[height=1.8cm]images/logo-iu.pdf Smart Edward Amanfo Graduate School of Global ... · 2019-11-06 · 15000 households in 1,000 Enumeration Areas (EAs) 561 of rural EAs (56.1%) and

https://www.genv.sophia.ac.jp/english/

Smart Edward AmanfoGraduate School of Global Environmental Studies

Sophia UniversityTokyo, Japan

A Microeconometric Analysis of Household Energy Consumption:Evidence from Ghana Household Living Standard Survey and Sustainability Implications

November 6, 2019

Amanfo S. Edward Sophia University November 6, 2019 1 / 15


Outline:

1 IntroductionBackgroundHousehold energy consumption

2 MethodologyData sampling strategyHousehold energy choice theoryEstimation Models

3 Empirical findingsDeterminants of household energy choiceDeterminants of household energy consumption dependency

4 Conclusions and sustainability implications

5 References



Introduction

Background and Trends

• Ghana attained a Lower-Middle-Income status in 2011 based on the World Bank’s WorldBank $1,006 to $3,975 per capita threshold (Huq and Tribe, 2018).

• Ghana joined oil exporting countries in 2011 (Huq and Tribe, 2018).

• Over 70% of total primary energy supply (TPES) comes from woodfuels, representing about60% of the final energy demand (GoG, 2015) .

• Between 2001 to 2018, Ghana lost about 1.09Mha of tree cover, representing a 16%decrease in tree cover due to deforestation, and 29Mt of carbon dioxide emissionsa

• Seven regions of Ghana were responsible for about 72% of all tree cover lost between 2001

and 2018, with the Upper West Region alone contributing 97% relative to the 330%

national average.

• Ghana’s annual deforestation rate is 3% (GoG, 2015)

• Household indoor pollution was responsible for 11,500 premature deaths in Ghana in 2018.

• These deaths are projected to grow in the near future, to about 13,820 deaths annually !b

aSource: Global Forest Watch databasebISSER and others, 2018.



IntroductionHousehold energy choices by carriers

Figure 1: Distribution of households by energy carries



IntroductionSpatial view of household energy choices

Figure 2: Distribution of households energy consumption carries by the ecological zones/locations



How would economists think by observing these trends?

Motivation

Figure 3: BOULDING, Kenneth Ewart.Born on 18 January 1910 in Liverpool, England.Died 19 March 1993 in Boulder, Colorado

⇒

Man is finally going to have to face thefact that he is a biological system livingin an ecological system, and that hissurvival power is going to depend onhis developing symbiotic relationshipsof a closed-cycle character with all theother elements and populations of theworld of ecological systems

(Boulding, 1956)



MethodologyData sampling strategy

Data • Use data from Ghana Living Standard Survey Round Seven 2016/7 (GLSS7, 2016/7) for the ten1 administrative regions of Ghana• Survey conducted by Ghana Statistical Service with support from World

Bank.• 15000 households in 1,000 Enumeration Areas (EAs)• 561 of rural EAs (56.1%) and 439 urban EAs (43.9%).

1Currently, Ghana has 16 regions–previous ten region re-partitioned



MethodologyTheories and empirical strategies

Household energy choice theories

1 The energy ladder theory (Energy Transition Theory)⇒ Household transits to clean carriesas incomes increases (Hosier and Dowd, 1987, Johansson et al., 2012)

2 Fuel Stacking Theory (Household use multiple sources of energy) (yo Cheng and Urpelainen,2014).

Unordered Choice Theory– Applied to household energy choices

1 Additive-Random Utility (Colin and Trivedi, 2009, Train, 2009).

2 Faced with alternative energy sources j = 1, 2, . . . k, a household i chooses energy service jthat maximizes its utility, Uij .

3 We assumed that Uij is an aggregation of deterministic and observed the component—sayVij and an unobserved part ηij , then:

Uij = Vij + ηij (1)

where Vij depends on an observed attributes– κij , and unknown parameter —νj ⇒Vij = νjκij

4 Household head behavioural statement: Given all energy alternatives, a household headchooses energy type j if Uij > Uik,∀ 6= k



MethodologyHypothesis and Study Objectives

Hypothesis 1

1 Households with comparatively higher income are less likely than those with relative lowlevels of income to use firewood and kerosene.

2 High income households are more likely to depend on gas and electricity perceived as cleanand efficient energy sources

Hypothesis 2

1 The more the household members (large household size), the more the likelihood ofhousehold heads adopting firewood, charcoal and kerosene as their main sources of energyservices.

2 Firewood, charcoal and kerosene are likely to take greater share of large households totalenergy expenditure.

Study Objectives

1 To estimate the covariates of household energy choices for cooking

2 To estimate influential factors of household intensity of dependency on a particular type ofenergy



MethodologyEconometric Model 1: Determinants of household energy consumption choice

Multivariate Probit Model: Objective 1

yi = 1 if αi x′+ εi > 0 (2)

and yi = 0 if αi x′+ εi ≤ 0, i = 1, 2, . . . n (3)

Where x is a vector of uniquely observed measurable characteristics of a household which mayinclude income level, household head’s gender, education of a household head and otherdemographic features, εi is a vector of the random components (unobserved household head’sintrinsic values, preference etc. for the purpose of this paper) of utility, normalized to zero meanand unitary standard deviation and nXn matrix

Why Multivariate Probit Model

1 Simultaneously analyzes the choice of energy by the source of energy.

2 Relaxes Independence of Irrelevant Alternatives (IIA) assumption

3 Average Ghanaian household engages in Fuel Stacking Behaviour



MethodologyEconometric Model 2: Intensity of household dependency of different energy sources

Two-Limit-Tobit Model: Objective 2

yi = α0 + αiZi + εi (4)

yi = 1 [y∗i ≥ δ] (5)

yi = 1 [y∗i < δ] (6)

where δ is a constructed threshold that differentiate households that consume a specific energytype from households who otherwise do so.

• The indicator function, 1[·] takes on the value one if the event in the bracket in equation 5 istrue, and takes the value zero if the events specified in the bracket of equation 6 is true.

• yi takes the value one if y∗i ≥ δ, a threshold in which a representative household is classifiedhaving used a type of energy service;

• yi takes the value zero if y∗i < δ, the threshold at which a household is regarded not a userof a particular source of energy captured in the survey datasets

(Wooldridge, 2015, chp. 17 ).



Econometric Model 2: Intensity of household dependency on differentenergy sources

Two-Limit-Tobit Model: Objective 2

Following equations 4, 5 and 6, we specify probability functions for non-users of particular energysource as

p (y∗i < δ) = Ψ

(αiZi

σ

)(7)

and probability density function for a representative household who used a type of energy sourceas,

f (yi|y∗i ≥ δ) =f (yi)

p(y∗i ≥ δ

) =

1σθ

(y∗−Ziαiiσ

)Ψ

(αiZi

σ

) (8)

and an aggregate function for user-household and non-user-household for a particular energy typeas:

lnL =∑y∗i<δ

ln

1− ΨαiZi

σ+∑yi≥

1

σθ

1

σθ

(y∗−Ziαii

σ

) (9)



Results for Objective 1 based on GLSS 2016/7: Table 1 ⇒ Household cookingenergy choice

• The marginal effects are presented.

• Firewood is used as the base-category energy choice

Table 1: Marginal effects of Estimated Multivariate Probit Model

(Charcoal) (Gas) (Other fuels)

Regressors

DemographicsFemale head 0.0624** 0.0170 -0.3949***

(0.0244) (0.0307) (0.0375)Head age 0.0077 0.0226** -0.0283***

(0.0080) (0.0110) (0.0098)Head age squared -0.0001 -0.0003*** 0.0002***

(0.0001) (0.0001) (0.0001)Household size =2 -0.0411 -0.3710*** -1.5137***

(0.0841) (0.0999) (0.1076)Household size =3 -0.1249 -0.4829*** -2.0813***

(0.0953) (0.1132) (0.1268)Household size = 4 -0.3588*** -0.7966*** -2.5471***

(0.1081) (0.1305) (0.1467)Household size > 4 -0.5062*** -1.2506*** -2.8021***

(0.1247) (0.1509) (0.1564)Affluence and welfare quintile

ln income (expenditure) 0.3126*** 0.8543*** 0.3914***(0.0653) (0.0835) (0.0781)

2nd Quintile 0.3869*** 0.1535 -0.4397***(0.0819) (0.1631) (0.1083)

3rd Quintile 0.5865*** 0.5128*** -0.5573***(0.0986) (0.1684) (0.1336)

4th Quintile 0.7161*** 0.8476*** -0.4936***(0.1181) (0.1828) (0.1542)

5th Quintile 0.6581*** 1.0044*** -0.5048***



Table 1 continued from previous page

Charcoal Gas Other fuels

(0.1530) (0.2175) (0.1952)Locations/Ecological Zones

Other urban areas 1.0900*** 1.2652*** -1.2860***(0.2610) (0.2629) (0.2796)

Rural Coastal -1.9623*** -2.1638*** -2.5942***(0.2665) (0.2738) (0.2997)

Rural Forest -2.4147*** -2.8253*** -2.6620***(0.2628) (0.2679) (0.2854)

Rural Savannah -2.7586*** -3.1952*** -1.7962***(0.2657) (0.2792) (0.2870)

Labour market statusRetired/unemployed/other inactive -0.2897*** -0.0719 -0.1824**

(0.0641) (0.0796) (0.0911)Private employee(non-agric) 0.1774** 0.1871** 0.3026***

(0.0700) (0.0795) (0.0933)Public employee -0.1210 0.1607 -0.3307**

(0.1090) (0.1147) (0.1545)Self-employed agric -1.1165*** -1.3741*** -0.7824***

(0.0597) (0.0901) (0.0895)Asset ownership

Renting 0.9299*** 1.0259*** 0.5759***(0.0640) (0.0739) (0.0910)

Rent-free 0.3699*** 0.1276* 0.2758***(0.0499) (0.0654) (0.0710)

Squatting 0.5034* -0.1295 0.3034(0.2617) (0.3416) (0.3228)

Human capitalBECE 0.2245*** 0.8308*** 0.1725*

(0.0677) (0.0824) (0.0933)MSLC 0.2547*** 0.7816*** 0.1190

(0.0632) (0.0805) (0.0985)SSS 0.2895*** 1.3266*** 0.4395***

(0.0853) (0.0946) (0.1034)Voc/Tec/Tea 0.3365*** 1.6614*** 0.2731**

(0.1026) (0.1085) (0.1357)Tertiary 0.2848 2.2504*** 0.8448***

(0.1803) (0.1774) (0.2164)Constant -1.6215*** -7.3132*** 0.0683

(0.5675) (0.7188) (0.6700)



Table 1 continued from previous page

Charcoal Gas Other fuels

Observations 14,009 14,009 14,009

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1



Results for Objective 2 based on GLSS 2016/7: Table 2 ⇒ Household energydependency

The marginal effects are presented.

Table 2: Estimated Marginal Effects from Two-Limit Tobit Model: Household expenditure share on differentsources of energy

Regressors Gas Kerosene Electricity Solid biomass

DemographicsFemale head 0.0224*** 0.0216 -0.0384*** 0.0458***

(0.0087) (0.0195) (0.0052) (0.0057)Age 0.0053 0.0076 -0.0008 0.0010

(0.0035) (0.0069) (0.0018) (0.0020)Age square -0.0001* -0.0000 -0.0000 0.0000

(0.0000) (0.0001) (0.0000 (0.0000)House size = 2 0.0255 0.0752 -0.0831*** 0.2616***

(0.0275) (0.0711) (0.0173) (0.0196)House size = 3 0.0633** 0.1832** -0.1321*** 0.2450***

(0.0297) (0.0801) (0.0189) (0.0215)House size = 4 0.0184 0.1362 -0.221*** 0.2634***

(0.0343) (0.0936) (0.0212) (0.0240)Household size > 4 -0.0540 0.1997* -0.1381*** 0.3227***

(0.0390) (0.1082) (0.0242) (0.02775)Affluence and Welfare Quintile

log of income(expenditure) 0.2187*** -0.0855 -0.0248* -0.0236(0.0225) (0.0573) (0.0129) (0.0145)

2nd Quintile 0.1902*** 0.0216 0.0552*** 0.0082(0.0663) (0.0698) (0.0183) (0.0189)

3rd Quintile 0.2851*** 0.0166 0.0784*** 0.0116(0.0661) (0.0836) (0.0214) (0.0234)

4th Quintile 0.3838*** 0.0600 0.0861*** 0.0220(0.0682) (0.0996) (0.0246) (0.0272)

5th Quintile 0.4239*** 0.0750 0.0680** -0.0053(0.0752) (0.1309) (0.0316) (0.0349)



Table 2 continued from previous pageRegressors Gas Kerosene Electricity Solid biomass

Location/Ecological ZonesOther urban areas 0.0564** 0.0992 -0.224 0.0208

(0.0269) (0.0943) (0.0202) (0.0226)Rural Coastal -0.0146 0.6065*** -0.0809*** 0.0732***

(0.0381) (0.1031) (0.0248) (0.0275)Rural Forest -0.1047*** 0.0954 -0.0167 0.0136

(0.0338) (0.1023) (0.0227) (0.0253)Rural Savanna -0.2083*** -0.3128** -0.1171*** 0.1181***

(0.0435) (0.1222) (0.0245) (0.0271)Labour Market Status

Retired/unemployed/other-inactive 0.0172 -0.0720 -0.0297** -0.0033(0.0239) (0.0525) (0.0141) (0.0154)

Private employee(non-agric) 0.0191 0.0514 -0.0226 0.0095(0.0208) (0.0514) (0.0138) (0.0153)

Public employee 0.0793*** -0.3095*** -0.0442** 0.0320(0.0267) (0.1130) (0.0196) (0.0220)

Self-employed (agric) -0.2663*** -0.1180** 0.0398*** -0.0594***(0.0305) (0.0508) (0.0138) (0.0150)

Asset ownershipRenting 0.0350* 0.0101 0.0417*** -0.0103

(0.0201) (0.0517) (0.0125) (0.0139)Rent-free -0.0185 0.1759*** -0.0606*** 0.0672***

(0.0204) (0.0414) (0.0114) (0.0125)Squatting -0.2033* 0.2092 -0.2262*** 0.2828***

(0.1124) (0.2058) (0.0599) (0.0624)Human capital

BECE 0.1838*** -.1811*** 0.0834*** -0.1037***(0.0250) (0.0588) (0.0145) (0.0160)

MSLC 0.1728*** 0.1016** 0.0869*** -0.1083***(0.0246) (0.0512) (0.0140) (0.0153)

SSS 0.3093*** -0.2459*** 0.0979*** -0.1936***(0.0260) (0.0792) (0.0164) (0.0185)

Voc/Tec/Tea 0.3604*** -0.1890** 0.1189*** -0.2607***(0.0270) (0.0830) (0.0183) (0.0208)

Tertiary 0.4417*** -0.2259* 0.0949*** -0.3970***(0.0331) (0.1351) (0.0247) (0.0291)

Constant -3.0733*** -0.9551** 0.8975*** 0.1578(0.1994) (0.4506) (0.1051) (0.1168)

Observations 10,931 10,931 10,931 10,931



Table 2 continued from previous pageRegressors Gas Kerosene Electricity Solid biomass

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1



Conclusions and Sustainability Policy Implications

General Observations

Ghanaian households energy consumption choices and the intensity of dependency on a particulartype of carrier depend on:

1 Human capital

2 Location/Ecological factors

3 Labour Market Status

4 Socio-demographic characteristics

5 Asset ownership

6 Affluence and Income Class

Sustainability Implications

• Monetary poverty–Energy Poverty Nexus

• Addressing problems of affordability and accessibility gaps simultaneously

• Household energy dependency data paucity



End Of Presentation

Q & A



References

Boulding, K. E. (1956). General systems theory—the skeleton of science. Managementscience 2(3), 197–208.

Colin, C. A. and P. K. Trivedi (2009). Microeconometrics using STATA. S.

GoG (2015, December). Sustainable energy for all (SE4ALL): Action Agenda of Ghana. Accessedon : 12-August-2019. Available athttp://energycom.gov.gh/files/SE4ALL-GHANA%20ACTION%20PLAN.pdf,.

Hosier, R. H. and J. Dowd (1987). Household fuel choice in Zimbabwe: an empirical test of theenergy ladder hypothesis. Resources and energy 9(4), 347–361.

Huq, M. and M. Tribe (2018). The Economy of Ghana: 50 Years of Economic Development.Springer.

Johansson, T. B., A. P. Patwardhan, N. Nakicenovic, and L. Gomez-Echeverri (2012). Globalenergy assessment: toward a sustainable future. Cambridge University Press.

Train, K. E. (2009). Discrete choice methods with simulation (Second ed.). Cambridge universitypress.

Wooldridge, J. M. (2015). Introductory econometrics: A modern approach (S ed.). NelsonEducation.

yo Cheng, C. and J. Urpelainen (2014, nov). Fuel stacking in India: Changes in the cooking andlighting mix, 1987–2010. Energy 76, 306–317.


http://energycom.gov.gh/files/SE4ALL-GHANA%20ACTION%20PLAN.pdf

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[height=1.8cm]images/logo-iu.pdf Smart Edward Amanfo Graduate School of Global ... · 2019-11-06 · 15000 households in 1,000 Enumeration Areas (EAs) 561 of rural EAs (56.1%) and