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Energy and Human Development: Panel Co-integration and Causality Testing of the Energy, Electricity and Human Development Index (HDI) Relationship. Nadia Ouedraogo PhD student Centre of Geopolitics of Energy and Raw Materials (GEMP) University of Paris-Dauphine
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Energy and Human Development: Panel Co-integration and Causality Testing of the Energy, Electricity and Human Development Index (HDI) Relationship Nadia Ouedraogo PhD student Centre of Geopolitics of Energy and Raw Materials (GEMP) University of Paris-Dauphine 30th USAEE/IAEE NorthAmericaConference, OCT. 9-12, 2011
Outline 1. Overview 2. Methodology 3. Results 4. Conclusion
Overview (1) • The increasing attention given to global energy issues and the international policies needed to reduce greenhouse gas emissions have given a renewed stimulus to research interest in the linkages between the energy sector and economic performance at country level. • The existence or non-existence of a long run causal relationship between energy consumption and economic growth in these countries should lead to the choice of an optimal energy policy for energy poverty reduction, economic growth and climate mitigation. • They may exist 3 types of causality: • Unidirectional causality runs from energy consumption to growth • Unidirectional causality runs from economic growth to energy consumption • Finally a bi-directional causality: energy causes economic growth and growth leads to increase of energy consumption
Overview (2) • The causal relationship between energy consumption and income is a well-studied topic in the literature of energy economics. The causality is in the sense of Granger causality (Granger, 1969). • Granger-causality implies causality in the prediction (forecast) sense rather than in a structural sense. It starts with the premise that ‘the future cannot cause the past’; if event A occurs after event B, then A cannot cause B (Granger 1969). • The large number of studies in this area, unfortunately, found different results for different countries as well as for different time periods within the same country.
1. Overview (3) • However, very little attention in the literature has been paid to the development indicators other than GDP, particularly the HDI. This can be partly explained by the difficulties in terms of data availability. For instance, although the HDI index was developed in 1990, the UN undertook several major revisions of the index, so that the data from different years are not comparable over time and cannot be used as a single series.
Overview (4) • The purpose of this paper is to investigate the relationship between economic growth, energy use, poverty alleviation and development. • To perform that, we are using: • a 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.
DATA MODEL: • HDIit=ait+βit+ d1iLENER+ d2iLELEC+ d3iLPX+ eit (1) • The observable variables are in natural logarithm form, t =1,.....T is time periods; i=1,.....N members of the panel; αiis the country-specific effects, diis the deterministic time trends and eit is the estimated residual. • Data used in this analysis are annual time series on Human Development Index (hereafter referred to as HDI); per capita energy consumption (referred to as ENER hereafter) and per capita electricity consumption (referred to as ELEC) for 15 ECOWAS countries for the years 1988 to 2008. HDI data is obtained from the United Nation Development Program (UNDP), and the energy data is obtained from ENERDATA. International energy price in us $ /brent is from Statistical review of World Energy 2010 • All variables used, except the HDI, are in natural logarithm.
FMOLS & DOLS Results • FMOLS and DOLS models estimation give different results. The t-statistics of FMOLS model are systematically lower than that of the DOLS, especially when the model is estimated without trends. • We must notice that the DOLS method has the drawback of reducing the number of degrees of freedom by including leads and lags in the variables studied. This reduces the robustness of estimations. As, the size of our sample is already low in both dimensions of time and the number of countries, the DOLS results would therefore not robust. • The DOLS estimation method, however, allows us to confirm the general trend and direction of causality obtained by the FMOLS method. • It is interesting to note that the within-dimension results do not differ from between- dimension results. • Modeling intra-dimension (Within) allows taking into account the heterogeneity of individuals in their temporal dimension and / or individual. Within estimator eliminates the individual effects (persistent differences between the countries over the period). He favors the temporal information.
Panel Results (1) • The panel long-run income elasticity is 0.8 in energy model, which is statistically significant at the 5% level, and the effect is negative. • This implies that a 1 % increase in per capita energy consumption decrease the HDI by 0,5%, Moreover, the panel long-run energy price elasticity is -0,11 in this model, which is statistically significant at the 5% level, and the effect is negative. This implies that a 1% increase in energy price reduces the HDI by around 0.11%, when the dependent variable is energy consumption. • In the electricity per capita model, the panel long-run income elasticity is 0.22 % which is statistically significant at the 5% level, and the effect is positive. Hence, panel long-run electricity consumption increases the HDI by 0.22%.
Panel Results (2) • This negative impact of energy on the HDI supports our assertion. Indeed, several hypotheses can be formulated to explain the negative impact of energy on the HDI: • -excessive consumption of energy in unproductive sectors of the economy; • -a capacity constraint; • -inefficient supply of energy. • Regarding our sample of countries, one of the most obvious first explanations is the inefficiency of energy supply. In fact, energy consumption in the region is composed of 80% biomass. Outside, the use of biomass has a negative and hard on many aspects of the HDI: • -expectancy at birth (through its negative impact on health and nutrition, for example)-level of education (through such non-schooling of girls whose time is devoted to tasks such as searching for wood but also through the reduction of study time due to lack of lighting, the lack of access to the New Technologies etc.). • The coefficients for electricity consumption and ECT in the electricity equation are significant at the 5% level, respectively, and the two variables are jointly statistically significant at the 1% level. This clearly shows that there is a unidirectional Granger Causality running from electricity consumption to HDI in the long-run.
Conclusion • The long-term results allow us to draw some conclusions: • -A Long-term increase in the quantities consumed energy is necessary for economic growth but an improvement in the quality of this consumption is vital, especially if we are targeting the human development of people involved (the current structure of consumption has a negative impact on HDI and we have demonstrated the positive impact of electricity on economic development in the long run). -Thus, an improvement in income, followed by non-availability of supply of electricity, cooking gas and other forms of energy that can reduce the pressure on biomass is not sufficient for sustainable development. Measures target the decline in the share of biomass in energy consumption should be encouraged because the use of this form of energy is also a real threat to the environment. By making electricity accessible to all, this could help reducing poverty but also to improve the quality of living. • The relatively low magnitudes of the estimated own price elasticity suggest that the potential implementation of pricing policies to curtail energy demand may not be useful. The small price sensitivities also indicate that little substitution between alternative energy options is possible.
