover, the marginal effect of IT adoption on the exchange rate flexibility increases with the duration of the IT regime in place, and with the propensity scores to adopt it.

Keywords: exchange rate regime; inflation targeting; macroeconomic conditions

JEL Classification: E5; C1; F3; F6

Acknowledgement

The views expressed in this paper are those of the authors and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Appendix

Table 4:

De Facto Exchange Rate Regime Classification.

Codes	Regimes
1	No separate legal tender
1	Pre announced peg or currency board arrangement
1	Pre announced horizontal band that is narrower than or equal to ±2 percent
1	De facto peg
2	Pre announced crawling peg
2	Pre announced crawling band that is narrower than or equal to ±2 percent
2	De factor crawling peg
2	De facto crawling band that is narrower than or equal to ±2 percent
3	Pre announced crawling band that is wider than or equal to ±2 percent
3	De facto crawling band that is narrower than or equal to ±5 percent
3	Moving band that is narrower than or equal to ±2 percent (i.e. allows for both appreciation and depreciation over time)
3	Managed floating
4	Freely floating
5	Freely falling
6	Dual market in which parallel market data is missing

Source: Course classification from Reinhart and Rogoff.

Table 5:

Sample.

IT countries	Non-IT countries
Brazil (1999)	Algeria	Saudi Arabia
Chile (1999)	Argentina	Tunisia
Colombia (1999)	Bulgaria	Ukraine
Czech Republic* (1997)	China	Venezuela, RB
Hungary (2001)	Ecuador
Indonesia (2005)	Egypt, Arab Rep.
Israel* (1997)	Hong Kong SAR, China
Korea, Rep. (2001)	India
Mexico (2001)	Jordan
Peru (2002)	Kenya
Philippines (2002)	Kuwait
Poland (1998)	Libya
Romania (2005)	Malaysia
South Africa (2000)	Morocco
Thailand (2000)	Nigeria
Turkey (2006)	Pakistan

Inflation targeting adoption date in parentheses. Czech Rep. and Israel are now considered Advanced Economies by the IMF.
Source: Roger (2009).

Table 6:

Data and sources.

Variable	Description	Source
ERR	De facto exchange rate regime classification	Reinhart and Rogoff data
Trade openness	Imports + exports of goods and services in percent of GDP	WDI, World Bank
Growth	Growth rate of GDP	WDI, World Bank
Economic development	Log of real GDP per capita	WDI, World Bank
Financial development	Domestic credit to private sector in percent of GDP	WDI, World Bank
Inflation	Percentage change in consumer price index	WDI, World Bank
Reserves	Total reserves in months of imports	WDI, World Bank
KAOPEN	Index of capital openness	Chinn and Ito (2008), updated 2011)
Politics	Index of political instability	ICRG
Fiscal	Change in government total debt	WEO, International Monetary Fund
Net imports	(Imports – exports of goods and services) in percent GDP	WDI, World Bank
External debt	Total external debt in percent of GDP	WEO, International Monetary Fund
Inverse of CBI	Five-year central bank governors turnover rate	Dreher, De Haan, and Sturm (2008)
Banks assets/liabilities	Banking institutions’ assets / liabilities	IFS, International Monetary Fund
Financial openness	De facto index of financial openness = (external financial liabilities + assets) in percent of GDP	Lane and Milesi-Ferretti (2007, updated 2011)

Table 7:

Robustness–random effects ordered probit estimates (controlling for crisis dummies).

	Dependent variable: de facto exchange rate regime
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)
IT	1.380***	1.384***	1.556***	1.298***	0.695**	0.449	0.438	1.486***	1.230***	1.414***	0.375
	(7.513)	(7.549)	(7.260)	(6.951)	(2.419)	(1.382)	(1.458)	(7.176)	(6.084)	(6.841)	(1.514)
IT*Trade openness		0.0142***
		(2.941)
IT*Financial openness			0.0198***
			(3.889)
IT*Financial development				0.0183***
				(3.820)
IT*Banks foreign assets/total assets					−0.0334**
					(−2.157)
IT*Banks foreign liabilities/total assets						−0.00877***
						(−2.690)
IT*Inflation							−0.142***
							(−3.894)
IT*Net imports								−0.0984***
								(−4.816)
IT*External debt									−0.0193***
									(−2.641)
IT*Pscore										2.226**
										(2.415)
IT*Time											0.223***
											(5.828)
Controls included?	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	640	640	642	640	594	594	640	588	624	602	640
Number of id	36	36	36	36	35	35	36	36	36	35	36
Wald chi2 stat	90.83	98.79	92.60	100.3	81.27	82.20	102.8	121.1	96.47	92.00	115.6

Random effects probit model with panel data; constant included but not reported; the control variables as well as interaction terms (not reported) are the same as in Table 1, in addition to Banking crisis, Currency crisis, and Sovereign debt crisis dummies; all the controls (except IT) are included with 1 year lag; the Wald chi2 test is a test for the null hypothesis that all the coefficients except the constant, are jointly equal to zero; ***, **, * indicate the statistical significance at 1, 5, and 10 percent respectively.
Source: Authors’ estimates.

Table 8:

Robustness–random effects ordered probit estimates (controlling for Central Bank Independence).

	Dependent variable: de facto exchange rate regime
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)
IT	1.390***	1.388***	1.536***	1.309***	0.674**	0.465	0.453	1.502***	1.239***	1.391***	0.396
	(7.587)	(7.597)	(7.229)	(7.035)	(2.356)	(1.443)	(1.509)	(7.305)	(6.162)	(6.683)	(1.609)
IT*Trade openness		0.0128***
		(2.694)
IT* Financial openness			0.0189***
			(3.738)
IT* Financial development				0.0177***
				(3.687)
IT*Banks foreign assets/total assets					−0.0349**
					(−2.265)
IT*Banks foreign liabilities/total assets						−0.00863***
						(−2.683)
IT*Inflation							−0.140***
							(−3.872)
IT*Net imports								−0.0977***
								(−4.792)
IT*External debt									−0.0193***
									(−2.677)
IT*Pscore										2.175**
										(2.356)
IT*Time											0.218***
											(5.822)
Controls included?	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	628	628	630	628	583	583	628	576	614	602	628
Number of id	35	35	35	35	34	34	35	35	35	35	35
Wald chi2 stat	89.16	95.97	89.73	97.86	78.72	79.00	101.2	118.1	96.37	90.24	114.7

Random effects logit model with panel data; constant included but not reported; control variables as well as interaction terms (not reported) are the same as in Table 1, in addition to a proxy for central bank independence, all the control variables (except IT) are included with 1 year lag; the Wald chi2 test is a test for the null hypothesis that all the coefficients except the constant, are jointly equal to zero;***, **, * indicate the statistical significance at 1, 5, and 10 percent respectively.
Source: Authors’ estimates.

Table 9:

Robustness–linear probability model.

	Dependent variable: de facto exchange rate regime
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)
IT	0.504***	0.529***	0.468***	0.457***	0.262*	0.160	0.0940	0.494***	0.378***	0.489***	0.148
	(6.105)	(6.419)	(5.754)	(5.464)	(1.931)	(1.043)	(0.726)	(6.069)	(4.591)	(5.208)	(1.402)
IT*Trade openness		0.00689***
		(3.115)
IT*Financial openness			0.00251**
			(2.032)
IT*Financial development				0.00546***
				(2.981)
IT*Banks foreign assets/total assets					−0.0124*
					(−1.782)
IT*Banks foreign liabilities/total assets						−0.00336**
						(−2.259)
IT*Inflation							−0.0619***
							(−4.079)
IT*Net imports								−0.0361***
								(−4.515)
IT*External debt									−0.00861***
									(−2.956)
IT*Pscore										0.984**
										(2.335)
IT*Time											0.0764***
											(5.221)
Constant	0.860	0.761	1.007	0.601	2.010	2.193*	0.954	1.973*	−0.732	−0.921	1.711
	(0.727)	(0.648)	(0.849)	(0.510)	(1.532)	(1.672)	(0.817)	(1.728)	(−0.612)	(−0.725)	(1.464)
Controls included?	yes	yes	yes	yes	yes	yes	yes	yes	yes	yes	yes
Observations	640	640	642	640	594	594	640	588	624	602	640
R-squared	0.118	0.133	0.115	0.131	0.110	0.112	0.142	0.208	0.173	0.129	0.157
Number of id	36	36	36	36	35	35	36	36	36	35	36
F stat	7.976	8.239	7.019	8.155	6.179	6.274	8.954	12.91	10.01	6.854	10.05

OLS panel fixed effects estimates; all the control variables as well as interaction terms (not reported) are the same as in Table 1; control variables (except IT) are included with 1 year lag; robust T-statistics in parentheses; ***, **, * indicate the statistical significance at 1, 5, and 10 percent respectively.
Source: Authors’ estimates.

Figure 2:

Probit model of the matching estimates.

Table 10:

Matching estimates – additional controls.

	Neighbor matching			Radius matching			Kernel matching
	Nearest neighbor	3 nearest neighbors	5 nearest neighbors	r = 0.1	r = 0.05	r = 0.02
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Controlling for political risk
	1.115***	1.026***	0.970***	0.892***	0.885***	0.929***	0.930***
	(7.767)	(7.774)	(8.166)	(10.09)	(9.176)	(9.206)	(8.657)
Obs.	617	617	617	617	617	617	617
	Controlling for banking crisis
	0.951***	0.788***	0.739***	0.789***	0.795***	0.844***	0.827***
	(5.121)	(4.764)	(5.099)	(8.679)	(6.881)	(6.366)	(5.888)
Obs.	490	490	490	490	490	490	490
	Controlling for stock market crisis
	0.937***	0.925***	0.903***	0.909***	0.918***	0.919***	0.903***
	(4.750)	(5.577)	(5.821)	(8.342)	(6.900)	(6.674)	(6.355)
Obs.	404	404	404	404	404	404	404

A 0.06 fixed bandwidth and an Epanechnikov kernel are used for kernel regression matching. T-statistics based on bootstrapped standard errors are reported in parentheses. The bootstrap is based on sampling from our sample of observation 500 times, with replacement. The estimated standard errors are the standard deviations of the estimated impact of the IT adoption across the 500 replications. ***, **, and * indicate statistical significance at the 1, 5, and 10 percent levels, respectively.

References

Abo-Zaid, S., and D. Tuzemen. 2012. “Inflation Targeting: A Three-Decade Perspective.” Journal of Policy Modeling 34: 621–645.10.1016/j.jpolmod.2011.08.004Search in Google Scholar

Aghion, P., P. Bacchetta, R. Rancière, and K. Rogoff. 2009. “Exchange Rate Volatility and Productivity Growth: The Role of Financial Development.” Journal of Monetary Economics 56 (4): 494–513.10.1016/j.jmoneco.2009.03.015Search in Google Scholar

Ai, C., and E. C. Norton. 2003. “Interaction Terms in Logit and Probit Models.” Economics Letters 80: 123–129.10.1016/S0165-1765(03)00032-6Search in Google Scholar

Ball, C. P. and J. Reyes. 2008. “Inflation Targeting or Fear of Floating Disguise? A Broader Perspective.” Journal of Macroeconomics 30 (1): 308–326.10.1016/j.jmacro.2006.08.005Search in Google Scholar

Chinn, M. D., and H. Ito. 2008. “A New Measure of Financial Openness.” Journal of Comparative Analysis: Research and Practice 10 (3): 309–322.10.1080/13876980802231123Search in Google Scholar

Dreher, A., J. De Haan, and J. E. Sturm. 2008. “Does High Inflation Cause Central Bankers to Lose Their Job? Evidence Based on a New Dataset.” European Journal of Political Economy 24: 778–787.10.1016/j.ejpoleco.2008.04.001Search in Google Scholar

Edwards, S. 1996. “The Determinants of the Choice between Fixed and Flexible Exchange-Rate Regimes.” NBER Working Paper No 5756.10.3386/w5756Search in Google Scholar

Garcia, C. J., J. E. Restrepo, and S. Roger. 2011. “How Much Should Inflation Targeters Care About the Exchange Rate.” Journal of International Money and Finance 30: 1590–1617.10.1016/j.jimonfin.2011.06.017Search in Google Scholar

Eichengreen, B., and R. Razo-Garcia. 2013. “How Reliable are De Facto Exchange Rate Regime Classifications?” International Journal of Finance and Economics 18: 216–239.10.1002/ijfe.1456Search in Google Scholar

Gonçalves, C., and J. Salles. 2008. “Inflation Targeting in Emerging Economies: What Do the Data Say?” Journal of Development Economics 85 (1–2): 312–318.10.1016/j.jdeveco.2006.07.002Search in Google Scholar

Güçlü, M. 2008. “The Determinants of Exchange Rate Regimes in Emerging Market Economies.” In Proceedings of the Conference on Emerging Economic Issues in a Globalizing World, 177–191. Izmir University of Economics, Papers of the Annual IUE-SUNY Cortland Conference in Economics.Search in Google Scholar

Klein, M., and J. C. Shambaugh. 2010. Exchange Rate Regimes in the Modern Era. Cambridge, MA: The MIT Press.10.7551/mitpress/9780262013659.001.0001Search in Google Scholar

Lane, P. R., and G. M. Milesi-Ferretti. 2007. “The External Wealth of Nations Mark II: Revised and Extended Estimates of Foreign Assets and Liabilities, 1970–2004.” Journal of International Economics 73: 223–250.10.1016/j.jinteco.2007.02.003Search in Google Scholar

Lin, S. 2010. “On the International Effect of Inflation Targeting.” The Review of Economics and Statistics 92 (1): 195–199.10.1162/rest.2009.11553Search in Google Scholar

Lin, S., and H. Ye. 2009. “Does Inflation Targeting Make a Difference in Developing Countries?” Journal of Development Economics 89 (1): 118–123.10.1016/j.jdeveco.2008.04.006Search in Google Scholar

Markiewicz, A. 2006. “Choice of Exchange Rate Regime in Transition Economies: An Empirical Analysis.” Journal of Comparative Economics 34 (3): 484–498.10.1016/j.jce.2006.06.004Search in Google Scholar

Martin, P., T. Maryer, and M. Thoeing. 2012. “The Geography of Conflicts and Free Trade Agreements.” American Economic Journal: Macroeconomics 4 (4): 1–35.Search in Google Scholar

McKinnon, R. 1963. “Optimum Currency Areas.” American Economic Review 53: 717–725.Search in Google Scholar

Méon, P.-G., and J.-M. Rizzo. 2002. “The Viability of Fixed Exchange Rate Commitments: Does Politics Matter? A Theoretical and Empirical Investigation.” Open Economies Review 13: 111–132.10.1023/A:1013916013825Search in Google Scholar

Minea, A., and R. Tapsoba. 2014. “Does Inflation Targeting Improve Fiscal Discipline?” Journal of International Money and Finance 40: 185–203.10.1016/j.jimonfin.2013.10.002Search in Google Scholar

Mishkin, F. S., and K. Schmidt-Hebbel. 2007. “Does Inflation Targeting Make a Difference?” In Monetary Policy Under Inflation Targeting, edited by F. S. Mishkin and K. Schmidt-Hebbel, 291–372, Central Bank of Chile.10.3386/w12876Search in Google Scholar

Mundell, R. 1961. “A Theory of Optimal Currency Areas.” Amarican Economic Review 53: 657–665.Search in Google Scholar

Rizzo, J. M. 1998. “The Determinants of the Choice of an Exchange Rate Regime: A Probit Analysis.” Economic Letters 59: 283–287.10.1016/S0165-1765(98)00056-1Search in Google Scholar

Roger, S. 2009. “Inflation Targeting at 20: Achievements and Challenges.” IMF Working Paper No. 09/236.10.5089/9781451873832.001Search in Google Scholar

Rose, A. 2011. “Exchange Rate Regimes in the Modern Era: Fixed, Floating, and Flaky.” Journal of Economic Literature 49 (3): 652–672.10.1257/jel.49.3.652Search in Google Scholar

Rosenbaum, P. R. 2002. Observational Studies. New York: Springer.10.1007/978-1-4757-3692-2Search in Google Scholar

Stone, M., T. Kisinbay, A. Nordstrom, J. Restrepo, S. Roger, and S. Shimizu. 2009. “The Role of the Exchange Rate in Inflation-Targeting Emerging Economies.” IMF Occasional Paper No. 267.10.5089/9781589067967.084Search in Google Scholar

Vega, M., and D. Winkelried. 2005. “Inflation Targeting and Inflation Behavior: A Successful Story?” International Journal of Central Banking 1: 153–175.Search in Google Scholar

von Hagen, J., and J. Zhou. 2005. “The Choice of Exchange Rate Regimes: An Empirical Analysis for Transition Economies.” Economics of Transition 13 (4): 679–703.10.1111/j.0967-0750.2005.00237.xSearch in Google Scholar

von Hagen, J., and J. Zhou. 2007. “The Choice of Exchange Rate Regimes in Developing Countries: A Multinomial Panel Analysis.” Journal of International Money and Finance 26: 1071–1094.10.1016/j.jimonfin.2007.05.006Search in Google Scholar

Article note

The views expressed in this paper are those of the authors and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Published Online: 2018-07-11

You are currently not able to access this content.

Articles in the same Issue

https://doi.org/10.1515/bejm-2017-0146

Keywords for this article

exchange rate regime; inflation targeting; macroeconomic conditions