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An empirical test of the Balassa-Samuelson hypothesis: Evidence from eight middle-income countries in Africa
Accepted Manuscript Title: An empirical test of the Balassa-Samuelson hypothesis: Evidence from eight middle-income countries in Africa Authors: Bernard Njindan Iyke, Nicholas M. Odhiambo PII: DOI: Reference:
S0939-3625(17)30030-4 http://dx.doi.org/doi:10.1016/j.ecosys.2016.10.001 ECOSYS 605
To appear in:
Economic Systems
Received date: Revised date: Accepted date:
22-2-2016 23-8-2016 7-10-2016
Please cite this article as: Iyke, Bernard Njindan, Odhiambo, Nicholas M., An empirical test of the Balassa-Samuelson hypothesis: Evidence from eight middle-income countries in Africa.Economic Systems http://dx.doi.org/10.1016/j.ecosys.2016.10.001 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
An empirical test of the Balassa-Samuelson hypothesis: Evidence from eight middle-income countries in Africa
Bernard Njindan Iyke* and Nicholas M. Odhiambo** Department of Economics, University of South Africa, P. O. Box 392, UNISA 0003, Pretoria, South Africa * Corresponding author. E-mail address: [email protected] / [email protected] **E-mail address: [email protected] / [email protected]
HIGHLIGHTS
We test the validity of the Balassa-Samuelson hypothesis (BSH).
Our sample is based on a panel of eight middle-income African countries for the period 1960-2009.
These countries exhibit a mixture of relative productivity growth and real exchange rate misalignment befitting the characterization of the BSH.
We use the within-effects and GMM estimators for the empirical estimations and find the BSH to hold.
Therefore, as these countries become more productive, their currencies appreciate in real terms.
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Abstract The paper tests the validity of the Balassa-Samuelson hypothesis (BSH) using the withineffects and the dynamic panel generalised methods of moment (GMM) techniques for a panel of eight middle-income African countries over the period 1960-2009. We selected these countries because they exhibited a mixture of relative productivity growth and real exchange rate misalignment that fits the characterization of the BSH well. The results strongly support the BSH for this group of countries. The results are valid even after we controlled for potentially omitted variables and endogeneity. The implication is that as these countries become more productive, their currencies appreciate in real terms.
Keywords: Balassa-Samuelson hypothesis, Purchasing power parity, Real exchange rate, Relative productivity
1. Introduction The law of one price postulates that identical goods and services should trade at the same prices, given that transaction costs and trade barriers are insignificant. This was the idea long held by Cassel (1918) when he was developing what is known today as the purchasing power parity (PPP) theory. Cassel argues that, if the PPP theory holds, the exchange rate between two countries should be seen as the relative general prices of the two countries (see Samuelson, 1994; Krugman and Obstfeld, 2009).
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The validity of the PPP theory was first questioned when Ricardo (1911), Harrod (1933) and Viner (1937) observed that long-run real exchange rates actually deviated from PPP. Concrete evidence against the PPP theory was first advanced by Balassa (1964) and Samuelson (1964), who found that real exchange rates persistently deviated from PPP due to the presence of productivity differentials in traded goods between countries. These productivity differentials, they argue, stimulate price and wage differentials, which widens the deviations between real exchange rates and PPP. In honour of their findings, the contribution of productivity differentials to the deviation of real exchange rates from PPP has been termed the Balassa-Samuelson effect or hypothesis in the literature (see Officer, 1976). We note briefly that the “appropriate” name for this phenomenon remains controversial. Variant terminologies can be found in the literature, such as the Penn effect, Balassa’s theory or proposition, and Ricardo-Viner-Harrod-BalassaSamuelson-Penn-Bhagwati effect, among others (see Bahmani-Oskooee and Nasir, 2005). The Balassa-Samuelson effect or hypothesis (referred hereinafter as BSH) has been tested widely and empirically. The evidence from time series and panel data techniques appears to validate the BSH more often than that from cross-sectional techniques. Nonetheless, the jury is still wide open. In this paper, we aim to provide new evidence of the BSH from a panel of eight middle-income countries in Africa that are best described as ‘developing’.1 Our choice of countries makes this empirical exercise more persuasive, in that these countries are more likely to exhibit significant economic growth than their developed counterparts, whose growth has already plateaued. This higher likelihood of rapid economic growth of the chosen countries fits perfectly into the “productivity differentials factor” identified in Balassa (1964) and Samuelson (1964). In other words, rapid economic growth should manifest clearly in the real exchange rate – in the form of real appreciation, which stimulates the deviation of the real exchange rate from 1
These countries are Botswana, Ghana, Lesotho, Mauritius, Namibia, Nigeria, South Africa and Zambia.
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PPP – than slow or balanced economic growth. This is a key feature of the countries chosen for our paper. In Figure 1, we present a simple graphical relationship between the real exchange rate and relative productivity for the panel of eight middle-income SSA countries in our study. The real exchange rate is measured as ln(XRAT/PPP)2, where XRAT is the exchange rate3 and PPP is the purchasing power parity conversion factors, and relative productivity is measured as the logarithm of real GDP per capita of the home countries divided by the real GDP per capita of the US. These variables are extracted from the Penn World Tables, version 7.1, compiled by Heston et al. (2012). From Figure 1, it appears that the relationship between the real exchange rate and relative productivity is negative. Both the quadratic and the lowess fits show an inverse relationship between the real exchange rate and relative productivity. In addition, the points appear somehow distributed around these line plots. The observed negative relationship implies that whenever these countries experience relative productivity increases, their real exchange rates appreciate. This is an indication of the Balassa-Samuelson effect (see Rodrik, 2008). The shortcoming of Figure 1 is that majority of the points appear to scatter above and below the linear regression line. Thus, the negative relationship between the real exchange rate and productivity seems weak.
To further illustrate the relationship between the real exchange rate and relative productivity growth, we present graphical evidence for two countries, namely Botswana and
2 3
See Rodrik (2008) for this definition. This is the domestic currency per US dollar exchange rate.
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Mauritius, in Figures 2 and 3. Note that the real exchange rates and the relative productivity plotted in Figures 1, 2 and 3 are based on five-year averages. These two countries have endured periods of real appreciation and depreciation of their exchange rates. In addition, they have experienced periods of rapid productivity growth as well as periods of productivity decline. These dynamic features make Botswana and Mauritius particularly captivating for our spotlight. Figures 2 and 3 show that both countries have experienced regimes of high relative productivity growth, barring some declines. At the same time, their real exchange rates have appreciated in real terms during this period. While we may be committing a post hoc fallacy by suggesting that the real productivity increment in these countries stimulated the real appreciation of their exchange rates, the evidence from the data appears to be so. In the remaining sections, we provide concrete econometric evidence to support these figures.
The rest of the paper is organised in three sections. In Section 2, we present the relevant theoretical and empirical literature on the BSH. In Section 3, we present a simple economic model that enables us to examine the BSH. In Section 4, we present our results and discuss our findings. In the final section, we provide some concluding remarks.
2. The relevant theoretical and empirical literature on the BSH 2.1 The theory The formal theoretical model that explores the BSH was first discussed in Rogoff (1992a). Before Rogoff’s rigorous theoretical model, the BSH was virtually descriptive in nature.
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For instance, Harrod (1933) and Samuelson (1964) only described the main elements of the BSH, and Balassa (1964) then provided a formal empirical verification. In addition to Balassa (1964), other authors such as Kravis and Lipsey (1983) and Bhagwati (1984) also documented empirical evidence on the BSH in their papers. A feature of these studies was that they mostly focused on the supply side of the economy and modelled linear relationships between the relative price level and the level of productivity (see Officer, 1976; Hsieh, 1982; Marston, 1987). In contrast to these earlier descriptive and empirical works, Rogoff (1992a) proposed a fully-fledged model for the BSH. His model was derived within the general equilibrium setting, with two Cobb-Douglas production functions for two domestically produced goods. The two domestically produced goods were tradable “T” and non-tradable “N”, which originated from the tradable sector and the non-tradable sector, respectively. In addition, these two goods were produced with three factors: labour “L”, capital “K”, and technology “A”. According to Rogoff (1992a), the two goods follow production functions of the form: 1−𝜃𝑇
𝜃𝑇 𝑌𝑇𝑡 = 𝐴𝑇𝑡 𝐾𝑇𝑡 𝐿𝑇𝑡
(1)
1−𝜃𝑁
𝜃𝑁 𝑌𝑁𝑡 = 𝐴𝑁𝑡 𝐾𝑁𝑡 𝐿𝑁𝑡
(2)
where 𝑌𝑇𝑡 and 𝑌𝑁𝑡 are the quantities of the tradable and the non-tradable goods at time 𝑡, respectively. 𝜃𝑇 and 𝜃𝑁 are the share of 𝐿 and 𝐾 in the production of 𝑇 and 𝑁, respectively, and 𝐴𝑇 and 𝐴𝑁 denote the stochastic productivity shocks in sectors 𝑇 and 𝑁, respectively. Rogoff (1992a) makes four assumptions: (i) the law of one price holds in the tradable sector; (ii) there is perfect international capital mobility; (iii) there is perfect market competition; and (iv) there is perfect factor mobility between the sectors of the economy. Relying on these assumptions, Rogoff shows that a change in the relative price of non-tradable goods depends on a change in the relative productivity of the two sectors in the form: 6
𝑑𝑝 = (𝜃𝑁 ⁄𝜃𝑇 )𝑑𝑎 𝑇 − 𝑑𝑎𝑁
(3)
where 𝑑 is the differential operator and the lower cases represent the logarithm of the variables. 𝑝 is the relative price of non-tradable goods in terms of tradable goods, and 𝑎 𝑇 and 𝑎𝑁 are the stochastic productivity shocks in the tradable and non-tradable sectors, respectively. Rogoff (1992a) argues that in order to arrive at a more realistic result, we must take capital and labour as given in each of the sectors and assume that the capital markets are closed to international borrowing and lending. Given these two assumptions, we obtain the following result: 𝑑𝑝 = 𝛽𝑇 𝑑𝑎 𝑇 − 𝛽𝑁 𝑑𝑎𝑁 − [(𝛽𝑇 − 1)𝑑𝑔𝑇 − (𝛽𝑁 − 1)𝑑𝑔𝑁 ]
(4)
where 𝛽 is the output-consumption ratio and 𝑔 is the logarithm of government consumption. According to Rogoff (1992a), (4) is identical to the Balassa-Samuelson result in (3) because the productivity shocks in (4) have an isomorphic effect to the one in (3). This model allows empirical studies to explore the impact of the demand side of economies on long-term relative price levels. Obstfeld and Rogoff (1996) have since provided an extension to Rogoff’s (1992a) model. Their model includes an additional factor of production. They also relaxed the axiom of international capital movement. Obstfeld and Rogoff (1996) then derived the BSH by basing it on the factor-price equalization concept of the trade theory. Moreover, other theoretical advancements have been made since Rogoff’s (1992a) paper. The discussion of these theoretical advancements is beyond the scope of our paper. We refer the interested reader to Asea and Mendoza (1994) and De Gregorio et al. (1994a) for earlier studies. For the most recent studies, the reader should consider Ghironi and Melitz (2005), Bergin et al. (2006), Méjean (2008) and Hassan (2016).
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2.2 The empirical evidence On the empirical front, the BSH has been tested by various studies. Different techniques have been employed with varying degrees of success. A review of the empirical literature can be found in Officer (1976), and more recently in Bahmani-Oskooee and Nasir (2005). The available empirical evidence on the BSH could be streamlined under three headings: (i) cross-sectional evidence, which includes studies such as Balassa (1964), De Vries (1968), Officer (1976), Heston et al. (1994) and Choudhri and Schembri (2010); (ii) time series evidence, which includes studies such as Hsieh (1982), Rogoff (1992b), Strauss (1995), Bahmani-Oskooee and Nasir (2004), Gente (2006), Lothian and Taylor (2008), Thomas and King (2008) and Chowdhury (2012); and (iii) panel evidence, which includes studies such as Asea and Mendoza (1994), De Gregorio et al. (1994a,b), Chinn (2000), Bahmani-Oskooee and Nasir (2002), Choudhri and Khan (2005), Genius and Tzouvelekas (2008), Peltonen and Sager (2009), Guo and Hall (2010), Chong et al. (2012) and Wang et al. (2016). These studies have produced conflicting findings, thereby leaving the relationship between the real exchange rate and relative productivity (growth) or the BSH open to further investigation. In his paper, Balassa (1964), for example, provided the earliest empirical evidence of the BSH. Balassa found two crucial pieces of evidence in support of the BSH, employing data from 12 OECD countries. First, by computing sectorial purchasing power parity (PPP) for the year 1950, he found that the prices of non-tradable goods were relatively lower in economies with relatively low incomes. Second, by using the data for the year 1960, he regressed the deviation of PPP from the equilibrium exchange rate on GNP per capita. He found the GNP per capita to have a positive and statistically significant coefficient. These two pieces of evidence, according to Balassa (1964), confirmed the BS effect.
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Officer (1976) later argued against Balassa’s (1964) results and noted that, instead of productivity, Balassa should have employed relative productivity. Officer estimated a crosscountry regression for each year from 1950 to 1973, varying the measure of productivity (i.e. between GDP per capita and GDP per worker), the ratio of productivity between the tradable and the non-tradable goods sectors, and the base country (alternating between the USA and Germany). Using a sample of 15 industrialised countries, Officer found no evidence in favour of the BSH. Bahmani-Oskooee and Niroomand (1996) found similar results, replicating Officer’s (1976) study by estimating a cross-sectional regression from 1974 to 1989. Lothian and Taylor (2008) argued that nonlinear models would fit the relationship between the real exchange rate and relative productivity (growth) better, suggesting that the previous findings may be questionable. The authors therefore examined the BSH in a non-linear setting, which allowed for shifts in the real exchange rate volatility across nominal regimes. They characterised, statistically, the non-linearity of the real exchange rate using the exponential smooth transition autoregressive (ESTAR) model. The study was based on three industrialised countries: the UK (1820-2001), the US (1820-2001) and France (1890-1998). Their results were found to support the BSH for the UK-US real exchange rate, but not for the UK-French real exchange rate. Lothian and Taylor argued that the BSH was not supported for the UK-French real exchange rate because of the parallel industrial development of the UK and France, which was not the case for the UK and the USA. Chowdhury (2012) attempted to improve estimation precision by utilising the ARDL approach, which performs well in small samples. He found two endogenous structural breaks in the data and incorporated them into the empirical model to capture any non-linearity in the series. Chowdhury found no evidence in support of the BSH in 6 out of the 7 South Asian
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countries, namely Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka. However, his results supported the BSH for Bangladesh over the period 1959-2003. Specifically, his results suggested that a percentage increment in the productivity of labour in Bangladesh relative to the US led to about 2.5% appreciation of the real exchange rate in Bangladesh. Apart from the conflicting evidence documented in the existing literature, the BSH has received less attention in the context of Africa. Most studies only incorporated few African countries in their samples. Among the existing studies which include African countries are Bahmani-Oskooee and Nasir (2001, 2002, 2004), Bahmani‐Oskooee and Miteza (2004), Genius and Tzouvelekas (2008), Peltonen and Sager (2009), Dumrongrittikul (2012), Chen et al. (2015), Hassan (2016) and Wang et al. (2016). Among these studies, only Bahmani‐Oskooee and Miteza (2004), Bahmani-Oskooee and Nasir (2001, 2004) and Genius and Tzouvelekas (2008) focused on African countries in particular. We add to this growing literature by focusing on selected middle-income countries in Africa.
3. The empirical model In order to test the BSH, we specify a simple model in the spirit of Rodrik (2008). The first step is to extract the exchange rates (XRAT) and purchasing power parity (PPP) conversion factors from the Penn World Tables, version 7.1, compiled by Heston et al. (2012). Next, we construct the real exchange rate index as follows: 𝑙𝑛𝑅𝐸𝑅𝑖𝑡 = 𝑙𝑛(𝑋𝑅𝐴𝑇𝑖𝑡 ⁄𝑃𝑃𝑃𝑖𝑡 )
(5)
where 𝑖 is the country under consideration and 𝑡 is a five-year time period. The basis for constructing the variables using five-year averages is to eliminate noise effects that are often inherent in annual data. Besides, real exchange rate effects are known to take more than a year to
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appear (see Freund and Pierola, 2008; Rodrik, 2008; Aghion et al., 2009; and Rapetti et al., 2011). We also construct variables based on one-year annual data as a robustness check (i.e., we set t=1 instead of t=5). Note that XRAT and PPP are denoted in national currency units per US dollar. When RER is more than unity, it implies that the currency is more depreciated than the purchasing power parity implies. The simple model relating the real exchange index that we have constructed to the relative productivity index is of the form: 𝑙𝑛𝑅𝐸𝑅𝑖𝑡 = 𝛼 + 𝛽𝑙𝑛𝑃𝑅𝑂𝐷𝑖𝑡 + 𝑓𝑖 + 𝑓𝑡 + 𝜀𝑖𝑡
(6)
where 𝛼 and 𝛽 are parameters of the model, and 𝑃𝑅𝑂𝐷𝑖𝑡 is the index of relative productivity of country 𝑖 in period 𝑡, which is proxied by the real GDP per capita of the home country relative to that of the US. Officer (1976) recommends that we use relative productivity or relative productivity growth. Hence, our paper is in line with his recommendation. Studies such as Bahmani-Oskooee (1992), Choudhri and Khan (2005), Peltonen and Sager (2009) and Chowdhury (2012) have also utilized relative productivity. 𝑙𝑛 is the natural logarithm, and 𝑓𝑖 and 𝑓𝑡 are the fixed effects for country 𝑖 and time period 𝑡, respectively. 𝜀𝑖𝑡 is the error term for country 𝑖 at time period 𝑡. It has been argued that non-traded goods are cheaper in poorer countries than in richer countries (see Harrod, 1933; Balassa, 1964; Samuelson, 1964; and Bhagwati, 1984). Thus, for the BSH to be valid, 𝛽 must be negative and significant (see Rodrik, 2008; Rapetti et al., 2011; Gluzmann et al., 2012; and Vieira and MacDonald, 2012). Some empirical studies have indeed found 𝛽 to be negative and significant (see, for instance, Ito et al., 1999; Gala, 2008; Rodrik, 2008; Gluzmann et al., 2012; and Vieira and MacDonald, 2012).
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Equation (6) may suffer from misspecification problems, since other factors could stimulate overvaluation or undervaluation of the real exchange rate. We avoid the misspecification problem by fitting a model with control variables of the form: 𝑙𝑛𝑅𝐸𝑅𝑖𝑡 = 𝛼 + 𝛽𝑙𝑛𝑃𝑅𝑂𝐷𝑖𝑡 + Ψ𝑋𝑖𝑡 + 𝑓𝑖 + 𝑓𝑡 + 𝜀𝑖𝑡
(7)
All the variables in Equation (7), except 𝑋, retain their definitions as before. 𝑋 is a vector of 1xk variables representing the standard determinants of the real exchange rate considered in the exchange rate literature. Ψ is a vector of kx1 parameters to be estimated. 𝜀 represents the white-noise error term. The real GDP per capita for each country, measured as real GDP at constant 2005 national prices (in mil. 2005 US$), is taken from the Penn World Tables, version 8.0, compiled by Feenstra et al. (2013). To arrive at the relative productivity of each country, we divide the real GDP per capita of each of the countries by the real GDP per capita of the US. The control variables are terms of trade, trade openness, and government debt burden. Terms of trade is measured as 𝑝𝑙_𝑥/𝑝𝑙_𝑚, where 𝑝𝑙_𝑥 is the price level of exports at constant 2005 US$, and 𝑝𝑙_𝑚 is the price level of imports at constant 2005 US$. Government debt burden is measured as 𝑐𝑠ℎ_𝑔, which is the share of government consumption at current purchasing power parities. Both variables are extracted from the Penn World Tables, version 8.0, compiled by Feenstra et al. (2013). Trade openness, which is defined as openness at 2005 constant prices (%) (i.e. 𝑜𝑝𝑒𝑛𝑘), is extracted from the Penn World Tables, version 7.1, compiled by Heston et al. (2012). We estimate Equations (6) and (7) using the fixed-effects or within effects estimator. The presence of endogeneity problems would render our results from the fixed-effects estimation meaningless. Therefore, we controlled for potential endogeneity problems using the generalized method of moments (GMM) developed for dynamic panels by Arellano and Bond (1991),
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Arellano and Bover (1995) and Blundell and Bond (1998). Monte Carlo simulations have shown that GMM system estimators perform better than GMM difference estimators when the instruments exhibit high degrees of persistence (see Blundell and Bond, 1998). For this reason, we provide the results for both estimators and check for model adequacy using the Sargan test for orthogonality of the instruments and error terms. The countries included in our sample are Botswana, Ghana, Lesotho, Mauritius, Namibia, Nigeria, South Africa and Zambia. Our data spans the period 1960–2009.
4. Results and findings As we have discussed in the preceding section, we estimated (6) using the within effects estimator with the robust variance option. We did this for the one-year and the five-year time windows. The results are shown in Panels (1) and (2) of Table 1. For the 1-year time window, the estimate of 𝛽 was negative and highly significant (𝛽 = −0.013 and 𝑡 = −2.42). This suggests strong evidence in support of the BSH. The result suggests that if relative productivity increases by 10 percent, the real exchange rate will appreciate by approximately 0.13 percent. In the case of the 5-year time window, the estimated value of 𝛽 was also negative, i.e. −0.014 and highly significant, i.e. 𝑡 = −4.28. Again, this suggests a strong and well-established BSH. Note that the t-statistic for the 1-year time window is lower than the one for the 5-year window (i.e. 2.42>-4.28). This is probably due to the inherent noise effect of annual data that we have discussed earlier. Panels (3) to (6) in Table 1 show the results we obtained after controlling for potential endogeneity problems. Note here that panels (3) and (4) are the two-step difference GMM results, while (5) and (6) are the two-step system GMM results. The estimated values of 𝛽
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remain negative and significant in all cases. Again, these results show support for the BSH. However, the Sargan test for overidentification restriction rejected all but one of the system and difference GMM results. This implies that the simple model in (6) is misspecified. Therefore, the results could be misleading.
In order to present more convincing results, we added terms of trade (lnTOT), trade openness (lnOPEN), and government debt burden (lnGDB) as control variables. We then estimated the resulting equation (i.e. Equation (7)) with the within effects and the GMM estimators. The results are presented in Table 2. Panel (1) and (2) in Table 2 show the withineffects results for the one-year and five-year time windows. As in the simple model, the estimated values of 𝛽 are negative and very significant (i.e. 𝛽 = −0.011, 𝑡 = −2.64 and 𝛽 = −0.017, 𝑡 = −3.75, for the one-year and five-year time windows, respectively). These suggest a strong and well-supported BSH. The results imply that a 10 percent increase in relative productivity leads to approximately between 0.11 to 0.17 percent appreciation of the real exchange rate. Panels (3) to (6) show the results for the one-year and five-year time windows, which we have estimated using the system and difference GMM estimators. Note that panels (3) and (4) are the two-step difference GMM results, while (5) and (6) are the two-step system GMM results. The estimated values of 𝛽 have remained negative and fairly significant. We must note that the estimated impact of productivity changes has lessened after controlling for potential endogeneity and variable omission when compared to the results of the within effects estimation.
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Nevertheless, our main concern is the sign and significance of the estimated values of 𝛽. We have found evidence in support of the BSH, since all the values of 𝛽 are negative and also statistically significant. Finally, the estimated probabilities of the Sargan test are all greater than 1 percent, 5 percent and 10 percent. Thus, we have no reason to worry about model adequacy. In other words, our model is correctly specified.
5. Concluding remarks The concept of purchasing power parity (PPP) has long been held as the main theory of the real exchange rate, at least since it first gained popularity in 1918. Cassel (1918) and some of his contemporaries argued that, in the absence of transaction costs and trade barriers, identical goods would command the same price when expressed in different currencies. On this basis, Cassel argued that the exchange rate between two countries should be estimated as the relative general prices of these two countries. From this stance, deviations in the real exchange rates from PPP were attributed to transaction costs and trade barriers. However, Balassa (1964) and Samuelson (1964) found that the deviations were actually the consequence of productivity differentials between countries – the Balassa-Samuelson effect. Since this revelation, the BSH has been tested by different authors with various techniques. This paper adds to the growing list of papers by providing new empirical evidence for the BSH. The uniqueness of our contribution lies in the fact that we employed similar developing countries that pursue similar growth and exchange rate strategies to examine this hypothesis. Indeed, these countries have recorded a mixture of rapid productivity growth (both declines and increments) and real exchange rate
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misalignments (both appreciations and depreciations) over the last two decades, features which are necessary to efficiently test the BSH. We employed within effects and dynamic panel methods using one-year and five-year time windows for a panel dataset of eight middle-income countries in Africa covering the period 1960–2009. Our results strongly support the BSH. On the basis of our results, we argue that productivity changes, especially increments, have resulted in real appreciation of the currencies of these countries.
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Cassel, G. (1918). Abnormal Deviations in International Exchanges. The Economic Journal 28(112): 413—415. Chen, L.-L., Choi, S. and Devereux, J. (2015), Explaining price level differences: New evidence on the Balassa–Samuelson effect. Southern Economic Journal, 82: 81–99. Chinn, M. D. (2000). The Usual Suspects? Productivity and Demand Shocks and Asia-Pacific Real Exchange Rates. Review of International Economics 8: 20–43. Chong, Y., Jorda, O. and Taylor, A. M. (2012). The Harrod–Balassa–Samuelson Hypothesis: Real Exchange Rates and their Long-Run Equilibrium. International Economic Review 53(2): 609–633. Choudhri, E. U. and Khan, M. S. (2005). Real Exchange Rates in Developing Countries: Are Balassa-Samuelson Effects Present? IMF Staff Papers 52 (3): 387–409. Choudhri, E. U. and Schembri, L. L. (2010). Productivity, the Terms of Trade, and the Real Exchange Rate: Balassa–Samuelson Hypothesis Revisited. Review of International Economics 18(5): 924–936. Chowdhury, K. (2012). The Real Exchange Rate and the Balassa–Samuelson Hypothesis in SAARC Countries: An Appraisal. Journal of the Asia Pacific Economy 17(1): 52–73. De Gregorio, J., Giovannini, A. and Kruegar, T. H. (1994a). The Behavior of Nontradable Goods Prices in Europe: Evidence and Interpretation. Review of International Economics 2(3): 284– 305. De Gregorio, J., Giovannini, A. and Wolf, H. C. (1994b). International Evidence on Tradables and Nontradables Inflation. European Economic Review 38: 1225–1244. De Vries, M. G. (1968). Exchange Depreciation in developing countries, IMF Staff Papers, 15: 560–578. Dumrongrittikul, T. (2012), Real Exchange Rate Movements in Developed and Developing Economies: A Reinterpretation of the Balassa-Samuelson Hypothesis. Economic Record, 88: 537–553. Feenstra, R. C., Robert I. and Timmer, M. P. (2013). The Next Generation of the Penn World Table Available for Download at www.ggdc.net/pwt Freund, C., and Pierola, M. D. (2008). Export Surges: The Power of a Competitive Currency, World Bank, August, 2008. Gala, P. (2008). Real Exchange Rate Levels and Economic Development: Theoretical Analysis and Empirical Evidence. Cambridge Journal of Economics 32: 273–288. Genius, M. and Tzouvelekas, V. (2008).The Balassa-Samuelson Productivity Bias Hypothesis: Further Evidence Using Panel Data. Agricultural Economics Review 9(2): 31–41. Gente, K. (2006). The Balassa–Samuelson Effect in a Developing Country. Review of Development Economics 10(4): 683–699.
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Ghironi, F., Melitz, M. (2005). International Trade and Macroeconomic Dynamics with Heterogeneous Firms. Quarterly Journal of Economics 120: 865–915. Gluzmann, P. A., Levy-Yeyati, E., and Sturzenegger, F. (2012). Exchange Rate Undervaluation and Economic Growth: Díaz Alejandro (1965) Revisited. Economics Letters 117(3): 666–672. Guo, Q. and Hall, S. G. (2010). A Test of the Balassa-Samuelson Effect Applied to Chinese Regional Data. Romanian Journal of Economic Forecasting 2: 57–78. Harrod, R. (1933). International Economics. London. James Nisbet and Cambridge University Press. Hassan, F. (2016), The Price of Development: the Penn-Balassa-Samuelson effect revisited, Journal of International Economics http://dx.doi.org/10.1016/j.jinteco.2016.07.009. Heston, A., Nuxoll, D. A. and Summers, R. (1994). The Differential-Productivity Hypothesis and Purchasing-Power Parities: Some New Evidence. Review of International Economics 2(3): 227–243. Heston, A., Summers, R. and Aten, B. (2012). Penn World Table Version 7.1, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania. Available at: https://pwt.sas.upenn.edu/php_site/pwt_index.php Hsieh, D. (1982). The Determination of the Real Exchange Rate: The Productivity Approach. Journal of International Economics 12: 355–62. Ito, T., Isard, P., Symansky, S. (1999). Economic Growth and Real Exchange Rate: An Overview of the Balassa-Samuelson Hypothesis in Asia, pp. 109-132, NBER-EASE, University of Chicago Press, Vol. 7. Kravis, I. B. and Lipsey, R. E. (1983). Toward an Explanation of National Price Levels. Princeton Studies in International Finance No. 52. Krugman, P. and Obstfeld, M. (2009). International Economics. Pearson Education Inc. Lothian, J. R. and Taylor, M. P. (2008). Real Exchange Rates over the Past Two Centuries: How Important is the Harrod-Balassa-Samuelson Effect? The Economic Journal 118(October): 1742– 1763. Marston, R. (1987). “Real Exchange Rates and Productivity Growth in the United States and Japan" in Real Financial Linkages among Open Economies. Sven W. Arndt and David J. Richardson, eds. Cambridge: MIT Press, pp.71-96. Méjean, I. (2008). Can Firms' Location Decisions Counteract The Balassa–Samuelson Effect? Journal of International Economics 76: 139–154. Obstfeld, M. and Rogoff, K. (1996). Foundations of International Macroeconomics. Cambridge: MIT Press. Officer, L. H. (1976). The Productivity Bias in Purchasing Power Parity: An Econometric Investigation. IMF Staff Papers 23: 545–79.
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Peltonen, T. A. and Sager, M. (2009). Productivity Shocks and Real Exchange Rates a Reappraisal. Working Paper Series No. 1046. Rapetti, M., Skott, P., and Razmi, A. (2011). The Real Exchange Rate and Economic Growth: Are Developing Countries Different? Working Paper No. 2011‐07, Department of Economics, University of Massachusetts. Ricardo, D. (1911). The Principles of Political Economy and Taxation. London: J. M. Dent and Sons. Rodrik, D. (2008). The Real Exchange Rate and Economic Growth, Working Paper, John F. Kennedy School of Government, Harvard University, Cambridge, MA 02138, Revised, September 2008. Rogoff, K. (1992a). Traded Goods Consumption Smoothing and the Random Walk Behaviour Walk Behavior of the Real Exchange Rate. NBER Working paper No. 4119. Rogoff, K. (1992b). Traded Goods Consumption Smoothing and the Random Walk Behavior of the Real Exchange Rate. Bank of Japan Monetary and Economic Studies 10(2): 1–29. Samuelson, P. A. (1964). Theoretical Notes on Trade Problems. Review of Economics and Statistics 46: 145–54. Samuelson, P. A. (1994). Facets of Balassa-Samuelson Thirty Years Later. Review of International Economics 2 (3): 201–226. Strauss, J. (1995). Real Exchange Rates, PPP and the Relative Prices of Non-Traded Goods. Southern Economic Journal 61(4): 991–1005. Thomas A. and King, A. (2008). The Balassa–Samuelson Hypothesis in the Asia-Pacific Region Revisited. Review of International Economics 16(1): 127–141. Vieira, F. V. and MacDonald, R. (2012). A Panel Data Investigation of Real Exchange Rate Misalignment and Growth. Estudos Econômicos (São Paulo), 42(3), 433-456. Viner, J. (1937). Studies in the Theory of International Trade. New York: Harper and Sons. Wang, W., and Xue, J., and Du, C. (2016). The Balassa–Samuelson hypothesis in the developed and developing countries revisited, Economics Letters, Volume 146, September 2016, Pages 3338.
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1
2
3
Figure 1. The relationship between the real exchange rate and relative productivity
Actual Data
0
Quadratic Fit Lowess
-6
-4
-2
0
Relative Productivity
Note: The variables are normalised and based on 5-year averages in order to minimise shocks. Source: Plotted from the Penn World Tables, version 7.1, compiled by Heston et al. (2012).
21
2
Figure 2. Real exchange rate and relative productivity in Botswana 2.5
0 -0.5
2
-1 -1.5
1.5
-2 -2.5
1
-3 -3.5
0.5
-4 -4.5
0
-5 1960
1961
1962
1963
1964
1965
lnrer
1966
1967
1968
1969
lnprod
Note: The variables are normalised and based on 5-year averages in order to minimise shocks. Source: Plotted from the Penn World Tables, version 7.1, compiled by Heston et al. (2012).
Figure 3. Real exchange rate and relative productivity in Mauritius 3
-2.2 -2.3
2.5
-2.4 2
-2.5
1.5
-2.6 -2.7
1
-2.8 0.5
-2.9
0
-3 1960
1961
1962
1963
1964
1965
lnrer
1966
1967
1968
lnprod
Note: The variables are normalised and based on 5-year averages in order to minimise shocks.
22
1969
Source: Plotted from the Penn World Tables, version 7.1, compiled by Heston et al. (2012).
23
Table 1. Evidence of BSH from within and dynamic panel estimations (1)
(2)
FE (Within) [One-Year]
FE (Within) [Five-Year]
lnPROD
-0.013** (-2.42)
Time dummies Country dummies
lnRER
Sargan Test [Prob > chisquared]
(3)
(4)
(5)
(6)
Diff-GMM [One-Year]
Diff-GMM [Five-Year]
Sys-GMM [One-Year]
Sys-GMM [Five-Year]
-0.014*** (-4.28)
-0.019** (-2.88)
-0.023** (-2.83)
-0.016** (-2.83)
-0.021* (1.91)
yes
yes
yes
yes
yes
yes
yes
yes
0.025
0.078
0.000
0.296
Observations 408 80 408 80 408 80 Notes: (1) The t-statistics are in parentheses. (2) ** and *** imply significance at 5% and 1%, respectively. (3) FE = fixed-effects estimator, Diff = two-step difference estimator, and Sys = two-step system estimator.
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Table 2. Evidence of BSH after controlling for omitted variables (1)
(2)
FE (Within) [One-Year]
FE (Within) [Five-Year]
Diff-GMM [One-Year]
Diff-GMM [Five-Year]
Sys-GMM [One-Year]
Sys-GMM [Five-Year]
lnPROD
-0.011** (-2.64)
-0.017*** (-3.75)
-.009** (-2.56)
-0.010* (-1.83)
-0.008** (-2.62)
-0.012** (-2.54)
lnTOT
0.067** (2.48)
0.015*** (5.10)
0.353** (2.31)
0.089** (2.12)
0.062*** (6.53)
0.011 (0.01)
lnOPEN
-0.188** (-2.50)
-0.167 (-1.61)
0.381** (2.48)
0.202 (1.04)
0.478** (2.92)
0.112** (2.42)
lnGDB
0.173 (1.37)
0.159 (1.25)
0.410 (0.97)
-0.135 (-0.43)
0.350 (0.87)
-0.047* (-1.84)
Time Dummies
yes
yes
yes
yes
yes
yes
Country Dummies
yes
yes
0.208
0.156
0.136
0.290
lnRER
Sargan Test [Prob > chi-squared]
(3)
(4)
(5)
(6)
Observations 408 80 408 80 408 80 Notes: (1) The t-statistics are in parentheses. (2) *, ** and *** imply significance at 10%, 5% and 1%, respectively. (3) FE = fixed-effects estimator, Diff = two-step difference estimator, and Sys = two-step system estimator.
25
S0939-3625(17)30030-4 http://dx.doi.org/doi:10.1016/j.ecosys.2016.10.001 ECOSYS 605
To appear in:
Economic Systems
Received date: Revised date: Accepted date:
22-2-2016 23-8-2016 7-10-2016
Please cite this article as: Iyke, Bernard Njindan, Odhiambo, Nicholas M., An empirical test of the Balassa-Samuelson hypothesis: Evidence from eight middle-income countries in Africa.Economic Systems http://dx.doi.org/10.1016/j.ecosys.2016.10.001 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
An empirical test of the Balassa-Samuelson hypothesis: Evidence from eight middle-income countries in Africa
Bernard Njindan Iyke* and Nicholas M. Odhiambo** Department of Economics, University of South Africa, P. O. Box 392, UNISA 0003, Pretoria, South Africa * Corresponding author. E-mail address: [email protected] / [email protected] **E-mail address: [email protected] / [email protected]
HIGHLIGHTS
We test the validity of the Balassa-Samuelson hypothesis (BSH).
Our sample is based on a panel of eight middle-income African countries for the period 1960-2009.
These countries exhibit a mixture of relative productivity growth and real exchange rate misalignment befitting the characterization of the BSH.
We use the within-effects and GMM estimators for the empirical estimations and find the BSH to hold.
Therefore, as these countries become more productive, their currencies appreciate in real terms.
1
Abstract The paper tests the validity of the Balassa-Samuelson hypothesis (BSH) using the withineffects and the dynamic panel generalised methods of moment (GMM) techniques for a panel of eight middle-income African countries over the period 1960-2009. We selected these countries because they exhibited a mixture of relative productivity growth and real exchange rate misalignment that fits the characterization of the BSH well. The results strongly support the BSH for this group of countries. The results are valid even after we controlled for potentially omitted variables and endogeneity. The implication is that as these countries become more productive, their currencies appreciate in real terms.
Keywords: Balassa-Samuelson hypothesis, Purchasing power parity, Real exchange rate, Relative productivity
1. Introduction The law of one price postulates that identical goods and services should trade at the same prices, given that transaction costs and trade barriers are insignificant. This was the idea long held by Cassel (1918) when he was developing what is known today as the purchasing power parity (PPP) theory. Cassel argues that, if the PPP theory holds, the exchange rate between two countries should be seen as the relative general prices of the two countries (see Samuelson, 1994; Krugman and Obstfeld, 2009).
2
The validity of the PPP theory was first questioned when Ricardo (1911), Harrod (1933) and Viner (1937) observed that long-run real exchange rates actually deviated from PPP. Concrete evidence against the PPP theory was first advanced by Balassa (1964) and Samuelson (1964), who found that real exchange rates persistently deviated from PPP due to the presence of productivity differentials in traded goods between countries. These productivity differentials, they argue, stimulate price and wage differentials, which widens the deviations between real exchange rates and PPP. In honour of their findings, the contribution of productivity differentials to the deviation of real exchange rates from PPP has been termed the Balassa-Samuelson effect or hypothesis in the literature (see Officer, 1976). We note briefly that the “appropriate” name for this phenomenon remains controversial. Variant terminologies can be found in the literature, such as the Penn effect, Balassa’s theory or proposition, and Ricardo-Viner-Harrod-BalassaSamuelson-Penn-Bhagwati effect, among others (see Bahmani-Oskooee and Nasir, 2005). The Balassa-Samuelson effect or hypothesis (referred hereinafter as BSH) has been tested widely and empirically. The evidence from time series and panel data techniques appears to validate the BSH more often than that from cross-sectional techniques. Nonetheless, the jury is still wide open. In this paper, we aim to provide new evidence of the BSH from a panel of eight middle-income countries in Africa that are best described as ‘developing’.1 Our choice of countries makes this empirical exercise more persuasive, in that these countries are more likely to exhibit significant economic growth than their developed counterparts, whose growth has already plateaued. This higher likelihood of rapid economic growth of the chosen countries fits perfectly into the “productivity differentials factor” identified in Balassa (1964) and Samuelson (1964). In other words, rapid economic growth should manifest clearly in the real exchange rate – in the form of real appreciation, which stimulates the deviation of the real exchange rate from 1
These countries are Botswana, Ghana, Lesotho, Mauritius, Namibia, Nigeria, South Africa and Zambia.
3
PPP – than slow or balanced economic growth. This is a key feature of the countries chosen for our paper. In Figure 1, we present a simple graphical relationship between the real exchange rate and relative productivity for the panel of eight middle-income SSA countries in our study. The real exchange rate is measured as ln(XRAT/PPP)2, where XRAT is the exchange rate3 and PPP is the purchasing power parity conversion factors, and relative productivity is measured as the logarithm of real GDP per capita of the home countries divided by the real GDP per capita of the US. These variables are extracted from the Penn World Tables, version 7.1, compiled by Heston et al. (2012). From Figure 1, it appears that the relationship between the real exchange rate and relative productivity is negative. Both the quadratic and the lowess fits show an inverse relationship between the real exchange rate and relative productivity. In addition, the points appear somehow distributed around these line plots. The observed negative relationship implies that whenever these countries experience relative productivity increases, their real exchange rates appreciate. This is an indication of the Balassa-Samuelson effect (see Rodrik, 2008). The shortcoming of Figure 1 is that majority of the points appear to scatter above and below the linear regression line. Thus, the negative relationship between the real exchange rate and productivity seems weak.
To further illustrate the relationship between the real exchange rate and relative productivity growth, we present graphical evidence for two countries, namely Botswana and
2 3
See Rodrik (2008) for this definition. This is the domestic currency per US dollar exchange rate.
4
Mauritius, in Figures 2 and 3. Note that the real exchange rates and the relative productivity plotted in Figures 1, 2 and 3 are based on five-year averages. These two countries have endured periods of real appreciation and depreciation of their exchange rates. In addition, they have experienced periods of rapid productivity growth as well as periods of productivity decline. These dynamic features make Botswana and Mauritius particularly captivating for our spotlight. Figures 2 and 3 show that both countries have experienced regimes of high relative productivity growth, barring some declines. At the same time, their real exchange rates have appreciated in real terms during this period. While we may be committing a post hoc fallacy by suggesting that the real productivity increment in these countries stimulated the real appreciation of their exchange rates, the evidence from the data appears to be so. In the remaining sections, we provide concrete econometric evidence to support these figures.
The rest of the paper is organised in three sections. In Section 2, we present the relevant theoretical and empirical literature on the BSH. In Section 3, we present a simple economic model that enables us to examine the BSH. In Section 4, we present our results and discuss our findings. In the final section, we provide some concluding remarks.
2. The relevant theoretical and empirical literature on the BSH 2.1 The theory The formal theoretical model that explores the BSH was first discussed in Rogoff (1992a). Before Rogoff’s rigorous theoretical model, the BSH was virtually descriptive in nature.
5
For instance, Harrod (1933) and Samuelson (1964) only described the main elements of the BSH, and Balassa (1964) then provided a formal empirical verification. In addition to Balassa (1964), other authors such as Kravis and Lipsey (1983) and Bhagwati (1984) also documented empirical evidence on the BSH in their papers. A feature of these studies was that they mostly focused on the supply side of the economy and modelled linear relationships between the relative price level and the level of productivity (see Officer, 1976; Hsieh, 1982; Marston, 1987). In contrast to these earlier descriptive and empirical works, Rogoff (1992a) proposed a fully-fledged model for the BSH. His model was derived within the general equilibrium setting, with two Cobb-Douglas production functions for two domestically produced goods. The two domestically produced goods were tradable “T” and non-tradable “N”, which originated from the tradable sector and the non-tradable sector, respectively. In addition, these two goods were produced with three factors: labour “L”, capital “K”, and technology “A”. According to Rogoff (1992a), the two goods follow production functions of the form: 1−𝜃𝑇
𝜃𝑇 𝑌𝑇𝑡 = 𝐴𝑇𝑡 𝐾𝑇𝑡 𝐿𝑇𝑡
(1)
1−𝜃𝑁
𝜃𝑁 𝑌𝑁𝑡 = 𝐴𝑁𝑡 𝐾𝑁𝑡 𝐿𝑁𝑡
(2)
where 𝑌𝑇𝑡 and 𝑌𝑁𝑡 are the quantities of the tradable and the non-tradable goods at time 𝑡, respectively. 𝜃𝑇 and 𝜃𝑁 are the share of 𝐿 and 𝐾 in the production of 𝑇 and 𝑁, respectively, and 𝐴𝑇 and 𝐴𝑁 denote the stochastic productivity shocks in sectors 𝑇 and 𝑁, respectively. Rogoff (1992a) makes four assumptions: (i) the law of one price holds in the tradable sector; (ii) there is perfect international capital mobility; (iii) there is perfect market competition; and (iv) there is perfect factor mobility between the sectors of the economy. Relying on these assumptions, Rogoff shows that a change in the relative price of non-tradable goods depends on a change in the relative productivity of the two sectors in the form: 6
𝑑𝑝 = (𝜃𝑁 ⁄𝜃𝑇 )𝑑𝑎 𝑇 − 𝑑𝑎𝑁
(3)
where 𝑑 is the differential operator and the lower cases represent the logarithm of the variables. 𝑝 is the relative price of non-tradable goods in terms of tradable goods, and 𝑎 𝑇 and 𝑎𝑁 are the stochastic productivity shocks in the tradable and non-tradable sectors, respectively. Rogoff (1992a) argues that in order to arrive at a more realistic result, we must take capital and labour as given in each of the sectors and assume that the capital markets are closed to international borrowing and lending. Given these two assumptions, we obtain the following result: 𝑑𝑝 = 𝛽𝑇 𝑑𝑎 𝑇 − 𝛽𝑁 𝑑𝑎𝑁 − [(𝛽𝑇 − 1)𝑑𝑔𝑇 − (𝛽𝑁 − 1)𝑑𝑔𝑁 ]
(4)
where 𝛽 is the output-consumption ratio and 𝑔 is the logarithm of government consumption. According to Rogoff (1992a), (4) is identical to the Balassa-Samuelson result in (3) because the productivity shocks in (4) have an isomorphic effect to the one in (3). This model allows empirical studies to explore the impact of the demand side of economies on long-term relative price levels. Obstfeld and Rogoff (1996) have since provided an extension to Rogoff’s (1992a) model. Their model includes an additional factor of production. They also relaxed the axiom of international capital movement. Obstfeld and Rogoff (1996) then derived the BSH by basing it on the factor-price equalization concept of the trade theory. Moreover, other theoretical advancements have been made since Rogoff’s (1992a) paper. The discussion of these theoretical advancements is beyond the scope of our paper. We refer the interested reader to Asea and Mendoza (1994) and De Gregorio et al. (1994a) for earlier studies. For the most recent studies, the reader should consider Ghironi and Melitz (2005), Bergin et al. (2006), Méjean (2008) and Hassan (2016).
7
2.2 The empirical evidence On the empirical front, the BSH has been tested by various studies. Different techniques have been employed with varying degrees of success. A review of the empirical literature can be found in Officer (1976), and more recently in Bahmani-Oskooee and Nasir (2005). The available empirical evidence on the BSH could be streamlined under three headings: (i) cross-sectional evidence, which includes studies such as Balassa (1964), De Vries (1968), Officer (1976), Heston et al. (1994) and Choudhri and Schembri (2010); (ii) time series evidence, which includes studies such as Hsieh (1982), Rogoff (1992b), Strauss (1995), Bahmani-Oskooee and Nasir (2004), Gente (2006), Lothian and Taylor (2008), Thomas and King (2008) and Chowdhury (2012); and (iii) panel evidence, which includes studies such as Asea and Mendoza (1994), De Gregorio et al. (1994a,b), Chinn (2000), Bahmani-Oskooee and Nasir (2002), Choudhri and Khan (2005), Genius and Tzouvelekas (2008), Peltonen and Sager (2009), Guo and Hall (2010), Chong et al. (2012) and Wang et al. (2016). These studies have produced conflicting findings, thereby leaving the relationship between the real exchange rate and relative productivity (growth) or the BSH open to further investigation. In his paper, Balassa (1964), for example, provided the earliest empirical evidence of the BSH. Balassa found two crucial pieces of evidence in support of the BSH, employing data from 12 OECD countries. First, by computing sectorial purchasing power parity (PPP) for the year 1950, he found that the prices of non-tradable goods were relatively lower in economies with relatively low incomes. Second, by using the data for the year 1960, he regressed the deviation of PPP from the equilibrium exchange rate on GNP per capita. He found the GNP per capita to have a positive and statistically significant coefficient. These two pieces of evidence, according to Balassa (1964), confirmed the BS effect.
8
Officer (1976) later argued against Balassa’s (1964) results and noted that, instead of productivity, Balassa should have employed relative productivity. Officer estimated a crosscountry regression for each year from 1950 to 1973, varying the measure of productivity (i.e. between GDP per capita and GDP per worker), the ratio of productivity between the tradable and the non-tradable goods sectors, and the base country (alternating between the USA and Germany). Using a sample of 15 industrialised countries, Officer found no evidence in favour of the BSH. Bahmani-Oskooee and Niroomand (1996) found similar results, replicating Officer’s (1976) study by estimating a cross-sectional regression from 1974 to 1989. Lothian and Taylor (2008) argued that nonlinear models would fit the relationship between the real exchange rate and relative productivity (growth) better, suggesting that the previous findings may be questionable. The authors therefore examined the BSH in a non-linear setting, which allowed for shifts in the real exchange rate volatility across nominal regimes. They characterised, statistically, the non-linearity of the real exchange rate using the exponential smooth transition autoregressive (ESTAR) model. The study was based on three industrialised countries: the UK (1820-2001), the US (1820-2001) and France (1890-1998). Their results were found to support the BSH for the UK-US real exchange rate, but not for the UK-French real exchange rate. Lothian and Taylor argued that the BSH was not supported for the UK-French real exchange rate because of the parallel industrial development of the UK and France, which was not the case for the UK and the USA. Chowdhury (2012) attempted to improve estimation precision by utilising the ARDL approach, which performs well in small samples. He found two endogenous structural breaks in the data and incorporated them into the empirical model to capture any non-linearity in the series. Chowdhury found no evidence in support of the BSH in 6 out of the 7 South Asian
9
countries, namely Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka. However, his results supported the BSH for Bangladesh over the period 1959-2003. Specifically, his results suggested that a percentage increment in the productivity of labour in Bangladesh relative to the US led to about 2.5% appreciation of the real exchange rate in Bangladesh. Apart from the conflicting evidence documented in the existing literature, the BSH has received less attention in the context of Africa. Most studies only incorporated few African countries in their samples. Among the existing studies which include African countries are Bahmani-Oskooee and Nasir (2001, 2002, 2004), Bahmani‐Oskooee and Miteza (2004), Genius and Tzouvelekas (2008), Peltonen and Sager (2009), Dumrongrittikul (2012), Chen et al. (2015), Hassan (2016) and Wang et al. (2016). Among these studies, only Bahmani‐Oskooee and Miteza (2004), Bahmani-Oskooee and Nasir (2001, 2004) and Genius and Tzouvelekas (2008) focused on African countries in particular. We add to this growing literature by focusing on selected middle-income countries in Africa.
3. The empirical model In order to test the BSH, we specify a simple model in the spirit of Rodrik (2008). The first step is to extract the exchange rates (XRAT) and purchasing power parity (PPP) conversion factors from the Penn World Tables, version 7.1, compiled by Heston et al. (2012). Next, we construct the real exchange rate index as follows: 𝑙𝑛𝑅𝐸𝑅𝑖𝑡 = 𝑙𝑛(𝑋𝑅𝐴𝑇𝑖𝑡 ⁄𝑃𝑃𝑃𝑖𝑡 )
(5)
where 𝑖 is the country under consideration and 𝑡 is a five-year time period. The basis for constructing the variables using five-year averages is to eliminate noise effects that are often inherent in annual data. Besides, real exchange rate effects are known to take more than a year to
10
appear (see Freund and Pierola, 2008; Rodrik, 2008; Aghion et al., 2009; and Rapetti et al., 2011). We also construct variables based on one-year annual data as a robustness check (i.e., we set t=1 instead of t=5). Note that XRAT and PPP are denoted in national currency units per US dollar. When RER is more than unity, it implies that the currency is more depreciated than the purchasing power parity implies. The simple model relating the real exchange index that we have constructed to the relative productivity index is of the form: 𝑙𝑛𝑅𝐸𝑅𝑖𝑡 = 𝛼 + 𝛽𝑙𝑛𝑃𝑅𝑂𝐷𝑖𝑡 + 𝑓𝑖 + 𝑓𝑡 + 𝜀𝑖𝑡
(6)
where 𝛼 and 𝛽 are parameters of the model, and 𝑃𝑅𝑂𝐷𝑖𝑡 is the index of relative productivity of country 𝑖 in period 𝑡, which is proxied by the real GDP per capita of the home country relative to that of the US. Officer (1976) recommends that we use relative productivity or relative productivity growth. Hence, our paper is in line with his recommendation. Studies such as Bahmani-Oskooee (1992), Choudhri and Khan (2005), Peltonen and Sager (2009) and Chowdhury (2012) have also utilized relative productivity. 𝑙𝑛 is the natural logarithm, and 𝑓𝑖 and 𝑓𝑡 are the fixed effects for country 𝑖 and time period 𝑡, respectively. 𝜀𝑖𝑡 is the error term for country 𝑖 at time period 𝑡. It has been argued that non-traded goods are cheaper in poorer countries than in richer countries (see Harrod, 1933; Balassa, 1964; Samuelson, 1964; and Bhagwati, 1984). Thus, for the BSH to be valid, 𝛽 must be negative and significant (see Rodrik, 2008; Rapetti et al., 2011; Gluzmann et al., 2012; and Vieira and MacDonald, 2012). Some empirical studies have indeed found 𝛽 to be negative and significant (see, for instance, Ito et al., 1999; Gala, 2008; Rodrik, 2008; Gluzmann et al., 2012; and Vieira and MacDonald, 2012).
11
Equation (6) may suffer from misspecification problems, since other factors could stimulate overvaluation or undervaluation of the real exchange rate. We avoid the misspecification problem by fitting a model with control variables of the form: 𝑙𝑛𝑅𝐸𝑅𝑖𝑡 = 𝛼 + 𝛽𝑙𝑛𝑃𝑅𝑂𝐷𝑖𝑡 + Ψ𝑋𝑖𝑡 + 𝑓𝑖 + 𝑓𝑡 + 𝜀𝑖𝑡
(7)
All the variables in Equation (7), except 𝑋, retain their definitions as before. 𝑋 is a vector of 1xk variables representing the standard determinants of the real exchange rate considered in the exchange rate literature. Ψ is a vector of kx1 parameters to be estimated. 𝜀 represents the white-noise error term. The real GDP per capita for each country, measured as real GDP at constant 2005 national prices (in mil. 2005 US$), is taken from the Penn World Tables, version 8.0, compiled by Feenstra et al. (2013). To arrive at the relative productivity of each country, we divide the real GDP per capita of each of the countries by the real GDP per capita of the US. The control variables are terms of trade, trade openness, and government debt burden. Terms of trade is measured as 𝑝𝑙_𝑥/𝑝𝑙_𝑚, where 𝑝𝑙_𝑥 is the price level of exports at constant 2005 US$, and 𝑝𝑙_𝑚 is the price level of imports at constant 2005 US$. Government debt burden is measured as 𝑐𝑠ℎ_𝑔, which is the share of government consumption at current purchasing power parities. Both variables are extracted from the Penn World Tables, version 8.0, compiled by Feenstra et al. (2013). Trade openness, which is defined as openness at 2005 constant prices (%) (i.e. 𝑜𝑝𝑒𝑛𝑘), is extracted from the Penn World Tables, version 7.1, compiled by Heston et al. (2012). We estimate Equations (6) and (7) using the fixed-effects or within effects estimator. The presence of endogeneity problems would render our results from the fixed-effects estimation meaningless. Therefore, we controlled for potential endogeneity problems using the generalized method of moments (GMM) developed for dynamic panels by Arellano and Bond (1991),
12
Arellano and Bover (1995) and Blundell and Bond (1998). Monte Carlo simulations have shown that GMM system estimators perform better than GMM difference estimators when the instruments exhibit high degrees of persistence (see Blundell and Bond, 1998). For this reason, we provide the results for both estimators and check for model adequacy using the Sargan test for orthogonality of the instruments and error terms. The countries included in our sample are Botswana, Ghana, Lesotho, Mauritius, Namibia, Nigeria, South Africa and Zambia. Our data spans the period 1960–2009.
4. Results and findings As we have discussed in the preceding section, we estimated (6) using the within effects estimator with the robust variance option. We did this for the one-year and the five-year time windows. The results are shown in Panels (1) and (2) of Table 1. For the 1-year time window, the estimate of 𝛽 was negative and highly significant (𝛽 = −0.013 and 𝑡 = −2.42). This suggests strong evidence in support of the BSH. The result suggests that if relative productivity increases by 10 percent, the real exchange rate will appreciate by approximately 0.13 percent. In the case of the 5-year time window, the estimated value of 𝛽 was also negative, i.e. −0.014 and highly significant, i.e. 𝑡 = −4.28. Again, this suggests a strong and well-established BSH. Note that the t-statistic for the 1-year time window is lower than the one for the 5-year window (i.e. 2.42>-4.28). This is probably due to the inherent noise effect of annual data that we have discussed earlier. Panels (3) to (6) in Table 1 show the results we obtained after controlling for potential endogeneity problems. Note here that panels (3) and (4) are the two-step difference GMM results, while (5) and (6) are the two-step system GMM results. The estimated values of 𝛽
13
remain negative and significant in all cases. Again, these results show support for the BSH. However, the Sargan test for overidentification restriction rejected all but one of the system and difference GMM results. This implies that the simple model in (6) is misspecified. Therefore, the results could be misleading.
In order to present more convincing results, we added terms of trade (lnTOT), trade openness (lnOPEN), and government debt burden (lnGDB) as control variables. We then estimated the resulting equation (i.e. Equation (7)) with the within effects and the GMM estimators. The results are presented in Table 2. Panel (1) and (2) in Table 2 show the withineffects results for the one-year and five-year time windows. As in the simple model, the estimated values of 𝛽 are negative and very significant (i.e. 𝛽 = −0.011, 𝑡 = −2.64 and 𝛽 = −0.017, 𝑡 = −3.75, for the one-year and five-year time windows, respectively). These suggest a strong and well-supported BSH. The results imply that a 10 percent increase in relative productivity leads to approximately between 0.11 to 0.17 percent appreciation of the real exchange rate. Panels (3) to (6) show the results for the one-year and five-year time windows, which we have estimated using the system and difference GMM estimators. Note that panels (3) and (4) are the two-step difference GMM results, while (5) and (6) are the two-step system GMM results. The estimated values of 𝛽 have remained negative and fairly significant. We must note that the estimated impact of productivity changes has lessened after controlling for potential endogeneity and variable omission when compared to the results of the within effects estimation.
14
Nevertheless, our main concern is the sign and significance of the estimated values of 𝛽. We have found evidence in support of the BSH, since all the values of 𝛽 are negative and also statistically significant. Finally, the estimated probabilities of the Sargan test are all greater than 1 percent, 5 percent and 10 percent. Thus, we have no reason to worry about model adequacy. In other words, our model is correctly specified.
5. Concluding remarks The concept of purchasing power parity (PPP) has long been held as the main theory of the real exchange rate, at least since it first gained popularity in 1918. Cassel (1918) and some of his contemporaries argued that, in the absence of transaction costs and trade barriers, identical goods would command the same price when expressed in different currencies. On this basis, Cassel argued that the exchange rate between two countries should be estimated as the relative general prices of these two countries. From this stance, deviations in the real exchange rates from PPP were attributed to transaction costs and trade barriers. However, Balassa (1964) and Samuelson (1964) found that the deviations were actually the consequence of productivity differentials between countries – the Balassa-Samuelson effect. Since this revelation, the BSH has been tested by different authors with various techniques. This paper adds to the growing list of papers by providing new empirical evidence for the BSH. The uniqueness of our contribution lies in the fact that we employed similar developing countries that pursue similar growth and exchange rate strategies to examine this hypothesis. Indeed, these countries have recorded a mixture of rapid productivity growth (both declines and increments) and real exchange rate
15
misalignments (both appreciations and depreciations) over the last two decades, features which are necessary to efficiently test the BSH. We employed within effects and dynamic panel methods using one-year and five-year time windows for a panel dataset of eight middle-income countries in Africa covering the period 1960–2009. Our results strongly support the BSH. On the basis of our results, we argue that productivity changes, especially increments, have resulted in real appreciation of the currencies of these countries.
16
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Ghironi, F., Melitz, M. (2005). International Trade and Macroeconomic Dynamics with Heterogeneous Firms. Quarterly Journal of Economics 120: 865–915. Gluzmann, P. A., Levy-Yeyati, E., and Sturzenegger, F. (2012). Exchange Rate Undervaluation and Economic Growth: Díaz Alejandro (1965) Revisited. Economics Letters 117(3): 666–672. Guo, Q. and Hall, S. G. (2010). A Test of the Balassa-Samuelson Effect Applied to Chinese Regional Data. Romanian Journal of Economic Forecasting 2: 57–78. Harrod, R. (1933). International Economics. London. James Nisbet and Cambridge University Press. Hassan, F. (2016), The Price of Development: the Penn-Balassa-Samuelson effect revisited, Journal of International Economics http://dx.doi.org/10.1016/j.jinteco.2016.07.009. Heston, A., Nuxoll, D. A. and Summers, R. (1994). The Differential-Productivity Hypothesis and Purchasing-Power Parities: Some New Evidence. Review of International Economics 2(3): 227–243. Heston, A., Summers, R. and Aten, B. (2012). Penn World Table Version 7.1, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania. Available at: https://pwt.sas.upenn.edu/php_site/pwt_index.php Hsieh, D. (1982). The Determination of the Real Exchange Rate: The Productivity Approach. Journal of International Economics 12: 355–62. Ito, T., Isard, P., Symansky, S. (1999). Economic Growth and Real Exchange Rate: An Overview of the Balassa-Samuelson Hypothesis in Asia, pp. 109-132, NBER-EASE, University of Chicago Press, Vol. 7. Kravis, I. B. and Lipsey, R. E. (1983). Toward an Explanation of National Price Levels. Princeton Studies in International Finance No. 52. Krugman, P. and Obstfeld, M. (2009). International Economics. Pearson Education Inc. Lothian, J. R. and Taylor, M. P. (2008). Real Exchange Rates over the Past Two Centuries: How Important is the Harrod-Balassa-Samuelson Effect? The Economic Journal 118(October): 1742– 1763. Marston, R. (1987). “Real Exchange Rates and Productivity Growth in the United States and Japan" in Real Financial Linkages among Open Economies. Sven W. Arndt and David J. Richardson, eds. Cambridge: MIT Press, pp.71-96. Méjean, I. (2008). Can Firms' Location Decisions Counteract The Balassa–Samuelson Effect? Journal of International Economics 76: 139–154. Obstfeld, M. and Rogoff, K. (1996). Foundations of International Macroeconomics. Cambridge: MIT Press. Officer, L. H. (1976). The Productivity Bias in Purchasing Power Parity: An Econometric Investigation. IMF Staff Papers 23: 545–79.
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1
2
3
Figure 1. The relationship between the real exchange rate and relative productivity
Actual Data
0
Quadratic Fit Lowess
-6
-4
-2
0
Relative Productivity
Note: The variables are normalised and based on 5-year averages in order to minimise shocks. Source: Plotted from the Penn World Tables, version 7.1, compiled by Heston et al. (2012).
21
2
Figure 2. Real exchange rate and relative productivity in Botswana 2.5
0 -0.5
2
-1 -1.5
1.5
-2 -2.5
1
-3 -3.5
0.5
-4 -4.5
0
-5 1960
1961
1962
1963
1964
1965
lnrer
1966
1967
1968
1969
lnprod
Note: The variables are normalised and based on 5-year averages in order to minimise shocks. Source: Plotted from the Penn World Tables, version 7.1, compiled by Heston et al. (2012).
Figure 3. Real exchange rate and relative productivity in Mauritius 3
-2.2 -2.3
2.5
-2.4 2
-2.5
1.5
-2.6 -2.7
1
-2.8 0.5
-2.9
0
-3 1960
1961
1962
1963
1964
1965
lnrer
1966
1967
1968
lnprod
Note: The variables are normalised and based on 5-year averages in order to minimise shocks.
22
1969
Source: Plotted from the Penn World Tables, version 7.1, compiled by Heston et al. (2012).
23
Table 1. Evidence of BSH from within and dynamic panel estimations (1)
(2)
FE (Within) [One-Year]
FE (Within) [Five-Year]
lnPROD
-0.013** (-2.42)
Time dummies Country dummies
lnRER
Sargan Test [Prob > chisquared]
(3)
(4)
(5)
(6)
Diff-GMM [One-Year]
Diff-GMM [Five-Year]
Sys-GMM [One-Year]
Sys-GMM [Five-Year]
-0.014*** (-4.28)
-0.019** (-2.88)
-0.023** (-2.83)
-0.016** (-2.83)
-0.021* (1.91)
yes
yes
yes
yes
yes
yes
yes
yes
0.025
0.078
0.000
0.296
Observations 408 80 408 80 408 80 Notes: (1) The t-statistics are in parentheses. (2) ** and *** imply significance at 5% and 1%, respectively. (3) FE = fixed-effects estimator, Diff = two-step difference estimator, and Sys = two-step system estimator.
24
Table 2. Evidence of BSH after controlling for omitted variables (1)
(2)
FE (Within) [One-Year]
FE (Within) [Five-Year]
Diff-GMM [One-Year]
Diff-GMM [Five-Year]
Sys-GMM [One-Year]
Sys-GMM [Five-Year]
lnPROD
-0.011** (-2.64)
-0.017*** (-3.75)
-.009** (-2.56)
-0.010* (-1.83)
-0.008** (-2.62)
-0.012** (-2.54)
lnTOT
0.067** (2.48)
0.015*** (5.10)
0.353** (2.31)
0.089** (2.12)
0.062*** (6.53)
0.011 (0.01)
lnOPEN
-0.188** (-2.50)
-0.167 (-1.61)
0.381** (2.48)
0.202 (1.04)
0.478** (2.92)
0.112** (2.42)
lnGDB
0.173 (1.37)
0.159 (1.25)
0.410 (0.97)
-0.135 (-0.43)
0.350 (0.87)
-0.047* (-1.84)
Time Dummies
yes
yes
yes
yes
yes
yes
Country Dummies
yes
yes
0.208
0.156
0.136
0.290
lnRER
Sargan Test [Prob > chi-squared]
(3)
(4)
(5)
(6)
Observations 408 80 408 80 408 80 Notes: (1) The t-statistics are in parentheses. (2) *, ** and *** imply significance at 10%, 5% and 1%, respectively. (3) FE = fixed-effects estimator, Diff = two-step difference estimator, and Sys = two-step system estimator.
25