Download The Pedroni panel cointegration test results based on seven

Energy and Human Development: Panel Co-integration and Causality Testing of the Energy, Electricity and
Human Development Index (HDI) Relationship
[Ouedraogo Nadia, CGEMP, University Paris-Dauphine, Place Maréchal de Lattre de Tassigny, 75016 Paris
[email protected]/ [email protected]/ [email protected]
+33 6 81 04 52 03]
Overview
The causal relationship between energy consumption and income is a well-studied topic in the literature of energy
economics. This reflects the growing awareness of the international community of the close correlation between income
levels and access to modern energy: countries with low income tend to have low energy access and a high proportion of the
population relying on traditional biomass. The energy is thus an important vector for triggering economic development and
for reaching the Objectives of the Millennium.
The theoretical relationship between energy and economic growth has been therefore the subject of increasing attention
over recent years especially, since the studies of Granger (1969), Sims (1972) and Kraft and Kraft (1978). The purpose of
most of the studies, utilizing diverse methodologies and data sets for various time periods, has been to uncover the temporal
relationship between energy consumption and economic growth. Thus, in the literature, there is evidence supporting the
precedence of energy consumption to growth and vice versa as well as neutrality and bi-directional relation. The evidence
of a bidirectional causal relationship between energy consumption and GDP confirms the value of energy in economic
growth. Furthermore, these results reinforce the importance of energy access for social and economic development in
developing countries.
However, since the growing awareness of the international community that the access to modern energy is an important
vector for triggering economic development and for reaching the OMDs, and since that the shortcomings of GDP as
method of measuring human well-being are well known, it becomes interesting to explore the link between energy
consumption and indicators taking account others dimension of development such as the Human Development Index.
Hence, the purpose of this paper is to establish the empirical relationship among energy use, economic and social growth
measured by HDI.
It is well recognized that access to adequate energy services, affordable, reliable, effective and sustainable in environmental
terms is crucial for economic growth and human development. At the most basic level, energy is required for cooking,
heating and lighting. Without meeting these needs, survival is directly imperiled. Moving beyond survival, energy becomes
an essential component to every aspect of enhanced development: providing motive force for industry; refrigerating
medicines, lighting schools, preserving foods, transporting products, and communication. It seems reasonable to assume
that ‘energy-poor’ nations seeking to improve their quality of life will need increased access to cleaner, more reliable and
efficient energy sources. Thus, it follows that part of global energy transition planning entails reasonable evaluation of the
goals society should set to address the imperative of increasing energy access to people seeking to improve their current
conditions.
The importance, the direction of the empirical causality between energy consumption and the HDI can give a realistic
energy transitions plan (including the type of energy sources to promote and the good energy mix) for the development of
poor world.
This paper applies recently developed panel unit root, panel cointegration, and panel causality techniques to examine the
relationship between human development index and the total energy consumption as well as the electricity and oil price for
the fifteen’ (15) Economic Community of West African States (ECOWAs) from 1988 to 2008.
Methodology
Model specification and data
In our empirical analysis, we use the following model specification to investigate the long-run relationship between energy
and socio-economic development for the 15 ECOWAS countries.
HDIit= αit+βit +1iLENERit+2iLELECit+3iLPXit+it
Where i= 1; . . . ; N for each country in the panel and t = 1; . . . ; T refers to the time period. The parameters αi and i allow
for the possibility of country-specific fixed effects and deterministic trends, respectively. εit denote the estimated
residuals which represent deviations from the long-run relationship.
HDI= human development index, takes from the UNDP database; LENER= log per capita total energy consumption in
tep/hbt takes from Enerdata; LELEC=log per capita electricity consumption in kWh/hab and PX= international energy
price in us $ /brent from Statistical review of World Energy 2010.
The natural logarithm of all the variables was taken to standardize the data
Estimating Technique
Most economic time series are non-stationary, and the first step in the estimation is to determine the presence of a unit root.
Various tests are currently available to test for unit roots in panel data. Therefore, we adopt the recently developed panel
unit root tests suggested by Levin and Lin (1992), Im, Pesaran and Shin (2003) (IPS), Maddala and Wu (1999), Breitung
(2000), and Hadri (2000). For the first three tests, the null hypothesis is that the individual time series in the panel are
Ouédraogo.Nadia USAEE 2011
jointly non-stationary. For the Hadri test, the null hypothesis is that the time series for each cross-section member is
stationary around a deterministic trend.
Following the tests for unit roots is the test for co-integration. In an economic sense, two or more series are co-integrated
when there exists a long-term equilibrium to which they converge over time, and the white noise error can be interpreted as
the disequilibrium error. Thus, if series xt and yt are both stationary in the first differences (I(1)) and the error term ut is
stationary in the levels (I(0)), then the two series would be integrated of order CI(1,1). Three co-integration tests are
commonly employed in panel data studies, namely those of Pedroni (1999), Kao (1999) and McCoskey and Kao (1998).
We will use in this paper the Pedroni test. Pedroni (1995, 1999, 2000) has proposed seven test statistics for cointegration in
a panel context. Four of the statistics developed by Pedroni (1995, 1999), called panel cointegration statistics, are pooled
within-dimension based statistics. The other three statistics developed by Pedroni (2000), called group-mean panel
cointegration statistics, are between-dimension panel statistics. The seven residual-based co-integration tests by Pedroni are
based on the null hypothesis of no co-integration and allow for heterogeneous variances across countries in the panel.
If our variables are cointegrated, the next step is estimation of the long-run relationship. There are various methods for
estimating the long-run equilibrium relationship among the variables in question. We use the fully modified OLS (FMOLS)
and dynamic OLS (DOLS) estimators. The OLS estimator is a biased and inconsistent estimator when applied to
cointegrated panels. Therefore, we estimate the long-run relationship using FMOLS approach suggested by Pedroni (2000,
2001) and the DOLS(Harris and Sollis, 2003). The FMOLS is a non-parametric approach to dealing with corrections for
serial correlation – it therefore takes into account the possible correlation between the error term and the first differences of
the regressors as well as the presence of a constant term. DOLS is a parametric approach where the lagged first-difference
terms are explicitly estimated. With DOLS, the errors are augmented with leads, lags and contemporaneous values of the
regressors.
All the analyses were completed using the programs EViews 6 (Quantitative Micro Software, Irvine, CA, USA) and RATS
7.0 (Estima, Evanston, IL, USA).
Empirical Findings & Conclusions
If the correlation between gross domestic product (GDP) and energy consumption is well-established, the relationship
between energy and indicator of human welfare or quality-of-life (HDI) is not overwhelmingly compelling. This paper
investigates the long-run relationship between the energy consumption and socio-economic development measure by the
Human Development Index (HDI) for a panel of 15 developing countries over the period 1970–2008 by using recently
developed panel data unit root tests and Pedroni panel data cointegration techniques. The IPS, the Fisher ADF and Breitung
panel unit root test results show that the series in the panel are integrated of the order one.
The Pedroni panel cointegration test results based on seven test statistics show that there is a long-run relationship between
HDI and the energy consumption as well as electricity. Furthermore, the paper generates consistent estimates by employing
the Pedroni FMOLS procedure and finds that electricity consumption has a positive and significant effect on HDI for
ECOWAS countries. Access to modern and clean sources of energy has thus an important relationship to human
development indices (HDI). Mostly findings were consistent with economic theory and the implications of the model can
be use for policy making.
References
Glasure, YU. and A.R. Lee (1998). "Cointegration, error-correction, and the relationship between GDP and electricity: the case of South
Korea and Singapore." Resource and Energy Economics, 20(1): 17-25.
Hadri, K. (2000). "Testing for stationarity in heterogeneous panel data." Econometrics Journal, 3(2): 148-161.
Hu, J.L. and C.H. Lin (2008). "Disaggregated energy consumption and GDP in Taiwan: A threshold co-integration analysis." Energy
Economics, 30(5): 2342-2358.
Im, K.S., M.H. Pesaran and Y. Shin (2003). "Testing for unit roots in heterogeneous panels." Journal of Econometrics, 115(1): 53-74.
Johansen, S. (2002). "A small sample correction for the test of cointegrating rank in the vector autoregressive model." Econometrica,
70(5): 1929-1961.
Kao, C. ( 1999). "Spurious regression and residual-based tests for cointegration in panel data." Journal of Econometrics, 90-44.
Kim, H., Oh, K.Y. and C.W. Jeong (2005). "Panel cointegration results on international capital mobility in Asian economies." Journal of
International Money and Finance, 24, 71-82.
Kraft, J. and A. Kraft (1978). "On the relationship between energy and GNP." Journal of Energy and Development, 3(2): 401-403.
Maddala, G.S. and I.M. Kim (1998). Unit Roots, Cointegration, and Structural Change. Cambridge University Press.
Masih, A.M.M. and R. Masih (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(3): 165-183.
Pedroni, P. (2001). "Purchasing power parity tests in cointegrated panels." The Review of Economics and Statistics, &3{A): 121-1.
WDI (2006), World Development Indictors, World Bank,
WoIde-Rufael, Y, (2006), "Electricity consumption and economic growth: a time series experience for 17 African countries," Energy
Policy, 34(10): 1106-1114
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