Energy Economics 34 (2012) 1882–1887

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Energy Economics

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Cross-country convergence in energy and electricity consumption, 1971–2007

Hassan Mohammadi ⁎, Rati RamIllinois State University, United States

⁎ Corresponding author at: Economics Department, IlIL 61790‐4200, United States. Tel.: +1 309 438 7777; fa

E-mail address: hmohamma@ilstu.edu (H. Mohamm1 Islam (2003) and Abreu et al. (2005) provide a flav

scholarship that has gone into studies of income conver

0140-9883/$ – see front matter © 2012 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.eneco.2012.08.001

a b s t r a c t

a r t i c l e i n f o

Article history:Received 31 July 2011Received in revised form 20 July 2012Accepted 3 August 2012Available online 25 August 2012

JEL classification:C31O40Q40Q50

Keywords:Cross-country convergenceEnergy consumptionElectricity consumptionQuantiles

Patterns of convergence in per-capita consumption of energy and electricity are studied from a largecross-country data set covering the period 1971–2007. Along with unconditional β-convergence, we useσ-convergence criterion and a simple model of conditional β-convergence. The exploration is done for theentire period and several subperiods. In addition to OLS for the global sample, β-convergence is studied fortop and bottom deciles through quantile regressions. Ten points are noted. First, global convergence in ener-gy consumption is generally weak. Second, convergence in electricity usage is strong in most cases. Third, de-spite some variations, the patterns are fairly similar across the four periods. Fourth, energy convergence inboth top and bottom deciles is generally weak, but there are some variations. Fifth, for electricity, conver-gence is noted in the top, but not in the bottom, decile. Sixth, unconditional β-convergence patterns are con-sistent with σ-convergence scenarios. Seventh, as is usually noted, convergence is more marked inconditional β-format than in the unconditional models. However, interpretation of conditional convergencein usage of energy or electricity is somewhat ambiguous. Eighth, weak convergence in energy usage mightreflect a modestly larger increase in low-usage contexts relative to high-usage cases, and might not be of con-cern from the sustainability perspective. Ninth, strong convergence in electricity usage is associated with amuch higher rate of global increase than the weakly-convergent energy usage. Last, the difference in conver-gence patterns for energy- and electricity-usage seems to merit further exploration.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

A study of the distribution of energy consumption in terms ofcross-country convergence is important from the perspective of eco-nomic development and in the context of sustainability. Perceived asan important input in the production process, relevance of energyusage to output and growth is evident. The relation between energyusage and environmental qualitymakes cross-country patterns of ener-gy consumption a significant dimension of sustainability issues, includ-ing greenhouse emissions, global warming, and emissions-relatedinternational initiatives.

Numerous scholars have investigated convergence of income acrosscountries and regions, and much of that discussion has usually beenconducted in the context of economic growth.1 The energy-related liter-ature includes studies of cross-country convergence in carbon-dioxideemissions by Aldy (2006), Ezcurra (2007a) and others. It also includesresearch on convergence in energy intensity or energy productivity byDuro and Padilla (2011), Ezcurra (2007b), Le Pen and Sevi (2010),Liddle (2010), Markandya et al. (2006), Miketa and Mulder (2005),Mulder and de Groot (2007) and others. Liddle (2009) studied

linois State University, Normal,x: +1 309 438 5228.adi).or of the enormous amount ofgence.

rights reserved.

convergence in electricity intensity, and Maza and Villaverde (2008)considered convergence in residential electricity consumption as an indi-cator of economic well-being. While numerous studies have considereddrivers of energy consumption, to our knowledge, there has been no re-search on cross-country convergence in energy usage, which should bean important complement to the research on carbon-dioxide emissionsand energy and electricity intensities. First, energy usage is a major indi-cator of development and it is useful to study its cross-country distribu-tion from that perspective. Second, carbon-dioxide emissions constitutea partial outcome of energy usage which has wider consequences.Third, study of energy intensity entails a joint study of the distributionsof GDP and energy usage and it is difficult to judge patterns in energyusage from that research. Fourth, while energy intensity has recentlybeen a declining variable, energy usage is typically increasing, and con-vergence structures for the twovariablesmaybe different. Themain pur-pose of this study, therefore, is to complement the existing research oncarbon-dioxide emissions and energy-intensity by providing evidenceon cross-country convergence in per-capita energy usage. Five additionalcontributions are also sought to be made. First, besides exploring globalconvergence in energy consumption, we use quantile regression meth-odology to consider variations in the structure across country-groups lo-cated in the top and the bottom parts of the distribution of change inenergy usage. That should provide a structured analysis of variations inthe convergence parameters across the top and bottom quantiles. Sec-ond, we study possible temporal patterns by considering three subpe-riods in addition to the 37-year period from 1971 to 2007. Third, along

2 For instance, relative to electricity and energy intensity in the IEA countries, Liddle(2009) studied mobility within the distribution through gamma-convergence. Similar-ly, in the study of global convergence in energy intensity, Liddle (2010) examinedchange in the shape of the distribution over time through kernel density estimates.He also considered intra-distribution mobility through the gamma-convergence index.

1883H. Mohammadi, R. Ram / Energy Economics 34 (2012) 1882–1887

with energy usage, we consider convergence in electricity usage. WhileMaza and Villaverde (2008) have studied convergence in electricityusage, theirwork covered the shorter period 1980–2007 andwas limitedto the residential component. We work with total electricity usage andthe longer period 1971–2007. Moreover, as for energy usage, we com-pare temporal patterns in electricity usage across several periods andalso compare patterns across the groups of countries in top and bottomquantiles. Fourth, we use models of unconditional β-convergence alongwith a simple conditional-convergence format for each period andcountry-group. Fifth,we doquick comparisons of scenarios for (uncondi-tional) β- and σ-convergence.

Section 2 contains a selective narrative on related studies.Section 3 describes the model, data, and the main results. Section 4provides a brief discussion, and Section 5 contains a few concludingreflections.

2. A selective narrative on related research

The preceding section noted several energy-related studies ofcross-country convergence. The main purpose of this section is to de-scribe those studies in slightly greater detail so as to indicate the po-tential usefulness of our research.

In the energy-related convergence literature, three strands of re-search can be identified. One of these deals with convergence incarbon-dioxide emissions. For example, Aldy (2006) explored the sta-tus of convergence in carbon-dioxide emissions during the period1960–2000. Using several procedures, he noted convergence in theOECD group, but lack of convergence in the broader sample of 88countries. Ezcurra (2007a), who worked with data for 87 countriescovering the period 1960–1999, used a non-parametric approachand concluded that the evidence indicates convergence. He also con-sidered determinants of emissions and found temperature andper-capita income to be significant, but trade openness did notseem important. It is interesting to note that Aldy (2006) andEzcurra (2007a) reached different conclusions on emissions conver-gence despite the similarity of their samples and periods. Anothergroup of energy-related studies has explored cross-country conver-gence in energy intensity, defined broadly as energy usage per unitof output, and energy productivity, which is the inverse of energy in-tensity. For instance, Markandya et al. (2006) considered whether en-ergy intensity in 12 transition economies was converging to the EU15levels during 1992–2002, and concluded that the data indicatedβ-convergence. In a broader cross-country context, Ezcurra (2007b)found convergence in energy intensity across 98 countries over theperiod 1971–2001. His conclusion was based mainly on a decline inthe dispersion of the distribution and thus reflected σ-convergence.Le Pen and Sevi (2010) studied energy intensity for 97 countriesover the period 1971–2003. Using Pesaran's method of pair-wisecomparison to judge stochastic convergence, they rejected globalconvergence, but indicated some signs of convergence in themiddle-east region and possibly in OECD. Liddle (2010) consideredenergy-intensity distributions for 111 countries covering the period1971–2006 and 134 countries for the shorter period 1990–2006.Using several methods, he indicated convergence at the global level,but noted different geographical regions to be converging at differentrates, and some not converging. In a related work, Duro and Padilla(2011) investigated cross-country inequalities in energy intensity toidentify the roles of energy transformation and final energy consump-tion. They indicated the pre-eminence of divergence in final energyintensity in explaining global primary energy intensity inequality. Inan earlier study, Miketa and Mulder (2005) used data on energy pro-ductivity for 10 manufacturing sectors in 56 developed and develop-ing countries covering the period 1971–1995. They employed severaldifferent formats, including σ-convergence and unconditional andconditional β-convergence in 5-year panels. This was an elaboratework since they examined 10 sectors for 56 countries over a

25-year period. The general conclusion was that convergence islocal rather than general. Mulder and de Groot (2007) did a similarstudy for 14 sectors in 14 OECD countries covering the period1970–1997, and indicated that while σ-convergence shows varyingpatterns, conditional β-convergence suggested catch-up in mostcases. The third strand of literature deals with convergence in elec-tricity variables. For example, Maza and Villaverde (2008) studiedthe status of convergence in per-capita residential electricity usagein panel data for 98 countries over the period 1980–2007. They ex-plored σ-convergence and studied the distributional dynamics. Forβ-convergence, their basic model has the unconditional format, butthey did fixed-effects estimation with intercept country dummies.Their main finding is of a weak process of convergence. Liddle (2009)studied convergence in sectoral end-use electricity (and energy) inten-sity in 22 developed countries over the period 1960–2006, and indicat-ed σ-convergence in electricity intensity, but a diverse convergencepattern for end-use sectors. There is also a huge literature on the envi-ronmental Kuznets curve that looks at the evolution of emissions duringthe course of economic development. However, that literature has lim-ited relevance to studies of convergence.

Without going into the differences in the conclusions stated in theforegoing research about convergence in carbon-dioxide emissions andenergy intensity, we note again that, although many scholars haveconsidered drivers of energy consumption, there seems to have beenno study on convergence in per-capita energy usage. We also note that(a) while several studies have examined movements within thedistribution, there has been a lack of consideration of convergence intop and bottomquantiles of the distributions of energy-related variables;(b) although some scholars have looked at temporal patterns throughplots of σ-convergence, kernel estimations or histograms at variouspoints of time, few studies have directly considered temporal patternsin convergence across several periods; and (c) there are no direct com-parisons of convergence patterns in usage of energy and electricity.2

Ourmain contribution, therefore, lies in (a) exploring cross-country con-vergence in energy usage over the 37-year period 1971–2007, (b) com-paring the convergence patterns in top and bottom quantiles of thedistribution of change in energy usage, (c) directly studying temporalpatterns in the status of convergence in energy usage for several periods,(d) comparing patterns across the top and bottom quantiles and acrossseveral periods in per-capita electricity usage, and (c) providing a com-parison of convergence patterns in energy and electricity usage.

3. Model, data, and the main results

Before our models are specified, it is useful to recall briefly variousconcepts of cross-country convergence. As noted in Section 2,σ-convergence refers to a decline in the dispersion of the variable,and the dispersion is often measured in terms of coefficient of varia-tion (CV). The concept thus refers to a decline in cross-country in-equality in the variable. For an increasing variable, β-convergenceimplies that the variable increases at a slower rate in countries withhigh values and at a higher rate in countries with low values. This ver-sion of convergence is judged by regressing the rate of increase dur-ing a period on the initial value of the variable and testing whetherthe initial-value coefficient is significantly negative. When the regres-sion includes only the initial-value variable, it models “unconditional”β-convergence in which all countries tend to move toward a commonvalue. If the regression includes other variables also, it depicts amodel of “conditional” β-convergence. The other terms are treatedas “conditioning” variables and the model reflects the proposition

Table 1Descriptive statistics for the main variables.

Mean SD CV Min. Max. N

A. Per capita consumptionPrimary energy consumption per capita (kg of oil equivalent)1971 1641 1977 121 80 11774 1081980 2111 2564 121 93 15150 1081990 2215 2540 115 110 14732 1082007 2732 3335 122 163 19504 108

Electricity consumption per capita (kW h)1971 1495 2401 161 6 14085 1081980 2316 3288 142 12 18701 1081990 3060 4218 138 22 23354 1082007 4448 5827 131 30 36852 108

Mean SD Min. Max. N

B. Proportionate increase [ln(E1/E0)] during the periodPrimary energy consumption per capita1971–2007 (37 years) 0.48 0.58 −0.54 2.18 1081971–1990 (19 years) 0.29 0.44 −0.69 1.66 1081990–2007 (17 years) 0.19 0.27 −0.76 0.94 1081980–2000 (20 years) 0.17 0.36 −0.71 1.31 108

Electricity consumption per capita1971–2007 (37 years) 1.35 0.80 −0.50 3.58 1081971–1990 (19 years) 0.84 0.57 −0.42 2.50 1081990–2007 (17 years) 0.51 0.47 −0.66 2.46 1081980–2000 (20 years) 0.60 0.51 −0.57 2.19 108

C. Urban population as percent of total (initial value at the start of the period)1971 47.9 23.9 4.2 100 1081980 52.7 23.8 6.1 100 1081990 57.9 23.1 8.9 100 108

Notes. The numbers in part B (consumption increase) are proportionate (logarithmic)

1884 H. Mohammadi, R. Ram / Energy Economics 34 (2012) 1882–1887

that different countries may be moving (converging) toward differentvalues that depend on the conditioning variables. Therefore, σ- and un-conditionalβ-convergencemay be perceived as primary conceptswhilethere is some ambiguity associated with conditional β-convergence.3

That is particularly so in studies of convergence of energy or electricityusage since it is difficult to interpret conditional convergence in whichcountries may be converging toward different values.

For studying β-convergence, our starting point is the followingsimple model which is similar to the one underlying Liddle's (2010,p. 3220) Table 1, Table 2 of Maza and Villaverde (2008, p. 4257),and Eq. (1) of Miketa and Mulder (2005, p. 438) who used panel data:

ln E1=E0ð Þi ¼ aþ blnE0i þ ui ð1Þ

where E1 denotes per capita consumption of energy or electricity inthe last year of the period, E0 is the corresponding value for the initialyear of the period, ln denotes natural logarithm, subscript i is thecountry identifier, and u's are well-behaved stochastic error terms.The expression on the left-hand side is proportionate (logarithmic)increase in the usage over the period and not at the annual rate.This follows the specification in Mankiw et al. (1992) and manyother studies. The variable on the right-hand side (lnE0) is the basicterm and enables one to judge whether there is β-convergence inthe sense that the rate of increase of consumption in countries withhigh (low) initial values is low (high). A negative sign on the coeffi-cient of lnE0 (b) thus implies β-convergence.

It may be recalled that Eq. (1) represents what is usually called un-conditional β-convergence which implies that countries are convergingtoward a common value of energy (electricity) usage per capita. Howev-er, it is recognized in the literature that all countries may not be movingtoward a common steady-state value, but may converge to differentlevels that depend on country-specific characteristics in terms of “condi-tional” β-convergence. A model for that can be specified by adding toEq. (1) appropriate “conditioning” variables that may affect the changein consumption over the period. However, choice of such variables forenergy (or electricity) consumption is not easy since there is no studyof convergence in energy usage nor any on electricity usage that includedconditioning variables. In their analysis of sectoral convergence in energyintensity, Miketa and Mulder (2005) considered energy price, invest-ment ratio, and energy-mix as conditioning variables, but found verylimited support for the significance of energy prices and investmentratio and even energy-mix.Moreover, conditional convergence in energyor electricity usage might be of limited interest. If countries are converg-ing to different steady-state values, it is difficult to interpret the phenom-enon relative to the discussions on emissions or sustainability. Therefore,while focusing on the unconditional format of Eq. (1), we explore therole of urbanization as a structural factor that is likely to affect changein energy (electricity) consumption and may serve as a reasonable con-ditioning variable. Miketa andMulder (2005, p. 442) had noted structur-al change as a potential conditioning factor. We also experimented with“openness” as another conditioning variable, but, as noted in some otherstudies, it lacked significance in most cases and was omitted from themodel. With the addition of urbanization (URB), the specification maybe written as

ln E1=E0ð Þi ¼ aþ blnE0i þ cURBi þ ui: ð2Þ

All raw data are taken from World Bank (2010). The proxy for en-ergy usage is primary energy consumption per capita (in kilograms ofoil equivalent) and is fairly standard. For electricity, electric powerusage per capita (in kW h) is used and appears appropriate. Urbani-zation is proxied by the percentage of population that lives in urban

3 As a perceptive referee noted, σ-convergence is more interesting partly (but notentirely) because β-convergence is a necessary, but not a sufficient, condition for σ-convergence.

areas and is entered for the first year of the period so as to make itpredetermined.

Table 1 reports descriptive statistics for the variables and suggestsfour points. First, simple (unweighted) means in section A indicatethat the increase in primary energy usage slowed after 1980. Whilethe mean increased by 29% during the 9-year period from 1971 to1980, the increase was of the same order over the 27-year period1980–2007. Second, there is a similar slowdown in the increase inelectricity consumption. The mean increased by 55% from 1971 to1980, but increased at slower rates during the subsequent decadesand the increase is 92% over the 27-year period 1980–2007. Relativeto the consideration of emissions and sustainability, the slowdownsmay be deemed desirable. Third, cross-country dispersion in energyusage, as reflected in CV, seems fairly stable over the period. The pat-tern implies absence of σ-convergence which would be indicated by adecline in the dispersion. This is an interesting preliminary finding onconvergence in energy usage. Fourth, there is an almost steady de-cline in the CV for electricity usage, which implies σ-convergencefor the variable. Therefore, energy- and electricity-usage show oppo-site patterns for σ-convergence.

Before estimates of Eqs. (1) and (2) are presented, it is useful to statebriefly the methodology for quantile regressions. The methodology isnow fairly well known and has been described, besides others, byChamberlain (1994, p. 181), Deaton (1997, pp. 83–84) and Koenker(2005), and was applied by Ram (2008) in a study of cross-country in-come convergence. While the technical details are omitted here, basi-cally it enables one to judge parametric variations in different parts ofthe distribution of the dependent variable without truncation of thesample. Relative to Eqs. (1) and (2), quantile estimates enable one tosee whether the convergence patterns are different for countries withdifferent levels of predicted usage change. The procedure can generateestimates for the coefficients of right-hand variables for any part of

increases during the entire period, and need to be interpreted carefully. For example,the mean energy-consumption increase for 1971–2007 is approximately 48% overthe 37-year period.All data have been taken fromWorld Bank'sWorld Development Indicators CD-ROM 2010.

Table 2Tests of cross-country convergence in per capita energy consumption, 1971–2007: dependent variable is ln[(consumption in last year)/(consumption in first year)].

1971–2007 1971–1990 1990–2007 1980–2000

(1) (2) (3) (4) (5) (6) (7) (8)

OLSC 1.141⁎

(3.15)1.876⁎

(4.83)0.573⁎

(2.05)1.191⁎

(4.01)0.365⁎

(2.11)0.404⁎

(2.10)0.435+

(1.92)0.787⁎

(3.09)LEo −0.097+

(−1.86)−0.288⁎

(−4.15)−0.042(−1.04)

−0.202⁎

(−3.82)−0.025(−1.02)

−0.036(−1.05)

−0.038(−1.20)

−0.131⁎

(−2.86)URBo 0.012⁎

(3.89)0.010⁎

(4.28)0.001(0.48)

0.006⁎

(2.74)R2 0.03 0.15 0.01 0.16 0.01 0.01 0.01 0.08Lambda (%) 0.28 0.94 0.23 1.19 0.15 0.22 0.22 0.70N 108 108 108 108 108 108 108 108

Q10 (0.10 quantile)C −0.005

(−0.01)0.673(1.42)

0.097(0.18)

0.304(0.50)

−0.037(−0.16)

0.343+

(1.87)−0.018(−0.04)

0.452(0.97)

LEo −0.031(−0.44)

−0.192⁎

(−2.32)−0.040(−0.46)

−0.129(−1.24)

−0.007(−0.18)

−0.103⁎

(−2.79)−0.038(−0.59)

−0.150+

(−1.66)URBo 0.011⁎

(3.03)0.008⁎

(2.23)0.006⁎

(2.86)0.007(1.42)

Lambda (%) 0.09 0.59 0.21 0.73 0.04 0.64 0.66 0.81

Q90 (0.90 quantile)C 2.197⁎

(3.14)2.446⁎

(2.51)1.389⁎

(2.09)2.191⁎

(3.17)0.240(0.61)

0.099(0.25)

1.035+

(1.88)1.082⁎

(2.16)LEo −0.129

(−1.19)−0.218(−1.07)

−0.083(−0.87)

−0.277⁎

(−2.08)0.046(0.71)

0.092(1.11)

−0.059(−0.74)

−0.098(−1.08)

URBo 0.006(0.64)

0.011(1.57)

−0.003(−0.076)

0.003(0.82)

Lambda (%) 0.38 0.68 0.46 1.71 a a 0.31 0.53

Notes. The estimates correspond to Eqs. (1) and (2) of the text. LEo stands for the logarithm of energy consumption per capita in the first year of the period. A superscripted asteriskindicates statistical significance at least at the 5% level, and +denotes significance at the 10% level. Related t-statistics are in parentheses. Standard errors for quantile regressions arebased on 1000 replications. STATA 11.2 has been used for all computations.Lambda indicates the implied annual speed of convergence, and the value ‘a’ is entered for models where convergence is not observed and the coefficient of initial value (LEo) ispositive. Speed of convergence is calculated as [− ln(1+b)/N]100 (percent per year), where b is the initial-value parameter, N is the number of years covered by the period, and lndenotes natural logarithm.

1885H. Mohammadi, R. Ram / Energy Economics 34 (2012) 1882–1887

the distribution of (predicted) change in energy- or electricity-usage,including its top and bottom segments.We focus on the top and bottomdeciles so as to show sharply the possible variations in the coefficientsof the initial-value variable and thus in the convergence patterns inthe top and bottom parts of the distribution.

Table 2 reports estimates of Eqs. (1) and (2) for energy consumption.The estimates are shown for the entire period 1971–2007 and also forthe subperiods 1971–1990, 1990–2007 and 1980–2000 so as to revealtemporal patterns. In addition to the conventional OLS results, quantileregression estimates for the top (0.90) and the bottom (0.10) decilesare also shown.

Several points are suggested by Table 2. First, support for globalconvergence in energy usage is weak. In columns (1), (3), (5) and (7),which are based on unconditional format of Eq. (1), coefficient ofinitial-consumption variable is barely significant for 1971–2007 andlacks significance at conventional levels in all other periods.4 This scenar-io is consistent with part A of Table 1 which shows a largely unchangingCV and thus lack of σ-convergence. The weak tendency toward conver-gence is also reflected in very low convergence speeds which are around0.2% per year. Absence of global σ-convergence and weak unconditionalβ-convergence in per-capita energy consumption is perhaps a significantoutcome of this study.

The observed weak convergence in energy usage merits a brief re-flection. It indicates that the negative relation between level of usage

4 Unreported estimates show that initial-consumption coefficient in the uncondi-tional format lacks significance at almost any level in each of the four decades. The co-efficients (with t-statistics) are −0.000 (−0.01), −0.020 (−0.89), −0.013 (−0.66),and −0.012 (−0.95) for 1971–1980, 1980–1990, 1990–2000, and 2000–2007 respec-tively. It may, however, be noted that convergence indication is somewhat sharper insome cases if Netherlands Antilles and Oman, which are huge outliers, are included.

and its rate of increase is weak. It seems that rates of increase inlow-usage contexts are higher than those in high-usage countries,but not significantly so. That might be comforting from the sustain-ability perspective even though one may worry about the relativelyunchanging cross-country inequality in energy usage.

Second, conditional convergence is indicated in columns (2), (4)and (8) at the global level. However, since countries may be converg-ing to different steady-state values, it is difficult to interpret the phe-nomenon relative to sustainability or cross-country energy inequality.

Third, top and bottom quantiles show a similar pattern relative tounconditional convergence. Much like the OLS estimates, seven of theeight quantile estimates in columns (1), (3), (5) and (7) have nega-tive signs, but none is significant at the conventional levels, and oneis insignificantly positive. Therefore, it is difficult to discern any “con-vergence clubs” in energy usage.

Fourth, in the conditional format of columns (2), (4), (6) and (8),the quantile estimates show varying patterns. For 1971–1990, con-vergence is indicated for the top (0.90) quantile, but not for the bot-tom (0.10) decile. For the other three periods, the bottom quantiletends to suggest convergence, but not the top quantile. No clear pat-tern, therefore, emerges.

Fifth, urbanization coefficient is significant in three of the four OLSestimates suggesting that, in a global perspective, more urbanizedcountries are likely to converge to higher levels of energy consump-tion. In the quantile regressions, the variable shows positive and sig-nificant coefficients mainly in the bottom quantile, indicating thaturbanization is not a significant factor in the top quantile.

Table 3 reports estimates of Eqs. (1) and (2) for electricity con-sumption per capita. As in Table 2, OLS results for each of the four pe-riods are shown along with the quantile estimates for each period.This table also suggests several points.

5 Preliminary estimates from another project show strong global convergence in en-ergy intensity in a sample similar to that of Tables 1 and 2 which show a weak (or no)convergence in per-capita energy consumption.

Table 3Tests of cross-country convergence in per capita electricity consumption, 1971−2007: Dependent variable is ln[(consumption in last year)/(consumption in first year)].

1971–2007 1971–1990 1990–2007 1980–2000

(1) (2) (3) (4) (5) (6) (7) (8)

OLSC 2.448⁎

(9.66)2.686⁎

(10.42)1.351⁎

(7.13)1.578⁎

(8.40)1.113⁎

(6.19)1.135⁎

(6.08)1.057⁎

(5.67)1.170⁎

(5.91)LELo −0.180⁎

(−4.51)−0.329⁎

(−5.16)−0.083⁎

(−2.79)−0.225⁎

(−4.85)−0.087⁎

(−3.47)−0.102⁎

(2.48)−0.070⁎

(−2.56)−0.132⁎

(−2.79)URBo 0.014⁎

(2.93)0.013⁎

(3.84)0.001(0.46)

0.006(1.61)

R2 0.16 0.22 0.07 0.18 0.10 0.10 0.06 0.08Lambda (%) 0.55 1.11 0.44 1.38 0.55 0.62 0.36 0.70N 108 108 108 108 108 108 108 108

Q10C 0.928⁎

(3.98)1.054⁎

(4.44)0.170(0.40)

0.298(0.85)

−0.344(−0.87)

−0.212(−0.52)

−0.248(−0.89)

−0.080(−0.42)

LELo −0.064+

(−1.73)−0.230⁎

(−2.60)0.007(0.13)

−0.111(−1.54)

0.041(0.89)

−0.021(−0.37)

0.044(1.11)

−0.105⁎

(−2.01)URBo 0.019⁎

(2.71)0.012⁎

(2.77)0.006(1.62)

0.015⁎

(3.48)Lambda (%) 0.17 0.73 a 0.61 a 0.12 a 0.58

Q90C 4.519⁎

(8.63)4.687⁎

(8.30)2.862⁎

(7.11)2.835⁎

(7.46)2.265⁎

(2.99)2.393⁎

(3.21)2.318⁎

(6.34)2.464⁎

(6.77)LELo −0.358⁎

(−4.61)−0.439⁎

(−3.89)−0.226⁎

(−4.07)−0.299⁎

(−4.12)−0.177+

(−1.83)−0.212+

(−1.93)−0.168⁎

(−3.55)−0.223⁎

(−3.57)URBo 0.006

(0.68)0.008(1.16)

0.002(0.41)

0.003(0.94)

Lambda (%) 1.24 1.61 1.38 1.88 1.17 1.39 0.93 1.24

Notes. The estimates correspond to Eqs. (1) and (2) of the text. LELo stands for the logarithm of electricity consumption per capita in the first year of the period.Related t-statistics are in parentheses. A superscripted asterisk indicates statistical significance at least at the 5% level, and +denotes significance at the 10% level. Standard errors forquantile regressions are based on 1000 replications. STATA 11.2 has been used for all computations.Lambda indicates the implied annual speed of convergence, and the value ‘a’ is entered for models where convergence is not observed and the coefficient of initial value (LELo) ispositive. Speed of convergence is calculated as [− ln(1+b)/N]100 (percent per year), where b is the initial-value parameter, N is the number of years covered by the period, and lndenotes natural logarithm.

1886 H. Mohammadi, R. Ram / Energy Economics 34 (2012) 1882–1887

First, OLS results in columns (1), (3), (5) and (7) show uncondi-tional β-convergence at the global level in every period. All estimatesof the initial-value coefficient are negative and significant at least atthe 5% level. This is consistent with the scenario in part A of Table 1which depicts a steady decline in the CV and thus σ-convergence inelectricity usage.

Second, the highly significant global convergence in electricityusage is a contrast from the weak (or no) convergence in energyusage suggested by Tables 1 and 2. The implications of the strong con-vergence in electricity usage might, however, be somewhat ambigu-ous. On the one hand, it implies a reduction of cross-countryinequality in this important measure. On the other hand, it is possiblethat the implied rapid increases in low-usage countries might not beconducive to sustainability.

Third, columns (2), (4), (6) and (8) show strong conditional con-vergence at the global level. In every period, the initial-value coeffi-cient is larger in conditional models than in the unconditionalformat, and the implied convergence speeds are higher. However, asnoted for Table 2, since countries may be converging to differentsteady states, it is difficult to provide a sharp interpretation of theestimates.

Fourth, top and bottom quantiles show considerable differences. Inunconditionalmodels, the top quantile (0.90) shows significant conver-gence, but the bottom quantile is marked by a weak convergence onlyfor 1971–2007, and the convergence parameters are positive in theother periods. The pattern is broadly similar for conditional models,and the top quantile shows convergencemore sharply than the bottomquantile in all periods. The implication is that the negative covariationbetween the level of usage and its rate of increase is sharper in thetop quantile than in the bottom segment.

Fifth, OLS urbanization coefficients are positive and significant intwo periods. The coefficients, however, differ sharply across the topand bottom quantiles; while being positive and mostly significant inthe bottom quantile, these lack significance in the top quantile. Thisis similar to the pattern noted in Table 2.

4. A brief discussion on convergence patterns

One major point of our work is the observation of weak convergencein per-capita energy usage. Since there is apparently no study ofcross-country convergence in per-capita energy usage, it is difficult tocompare our resultswith the existing research. Among studies of conver-gence in energy intensity across broad country groups, Ezcurra (2007b)and Liddle (2010) reported evidence in favor of σ- or β-convergence,but Le Pen and Sevi (2010) rejected global convergence in terms of a sto-chastic criterion. Since patterns of convergence in energy-usage may bedifferent from those in energy-intensity, we do not undertake a detailedcomparison of our results with studies of energy-intensity convergence,but comment very briefly on a few implications of theweak convergencenoted by us.5 As stated in Section 3, one obvious implication of our esti-mates is the likely persistence of cross-country disparities in energyusage. Relative to the considerations of emissions and sustainability,however, the implication of weak convergence is somewhat ambiguous.It reflects a situation inwhich the rate of increase has aweaknegative as-sociation with the usage levels, which might mean that countries withlower (higher) usage have modestly, and not sharply, higher (lower)

1887H. Mohammadi, R. Ram / Energy Economics 34 (2012) 1882–1887

rates of increase. Since there seems to be some slowdown in the overallrate of increase, it is possible that the rate of increase in low-usage coun-tries is modest. Therefore, absence of strong convergence in energyusage may not be worrisome relative to considerations of emissionsand sustainability.

Another main point emerging from our work is the indication ofstrong convergence in electricity usage in both σ and β formats.This is broadly consistent with the pattern reported by Maza andVillaverde (2008). As noted by them, the convergence is apparentlyrelated to rapid economic changes in developing countries and con-servation policies adopted by developed countries. While the implieddecline in cross-country inequality is good, two aspects of the ob-served convergencemerit brief comments. First, it may seem puzzlingthat while energy-usage shows a weak (or little) convergence, that inelectricity-usage is strong. The contrast suggests that evolution ofcross-country distributions of energy- and electricity-usage mightbe different. As the simple means in Table 1 show, global rate of in-crease in per-capita electricity usage was nearly three times that ofincrease in energy usage.6 Moreover, only about 36% of primaryenergy is used for production of electricity, and a large componentof the rest is probably used in transportation or heating.7 While wenote the difference between the convergence patterns for energy-and electricity-usage, a full analysis of the difference is beyond thescope of our study and merits further research.

Second, although convergence is often perceived as a desirablephenomenon, it is observed that convergence in electricity usage isassociated with a rate of increase that is three times that in theweakly-convergent energy-usage, and perhaps reflects big increasesin electricity consumption in low-usage countries. Therefore, fromthe perspective of emissions and sustainability, convergence in elec-tricity usage with a much higher rate of increase may not necessarilybe preferable to weaker convergence in energy usage that has a muchlower rate of increase. It is, however, important to note that, relativeto the possible concern about emissions due to increased electricityusage, what matters is the carbon intensity of primary sources fromwhich electricity is generated.

5. Concluding observations

This research seeks to make a contribution along five dimensions.First, it is probably the first study of cross-country convergence inper-capita usage of energy. Second, we provide a comparison of theconvergence patterns in per-capita usage of energy and electricity.Third, we use quantile regressions to study possible differences in con-vergence parameters in the top and bottom segments of the distribu-tions of changes in energy- and electricity-usage. Fourth, an effort ismade to judge temporal patterns by considering four different periodsfrom 1971 to 2007. Fifth, unconditional β-convergence scenarios arecompared with those indicated by σ-convergence and a simple condi-tional β-model.

Tenobservations summarize ourfindings. First, the evidence generallyindicates weak or little convergence in per-capita energy usage. That isparticularly true for σ-convergence and the unconditional β-format. Sec-ond, stronger convergence is indicated in electricity usage in all formats.Third, therefore, we note a contrast between the convergence patternsin the usage of energy and electricity. Fourth, quantile regressions for en-ergy usage indicate a mixed position. While the unconditional formatindicates lack of convergence in all periods in both top and bottomquantiles, the conditional format shows a variable pattern. Fifth,for electricity-usage, convergence is considerably stronger in the top

6 Annual data from World Bank's WDI online show the rate of increase in per-capitaconsumption of energy and electricity to be 0.76% and 2.18% per year during the period1971–2009.

7 This is indicated in the entry for electricity generation in Wikipedia (accessed 21May 2012).

quantile. Sixth, urbanization coefficient is positive and statistically signif-icant in most OLS regressions, indicating that urbanization raises the in-crease in usage of both energy and electricity. Seventh, however,urbanization coefficients tend to be weak and insignificant in the topquantile while being positive and significant in the bottom quantile.Eighth, weak convergence in energy usage could be interpreted as indic-ative of amodestly, and not sharply, higher rates of increase in low-usagecountries, and may perhaps not be worrisome from the perspective ofemissions and sustainability. Ninth, stronger convergence is electricityusage perhaps indicates sharply higher rates of increase in low-usagecountries and, although desirable in terms of reduction in cross-countryinequality, may have ambiguous implications relative to emissions andsustainability. That thought is reinforced by the fact that weak conver-gence in energy usage is associated with amuch smaller global rate of in-crease than that in the strongly-convergent electricity usage. Tenth, weoffer a few conjectures on the different convergence patterns in energy-and electricity-usage, but leave it as a potentially useful area for future re-search. Methodologically, we note consistency between σ- and uncondi-tional β-convergence patterns. As is typical, conditional β-convergenceis more marked than that in the unconditional format. However, wenote the difficulty of interpreting conditional β-convergence in terms ofcross-country inequality or relative to considerations of emissions andsustainability.

Acknowledgments

Perceptive comments from two anonymous reviewers are gratefullyacknowledged. The usual disclaimer, however, applies.

References

Abreu, M., de Groot, H.L.F., Florax, R.J.G.M., 2005. A meta-analysis of β-convergence:the legendary 2%. J. Econ. Surv. 19, 389–420.

Aldy, J.E., 2006. Per capita carbon dioxide emissions: convergence or divergence? Environ.Resour. Econ. 33, 533–555.

Chamberlain, G., 1994. Quantile regression, censoring, and the structure of wages. In:Sims, C.A. (Ed.), Advances in Econometrics. : SixthWorld Congress, vol. 1. CambridgeUniversity Press, pp. 171–209.

Deaton, A., 1997. The Analysis of Household Surveys. Johns Hopkins University Press.Duro, J.A., Padilla, E., 2011. Inequality across countries in energy intensities: an analysis

of the role of energy transformation and final energy consumption. Energy Econ.33, 474–479.

Ezcurra, R., 2007a. Is there cross-country convergence in carbon dioxide emissions?Energy Policy 35, 1363–1372.

Ezcurra, R., 2007b. Distribution dynamics of energy intensities: a cross-country analysis.Energy Policy 35, 5254–5259.

Islam, N., 2003. What have we learnt from the convergence debate? J. Econ. Surv. 17,309–362.

Koenker, R., 2005. Quantile Regression. Cambridge University Press.Le Pen, Y., Sevi, B., 2010. On the non-convergence of energy intensities: evidence from

a pair-wise econometric approach. Ecol. Econ. 69, 641–650.Liddle, B., 2009. Electricity intensity convergence in IEA/OECD countries: aggregate and

sectoral analysis. Energy Policy 37, 1470–1478.Liddle, B., 2010. Revisiting world energy intensity convergence for regional differences.

Appl. Energy 87, 3218–3225.Mankiw, N.G., Romer, D., Weil, D.N., 1992. A contribution to the empirics of economic

growth. Q. J. Econ. 107, 407–437.Markandya, A., Pedroso-Galinato, S., Streimikiene, D., 2006. Energy intensity in transition

economies: is there convergence towards the EU average? Energy Econ. 28, 121–145.Maza, A., Villaverde, J., 2008. The world per capita electricity consumption distribution:

signs of convergence? Energy Policy 36, 4255–4261.Miketa, A., Mulder, P., 2005. Energy productivity across developed and developing countries

in 10 manufacturing sectors: patterns of growth and convergence. Energy Econ. 27,429–453.

Mulder, P., de Groot, H.L.F., 2007. Sectoral energy- and labour-productivity convergence.Environ. Resour. Econ. 36, 85–112.

Ram, R., 2008. Parametric variability in cross-country growth regressions: an applicationof quantile-regression methodology. Econ. Lett. 99, 387–389.

World Bank, 2010. World Development Indicators CD-ROM 2010 . Washington DC.