Energy prices, energy efficiency, and fuel poverty 1. Vivien Foster, JeanPhilippe Tre, and Quentin Wodon. World Bank. September 2000.


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1 Energy prces, energy effcency, and fuel poverty 1 Vven Foster, JeanPhlppe Tre, and Quentn Wodon World Bank September 2000 Abstract Because electrcty s much more effcent than other sources of energy for certan uses such as lghtng, access to electrcty can help n promotng energy affordablty n developng countres. Usng data from Guatemala, ths note suggests that the prce reducton per effcent klowatthour whch can be expected from access to electrcty s substantal. Ths prce reducton could generate a large reducton n measures of fuel poverty whch capture the nablty of households to meet ther basc energy needs. JEL categores: I31, Q42 Correspondng author: Emal: 1 Ths paper s part of a research project on Infrastructure reform and the poor n the Latn Amerca Regon of the World Bank. The authors benefted from dscussons wth Antono Estache and Kathy Lndert. The vews expressed here are those of the authors and need not reflect those of the World Bank.
2 1. Introducton Many households n developng countres do not have the means to satsfy ther basc energy needs. Part of the problem les n the technologes used by those wthout access to electrcty for lghtng and powerng applances, such as candles, kerosene lamps, and batteres 2. These technologes are orders of magntude more expensve per effcent klowatthour than electrcty. Access to electrcty could provde sgnfcant savngs n energy costs for the populaton not yet connected to the grd. Usng data from Guatemala, ths note provdes a smple method for measurng the reducton n the prce of energy that can be expected from access to electrcty. It also provdes estmates of the reducton n fuel poverty whch could be acheved wth better access to electrcty, where fuel poverty defned as the nablty by households to meet ther energy needs. Secton 2 descrbes the method, and secton 3 provdes the results. 2. Methodology A household s sad to be fuel poor f ts energy consumpton does not meet basc energy needs. Followng the ncome poverty lterature, n order to measure fuel poverty, we use the frst three measures of the FGT (Foster, Greer, and Thorbecke, 1984) class. The frst measure s the headcount ndex of fuel poverty, whch s smply the percentage of the populaton lvng n households wth an equvalent energy consumpton below the fuel poverty lne. Ths s denoted by P 0. The second measure, whch captures the depth of fuel poverty, s the fuel poverty gap ndex P 1. It estmates the average dstance separatng the fuel poor from the fuel poverty lne as a proporton of that lne (the mean s taken over the whole sample wth a zero dstance allocated to the households who are not poor). The thrd measure, whch captures the severty of fuel poverty, s the squared fuel poverty gap ndex P 2. It takes nto account not only the dstance separatng the fuel poor from the fuel poverty lne, but also the nequalty among the poor. Denotng by E the energy consumpton for household, by Z the fuel poverty lne, by N populaton sze, by w the weght for household (equal to the household sze tmes the expanson factor, the sum of the weghts beng N), the three fuel poverty measures are obtaned for values of equal to 0, 1, and 2 n: P E = ( w / N) 1 (1) Z E Z When measurng the energy consumpton of household, t s mportant to take nto account the qualty or effcency of varous fuels. In the case of Guatemala, the 1998/99 ENIGFAM (Encuesta 2 The energy lterature suggests the exstence of a transton process whereby households gradually ascend an energy ladder. The ladder begns wth bomass fuels (frewood and charcoal), moves to modern commercal fuels (kerosene and LPG), and culmnates wth electrcty (e.g., Albouy and Nadf, 1999). The realty s somewhat more complex and the emprcal work suggests that at any gven pont n tme, households rely on a range of fuels that encompasses at least two steps of the energy ladder (e.g., Barnes and Qan, 1992; Hoser and Kpondya, 1993; ESMAP, 1994; Eberhard and van Horen, 1995). In Guatemala, the country used for ths note, our data ndcates that households use on average 2.6 dfferent types of fuels.
3 Naconal de Ingresos y Gastos Famlares) ncome and expendture survey provdes monthly household expendtures for batteres, candles, electrcty, fuelwood, kerosene and butane gas (n the case of fuelwood, for the households who gather ther own wood, the survey provdes an estmate provded by the household as to the cost of purchasng an equvalent amount of fuelwood n the market place). We convert these expendtures nto comparable unts of effcent energy consumpton by usng not only regonal unt prces from Guatemala s Consumer Prce Index, but also energy effcency factors from the Unted Natons (1987). Formally, denotng by P k the market or gross prce of fuel k wth k=1,, K, for household, by C k the household expendtures for that fuel, by EF k the effcency factor reflectng the qualty of fuel k, and by E k the amount of energy provded by fuel k n standardzed effcent klowatthours, we obtan the total amount of energy provded by the varous fuels for household as: E = C K K k Ek = k = 1 k = 1 Pk / EF k (2) In equaton (2), the net prce per effcent klowatthour for fuel k s equal to P k /EF k. Table 1 compares the gross and net prces per klowatthour for each fuel. For cookng, the net prces show that despte a low gross prce, fuelwood s as costly as propane gas on a comparable effcency bass. Both fuels are slghtly cheaper than electrcty n net terms. For lghtng, the converson from gross to net terms dramatcally wpes out any apparent cost advantage of kerosene. Candles reman by far the most expensve source of lghtng, whether n gross or net terms. For applances, the gross to net converson s not relevant snce all alternatves are based on electrcty. However, the fgures show that batteres are substantally more expensve per klowatthour than mans electrcty. Several methods can be used n order to estmate the fuel poverty lne Z. One method conssts n computng the average energy consumpton of households whose overall per capta consumpton level falls wthn plus or mnus 10 percent of the US$ 1 (Purchasng Power Party adjusted) ncome poverty lne used n the nternatonal lterature on ncome poverty. In the case of Guatemala n 1998/99, ths gves a subsstence energy threshold of 2,125 klowatthours per year (5.8 klowatthours per day). A second method conssts n defnng a basc set of energy needs. A consultaton wth energy experts n Guatemala led to the suggeston that a household should be able to run two 60 watt lght bulbs and one 16 watt rado for four hours each day. A household should also be able to use fve twoklogram logs of fuelwood each day for cookng. Ths leads to a fuel poverty lne of 2,154 klowatthours per year (5.9 klowatthours per day). Gven the smlarty between the two estmates, we wll adopt the frst value as the fuel poverty lne snce t has the advantage of beng derved drectly from the household survey data set. Next, to measure the mpact of access to electrcty on fuel poverty, we start by notng that the average net prce pad by households for each effcent klowatthour of energy consumed s:
4 P = K k = 1 ( C K k = 1 k C k / P ) EF k k (3) Gven that electrcty s much more effcent and thereby cheaper than other fuels for lghtng, households wthout access to electrcty are lkely to have a hgher net prce per effcent klowatthour than those wth access. It turns out that the average value of P for households wth access to electrcty s Qz (1 US$ 7.65 Qz) per effcent klowatthour. The average value for households wthout access to electrcty s Qz. Whle part of ths dfference may be due to access to electrcty tself, part may also be due to other dfferences n characterstcs between households wth access and households wthout access. To fnd out the margnal mpact of access to electrcty on effcent energy prces controllng for other household characterstcs, regresson analyss s needed. Let L represent a vector of geographc locaton dummes, H a vector of characterstcs of the resdents n the household ncludng quntle dummes for the household s poston n the dstrbuton of per capta ncome, R a vector descrbng the physcal characterstcs of the household s resdence, G a dummy varables for access to the electrcty grd, and O a vector of dummes for access to other sources of energy. We estmate the followng regresson: log( P ) β O + ε (4) = 0 + β1l + β 2H + β 3R + β 4G + β 5 A negatve and statstcally sgnfcant estmate for β 4 would suggest that access to electrcty reduces effcent energy prces by β 4 percent. When households wthout access gan access to electrcty, ther energy prce would then be reduced to P (1+ β 4 ). If we assume that the amount of resources devoted to energy by the household remans unchanged after the reducton n prce provded by access to electrcty (ths may underestmate the mpact of the reducton n prce on fuel poverty f energy s a normal good), the new level of consumpton s gven by E /(1+ β 4 ), n whch case fuel poverty becomes: P = ( w E Z E /(1 + β 4 ) / N) 1 Z (5) 3. Results Table 2 provdes estmates of fuel poverty based on the fuel poverty lne of 2,125 klowatthours per household per year. Among the households wth access to electrcty, the average prce pad per effectve klowatthour s 0.52 Qz, and the average yearly consumpton s kwh. The households wthout access to electrcty have a lower consumpton (2,892 kwh) and they pay a hgher prce (1.35 Qz/kwh). One fourth of the populaton wth access to electrcty s fuel poor (headcount ndex of fuel
5 poverty equal to 0.255), as compared to half of the populaton wthout access (headcount of 0.509). The dfferences between the two household groups are even larger wth the poverty gap and square poverty gap. If the households wthout access were gven access to electrcty, the headcount ndex of fuel poverty among that group would be reduced to 0.365, and the other fuel poverty measures would be reduced smlarly. The relatvely large mpact of access to electrcty on fuel poverty comes from the regresson estmates provded n table 3. At the natonal level, the coeffcent for access to the publc electrcty grd s 0.277, wth a confdence nterval of [0.304,0.238]. Farly smlar results are obtaned for prvate access to electrcty (less than 2 percent of households are n ths stuaton). The coeffcent estmates are also smlar n the urban and rural subsamples. Although a few other varables have a statstcally sgnfcant mpact at the 5 percent level on the net prce per effcent klowatthour, none of those varables has an mpact as large as that of access to electrcty (although ths s not shown n table 3, the regresson also contans a large number of housng varables, but most of these are not statstcally sgnfcant at the fve percent level). To conclude, whle access to electrcty would not equalze the fuel poverty status of the two groups of households (those wth and wthout access to electrcty), t would go a long way n reducng dfferences between the two groups. References Albouy, Y. and Nadf, N. (1999) Impact of Power Sector Reform on the Poor: A Revew of Issues and the Lterature, Mmeo, World Bank, Washngton DC. Barnes, D. and Qan, L. (1992) Urban Interfuel Substtuton, Energy Use and Equty n Developng Countres: Some Prelmnary Results, Mmeo, World Bank, Washngton DC. Hoser, R. and Kpondya, W. (1993) Urban household energy use n Tanzana. Prces, substtutes and poverty. Energy Polcy, 21, pp ESMAP (1994) Ecuador: Energy Prcng, Poverty and Socal Mtgaton, Report No EC, Energy Sector Management Assstance Program. Eberhard, A. and van Horen, C. (1995) Poverty and Power: Energy and the South Afrcan State, Pluto Press, East Haven, Connectcut. Foster, J., Greer, J., and Thorbecke, E. (1984). A Class of Decomposable Poverty Measures, Econometrca, 52: Leach, G. and Gowen, M. (1987) Household Energy Handbook: An Interm Gude and Reference Manual, World Bank Techncal Paper Number 67, The World Bank Group, Washngton DC. Unted Natons (1987) Energy Statstcs: Defntons, Unts of Measure and Converson Factors, Studes n Methods No. 44, Department of Internatonal Economc and Socal Affars, Untes Natons, New York. Van der Plas, R. and De Graaff, A. (1988) A Comparson of Lamps for Domestc Lghtng n Developng Countres, Energy Seres Paper No. 6, Industry and Energy Department, World Bank, Washngton DC.
6 Table 1: Gross and net unt prces for dfferent fuels (US$ per kwh) Cookng fuels Lghtng fuels Applances Gross Net Gross Net Gross Net Electrcty Electrcty Electrcty Propane Kerosene Batteres Fuelwood Candles Car batteres Sources: Authors computatons from Leach and Gowen (1987), Van der Plas and De Graaff (1988), and Guatemala s Consumer Prce Index. Table 2: Fuel poverty estmates wth and wthout access to electrcty Households wth access Households wthout access to electrcty to electrcty Current stuaton After ganng access Prce per effectve kwh Net consumpton (kwh) Fuel poverty Headcount Poverty gap Squared poverty gap Source: Authors estmaton usng ENIGFAM 1998/99.
7 Table 3: Determnants of the logarthm of the prce per effcent klowatthour, Guatemala 1998/99 [The regresson also contans a large number of housng varables, most of whch are not statstcally sgnfcant] Natonal Urban Rural Coef. St. Err. 95% Conf. Int. Coef. St. Err. 95% Conf. Int. Coef. St. Err. 95% Conf. Int. Energy Prvate elec Publc electrcty Access to butane Cookng wth elec Demographcs Babes Chldren Adults Babes squared Chldren squared Adults squared Female head Age of head Educaton Head 68 years Head > 9 years Spouse 0 years Spouse 68 years Spouse > 9 years Employment Head ndustry Head famly wk Head publc wk Head employed Head searchng Spouse ndustry Sp. Famly wk Sp. Publc wk Sp. Employed Sp. Searchng Locaton Norte Nororente Surorente Central Suroccdente Noroccdente Peten Income 1 st quntle nd quntle rd quntle th quntle Constant Source: Authors estmaton usng ENIGFAM 1998/ observatons (5229 urban, 1898 rural). Adjusted R2 of for the natonal sample (0.089 urban, rural).
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