This Selected Issues paper on the Arab Republic of Egypt examines the dynamic relationship between the nominal exchange rate and prices during Egypt’s exit from a managed exchange rate regime. The exit from the peg went through several phases, including a series of step devaluations between 2000 and 2002, a first attempt at a float in January 2003, and the successful transition to a unified, flexible exchange rate system in late-2004. From 2000 to 2004, the Egyptian pound experienced a cumulative depreciation of 68 percent against the U.S. dollar.

Abstract

This Selected Issues paper on the Arab Republic of Egypt examines the dynamic relationship between the nominal exchange rate and prices during Egypt’s exit from a managed exchange rate regime. The exit from the peg went through several phases, including a series of step devaluations between 2000 and 2002, a first attempt at a float in January 2003, and the successful transition to a unified, flexible exchange rate system in late-2004. From 2000 to 2004, the Egyptian pound experienced a cumulative depreciation of 68 percent against the U.S. dollar.

I. Exchange Rate Pass-Through1

A. Introduction

1. After pegging its currency to the U.S. dollar for almost a decade, Egypt began a transition to a flexible exchange rate system in 2000. The exit from the peg went through several phases, including a series of step devaluations between 2000 and 2002, a first attempt at a float in January 2003, and the successful transition to a unified, flexible exchange rate system in late–2004. From 2000 to 2004, the Egyptian pound experienced a cumulative depreciation of 68 percent against the U.S. dollar. During this period, there was an active parallel market for foreign exchange, with a premium that reached as high as 15 percent over the official (banking) rate. The parallel market rate converged with the banking rate in mid–2004, prior to the establishment of a formal interbank market for foreign exchange.

2. This chapter examines the dynamic relationship between the nominal exchange rate and prices during Egypt’s exit from a managed exchange rate regime. In theory, the movements in the nominal exchange rate since 2000 should have affected domestic prices and inflation through changes in import prices (both final and intermediate goods), as well as through expectations. A cursory examination of the data, however, suggests otherwise: inflation as measured by the consumer price index (CPI) remained remarkably low and stable from 2000 to 2003, despite the sizable cumulative depreciation of the pound (Figure 1). CPI inflation started rising only after July 2003, the first observation of a new CPI series with updated weights introduced in early 2004.2 The effects of the 2000–04 exchange rate movements on the wholesale price index (WPI) appear to have been quantitatively larger than on the CPI, but also exhibited long lags.

3. The methodology used in the chapter to measure the impact of exchange–rate movements on wholesale and consumer prices is based on a “price chain” model that seeks to identify the effects of exchange rate “shocks” at each stage of the distribution process through the estimation of a recursive vector autoregressive (VAR) model.3 This method was put forward by McCarthy (1999), and has become standard for examining empirical pass–through issues.4

Figure 1.
Figure 1.

Egypt: Consumer Price Index, Wholesale Price Index, and the Egyptian Pound (L.E.) per U.S. Dollar Exchange Rate, 1995–2004

Citation: IMF Staff Country Reports 2005, 179; 10.5089/9781451811858.002.A001

Source: Central Bank of Egypt.1/ The exchange rate series is the official (banking) rate from January 1995 to December 2000, and from October 2004 to December 2004. From January 2001 to September 2004, the chart depicts the parallel market rate.

4. The main findings of the chapter can be summarized as follows:

  • The exchange rate pass–through to the CPI in Egypt from the late 1990s to 2004 was low (ranging between 6 and 27 percent) and not statistically significant. The pass–through to the wholesale price index was much higher (from 30 percent to 60 percent) and statistically significant.

  • The pass–through in Egypt was slow. It took between six to 12 months for exchange rate changes to have a quantitatively significant impact on the WPI, and between 12 to 24 months to affect the CPI, though not significantly.

  • The pass–through in Egypt was much lower and slower than the one observed in other emerging markets in recent years.

  • A “counterfactual” model–based CPI suggests that the 12–month CPI inflation rate would have been 2 to 3 percentage points higher during the 2001-03 period, if the dynamic relationship between the exchange rate and prices identified for the late 1990s had been maintained.

B. Exchange Rate Pass–Through in Emerging Markets

5. The VAR methodology for estimating the pass–through was applied to a selected group of emerging market countries to provide a benchmark for the results obtained with Egypt’s data.5 The group consisted of Brazil, the Czech Republic, Israel, Mexico, Poland, South Africa, and Turkey. The same methodology, variable definitions, and sample period (1995–2004) were used in all cases in order to maximize comparability.6

6. The dynamic relationship between exchange rates and prices identified with the VAR can be usefully summarized by estimated measures of the level and speed of the pass–through at several horizons. The level of the pass–through at horizon j is defined as the ratio of cumulative responses of the price level and the exchange rate j periods after the exchange–rate shock.7 The speed of the pass–through measures the time it takes for the exchange rate shock to build up in the system, and is calculated as the ratio of the pass–through coefficient at horizon j over the long–run pass–through (assumed in this exercise to be the level of the pass–through after 24 months).

7. Results from the estimation are presented in Table 1. In terms of the level of the pass–through, countries fall into two broad groups: in the first group (Israel, Mexico, and Turkey), the level of pass–through is high; in the second group (Brazil, the Czech Republic, Poland, and South Africa), the pass–through is low to moderate.8 All the impulse responses of the (PPI) (or the WPI) and the CPI, even when quantitatively small (e.g., South Africa), are statistically significant at several horizons. Also, in all cases, the pass–through to the PPI (or the WPI) is not much different from the pass–through to the CPI.

Table 1.

Exchange Rate Pass–Through in Selected Emerging Markets

(1995–2004) 1/

article image
Sources: IFS; national authorities; and IMF staff estimates.

Response of the wholesale price index (WPI), producer price index (PPI), and consumer price index (CPI) to an exchange rate shock.

Number of months after the shock.

8. The differences in the speed of the pass–through are relatively small across countries. In all cases, most of the build–up of the pass–through to the WPI (at least two–thirds of the total) occurs between three and six months after the exchange–rate shock. In Israel, Poland, and South Africa, virtually all of the pass–through to the CPI (more than 90 percent of the total) is completed after 12 months. In the remaining countries (Brazil, the Czech Republic, Mexico, and Turkey), the pass–through is slightly slower, with about 75 percent to 85 percent completed after twelve months.

9. Mexico and Turkey, two of the countries that displayed high pass–through coefficients, experienced the highest and most volatile inflation rates during the sample period (Table 2). Countries that adopted inflation targeting relatively early in the period (e.g., the Czech Republic, Poland, and South Africa) generally had lower pass–through coefficients. The exception was Israel, which displayed high pass–through coefficients, despite having adopted inflation targeting in the early 1990s and having lowered the level and variability of inflation.

Table 2.

Annual CPI Inflation Rate in Selected Emerging Markets, 1995–2004

article image
Source: IFS.

C. Estimating the Exchange Rate Pass–Through in Egypt9

10. Since the period with flexible exchange rates in Egypt is short, the estimation of the VAR includes data from the fixed exchange rate years. Concretely, the estimates were obtained with monthly data for the period 1995–2004. This choice could introduce two potential biases: first, possible changes in the behavior of price setters in response to the change in exchange rate regime may render the estimated regression coefficients unstable; and second, the absence of exchange rate shocks during the first six years of the sample (the fixed exchange rate period) may lower the statistical significance of the estimates.10

11. The main results of the estimation are presented in Figure 2, and the top panel of Table 3. The results show significant differences in the responses of Egypt’s WPI and CPI to an exchange rate shock. The long–run pass–through to the WPI is about one third, and its response is statistically significant at the 95 percent level at several horizons. However, the response of the CPI to exchange rate shocks is not statistically significant at conventional levels, and the point estimate for the long–run pass–through is very low (11 percent). For both price indices, the pass–through is slow: it took between six and 12 months for exchange rate shocks to affect the WPI, and from 12 to 24 months for those same shocks to have a full impact on the CPI.

Figure 2.
Figure 2.

Egypt: Cumulative Response to a LE/U.S. Dollar Exchange Rate Shock

(+/-2 Standard Error Bands)

Citation: IMF Staff Country Reports 2005, 179; 10.5089/9781451811858.002.A001

Source: IMF staff estimates.
Table 3.

Egypt: Estimated Pass–Through Coefficients under Several VAR Specifications 1/

(1995–2004)

article image
Sources: IMF’s Information Notice System; and IMF staff estimates.

Response of the wholesale price index (WPI) and the consumer price index (CPI) to an exchange rate shock.

Number of months after the shock.

Baseline specification.

12. The weak relationship between exchange rate shocks and the CPI found for Egypt is at odds with most of the findings in the pass–through literature, including those reported in Table 1. One feature that may have contributed to this result is the relatively large share of goods with administered prices in Egypt’s CPI.11 In terms of the price chain implied by the VAR (see Appendix I.1), a large share of goods with administered prices would tend to compress the markups along the distribution process and weaken the transmission of exchange–rate shocks, at least temporarily.

13. The basic results for Egypt were not altered when the estimations were conducted using alternative measures of the exchange rate (the nominal effective exchange rate instead of the bilateral L.E./U.S. dollar exchange rate), and of supply shocks (commodity prices instead of oil prices)—see Table 3.12 In all cases, the cumulative response of the WPI remained significant at several horizons after the shock, while the responses of the CPI were not statistically significant from zero.13

D. A “Counterfactual” CPI

14. The large difference in the level and the speed of the pass–through coefficients between the WPI and the CPI is one salient result obtained in the Egypt case. This result corroborates the prima facie evidence presented in Figure 1: from 2001–03, the nominal exchange rate displayed large volatility, but CPI inflation was remarkably low and stable. Infact, the sample mean for the 12#x2013;month CPI inflation in Egypt during that period was 3.1 percent, and the standard deviation was 1.2 percent. These statistics are much lower than those observed in Egypt and other emerging markets during the whole sample period (see Table 2).

15. One factor that may account for this result is the highly persistent compression of margins that would result from a large share of administered prices in the price index. As noted earlier, this would tend to weaken the “price chain” model in its final stage (from the WPI to the CPI). To explore this possibility, three different “counterfactual” CPI indices were constructed for the years 2001–04, using the economic relationships derived from the VAR estimates. The first counterfactual consisted of an “unconditional” forecast of all five variables in the VAR model, with estimates obtained using data from January 1995 to December 2000. Since this model had difficulty forecasting the large depreciations of 2002–03, the second model used the actual values of the parallel market exchange rate and the estimated parameters from the VAR to produce the CPI counterfactual. The third counterfactual was produced using the estimated VAR parameters and the actual values of all the other variables in the system.

16. The unconditional forecast produced a CPI inflation rate that is 2 to 3 percentage points higher than the one recorded during 2001–03 (Figure 3). However, this model cannot replicate the sharp increase in the inflation rate that followed the release of the new CPI. This forecast converges with the actual CPI by end–2004.

17. The forecast conditional on the actual path of the nominal exchange rate differed from the unconditional forecast only after mid–2002. During 2002–03, the CPI inflation forecasts obtained with this model were 1–1 ½ percentage points above those obtained with the unconditional forecast, and more than 4 percentage points higher than the actual CPI inflation rate. By end–2004, this conditional forecast was about 5 percentage points above both the actual CPI figure and the unconditional forecast.

18. Compared to the first two counterfactuals, the third CPI forecast predicted a lower inflation rate during 2001–02, and a higher rate during 2003–04. This is most likely due to the influence of the lagged values of the actual WPI that were used to produce the forecasts. This third counterfactual CPI would have been about 4 percentage points higher than the actual CPI by end–2004.

19. All considered, the results from this exercise are broadly supportive of the hypothesis that Egypt’s CPI underestimated the underlying level of, and changes in, the CPI during 200 1–03. The forecasts obtained with the three models suggest that, in the initial stages of the transition to a flexible exchange rate, the dynamic relationships embedded in a VAR estimated with data for the 1995–2000 period were not operating fully. The new CPI index released in 2004, as well as price adjustments in some administered items, have helped to partially offset this compression of markups.

Figure 3.
Figure 3.

Egypt: Counterfactual CPI Based on VAR Model July 2000–December 2004

Citation: IMF Staff Country Reports 2005, 179; 10.5089/9781451811858.002.A001

Source: IMF staff estimates.

E. Conclusions

20. This paper has examined the pass–through of exchange rate fluctuations on wholesale and consumer prices in Egypt from 1995 to 2004. The pass–through of changes in the exchange rate to the WPI, and especially the CPI, was small and slow. The results based on a VAR model that identifies exchange rate shocks and their effects along the distribution process suggest that the pass–through to the WPI was in a range from 30 percent to 60 percent. The pass–through to the CPI was much lower, and was not statistically significant.

21. The large differences in the reaction of the WPI and the CPI point to structural problems with the CPI, which may have resulted in a temporary compression of price markups. Forecasts of the CPI produced in a counterfactual exercise suggest that CPI inflation in Egypt might have been higher in the 2001–03 period, if the dynamic relationships that existed in earlier periods had been maintained.

APPENDIX I.1 The VAR Model

The methodology employed in the chapter was first proposed by McCarthy (1999) and is based on a vector autoregressive (VAR) model that incorporates a recursive distribution chain of pricing. A five–variable VAR, that includes the price of oil in domestic currency (ptoil), output (yt), the nominal exchange rate (et), the wholesale price index (wpit), or, if available, the producer price index (ppit) and the consumer price index (cpit) is estimated. All variables are introduced in logs and first differences to render them stationary. Formally, the system is:

[ΔptoilΔytΔetΔwpitΔcpit]=Et1[ΔptoilΔytΔetΔwpitΔcpit]+[ϵtsα1ϵts+ϵtdβ1ϵts+β2ϵtd+ϵterγ1ϵts+γ2ϵtd+γ3ϵter+ϵtwpiδ1ϵts+δ2ϵtd+δ3ϵter+δ4ϵtwpi+ϵtcpi]

The rationale for this model is that oil shocks identify supply shocks, while output identifies demand shocks. The inclusion of output also measures the cyclical position of the economy. The exchange rate is therefore allowed to respond to supply and demand shocks, and to its own shock. Wholesale prices respond to these shocks, and to their own shock. The CPI responds to all the shocks of the system. The ordering of the variables denotes the “price chain” structure of the model, as it seeks to identify the effects of exchange rate innovations at each stage of the distribution process. The shocks are identified (orthogonalized) using the Cholesky decomposition of the variance–covariance matrix of the reduced form residuals.

In all cases, the sample covers the period 1995–2004, and uses monthly data. In all cases, six lags of each variable are introduced in the VAR. Due to data limitations, the starting date for Poland is November 1996; the starting date for Brazil is January 1996 in order to exclude observations from that country’s last hyperinflationary period.

APPENDIX I.2

Variables AND Data Sources

The data series used for the estimation of the VAR for Egypt were:

  • Price of oil in domestic currency: the Suez Canal blend price (in U.S. dollars per barrel) converted to Egyptian pounds using the nominal exchange rate.

  • Monthly real output: monthly electricity consumption by industry. Source: CBE.14

  • Exchange rate: the official (banking) nominal exchange rate with the U.S. dollar, except in the period January 2001–September 2004, when the official exchange rate was replaced with the parallel market exchange rate. Source: CBE.

  • Wholesale and the consumer price indices. Source: CAPMAS.

In the VAR estimation, all variables are introduced in logs and first differences after running the appropriate unit root tests. The monthly electricity series exhibits a strong seasonal pattern, and was therefore seasonally adjusted using the X–1 1 method. Six lags of the endogenous variables are used. While the Akaike and Schwartz criteria suggest two lags, the likelihood ratio test suggest twelve. An intermediate choice of six lags is made, in order to allow for enough endogenous transmission of the shocks in the system and obtain white noise residuals.

References

  • Belaisch, A., 2003, “Exchange Rate Pass–Through in Brazil,IMF Working Paper 03/141.

  • Bhundia, A., 2002, “An Empirical Investigation of Exchange Rate Pass–Through in South Africa,IMF Working Paper 02/165.

  • Billmeier, A., and L. Bonato, 2002, “Exchange Rate Pass-Through and Monetary Policy in Croatia,IMF Working Paper 02/109.

  • Hassan, M, 2004, “Driving Forces behind the Real Effective Exchange Rate: The Case of Egypt,unpublished manuscript, Central Bank of Egypt and Cairo University.

    • Search Google Scholar
    • Export Citation
  • Kara, H., Küçük Tuğer, H., Özlale, Ü., Tuğer, B., Yavuz, D., and E. Yücel, 2005, “Exchange Rate Pass–Through in Turkey: Has It Changed and to What Extent?,Central Bank of the Republic of Turkey Working Paper 05/04.

    • Search Google Scholar
    • Export Citation
  • Leigh, D., and M. Rossi, 2002, “Exchange Rate Pass–Through in Turkey,IMF Working Paper 02/204.

  • McCarthy, J., 1999, “Pass–Through of Exchange Rate and Import Prices to Domestic Inflation in Some Industrialized Economies,BIS Working Paper No. 79.

    • Search Google Scholar
    • Export Citation
  • Rabanal, P., and G. Schwartz, 2001, “Exchange Rate Changes and Consumer Price Inflation” 20 Months after the Floating of the Real,” in Brazil: Selected Issues and Statistical Appendix, IMF Country Report 01/10.

    • Search Google Scholar
    • Export Citation
1

Prepared by Pau Rabanal. The author would like to thank the Monetary Policy Unit of the Central Bank of Egypt (CBE) for useful comments on an earlier version of this paper.

2

The new series was released in January 2004, with the initial observation going back to July 2003. A further backward revision with the new weights has not been produced.

3

The main advantage of this methodology over single–equation regressions or cumulative pass–through calculations is that it takes into account the influence of other macroeconomic variables (e.g., supply shocks, the cyclical position of the economy, the effects of commodity prices) on the price level. One potential shortcoming is that the model is linear: it assumes that large and small exchange–rate shocks (in either direction) have the same proportional effect on prices. The methodology is also not well equipped to deal with parameter instability, a likely consequence of changes in the exchange rate regime, or with very short sample periods.

4

See, for instance, Rabanal and Schwartz (2001) and Belaisch (2003) for the case of Brazil; Billmeier and Bonato (2002) for Croatia; Leigh and Rossi (2002) for Turkey; and Bhundia (2002) for South Africa.

5

See Appendix I.1 for a brief description of the VAR methodology and other details of the estimation.

6

The estimations do not take into account possible changes in the monetary policy and exchange–rate regime. Belaisch (2003) and Kara, et. al (2005) investigate this possibility for the cases of Brazil and Turkey, and find evidence of a decline in the pass–through coefficients in both countries in recent years.

7

The pass–through at horizon j is defined as PTt,t+j = PTt,t+j/Et,t+j, where Pt,t+j is the cumulative response of the price level j periods after the shock, and Et,t+j is the cumulative response of the nominal exchange rate. For example, if six months after the shock, the nominal exchange rate has depreciated by 3 percent and the price level has increased by 2 percent, the pass–through level would be 66.6 percent.

8

The results for Brazil, South Africa, and Turkey are similar to those obtained in the studies cited in footnote 4. The VAR included the producer price index (PPI) in the four countries where it is available (the Czech Republic, Mexico, Poland, and South Africa). In the other three countries (Brazil, Israel, and Turkey), the VAR was estimated with the WPI instead.

9

The data used in the estimation is described in Appendix I.2. The estimations used the parallel market rate from January 2001 to September 2004 (i.e., the exchange rate series plotted in Figure 1) under the assumption that the parallel market rate was the one that influenced the pricing behavior of retail importers. The results were broadly similar when the estimations were done using the official exchange rate.

10

The behavior of CPI and WPI inflation during 2003–04 presented an additional complication for the choice of the sample period. Because these series exhibited low and stable values between 1999–2003, and started rising afterwards, a VAR estimated with a starting date in 1999 or 2000 delivered unstable roots. Hence, more observations from the past, when both inflation and the exchange rate were stable, had to be added to make the system stationary.

11

Roughly one third to one half of the items in the CPI series that was used until July 2003 is believed to have consisted of goods with administered prices, including food items, utilities, transportation, and rent.

12

The two series of commodity prices used in those estimations were obtained from the IMF Research Department.

13

Additional robustness checks (not reported in Table 3) included: starting the sample period in 1997, introducing M2 in domestic currency (M2D) in the system, or changing the ordering of the variables. The results did not change significantly with any of these changes.

14

This proxy is often used in empirical work for Egypt; see, for example, Hassan (2004).

Arab Republic of Egypt: Selected Issues
Author: International Monetary Fund
  • View in gallery

    Egypt: Consumer Price Index, Wholesale Price Index, and the Egyptian Pound (L.E.) per U.S. Dollar Exchange Rate, 1995–2004

  • View in gallery

    Egypt: Cumulative Response to a LE/U.S. Dollar Exchange Rate Shock

    (+/-2 Standard Error Bands)

  • View in gallery

    Egypt: Counterfactual CPI Based on VAR Model July 2000–December 2004