EXPORT LED GROWTH vs. GROWTH LED EXPORTS: LDCs EXPERIENCE
Bahmani-Oskooee, Mohsen, The Journal of Developing Areas
Previous studies that investigated the export led growth hypothesis, relied upon Granger or Sims causality detection approach. Since these approaches employ first differenced variables, any inference could be short-run in nature. In this paper we consider the relation between exports and economic growth to be a long-run phenomenon. After applying Johansen's cointegration technique to establish the long-run relationship between exports and output (in presence of other factors), we rely upon weak exogeneity tests proposed by Johansen to establish exogeneity of exports or output. Annual data over 1960-99 period from 61 countries are used for analysis. The results are country specific and there is no uniform pattern.
JEL Classifications: F10
Keywords: Export Growth, Income Growth, Cointegration
One of the areas in international and development economics that has received a great deal of attention by researchers is the relation between export growth and economic growth. Based on economic theory, one could easily postulate that an increase in exports leads to an increase in real GDP through the well-known multiplier effect. On the other hand, increase in real GDP could lead to realization of economies of scale and cost reduction that could, in turn, boost exports.
Due to lack of long time-series data, early studies employed either crosssectional data or panel data to establish the relation between export growth and economic growth. The list includes Kravis (1970), Michalopoulos and Jay (1973), Voivodas (1973), Michaely (1977), Balassa (1978a, 1978b, 1982, 1985), Heller and Porter (1978), Tyler (1981), Feder (1983), and Kavoussi (1984). In general, these studies provide support for the positive relation between export growth and economic growth. However, these studies have well-known weaknesses, including uncertainty about exogeneity of export variable. Furthermore, they do not incorporate country specific factors into analysis due to the nature of the data (cross-sectional).
To overcome the deficiencies associated with cross-sectional studies, for any country that enough time-series observations are available, researchers employed timeseries data and using ordinary least squares estimation method tried to establish the relation between export growth and economic growth. The list includes Krueger (1978), Ram (1987), Salvatore and Hatcher (1991) and Sengupta (1993). These studies did not investigate whether causality is from export growth to economic growth or vice versa. Given the evidence of unit root in most macro variables, they used non-stationary data. Thus, their estimates could be spurious.
Since introduction of Granger's (1969) and Sims' (1972) concept of causality within time-series framework, researchers shifted their emphasis toward investigating causality between export growth and output growth. The list includes Jung and Marshall (1985), Chow (1987), Ahmad and Kwan (1991), Bahmani-Oskooee et al. (1991), Ahmad and Harnhirun (1992), Hutchison and Singh (1992) and Dodaro (1993). In general, these studies have failed to provide strong support for export led growth (ELG) or growth led exports (GLE) hypothesis. One reason, as pointed out by Bahmani-Oskooee and Alse (1993) is that they have ignored to incorporate the cointegrating properties of exports and output in their testing procedure. Engle and Granger (1987) show that when two variables are cointegrated, there is an additional channel through which one variable can Granger cause the other variable and that is through what they call an error-correction term. Indeed, when Bahmani-Oskooee and Alse (1993) employ granger causality test inclusive of an error-correction term, they do provide strong support for bi-directional causality between exports and real GDP in almost all countries that they consider. Subsequent authors such as Kugler and Dridi (1993), van den Berg and Schmidt (1994), and Ahmad and Harnhirun (1995) who employed cointegration and error-correction modeling, also provided some support for bi-directional causality.1 In these studies causality is usually established by testing for the joint significance of the lagged first differenced variables that are suspected to cause the dependent variable (a short-run causality detection). Table 1 summarizes the main features of studies reviewed above and other related work.
However, the relation between export growth and economic growth seems to be a long-run process and rather than using the short-run dynamics to detect causality, one could concentrate on the long-run characteristics of the relation and establish exogeneity or endogeneity of the variables.2 Johansen's (1988) multivariate cointegration analysis offers a framework within which this goal could be achieved. A study based on cointegrating vectors that indicate a long-run relation, should provide a useful supplement to the existing research, especially if the number of countries is large and a multivariate framework is used.
Thus, the main purpose of this paper is to revisit the relation between export growth and economic growth using Johansen's cointegration technique. For this purpose, section II briefly outlines Johansen's cointegration method and explain how one could establish exogeneity or endogeneity of each variable in the model. Section III provides the results for 61 developing countries, the most comprehensive study in the literature. Section IV concludes. Data sources and definitions are cited in an appendix.
Let Z be a vector of p variables that are all integrated of order one. Assuming ÄZ is integrated of order zero or stationary, Johansen (1988) and Johansen and Juselius (1990) then express ÄZ as VAR systems outlined by model (1) below:
ΔZ^sub t^ = Γ^sub 1^ΔZ^sub t-1^ + . . . . . . . . .+ Γ^sub K-1^ΔZ^sub t-K+1^ + π Z^sub t-K^ + ε^sub t^ (1)
In (1) π contains information about the long-run relationships among the variables included in vector Z. Johansen (1988) demonstrates that π could be decomposed into the product of two p ?× r matrices, i.e., π = αβ' where elements of β matrix form the long-run cointegrating coefficients and elements of α matrix form the loading parameters. Number of cointegrating vectors r is determined by the rank of π. Once (1) is estimated using maximum likelihood estimation technique, two test statistics, i.e., trace and λ-max are used to determine number of cointegrating vectors. They then demonstrate that testing for significance of the elements of β matrix amounts to testing whether each variable in Z belongs to the cointegrating space (i.e., exclusion test) and testing for elements of α matrix amounts to establishing "weak exogeneity" of each variable.
Most studies mentioned above investigated the causality between output and exports only. However, in this paper following Solow's (1957) growth model we assume that the vector Z includes real GDP (denoted by Y), labor (L) and Capital (K). Exports and imports (openness) are not proper arguments of the production function in that they are not production inputs in the neoclassical sense. They are intended to reflect those international factors influencing productivity but not captured in L or K. Exports and imports may be viewed as a systematic error term affecting Y, so that the conditional expectation E(X / L, K) is non-zero. Hence, estimates of the effects of L and K on Y may be biased or inconsistent unless exports and/or imports are appropriately controlled for. Furthermore, imports may include raw materials as well as capital goods (inclusive of technology) that are used in production process. If this is the case, they could boost economic growth (Liu et al. 1997). Thus, we will assume that vector Z includes Y, L, K, X, and M.
In this section we apply Johansen's cointegration technique among the five variables for 62 developing countries using annual data over the period 1960-99. The main criterion for selecting these countries was availability of data for all variables in the model. The first task in applying the cointegration technique is to make sure that all variables are nonstationary. While earlier research used a univariate test such as ADF test to determine degree of integration of each variable, in this paper we follow Johansen (1988) and test for stationarity of a variable given the number of cointegrating vectors. Thus, in this likelihood-based procedure the test is applied in connection with the determination of the cointegration rank. To this end, we must first determine the cointegration rank or number of cointegrating vectors. In doing so, we must first ascertain the order of VAR outlined by (1). Following Juselius (1996) we begin with one lag and make sure that the residuals are autocorrelation free. If they are not, we then increase the lag length. Since the data is annual, in most cases one lag was sufficient enough to yield autocorrelation (first order) free residuals.
After imposing the optimum number of lags, the ë-max and trace statistics are calculated and the results are reported in Table 2. Note that Cheung and Lai (1993) demonstrate that both statistics must be adjusted for number of observations (T), the order of VAR (k) and number of variables in cointegrating space (n). They suggest multiplying the two statistics by (T-nk)/T to arrive at adjusted statistics. Thus, results reported in Table 2 are adjusted.
By relying on at least one of the test statistics we gather that there are 14 countries (i.e., Cote d' Ivoire, Mauritania, Mauritius, Morocco, China, Malaysia, Pakistan, the Philippines, Guyana, Haiti, Jamaica, Paraguay, Peru and Uruguay) for which the null of no cointegration, i.e., r = 0 cannot be rejected. This is because for these countries trace statistic is less than its critical value. For the remaining countries, the null of no cointegration is rejected indicating that there is at least one cointegrating vector among five variables. Concentrating on the trace statistic and the first country, Algeria, the null of at most one vector is rejected in favor of two vectors and the null of at most two is rejected in favor of three. However, the null of at most three vectors cannot be rejected in favor of four. Thus for Algeria, there are three cointegrating vectors. Following the same intuition we determine number of cointegrating vectors in each case (using ë-max or trace test which ever yields highest number of cointegrating vectors) and report estimates of these vectors normalized on Log Y in Table 3.
In addition to reporting the estimates of cointegrating vectors, we also report the result of exclusion test for each variable in cointegrating space. As indicated before, the exclusion test statistic is distributed as ÷2 with degrees of freedom equal to the number of cointegrating vectors. For weak exogeneity test, we concentrate only on countries for which output and exports pass the exclusion test. For example, in case of Cameroon, it appears that coefficient obtained for output is insignificant indicating that output does not belong to cointegrating space. Therefore, for exogeneity test we exclude countries for which either there was no evidence of cointegration or countries for which either output or export did not pass the exclusion test. The results of weak exogeneity test for remaining countries (26 in total) are reported in Table 4.
We classify countries in Table 4 into four groups. The first includes countries for which the assumption of weak exogeneity is rejected for both output and exports. The list includes Algeria, Gambia, Ghana, Malawi, Senegal, Hungary, El Salvador and Honduras. For these countries, it appears that in the long-run increased exports do contribute to output growth and increased output contributes to export growth. Thus, any inference based on single equation analysis, such as Granger causality will be misleading. The second group includes countries for which the hypothesis of weak exogeneity cannot be rejected for both output and exports. The list includes Burkina Faso, Burundi, Gabon, Kenya, Lesotho, Mali, Niger, Nigeria, Togo, India, Korea, Thailand, Egypt, Israel, Argentina, Bolivia, Brazil, Costa Rica, Dominican Republic, Guatemala, Mexico and Trinidad and Tobago. Thus, in these countries it is difficult to establish the direction of the long-run relation between output and export. The third group includes countries for which weak exogeneity is rejected for output but not for exports. The list includes Congo, South Africa, Swaziland, Tunisia, Ecuador and Nicaragua. In these countries since export is exogenous, we may conclude that in the long-run it is the increased exports that stimulates output. Finally, the fourth group includes countries for which the hypothesis of weak exogeneity is rejected for exports but not for output. This group includes Benin, Guinea Bisu, Rwanda, Zambia, Bangladesh, Indonesia, Papua New Guinea, Chile and Colombia. In these countries output growth leads to export growth in the long-run.
SUMMARY AND CONCLUSION
Export led growth hypothesis has received a great deal of attention in the literature. Studies investigating this hypothesis have concentrated on establishing whether export growth causes output growth or vice versa. Empirical literature could be divided into three categories. The first category includes early studies that used cross sectional data to establish the relation between exports and output. Since they do not take account of country specific factors, their results could be misleading. The second group concentrates on an individual country and use time-series data and ordinary least square technique. Their findings could also be misleading because they suffer from "spurious regression problem". The third group tackles the shortcomings of the first and second group by using stationary time-series data from an individual country and Granger or Sims causality to establish the direction of causation. Since Granger or Sims technique relies upon first differenced data and lag structure imposed on the variables in the model, they yield results that are only pertinent to the short run.
In this paper we consider the relation between exports and output to be a longrun in nature. After allowing for short-run dynamic adjustment in relevant macro variables such as stock of labor and capital in addition to adjustment of output and exports as well as feedback effect among all these variables, we employ Johansen's cointegration in conjunction with weak exogeneity test to determine which aggregate (output or exports) is exogenous in cointegrating space in the long-run. After testing the model for 61 developing countries using annual data over 1960-99 period. We conclude that the results are country specific. In countries that there was evidence of long-run relation among the variables in the model, there was evidence of both variables being endogenous in some cases and exogenous in others. In cases where export was exogenous, clearly output growth will depend on export growth and in cases where output was exogenous, export growth will depend on output growth. Overall policy implication of these results is that in developing countries export promotion policies and growth oriented policies work together in making these countries grow and enjoy economic prosperity.
? except for the following 11 countries: Burkina Faso: 1965-1999; The Gambia: 1966-1999; Guinea Bisu: 1970-1999; Mali: 1967-1999; Morocco: 1965-1999; Swaziland: 1970-1999; Tunisia: 1961- 1999; China (mainland): 1970-1999; India: 1970-1999; Papua Nea Guinea: 1961-1999, Hungary: 1970-1999
Valuable comments of an anonymous referee are greatly appreciated. Any error, however, is ours.
1 For a comprehensive review of the literature see Giles and Williams (2000).
2 For a discussion on exogeneity see Engle, Hendry and Richard (1983)
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The University of Wisconsin-Milwaukee, USA
Corresponding Author's Email Address: Bahmani@uwm.edu
Sources and Definitions of the Variables
All data are annual over the period 1960 - 1999? and collected from World Development Indicators (WDI, 2001) by the World Bank.
Y, Gross Domestic Product in Constant 1995 US $.
K, Gross Capital Formation in Constant 1995 US $.
L, Total Labor Force in Thousands.
X, Exports of Goods and Services in Constant 1995 US $.
M, Imports of Goods and Services in Constant 1995 US $.
1. Series: GDP (Constant 1995 US$) (NY.GDP.MKTP.KD)
GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 1995 U.S. dollars. Dollar figures for GDP are converted from domestic currencies using 1995 official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used. For more information, see WDI table 4.1.
2. Series: Exports of Goods and Services (Constant 1995 US$) (NE.EXP.GNFS.KD)
Exports of goods and services represent the value of all goods and other market services provided to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude labor and property income (formerly called factor services) as well as transfer payments. Data are in constant 1995 U.S. dollars. For more information, see WDI table 4.10.
3. Series: Imports of Goods and Services (Constant 1995 US$) (NE.IMP.GNFS.KD)
Imports of goods and services represent the value of all goods and other market services received from the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude labor and property income (formerly called factor services) as well as transfer payments. Data are in constant 1995 U.S. dollars. For more information, see WDI table 4.10.
4. Series: Gross Capital Formation (Constant 1995 US$) (NE.GDI.TOTL.KD)
Gross capital formation (gross domestic investment) consists of outlays on additions to the fixed assets of the economy plus net changes in the level of inventories. Fixed assets include land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. Inventories are stocks of goods held by firms to meet temporary or unexpected fluctuations in production or sales, and "work in progress." According to the 1993 SNA, net acquisitions of valuables are also considered capital formation. Data are in constant 1995 U.S. dollars. For more information, see WDI table 4.10.
5. Series: Labor Force, Total (SL.TLF.TOTL.IN)
Total labor force comprises people who meet the International Labor Organization definition of the economically active population: all people who supply labor for the production of goods and services during a specified period. It includes both the employed and the unemployed. While national practices vary in the treatment of such groups as the armed forces and seasonal or part-time workers, in general the labor force includes the armed forces, the unemployed, and first-time jobseekers, but excludes homemakers and other unpaid caregivers and workers in the informal sector. For more information, see WDI table 2.2.…
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Publication information: Article title: EXPORT LED GROWTH vs. GROWTH LED EXPORTS: LDCs EXPERIENCE. Contributors: Bahmani-Oskooee, Mohsen - Author. Journal title: The Journal of Developing Areas. Volume: 42. Issue: 2 Publication date: Spring 2009. Page number: 179+. © Journal of Developing Areas Fall 2008. Provided by ProQuest LLC. All Rights Reserved.
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