Household energy demand in Kenya: An application of the linear approximate almost ideal demand system (LA-AIDS)

Energy Policy 39 (2011) 7084–7094

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Household energy demand in Kenya: An application of the linear approximate almost ideal demand system (LA-AIDS) Dianah Ngui a,c,n, John Mutua b, Hellen Osiolo c,1, Eric Aligula c,1 a

Kenyatta University, P.O. Box 43844-00100, Nairobi, Kenya Energy Regulatory Commission, P.O. Box 42681-00100, Nairobi, Kenya c Kenya Institute for Public Policy Research and Analysis, P.O. Box, 56445-00200, Nairobi, Kenya b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 1 December 2010 Accepted 10 August 2011 Available online 7 September 2011

This paper estimates price and fuel expenditure elasticities of demand by applying the linear Approximate Almost Ideal Demand system (LA-AIDS) to 3665 households sampled across Kenya in 2009. The results indicate that motor spirit premium (MSP), automotive gas oil (AGO) and lubricants are price elastic while fuel wood, kerosene, charcoal, liquefied petroleum gas (LPG) and electricity are price inelastic. Kerosene is income elastic while fuel wood, charcoal, LPG, electricity, MSP and AGO are income inelastic. The results also reveal fuel stack behaviour, that is, multiple fuel use among the households. Main policy implications of the results include increasing the penetration of alternative fuels as well as provision of more fiscal incentives to increase usage of cleaner fuels. This not withstanding however, the household income should be increased beyond a certain point for the household to completely shift and use a new fuel. & 2011 Elsevier Ltd. All rights reserved.

Keywords: Energy Demand LA-AIDS model

1. Introduction There are compelling reasons underlying the importance of research on energy demand in developing countries. Although developing countries currently consume a limited share of the world’s commercial energy, the faster income growth of their economies suggests that they may soon come to consume the majority of the world’s energy (Dahl, 1994). The International Energy Agency (IEA) predicts that developing countries will increase their share of global oil consumption from 20.5% in 1999 to 35.8% in 2020 (IEA, 2002). Various authors (see, for example, Levine et al., 1995) also point to the extensive investments required in new generation capacity to meet the growing demand for electricity in developing countries. For regions such as sub-Saharan Africa the investments necessary to produce the required increase in all forms of commercial energy are major compared to traditional gross capital formation in society and net capital inflows. Overinvestment in energy infrastructure and investments made long before they are needed, represent costly drains on scarce resources. Under-investment, or investments made too late, can also carry significant economic costs. With a significant potential for energy demand growth in the developing world, but an equally great uncertainty over the time and magnitude of this growth, providing

n Corresponding author at: Kenyatta University, P.O. Box 43844-00100, Nairobi, Kenya. Tel.: þ254 2 810910, þ 254 2 2719933/4. E-mail addresses: [email protected], [email protected] (D. Ngui). 1 Tel.: þ254 2 2719933/4.

0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.08.015

information that may decrease this uncertainty should prove valuable to policy makers. Despite the above, there is still paucity of research on energy demand in the developing world and, of the scarce literature that exists, only a small proportion presents formal econometric studies of the response of energy consumption to changes in income, prices and other relevant regressors. Moreover, most of these studies focus on Asia (see, for example, Brenton, 1997; Pesaran et al., 1998; ¨ Pourgerami and von Hirschhausen, 1991; Gundimeda and Kohlin, 2006; Rajmohan and Weerahewa, 2007; Al- Salman, 2007; Khattak et al., 2010; Athukorala and Wilson, 2010) and Latin America (Balabanoff, 1994; Ibrahim and Hurst, 1990; Hunt et al., 2000; Ghilardi et al., 2007; Sterner, 2007; Mariana et al., 2009; de Freitas and Kaneko, 2011) leaving a glaring gap for sub-Saharan Africa, and Kenya in particular. Earlier studies in Kenya (see, for example, Mwakubo et al., 2007; Wasike et al., 2007; Onuonga, 2008; Mutua et al., 2009) have analysed energy demand at the macro-level. However, the main limitation of all the studies at macro-level is that the projections that were made only took into account the aggregates such as population growth rate, increase in GDP, urbanisation and technological advancements. The fundamental problem with these studies is that although macro-factors can influence energy consumption patterns indirectly, the actual determinants of household energy consump¨ tion are found at the household level (Gundimeda and Kohlin, 2006; Israel, 2002). Aggregate fuel demand is made up by the day-to-day decisions at the household level. These decisions are affected by budget and time constraints of the household, their opportunity costs of time, the relative accessibility of fuels (relative prices) as

D. Ngui et al. / Energy Policy 39 (2011) 7084–7094

well as social and cultural factors. In addition, most of these studies have used single equation models whose imposition of implausible separability restrictions, make them unable to estimate cross-price effects between different energy goods. The purpose of this paper is to use data collected on individual households, that is, micro-data, to estimate the income and price elasticities of household demand for different kinds of fuels. The micro-data used in this paper was taken from a comprehensive survey of 3665 households sampled across Kenya. There are a number of motivations for this in addition to dearth of reliable and readily available estimates in Kenya. Energy being an important necessity for any household, the households need to choose not only how much but also which fuel to use. These decisions can have important consequences for the household budget, time allocation and health. They can also lead to negative environmental externalities at the local, regional or global level. Price and income elasticities of demand are important for the choice of domestic energy policies. We model energy demand as a multistage budgeting problem considering different types of fuels that can be used by a given household. The allocation of energy expenditure across fuel types is analysed using the Linear Approximate Almost Ideal Demand System (LA-AIDS) specification proposed by Deaton and MuellBauer (1980). The LA-AIDS model is commonly used to estimate price and income elasticities of the demand for goods when expenditure share data are available. We compute own- and cross- price elasticities between different fuel types in the household sector. The structure of the article is as follows. Section 1 presents the introduction and motivation of the study while Section 2 discusses energy sector Kenya. In Section 3, we sketch the application of the LA-AIDS model to the demand of energy while in Section 4 data description is presented. Section 5 discusses the results while Section 6 provides summary and conclusions. Section 7 provides policy implications and recommendations.

2. The energy sector in Kenya In Kenya, energy resources comprise commercial and noncommercial. Commercial energy mainly comprises of petroleum products and electricity, while non-commercial comprises of biomass, and to a lesser extent solar energy, wind power and biogas (UNEP, 2006; Mwakubo et al., 2007). The energy sector contributes about 9.49% to GDP with the petroleum sector, electricity and fuel wood sector contributing 8.4%, 0.6% and 0.4%, respectively. Petroleum fuel accounts for about 20% of the total primary energy consumption while electricity and biomass accounts for about 10% and 70% (UNEP, 2006; Mwakubo et al., 2007). Transport sector is the largest consumer of petroleum products followed by the manufacturing sector and others (agriculture, tourism, power generation and government). Over the years, the transport sector generally consumed 70% of the total net domestic sales of petroleum products as compared to the manufacturing sector, which consumed only 25% of the total net domestic sales of petroleum products (Republic of Kenya, 2010). The use of LPG in homes, educational and health institutions has risen from slightly over 40 thousand metric tons in 2003 to 80 thousand metric tons in 2009 (Republic of Kenya, 2004; 2010). Motor gasoline, which is mostly used in the transport of passengers and goods, may not have made any remarkable growth owing to the efficiency of the vehicles entering the domestic market, in spite of the rise in numbers. Illuminating kerosene, the most popular fuel for use by households in lighting and cooking, recorded consumption of about 360,000 m3 in 2009 as compared to about 200,000 m3 consumed in 2003. In 1993, it is estimated

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that about 64% of urban households used kerosene for cooking (Kamfor, 2002). As proof of its popularity, over 75% of households surveyed reported owning a paraffin stove. Demand for petroleum products increased by about 10% from 3.33 million m3 in 2005 to about 3.66 million m3 in 2009 (Kamfor, 2002). This demand growth comprised of 60% liquefied petroleum gas (LPG) and 36% gas oil. Electricity in the household is mainly used for lighting and powering various equipments/machines. The mix of electricity generation has varied over time and seasons. In Kenya, electricity over the years has been generated through hydropower, fossil fuels (thermal), geo-thermal and wind. Importing power from Uganda and Tanzania in the earlier days was an alternative source. However, as the country developed, there was a shift from imports to local generation in line with objectives of energy supply and security issues. Hydro power generation has dominated the electricity sub-sector since the 1970s when it accounted for 41.6% of all electricity used. Thermal power generation accounted for 25.9% of the total power consumed in the country then. However, in the 1980s and 1990s, it gained more prominence and achieved an all time high of 80% in late 1980s and early 90s before declining to about 50% today. Over the last three years, thermal generation has increased tremendously due to poor rains, which have led to a drop in water levels in main dams such as Gitaru and closure of others such as Masinga. Therefore, Kenya has had to rely heavily on thermal generation, which has in turn increased the amount of diesel required to power these plants in order to generate enough power to meet current demand and avoid blackouts. Fuel wood is the most important source of energy in Kenya, supplying over 70% of Kenya’s total energy requirements. The Kenyan population depends on fuel wood for domestic energy needs. In the rural areas, fuel wood is mainly used in the form of firewood whereas charcoal dominates in the urban areas. Household expenditure surveys show that firewood is the most popular source of cooking fuel while kerosene remained popular for lighting. In 1989, 1999 and 2005, 87.3%, 78.8% and 76.4% of Kenyan households preferred kerosene for lighting, respectively. During the same period, those opting for firewood were 15.5%, 17.1% and 13.2% in that order. In 1989, 5.6% of Kenyan households used firewood for lighting while 73% used it for cooking (Kamfor, 2002). Similar data for 1999 and 2005 were 4.9% and 4.6% for light and 68.6% and 68.4% for cooking, respectively. It remains extremely hard to assess the quantity and value of firewood utilised by Kenyans. Although charcoal is primarily used outside the monetary economy, there is a market for it in urban domestic, industrial and export sectors with about 40% entering the commercial market. Its use has been on the rise since 1989 when 7.2% of households used it, rising to 9.6% in 1999 and 13.3% in 2005. Urban households prefer charcoal over gas and electricity because it is cheaper.

3. Methodology In this section we present a model of demand for fuel based on the Linear Approximate Almost Ideal Demand system (LA-AIDS) model. The LA-AIDS model is linear, flexible and satisfies the axioms of demand theory. It is derived from a well-behaved utility function and hence is consistent with demand theory. We hypothesise that the individual utility derived from the purchase of fuels is weakly separable from quantities of all other types of goods purchased by the household. Consequently, households follow a multistage process to allocate their budget to fuels. In the first stage, the total spending is allocated to broad categories of goods, namely fuels, and all other non-fuels, and in the second

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D. Ngui et al. / Energy Policy 39 (2011) 7084–7094

stage, it is allocated among various commodities in that group depending on the prices of individual commodities and the expenditure allocated to that group in the first stage. 3.1. Theoretical model The underlying theoretical model used for our empirical specification is based on the paper Blundell (1988) and Baker et al. (1989). Let x1 ,. . .,xm denote all consumption goods and q1 ,. . .,qs denote a smaller number of commodity groups to which x1 ,. . .,xm can be uniquely allocated. For the purposes of this paper, let us consider two commodity groups, namely fuel (qf) and nonfuels (qnf). Then if utility is weakly separable across these groups, direct utility may be written as (Blundell, 1988: p. 19) Uðx1 ,. . .,xm Þ ¼ F½Uf ðqf Þ,Unf ðqnf Þ

ð1Þ

While the allocation of expenditure to any xi in qs may then be expressed as Pi xi ¼ fi ðps ,ys Þ

for i ¼ 1,. . .,m and s ¼ f ,nf :

ð2Þ

This is the second stage of the two-stage budgeting where fi is related to the utility function, Ps is the vector of prices corresponding to qs and ys is the allocation of total expenditure to group s. U(  ), F(  ) and each Us(qs) are assumed to be concave and continuous, with the budget constraint being assumed to be linear. This implies that the expenditure Eq. (2) is linear homogeneous in Ps and ys and that the Hicksian or compensated price derivatives are symmetric forming a negative semi-definite Slutsky substitution matrix. Note that once ys is determined at the first stage, each qs can be determined without reference to prices outside this group. Assuming homothetic preferences within the group that is, elasticity with respect to total within group expenditure is unity, the expenditures in Eq. (2) can be written as Pi xi ¼ fi ðps Þys

for i ¼ 1,. . .,m and s ¼ f ,nf :

ð3Þ

So that each expenditure share w of good i out of group s, given by wis ¼ fi ðps Þ

ð4Þ

Generalising Eq. (3) to allow linear Engel (expenditure/ income) curves with non-zero intercepts, expenditure on good i may be written as Pi xi ¼ ai ðps ÞPi ¼ þ fi ðps Þys

ð5Þ

From Eq. (5), the cost of achieving a level of utility Us(qs) is given as (Baker et al., 1989: p. 723) CðPs ,Us Þ ¼ aðPs Þ þ bðPs ÞUs ðqs Þ ð6Þ P P where as ðPs Þ ¼ i pi asi ðPs Þ and bs ðPs Þ ¼ i pi bsi ðPs Þ. Differentiating the cost function (6) with respect to price (Hicksian or compensated demand), and substituting the utility term Us(qs) in the compensated demand function gives the following Marshallian demand functions: xi ¼ ai ðPs Þ þ fi ðPs Þ

½ys aðPs Þ bs ðPs Þ bðPs Þ

for s ¼ f ,n

ð7Þ

where ai ðps Þ and bi ðps Þrefer to the corresponding price derivatives of a(  ) and b(  ), respectively 3.2. Empirical model

consumer theory is aggregated across individuals to obtain the fuel expenditure. Muellbauer (1975, 1976) showed that exact aggregation is possible within the PIGLOG class of preferences. Assuming a two-stage budgeting procedure between fuel and non-fuel items, and weak separability, the Almost Ideal Demand System cost function (Deaton and MuellBauer, 1980: p. 313) can be written as lnCðU,PÞ ¼ a0 þ

X

aj lnpj þ

j

1XX b l lnpj lnpl þ b0 U Ppj j 2 j l jl j

ð8Þ

where lnCðU,PÞis the cost function for utility U at price vector P,

a0 , aj , b0 , bj and ljl are constants, and j and l are indexes representing different fuel groups, that is, fuel wood, charcoal, electricity, liquefied petroleum gas, automotive gas oil, motor spirit and lubricants. By applying the Shephard’s lemma and substituting in the indirect utility function, we then obtain the expenditure share of the jh group of fuels:   X y ð9Þ gjl lnpj þ bj ln wj ¼ aj þ p j P where y is the total expenditure on the fuels given by y ¼ j pj qj h where qj is the quantity demanded for j group of fuels by the representative household. bj are the expenditure coefficients, indicating whether commodities are necessities or luxuries. If bj o0, wj decreases when y increases so that fuel j is a necessity. Conversely if bj 40, wj increases with y so that fuel j is a luxury. p is a general price index defined by ln p ¼ a0 þ

X j

aj ln pj þ

1XX g ln pj lnpl 2 j l jl

ð10Þ

To comply with the theoretical properties of consumer theory, the following restrictions are imposed on the parameters of the AIDS model X X X aj ¼ 1, bj ¼ 0, gjl ¼ 0 ðAdding-upÞ ð11Þ j

X

j

j

gjl ¼ 0 ðHomogeneityÞ

ð12Þ

gjl ¼ glj 8j, lðj alÞ ðSymmetryÞ

ð13Þ

j

Eq. (11) allows the budget share to sum up to unity, Eq. (12) is based on the assumption that a proportional change in all prices and expenditures does not affect the quantities purchased, while Eq. (13) represents consistency of consumer choices. From the literature reviewed, demand for energy is influenced by other social-demographic variables other than price and expenditure, for example, regions: Central, Nairobi, Coast, Eastern, North Eastern, Nyanza, Rift Valley and Western (see Appendix A), employment status, household size, total expenditure, fuel price, gender and education. To capture the effect of these variables on the energy demand functions, the intercept of Eq. (9) was modified according to the demographic translating method (Heien and Wessells, 1990: p. 365), which assumes that the other parameters in the demand system do not depend upon the social-demographic variables. According to the translating method, aj was modified as X aj ¼ oj0 þ ojk dk ð14Þ k

Blundell (1988) observes that the choice of functional form for the representation of consumer preferences must stand as one of the most important issues in any aspect of the empirical analysis of consumer behaviour. For this paper, we choose the Price Independent Generalised Logarithmic (PIGLOG) functional form, since the individual expenditure function derived from the

oj0 and ojk are parameters to be estimated, dk are demographic variables of which there are K. Incorporating Eq. (14) into Eq. (9) yields   X X y ð15Þ wj ¼ oj0 þ ojk dk þ gjl ln pl þ bj ln p* j k

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pn is Stone’s price index given by X wj ln pj ln p* ¼

ð16Þ

j

where wj is the mean of the share equation. To preserve adding property, Eqs. (11)–(13) should hold with P j aj ¼ 1 replaced with X X ojo ¼ 1, ojk ¼ 0 ð17Þ j

j

The presence of zero expenditure for some commodities for some households is a common feature in household data. This can be caused by the study period being too short to allow consumers to report any purchase of a specific product (infrequency of purchase) or consumers not willing to buy the product (abstention), and consumers not purchasing the product at current prices and income levels (corner solutions) (see Angulo et al., 2001). Including nonzero observations would result in selection bias, if non-purchasing households behave systematically different from purchasing households. To solve this problem, a two-step estimation procedure based on the Amemiya—Tobin approach is used to include all the observations at both steps to estimate a system of petroleum consumption equations (Heien and Wessells, 1990). Following Heien and Wessells (1990), two decisions—whether or not to consume and how much to consume—are thus estimated separately. In the first step, the decision that a given household will purchase a specific commodity is determined from a probit regression of all available observations, taking the form Zjh ¼ f ðp1h ,. . .,pjh ,yh ,d1h ,. . .,dkh Þ

ð18Þ

where Zjk is 1 if the hth household buys the jth fuel (that is, if wjk 40) and zero otherwise. The other variables are as earlier defined. The maximum likelihood estimates from Eq. (18) are then used to compute the inverse Mill’s ratio for each household h and each fuel group. The inverse Mill’s ratio for the hth household that consumes the jth fuel is derived as

wjh ¼

yðp1h ,. . .,pjh ,yh ,d1h ,. . .,dkh Þ Yðp1h ,. . .,pjh ,yh ,d1h ,. . .,dkh Þ

ð19Þ

where y and Y are the standard normal density and cumulative probability functions, respectively. The inverse Mill’s ratio for the hth household that does not consume the jth fuel is derived as

wjh ¼

yðp1h ,. . .,pjh ,yh ,d1h ,. . .,dkh Þ 1Yðp1h ,. . .,pjh ,yh ,d1h ,. . .,dkh Þ

ð20Þ

In the second step, the inverse Mill’s ratio for each household for each item is then used in Eq. (15) as an instrumental variable. The estimating model then becomes   X X y wj ¼ oj0 þ þ xj wjh þ ej ojk dk þ gjl ln pl þ bj ln ð21Þ p* j k where ej is an error term. Heien and Wessells (1990: p. 370) observe that the system will not add up if all n equations are specified as Eq. (21), which pertains only to the first n–1 demand relations. In this case, adding up would require xjwjh ¼0, a restriction, which is impossible since wjh can assume any value. However, Heien and Wessells (1990: p. 370) observe that the adding-up constraint could be preserved by specifying the deleted equation as   n1 X X X y þ xi wih  wj ¼ oj0 þ ojk dk þ gjl ln pl þ bj ln xj wjh þ ej p* j j¼1 k ð22Þ The complete demand model of the allocation of the fuel budget can be estimated using Iterated Seemingly Unrelated

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Regression (ITSUR) technique together with homogeneity and symmetry restrictions maintained. Since the adding-up condition makes the covariance matrix of the residuals singular, one equation has to be dropped from the system and the parameters of this equation calculated using the parameter restrictions of the system. 3.2.1. Expenditure and price elasticities Given the above specifications, Marshallian (uncompensated) and Hicksian (compensated) elasticities can be computed from the estimated parameters of the LA-AIDS model using the formulae suggested by Green and Alston (1990), which are given as follows: ej ¼ 1 þ

bj

ðexpenditure elasticityÞ

wj

Zjl ¼ djl þ wl þðgjl =wj Þ ðHicksianÞ

ð23Þ ð24Þ

where djl is the Kronecker delta, djl ¼1 for j ¼l; djl ¼0 for j al.

Zjl ¼

gjl wj

bj

Zjj ¼ 1 þ

wj wl

gjj wj

bj

ðMarshallianÞ

ðMarshallianÞ

ð25aÞ

ð25bÞ

4. Data description The data used for the analysis was taken from a comprehensive survey on energy consumption patterns in Kenya carried out by the Kenya Institute for Public Policy Research and Analysis (KIPPRA) and Energy Regulatory Commission (ERC) in 2009. The data comprises information collected from 3665 households across the country. The survey includes detailed information on demographic characteristics and household expenditure shares of fuels. The variables used in the model are defined in Table 1. To make the data suitable for analysis, the data were transformed in various ways. Some of the variables were first converted to natural logarithms and/or percentages before regression. These included prices of the various fuels, budget shares, fuel expenditure and household size. The data was also subjected to various tests. To prevent the effects of outliers, squared Mahalabonis distance to the mean vector was computed. Squared Mahalabonis distance points out to observations for which the explanatory part lies far from that of the bulk of the data (Rousseeuw and Leroy, 1986). The values of squared Mahalabonis distance were then compared with 95% quantiles of the Chi-square distribution with m 1 degrees of freedom, where m represents the number of independent variables, and the observations with extreme values corrected. 10 out of the 3665 observations were corrected for outliers. The models were evaluated for heteroscedasticity using a White test, and multicollinearity using variance inflation factor. The variance inflation factor measures the impact of collinearity among the independent variables in a regression model on the precision of estimation. It shows how the variance of an estimator is inflated by the presence of multicollinearity (see Greene, 2008). A variance inflation factor will be 1 if there is no collinearity between any two independent variables while it will increase as the extent of collinearity increases, and in the limit it can become infinite (Greene, 2008). Variance inflation factors greater than 10 are generally seen as indicative of severe multicollinearity. The White test indicated that there was no heteroscedasticity while the variance inflation factors indicated that collinearity among the analysed variables was not high with values ranging between 1.07 and 5.06.

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D. Ngui et al. / Energy Policy 39 (2011) 7084–7094

Table 1 Variable description. Variable

Description and measurement

Fuel budget share Regions

Budget share of the jth fuel group. The part of Kenya each household belongs to. Dummy variables: 1 if Central otherwise 0, 1 if Coast otherwise 0, 1 if Eastern otherwise 0, 1 if North Eastern otherwise 0, 1 if Nyanza otherwise 0, 1 if Rift Valley otherwise 0 and 1 if western otherwise 0. Type of occupation. Dummy variables, 1 if formal employment (legislators, professionals, technicians, secretarial, clerical and related workers) otherwise 0 and 1 if informal employment (service workers, shop and sales workers, farmers, craft and related trade workers, hawkers, plant and machine assemblers and cleaners) otherwise 0. Number of persons living together in one house. Price of the jth fuel group in Kenyan shillings (1 Kshs E 77.51 USD). Real expenditure allocated to fuels. Sex of the household head. Dummy variable, 1 if female, 0 otherwise. The highest level of education completed. Dummy variables: 1 if primary otherwise 0, 1 if secondary otherwise 0 and 1 if vocational/diploma or no education otherwise 0.

Employment categories Household size Fuel price Expenditure Gender Education

Cross-sectional data typically suffer from limited variation in prices. This occurs when the structure of the demand is relatively constant, and price variation can be attributed to different supply conditions and be used to identify commodity demand curves. This sample has sufficient variation in supply conditions and therefore does suffer from the said problem. Another problem with expenditure surveys is that if a household does not consume a particular type of fuel, there is no data on the price of that fuel for the household. On average, 10–15% of the households were not using at least one fuel type at the time of survey. In order to account for the fuel expenditure function and the complete system of fuel share equations, price must be available for all types of fuel for all households. Hence, we used the average price of that particular kind of fuel within the same cluster/town as a proxy for missing price.

Table 2 Summary statistics for the fuel demand model variables. Variable

Mean

Std. dev.

Min.

Max.

House hold size Expenditure Kerosene price(Kshs/l) Fuel wood price(Kshs/bundle) LPG price(Kshs/kg) Electricity price(Kshs/Kwh) Charcoal price(Kshs/90 kg sack) MSP price(Kshs/l) AGO price(Kshs/litre) Lubricant price(Kshs/ l) Formal employment Informal employment Gender Primary education Secondary education Vocation education No education

5.145 15744.390 72.094 760.664 176.405 11.62 431.137 76.092 66.13 382.657 0.466 0.242 0.613 0.290 0.314 0.211 0.068

3.011 18041.710 89.735 918.625 63.261 6.297 256.881 6.819 3.457 193.883 0.499 0.428 0.487 0.454 0.464 0.408 0.252

1 300 1 1 22 0.0002 0.34 67.45 60.249 50 0 0 0 0 0 0 0

50 355,000 2400 15,000 1800 100 7000 110 79 1000 1 1 1 1 1 1 1

5. Results 1 Kshs E77.51 USD.

5.1. Descriptive statistics Table 2 shows that the average value of total expenditure was Kenya Shillings (Kshs) 15,744 (USD 203.12) with a standard deviation of 18041.71. This implies that the total expenditure, which proxied income differed across the households with a minimum of Kshs 300 (USD 3.87) and a maximum of Kshs 355,000 (USD 4580.05). The average kerosene price was Kshs. 72/l (USD 1.01) compared to Kshs 760 per bundle (USD 9.81) for fuel wood. The statistics showed that majority of the household heads were female with an average household size of 5. The budget shares for households differed across the provinces and fuels. Electricity had the highest energy budget share on average (14.01%) compared to kerosene (13.41%), fuel wood (12.60%), liquefied petroleum gas (LPG) (10.09%) and charcoal (5.39%). Among the transport fuels motor spirit premium (MSP) had the highest budget share of (22.29%) compared to automotive gas oil (AGO), which had 14.25%. The findings indicated that Nyanza households utilise a larger proportion of their energy budget on charcoal. This may be occasioned by income disparities. All other regions are split into two groups of three with the first spending less than 2% of their energy budget on charcoal and the second group using over 2% of energy budget on charcoal. As anticipated, electricity had the highest budget share in Nairobi compared to the rest of the provinces. The highest budget share was recorded in rural North Eastern region at nearly 16% of their energy budget. There were very few households who used MSP

Table 3 Mean energy consumption per month. Energy type

Kshs/month

kWh/month

Fuel wood Charcoal Electricity Kerosene LPG Lubricants MSP AGO

1983.74 848.60 2205.73 2111.27 1588.57 1254.80 3509.34 2243.52

174.32 74.57 193.83 185.53 139.39 110.26 308.38 197.15

1 kilowatt hour (1 kWh)E Kshs 11.38, 1 Kshs E77.51 USD.

as a source of fuel with Nairobi having the highest share of users at 10%. Table 3 shows the mean energy consumption per month. The mean energy consumption differed across the different fuel types with MSP having the highest average among the transport fuels and the highest budget share. Note that for non-transport fuels, electricity had the highest mean energy consumption followed by kerosene an implication of a high allocation of energy budget to cater for this average monthly consumption.

D. Ngui et al. / Energy Policy 39 (2011) 7084–7094

5.2. Empirical analysis 5.2.1. Fuel demand determinants The complete demand system for allocation of fuel budget was estimated for all the households in general using the Iterated Seemingly Unrelated Regression. The results are presented in the Appendix Tables B1 and C1. In estimation, particular attention is given to the presence of zero expenditure and the effects of household size, energy type and related products prices, human capital, occupation, and regional dummies. The results support a number of the trends that we have already seen in terms of significance and expected signs. The kerosene results show that household size, total expenditure, kerosene price, fuel wood price, LPG price, gender and education level, are some of the key determinants of kerosene budget share. As the household size increases, the budget share on kerosene declines. This could be explained by the fact that as the household size increases, the household switches to other fuel types such as charcoal, fuel wood and even LPG to meet increased demand for energy. This is an indication that most households use multiple fuels as a safety net to cushion themselves against the failure of one source. A good example is the application of firewood and charcoal for cooking and kerosene for lighting. This finding is consistent with the ‘‘multiple fuel, or fuel stacking, model’’, which states that household do not simply switch to a new fuel as income increases, but will continue to use more than one fuel (see Masera et al., 2000). The regional dummies for Eastern, Rift Valley and Western were also statistically significant but had negative coefficients an indication that Kerosene was used more in these regions compared to Nairobi, which was the reference point. The budget share for LPG in Kenya is mainly driven by household size, total expenditure, kerosene price, fuel wood price, electricity price and its own price. As the price of LPG increases, the budget share also increases. Also as the price of charcoal increases, the budget share of LPG increases. This could be explained by the household opting to use more of LPG when prices of charcoal increase, since it is cleaner and faster in food preparation compared to the latter. In terms of gender, a household headed by female, is more likely to reduce demand for LPG than that headed by a male. Budget share for MSP is mainly driven by employment status, household size, education, gender, its own price, which is inversely related to the budget share, i.e. as its own price increases, the budget share allocated to MSP decreases as well. However the budget share is positively related to the price of AGO, i.e. as the price of AGO declines, the budget share allocated to this fuel decreases and vice versa. This could mean that households that have a choice to use an MSP powered vehicle are likely to use the alternative that consumes AGO. Note that MSP and AGO cannot be substituted in terms of automobile operations. However in instances where a household has two cars, those using MSP powered engines could opt to use AGO powered due to price changes. In addition, motorists using MSP powered cars are likely to opt to use public transport due to price increases of the product. The regional dummies for Eastern, Rift Valley and Western had negative coefficients and were statistically significant implying that household in such regions are likely to experience reduction in budget share due to certain characteristics unique to these regions. Automotive Gas Oil budget share is mainly driven by the following factors: formal employment, total fuel expenditure, its own price, price of MSP, which is positively related to the budget share, and whether household is headed by female, primary and secondary education. In the case of whether rural or urban, the same factors are important drivers of demand. In the rural areas, price of lubricants, MSP price and its own price are some of the key drivers

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of demand. In urban areas, only its own price and price of MSP are statistically significant. In the case of rural households, regional dummies for Rift Valley and Western are significant and negative implying that households in these regions are less likely to demand AGO due to certain characteristics unique to them that make them consume less of the product. The main factors driving consumption of fuel wood include its own price, which is positively related to its budget share. Charcoal, electricity and LPG prices are inversely related to the budget share implying that, as the price of these fuels increases, fuel wood budget share declines. Gender, total expenditure, education and employment status are also highly significant. The budget share for electricity in Kenya is mainly driven by household size, total expenditure, fuel wood price, charcoal price and LPG price. As the price of kerosene and fuel wood increases, the budget share on electricity decreases. However as the price of charcoal and LPG increases, the budget share on electricity increases. This could be explained by the household opting to use more electricity when prices of LPG and charcoal increases, since they can be substituted as far as their use are concerned. In terms of education those with lower education allocate lower budget shares to electricity than those with higher education. The budget share for charcoal is driven by various factors ranging from socioeconomic, prices, education and location of households among other factors. From the analysis, demand for charcoal is inversely related to formal employment. This implies that as the head of the household moves to formal employment the demand for charcoal declines at that particular household. Other important factors in the demand for charcoal include, household size, price of LPG, as well as primary education, which is inversely related to demand.

5.2.2. Elasticities The Hicksian, Marshallian and expenditure elasticities are reported in Tables 4–6, respectively. The uncompensated (Marshallian) own-price elasticities for the different energy sources are presented in Table 5. The uncompensated elasticities should be interpreted as conditional elasticities, where it is assumed the relative price changes within fuel categories would not affect the real on expenditure fuel. The uncompensated own-price elasticities have the expected negative signs for all the demands. The results indicate that MSP, AGO and lubricants are price elastic while fuel wood, kerosene, charcoal, LPG and electricity are price inelastic. Gebreegziabher et al. (2010) found that the demand for firewood, charcoal and kerosene were price inelastic with own price elasticity of less than 1, while Athukorala and Wilson (2010) found that the demand for electricity to be price inelastic. Arnold et al. (2006) found that, with the exception of evidence in India, most estimates of ownprice elasticity reflect that the demand for fuel wood and charcoal, particularly in urban areas are price inelastic. The fact that the demand for firewood and charcoal, in our case, turned out price inelastic was consistent with their findings. Nonetheless, the magnitude found by Gebreegziabher et al. (2010) were substantially lower, –0.15, –0.095 and –0.391 in the case of kerosene, charcoal and electricity, respectively, than suggested by this study (see Table 5), which implies that households in Kenya are relatively more price responsive to these fuels than in Ethiopia. Note that the highest own price elasticities are found in the fuel categories with the highest budget share. i.e. AGO and MSP. The rationale is that households are more likely to increase or reduce the share of budget expenditure of these fuels when their price changes. These findings contradict Ramanathan (1999) who found gasoline demand to be relatively inelastic to price changes, both in the long and short terms.

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Table 4 Elasticities (Hicksian-compensated) own and cross price elasticities.

Fuel wood Kerosene Charcoal LPG Electricity MSP AGO Lubricants

Fuel wood

Kerosene

Charcoal

LPG

Electricity

MSP

AGO

Lubricants

 0.57a (0.03) 0.222a (0.02) 0.44a (0.04) 0.222a (0.04) 0.54a (0.02) 0.23 (0.18) 0.098a (0.03) 0.85

 0.55a (0.02) 0.16b (0.02) 0.43 (0.03) 0.14 (0.03) 0.03 (0.02) 0.25 (0.05) 1.37

 0.76a (0.04) 0.09a (0.01) 0.147a (0.02) 0.103a (0.01) 0.04 (0.025) 0.39

 0.38a (0.11) 0.235a (0.02) 0.403a (0.05) 0.23a (0.1) 0.57

 0.631a (0.07) 0.307b (0.126) 0.31a (0.06) 0.41

 6.73a (0.18) 5.59 (0.36) 0.37

 8.49a (0.21) 0.48

 13.4

The prices are across the top. Standard errors given in parenthesis are for those elasticities not obtained from restrictions on parameters. a b

Indicates 1% significance level. Indicates 5% significance level.

Table 5 Elasticities (Marshallian-uncompensated) own and cross price elasticities. Fuel wood Fuel wood Kerosene Charcoal LPG Electricity MSP AGO Lubricants

a

 0.629 (0.032) 0.087a (0.015)  0.555a (0.037)  0.33a (0.02)  0.646a (0.021)  0.254a (0.014)  0.187a (0.014)  1.388

Kerosene a

0.110 (0.024)  0.693a (0.022) 0.04 (0.037)  0.031 (0.03) 0.021 (0.029)  0.003 (0.018)  0.045 (0.028)  1.94

Charcoal a

 0.24 (0.016) 0.092a (0.015)  0.679a (0.074) 0.09a (0.001) 0.102a (0.021)  0.114a (0.013)  0.00 (0.14)  0.614

LPG

Electricity a

 0.272 (0.016)  0.427a (0.022) 0.079a (0.019)  0.286a (0.109) 0.196a (0.021)  0.37a (0.035)  0.25a (0.056) 0.137

a

 0.073 (0.024)  0.009a (0.03) 0.26a (0.056) 0.269a (0.03)  0.88a (0.036) 0.278a (0.013) 0.215a (0.035)  0.19

MSP

AGO a

 0.61 (0.02)  0.196a (0.037)  0.622a (0.056) 0.35 (0.297) 0.214a (0.036)  6.78a (0.18)  0.215 (0.204) 0.361

Lubricants a

 0.2 (0.02)  0.1a (0.03)  0.024 (0.037) 0.34 (0317) 0.202a (0.036) 3.55a (0.14)  8.59a (0.211)  0.485

0.424a (0.06)  0.200b (0.149) 0.621a (0.222)  1.127a (0.119)  0.141 (0.099) 2.704a (0.126) 3.49a (0.161)  12.68

The prices are across the top. Standard errors given in parenthesis are for those elasticities not obtained from restrictions on parameters. a b

Indicate 1% significance level. Indicate 5% significance level.

Table 6 Expenditure elasticity. Fuel source

Elasticity

Fuel wood Kerosene Charcoal LPG Electricity MSP AGO Lubricants

0.937 (0.035)a 1.06 (0.032)a 0.889 (0.134)a 0.871 (0.077)a 0.850 (0.057)a 0.205 (0.139) 0.710 (0.066)a 4.24

Standard errors are given in parenthesis are for those elasticities not obtained from restrictions on parameters. a

Indicate 1% significance level.

For some cross price elasticities, while Marshallian estimates are negative, Hicksian estimates are positive. See for instance, charcoal, LPG and electricity vs. fuel wood; and fuel wood and kerosene vs. LPG. This suggests that the income effect in these cases outweighs the substitution effect. Thus, if the price of one fuel decreases, real income goes up enough to increase the consumption of other fuels. However, an increase in income alone will not cause households consume other fuels. This is evidenced by the Hicksian own price elasticity for kerosene, electricity, MSP and AGO, which is smaller in magnitude compared to the Marshallian own price elasticity implying that the pure effect of substitution is only partially compensated by the income effect.

Income has been the single most important explanatory factor in the literature on the choice of domestic fuel over the last ¨ decades (Gundimeda and Kohlin, 2006). It is also the basis for the energy ladder model and although this model has been elaborated lately (Heltberg et al., 2000), income, or its proxy expenditure, remains as the most important variable in explaining fuel demand. The energy ladder hypothesis explains the movement of energy consumption from traditional sources to more sophisticated sources along an imaginative ladder with the improvement in the economic (income) status of households. Biomass fuels occupy the bottom of the list while electricity, that is much cleaner, lies at the top. It is assumed that energy transition occurs from the bottom to the top with increasing socioeconomic status of households either through a rise in income or a fall in price (Hosier and Dowd, 1987). For instance, if income increases holding all other factors constant, households will substitute fuel wood with charcoal, charcoal with kerosene and kerosene with LPG. Expenditure elasticities of demand for the different fuel categories are given in Table 6. The elasticities obtained from the AIDS model are with respect to the expenditures on fuels only. The magnitude of the income elasticities, however, varied for the different fuels. For example, while demand for kerosene was income elastic (41), the demand for fuel wood, charcoal, LPG and electricity were income inelastic. Moreover, the magnitude of the income elasticity of demand for charcoal was substantially larger than suggested by Arnold et al. (2006) and Hughes-Cromwick (1985), but consistent with Gebreegziabher et al. (2010). Contrary to Hunges et al., (2006) and Olivia and Gibson (2008) who found gasoline and oil to be luxury goods, MSP and AGO were found to be normal goods. Lubricants were found to be luxury

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goods. As the results also show, income alone is not enough to explain the way in which MSP is used as stipulated by the energy ladder hypothesis. The price of MSP for example, can have a dissuasive effect in the choice of using personal cars as means of transport. Note that the Marshallian estimates indicate that the demand for MSP fuel is more price elastic compared to the other fuels. The expenditure elasticities for LPG, charcoal and electricity were positive and significantly different from zero, a finding consistent with Gebreegziabher et al. (2010) and Rajmohan and Weerahewa (2007). Contrary to some studies on developing countries (see for ¨ example, Gundimeda and Kohlin, 2006; Arnold et al., 2006; Rajmohan and Weerahewa, 2007; Gebreegziabher et al., 2010; Athukorala and Wilson, 2010), kerosene had the highest elasticity (greater than one) implying that an increase in total energy expenditure will lead to more than proportionate increase in the expenditure shares. This could be attributed to the fact that kerosene has multiple uses and is utilised intermittently. In addition, it is easily available and affordable in smaller quantities compared to the other fuels. Arnold et al. (2006) argued that in most studies the effect of income on fuel wood consumption turns out to be small, irrespective of how income is measured. Their results were in the range of –0.31 to 0.06 and relatively few of these observed income elasticities were significantly different from zero. In our case, however, income/expenditure elasticities for all fuel goods were positive and significantly different from zero (with an exception of MSP), implying that none of the fuels we considered were inferior goods. In fact, there is no support for the energy ladder hypothesis contrary to what Arnold et al. suggested, a finding consistent Gebreegziabher et al. (2010). Note that the cross price elasticities for charcoal, LPG and electricity vs. fuel wood suggests that the income effect outweigh the substitution effect. However, as the results show, income alone is not enough to explain the way in which fuel wood is used. The price of wood, for example, can have a dissuasive effect on the choice of wood as the main source of heating and/or cooking energy.

6. Summary and conclusions The study utilises a Linear Approximate-Almost Ideal Demand System (LA-AIDS). This complete energy demand model is estimated using Iterated Seemingly Unrelated Regression (ITSUR) technique together with homogeneity and symmetry restrictions maintained. With regard to global energy demand, the study finds that different factors affect the demand at the fuel level and hence policy advice should be fuel specific. For instance, kerosene budget share is determined by household size, kerosene total expenditure, kerosene price, fuel wood price, charcoal price, LPG price, female and education level. The share of budget for LPG is mainly driven by household size, total expenditure on LPG, kerosene prices, fuel wood price and its own price. Note that as the household size increases, the household switches to other fuel types such as charcoal, fuel wood and even LPG to meet increased demand for energy for example for food preparation. From the analysis, demand for charcoal is inversely related to formal employment. This implies that as the head of the household moves to formal employment the demand for charcoal declines at that particular household Other important factors in the demand for charcoal include, household size, price of LPG, as well as primary education which is inversely related to demand. In the transport fuels, the study has established that the budget share for MSP is mainly driven by employment both

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formal and informal, its own price which is positively related to the budget share, i.e. as its own price increases, the budget share allocated to MSP increases as well. However the budget share is inversely related to price of AGO, i.e. as the price of AGO declines, the budget share allocated to this fuel increases and vice versa. This could mean that households that have a choice to use an MSP powered vehicle are likely to use the alternative that consumes AGO. The main factors driving consumption of lubricants include its own price which is positively related to its budget share, total expenditure on lubricants, AGO price, which is inversely related meaning that as the price of AGO increases the budget share declines. This is because lubricants and AGO are compliments. Lubricants lubricate engines and this is common in AGO driven vehicles. However it is positively related to the price of MSP. With regard to elasticities, the uncompensated (Marshallian) own-price elasticities for the different energy sources were calculated. The uncompensated elasticities should be interpreted as conditional elasticities, where it is assumed the relative price changes within fuel categories would not affect the real expenditure on fuel. The uncompensated own-price elasticities have the expected negative signs for all the fuels. The estimates indicate that the demand for MSP, AGO and lubricants are more price elastic compared to the other fuels. For some cross price elasticities, while Marshallian estimates are negative, Hicksian estimates are positive. See for instance, charcoal, LPG and electricity vs. fuel wood; and fuel wood and kerosene vs. LPG. This suggests that the income effect in these cases outweigh the substitution effect, such that if the price of one fuel falls, the resulting increase in real income will cause household to cleaner fuels on the energy ladder. For instance, increasing household income to a certain threshold, the households will completely substitute kerosene for LPG, with all other factors held constant. Note that however, an increase in income alone will not cause households to move up the energy ladder. This is evidenced by the Hicksian own price elasticity for kerosene, electricity, MSP and AGO, which is smaller in magnitude compared to the Marshallian own price elasticity. This suggests that the pure effect of substitution is only partially compensated by the income effect, hence as indicated earlier, the fuel stack hypothesis is depicted in the results where household would prefer substituting alternative fuels other than fully switching to a different fuel even if income is increased. This implies that other factors than income as shown in the demand models significantly affects the demand for these fuels. The income/expenditure elasticities for all fuel goods were positive and significantly different from zero, implying that none of the fuels we considered were inferior goods. The highest elasticity (greater than one) is for kerosene implying that an increase in total energy expenditure will lead to more than proportionate increase in the expenditure shares. This could be attributed to the fact that kerosene has multiple uses and is utilised intermittently. In addition, it is easily available and affordable in smaller quantities compared to the other fuels. The high expenditure elasticity indicates that the use for kerosene will be pervasive in Kenya. Hence, when simulating future demand, one should consider not only income and population growth but distribution. This study investigated the household energy demand in Kenya using a Linear Approximate Almost Ideal Demand System (LA-AIDS). However, despite the study employing data taken from a comprehensive survey on energy consumption patterns in Kenya, the effects of household fuel choice on environment and health was not covered. Hence, further research to determine whether household fuel choice has effects on environment and health is necessary. Furthermore, there is a need for a

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further investigation on the cost benefit analysis of the main household fuels to the economy and the level of fuel switching in Kenya.

7. Policy implications and recommendations Based on the findings, the following recommendations can be made.

7.1. Fuel wood and charcoal Since the use of fuel wood and charcoal may continue for a period before consumers appreciate the dangers associated with smoke as well as petroleum products and other emissions from these products, there is need for the government through the ministry of health, Ministry of Energy (MOE), National Environment Management Authority (NEMA) and nongovernmental organisations to proactively provide information and educate citizens on the best ways to ensure best use as well as how to sustain provision of clean biomass. Specifically, policy measures that simultaneously address household income and fuel price are required. Increasing household income, directly with income supplements or indirectly with the provision of energy-efficient cook stoves, has the potential to decrease charcoal consumption. Similarly, as Kidane (1991) observes, by manipulating the price variable, the government may be able to control the high rate of depletion of forest resources. Hence, price reforms that force the price of energy to reflect its real economic cost could encourage more efficient consumption. In addition there is need to encourage users to shift to modern energy sources by encouraging marketers as well as providing incentives to increase its production. There is need to provide economic instruments to regulate charcoal production and use so as to achieve sustainability of supply and protect the environment following Kyoto protocol and Copenhagen resolutions among other environmental protection conventions. This is also in line with the Cancun meeting that recognises that climate change represents an urgent and potentially irreversible threat to human societies and the planet, which needs to be addressed by all parties. It advocates for deep cuts in green house gas emissions.

7.2. Electricity Although connectivity of electricity both to households has greatly improved in the last six years, there is need for a strategic move to reduce the initial cost of connection to electricity as well as to allocate more funding in the sub-sector not only on transmission and distribution, but also in the electricity sector to increase clean electricity generation from wind energy. This will not only put more electricity to the national grid, but also ensure improved access and reduction in cost of power as well as protect the environment from carbon dioxide emissions that are harmful to the environment. There is also need to put in place deliberate measures to improve penetration of renewable technologies by providing fiscal incentives as well as credit facilities for both consumers and providers of energy in this sub-sector. The renewable technologies (solar, wind and biogas) are the fuels for rural Kenya since they stand alone. If these are used to light rural homes, electricity may then be directed to industries and offices, in addition to urban homes. The challenge here is start-up costs to get systems up and running since those in rural areas may find such costs prohibitive.

7.3. Petroleum 7.3.1. Kerosene Due to health problems associated with smoke from petrol and use of kerosene, there is need for the government to increase the penetration of alternative fuels such as biogas and LPG to reduce the disease burden such as bronchitis that is associated with use of dirty fuels. 7.3.2. LPG In order to increase usage and penetration of LPG in the country, there is need to provide more fiscal incentives both to the users and suppliers and particularly for appliances such as cookers which are currently expensive and other peripherals that discourages prospective users. 7.3.3. MSP and AGO There is need to organise and improve the public transport system in Kenya and particularly in the urban areas so as to reduce the consumption of petroleum products. If measures to solve urban public transport are brought on board, more people will find it unnecessary to drive their personal cars and hence this may result in reduction in the amounts of MSP and AGO consumed. MSP and AGO may be then be channelled to other productive sectors in the economy. In addition, friendly regulations and policies should be implemented regarding petroleum product pricing.

Appendix A See Fig. A1.

Appendix B. Survey instrument The following table describes the main sections of the questionnaire used to collect the data used for analysis in this study. (See Table B1.)

Appendix C. Estimates of the linear approximate almost ideal demand system (LA-AIDS) See Table C1.

Fig. A1. Map of the regions. Key: 1—Central, 2—Coast, 3—Eastern Province, 4—Nairobi, 5—North Eastern, 6—Nyanza, 7—Rift Valley, 8—Western.

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Table B1 Sections of the energy consumers’ questionnaire. SECTION

Description

Profile of the Consumer

Gender, age, property tenancy, relationship to household head, type of dwelling unit, decision making regarding household energy issues, household expenditure data. Energy sources, distance to the source, energy choice determinants, energy uses, problems associated with the use of energy. Quantity of energy consumed, monthly energy expenditure and cost/unit for each of the energy consumed, energy budget as a share of total budget, energy efficiency and conservation measures, energy appliances, monthly savings from efficiency measures, energy switch options, household expenditure data. Household size, age, marital status, highest level of education reached household head’s main economic activity and occupation.

Energy Choices and uses Energy cost and expenditure

Socio-economic and demographic profile of the household head

The national energy survey was undertaken by KIPPRA in collaboration with the Ministry of Energy and Energy Regulatory Commission (ERC) between May and June, 2009. Using the Kenya National Bureau of Statistics’ frame of 1800 clusters, each with an average of 100 households, a representative sample of 3665 households was randomly selected from the eight provinces of Kenya namely, Nairobi, Coast, Central, Eastern, Western, North Eastern, Nyanza and the Rift Valley (see Appendix A). The questionnaire was administered on the respondents by the authors together with a team of research assistants.

Table C1 Estimates of the LA-AIDS model for demand. Kerosene Variables

Coef.

Central Coast Eastern North eastern Nyanza Rift Valley Western Formal employ Informal employ Household size Expenditure Kerosene price Fuel wood price Charcoal price LPG price Electricity price Lubricant price AGO price MSP price Gender Primary educ. Secondary educ. Vocation educ. No educ Inverse mills Constant

(dropped) (Dropped)  1.314a (dropped)  0.820a  1.002a  1.217a 0.004 (Dropped)  0.005a 0.008a 0.042a 0.013a 0.001  0.056a 0.000  0.039  0.012a  0.024a  0.027a  0.595a  0.550a  0.415a (Dropped)  1.417a 2.26a

Charcoal Std. err. Coef.

0.016 0.013 0.013 0.019 0.006 0.001 0.005 0.003 0.002 0.002 0.003 0.004 0.019 0.004 0.005 0.005 0.013 0.013 0.011 0.024 0.125

(Dropped) (Dropped)  0.660a (dropped)  0.652a  0.648a  0.657a  0.049a (Dropped) 0.003c  0.006a 0.001a  0.031a 0.017a 0.004b 0.013a 0.033b  0.002  0.035a  0.006  0.037b 0.003 0.012 (Dropped) 0.102 0.000b

Electricity Std. err. Coef.

0.016 0.016 0.015 0.019 0.010 0.002 0.007 0.002 0.002 0.004 0.001 0.003 0.012 0.002 0.003 0.010 0.017 0.016 0.015 0.135 0.871

(Dropped) (Dropped)  0.668a (dropped)  0.608a  0.744a  0.677a  0.183a (Dropped) 0.023a  0.021a 0.000  0.093a 0.013a 0.025a 0.014b  0.021 0.025a 0.037a  0.010 0.124a 0.058a  0.005 (Dropped)  0.368a 1.976a

LPG Std.err Coef.

0.013 0.014 0.011 0.017 0.009 0.001 0.008 0.004 0.003 0.003 0.003 0.005 0.014 0.005 0.005 0.007 0.014 0.011 0.011 0.015 0.051

(Dropped) (Dropped) 0.102a (dropped)  0.181a  0.637a  0.136a  0.078a (Dropped) 0.015a  0.013a  0.056a  0.035a 0.004b 0.129a 0.025a  0.115a  3.512a 3.561a  0.015a 0.183a 0.078a 0.023a (Dropped)  0.212a 0.714a

Fuel wood Std. err. Coef.

0.010 0.011 0.008 0.013 0.005 0.001 0.008 0.003 0.002 0.001 0.011 0.003 0.012 0.032 0.030 0.003 0.014 0.007 0.005 0.014 0.050

(Dropped) (Dropped)  0.533a (dropped)  0.384a  0.543a  0.435a 0.063a (Dropped)  0.022a  0.008a 0.013a 0.204a  0.031a  0.035a  0.093a 0.053a  0.032a  0.079a 0.030a  0.217a  0.181a  0.160a (Dropped)  0.511a 1.192a

MSP

AGO

Std. err. Coef.

Std. err. Coef.

0.010 0.010 0.008 0.012 0.006 0.001 0.004 0.002 0.004 0.002 0.002 0.003 0.008 0.002 0.002 0.005 0.010 0.009 0.009 0.006 0.026

(Dropped) (Dropped)  1.383a (dropped) 0.150a  0.981a  0.384a  2.118a (Dropped)  0.045a  0.177a  0.024a  0.079a  0.035a  0.102a 0.037a 0.765a 0.766a  1.328 0.399a  0.108a 0.691a  0.338a (Dropped)  1.816a 13.85a

0.021 0.022 0.012 0.021 0.038 0.001 0.031 0.005 0.002 0.003 0.008 0.005 0.028 0.029 0.040 0.009 0.009 0.015 0.010 0.033 0.000

(Dropped) (Dropped)  0.955a (dropped)  0.604a  0.673a  0.707a  0.141 (Dropped)  0.002a  0.041a  0.012a  0.032a  0.002a  0.040a 0.025a 0.382a  1.087a 0.766a 0.009a 0.265a 0.175a  0.074a (Dropped)  0.530a  1.816a

Std. err

0.020 0.013 0.011 0.017 0.006 0.001 0.009 0.004 0.002 0.002 0.008 0.005 0.023 0.030 0.029 0.005 0.011 0.009 0.008 0.013 0.033

Dropped indicates insignificant variables that were eliminated and the equations re-estimated. a b c

Indicate 1% significance level. Indicate 5% significance level. Indicate 10% significance level.

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