1. 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 North America Conference,
OCT. 9-12, 2011

2. Outline
1. Overview
2. Methodology
3. Results
4. Conclusion

3. OVERVIEW

4. 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
If causality is running from energy consumption to growth, then energy conservation may harm economic growth. In such cases, countries may choose to invest in technology that discovers and makes alternative energy sources economically feasible, in the meantime following policies that
mitigate carbon emissions such as increasing energy efficiency and decreasing energy intensity via substituting in cleaner (i.e. natural gas, solar, wind energy) sources for fossil fuels like coal and oil. On the other hand, if there is uni-directional causality running in the opposite direction,
then decreasing domestic energy consumption and encouraging energy conservation may become the key actions in reducing domestic emissions. Finally, if there is bi-directional causality, then a carefully integrated mixture of alternative policy actions may be possible.

5. 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.

6. 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.

7. 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.

8. 2. Methodology and Data Sources

9. Methodology
Cointegration analysis is the appropriate technique to investigate the long-run relationship between energy consumption and economic development. Before applying the cointegration technique, the first step is to investigate the stationarity properties of the variables. The power of standard time-series unit root tests may be quite low given the sample sizes and time spans. Therefore, we adopt the recently developed panel unit root tests suggested by Im, Pesaran and Shin (2003) (IPS), Maddala and Wu (1999), and Breitung (2000) to test for the presence of a unit root in the panel data series. The second step is to test for the existence of a long-run relationship between financial development and economic growth. The Pedroni panel cointegration test, which takes into account heterogeneity by using specific parameters, is applied in this study to examine the long-run relationship. Finally, on finding cointegration in the second step, we estimate the coefficients on economic growth by using panel fully modified ordinary least squares (FMOLS) and Dynamic OLS (DOLS) methods.

10. 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; αi is the country-specific effects, di is 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 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.

11. RESULTS

12. Unit root Results
The results of the IPS, LLC and Hadri panel unit root tests for the series HDI ENER and ELEC are shown in table 1. The unit root statistics reported are for the level and first differenced series of HDI ENER and ELEC. At a 1% significance level the statistics confirm that the two series in each model have a panel unit root.
Overall, all the three panel unit test techniques reject the null hypothesis for the differenced series and thus show that HDI and LELEC as well as HDI and ENER are integrated of order one or I(1).

13. Cointegration Results (1)
Table 2 reports the results of the panel cointegration. The tests reject the null of no cointegration, and thus we can conclude that HDI and energy consumption or HDI and ENER move together in the long-run. The implication is that there is a long-run relationship between energy consumption and GDP for a cross section of the countries after allowing for a country-specific effect.

14. Cointegration Results (2)

15. 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.

16. 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%.

17. 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.

18. 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.