check, we also estimate the models using the balanced panel and with data from the Penn World Tables (1980‐2014). Conclusions of the paper are robust to these alternative data sources.

Table 1 reports descriptive statistics for the 137 economies in the unbalanced sample. The variables reported are GDP (PPP) per capita, total final energy consumption and population size. The numbers are period averages. The sample covers the entire spectrum from poor to rich countries.

# **III. Relationship between energy intensity and economic development**

# **A. Descriptive analysis**

We are interested in the relationship between energy intensity and economic development. One hypothesis that can be derived from the literature is that the relationship between energy intensity and economic development follows a Kuznet’s curve (inverted u‐shape). Energy intensity increases in early stages of development (industrialization) and it decreases in later stages of development (transition to a services economy). Structural change of the economy is a long process that is unlikely to be reflected in a panel of only 44 years. However, it is still possible to study this hypothesis. If it were true, we would expect a negative association between energy intensity and GDP per capita for developed countries and a positive association for developing countries (and possibly no association for countries that are in the middle of the transition).

Another hypothesis is that there is a negative association between energy intensity and economic development with no trend shift. The theoretical justification behind this hypothesis is that economic development increases opportunities to make production processes more efficient and therefore reduces energy intensity. If this negative association was preceded by an initial positive one, as the EKC hypothesis suggests, it would have pre‐dated the compilation of reliable cross‐country data. The remaining possibilities are that there is no association between growth and energy intensity or that energy intensity keeps rising with incomes (a positive association). These patterns are not supported by any theories or evidence.

Exploratory data analysis suggests it is appropriate to take logs of both variables. Taking logs does not change the direction of the relationship and therefore does not affect our conclusions regarding the main hypotheses. But the logarithm makes an exponential shape linear and allows correlation and regression analysis of the data.

As a first step we calculate the correlation coefficient between energy consumption per $PPP and GDP per capita PPP for all 137 economies in the data set (Table A1). For about 40 percent of the countries we find a very strong negative linear association between the two variables with a correlation coefficient of ‐0.9 or smaller. For another 35 percent of the countries we find a moderate to strong negative association with correlation coefficients between ‐0.9 and ‐0.5. Of the remaining countries, 16 show a weak to very weak negative association and 15 countries show a weak positive association. The overall impression from the correlation analysis is that there is a fairly strong negative association between energy consumption per $PPP and GDP per capita PPP for most countries. If the relationship followed a Kuznets curve, we would expect a negative association in developed countries and a positive association in developing countries. Even a cursory look at Table A1 contradicts this hypothesis: We find the same strong negative linear association for countries like India, Nigeria and 4