Energy Consumption, Total Factor Productivity and Growth in Latin America Roberto Pasten a, Roberto Contreras b, Claudio Molina c a Economics Department, FACE, Universidad de Talca, Talca, Chile. b Economics Department, FAE, Universidad Tecnológica Metropolitana, Santiago, Chile. c Finance and Accounting Department, FAE, Universidad Tecnológica Metropolitana, Santiago, Chile. Abstract: This paper uses country by country regressions and random coefficient regressions to investigate the determinants and the causal relationship between energy use and real GDP in a production function that includes energy, capital stock and labor input for 19 Latin American and Caribbean countries for the 1971 2001 period. Preliminary results show that when energy enters as factor of production, the empirical results fully support a positive long-run co-integrated relationship between real GDP and energy consumption for most countries in the sample. Furthermore, it is found that there is longrun unidirectional causality running from energy to economic growth. Hence, in our sample of countries, reducing energy consumption does adversely affect GDP. Under the total factor productivity approach, the economic consequences of energy shortages are investigated. Keywords: Energy consumption, Economic growth, GDP, JEL classification: C22; Q43 1. Introduction What are the economic consequences of a shortage in energy supply for developing countries? The answer to this question is relevant, at least as relevant or even more- as the related question about what is the impact of economic growth on energy consumption? However, in order to answer these kinds of questions we may have first to ask ourselves; it 1
is true that energy reduction has a negative impact in growth rate? Or it is the reduction in the growth rate what adversely affects energy consumption? And if the causality runs from energy to income, how important this effect is? This paper aim to answer these and other related question regarding income growth and energy use for a representative group of Latin-American countries. For some authors energy reductions does adversely affect income ( Stern, 2000; Oh and Lee, 2004; Ghali and El-Sakka, 2004, Beaudreau, 2005), for other it is the other way around (Yu and Choi, 1985; Masih and Masih, 1996, 1998; Soytas and Sari, 2003; Shiu and Lam, 2004). In this paper, we advance the literature on the relation between energy and income in several ways. First, we estimate production functions with energy as a factor of production for Latin-American countries, a case scarcely studied in the literature. 1 Second we improves over a common misspecification in this time of model due to finite time series samples, we here use random coefficient estimation in order to avoid this bias and to increase the degree of freedom.. Thirdly, we estimate country-by country vector error correction models (VECM) that allow us to distinguish between short and long-run elasticities. Finally the impact of energy shortages on economic growth are estimated using 1 Apergis and Payne (2009) studied and extended the empirical literature on the causal relationship between energy consumption and economic growth to the case of Central America. They studied the relationship between energy consumption and economic growth for six Central American countries over the period 1980 2004 within a multivariate framework. Given the relatively short span of the time series data, a panel cointegration and error correction model is employed to infer the causal relationship. Based on the heterogeneous panel cointegration test by Pedroni 1999 and 2004.. The Granger causality results indicate the presence of both short-run and long-run causality from energy consumption to economic growth which supports the hypothesis of energy as a factor of production. 2
a Total Factor Productivity (TFP) approach which is a totally new feature in these kinds of models. The income energy relationship has been extensively studied in the literature. Since the path breaking study presented by Kraft and Kraft (1978) that found evidence of causality running from income to energy consumption in the United States several authors have studied this relation (Erol and Yu, 1987; Masih and Masih, 1996; Asafu-Adjaye, 2000; Morimoto and Hope, 2004; Lee, 2006; Lee and Chang, 2008). However, several flaws have been observed, particularly in earlier studies (Lee and Chang, 2008) some of these flaws are; short time span of the datasets -most data cover a 30- to 40 year span- Disregard for the order of integration of the series, and absence of exogeneity analysis for the right hand side variables in the regression equations. In order to avoid some of the aforementioned flaws - particularly the fixed sample bias - in the present paper, random coefficient along with OLS regressions are estimated. Coefficients estimated as random coefficients are weighted average between the (overall) random coefficient estimator and country by country OLS estimators. The random coefficient estimator for each country tends to shrink the country s OLS estimator toward the overall estimator common to all countries and therefore this is relevant in the fixed time series context since each country is drawing information examining the behavior of others. For countries that belong to a similar group, the expected response should be similar with actual differences given only by a random component. If random coefficient estimators and OLS -- country by country estimators are similar it will be possible to conclude that the fixed sample bias is negligible. 3
The remainder of this paper is organized as follows: In Section 2, we provide a brief discussion of the definitions of the variables and the data description. Section 3 presents the theoretical background and some preliminary results. Finally, Section 4 concludes and presents the following steps in the development of this research. 2. Variables definitions and data description Our study uses annual time series for 19 Latin-American and Caribbean countries. The sample includes: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Mexico, Paraguay, Peru, Trinidad and Tobago, Uruguay, Venezuela RB. Annual data for real GDP, energy use in kilograms of equivalent oil, labor force and real gross capital formation are taken from the World Development Indicator (WDI, 2003). The dataset is balanced and cover the 1971 2001 period. All variables are expressed in natural logarithms. In our multivariate framework, capital stock is an independent variables. However, data on capital stock is not easily available. Similar problem has been faced by other researchers (Paual and Rabindra, 2004; Beaundreau, 2005; Thompson, 2006 and Sari and Soytas, 2007). However, even though an accurate measure of total fixed capital stock is not available, capital formation or investment input can be used as reliable proxies for capital stock (Sharma and Dhakal, 1994; Nourzad, 2000 and Paual and Rabindra, 2004). 3. Theoretical background and empirical results 3.1 Theoretical background Traditional economic growth theories do not include energy into the production function. However, recent models of economic growth with environment considers energy as a factor 4
of production in the production process (Stern, 1997; Pokrovski, 2003, Thompson, 2006) and consequently, production is determined by energy use, capital stock and labor. Therefore, if energy enters as a factor of production, we consider the following aggregate production function:,, (1) Y is the aggregate output or real GDP; and KS, L and E represent real capital stock, labor and energy input, respectively. Assuming a Cobb Douglas production function and taking logarithms it is possible to express (1) as: (2) where,,, correspond to the natural logarithm of real GDP, energy, labor, and capital input respectively in country in period. Coefficients on independent variables correspond to elasticieties. The long run elasticity of energy on income for country is. Finally, is the error component In order to test the direction of causality in (2) it is necessary for the energy variable to be exogenous. There are two classes of tests to testing for weak homogeneity in dynamic regression models. The first approach tests the orthogonality between innovations and the conditioning regressors, the so called Durbin-Wu-Hausman test (Durbin, 1954; Wu, 1973; Hausman, 1978). Nevertheless, a more recently developed approach consisting in the design of a error correction model where weak exogeneity is tested trough the error correcting behavior is utilized here. The general framework to model a vector error correction model of order p (VECM(p)) for cointegrated variables is: 5
Δ Y it = Π iδyit 1 + Γi 1ΔYit 1 +... + Γip 1ΔYit p+ 1 + U it (3) where Yit is a 4Χ 1 vector of country i time series with the variables appearing in the income-energy relationship (i.e. GDP, labor, energy and capital). Γ ij with j = 1,... p are 4Χ 4 coefficient matrices and zero mean and covariance matrix U it is a normally distributed 4Χ1 white noise process, with Σ ui In cointegrated models Π has reduced rank r = rk( Π) < 4 and can be decomposed as i Π i = α iβ i where α i and β i are 4 Χr matrices containing the loading (or feedback) coefficients and the cointegrating vector respectively. Therefore, in this context, testing for weak exogeneity is equivalent to test for zero restrictions on the α i matrix In order to fit a VECM as in (5) several previous steeps are necessary. Firs it is necessary to check that the country-specific income energy equations in (2) are effectively cointegration relationships. The method of Johansen and Jubelius is used here to test the hypothesis that only one cointegrating vector exists in our country specific income-energy relations. In general all the cointegrating relationship supports the presence of only one cointegrationg vector (results not reported). Secondly, in order to implement the Engel and Granger procedure, the residuals for every country specific EKC are estimated and recorded. Therefore, (4) And next the loading factors α i are estimated. 6
3.2 Empirical results Table 1 reports our empirical results applying Random Coefficients to estimate equation (2). The second row presents the overall regression. It is possible to observe that all the coefficients have the correct sign and their magnitude is reasonable. Furthermore it is possible to observe that the sum of coefficients is very close to one ( 0.98 in fact) and consequently it is possible to entertain the idea that the overall production function has constant returns to scale in the three factors of production. Columns 2,3 and 4 in the same Table present the coefficients estimated for labor, capital and energy inputs. From the second row in Table 1 Swamy-Mehta estimators (Swamy and Mehta, 1975) are displayed to predict individual coefficients within the Latin America group. For most of the countries in the sample the coefficients have the right sign and are highly significant ( t statistics in brackets) and the order of magnitude is reasonable. However, for a few countries in the sample (Bolivia, Haiti, Jamaica, and Venezuela) the coefficients have the wrong sign or are outside a reasonable boundary and this is surely explained by the quality of the data in these particular countries. It is worth to note that for most countries in the sample energy is an important factor of production having a participation greater than 50 % in some cases. 7
Table 1: Random Coefficients Regression by GLS Country grouping Lf Lks Lec Global 0.45 (4.86) 0.20 (9.01) 0.33 (4.47) Argentina 0.67 (5.18) 0.29 (14.57) 0.26 (2.47) Bolivia -0.00 (-0.05) 0.20 (8.86) 0.36 (6.99) Brazil 0.36 (3.30) 0.21 (4.83) 0.54 (4.34) Chile 0.48 (6.68) 0.14 (5.45) 0.63 (11.36) Colombia 0.76 (8.91) 0.12 (3.19) 0.24 (1.78) Costa Rica 0.46 (6.58) 0.20 (5.53) 0.39 (0.39) Dominican Republic 0.51 (4.33) 0.24 (3.51) 0.40 (6.24) Ecuador 0.40 (4.33) 0.14 (3.51) 0.51 (6.24) El Salvador 0.09 (1,23) 0.033 (10.76) 0.25 (2.64) Guatemala 0.62 (6.53) 0.14 (3.29) 0.28 (2.47) Haiti -0.02 (-0.24) 0.28 (10.19) -0.13 (-1.65) Honduras 0.28 (2.91) 0.08 (2.35) 0.72 (5.95) Jamaica 0.19 (3.49) 0.15 (5.06) 0.16 (2.58) Mexico 0.33 (6.60) 0.18 (8.48) 0.48 (0.48) Paraguay 0.77 (0.77) 0.35 (15.30) 0.07 (0.85) Peru 0.32 (9.71) 0.10 (3.10) 0.88 (8.38) Trinidad and 0.62 0.16 0.19 Tobago (3.75) (4.36) (2.48) Uruguay 1.22 (27.35) 0.23 (12.12) 0.11 (1.88) Venezuela, RB 0.54 (5.72) 0.18 (7.78) -0.05 (-0.48) 8
Table 2 reports our empirical results applying country by country OLS regressions. It can be observed that the results are very similar to those encountered in Table 1 and thus the finite sample bias seems in consequence not to be a problem. Table 2: Country-by country OLS regressions Country grouping Lf Lks Lec Argentina 0.69 (3.25) 0.29 (13.35) 0.23 (1.40) Bolivia -0.06 (-0.54) 0.21 (8.51) 0.39 (6.44) Brazil 0.27 (1.60) 0.22 (2.88) 0.61 (3.00) Chile 0.48 (6.42) 0.13 (5.07) 0.64 (11.52) Colombia 0.80 (7.38) 0.12 (2.32) 0.20 (1.11) Costa Rica 0.44 (5.91) 0.20 (4.52) 0.42 (4.06) Dominican Republic 0.51 (5.71) 0.25 (6.12) 0.39 (6.59) Ecuador 0.37 (3.01) 0.12 (1.68) 0.55 (4.84) El Salvador 0.06 (0.67) 0.34 (10.55) 0.26 (2.55) Guatemala 0.66 (4.51) 0.13 (1.93) 0.25 (1.32) Haiti -0.12 (-0.17) 0.28 (9.87) -0.19 (-2.30) Honduras 0.31 (1.60) 0.06 (1.52) 0.72 (3.00) Jamaica 0.18 (3.42) 0.14 (4.44) 0.16 (2.64) Mexico 0.32 (6.57) 0.17 (8.33) 0.48 (12.27) Paraguay 0.85 (6.55) 0.36 (15.70) -0.00 (-0.00) Peru 0.32 (9.74) 0.08 (2.30) 0.96 (7.96) Trinidad and Tobago 1.40 (2.10) 0.11 (1.88) -0.08 (-0.33) Uruguay 1.23 (28.58) 0.23 (12.30) 0.10 (1.83) Venezuela, RB 0.58 (5.80) 0.16 (6.82) -0.09 (-0.97) 9
4. Conclusions and further work This paper presents preliminary results for the relation between income and energy use in a set of Latin-American countries. The results fully support the presence of a long run cointegration relation between income and energy. In the following phases of this research we will report the results of the weak exogeneity test along with t-values. What is expected is that the null hypothesis of weak exogeneity cannot be rejected for the level variables (particularly for energy input). Contrarily, in the case of income, the null hypothesis of weak exogeneity we expect will be rejected for most of LA countries. Therefore, our hypothesis is that energy use is a (weakly) exogenous variable while real GDP is endogenous. Therefore, it makes sense to put real GDP at the left hand side and energy use at the right hand side of our country-by-country income-energy regressions. Finally, we expect to be able to investigate the quantitative impact of energy reductions on economic growth through the use of the Total Factor Productivity (TFP) approach. 5. References Apergis, N., Payne J., 2009. Energy consumption and economic growth in Central America: Evidence from a panel cointegration and error correction model. Energy Economics 31 (2009) 211 216. Asafu-Adjaye, J., 2000. The relationship between energy consumption, energy prices and economic growth: time series evidence from Asian developing countries. Energy Econ. 22, 615 625. Beaudreau, B., 2005. Engineering and economic growth. Energy Economics 16, 211 220. Engle, R.F., Granger, C.W.J., 1987. Co integration and error correction: representation, estimation, and testing. Econometrica55 (2), 251 276. Erol, U., Yu, E.S.H., 1987. On the causal relationship between energy and income for industrialized countries. J. Energy Dev. 13, 113]122. 10
Ghali, K.H., El-Sakka, M.I.T., 2004. Energy consumption and output growth in Canada: a multivariate cointegration analysis. Energy Economics 26, 225 238. Greene, W.H., 2000. Econometric Analysis. Prentice Hall, New York. Glasure, Y.U., 2002. Energy and national income in Korea: further evidence on the role of omitted variables. Energy Economics 24, 355 365. Kraft, J., Kraft, A., 1978. On the relationship between energy and GNP. Journal of Energy and Development 3, 401 403. Lee, C.C., Chang, C.P., Chen, P.F., 2008. Energy income causality in OECD countries revisited: the key role of capital stock. Energy Economics 30, 2359 2373. Maddala, G.S., Kim, I. M., 1998. Unit roots, cointegration and structural change. Cambridge University Press, Cambridge, United Kingdom. Masih, A.M.M., Masih, R., 1996. Energy consumption, real income and temporal causality: results from a multi country study based on cointegration and error correction modelling techniques. Energy Economics 18, 165 183. Masih, A.M.M., Masih, R., 1997. On the temporal causal relationship between energy consumption, real income, and prices: some new evidence from Asian energy dependent NICs based on a multivariate cointegration/vector errorcorrection approach. Journal of Policy Modeling 19 (4), 417 440. Morimoto, R., Hope, C., 2004. The impact of electricity supply on economic growth in Sri Lanka. Energy Economics 26, 77 85. Oh, W., Lee, K., 2004a. Energy consumption and economic growth in Korea: testing the causality relation. Journal of Policy Modeling 26, 973 981. Oh, W., Lee, K., 2004b. Causal relationship between energy consumption and GDP revisited: the case of Korea 1970 1999. Energy Economics 26, 51 74. Paul, S., Bhattacharya, R.N., 2004. Causality between energy consumption and economic growth in India: a note on conflicting results. Energy Economics 26, 977 983. Phillips, P.C.B., Perron, P., 1988. Testing for a unit root in time series regression. Biomètrika 75 (2), 336 346. Ram, R., 1986. Government size and economic growth: a new framework andsome evidence from cross-sectionand time-series data. American Economic Review 76 (1), 191 203. Shiu, A., Lam, P., 2004. Electricity consumption and economic growth in China. Energy Policy 32, 47 54. 11
Soytas, U., Sari, R., 2003. Energy consumption and GDP: causality relationship in G-7 countries and emerging markets. Energy Economics 25, 33 37. Stern, D.J., 2000. Multivariate cointegration analysis of the role of energy in the U.S.macroeconomy. Energy Economics 22, 267 283. Toda, H.Y., Yamamoto, T., 1995. Statistical inference in vector autoregressions with possible integrated processes. Journal of Econometrics 66, 225 250. Torras, M., Boyce, J.K., 1998. Income inequality and pollution: a reassement of the Environmental Kuznets Curve. Ecological Economics 25 (2), 147 160. World Bank, World Development Indicators 2003. Yang, H.Y., 2000. A note on the causal relationship between energy and GDP in Taiwan. Energy Economics 22, 309 317. Yao, S., 2006. On economic growth, FDI and exports in China. Applied Economics 38, 339 351. Yildirim, J., Sezgin, S., Öcal, N., 2005. Military expenditure and economic growth in middle eastern countries: a dynamic panel data analysis. Defence and Peace Economics 16 (4), 283 295. Yu, S.H., Choi, J.Y., 1985. The causal relationship between energy and GNP: an international comparison. Journal of Energy Development 10, 249 272. Yu, E.S.H., Hwang, B.K., 1984. The relationship between energy and GNP: Further results. Energy Economics 6, 186 190. Yu, S.H., Jin, J.C., 1992. Cointegration Tests of energy consumption, income and employment. Resources and Energy 14, 259 266. Yu, E.S.H., Choi, P.C.Y., Choi, J.Y., 1988. The relationship between energy and employment: a re examination. Energy Systems Policy 11, 287 295. 12