Stacking up the ladder: A panel data analysis of Tanzanian household energy choices

World Development 115 (2019) 222–235

Contents lists available at ScienceDirect

World Development journal homepage: www.elsevier.com/locate/worlddev

Stacking up the ladder: A panel data analysis of Tanzanian household energy choices Johanna Choumert-Nkolo a,⇑, Pascale Combes Motel b, Leonard Le Roux c a

Economic Development Initiatives (EDI) Limited, High Wycombe, United Kingdom Université Clermont Auvergne, CNRS, IRD, CERDI, F-63000 Clermont-Ferrand, France c School of Economics, University of Cape Town, Cape Town, South Africa b

a r t i c l e

i n f o

Article history: Accepted 24 November 2018

JEL classification: O13 Q41 N5 Keywords: Fuel choices Cooking Lighting Sub-Saharan Africa

a b s t r a c t Energy-use statistics in Tanzania reflect the country’s low level of industrialization and development. In 2016, only 16.9% of rural and 65.3% of urban inhabitants in mainland Tanzania were connected to some form of electricity. We use a nationally representative three-wave panel dataset (2008–2013) to contribute to the literature on household energy use decisions in Tanzania in the context of the stacking and energy ladder hypotheses. We firstly adopt a panel multinomial-logit approach to model the determinants of household cooking- and lighting-fuel choices, using night time lights data to proxy for electricity access. Secondly, we focus explicitly on energy stacking behaviour, proposing various ways of measuring what is inferred when stacking behaviour is thought of in the context of the energy transition and presenting household level correlates of energy stacking behaviour. Thirdly, since fuel uses have gender-differentiated impacts, we investigate the relevance of common proxies of women’s intrahousehold bargaining power in the decision-making process of household fuel choices. We find that whilst higher household incomes are strongly associated with a transition towards the adoption of more modern fuels, especially for lighting, this transition takes place in a context of significant fuel stacking. In Tanzania, government policy has been aimed mostly at connecting households to the electricity grid. However, the public health, environmental and social benefits of access to modern energy sources are likely to be diminished in a context of significant fuel stacking. Our analysis using proxies of women’s intra-household bargaining power suggests that the level of education of the spouse is also a major factor in the transition towards the use of modern fuels. Ó 2018 Elsevier Ltd. All rights reserved.

1. Introduction Tanzanian energy-use statistics reflect the country’s low level of industrialization and development. The household energy landscape is characterised by a very high prevalence of traditional fuel use, especially charcoal and firewood. In 2016, only 32.8% of households in mainland Tanzania were connected to some form of electricity (16.9% in rural areas and 65.3 in urban areas), of which 74.9% were connected to the grid, 24.7% to solar power and 0.3% to individual electricity generated from a privately-owned source (The United Republic of Tanzania, 2017). The types of fuels used by households for everyday activities have an important bearing on various factors influencing well⇑ Corresponding author at: Economic Development Initiatives (EDI) Limited, High Wycombe, United Kingdom. E-mail addresses: [email protected] (J. Choumert-Nkolo), pascale. [email protected] (P. Combes Motel). https://doi.org/10.1016/j.worlddev.2018.11.016 0305-750X/Ó 2018 Elsevier Ltd. All rights reserved.

being. These factors range from health outcomes and exposure to safety and financial risks to aspects of individual and collective time use (Bonan, Pareglio, & Tavoni, 2017; Bos, Chaplin, & Mamun, 2018; Köhlin et al., 2015). In addition, the societal relations that dictate who is exposed to the adverse effects associated with the use of traditional, ‘dirty’ fuels, such as firewood and charcoal, intimately link fuel use to the risks faced and the work carried out by women and children. These immediate linkages between fuel use and well-being are also not divorced from the broader effects of a reliance on traditional fuels such as forest degradation and pollution which endanger environmental systems (Allen, 1985, Heltberg, Arndt, & Sekhar, 2000; Hofstad, 1997). As such, both the short-term determinants of demand and the mechanisms of a longer-term energy transition are important to understand in order to work effectively against these adverse effects. Given the importance of fuel use in development outcomes, and in recognition of the various benefits associated with the adoption of

J. Choumert-Nkolo et al. / World Development 115 (2019) 222–235

‘clean’ fuels such as electricity and gas, there has been much engagement in the literature on theories of household energy transition. A key early concept is the energy ladder hypothesis, which views the process by which households substitute traditional fuels for modern fuels as one driven mostly by increases in income and socioeconomic status (Hosier & Dowd, 1987; van der Kroon, Brouwer, & van Beukering, 2013). In recent decades, this conception has been challenged by a growing body of empirical evidence showing that households simultaneously use multiple different fuels, a characteristic described as fuel stacking or fuel mixing (Heltberg, 2004; Masera, Saatkamp, & Kammen, 2000; Ruiz-Mercado & Masera, 2015). The implication is that a clean-break with the use of traditional fuels is unlikely to be occurring in many developing regions today. The main goal of this paper is to develop an understanding of the nature of household energy use in Tanzania in the context of the energy ladder and stacking hypotheses. We contribute to the literature on the multifaceted energy transitions taking place in Sub-Saharan Africa (SSA) today, an understanding of which is vital to achieving higher well-being in the region. SSA has experienced an impressive increase in energy demand over the last decade. However, access to energy services is still limited while grid connection rates lag behind the rest of the globe (International Energy Agency, 2014). Monopolised public utilities that constitute the supply side suffer from high connection charges and weak costrecovery (Blimpo, McRae, & Steinbuks, 2018). From this perspective, the case of Tanzania provides useful insights for an understanding of the energy transition in SSA. Rapid population growth, a heavy reliance on wood and charcoal for meeting basic energy needs, coupled with the slow pace of mass electrification and a lack of infrastructure to support economic growth and human development are salient features shared by Tanzania and many other SSA economies. Mass electrification programs across high-income and emerging economies have known challenges (See Kitchens & Fishback, 2015; Lee, Miguel, & Wolfram et al., 2016). Now the challenges lie with low-income economies, like Tanzania, with one of the key questions being ‘‘Does Africa’s energy future even lie with the grid?” (Lee, Miguel et al., 2016). The Tanzanian government has become involved on many steps of the energy ladder. The country has integrated the World BankInternational Finance Corporation’s Lighting Africa initiative that intends to promote off-grid solar products in SSA. It is also striving to decrease charcoal consumption by using fiscal incentives to stimulate the use of Liquefied Petroleum Gas (LPG) (2015 Tanzania National Energy Policy). Our contribution is threefold. Firstly, we adopt a panel multinomial logit approach to model the determinants of household cooking and lighting fuel choices. This is the first-time household fuel choices are modelled in this manner in Tanzania using a nationally representative panel survey. Whilst some authors, such as Mekonnen and Köhlin (2009) and Alem, Beyene, Köhlin, and Mekonnen (2016) in Ethiopia, and Zhang and Hassen (2017) in urban China, have recently made use of panel data in approaching the modelling of household fuel choices, the majority of past empirical studies on this subject has been reliant on cross-sectional data. Secondly, we focus on fuel stacking behaviour. Many microeconomic studies typically examine one type of fuel (Rahut, Behera, Ali, & Marenya, 2017, electricity; D’Agostino, Urpelainen, & Xu, 2015, charcoal expenditures) or the adoption of solar systems (Klasen & Mbegalo, 2016). We argue that households are likely to choose a portfolio of different fuels for different uses, especially in situations characterised by variable supply. As such, the effectiveness of policy interventions in the energy sector are likely to be limited if fuel stacking is not explicitly accounted for in their design. Existing evidence from large-scale experiments (e.g. Lee, Brewer et al., 2016, in Kenya, and Chaplin et al., 2017, in Tanzania)

223

find that household demand for grid connection is low even at subsidized rates. A focus on stacking behaviour enables us to explicitely account for the simultaneous use of multiple fuels. We propose five ways of measuring fuel stacking and derive household correlates with these. Finally, given the gender-differentiated impacts of fuel use, we investigate the relationships between the level of education of the spouse as well as other proxies of the intra-household bargaining power of the spouse in fuel choices. We find that an increase in household socio-economic status, while associated with a transition towards modern fuels, is also associated with an increase of the variety of fuels purchased. Secondly, the major transition in cooking fuels taking place in Tanzania today is away from the primary use of firewood and towards the use of charcoal. In particular, our results suggest that access to electricity alone does not secure a transition to modern fuels. We also find that households are much more likely to adopt modern lighting fuels than cooking fuels as socio-economic status increases. Lastly, the level of education of the spouse as well as a composite index comprised of the level of education, age and labour market access of the spouse are positively correlated with a transition away from firewood and animal residue for cooking, towards charcoal, but also towards modern lighting fuels. 2. Overview of the data 2.1. Presentation of the Tanzanian National panel survey The compilation of the Tanzanian National Panel Survey (TZNPS) over three waves, 2008/2009, 2010/2011 and 2012/2013, resulted in the production of a nationally representative panel survey which provides information on a range of socio-economic indicators at the household, individual and community levels (NBS, 2014a).1 We chose to restrict this analysis to those households that participated in all three waves of the survey. This is grounded in the desire to observe the factors affecting household energy choices over time and the sample size is sufficiently large to allow for a balanced panel of 3088 households interviewed in each of the three rounds.2 2.2. Energy expenditure and use variables The TZNPS contains information on the main cooking and lighting fuels used by households, the type and amount spent on four different fuels (kerosene, charcoal, electricity, and gas) and information on fuel prices in the enumeration area in which the household is located. In our approach to the energy ladder model, we adopt the classical typology of cooking fuels: (i) Traditional fuels: wood fuels and animal residue; (ii) Transition fuels: charcoal and (iii) Modern fuels: LPG, kerosene and electricity. We classify lighting fuels similarly. We identify variables in the TNZPS household questionnaires which relate to the major3 cooking and lighting fuels used by households based on the typology outlined above (See Table 1). In order to create a sound basis for explicitly testing the energy stacking model, a key step involves creating a well-defined outcome variable capturing the qualitative and quantitative aspects of stacking behaviour. In order to do this, some theoretical decisions need to be taken. There are two clear aspects of 1 The dataset and sampling notes are publicly available and published by the World Bank as part of its Living Standards Measurement Study (LSMS) programme. 2 See Choumert, Combes, and Roux (2017) for more detailed information on the data used. 3 The original conception of the energy ladder model concerns the exclusive use of different fuels. Unfortunately, the TZNPS only contains information on the major fuel used for lighting and cooking.

224

J. Choumert-Nkolo et al. / World Development 115 (2019) 222–235

Table 1 Constructed variables for the energy ladder model. Name

Description

Household’s major cooking fuel

Firewood, animal residue Charcoal Electricity, gas or kerosene

= 1 if the household’s major cooking fuel is either firewood or animal residue. = 2 if main cooking fuel is charcoal. = 3 if main cooking fuel is electricity, gas or kerosene.

Household’s major lighting fuel

Candles, firewood Paraffin lamps Electricity, solar or gas

= 1 if major lighting fuel is either candles or firewood. =2 if major lighting fuel is paraffin lamps. = 3 if major lighting fuel is either electricity, solar lamps, or gas.

household fuel choices a good measure should reflect. The first relates to which types of fuels to use, while the second is an allocative decision on how to apportion a limited budget for energy expenditure between different fuels. Below, in Table 2 we present five different indicators of stacking behaviour and illustrate their respective strengths and weaknesses. Each one relies on a particular conception of stacking behaviour and, whilst linked, they measure different types of household decisions. 2.3. Control variables 2.3.1. Socio-demographic variables For both the energy ladder and stacking model approaches, we identify in the TZNPS numerous variables, shown in Table 3 (summary statistics are provided in Annex 1), which are likely to have relevance to household fuel choices. Among these are variables capturing demographic household characteristics4 and household socio-economic status measured both by real per-adult-equivalent household expenditure, and a constructed asset index.5 Thirdly, locational aspects, such as the district and time dummy variables, are also included. 2.3.2. Luminosity data as a proxy for electricity access Developing regions are characterised by a low coverage of the power grid supply and thus the assumption that households face the choice whether or not to use electricity in an indirect utility maximising context may be flawed. Prices are one way of getting around this factor, by assuming that prices would reflect relative levels of supply, but in the case of electricity where prices are centrally set, this would also be inadequate. In the absence of data on whether or not households are directly connected to the electricity grid,6 we control for electricity access using luminosity data. We make use of the DMSP-OLS Nighttime Lights data published by the U.S. National Oceanic and Atmospheric Administration (NOAA, 2008–2013). Relying on the assumption that at least some of the households in the surrounding area would have been connected to electricity grid (which would be reflected in the luminosity data), we use mean rates of luminosity at the ward7 level as a proxy for electricity access. There are several convincing arguments in favour of using this measure as a proxy for electricity consumption (See Doll & 4 The data sets do not contain information on ethnicity. For decades, political discourse in Tanzania has purposively omitted references to ethnic groups (Miguel, 2004) and national household surveys do not ask questions related to ethnicity. 5 This was constructed using household asset counts and living conditions, using principal component analysis. Steps taken in the derivation of the asset index are available from the authors upon request. 6 In the questionnaire, there is no specific question asking respondents whether they are connected to the grid network or not. 7 A ward is an administrative unit of the Tanzanian local governance structure that contains sub-divisions of urban and rural areas. Rural wards encompass several villages. In deriving the luminosity measure, the luminosity score of a household is given by the average level of luminosity emanating from the area covered by the ward in which it is located. Ward shape files come from the Tanzanian National Bureau of Statistics, available here: https://goo.gl/nnoiqo. The resolution of the nighttime lights data is 30 arc seconds, which equates to roughly 1 km squared (Henderson, Storeygard, & Weil, 2012).

Pachauri, 2010; Dugoua, Kennedy, & Urpelainen, 2018 for a review). Indeed, the light captured by the satellites is mainly the result of electricity-powered illumination. Several studies corroborate the high correlation between nighttime lights data and household electrification data, with the caveat that nighttime data tend to overestimate the population without access to electricity, as the satellite sensors may not capture low density energy usage or indoor lighting. Even so, in the absence of publicly available information on the electricity grid in Tanzania, nighttime lights data appears to be a good and relevant proxy for electricity access.

2.3.3. Measuring women’s intra-household bargaining power Since fuel use has gender-differentiated impacts, we also investigate women’s bargaining power in the decision-making process of household fuel choices. As stressed by Pachauri and Rao (2013) most studies on fuel choices fail to address the gender dimension, although there is evidence of a relationship between intra-household dynamics and the choice of cooking technologies and fuel choices (Miller & Mobarak, 2013). In Tanzanian families, the husband and the wife typically form the central bond of the household with the former having primary authority, the latter usually being the main caregiver and homemaker. This is why scholars who have investigated the impact of intra-household dynamics on decision-making in the Tanzanian context have focused on wives and husbands (e.g. Anderson, Reynolds, & Gugerty, 2017). It is common for women to carry out most of the house-related work (Zambelli, Roelen, Hossain, Chopra, & Twebaze, 2017). The TZNPS data reflects this. On average, households report spending 40 min in total collecting firewood in the previous day. However, women report spending four times longer than men collecting firewood. We can therefore assume that men and women have different preferences regarding energy choices. For these reasons, we include various proxies for the intra-household decision making power for a sub-sample to the 1901 households with a male head and female spouse, present in all waves of the survey. The literature proposes three main ways to proxy women’s bargaining power, recognizing that it is context-specific and multidimensional. These are field experiments, survey data on who makes decisions within the household, and socio-demographic characteristics (see Doss, 2013; Glennerster, Walsh, & DiazMartin, 2018; Jensen & Thornton, 2003; Lépine & Strobl, 2013; Schaner, 2017). As the TZNPS data does not provide information on who makes consumption decision in the household, we rely on socio-demographic characteristics, education, age and whether the spouse has a waged job. These characteristics are often considered as proxies for bargaining power; for example, Schaner (2017) argues ‘‘that demographic and economic characteristics that improve an individual’s utility outside the marriage (or in a noncooperative equilibrium within the marriage) translate into greater household bargaining power” and ‘‘that having higher income, having more years of education, being more literate, and being older than a spouse correlate with greater relative bargaining power”. This is further corroborated in the context of Tanzania (see Van Aelst, 2014; Anderson et al., 2017).

J. Choumert-Nkolo et al. / World Development 115 (2019) 222–235

225

Table 2 Various approaches to measuring household fuel stacking. Name

Specification

Description

Explanation

1. Simple stacking

W it ¼ ð0; 1; 2; 3; 4Þ

Where Wit refers to the number of different fuels purchased by household i in wave t, among electricity, charcoal, gas or kerosene1

2. Directional stacking

Dit ¼ EGit =W it

3. Share stacking

SpendingonElec&Gasit Sit ¼ TotalEnergySpending

Wit as above. EGit ¼ Eit þ Git , whereEit andGit are dummy variables each taking a value of 1 if electricity and gas respectively are purchased. Sit is the share of electricity and gas expenditure in total energy expenditure by household i in wave t. Where ek is the share of total expenditure allocated to expenditure on a particular fuel k. Iit is the complement of the HerfindahlHirschman index of concentration. Where Iit and Sit are as defined above.

Only taking into account the number of fuel types purchased. Shortcomings: it does not consider the direction of stacking, i.e. a household which buys charcoal and firewood, and one which buys electricity and gas, would have the same stacking score by this measure. The ratio of electricity and gas in the number of fuels purchased. Considers the nature of fuel stacking by favouring stacking ‘‘up the ladder” in terms of purchases of clean fuels.

it

P4

4. Gini-Simpson diversity index2

Iit ¼

5. Stacking up the ladder index

Lit ¼ Iit ðSit þ 1Þ

k¼1 ek ð1

 ek Þ ¼ 1 

P4

2 k¼1 ek

Households which dedicate more of their energy budget to modern fuels score higher on this measure. Households that have a more diversified energy expenditure portfolio (i.e. stack more) score higher on this index. Shortcomings: This measure does not provide an indication of the direction of fuel stacking. This measure combines the undifferentiated level of fuel stacking with the share of energy expenditure going towards modern fuels. Strengths: a) By adding 1 to Sit we avoid multiplying by zero in cases where Sit ¼ 0; thus preserving information captured by the Gini-Simpson index. b) Improved ranking: In line with stacking model of the energy transition, if two households have the same stacking index score Iit – then the one with a higher Sit score would place higher on this index.

1 Households are asked whether they purchased electricity, charcoal, gas or kerosene in the preceding 30 days. If the answer to this question was yes, they were asked how much they spent. Sampling rounds were structured such that the households were interviewed at the same time-period of the year in each wave. 2 Adapted from Gini (1912) and Simpson (1949).

Using the information available in the TZNPS, we propose two proxies for women’s bargaining power presented in Table 3 (and their summary statistics in Annex 2). The variable Women_b combines age, education and wage information using principal component analysis, while the variable Spouse_education is the education level of the wife. 2.4. Stylized facts on household energy use in Tanzania High levels of traditional fuel dependency, especially for cooking (Fig. 1a), and a particularly low level of reported reliance on electricity characterise the fuel-use profile of most households. While better-off households are more likely to use modern fuels, over 70% of households in the top 10% of the expenditure distribution cook mainly with charcoal. Lighting fuels (Fig. 1b) show a much more rapid transition towards modern fuels as one moves up the expenditure distribution. Expenditure data presented in Fig. 2 clearly illustrates the notion of fuel mixing or stacking. Cumulatively Figs. 1 and 2 show that the poorest households rely nearly exclusively on firewood and animal residue as main cooking fuels and their energy expenditure goes mostly towards kerosene, which is used as the main source of lighting. At the median of the expenditure distribution, 80% of households still cook mainly with firewood and animal residue. Only 20% rely on modern lighting fuels. The best-off households (close to the top 5% of the expenditure distribution) cook mainly with charcoal and use mostly modern lighting sources but spend money on and likely use a range of different fuels. 3. Empirical strategy and econometric model 3.1. A multinomial logit model of household fuel choices to test the energy ladder hypothesis We categorize both cooking and lighting fuels, respectively, into three groups according to their position in the energy spectrum,

from traditional to modern fuels. In the case of cooking fuels, these categories are 1 = firewood and animal residue, 2 = charcoal and 3 = electricity, gas or kerosene. In the case of lighting fuels, the categories are 1 = candles and firewood, 2 = paraffin lamps and 3 = electricity, solar or gas. In addition, each household in the panel is observed in three time periods (T = 3). Thus, the tth observation of household i can take on a value of j = (1, 2, 3) where j refers to the category of the fuel choice. In general, specification taking into account unobserved household-specific effects, household i’s indirect utility associated with the choice of fuel j in time t is given by:

Vitj ¼ xit bj þ ui þ
it t ¼ 1; 2; 3; j ¼ 1; 2; 3

ð1Þ

where xit is a vector of observed explanatory variables particular to the household, and bj is a vector of parameters specific to fuel choice j. In addition, ui is the unobserved household-specific effect and
it is an independent and identically distributed error term. In a pooled multinomial logit model, the ui householdspecific effect is not controlled for explicitly, whilst both fixed and random effects models rely on the assumption that the explanatory variables are exogenous conditional on the household-specific term, i.e. Eðuit jxit ;
it Þ ¼ 0. A key additional assumption specific to random effects models is that Eðui jxit Þ ¼ Eðui Þ ¼ 0 i.e. that the unobserved household effect ui is independent of the explanatory variables. Under this condition and in the case where there are no omitted variables, random effects coefficient estimates are deemed to be consistent, as well as more efficient than fixed effects estimates. A fixed effects estimation relaxes this additional assumption, allowing for the derivation of consistent estimates in a case where there exist time-invariant unobserved factors related to the control variables. In a multinomial logit model with unobserved household heterogeneity, the conditional probability that household i chooses fuel type j in time t is thus given by:

226

J. Choumert-Nkolo et al. / World Development 115 (2019) 222–235

Table 3 Description of control variables.

Household characteristics1

Education level of the household head

Variable

Description

Less than primary (base category) Primary Junior secondary Secondary Tertiary

= 1 if household head’s highest level of education is below primary school, including no schooling. = 1 if household head completed primary but not junior secondary school. = 1 if household head completed junior secondary but not secondary school. = 1 if household head completed secondary school but not a tertiary qualification. = 1 if household head completed a tertiary qualification. Number of persons who normally live and eat their meals together in the household. Age of household head. Ratio of female residents to male residents. Ratio of number of members under 15 and over 65 years of age to number of members between 15 and 64 years of age. = 1 if household head is a woman. Variable constructed using a principal component analysis including the age of the spouse (Spouse_age), the number of schooling years (Spouse_education), and a binary variable for whether she earns a wage or other payment from a job (Spouse_wage). Years of completed schooling of the spouse. Real yearly household expenditure divided number by of adult equivalent members.2 Real yearly household expenditure is already constructed in the data and comprised of the sum of food and non-food expenditures for the household. Spatial price heterogeneity is accounted for by using Fischer food price indices. To account for inflation, we weight this by a yearly consumer price index3 based on 2010 prices. Score for asset-based wealth index. = 1 if household owns at least one electric and/or gas stove. = 1 if household owns another type of stove. Average level of luminosity (measured as a percentage) for the ward in which the household is located (Source: National Oceanic and Atmospheric Administration, 2008–2013) = 1 if household is classified by the TZNPS as being situated in a rural area. Sample is divided into 7 mainland administrative districts and Zanzibar. Based on CPI-adjusted mean reported per-litre price in the EA the household is situated in. Based on CPI-adjusted mean reported per-kilogramme price in the EA the household is situated in. Based on CPI-adjusted national energy tariffs for domestic consumption of between 50 and 283kWh/month. Based on CPI adjusted mean reported per-kilogramme price in the EA the household is situated in.

HH size Head age Ratio of women to men Dependency ratio

Proxies for women’s bargaining power

Socio-economic status indicators

Locational characteristics

Fuel prices5

Female-headed household Women_b

Spouse_education Log real per-adult-equivalent expenditure

Wealth index score Elec/Gas stove ownership Other stove ownership4 Luminosity Rural household Administrative District Dummy variables Log Kerosene price Log Firewood price Log electricity price6 Log Charcoal price

1 ‘‘The word ‘household’ refers to people who live together and share income and basic needs. In other words, residents of a household ‘‘share the same centre of production and consume from that centre.” (TZNPS, Household Questionnaire) 2 Information on adult equivalent weights used can be found in the Survey Basic Information Document (NBS, 2014b). 3 The World Bank World Development Indicators CPI is used (World Bank, 2017). 4 In the questionnaire, there is no distinction between regular stoves and improved stoves. 5 The community level questionnaire in the TZNPS contains price data on kerosene, charcoal, and firewood. From this data, the average per litre prices for kerosene and per kilogramme prices for firewood and charcoal are derived at the Enumeration Area level. 6 We use domestic-use national electricity tariffs which vary over the time of the panel but do not vary spatially. Within this domestic category, there is a lifeline price which applies to usage lower than 50 kWh per month, as well as a general tariff which applies to domestic use between 50 and 283 kWh per month. Given that a two-plate electric stove used for 3 h a day equates to around 135 kWh per month, we use the 50-283kWh tariff as an indicator of electricity prices faced by households. This tariff was subject to three adjustments throughout the panel: from 2008 until 2011 the price was 156 TZS/kWh, from 2011 to 2012 it was 195 TZH/kWh and from 2012 to 2013 it was 274 TZH/kWh.

Figure 1. Main cooking and lighting fuels across the expenditure distribution, Source: Authors’ calculations using real per-adult-equivalent expenditure across waves 1–3 of the TZNPS.

J. Choumert-Nkolo et al. / World Development 115 (2019) 222–235

Figure 2. Household energy expenditure shares, Source: Authors’ calculations using real per-adult-equivalent expenditure across waves 1–3 of the TZNPS.

8 > <

Pexpðxit bj þuij Þ ; j–B exp ðxit bk þuij Þ k–B Pr f it ¼ t j jxit ; ui ¼ > P 1 : ; j¼B 1þ exp ðxit bk þuij Þ k–B 



1þ

ð2Þ

where B refers to the base fuel type outcome category. The probability is conditional on the set of household level effects and on the observable household characteristics and is estimated by way of a maximum likelihood estimator. 3.2. Modelling of stacking behaviour In our analysis of stacking behaviour, we also control for unobserved household heterogeneity using the panel nature of the data. A Hausman test suggests the use of household fixed effects for the estimation and we regress the various measures for fuel stacking on household level covariates. 4. Results 4.1. Multinomial logit regression results Both household cooking and lighting decisions are modelled separately, using a multinomial logit approach. Below in Table 4, average marginal effects of the random effects regressions are displayed. Full regression outputs for both random and fixed effects estimates are presented in Annex 3. The last two rows present the marginal effects of the intra-household bargaining variable for the sub-sample of 1901 households with a wife and husband – with controls included. The variables Spouse education and Women_b are introduced in separate regressions (full regressions are available in Annex 4). Both random and fixed effects regression specifications show that per-adult-equivalent expenditure is significantly correlated with household fuel choices. Households with higher levels of per-adult-equivalent expenditures on average are less likely to use biomass fuels as their main cooking fuel, but more likely to use charcoal than electricity. These results are robust to the use of an alternative indicator of socio-economic status in the form of a wealth index.8 While households in areas where luminosity scores are higher are less likely to use firewood and animal residue, the relationship between luminosity scores and major cooking fuel choices is very weak and the marginal effects coefficient close to 8

Available from the authors on request.

227

zero. This alludes to the likelihood that ceteris paribus, access to electricity does not necessarily lead to the exclusive adoption of modern fuels and could be explained by the positive externalities of having neighbours or businesses with access to electricity or living in a town with street lights. Following van de Walle, Ravallion, Mendiratta, and Koolwal (2017), there is a distinction between the internal effects of a household’s electrification and the external effects of village electrification which can lead to different types of spillovers and hence behaviour in terms of energy choices. Rural households are substantially less likely to use clean fuels as their major cooking fuel than urban households. We see that rural households are 17% more likely to use firewood/animal residue, 16% less likely to use charcoal and 1% less likely than urban households to use clean fuels as their main cooking fuel. We also see that households which have access to electric or gas stoves are significantly more likely than those which do not to use clean fuels and less likely to use biofuels.9 Female-headed households are more likely than non-female-headed households to rely on charcoal and less likely to rely on biofuels as a main cooking fuel source. The level of education of the household head is positively associated with a transition towards the use of modern fuels. In particular, we see that households in which the head has obtained tertiary education are 7% more likely to use clean fuels as their main cooking fuels than households without primary education (the base case). When looking at the price effects, it is surprising to see that only the electricity price is significant (at the 5% level). The marginal effects illustrate that a 10% increase in the electricity price is associated with a 0.3% decrease in the likelihood of using modern fuels. The marginal effects estimates for the categorical lighting fuel variable presented in the last three columns of Table 4 also show that per-adult-equivalent expenditure is positively and significantly correlated with the use of modern fuels. In the case of lighting fuels, the effect of increases in per-adult-equivalent expenditure is largest for modern fuels. All else equal, rural households are also less likely than urban households to use more modern lighting sources. In addition, as expected, households in areas with higher rates of luminosity have a higher probability of mainly using modern lighting fuels. Female-headed households are less likely to use modern fuels than households headed by men; however, households with a higher ratio of women to men are more likely to use mainly modern lighting fuels. In addition, larger households are more likely to use modern fuels, perhaps alluding to the relative cost of modern fuels, which can be more easily incurred by larger households. A 10% increase in the kerosene price results in a 0.9% decrease in the likelihood of using lamp oil as a fuel source for lighting and an almost equivalent 0.9% increase in the likelihood of using modern fuels, suggesting that households easily switch between paraffin lamps and modern lights, such as solar or electric lamps. The results also suggest that the intra-household bargaining power of the spouse has implications for household fuel choices. In particular, households where the spouse has a higher level of education and scores higher on the bargaining power index are more likely to cook with charcoal, instead of with firewood and animal residue. This is likely due not only to the utility associated with the use of charcoal in the kitchen itself, but also 9 In their systematic review on who adopts improved fuels and cookstoves, Lewis and Pattanayak (2012) find that cookstoves programmes have low rates of adoption in developing economies, and this despite the triple dividends (household health, local environmental quality, and regional climate benefits) that could result from the use of improved cook stoves and clean fuels. Unfortunately, in our dataset, we cannot make a precise distinction between different types of cook stoves. Most household surveys do not provide such detailed information. This would deserve further attention in the context of Tanzania.

228

J. Choumert-Nkolo et al. / World Development 115 (2019) 222–235

Table 4 Average marginal effects results: Lighting and cooking fuels. Cooking Firewood/Animal Res. 0.083*** (0.007) Luminosity 0.003*** (0.000) Rural Household 0.167*** (0.014) Primary 0.048*** (0.009) Junior Secondary 0.131*** (0.023) Secondary 0.141*** (0.040) Tertiary 0.157* (0.081) Female Headed HH 0.024** (0.011) HH Size 0.002* (0.001) Ratio of Women to Men 0.001 (0.004) HH Head age 0.001*** (0.000) Elec/Gas stove 0.046*** (0.018) Other Stove 0.092*** (0.009) Log Kerosene Price 0.016 (0.022) Log Firewood Price 0.004 (0.007) Log Charcoal Price 0.005 (0.009) Log Electricity Price 0.020 (0.038) District fixed effects Yes Wave fixed effects Yes N 54,641 Intra-household bargaining variables Spouse education 0.005*** (0.001) Women_b 0.014*** (0.005) N for intra-household 3592 bargaining Log per. ad. eq. expenditure

Lighting Charcoal

Electricity/Kerosene/ Gas

Candles/ Firewood

Lamp Oil (Kerosene/ Paraffin)

Electricity/Gas/ Solar

0.077*** (0.007) 0.002*** (0.000) 0.161*** (0.015) 0.048*** (0.009) 0.123*** (0.023) 0.132*** (0.040) 0.087 (0.075) 0.032*** (0.011) 0.000 (0.001) 0.003 (0.004) 0.001** (0.000) 0.012 (0.018) 0.099*** (0.010) 0.001 (0.023) 0.005 (0.007) 0.006 (0.010) 0.053 (0.040) Yes Yes

0.006 (0.004) 0.000*** (0.000) 0.006 (0.005) 0.000 (0.005) 0.008 (0.007) 0.009 (0.012) 0.069* (0.038) 0.008** (0.004) 0.002*** (0.001) 0.005** (0.002) 0.000 (0.000) 0.034*** (0.010) 0.007 (0.005) 0.017* (0.009) 0.001 (0.003) 0.002 (0.003) 0.033** (0.016) Yes Yes

0.006** (0.003) 0.000** (0.000) 0.000 (0.002) 0.003 (0.002) 0.001 (0.003) 0.004** (0.002) 0.004** (0.002) 0.002 (0.001) 0.001** (0.000) 0.000 (0.001) 0.000 (0.000)

0.081*** (0.010) 0.003*** (0.000) 0.115*** (0.013) 0.080*** (0.011) 0.312*** (0.036) 0.454*** (0.077) 0.531*** (0.118) 0.028** (0.011) 0.003** (0.001) 0.011*** (0.004) 0.002*** (0.000)

0.087*** (0.010) 0.003*** (0.000) 0.115*** (0.013) 0.082*** (0.011) 0.313*** (0.040) 0.458*** (0.077) 0.534*** (0.118) 0.026** (0.011) 0.004*** (0.001) 0.011*** (0.004) 0.002*** (0.000)

0.001 (0.004) 0.003* (0.002)

0.090*** (0.023) 0.007 (0.007)

0.089*** (0.023) 0.009 (0.007)

0.018* 0.072 Yes Yes 4936

0.020 (0.043) Yes Yes

0.002 (0.042) Yes Yes

0.005*** (0.002) 0.014*** (0.005)

0.000 (0.001) 0.000 (0.002)

0.001** (0.000) 0.001 (0.001) 4706

0.010*** (0.002) 0.016*** (0.006)

0.010*** (0.002) 0.016*** (0.006)

* p < 0.10, ** p < 0.05, *** p < 0.010. These marginal effects estimates are calculated from random effects multinomial logit results presented fully in Annex 3. Huber-White cluster-robust sandwich standard errors in parentheses. 1 The sample size varies as a result of differing response rates for cooking and lighting fuel variables. In addition, the sample size is lower than 3088 in each wave, due to the low level of time-based variation in fuel choices (see Annex 1 and 2).

to the shadow price of firewood, which is the opportunity cost of the time spent collecting it. Descriptive results show that the task of firewood collection is mostly carried out by women. In addition, we find that households where the spouse has a higher level of education and scores higher on the bargaining power index are more likely to adopt electricity/gas/solar energy for lighting fuels. Our results suggest that when women are empowered within the household, the household is more likely to use modern fuels. This will impact not only their own well-being but also that of their children and other family members, in accordance with the literature emphasizing that the empowerment of women favours household investment in human capital and family public goods.

4.2. Household fuel stacking results Table 5 displays the results of panel regressions of the correlates of fuel stacking using the Gini-Simpson diversity index and the Stacking up the ladder index presented in Table 2 as the dependent

variable.10 We present results from a fixed-effects estimation, preferred by a Hausman test. The last two rows present the marginal effects coefficients of the intra-household bargaining variables for the sub-sample of households with a wife and husband.11 Using both measures of fuel stacking, we find significant correlations of fuel stacking behaviour and increases in per-adultequivalent expenditure, in line with the stacking hypothesis. Similar positive and significant effects for the luminosity coefficients suggest that household access to electricity is not necessarily a determinant of a full transition to the exclusive use of modern fuels. Indeed, the descriptive results presented in Fig. 2 shows that as incomes rise, expenditure shares on both charcoal and electricity rise simultaneously. The results also show significant urban–rural disparities in levels of fuel stacking, with rural households scoring significantly lower on both Stacking Indexes than urban

10 These two are the most comprehensive proxies of energy stacking. Results using other indices are available from the authors upon request. 11 Full regressions are available from the authors on request.

J. Choumert-Nkolo et al. / World Development 115 (2019) 222–235 Table 5 Fuel stacking panel regressions: Stacking Index as dependent variable.

Log per.ad.eq.expenditure Luminosity Rural Households Female Head HH Household size Ratio of women to men Log Kerosene Price Log Electricity Price Log Firewood Price Log Charcoal Price Constant Corr(ui, Xb) Overall R-Squared N Intra-household bargaining Spouse Education Women_b N *

p < 0.10, ** p < 0.05, parentheses.

***

(1) Gini-Simpson diversity index

(2) Stacking up the ladder index

0.0246*** (0.01) 0.00256*** (0.00) 0.0329*** (0.01) 0.0263 (0.02) 0.00290 (0.00) 0.00416 (0.00) 0.0124 (0.02) 0.0263 (0.02) 0.00388 (0.01) 0.000939 (0.01) 0.00670 (0.08)

0.0278*** (0.01) 0.00353*** (0.00) 0.0394** (0.02) 0.0316 (0.02) 0.00167 (0.00) 0.00631 (0.01) 0.00981 (0.02) 0.0257 (0.03) 0.00152 (0.01) 0.00135 (0.01) 0.00208 (0.10)

0.411 0.35 4936

0.414 0.35 4936

0.00134 (0.00) 0.00398 (0.01) 3208

0.00277 (0.00) 0.00559 (0.01) 3208

p < 0.010. White-Huber robust standard errors in

households, ceteris paribus. We do not find any significant correlation between our two proxies for women’s bargaining power and the fuel stacking measure we have derived. 5. Discussion and policy implications In this section we discuss the relevance our results to two key policy areas. Firstly, we discuss the role of prices in fuel choices. Secondly, we present the main thesis of our paper, and a discussion of Tanzanian household fuel choices in the context of the energy ladder and stacking models. 5.1. Do prices matter? Existing evidence of the price effects on fuel choices is deemed as not well understood (van der Kroon et al., 2013) or mixed (Muller & Yan, 2018). Data limitations also often restrict the analyses of fuel prices in the fuel choice literature. That said, several insights can be drawn from our analysis. Our results show that the price responsiveness of households is greater for lighting fuels than cooking fuels: kerosene prices are significantly correlated with the probability of adopting modern lighting fuels and a decline in the use of paraffin lamps. This finding is consistent with results from Uganda and Kenya (Blimpo et al., 2018; Lay, Ondraczek, & Stoever, 2013) as well as from a study on the effect of increasing oil prices on energy choices in Nigeria (Maconachie, Tankob, & Zakariya, 2009). The nonresponsiveness of cooking fuel choices to prices suggests that fuel levies on traditional fuels, encouraging the uptake of modern cooking fuels may be regressive. Policy makers may find it easier to

229

encourage a household transition towards the use of modern lighting fuels than cooking fuels. In the case of charcoal and firewood/animal residue use for cooking, the own-price elasticity of demand is relatively low. Our regression estimates show no significant own-price (and cross-price) effects on the choice of charcoal and firewood as the major cooking fuel. This result partly supports the idea that while fuel prices theoretically promote fuel shifts, this is unlikely when there are no alternatives, as suggested by van der Kroon, Brouwer, and van Beukering (2014) in the case of Kenya. Alem et al. (2016) in a sample of mostly urban households in Ethiopia find similar results to ours.12 Can central and local authorities rely on prices to promote the energy transition to modern fuels? The price of kerosene could trigger significant changes in households’ fuel choices, owing to its strong (in absolute terms) marginal impacts. Based on our results, an increase in kerosene price could favour the adoption of modern lighting devices while having no effect on stacking. This outcome adds to the existing literature on the mixed benefits of removing fuel subsidies in developing countries. Their social (Arze del Granado, Coady, & Gillingham, 2012) and environmental (Pitt, 1985) benefits are seriously questioned. The unresponsiveness of households to charcoal prices casts doubts on the efficiency of a tax on the widespread use of charcoal. Hence, policy reforms targeting the entire charcoal supply chain should likely be undertaken within the bounds of the politically and practically feasible (Sander, Gros, & Peter, 2013). 5.2. Stacking up the ladder Our results suggest that energy use decisions of Tanzanian households conform to the energy stacking hypothesis: An increase in per-capita equivalent expenditure is associated with an increase in fuel stacking, measured as the diversity of fuels purchased. In addition, Tanzanian households stack ‘‘up the ladder”. As household per capita equivalent expenditure rises, the major use of more modern cooking and lighting fuels increases. In the case of cooking fuels this transition is only a partial one, mostly characterised by a move away from the use of firewood and animal residue and towards charcoal, rather than modern fuels. Our results suggest that even relatively well-off Tanzanian households with access to electricity may still prefer to use charcoal as the major cooking fuel over modern fuels. Evidence of this partial transition is also reported in the literature (Peng, Zerriffi, & Pan, 2010). A much larger proportion of households use mainly modern lighting fuels and the propensity of households to transition to modern lighting fuels as incomes increase is much higher than that for cooking fuels. This may be due to a range of factors. Firstly, the cost of infrastructure associated with changing cooking fuels is likely to be high. Stoves and gas bottles are expensive in relation to solar lamps or fittings for electric lighting. This also speaks to the large expansion of low cost, micro-solar technology mainly used for lighting in Tanzania (Ondraczek, 2013). Secondly, preferences which may inhibit households from changing to modern cooking fuels are likely not to be present in the case of lighting fuels. Thirdly, the differentiated pattern of fuel transition between cooking and lighting fuels could be due to decisions about lighting fuels influencing a more diversified set of activities compared to cooking fuels. In addition, access to electricity as measured by the night-lights data is positively correlated with the fuel stacking measures we propose. Households in rural areas, where rates of electricity access are lower, also score lower on the fuel stacking index.

12 The Mekonnen and Köhlin’s (2009) study on urban Ethiopia find significant price effects.

230

J. Choumert-Nkolo et al. / World Development 115 (2019) 222–235

Descriptive data shown in Fig. 2 also illustrates how households can simultaneously increase their electricity and charcoal expenditures. These results are consistent with the idea that electricity is not a perfect substitute for traditional fuels such as charcoal and moreover that electricity access does not in itself guarantee a transition to modern fuels (See Chaplin et al., 2017; Lee, Brewer et al., 2016). This is likely to be especially true in situations where electricity supply is unreliable. A key question raised by the evidence of fuel stacking behaviour concerns whether this is desirable from a policy perspective and if not, how to disincentivise it. At the household level, there are clear reasons why households may choose to engage in fuel stacking. In addition to broad determinants such as irregularity of income flows, drivers of stacking behaviour include more specifically mitigating against the risks associated with dependence on only one fuel in a context where supply and prices may be variable (van der Kroon et al., 2013; Louw, Conradie, Howells, & Dekenah, 2008). The influence of cultural aspects has also emerged in the literature. For example, in a study done in Zanzibar, men emphasized the superior taste of food cooked on firewood (Winther, 2007). The broader consequences of fuel stacking may however outweigh individual benefits. Firstly, energy savings are likely to be much less than if modern fuels were fully adopted. In one study, for example, it was found that mixed fuel users consume more energy than households that rely only on firewood (Masera et al., 2000). Secondly, reductions of air pollution, within households, are likely to be modest in a fuel-mixing context (Ruiz-Mercado & Masera, 2015). Lastly, fuel stacking can also deliver detrimental environmental outcomes owing to the deforestation pressure. Evidence of stacking raises important, yet mostly unaddressed, policy questions, in regard to the potential consequences of energy stacking as described above. Firstly, should we give more weight to health arguments to implement energy policies? Acute respiratory infections are a major cause of death in children under five and are more likely to occur for those suffering from existing respiratory conditions due to indoor air pollution from the use of biomass fuels (Bruce, Perez-Padilla, & Albalak, 2000; Collings, Sithole, & Martin, 1990). In terms of health impacts, the most detrimental fuel is firewood, followed by charcoal, and then LPG. However, the health impact of these fuels depends on the use of appropriate equipment (Sepp, 2014). Households are aware of health problems associated with the use of ‘dirty’ fuels (e.g. van der Kroon et al., 2014). Moreover, despite significant progress in research on household air pollution (Jeuland, Pattanayak, & Bluffstone, 2015; Lewis et al., 2017), Rosenthal et al. (2017) rightly argue that ‘‘the challenge is to accelerate the widespread, sustained adoption of demonstrably clean cooking to promote public health”. Secondly, is lack of access to the electricity grid an impediment towards energy transition? Our results support the idea that a government energy policy aimed mostly at the rollout of electrical infrastructure would come up short in its attempt to address the various policy goals with which this rollout is associated. Thirdly, how should the use of cleaner technologies be promoted, especially by the poorer segments of the population? Relatedly, how should the fact that households cook on multiple stoves be managed, attenuating the benefits of using clean cookstoves? In this regard, policies which aid households in funding or accessing credit to purchase electric and gas stoves might be useful in encouraging the use of clean fuels for cooking (Edwards & Langpap, 2005).

6. Concluding remarks In Tanzania, a rapidly growing urban population and rising living standards is expected to lead to a significant increase in the

demand for energy over the next decade. The demand for charcoal is expected to double by 2030 without external intervention (The United Republic of Tanzania, 2017). Failure to address the effects of rapid population growth, urbanization and economic progress on energy demand will have massive implications in terms of human development, in Tanzania and SSA in general. For these reasons, an accurate understanding the underlying drivers and patterns of the energy transition is crucial. In this study, we provide an in-depth analysis of Tanzanian households cooking and lighting fuel choices. Relying on purposely designed indices, we have shown that as household incomes rise they use a wider variety of fuels, in line with the fuel stacking hypothesis. There are likely to be various reasons for this, including costs, variable electricity supply and individual preferences. We have also illustrated that household lighting and cooking fuel choices do not necessarily track one-another and should be seen as different decisions. In Tanzania today, the major transition taking place is away from the use of firewood and animal residue, towards the use of charcoal as a major cooking fuel. Modern lighting fuels are being adopted at a much faster pace. Our results also show that higher incomes and access to electricity are insufficient conditions for a transition towards modern fuels. Finally, women are often seen as ‘‘agents of change” (Pachauri & Rao, 2013). In this paper we have taken a preliminary step towards analysing the implications of the intra-household bargaining power of the spouse in fuel choice decisions. We find evidence to suggest that indeed the bargaining power of women is an important factor in household fuel choices and facilitating women’s access to education and the labour market are likely to have spillovers in the energy landscape as well. Our results point to the need for further research. Firstly, the supply side aspects of fuel choices deserve further attention. The electricity sector has been the subject of several studies, but the charcoal sector should be better understood given that it is not only a major source of energy, but also a major source of livelihood in Tanzania. As such there are likely to be important political economy aspects of charcoal use in Tanzania and beyond. Secondly, the reasons for and implications of fuel stacking need to be better understood. This would help to design policies that make the fuel choices closer to a social optimum. Lastly, further research into the gendered aspects of household fuel choices is necessary in order to understand the implications of fuel use on women’s health and well-being, and the decision to invest or not in improved cookstoves.

Conflicts of interest We declare no conflict of interest. Acknowledgements We are grateful to the ANR (project REVE, ANR-14-CE05-0008) and the French Ministry of Research for their financial support. We thank the Editor and two anonymous referees for their valuable suggestions. We’d also like to thank Olivier Santoni, who compiled the nighttime lights dataset, and Théophile Azomahou, Olivier Beaumais, Marc Jeuland, Koen Leuveld, Aude Pommeret, François Salanié, an anonymous referee of the French Association of Environmental and Resource Economists (FAERE), participants of the 2017 FAERE Conference, participants of the 2018 Association Française de Science Économique (AFSE) 67th International Congress, participants of the 2018 Sustainable Energy Transitions Initiative (SETI) third annual meeting, participants of the 2018 Workshop on ‘‘Sustainably Reducing Energy Poverty” for their valuable advice. The usual disclaimers apply.

231

J. Choumert-Nkolo et al. / World Development 115 (2019) 222–235

Appendix Annex 1 Yearly summary statistics of explanatory and outcome variables. 2008/2009

2010/2011

2012/2013

Variable

Mean

SD

Min

Max

N

Mean

SD

Min

Max

N

Mean

SD

Min

Max

N

Explanatory Variables Household Characteristics Less than primary Primary School Junior Secondary Secondary Tertiary HH Size Head Age Ratio of women to men Dependency ratio Female Headed Household

0.42 0.48 0.08 0.02 0.01 5.08 46.15 1.27 104.80 0.24

0.49 0.50 0.27 0.13 0.10 2.84 15.41 1.02 93.93 0.43

0 0 0 0 0 1 18 0 0 0

1 1 1 1 1 46 102 8 800 1

3018 3018 3018 3018 3018 3088 3088 2872 2981 3088

0.42 0.48 0.08 0.02 0.01 5.47 47.91 1.29 102.33 0.24

0.49 0.50 0.26 0.12 0.09 3.06 15.20 1.11 88.00 0.43

0 0 0 0 0 1 16 0 0 0

1 1 1 1 1 55 105 8 700 1

3045 3045 3045 3045 3045 3081 3088 2907 2982 3088

0.41 0.48 0.08 0.02 0.01 5.45 49.53 1.27 99.16 0.25

0.49 0.50 0.28 0.13 0.11 3.07 15.09 1.10 85.23 0.43

0 0 0 0 0 1 18 0 0 0

1 1 1 1 1 54 107 8 700 1

3047 3047 3047 3047 3047 3078 3088 2886 2966 3088

13.15 0.12 0.07 0.5

0.71 1.94 0.25 0.5

10.57 2.61 0 0

16.36 15.42 1 1

3087 3056 3088 3088

13.45 0.01 0.07 0.50

0.69 1.97 0.25 0.50

11.24 2.82 0 0

16.03 13.78 1 1

3080 3036 3086 3086

13.94 0.11 0.06 0.40

0.71 2.00 0.24 0.49

10.95 2.82 0 0

16.72 12.78 1 1

3078 3052 3088 3088

7.08 4.17 4.96 5.10

0.18 0.46 0.05 0.43

5.18 3.23 4.88 3.73

7.54 6.44 4.99 9.52

2739 2662 3087 2844

7.37 5.10 5.30 6.04

0.18 0.61 0.15 0.46

6.46 4.02 5.05 4.23

8.00 8.40 5.39 7.82

2557 1607 3088 2281

8.05 5.69 5.93 6.27

0.14 0.35 0.04 0.39

7.46 4.26 5.87 4.95

8.51 6.36 5.95 8.33

2562 2220 3088 2558

0.65 7.45

0.48 16.95

0 0

1 63

3088 2647

0.69 10.84

0.46 10.84

0 0

1 63

3088 3071

0.66 9.74

0.47 19.31

0 0

1 63

3088 3059

Administrative zone dummy variables Lake 0.10 Western 0.11 Northern 0.13 Central 0.05 Southern Highlands 0.11 Eastern 0.21 Southern 0.15 Zanzibar 0.15

0.29 0.31 0.33 0.21 0.32 0.41 0.36 0.35

0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1

3088 3088 3088 3088 3088 3088 3088 3088

0.09 0.11 0.12 0.05 0.11 0.21 0.15 0.14

0.29 0.31 0.33 0.22 0.32 0.41 0.36 0.35

0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1

3088 3088 3088 3088 3088 3088 3088 3088

0.10 0.11 0.12 0.05 0.11 0.21 0.15 0.14

0.30 0.31 0.33 0.21 0.32 0.41 0.36 0.35

0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1

3088 3088 3088 3088 3088 3088 3088 3088

Outcome Variables

Mean

SD

Min

Max

Mean

SD

Min

Max

Mean

SD

Min

Max

Main cooking fuel Firewood/Animal Residue Charcoal Electricity/Gas/Kerosene

0.72 0.23 0.04

0.45 0.42 0.20

0 0 0

1 1 1

3088 3088 3088

0.70 0.25 0.04

0.46 0.43 0.20

0 0 0

1 1 1

3088 3088 3088

0.70 0.26 0.03

0.46 0.44 0.18

0 0 0

1 1 1

3088 3088 3088

Main lighting fuel Candles/Firewood Paraffin Lamps Electricity/Solar/Gas

0.02 0.75 0.21

0.15 0.43 0.41

0 0 0

1 1 1

3088 3088 3088

0.02 0.68 0.23

0.14 0.47 0.42

0 0 0

1 1 1

3088 3088 3088

0.02 0.52 0.26

0.15 0.50 0.44

0 0 0

1 1 1

3088 3088 3088

Stacking Measures Simple Stacking Directional Stacking Share Stacking Gini-Simpson Index Stacking up the ladder index

1.44 0.09 0.10 0.16 0.20

0.78 0.19 0.22 0.22 0.30

0.00 0.00 0.00 0.00 0.00

4.00 1.00 1.00 0.71 1.17

3088 2965 2964 2964 2964

1.40 0.10 0.11 0.16 0.21

0.83 0.21 0.25 0.23 0.31

0.00 0.00 0.00 0.00 0.00

4.00 1.00 1.00 0.72 1.20

3088 2840 2840 2840 2840

1.25 0.14 0.15 0.17 0.23

0.92 0.25 0.28 0.23 0.32

0.00 0.00 0.00 0.00 0.00

4.00 1.00 1.00 0.71 1.19

3088 2472 2472 2472 2472

Socio-economic status indicators Log per-adult-eq. expend. Wealth index score Elec/Gas stove ownership Other stove ownership Fuel Prices Log Kerosene price Log Wood Price Log Electricity Price Log Charcoal price Locational characteristics Rural household Luminosity Score

Source: Authors’ own calculations using TZNPS (2008–2013) data.

Annex 2 Yearly summary statistics of intra-household bargaining power proxy variables. 2008/2009

2010/2011

2012/2013

Variable

Mean

SD

Min

Max

N

Mean

SD

Min

Max

N

Mean

SD

Min

Max

N

Spouse education Spouse wage Spouse age Women b1

5.03 0.11 37.05 0.07

3.64 0.31 12.51 1.19

0.00 0.00 15.00 3.27

18.00 1.00 80.00 3.92

1879 1901 1901 1879

4.93 0.13 38.97 0.02

3.65 0.34 12.57 1.21

0.00 0.00 16.00 3.38

18.00 1.00 82.00 3.63

1888 1901 1901 1888

5.07 0.15 40.76 0.06

3.66 0.36 12.68 1.23

0.00 0.00 17.00 3.27

18.00 1.00 81.00 3.96

1881 1901 1901 1881

Source: Authors’ own calculations using TZNPS (2008–2013) data. N = 1901 in each wave. 1 (PCA of Spouse_education, Spouse_wage and Spouse_age).

232

J. Choumert-Nkolo et al. / World Development 115 (2019) 222–235

Annex 3 Multinomial logit regression results – Cooking and Lighting Fuels. Cooking Fuels (Base: Firewood/Animal Res.)

Lighting Fuels (Base: Paraffin Lamps)

(1) Random Effects

(3) Random Effects

(2) Fixed Effects

Charcoal Log per.cap.eq.exp Luminosity Score Rural Primary Junior secondary Secondary Tertiary Female head Household size Elec/Gas stove Other stove Ratio of women to men Head age Log kerosene price Log wood price Log charcoal price Log electricity price Constant Electricity, kerosene or gas Log per.cap.eq.exp Luminosity Score Rural Primary Junior secondary Secondary Tertiary Female head Household size Elec/Gas stove Other stove Ratio of women to men Head age Log kerosene price Log wood price Log charcoal price

2.140*** (0.21) 0.0683*** (0.01) 3.131*** (0.24) 1.280*** (0.25) 2.617*** (0.39) 2.822*** (0.65) 2.775** (1.24) 0.663** (0.27) 0.0460 (0.04) 0.875** (0.39) 2.210*** (0.18) 0.00949 (0.10) 0.0213** (0.01) 0.318 (0.57) 0.106 (0.18) 0.133 (0.24) 0.721 (0.98) 37.76*** (7.08) 2.254*** (0.41) 0.0778*** (0.01) 2.754*** (0.46) 0.958* (0.50) 2.570*** (0.59) 2.755*** (0.91) 4.721*** (1.29) 0.337 (0.53) 0.256*** (0.07) 2.696*** (0.57) 1.095*** (0.41) 0.468** (0.22) 0.0273* (0.02) 1.844** (0.93) 0.0248 (0.28) 0.0385 (0.33)

(4) Fixed Effects

Candles/Firewood 0.963*** (0.20) 0.0306** (0.01)

0.262 (0.41) 0.0894 (0.06) 0.102 (0.34) 1.075*** (0.19)

1.516*** (0.29) 0.0517*** (0.01) 0.0921 (0.45) 0.572 (0.35) 0.200 (1.01) 29.70*** (0.66) 28.40*** (0.98) 0.570 (0.36) 0.265*** (0.08)

0.491* (0.29) 0.0455 (0.04)

1.666* (0.85) 0.249** (0.11)

0.0460 (0.14) 0.00709 (0.01) 0.491 (1.10) 0.748** (0.36)

0.916*** (0.31) 0.0270 (0.02)

0.498 (0.73) 0.368*** (0.11) 0.504 (0.42) 1.301*** (0.36)

4.966** (2.43) 9.098 (14.33) Electricity/Solar/Gas 2.373*** (0.29) 0.0849*** (0.01) 2.746*** (0.30) 2.326*** (0.36) 5.533*** (0.62) 7.412*** (1.00) 8.310*** (1.56) 0.743** (0.34) 0.114*** (0.04)

0.307*** (0.10) 0.0478*** (0.01) 2.440*** (0.67) 0.255 (0.19)

0.450** (0.21) 0.0512*** (0.01)

0.855 (0.55) 0.0695 (0.07)

233

J. Choumert-Nkolo et al. / World Development 115 (2019) 222–235 Annex 3 (continued)

Log electricity price Constant District dummy variables Wave dummy variables Var (RI2[hhid]) var(RI3[hhid]) cov(RI3[hhid],RI2[hhid]) Observations *

p < 0.10, ** p < 0.05, fixed effects.

***

Cooking Fuels (Base: Firewood/Animal Res.)

Lighting Fuels (Base: Paraffin Lamps)

(1) Random Effects

(3) Random Effects

(2) Fixed Effects

Charcoal

Candles/Firewood

2.530* (1.49) 32.54*** (11.66) YES YES 5.304*** (0.91) 6.471*** (2.32) 4.360*** (1.35) 5464

0.0751 (1.14) 59.76*** (8.73) YES YES 5.137*** (1.63) 16.31*** (3.82) 0.666 (0.43) 4936

YES YES

1350

(4) Fixed Effects

YES YES

1202

p < 0.010. White-Huber robust standard errors in parentheses. In regressions (2) and (4), time-invariant variables are absorbed into the household

Annex 4 Intra-household bargaining multinomial logit regression results. Cooking (Base: Firewood/Animal Residue)

Lighting (Base: Paraffin Lamps)

(1)

(2)

(3)

– – 0.480*** (0.18) 2.764*** (0.34) 0.0903*** (0.01) 3.757*** (0.38) 1.395*** (0.38) 2.309*** (0.54) 2.015** (0.81) 1.650 (2.13) 0.0779 (0.06) 0.922* (0.53) 2.346*** (0.26) 0.00974 (0.14) 0.0111 (0.02) 0.363 (0.72) 0.0443 (0.24) 0.114 (0.34) 2.061 (1.34) 53.27*** (9.72)

0.200*** (0.06) 1.549*** (0.32) 0.0346** (0.02) 0.0468 (0.53) 0.0573 (0.44) 0.635 (1.05) 35.26*** (0.79) 32.35*** (1.41) 0.170** (0.08) 0.168** (0.08)

– – 1.651*** (0.33) 0.0336** (0.02) 0.194 (0.54) 0.244 (0.41) 0.982 (1.05) 38.25*** (0.80) 35.75*** (1.44) 0.195** (0.09) 0.176** (0.09)

0.0523 (0.17) 0.0205 (0.01) 1.910* (1.11)

0.0398 (0.17) 0.0285 (0.02) 2.014* (1.12)

1.985 (1.96) 6.154 (12.02)

2.171 (1.98) 6.790 (12.25)

Charcoal Spouse education Women_b Log per.cap.eq.exp Luminosity Score Rural Primary Junior secondary Secondary Tertiary Household size Elec/Gas stove Other stove Ratio of women to men Head age Log kerosene price Log wood price Log charcoal price Log electricity price Constant

Spouse education Women_b

0.174*** (0.05) – – 2.720*** (0.34) 0.0902*** (0.01) 3.706*** (0.38) 1.240*** (0.39) 2.080*** (0.55) 1.828** (0.83) 1.305 (2.10) 0.0883 (0.06) 0.856 (0.52) 2.291*** (0.26) 0.00174 (0.15) 0.0268** (0.01) 0.393 (0.72) 0.0439 (0.24) 0.0999 (0.35) 2.232* (1.33) 53.60*** (9.70)

(4)

Candles/firewood

Electricity/Gas/Kerosene

Electricity/Solar/Gas

0.175 (0.11)

0.280*** (0.06) 0.403 (0.32)

0.485*** (0.16) (continued on next page)

234

J. Choumert-Nkolo et al. / World Development 115 (2019) 222–235

Annex 4 (continued) Cooking (Base: Firewood/Animal Residue)

Lighting (Base: Paraffin Lamps)

(1)

(3)

(2)

Charcoal Log per.cap.eq.exp Luminosity Score Rural Primary Junior secondary Secondary Tertiary Household size Elec/Gas stove Other stove Ratio of women to men Head age Log kerosene price Log wood price Log charcoal price Log electricity price Constant District dummy variables Wave dummy variables var(RI2[hhid]) Constant var(RI3[hhid]) Constant cov(RI3[hhid],RI2[hhid]) Constant Observations *

p < 0.10,

**

p < 0.05,

***

2.599*** (0.75) 0.127*** (0.02) 2.571*** (0.84) 1.109 (1.02) 2.925** (1.19) 0.793 (1.82) 2.998 (2.40) 0.286** (0.12) 4.722*** (1.09) 2.014*** (0.77) 0.192 (0.35) 0.0543* (0.03) 2.183 (1.62) 0.0916 (0.40) 0.208 (0.56) 0.191 (2.53) 56.13*** (17.85) YES YES 8.253*** (1.85) 16.91*** (5.27) 10.02*** (2.64) 3592

(4)

Candles/firewood 2.629*** (0.75) 0.126*** (0.02) 2.598*** (0.86) 1.260 (1.00) 3.152*** (1.14) 1.007 (1.78) 3.413 (2.35) 0.273** (0.12) 4.796*** (1.09) 2.050*** (0.77) 0.181 (0.35) 0.0420 (0.03) 2.162 (1.62) 0.0951 (0.40) 0.184 (0.56) 0.0289 (2.51) 55.20*** (17.81) YES YES 8.189*** (1.81) 16.17*** (5.07) 9.649*** (2.61) 3592

2.184*** (0.29) 0.0954*** (0.02) 3.120*** (0.38) 1.823*** (0.43) 4.161*** (0.63) 5.595*** (1.00) 7.321*** (2.34) 0.0776* (0.05)

2.278*** (0.29) 0.0957*** (0.02) 3.217*** (0.38) 2.211*** (0.46) 4.696*** (0.66) 6.135*** (1.02) 8.409*** (2.47) 0.0938** (0.05)

0.211 (0.14) 0.0406*** (0.01) 1.507** (0.61)

0.231* (0.14) 0.0507*** (0.02) 1.500** (0.60)

0.119 (1.25) 50.21*** (8.47) YES YES 4.958*** (1.36) 18.83*** (6.16) 0.420 (0.32) 4036

0.305 (1.25) 51.65*** (8.54) YES YES 5.155*** (1.37) 18.67*** (6.10) 0.528 (0.38) 4036

p < 0.010. WhiteHuber robust standard errors in parentheses.

References Allen, J. C. (1985). Wood energy and preservation of woodlands in semi-arid developing countries: The case of Dodoma region, Tanzania. Journal of Development Economics, 19(1), 59–84. https://doi.org/10.1016/0304-3878(85) 90039-2. Anderson, C. L., Reynolds, T. W., & Gugerty, M. K. (2017). Husband and wife perspectives on farm household decision-making authority and evidence on intra-household accord in rural Tanzania. World Development, 90, 169–183. https://doi.org/10.1016/j.worlddev.2016.09.005. Alem, Y., Beyene, A. D., Köhlin, G., & Mekonnen, A. (2016). Modeling household cooking fuel choice: A panel multinomial logit approach. Energy Economics, 59, 129–137. Arze del Granado, F. J., Coady, D., & Gillingham, R. (2012). The unequal benefits of fuel subsidies: A review of evidence for developing countries. World Development, 40(11), 2234–2248. https://doi.org/10.1016/j.worlddev.2012. 05.005. Blimpo, M., McRae, S., & Steinbuks, J., (2018). Why are connection charges so high? : An analysis of the electricity sector in sub-saharan africa. Policy Research Working Paper; No. 8407. World Bank, Washington, DC. Ó World Bank. https:// openknowledge.worldbank.org/handle/10986/29709 License: CC BY 3.0 IGO. Bonan, J., Pareglio, S., & Tavoni, M. (2017). Access to modern energy: A review of barriers, drivers and impacts. Environment and Development Economics, 22(5), 491–516. Bos, K., Chaplin, D., & Mamun, A. (2018). Benefits and challenges of expanding grid electricity in Africa: A review of rigorous evidence on household impacts in developing countries. Energy for Sustainable Development, 44, 64–77. https://doi. org/10.1016/j.esd.2018.02.007.

Bruce, N., Perez-Padilla, R., & Albalak, R. (2000). Indoor air pollution in developing countries: A major environmental and public health challenge. Bulletin of the World Health Organization, 78, 1078–1092. https://doi.org/10.1590/S004296862000000900004. Chaplin, D., Mamun, A., Protik, A., Schurrer, J., Vohra, D., Bos, K., ... Cook, T. (2017). Grid electricity expansion in Tanzania by MCC: Findings from a rigorous impact evaluation. Mathematica Policy Research. https://www.mathematica-mpr.com/ our-publications-and-findings/publications/grid-electricity-expansion-intanzania-by-mcc-findings-from-a-rigorous-impact-evaluation. Choumert-Nkolo, J., Combes, P., & Roux, L. L. (2017). Stacking up the ladder: A panel data analysis of Tanzanian household energy choices. Études et Documents, 2017/24. 2018. . Collings, D. A., Sithole, S. D., & Martin, K. S. (1990). Indoor woodsmoke pollution causing lower respiratory disease in children. Tropical Doctor, 20(4), 151–161. https://doi.org/10.1177/004947559002000403. D’Agostino, A. L., Urpelainen, J., & Xu, A. (2015). Socio-economic determinants of charcoal expenditures in Tanzania: Evidence from panel data. Energy Economics, 49, 472–481. https://doi.org/10.1016/j.eneco.2015.03.007. Doll, C. N. H., & Pachauri, S. (2013). Estimating rural populations without access to electricity in developing countries through nighttime light satellite imagery. Energy Policy, 38(10), 5661–5670. Doss, C. (2013). Intrahousehold Bargaining and resource allocation in developing countries. The World Bank Research Observer, 28(1), 52–78. Dugoua, E., Kennedy, R., & Urpelainen, J. (2018). Satellite data for the social sciences: Measuring rural electrification with night-time lights. International Journal of Remote Sensing, 39, 2690–2701. https://doi.org/10.1080/01431161.2017.1420936. Edwards, J. H. Y., & Langpap, C. (2005). Startup costs and the decision to switch from firewood to gas fuel. Land Economics, 81(4), 570–586. https://doi.org/10.3368/ le.81.4.570.

J. Choumert-Nkolo et al. / World Development 115 (2019) 222–235 Gini, C. (1912). Variablity and mutability (p. 156). Bologna: C. Cuppini. Glennerster, R., Walsh, C., & Diaz-Martin, L. (2018). A practical guide to measuring women’s and girls’ empowerment in impact evaluations. https://www. povertyactionlab.org/sites/default/files/resources/practical-guide-tomeasuring-women-and-girls-empowerment-in-impact-evaluations.pdf. Heltberg, R. (2004). Fuel switching: Evidence from eight developing countries. Energy Economics, 26, 869–887. https://doi.org/10.1016/j.eneco.2004.04.018. Heltberg, R., Arndt, T. C., & Sekhar, N. U. (2000). Fuelwood consumption and forest degradation: A household model for domestic energy substitution in rural India. Land Economics, 76, 213–232. https://doi.org/10.2307/3147225. Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring economic growth from outer space. The American Economic Review, 102, 994–1028. Hofstad, O. (1997). Woodland deforestation by charcoal supply to Dar es Salaam. Journal of environmental economics and management, 33(1), 17–32. https://doi. org/10.1006/jeem.1996.0975. Hosier, R. H., & Dowd, J. (1987). Household fuel choice in Zimbabwe. Resources and Energy, 9, 347–361. https://doi.org/10.1016/0165-0572(87)90003-X. International Energy Agency, 2014. Africa Energy Outlook. A Focus on Energy Prospects in Sub-Saharan Africa. World Energy Outlook Special Report. Paris: OECD/IEA. Available at: http://www.worldenergyoutlook.org/africa/. Jensen, R., & Thornton, R. (2003). Early Female Marriage in the Developing World. Gender and Development, 11(2), 9–19. Jeuland, M., Pattanayak, S. K., & Bluffstone, R. (2015). The economics of household air pollution. Annual Review of Resource Economics, 7, 81–108. https://doi.org/ 10.1146/annurev-resource-100814-125048. Klasen, S., & Mbegalo, T. 2016. The impact of livestock ownership on solar home system adoption in the northern and western regions of rural Tanzania. Courant Research Centre: Poverty, Equity and Growth – Discussion Papers, No. 218, Courant Research Centre Poverty, Equity and Growth, Göttingen. Available at: http://hdl.handle.net/10419/148393. Kitchens, C., & Fishback, P. (2015). Flip the switch: The impact of the rural electrification administration 1935–1940. The Journal of Economic History, 75(4), 1161–1195. https://doi.org/10.1017/S0022050715001540. Köhlin, G., Pattanayak, S., Sills, E., Mattsson, E., Ostwald, M., Salas, A., & Ternald, D. (2015). In search of double dividends from climate change interventions evidence from forest conservation and houshold energy transitions. Stockholm, 2015, 134. Lay, J., Ondraczek, J., & Stoever, J. (2013). Renewables in the energy transition: Evidence on solar home systems and lighting fuel choice in Kenya. Energy Economics, 40, 350–359. https://doi.org/10.1016/j.eneco.2013.07.024. Lee, K., Brewer, E., Christiano, C., Meyo, F., Miguel, E., Podolsky, M., ... Wolfram, C. (2016). Electrification for ‘‘Under Grid” households in Rural Kenya. Development Engineering, 1, 26–35. https://doi.org/10.1016/j.deveng.2015.12.001. Lee, K., Miguel, E., & Wolfram, C., (2016b). Experimental evidence on the demand for and costs of rural electrification. Working Paper No. w22292. National Bureau of Economic Research. Available at: http://www.nber.org/papers/w22292. Lépine, A., & Strobl, E. (2013). The effect of women’s bargaining power on child nutrition in rural Senegal. World Development, 45, 17–30. https://doi.org/ 10.1016/j.worlddev.2012.12.018. Lewis, J. J., Hollingsworth, J. W., Chartier, R. T., Cooper, E. M., Foster, W. M., Gomes, G. L., ... Pattanayak, S. K. (2017). Biogas stoves reduce firewood use, household air pollution, and hospital visits in Odisha, India. Environmental Science & Technology, 51, 560–569. https://doi.org/10.1021/acs.est.6b02466. Lewis, J. J., & Pattanayak, S. K. (2012). Who adopts improved fuels and cookstoves? A systematic review. Environmental Health Perspectives, 120, 637–645. https://doi. org/10.1289/ehp.1104194. Louw, K., Conradie, B., Howells, M., & Dekenah, M. (2008). Determinants of electricity demand for newly electrified low-income African households. Energy Policy, 36, 2812–2818. https://doi.org/10.1016/j.enpol.2008.02.032. Maconachie, R., Tankob, A., & Zakariya, M. (2009). Descending the energy ladder? Oil price shocks and domestic fuel choices in Kano, Nigeria. Land Use Policy, 26 (4), 1090–1099. Masera, O. R., Saatkamp, B. D., & Kammen, D. M. (2000). From linear fuel switching to multiple cooking strategies: A critique and alternative to the energy ladder model. World Development, 28, 2083–2103. https://doi.org/10.1016/S0305750X(00)00076-0. Mekonnen, A., & Köhlin, G., (2009). Determinants of Household Fuel Choice in Major Cities in Ethiopia. University of Gothenburg. Working Papers in Economics No 399. Available at: http://hdl.handle.net/2077/21490. Miguel, E. (2004). Tribe or nation?: Nation building and public goods in Kenya versus Tanzania. World Politics, 56(3), 327–362. https://doi.org/10.1353/ wp.2004.0018. Mekonnen, A., & Köhlin, G., (2009). Determinants of Household Fuel Choice in Major Cities in Ethiopia. Environment for Development: Discussion paper series, pp. 1–23.

235

Miller, G., & Mobarak, A. M. (2013). Gender differences in preferences, intra-household externalities, and low demand for improved cookstoves. National Bureau of Economic Research. Muller, C., & Yan, H. (2018). Household fuel use in developing countries: Review of theory and evidence. Energy Economics, 70, 429–439. https://doi.org/10.1016/j. eneco.2018.01.024. NBS. (2014a). Tanzania National Panel Survey Report (NPS) – Wave 3, 2013–2013. Dar es Salaam: Tanzania National Bureau of Statistics. NBS. (2014b). Tanzania National Panel Survey (NPS) Basic Information Document – Wave 3, 2012–2013. Dar es Salaam: Tanzania National Bureau of Statistics. National Oceanic and Atmospheric Administration (NOAA), (2008–2013). DMSPOLS Nighttime Lights Time Series Version 4. Image and Data processing by NOAA’s National Geophysical Data Center. DMSP data collected by the US Air Force Weather Agency. Ondraczek, J. (2013). The sun rises in the east (of Africa): A comparison of the development and status of solar energy markets in Kenya and Tanzania. Energy Policy, 56, 407–417. https://doi.org/10.1016/j.enpol.2013.01.007. Pachauri, S., & Rao, N. D. (2013). Gender impacts and determinants of energy poverty: Are we asking the right questions? Current Opinion in Environmental Sustainability, 5, 205–215. https://doi.org/10.1016/j.cosust.2013.04.006. Peng, W., Zerriffi, H., & Pan, J. (2010). Household level fuel switching in rural Hubei. Energy for Sustainable Development, 14(3), 238–244. https://doi.org/10.1016/j. esd.2010.07.001. Pitt, M. M. (1985). Equity, externalities and energy subsidies. The case of kerosene in Indonesia. Journal of Development Economics, 17(3), 201–217. https://doi.org/ 10.1016/0304-3878(85)90090-2. Rahut, D. B., Behera, B., Ali, A., & Marenya, P. (2017). A ladder within a ladder: Understanding the factors influencing a household’s domestic use of electricity in four African countries. Energy Economics, 66, 167–181. https://doi.org/ 10.1016/j.eneco.2017.05.020. Rosenthal, J., Balakrishnan, K., Bruce, N., Chambers, D., Graham, J., Jack, D., ... Yadama, G. (2017). Implementation science to accelerate clean cooking for public health. Environmental Health Perspectives, 125. https://doi.org/10.1289/ EHP1018. Ruiz-Mercado, I., & Masera, O. (2015). Patterns of stove use in the context of fueldevice stacking: Rationale and implications. EcoHealth, 12, 42–56. https://doi. org/10.1007/s10393-015-1009-4. Schaner, S. (2017). The cost of convenience? Transaction costs, bargaining power, and savings account use in Kenya. Journal of Human Resources, 52(4), 919–945. Sander, K., Gros, C., & Peter, C. (2013). Enabling reforms: Analyzing the political economy of the charcoal sector in Tanzania. Energy for Sustainable Development, 17(2), 116–126. Sepp, S. (2014). Multiple-household fuel use-a balanced choice between firewood, charcoal and LPG (liquefied petroleum gas). GIZ. Simpson, E. H. (1949). Measurement of diversity. Nature, 163, 688. https://doi.org/ 10.1038/163688a0. The United Republic of Tanzania, 2017. Energy Access Situation Report, 2016 Tanzania Mainland. Van Aelst, K. (2014). Household decision-making and gender relations in Tanzania: literature and theory review. IOB Working Papers 2014.02, Universiteit Antwerpen, Institute of Development Policy (IOB) van de Walle, D., Ravallion, M., Mendiratta, V., & Koolwal, G. (2017). Long-term gains from electrification in rural India. The World Bank Economic Review, 31(2), 385–411. https://doi.org/10.1093/wber/lhv057. van der Kroon, B., Brouwer, R., & van Beukering, P. J. H. (2013). The energy ladder: Theoretical myth or empirical truth? Results from a meta-analysis. Renewable and Sustainable Energy Reviews, 20, 504–513. https://doi.org/10.1016/j. rser.2012.11.045. van der Kroon, B., Brouwer, R., & van Beukering, P. J. H. (2014). The impact of the household decision environment on fuel choice behaviour. Energy Economics, 44, 236–247. https://doi.org/10.1016/j.eneco.2014.04.008. Winther, T. (2007). Rising electricity consumption: Driving forces and consequences. The case of rural Zanzibar (pp. 1835–1845). Oslo: ECEEE. World Bank, 2017. Tanzania Consumer Price Index (2010=100), Available: https://data.worldbank.org/indicator/FP.CPI.TOTL. Zambelli, E., Roelen, K., Hossain, N., Chopra, D., & Twebaze, J. (2017). ‘My mother does a lot of work’: Women balancing paid and unpaid care work in Tanzania, National Report. Brighton: Institute of Development Studies. Available at: http:// opendocs.ids.ac.uk/opendocs/handle/123456789/13283. Zhang, X.-B., & Hassen, S. (2017). Household fuel choice in urban China: Evidence from panel data. Environment and Development Economics, 1–22. https://doi.org/ 10.1017/S1355770X17000092.