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Dutch Disease Scare in Kazakhstan: Is it real?

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Abstract

In this paper we explore the evidence that would establish that Dutch disease is at work in, or poses a threat to, the Kazakh economy. Assessing the mechanism by which fluctuations in the price of oil can damage non-oil manufacturing—and thus long-term growth prospects in an economy that relies heavily on oil production—we find that non-oil manufacturing has so far been spared the perverse effects of oil price increases from 1996 to 2005. The real exchange rate in the open sector has appreciated over the last couple of years, largely due to the appreciation of the nominal exchange rate. We analyze to what extent this appreciation is linked to movements in oil prices and oil revenues. Econometric evidence from the monetary model of the exchange rate and a variety of real exchange rate models show that the rise in the price of oil and in oil revenues might be linked to an appreciation of the U.S. dollar exchange rate of the oil and non-oil sectors. But appreciation is mainly limited to the real effective exchange rate for oil sector and is statistically insignificant for non-oil manufacturing.

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Notes

  1. The discovery of new oil fields or an exogenous technological shock would have the same effect (Corden 1984).

  2. According to the relative version of the Balassa-Samuelson effect, an increase in productivity of the open sector exceeding that of the closed sector may go in tandem with increases in real wages in the open sector without any loss in competitiveness, provided relative PPP holds for the open sector (i.e. the real exchange rate is stable over time). Assuming wage equalization between the open and the market-based sheltered sectors, prices in the closed sector will increase. This productivity-driven inflation in market-based nontradables then results in higher overall inflation and a positive inflation differential, which in turn causes the real exchange rate to appreciate.

  3. Note that the expressions “open sector” and “tradable sector” are used interchangeably in the paper. The same applies to “closed sector,” “sheltered sector” and “nontradable sector.”

  4. It should be noted that the share of the nontradable sector in GDP and in total employment should decrease according to the resource movement effect and it should increase according to the spending effect (see Oomes and Kalcheva 2007, for a summary of the effects of the Dutch disease). Note, however, that an increase in the share of nontradables in total employment may also occur if productivity gains are higher in manufacturing than in nontradables. The resulting rise in nontradable prices (Balassa-Samuelson effect) gives rise to an increase in the share of nontradables in GDP measured in current prices. This is something which can be observed in many advanced countries over time (Rowthorn and Ramaswamy 1997)

  5. More generally, high dependence on natural resources as the engine of economic growth can impede long-term growth in particular (1) in the presence of ill-defined property rights, imperfect or missing markets and lax legal structures, (2) if the fight for resource rents and the concentration of economic and political power hampers democracy and growth, and finally (3) if too many people get stuck in low-skill intensive natural resource-based industries (Gylfason 2001). The implications of this are that strong institutions and a good educational system aimed at upgrading human capital (to enable new and higher value-added industries to settle in the country) may help avoid the Dutch disease.

  6. The analyzed data are not reported here due to space constraints. Interested readers should refer to the long version of our study (Égert and Leonard 2007).

  7. This revival comes after the seminal paper of Meese and Rogoff (1983), which showed that a random walk outperforms exchange rate models (among others the monetary model) in forecasting exchange rates.

  8. This implies that an increase (decrease) in the exchange rate is a depreciation (appreciation) of the domestic currency vis-à-vis the foreign currency.

  9. It has been first proposed by Clements and Frankel (1980) and applied recently to transition economies by Crespo-Cuaresma et al. (2005a, b).

  10. Some cautionary notes should be addressed here when applying the monetary model to transition economies mainly because of the fragility of some of the strong underlying assumptions. First, the stability of the money demand function is probably a strong hypothesis for transition economies with multiple changes in the real economy and in the monetary policy framework. Second, PPP fails not only for the overall real exchange rate but also for the real exchange rate of the open sector (crucial for establishing the relationship between the exchange rate and money demand) as documented in, e.g., Égert et al. (2006a). Finally, the homogeneity imposed on some of the elasticities in different versions of the monetary model may fail in practice. For instance, Knell and Stix (2003) emphasize systematic cross-country differences in the α 1 and α 2 terms (hence, \( \alpha _{1} \ne \alpha ^{*}_{1} \) and \( \alpha _{2} \ne \alpha ^{*}_{2} \)). The same applies to φ and φ* given that the share of nontradable goods in the consumer price index is considerably lower in developing countries (around 25% in Kazakhstan in 2005) as compared to industrialized countries (around 40% in the euro area).

  11. At the beginning of the transition process, there was a rush on foreign goods.

  12. Net foreign assets were also incorporated into real exchange rate models via the so-called stock-flow approach advocated by Faruqee (1995), Aglietta et al. (1997), Alberola et al. (1999, 2002) and via the NATREX (natural rate of exchange) model of Stein (1994, 1995).

  13. However, the expected sign is not clear-cut for transition economies. These economies need foreign savings to finance economic growth and catching-up. Thus, an inflow of foreign capital, mainly FDI, may cause the real exchange rate to appreciate. However, in the longer term, once net foreign liabilities attain a critical level, the home country will have to start servicing its net foreign liabilities. As a result, any additional increase in net foreign liabilities would lead to a depreciation of the real exchange rate. This corresponds to the long-run relationship between net foreign assets and the real exchange rate.

  14. See e.g. MacDonald (1998a, b) for a general discussion on the variables and Égert et al. (2006a) for a discussion for transition economies.

  15. Standard unit root and stationarity tests are used: the augmented Dickey-Fuller (ADF), Phillips-Perron (PP) and the Elliott-Rothenberg-Stock (ERS) point optimal unit root tests and the Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test. In some cases, the tests provide conflicting results. However, they never indicate unambiguously that the series are stationary in level. This is why we conclude that the series are I(1). These results are available from the authors upon request.

  16. Before jumping to the model estimations, it is important to make sure that no major initial undervaluation is observed for Kazakhstan at the earlier stages of the transition process. Maeso-Fernandez et al. (2005) were the first to note that in the presence of an initial undervaluation of the real exchange rate, the estimated coefficients and the constant term in the real exchange rate equation could be biased. A simple first check for a possible initial undervaluation consists in regressing the level of the real exchange rate on GDP per capita in purchasing power standards (PPS) against the USD for cross-sectional data. The results (not reported here) indicate a large initial undervaluation in 1994. This was corrected very quickly, followed by another, rather prolonged and stable undervaluation period. Although initial undervaluation might pose a problem for the econometric estimations for the period from 1994 to 1998, there appears to be no long-lasting and indeed a steadily declining undervaluation.

  17. These results are not reported here because of space constraints. However, they are available from the authors upon request.

  18. Nonetheless, this does not mean that the estimation results are very robust across different estimation methods and alternative foreign benchmarks (effective exchange rate or against the dollar). Despite the fact that the variables turn out to be occasionally insignificant, the main variables such as relative income, relative money supply and the interest differential have the expected sign. A notable exception is the productivity differential and the relative price variable, which usually bear a positive sign instead of the negative one that one might expect. The finding that an increase in the productivity differential or in the relative price of nontradables does not cause an appreciation but leads to a depreciation or has no effect at all on the nominal exchange rate corroborates the preliminary evidence from Table 1, where increases in productivity in the open sector are not accompanied by a rise in relative prices as the Balassa-Samuelson effect would have predicted.

  19. Note also that a sensitivity check is performed with regard to different data definitions. Not only nominal GDP but also industrial production as a proxy for nominal GDP—as often done in the literature (Crespo-Cuaresma et al. 2005b)—is used. The results do not change quantitatively.

  20. These results are also available from the authors upon request.

  21. Note that the Balassa-Samuelson effect should explain the difference between the CPI- and the PPI-based real exchange rate. If PPP holds for tradables, the B–S effect has the potential to drive overall exchange rate movements. Otherwise it has a partial influence. By contrast, if the relative price of nontradable goods enters with very similar coefficients both the PPI- and CPI-deflated real exchange rate equations, this indicates that something else is going on.

  22. Similar to the nominal exchange rate estimations, a Russian crisis dummy is used for the entire period.

  23. The signs mostly meet our expectations. For instance, public expenditures usually have a negative sign, as have net foreign assets and terms of trade. The sign on the openness and public debt variables is positive but on some occasions, these variables may also have the opposite positive sign. As for the productivity variable, the estimated coefficients have, as a rule, a positive sign.

  24. Estimation results for the real price of oil are not reported because they are fairly similar to the ones obtained using the oil revenue variable.

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Authors and Affiliations

  1. Foreign Research Division, Oesterreichische Nationalbank, Otto-Wagner-Platz 3, Vienna, 1090, Austria

    Balazs Egert & Carol S. Leonard

  2. CESifo, Munich, Germany

    Balazs Egert

  3. Economix at the University of Paris X-Nanterre, Paris, France

    Balazs Egert

  4. William Davidson Institute, Ann Arbor, Michigan, USA

    Balazs Egert

  5. University of Oxford, Oxford, UK

    Carol S. Leonard

Authors
  1. Balazs Egert
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Correspondence to Balazs Egert.

Appendix

Appendix

1.1 Data sources and definitions

Monetary model (monthly data if not indicated otherwise)

Nominal exchange rates of the Kazakh tenge:

  • Against the US dollar: period average (IFS/IMF via Datastream: KZI..RF)

  • Against the euro: computed using the USD/EUR cross rate (Datastream code: EMEBXUSD)

  • Against the Russian ruble: computed using the RUB/USD cross rate (Datastream code: RSXRUSD)

The nominal effective exchange rate is obtained as the weighted average of the three exchange rates using constant weights derived from foreign trade shares.

Nominal GDP (annualized and interpolated linearly from quarterly to monthly frequency):

Kazakhstan: KZI99B..A

US economy: main economic indicators/OECD via Datastream: USI99B.CB

Euro area: Eurostat via Datastream: EMESNGDPB

Russia: Datastream: RSOSN014B

Industrial production:

Kazakhstan: Datastream: KZIPTOTQA; nominal quarterly data interpolated to monthly frequency and deflated by the PPI

US economy: main economic indicators/OECD via Datastream: USOPRI38G

Euro area: Eurostat via Datastream: EMESINPRG

Russia: IMF/IFS via Datastream: RSIPTOT.H

Money supply (M2):

Kazakhstan: Datastream: KZM3....A

US economy: FED via Datastream: USM2....B

Euro area: ECB via Datastream: EMECBM2.B

Russia: Datastream: RSOMA002B

Short-term interest rates:

Kazakhstan: money market rate, Central Bank of Kazakhstan

US economy: treasury bill rate; IFS/IMF via Datastream: USI60C..

Euro area: 3-month money market rate; Eurostat via Datastream: EMESSFON

Russia: 3-month interbank rate; Datastream RSINTER3

The explanatory variables except the price of oil are constructed as the Kazakh series over the weighted average of the three foreign series (US, euro area and Russia) based on constant weights derived from foreign trade shares, if the nominal effective exchange rate is used as dependent variable.

Real exchange rate models (monthly data if not indicated otherwise)

Productivity:

Industrial production (quarterly data interpolated to monthly frequency) divided by employment figures in industry or manufacturing. As data are not available for services, productivity in this sector is assumed to be equal to 0 in all four economies. If productivity gains are comparable in the four economies, this zero growth assumption has little effect on the variable.

Employment in industry (quarterly data interpolated to monthly frequency):

  • Kazakhstan: IFS/IMF via Datastream: KZI67...F

  • US economy: Bureau of Labor Statistics via Datastream: USEMPMANO

  • Euro area: Eurostat via Datastream: EMESEMPIH

  • Russia: IFS/IMF via Datastream: RSI67...F

Real exchange rate (nominal exchange rate multiplied by foreign prices over domestic prices):

Real exchange rate, whole economy: CPI index is used

Real exchange rate, tradables: PPI index is used as a proxy for tradable price inflation

Real exchange rate, non-oil manufacturing/tradables: PPI excluding oil prices are used

The real effective exchange rate is constructed similarly to the nominal effective exchange rate

CPI:

Kazakhstan: Statistical Agency of the Republic of Kazakhstan via Datastream: KZCONPRCF

US economy: main economic indicators/OECD via Datastream: USOCP009E

Euro area: Eurostat via Datastream: EMCONPRCF

Russia: WIIW via Datastream: RSCONPR2F

PPI:

Kazakhstan: Statistical Agency of the Republic of Kazakhstan via Datastream: KZPROPRCF

Kazakhstan–non-oil PPI: Statistical Agency of the Republic of Kazakhstan; constructed on the basis of the PPI series for food processing; textile and sewing industry; chemical industry; rubber and plastic products; and machinery and equipments. As no weights are available, an arithmetic average is taken.

US economy: main economic indicators/OECD via Datastream: USOPP019F

Euro area: Eurostat via Datastream: EMESPPIIF

Russia: WIIW via Datastream: RSPROPRCF

Relative prices: CPI to PPI ratio

The productivity and relative price variables are obtained as the Kazakh series over the weighted average of the three foreign series (US, euro area and Russia) if the real effective exchange rate is used as dependent variable.

Terms of trade: Statistical Agency of the Republic of Kazakhstan

Openness: Statistical Agency of the Republic of Kazakhstan; export and imports of goods over nominal GDP

Public debt to GDP: cumulated government deficit to GDP; Datastream: KZQ80...A; (quarterly data interpolated to monthly frequency)

Net foreign assets: cumulated current account deficits; Statistical Agency of the Republic of Kazakhstan

Public expenditure to GDP: Datastream: KZQ82...A; (quarterly data interpolated to monthly frequency)

Ural crude: Datastream: OILURAL

Oil revenues: selling price of oil multiplied by quantity; Statistical Agency of the Republic of Kazakhstan

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Egert, B., Leonard, C.S. Dutch Disease Scare in Kazakhstan: Is it real?. Open Econ Rev 19, 147–165 (2008). https://doi.org/10.1007/s11079-007-9051-7

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