Electricity Availability and Economic Activity: Lessons ...

Electricity Availability and Economic Activity: Lessons from Developing

Countries

Jevgenijs SteinbuksEconomist

Development Research Group September 22, 2020

Electricity is Critical to Modern Economic Activity

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It is Also an Input to Important Public Services

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~800 million people still live without electricity

Proportion of population with access to electricity, rural areas. Source: UN SDG database, 2017

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Electricity Access is Particularly Dire in AfricaMenu Subscribe Search My account

Daily chart

More than half of sub-SaharanAfricans lack access to electricity

Graphic detail

Daily chart - More than half of sub-Saharan Africans lack access... https://www.economist.com/graphic-detail/2019/11/13/more-th...

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In many countries power supply is unreliable

Average hours of power outages in firms in a typical month, 2006-2018. Source: WDI, 2018

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Improving electricity availability is an international development priority, but also costly …

• UN SDG 7: “By 2030, ensure universal access to affordable, reliable and modern energy services”

• IEA: Providing electricity for all by 2030 would require annual investment of $52 billion per year

• more than twice the level mobilized under current and planned policies.

• For Africa alone, achieving the universal access goal costs no less than $19 billion per year

• 45% of the continent’s total official development assistance influx

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Understanding economic benefits of improved electricity availability is important for

ØDevelopment of electrification plansØInvestment in electricity generation,

transmission, and distributionØEconomic incentives to improve electricity

take up ØImproving power sector governance

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Electricity Consumption is Highly Correlated with Economic Development …

… but making causal claims is far more challenging

Taken from:Lee, Miguel, and Wolfram (2020b).

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Why is Quantifying Economic Impacts of Electricity Availability a Difficult Problem? • Problem 1: Measurement

• Electricity Access [non-binary] • The World Bank Multi-tier Framework (MTF)

• Electricity Reliability• Frequency vs. Duration of Outages

6B e y o n d C o n n e c t i o n s : E n e r g y A c c e s s R e d e f i n e d

T A B L E E S . 1Multi-tier Matrix for Measuring Access to Household Electricity Supply

TIER 0 TIER 1 TIER 2 TIER 3 TIER 4 TIER 5

ATTR

IBUT

ES

1. Peak Capacity

Power capacity ratings28 (in W or daily Wh)

Min 3 W Min 50 W Min 200 W Min 800 W Min 2 kW

Min 12 Wh Min 200 Wh Min 1.0 kWh Min 3.4 kWh Min 8.2 kWh

OR Services

Lighting of 1,000 lmhr/day

Electrical lighting, air circulation, television, and phone charging are possible

2. Availability (Duration)

Hours per day Min 4 hrs Min 4 hrs Min 8 hrs Min 16 hrs Min 23 hrs

Hours per evening

Min 1 hr Min 2 hrs Min 3 hrs Min 4 hrs Min 4 hrs

3. ReliabilityMax 14 disruptions per week

Max 3 disruptions per week of total duration <2 hrs

4. Quality Voltage problems do not affect the use of desired appliances

5. Afford-ability

Cost of a standard consumption package of 365 kWh/year < 5% of household income

6. LegalityBill is paid to the utility, pre-paid card seller, or authorized representative

7. Health & Safety

Absence of past accidents and perception of high risk in the future

T A B L E E S . 2Multi-tier Matrix for Measuring Access to Household Electricity Services

TIER 0 TIER 1 TIER 2 TIER 3 TIER 4 TIER 5

Tier criteria

Task lighting AND Phone charging

General lighting AND Phone Charging AND Television AND Fan (if needed)

Tier 2 AND Any medium-power appliances

Tier 3 AND Any high-power appliances

Tier 2 AND Any very high-power appliances

Source: Bhatia and Angelou (2015)

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Why is Quantifying Economic Impacts of Electricity Availability a Difficult Problem? • Problem 2: Data Availability

• Good quality aggregate data, but microdata lacking • Utilities have best data but rarely share with

researchers• Household (LSMS) and Enterprise Surveys have

very little about energy/electricity component• MTF Surveys are a promising and important

initiative

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Why is Quantifying Economic Impacts of Electricity Availability a Difficult Problem? • Problem 2: Data Availability

Pearson correlation coefficients(r) between log(number of electrified households) and log(sum of the 2011 shapefile night lights measure), state by state. Source: Dugoua, E., Kennedy, R., & Urpelainen, J. (2018).

If properly processed, satellite night lights data can be potentially good measure of long-term electricity access

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Why is Quantifying Economic Impacts of Electricity Availability a Difficult Problem? • Problem 3: Simultaneity

• Electricity availability affects economic decisions• Consumption (households’ appliance use)• Human capital accumulation (education, health)• Physical capital investment (machinery & equipment)• Public infrastructure investment (transactions costs)

• Electricity availability is itself a function of economic activity

• What is the willingness to pay for electricity availability?

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Example I: WTP For Electricity Access in SSA

WTP by electricity consumption level and country. Source: Sievert and Steinbuks (2020).

Poor households in Africa are willing to pay a lot (a 10% of their monthly income!) but can barely afford paying for a solar lantern

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Example II: Residential WTP for Electricity Reliability

Source: Nepal Electricity Authority (2020).

End of Nepal load shedding crisis (2008-2017):ü Shifting load from

industrial to residential customers

ü New generation capacityü Loss reduction initiatives ü Interconnection & imports

from India 0%

10%

20%

30%

40%

50%

60%

0

50

100

150

200

250

300

350

400

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Perc

ent t

ime

with

out e

lect

ricity

Hour

s with

out e

lect

ricity

Percent of the time without electricity

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Example II: Residential WTP for Electricity Reliability

• “Would you willing to pay a specified amount of money on top of their monthly bill to avoid going back to the situation of the year before?”

• A1: Quite a bit! On average 123.32 NR ($1.11) per month, or 65 percent of the actual average monthly bill.

• A2: Not enough! The WTP is lower than the marginal cost of delivering reliable supply!

Source: Alberini, Steinbuks and Timilsina (2020).

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Why is Quantifying Economic Impacts of Electricity Availability a Difficult Problem? • Problem 4: Selection (“Camels in Desert”)

Source: Perez-Sebastian and Steinbuks (2017)

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Why Quantifying Economic Impacts of Electricity Availability is a Difficult Problem? • Problem 5: Distorted power markets

• Creating functional power markets is difficult (Foster and Rana, 2019; IFC 2020)

• Upstream distortions• Distorted input markets (power generation)• Distorted input transportation modes

• Downstream distortions• Underpricing • Cross-subsidies • Market power / Double marginalization

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Example I: Upstream Distortion (India)

Source: Zhang (2019).

128 l IN THE DARK

FIGURE 4.12 Distance to coal mines is correlated with worse coal shortages and lower power generation in India

−50

0

50

100

Res

idua

l var

iati

on

in c

oal

sho

rtag

es(t

hous

and

s o

f to

ns)

0 500 1,000 1,500 2,000

Distance (kilometers)

a. Coal shortage of power plants is positively correlatedwith distance to coal mines

−5

0

5

10

Res

idua

l var

iati

on

in u

tiliz

atio

n ra

te (

per

cent

age

po

ints

)

0 500 1,000 1,500 2,000Distance (kilometers)

b. Utilization rate of power plants is negatively correlatedwith distance to coal mines

figure continues next page

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Example II: Downstream distortion (Uganda)

Source: Blimpo, McRae, and Steinbuks (2018).

Figure 9: Optimal connection charges and share of connected households, as a function ofregulated electricity price

0

200

400

600

800

10 20 30 40Electricity price (US cents/kWh)

Opt

imal

con

nect

ion

char

ge (U

S$)

0

5

10

10 20 30 40Electricity price (US cents/kWh)

% o

f con

nect

ed h

ouse

hold

s

Note: Calculation based on a wholesale electricity price of 10.4 cents/kWh, distribution losses of15%, a marginal connection cost of $200 per connection, annual costs of $41 per connection, anda discount rate for the distribution utility of 5%.

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Underpricing makes utilities raise connection charges,lowering electricity access

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Why is Quantifying Economic Impacts of Electricity Availability a Difficult Problem? • Problem 6: Network externalities

Source: Schiel et al., (2017)

ü Electricity storage is very costly

ü Load balancing happens real-time

ü Congestion is known to grid operator but otherwise difficult to detect

Extracting local exogenous variation in electricity availability is difficult and often impossible

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Assessing Economic Impacts of Electricity Availability: Approaches

• Approach 1: Time-series

• “High-frequency” approach to low frequency data

• Can only establish direction not magnitude

• A-theoretical

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• Approach 2: CGE models

• “Loosely” calibrated

• Stylized representation of power sector

• Typically comparative statics

-6.4

-2.5

-32.6

-6.9-5.4

-2.8

Grossdomesticproducts

Governmentrevenue

Investmentdemand

Totalindustrialoutput Imports Exports

Source: Timilsina, Steinbuks, and Sapkota (2018)

Annual average deviation from the Nepal CGE model baseline (%) during the 2008-2015 period

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• Approach 3: Randomized Control Trials

• Very difficult to implement

• Don’t trace long-term effects

• Partial equilibrium

• External validity issues

Source: Lee, Miguel, and Wolfram (2020a)

Example: Kenya Electricity Access RCT Figure 3—Experimental evidence on the social surplus implications of rural electrification

Panel A Panel B Panel C

Notes: Panel A combines the experimental demand curve with the population-weighted average total cost per connection (ATC) curve correspondingto the predicted cost of connecting various population shares, based on the nonlinear estimation of ATC = b0/M + b1 + b2M. Each point representsthe community-level, budgeted estimate of ATC at a specific level of coverage. Panel B demonstrates that the estimated total cost of communityelectrification is $62,618, based on average community density of 84.7 households. The area under the demand curve is estimated to be $12,421.These estimates suggest that a mass electrification program would result in a social surplus loss of $50,197 per community (i.e., $593 per household).Panel C combines the curves in panel A with the contingent valuation (CV) questions included in the baseline survey. The CV questions included:(1) whether the household would accept a hypothetical offer (i.e., at a randomly assigned price) to connect to the grid; (2) whether the householdwould accept the same offer if required to complete the payment in six weeks. The credit offer consisted of an upfront payment (ranging from $39.80to $79.60), a monthly payment (ranging from $11.84 to $17.22), and a contract length (either 24 or 36 months). We plot the net present value of thecredit offers, assuming a 15 percent discount rate. Additional details on the credit offers are provided in appendix table B9.

Copyright The University of Chicago 2019. Preprint (not copyedited or formatted). Please use DOI when citing or quoting. DOI: 10.1086/705415

Journal of Political Economy Downloaded from www.journals.uchicago.edu by Nottingham Trent University on 08/01/19. For personal use only.

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• Approach 4: “Exogenous variation”

• Specific to local context and technology

• Often unstable and not robust

• Need knowledge of power engineering and operations research

Notable studies:• Dinkelman (2011): topography as IV for

electricity access in South Africa • Rud (2012): availability of groundwater for

electric irrigation as IV for access in India• Lipscomb et al. (2013): hydropower

expansion plan as IV for access in Brazil • Allcott et al. (2016): hydrological conditions

as IV for reliability in India • Kassem (2020): power expansion plan based

on colonial incumbent infrastructure

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Assessing Economic Impacts of Electricity Availability: Takeaway

130 Journal of Economic Perspectives

Figure 2 Key Estimates of the Impacts of Rural Electrification

Source: Author calculations, based on the estimates presented in each of the cited articles.Note: In this figure, we present key estimates of the impact of electrification on labor supply (panel A) and education (panel B) outcomes. For each study, coefficient estimates have been expressed as a percentage of the mean of the dependent variable. Percentage point units are denoted as p.p.

India (Burlig and

Preonas 2016)[RD]

Kenya(Lee, Miguel, and Wolfram, forthcoming)

[RCT]

South Africa(Dinkelman

2011)[IV]

Nicaragua(Grogan and

Sadanand2013)[IV]

India(Van de Walle

et al. 2017)[IV]

9.5 p.p. increase in

female employment

18 p.p. increase in probability of

employment

23 p.p. increase in female propensity

to work outside the home

14.6 additional days per year of

regular wage work for men

0.5 p.p. increase for men in nonagricultural,

nonhousehold labor

Brazil(Lipscomb,

Mobarak, and Barham 2013)

[IV]

0.233increase in boys’

completed schooling

years

0.9 year increase in girls’

schooling0.5 increase in

completed schooling year

for girls

no statistically significant changes in enrollment

increase of 2 years of

schooling


proportion of women in

household-employedor own business

MaleFemaleBoth90% confidence interval

250

200

150

100

50

120

100

80

60

40

20

0

−20

0

−50Mai

n co

effic

ient

(as

per

cent

of s

ampl

e m

ean)

Vietnam(Khandker,Barnes, and Samad 2012)

[IV]

India(Burlig and

Preonas 2016)[RD]

Bangladesh(Khandker,Barnes, and Samad 2012)

[IV]

Brazil(Lipscomb,


[IV]

−7.0% on average

girl’s test score

Mai

n co

effic

ient

(as

per

cent

of s

ampl

e m

ean)

Kenya(Lee, Miguel, and Wolfram,forthcoming)

[RCT]

India(Van de Walle

et al. 2017)[IV]

A: Labor supply impacts

B: Education impacts


130 Journal of Economic Perspectives

Figure 2 Key Estimates of the Impacts of Rural Electrification

Source: Author calculations, based on the estimates presented in each of the cited articles.Note: In this figure, we present key estimates of the impact of electrification on labor supply (panel A) and education (panel B) outcomes. For each study, coefficient estimates have been expressed as a percentage of the mean of the dependent variable. Percentage point units are denoted as p.p.

India (Burlig and

Preonas 2016)[RD]

Kenya(Lee, Miguel, and Wolfram, forthcoming)

[RCT]

South Africa(Dinkelman

2011)[IV]

Nicaragua(Grogan and

Sadanand2013)[IV]

India(Van de Walle

et al. 2017)[IV]


female employment

18 p.p. increase in probability of

employment

23 p.p. increase in female propensity

to work outside the home

14.6 additional days per year of

regular wage work for men

0.5 p.p. increase for men in nonagricultural,

nonhousehold labor

Brazil(Lipscomb,


[IV]

0.233increase in boys’

completed schooling

years

0.9 year increase in girls’

schooling0.5 increase in

completed schooling year

for girls

no statistically significant changes in enrollment

increase of 2 years of

schooling


proportion of women in

household-employedor own business


250

200

150

100

50

120

100

80

60

40

20

0

−20

0

−50Mai

n co

effic

ient

(as

per

cent

of s

ampl

e m

ean)

Vietnam(Khandker,Barnes, and Samad 2012)

[IV]

India(Burlig and

Preonas 2016)[RD]

Bangladesh(Khandker,Barnes, and Samad 2012)

[IV]

Brazil(Lipscomb,


[IV]

−7.0% on average

girl’s test score

Mai

n co

effic

ient

(as

per

cent

of s

ampl

e m

ean)

Kenya(Lee, Miguel, and Wolfram,forthcoming)

[RCT]

India(Van de Walle

et al. 2017)[IV]

A: Labor supply impacts

B: Education impacts


Source: Lee, Miguel, and Wolfram (2020b).

• Empirical evidence is inconclusive • No methodological “silver bullet”

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Assessing Economic Impacts of Electricity Availability: What Should We Do?

• Use causal inference to• Calculate key [reduced-form] elasticities • Identify the channels [causal mechanisms] through which

electricity availability affects economic outcomes

• Use economic modeling to• Construct scenarios to determine best policies to maximize

economic gains from electricity availability improvements

• Use forecasting/posterior analysis to validate and rigorously test modeling assumptions and predictions

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Brazil as a “laboratory” for research on long-term electricity availability

• Massive Expansion of Electricity Access over the last 4 decades

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Source: Fidel-Sebastian, Steinbuks, Feres, and Trotter (2020)

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• Brazilian electricity sector highly dependent on renewable energy - in particular hydroelectricity - which is stochastic by nature.

• Climatic and hydrology conditions make Brazilian system vulnerable to high scale power outages since main hydropower facilities are located far from the consumption centers

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Events in the Brazilian electricity sector:2009-2015

Interruption > 100 MW; duration > 10 min.

Source: Boletim Mensal de Monitoramento do Sistema Elétrico Brasileiro

0

10

20

30

40

50

60

70

80

90

100

0

5000

10000

15000

20000

25000

30000

35000

Jan-

09Ap

r-09

Jul-0

9Oc

t-09

Jan-

10Ap

r-10

Jul-1

0Oc

t-10

Jan-

11Ap

r-11

Jul-1

1Oc

t-11

Jan-

12Ap

r-12

Jul-1

2Oc

t-12

Charge (MW) Nb events

0

2

4

6

8

10

12

14

16

18

0

2000

4000

6000

8000

10000

12000

Jan-

13M

ar-1

3M

ay-1

3Ju

l-13

Sep-

13No

v-13

Jan-

14M

ar-1

4M

ay-1

4Ju

l-14

Sep-

14No

v-14

Jan-

15M

ar-1

5M

ay-1

5Ju

l-15

Sep-

15No

v-15

charge (MW) Nb events

Interruption > 15 MW; duration > 10 min.

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• High quality spatially disaggregated panel data on various socio-economic characteristics going back to 1970s

• High quality firm-level panel microdata going back to 1995

• Strong domestic economic research infrastructure

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• Econometric analysis• Panel regression analysis of electricity access

measure on outcome variables (growth, sectoral GDP shares)

• Use power sector planning model of Lipscomb et al., (2013) based on topological and hydrological characteristics as an ‘instrumental’ variable to address simultaneity in electricity access and economic development

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• Dynamic General Equilibrium (GE) model of growth and structural transformation

• Captures interplay between households, firms, and [not necessarily benevolent] government

• 3 sectors in economy (agriculture, industry, services)• Electricity infrastructure enters the model through

• Firms’ production (accounting for different sector intensities of grid electricity use)

• Firms’ fixed and operational costs (coping with unreliable power supply, administrative costs)

• Government investment (accounting for mismanagement and resource rents, political preferences)

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• Lower fixed & operational costs accelerate growth of manufacturing and agriculture sectors along intensive margin and services along extensivemargin

• 1 percent growth in electricity availability results in a 1.4 percent increase in services share

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Figure 4: Comparative dynamics if G remains constant

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Final Remark: Electricity is only a Part of a Big Infrastructure Picture

20101970

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Final Remark: Electricity is only a Part of a Big Infrastructure Picture

Source: Blankespoor, Selod, Steinbuks, and Trotter (research in progress)

Thank you!

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ReferencesAlberini, A. Steinbuks, J. & Timilsina, G. (2020). How Valuable are Improvements in Residential Electricity Supply? Evidence from Nepal. World Bank Policy Research Paper 9311. World Bank, Washington, DCAllcott, H., Collard-Wexler, A., & O'Connell, S. D. (2016). How do electricity shortages affect industry? Evidence from India. American Economic Review, 106(3), 587-624.Bhatia, M., & N. Angelou. (2015). Beyond Connections: Energy Access Redefined. ESMAP Technical Report 008/15, The World Bank.Blimpo, M. P., Mcrae, S. D., & Steinbuks, J. (2018). Why are connection charges so high? An analysis of the electricity sector in Sub-Saharan Africa. World Bank Policy Research Paper 8407. World Bank, Washington, DC.Dinkelman, T. (2011). The effects of rural electrification on employment: New evidence from South Africa. American Economic Review, 101(7), 3078-3108.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(9), 2690-2701.Foster, V., & Rana. A. (2019). Rethinking Power Sector Reform in the Developing World. World Bank, Washington, DC International Energy Agency. (2018). World Energy Outlook. IEA/OECD.International Financial Corporation (2020). Creating Markets: Power Markets For Development, forthcoming. Kassem, D. (2020). Does Electrification Cause Industrial Development? Grid Expansion and Firm Turnover in Indonesia https://www.dropbox.com/s/ekn96nnik5sfblj/Electrification_Kassem.pdf?dl=0

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ReferencesLee, K., Miguel, E., & Wolfram, C. (2020a). Experimental evidence on the economics of rural electrification. Journal of Political Economy, 128(4), 1523-1565.Lipscomb, M., Mobarak, A. M., & Barham, T. (2013). Development effects of electrification: Evidence from the topographic placement of hydropower plants in Brazil. American Economic Journal: Applied Economics, 5(2), 200-231.Perez Sebastian, F., & Steinbuks, J. (2017). Public Infrastructure and Structural Transformation. World Bank Policy Research Working Paper 8285. World Bank, Washington, DCPerez-Sebastian, F., Steinbuks, J., Feres, J., & Trotter, I. (2020). “Electricity Access and Structural Transformation: Evidence from Brazil's Electrification”, World Bank Policy Research Paper 9182 . World Bank, Washington, DCRud, J. P. (2012). Electricity provision and industrial development: Evidence from India. Journal of development Economics, 97(2), 352-367.Schiel, C., Lind, P.G. & Maass, P. (2017). Resilience of electricity grids against transmission line overloads under wind power injection at different nodes. Sci Rep 7, 11562Sievert, M., & Steinbuks, J. (2020). Willingness to pay for electricity access in extreme poverty: Evidence from sub-Saharan Africa. World Development, 128, 104859.Timilsina, G. R., Sapkota, P., & Steinbuks, J. (2018). How Much Has Nepal Lost in the Last Decade Due to Load Shedding? An Economic Assessment Using a CGE Model. World Bank Policy Research Working Paper, 8468. World Bank, Washington, DCZhang, Fan. (2019). In the Dark: How Much Do Power Sector Distortions Cost South Asia? Washington, DC: World Bank

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