the impact of electricity outages on households...the smaller is the observed change in grid...

The Impact of Electricity Outages on Households

by

Ayesha Ali

A thesis submitted in conformity with the requirementsfor the degree of Doctor of PhilosophyGraduate Department of Economics

University of Toronto

Copyright c© 2016 by Ayesha Ali

Abstract

The Impact of Electricity Outages on Households

Ayesha Ali

Doctor of Philosophy

Graduate Department of Economics

University of Toronto

2016

Electricity outages are a common occurrence in developing countries and can have a substantial

impact on household welfare. In this dissertation, I assemble a unique data set with a district

level measure of outages, electricity generation prices, and labour market outcomes to examine

the effect of outages on employment and earnings. I also develop an analytical framework that

can be used to measure the welfare impact of outages on households arising from disruption of

electricity used in the home.

In chapter 1, I construct a district level measure of outages in Pakistan using the intra-annual

variability in night lights observed in meteorological satellite data. I estimate the elasticity be-

tween variability and reported outages, and use household electricity consumption data to show

that electricity consumption declines as variability increases within districts. These findings es-

tablish that night lights variability is a valid measure of outages that can be used in empirical

applications where subnational data on outages is not available.

In chapter 2, I estimate the effect of outages on labour market outcomes of adult males

in Pakistan. To address the problem of measurement error and potential endogeneity in night

lights variability, I use exogenous variation in the price of energy used to generate electricity at

thermal plants near a district, as an instrument. I find that outages have a significant negative

effect on employment, days worked, earnings and productivity. A larger effect on districts with

a greater reliance on electricity intensive industries and more educated workers suggests labour

demand driven reduction in productivity.

In chapter 3, I develop an analytical framework to measure the welfare impact of electricity

outages on households. The welfare effect or the willingness to pay for a reduction in outages is

larger the greater is the impact on home produced services affected by outages. It is also larger

ii

the smaller is the observed change in grid electricity expenditures. I find that the elasticity of

grid electricity consumption to outages is smaller the more outward shifted is the demand for

electricity, suggesting that households with a high value for electricity adapt by rescheduling

activities or acquiring off-grid supply.

iii

Dedication

To my parents, for their constant encouragement.

iv

Acknowledgements

I would like to thank my supervisors Dwayne Benjamin, Gustavo Bobonis, and Nicholas Li

for their guidance and support. My dissertation has also benefitted from valuable comments

and suggestions from Muneeza Alam, Abid Burki, Arthur Bloin, Loren Brandt, Christian Dip-

pel, Marco Gonzalez-Navarro, Jonathan Hall, Juan Morales, Palermo Penano, Laura Turner,

Genet Zinabou and seminar participants at the University of Toronto and Lahore University of

Management Sciences.

I would especially like to thank the Map and Data library at University of Toronto, Pak-

istan Bureau of Statistics, Center for Research in Economics and Business (Lahore School of

Economics) and the Lahore Electric Supply Company for assistance with data collection.

I gratefully acknowledge financial support from the University of Toronto, Canadian Labour

Market and Skills Researcher Network, Ontario Graduate Scholarship, Ranjit Kumar Graduate

Fellowship and Royal Bank Graduate Fellowship in Public and Economic Policy.

v

Contents

Introduction 1

1 Measuring Electricity Outages from Outer Space 5

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.2 Using Night Lights Data to Measure Outages . . . . . . . . . . . . . . . . . . . . 6

1.2.1 Night Lights Data Archive . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.2.2 Night Lights Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.2.3 Trends in Night Lights Variability . . . . . . . . . . . . . . . . . . . . . . 9

1.2.4 Reported Outages and Household Electricity Consumption Data . . . . . 10

1.3 Measurement Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.3.1 Measurement Error in Night Lights Variability . . . . . . . . . . . . . . . 12

1.3.2 Measurement Error in Reported Outages . . . . . . . . . . . . . . . . . . 15

1.4 Does Night Lights Variability Capture Outages? . . . . . . . . . . . . . . . . . . 16

1.4.1 Night Lights Variability and Reported Outages . . . . . . . . . . . . . . . 16

1.4.2 Night Lights Variability and Household Electricity Consumption . . . . . 17

1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2 Energy Prices, Electricity Outages and Employment 27

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.2 Electricity Sector in Pakistan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.2.1 Electricity Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.2.2 Electricity Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.2.3 Electricity Distribution and the Subsidy Regime . . . . . . . . . . . . . . 31

2.2.4 Oil Prices and the Electricity Shortage Crisis . . . . . . . . . . . . . . . . 32

2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.3.1 Thermal Power Plants and Energy Prices . . . . . . . . . . . . . . . . . . 33

2.3.2 Night Lights Variability and Outages . . . . . . . . . . . . . . . . . . . . . 34

2.3.3 Labour Market Outcomes and District Characteristics . . . . . . . . . . . 35

vi

2.4 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.4.1 Conceptual Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.4.2 The Instrument – Price of Energy Mix . . . . . . . . . . . . . . . . . . . . 37

2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

2.5.1 Outages and Price of Energy Mix . . . . . . . . . . . . . . . . . . . . . . . 41

2.5.2 Outages, Employment and Earnings – OLS and IV Results . . . . . . . . 42

2.6 Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

2.7 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

2.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3 The Welfare Effect of Electricity Outages on Households 69

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

3.2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.2.1 The Experience of Outages By Households . . . . . . . . . . . . . . . . . 71

3.2.2 Household Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

3.2.3 Willingness To Pay For a Change in Outages . . . . . . . . . . . . . . . . 73

3.2.4 Backup Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

3.2.5 Model Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

3.3 Measuring Willingness To Pay . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

3.4 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

3.4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

3.4.2 Electricity Demand Equations . . . . . . . . . . . . . . . . . . . . . . . . . 81

3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

3.5.1 Electricity Demand in Urban Areas . . . . . . . . . . . . . . . . . . . . . 84

3.5.2 Heterogeneity by Income and Appliance Type . . . . . . . . . . . . . . . . 85

3.5.3 Heterogeneity by Temperature . . . . . . . . . . . . . . . . . . . . . . . . 87

3.5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

3.6 Limitations of Welfare Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Bibliography 99

Appendix 105

Appendix A Data Appendix 105

A.1 Electricity Sector, Power Plants and Energy Prices Data . . . . . . . . . . 105

A.2 Household Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

vii

A.3 District Infrastructure and Weather Data . . . . . . . . . . . . . . . . . . 108

A.4 Standard Errors Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . 108

Appendix B Appendix Tables and Figures 109

viii

List of Tables

1.1 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.2 Night Lights Variability and Outage Duration . . . . . . . . . . . . . . . . . . . . 21

1.3 Correlation between Lights Variability and Household Electricity Demand . . . . 21

1.4 Household Electricity Demand Excluding Outages . . . . . . . . . . . . . . . . . 22

1.5 Correlation between Unpredicted Drop in Demand and Lights Variability . . . . 22

2.1 Thermal Power Plants and Energy Prices . . . . . . . . . . . . . . . . . . . . . . 52

2.2 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

2.3 Correlation between Baseline District Characteristics and Share of Oil Capacity . 54

2.4 Outages and Price of Energy Mix . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

2.5 Effect of Outages on Employment . . . . . . . . . . . . . . . . . . . . . . . . . . 56

2.6 Effect of Outages on Earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

2.7 Effect of Outages in Electricity Intensive Districts . . . . . . . . . . . . . . . . . 58

2.8 Effect of Outages in Districts with More Educated Workforce . . . . . . . . . . . 59

2.9 Effect of Outages in High Temperatures . . . . . . . . . . . . . . . . . . . . . . . 60

2.10 Effect of Outages in Remote Districts . . . . . . . . . . . . . . . . . . . . . . . . 61

2.11 Placebo Check – Correlation between Energy Prices and Prior Outcomes . . . . 62

2.12 Robustness checks – I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

2.13 Robustness checks – II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

3.1 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

3.2 Electricity Demand in Urban Areas . . . . . . . . . . . . . . . . . . . . . . . . . . 93

3.3 Effect of Outages on Demand by Income and Appliances . . . . . . . . . . . . . . 94

3.4 Effect of Outages on Demand by Temperature and Income . . . . . . . . . . . . . 95

3.5 Estimates of |pg∂Eg

∂q | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

B.1 Generator and Backup Supply Ownership Rates . . . . . . . . . . . . . . . . . . 109

B.2 Outages and Type of Employment . . . . . . . . . . . . . . . . . . . . . . . . . . 110

B.3 Effect of Outages – Additional Specification Checks . . . . . . . . . . . . . . . . . 111

ix

List of Figures

1.1 Measuring Night Lights Variability in Districts . . . . . . . . . . . . . . . . . . . 23

1.2 Night Lights Variability in Districts of Pakistan . . . . . . . . . . . . . . . . . . 24

1.3 Distribution of Lights Variability Pre and Post 2006 . . . . . . . . . . . . . . . . 25

1.4 Correlation between Growth in Average Lights and Lights Variability . . . . . . 25

1.5 Density of Residuals from Electricity Demand Equation . . . . . . . . . . . . . . 26

2.1 Aggregate Demand-Supply Gap and Oil Prices . . . . . . . . . . . . . . . . . . . 65

2.2 Energy Prices and Outages Measured by Variability in Night Lights . . . . . . . 66

2.3 Thermal Plants and Outages Measured by Variability in Night Lights . . . . . . 67

2.4 Energy Prices, Plant Cost and Plant Utilization Factor . . . . . . . . . . . . . . 68

3.1 Electricity Consumption and Income Per Capita in Developing Countries . . . . 97

3.2 Electricity Consumption and Temperature in Developing Countries . . . . . . . . 97

3.3 Electricity Consumption and Household Per Capita Expenditures . . . . . . . . . 98

3.4 Appliance Ownership and Household Per Capita Expenditures . . . . . . . . . . 98

B.1 Variation in Electricity Demand During the Day and Night . . . . . . . . . . . . 112

x

Introduction

Access to electricity is an important indicator of development that is associated with higher

incomes, increased productivity, greater labour force participation and better opportunities for

human capital development (Greenstone, 2014). Despite the rapid expansion of the electricity

infrastructure in many developing countries, the provision of reliable electricity remains a chal-

lenge for governments and electricity providers. Demand for electricity has gone up rapidly and

is projected to continue rising, due to growth in household incomes, urbanization, and adop-

tion of modern appliances (Wolfram, Shelef and Gertler, 2012). Many developing countries

subsidize electricity for residential and agricultural consumers (Brown and Mobarak, 2009). As

electricity prices are too low to recover the costs of provision, there is insufficient investment in

generation and distribution infrastructure (McRae, 2015 and Greenstone, 2014). The result is

that electricity providers often resort to outages or rolling blackouts to manage the gap between

supply and demand.

Electricity is an important input in many productive activities taking place in the factory,

shop or at home. Firm level data collected by the World Bank Enterprise Surveys carried

out between 2009 and 2015 in 135 developing countries, shows that on average electricity was

cut 6.3 times a month and a typical outage lasted 4.7 hours. One of the regions with the

highest incidence of outages is South Asia, where firms report that electricity was cut 25 times

in a month and a typical outage lasted 5.3 hours. Recent work on the impact of outages

on manufacturing firms, for example, Allcott, Collard-Wexler and O’Connell (2015), Fisher-

Vanden, Mansur and Wang (2015), Abeberese (2013) and Alam (2013) shows that big firms are

able to cope with outages by investing in self generation or adjusting their production processes

which mitigates the impact on total factor productivity.

In many developing countries, a large fraction of income generation activities are carried out

in small enterprises that may operate within the home. These establishments may not be able

to fully insure themselves against electricity outages if the fixed costs of adaptation are high. As

a result, frequent interruption in electricity supply due to outages can have a significant impact

on the extent to which workers and businesses can utilize their resources for income generation.

Outages can create unemployment or reduce the number of days worked as businesses adjust

1

2

or shut down to minimize losses. Labour productivity can also go down due to unavailability

of electricity as a complementary input for production. All of these channels will lead to a

negative effect on earned income that may also have an impact on present and future human

capital outcomes. Therefore, by focussing on the impact of outages on large manufacturing

firms, we will not be able to fully capture the effect of unreliable electricity supply on economic

activity in a broader sense.

In this dissertation, I analyze the effect of electricity outages on labour market outcomes and

electricity consumption within the home using district and household level data from Pakistan

from 2004 to 2011. Pakistan experienced a large increase in the frequency of outages, popularly

termed as “loadshedding” after 2006. According to conservative estimates, post 2006, rolling

blackouts took place for up to 6 to 8 hours each day in urban areas and 10 to 12 hours in

rural areas. One of the main causes of the gap between electricity supply and demand was that

electricity generation cost went up substantially due to a rise in world oil prices. Electricity

prices for consumers were not adjusted to reflect the higher costs and this created a cash flow

problem for electricity distribution companies forcing them to cut back electricity purchases

from generation companies.

In order to examine the impact of outages on households, I construct a yearly district level

measure of outages in Pakistan using satellite images of the earth observed at night. The

measure is a population weighted average of the intra-annual variability in night lights at a

given location, as captured by satellite observations taken over the course of the year. Despite

the problem of measurement error, I find that night lights variability is significantly correlated

with reported outage duration and household electricity consumption.

The results from a reduced form regression between variability and outage duration reported

by distribution companies suggest that an additional hour of outages per day, generates ap-

proximately 10 percent change in observed night lights variability. This is an under estimate

of the true elasticity between outages and variability, as reported outage duration itself is mea-

sured with error. A 10 percent increase in variability is also equivalent to 4.2 percent reduction

in monthly household expenditures on electricity within districts. The results of this chapter

establish that night lights variability is a valid measure of subnational outages and can be used

in empirical applications where subnational data on outages is not available.

Using nights lights variability as a measure of outages, I estimate its impact on employment,

earnings and labour productivity of adult males at the district level using data from 2004

to 2011. In order to address concerns about measurement error and unobserved correlation

between changes in outages and labour market conditions, I use an instrumental variables

strategy that relies on exogenous variation in energy input prices of thermal power plants due

to a global oil price shock. Using detailed data on plant inputs, plant capacity, and distance to

3

the districts, I construct an instrument that measures the district’s yearly average price of the

energy mix that is used to generate electricity. The instrument acts a local electricity supply

shifter, as districts closer to plants using oil based inputs experience an increase in outages due

to a reduction in electricity supply.

I find that a 10 percent increase in variability or approximately an additional hour of outages

each day of the month reduces the fraction employed by 2.1 percent and number of days worked

in the month by 8 percent. Total earnings fall by 18 percent and labour productivity is reduced

by 11 percent. I find that the effect is larger in districts that are likely to experience larger

demand shocks due to a greater reliance on electricity intensive industries and more educated

workforce. My results show that the negative effect of outages is amplified during periods of

high temperatures as the absence of electricity can also reduce labour efficiency. The effect of

outages is also larger in districts that are located close to national highways. These districts

may be growing faster, and thus energy price shocks result in a larger gap in electricity supply

and potential demand in these districts. The results are robust to adding a series of additional

controls for plant characteristics, plant entry, and other district time varying factors. The results

of this chapter emphasize that the frequent interruption in electricity can have a substantial

effect on incomes and productivity that may be missed when looking at large firms only.

Finally, I develop an analytical framework that can be used to measure the welfare impact

of outages on households arising from disruption of electricity used in the home for household

chores, indoor cooling or heating and for other purposes. The model shows that total welfare

effect is composed of the change in utility arising from change in home produced services

disrupted from outages, the change in time devoted to performing these services valued at the

opportunity cost of household time, and adjustment in electricity expenditures.

The model predicts that the larger is the difference between the utility change due to

disruption of electricity using services and the observed change in grid electricity expenditures,

the larger is the willingness to pay for a reduction in outages. This is because households

try to mitigate the effect of outages by rescheduling production and using off-grid technology.

If the scope of adjustment is limited then the welfare cost of outages is larger. The model

also predicts that if service disruption is higher when electricity demand is shifted outwards,

then the difference in the observed changes in electricity expenditures can be used to put a

lower bound on the difference in willingness to pay when demand is high versus low. Using

household electricity demand data, I estimate the elasticity and implied adjustment in electricity

expenditures in response to outages and how it varies with income and temperature.

My research is part of a growing literature on estimating the effects of electricity outages.

In contrast to the findings from previous work that generally found small effects for big manu-

facturing firms, I find a large and negative effect on household incomes and labour productivity.

4

This suggests that the average household and business is limited in its ability to mitigate the

effect of outages through off-grid supply. The empirical strategy used to identify the effect of

outages is based on a common feature of electricity markets in many developing countries. In

particular, I am able to utilize supply shocks induced by global movements in energy input

prices in a market where end user prices are subsidized and do not adjust completely to reflect

increase in generation cost.

My work is also related to the large literature on the effect of electrification programs on

development outcomes (see for example Dinkelman, 2011, Lewis, 2013, Lipscomb, Mobarak,

Barham, 2013 and Rud, 2012). The findings of my research emphasize that even though access

to electricity infrastructure is important for increasing income, productivity and generating

advances in human capital, the reliability (quality) of service also matters.

In order to understand the change in welfare arising from disruption of electricity used in

the home, I develop an analytical framework for quantifying the welfare effect. In contrast

to the previous literature on measuring the willingness to pay (see for example Ozbfali and

Jenkins, 2015 and Westley, 1984), my research suggests that a combination of stated and

revealed preference methods should be used to fully capture the welfare effect. I also show

that the household’s willingness to pay for a reduction in outages will vary with income and

temperature.

The rest of the dissertation is arranged as follows. In chapter 1, I construct and validate the

measure of electricity outages derived from night time lights data. In chapter 2, I estimate the

effect of outages on labour market outcomes and provide evidence on the mechanisms underlying

the observed impact. In chapter 3, I present the model used to quantify the welfare effect arising

from disruption of electricity used in the home, and use household electricity consumption data

to examine how the welfare effect varies with income and temperature.

Chapter 1

Measuring Electricity Outages from

Outer Space

1.1 Introduction

A key empirical challenge in understanding the impact of outages is that data on the frequency

and duration of outages over time are not easily available. Many developing countries do

not have digital information systems to monitor outages, and data is collected by means of

manual records that are susceptible to errors and omissions. Where digital records do exist,

governments and utility providers might not publicly release electricity outage data, as it can

reveal politically sensitive information about the allocation of scarce electricity.

Some cross country snapshots of the quality of electricity infrastructure are available from

firm level surveys that are carried out by the World Bank to assess the competitiveness of the

business environment. These surveys provide valuable information about how countries rank in

controlling outages to firms. However, the use of this data is limited, as there is no information

on the incidence of outages at a subnational level over time. This is a serious drawback from

a policy perspective as the relevant unit of performance evaluation and intervention is often

the local utility or distribution company. Furthermore, many electricity deficient countries

minimize the incidence of outages to firms, so estimates from firm level surveys likely understate

the magnitude of problem faced by other consumers such as households.

In this chapter, I construct a district level measure of outages in Pakistan from 2000 to 2012,

using satellite images of the earth observed at night. This measure is based on the intra-annual

variability in night lights at a given location, as captured by satellite observations taken over the

course of the year. It was first used by Alam (2013) to estimate the impact of outages on rice

and steel manufacturing plants in Indian districts. I depart from Alam (2013) in two important

ways. First, I use the lights data to measure outages experienced by households instead of

5

Chapter 1. Measuring Electricity Outages from Outer Space 6

firms. The persistence or variability of lighting is likely to be a better reflection of electricity

usage by households and market places, as compared to electricity usage of large manufacturing

firms that are more likely to operate during the day. While outages should be highly correlated

across manufacturing firms and households at any point in time, changes in outages over time

may not necessarily be correlated, if firms are favoured in the provision of electricity. Second,

in order to aggregate the gridded lights data to arrive at a district level measure, Alam (2013)

uses the median night lights variability observed within the district boundaries. This measures

the average variability in night lights experienced by a district over time. In contrast, I use

gridded population counts data, to create a population weighted average of variability in night

lights. Thus, my measure of outages captures the intra-annual variability in electricity provision

experienced by a population over time, and is likely to be a better measure of outages in districts

where population in not evenly spread across space.

I show that variability in night lights can be used to track the duration of outages and

household electricity expenditures over time. Using data on average reported duration of out-

ages provided by distribution companies in Pakistan, I estimate an elasticity between changes

in night lights and changes in duration of reported outages. This is a useful parameter that

can be used in future empirical work using lights variability as a measure of outages. I also use

household electricity expenditures data to estimate the responsiveness of household electricity

usage to light variability within districts over time. This results of this exercise show that night

lights variability is valid subnational measure of outages over time.

The rest of the chapter is arranged as follows. In section 1.2, I describe the night lights

data, the steps that are used to process the geospatial data into a yearly measure of subnational

night lights variability, as well as other data used in this chapter. In section 1.3, I discuss the

conceptual issues using surrounding the use of night lights variability as a measure of outages.

Section 1.4 presents the results of correlating this measure with reported outage duration and

electricity consumption. Section 1.5 concludes with a summary of findings.

1.2 Using Night Lights Data to Measure Outages

1.2.1 Night Lights Data Archive

The Earth Observation Group of National Atmospheric and Oceanic Administration (NOAA)

housed at the National Geophysical Data Center (NGDC) produces yearly composites of night

lights at a resolution of 30 arc seconds (approximately 1 km2 at the Equator). These composites

are derived from satellite observations taken by the US Department of Defense Meteorological

Satellite Program Operational Linescan System (DMSP–OLS).

The satellites orbit the earth in polar orbits and collect fourteen orbits per day. With a


3,000 km swath width, each OLS satellite is capable of collecting a complete set of images of

the Earth twice a day. The satellites are capable of detecting low levels of visible lights at night.

In order to produce the yearly composites, observations with cloud cover, natural light such

as sunlight or moonlight, and auroral emissions are removed. In a typical annual cloud free

composite most pixels have 20 -100 observations (Elvidge et al., 2014). The night time light

composites are coded as a digital number (DN) from 0 to 63 with a higher number indicating

brighter lights.

The night lights composites can be subject to measurement error due to the way data is

collected and processed. Saturation or top coding of pixels at 63 implies that the true brightness

of lights in urban cores is under estimated when using this data. Top coding will also lead us to

under estimate the true change in brightness over time. Lights data is also subject to blooming

effects – that is lights may actually bleed into neighbouring pixels (especially near water and

snow). This can cause an over estimation of the brightness and persistence of lights in pixels

close to brightly lit areas. Another source of noise can arise from differences in on-board satellite

sensor settings that can limit the comparability of digital numbers over time.1 Measurement

error can also arise due to geography, for example in places with more cloud cover, the number of

observations over which annual composites are calculated may be lower, reducing the accuracy

of the measurement.

Despite these known disadvantages, the DMSP-OLS annual archives that are publicly avail-

able from 1992 onwards have been widely used to study subnational development. For exam-

ple, Henderson, Storeygard, and Weil (2012), argue that income per capita growth estimated

from the lights data is more accurate than World Bank estimates especially for countries with

poor quality data. Chen and Nordhaus (2014) also find that night lights can be used as a

proxy for spatial distribution of economic activity in countries with weak statistical systems.

Michalopoulos and Papaioannou (2013) find that pre-colonial ethnic institutions in Africa corre-

late significantly with contemporary regional development measured by night time light density.

In addition to measuring economic development and growth, night lights also provide informa-

tion about allocation of electricity within countries (Baskaran, Min, and Uppal, 2015; Min and

Gaba, 2014). Brightness of lights has been used to estimate electric power consumption in India

and China (Chand et. al, 2009 and He et. al, 2013), and the variability in lights has been used

as a proxy for frequency of power outages (Alam, 2013).

1This is not a problem when comparing digital numbers from composites produced from the same satellitein a given year. In order to compare digital numbers across satellites and years, Elvidge et. al (2009, 2014) haveproduced a set of inter-calibration estimates that are designed to make the data comparable over time.


1.2.2 Night Lights Variability

I use the average lights (AL) and the average normalized lights (NL) composites that are publicly

available at a yearly frequency to construct a subnational measure of night lights variability in

Pakistan from 2000 to 2012. The AL composite measures the average luminosity or brightness

of lights, while the NL composites normalize the average by a percent frequency of detection

term. The inclusion of the percent frequency of detection term normalizes the resulting digital

numbers for variations in the persistence of lighting. For example, if in a given location a light

is observed only half of the time over the set of observations included in creating the composite,

the percent frequency of detection will be 0.50.

In order to measure subnational variability, I create two maps that identify: (i) the bound-

aries of areas served by distribution companies (or electricity utilities), and (ii) the boundaries

of cities, urban divisions and rural districts, collectively referred to as districts in this research. 2

To create these two maps, I use boundaries from the GADM database of Global Administrative

Areas, the list of districts served by distribution companies, and a map of urban population

extents produced by the Global Rural Urban Mapping Project (GRUMP) of the Center for In-

ternational Earth Science Information Network at Columbia University. I use the urban extents

and city coordinates from Google Maps to identify the boundaries of fourteen large cities in

Pakistan. I use the urban extents and names of districts included in the divisions to identify the

boundaries of urban divisions. The remaining areas falling within the administrative district

boundaries comprise the rural areas of the district.

Figure 1.1 shows a graphical illustration of how I measure night lights variability using AL

and NL data. For the set of pixels (h) included within the boundaries of the subnational unit

(d), I obtain the ratio of NL to AL pixel by pixel. This ratio gives the percent frequency of

detection of light for each pixel. I subtract this ratio from one to create a measure of night

light variability that should increase as outages go up. For example, if percent frequency of

observation is 0.75, night lights variability is 0.25. As the frequency of observation increases

variability decreases, and if a light is always observed then variability is zero.

In order to aggregate variability across space, Alam (2013) uses the median variability within

districts which is less susceptible to outliers than using the mean. However, such a measure

ignores the distribution of population across space. To address this, I construct a population

weighted average of variability. Aggregation using population weighting allows me to measure

the average variability experienced by a person as opposed to the average or median variability

2A district is the third level administrative unit after a province and a division. Each electricity distributioncompany provides electricity to several districts located within its jurisdiction. Officially a district consists ofboth rural and urban areas including cities. The lowest geographic unit identified in the household data that Ilater use to validate night lights variability is either a city, urban division, or a rural district. For ease of notation,I collectively refer to these geographic units as districts.


experienced in the subnational unit. I use the population counts data produced by GRUMP

(also at a resolution of 30 arc seconds), which is based on the last available population census

to obtain a population weighted average of variability across all pixels included within the unit.

Thus, my estimate variability in night time lights (Outagesdy) measured at the distribution

company or district level over the years 2000 to 2012 (y) is constructed as follows:

Outagesdy =∑

h∈dy

poph

Tpopd

(

1 −(AvgLight x precent frequency observed)h

AvgLighth

)

(1.1)

If night lights variability is a perfect measure of the incidence of outages, then a variability

of 0.50 implies that electricity outages occur twelve hours of the day. Table 1.1 shows that the

mean variability in the sample of districts from 2000 to 2012 was 0.15 or around 3.6 hours of

outages per day. An additional hour of outages would approximately translate into a change of

0.04 units in this measure or roughly 27 percent change in variability from the mean. However,

as will be discussed in section 1.3, night light variability actually tends to under estimate the

true incidence of outages due to different sources of measurement error.

1.2.3 Trends in Night Lights Variability

Pakistan experienced a large increase in the frequency of outages, popularly termed as “load-

shedding” after 2006. One of the main causes of the gap between electricity supply and demand

was that generation cost went up substantially due to a rise in world oil prices. Electricity prices

for consumers were not adjusted to reflect the higher costs and this created a cash flow prob-

lem for distribution companies forcing them to cut back electricity purchases from generation

companies (see chapter 2). As a result, electricity was unavailable for 6 to 8 hours each day in

urban areas and 10 to 12 hours in rural areas.3

The national electricity shortage crisis is well captured at the subnational level by the lights

data. Figure 1.2 shows the distribution of Outagesdy at the district level in 2004 and 2011,

together with the boundaries of distribution companies. All together there are nine distribution

companies supplying electricity to contiguous geographic areas.4 In 2004, prior to the start of

3See for example Dawn (2008), “No light at the end of the tunnel.” June 23 2008, and Dawn (2012), “ForPakistan Everyday is a Blackout With No End in Sight.” Aug 8 2012.

4Five of these companies (Islamabad Electric Supply Company, Lahore Electric Supply Company, FaisalabadElectric Supply Company, Gujranwala Electric Supply Company and Multan Electric Supply Company) servethe mostly densely populated province of Punjab. The north west province of Khyber-Pakhtunkhwa is servedby the Peshawar Electric Supply Company. The Quetta Electric Supply Company covers the western provinceof Balochistan which is the largest province by landmass but sparsely populated. In the south, the HyderabadElectric Supply Company supplies electricity to the entire province of Sindh except the port city of Karachi.The company serving Karachi and its surrounding areas, the Karachi Electric Supply Company (KESC) wasprivatized in 2005. KESC is vertically integrated – it owns its own generation capacity and also receives supplyfrom private producers and the NTDC system.


the electricity shortage crisis, lights variability in most urban areas was below 0.10 and in most

rural areas it was below 0.22. In contrast, by 2011 lights variability was in the range of 0.22 to

0.26 in majority of the districts.

Figure 1.3 shows the complete distribution of night lights variability or Outagesdy pre

and post 2006 separately for urban and rural areas of Pakistan. Pre 2006, urban areas were

persistently lit and mean lights variability was close to 0.05. In rural areas, the mean lights

variability was approximately 0.15. Post 2006, both distributions display a noticeable shift

to the right as the incidence of outages went up. Furthermore, more prosperous districts

experienced a greater increase in lights variability. Figure 1.4 shows that the relationship

between the mean annual change in variability post 2006 and the mean annual change in

average lights pre 2006 was positive. Districts with faster growth in average lights are more

likely to have experienced faster growth, as the brightness of lights is a good measure of economic

growth. The lights data suggests that these districts eventually experienced a larger increase

in outages. The correlation between average lights and variability post 2006 is also positive

but less significant as there can be a contemporaneous relationship between average lights (or

economic growth) and outages. As outages went up, a number of energy conservation measures

were introduced to reduce the use of electricity such as early closure of offices, markets and

restrictions on lighting in public spaces, which can also lead to dimming of lights in districts

with more outages.5

1.2.4 Reported Outages and Household Electricity Consumption Data

Electricity supply to end consumers is carried out by nine distribution companies that pur-

chase electricity from a centralized grid system and sell to the consumers within their areas

of jurisdiction shown in Figure 1.2. Data on actual incidence outages at a subnational level is

not available yearly during the period under study either at the distribution company or at a

smaller unit of observation. Since outages increased sharply after 2006, the national regula-

tor called National Electric Power Regulatory Authority (NEPRA) started gathering data to

monitor the performance of distribution companies in controlling outages and overall quality of

service provision (NEPRA, 2014). These performance evaluation reports available from 2010

to 2013, asked the distribution companies to report data on the average number of hours of

outages per day in the last year.

Table 1.1 shows the summary statistics of the distribution companies data. In addition

to the average duration of reported outages which was 5.39 hours, two additional measures

of electricity interruptions are available in the performance evaluation reports. The system

5Dawn (2010). “Power Conservation Measures Proposed.” Dawn, April 20, 2010.


average interruption frequency index (SAIFI) is a measure of how often an average customer

experienced a supply interruption during the last year. A SAIFI of 232 means that on average

customers connected to the distribution company lost supply 232 times during the past 12

months or 0.63 times per day. The second measure is the system average interruption duration

index (SAIDI) which captures the average number of minutes customers are without supply

during the past 12 months. The mean of this variable is 8,149 minutes or approximately 0.37

hours per day. The measures of interruption reported by the distribution companies tend to

under state the actual incidence of outages which was according to conservative estimates from

news reports was between 6 to 8 hours in urban areas and 10 to 12 hours in rural areas.6 In

fact, the performance evaluation reports of the regulator suggest that distribution companies

are likely under reporting the actual incidence of outages (NEPRA, 2014). Despite these issues,

these are the only official estimates available, and therefore I use them to validate the night

lights data, supplementing the analysis with household electricity data.

In addition to these measures related to reliability of supply, the reports also provide data

on a number of other aspects of service quality in the last year. The data available includes the

transmission and distribution losses which is the percentage of electricity lost while supplying

electricity from the regional grid to the consumer (19.8 percent), the number of faults in trans-

mission lines per kilometer (6.2), and the percentage of applications for new connections that

were delayed (16 percent) in the last year.

Since outage data from distribution companies is limited and likely to be under reported, I

also use data on household electricity consumption to validate the use of night lights variability

in measuring outages at a subnational level in Pakistan. The household electricity consumption

data comes from national household surveys carried out by the Pakistan Bureau of Statistics.

The lowest identifiable geographic unit in these surveys in the rural areas is the district, and

in the urban areas it is the city or an urban division. Table 1.1 shows that the data consists of

47,281 households observed in 117 districts from 2004 to 2011. The mean monthly electricity

consumption is Rs. 520 which is roughly 5 percent of the mean monthly total expenditures

of Rs. 12,287. The survey also provides access to a number of other household level variables

such as ownership of electrical appliances. Furthermore, I construct district level variables

measuring total monthly rainfall using data from the Climate Research Unit (CRU), and mean

monthly temperature using data from the European Center for Medium-Range Weather Fore-

casts (ECMWF) ERA-Interim database.

6See for example, Declan Walsh (2011). “Power Cuts Leave Pakistan Hot and Bothered.”The Guardian, July3rd, 2011; Declan Walsh and Imran Masood (2013). “Pakistan Faces Struggle to Keep Its Lights On.” New YorkTimes, May 27, 2013; and Economist (2014).“The Urdu rate of growth.” Economist, Feb 15, 2014.


1.3 Measurement Issues

In this section, I discuss potential sources of measurement error while using subnational vari-

ability in night lights as a measure of true incidence of outages. Attenuation bias is likely in

empirical work using variability as a right hand side variable due to these potential sources of

measurement error. I also use the error-in-variables framework to show that changes in inci-

dence of outages will not be completely reflected in changes in variability, if outages incidence

itself is reported with error.

1.3.1 Measurement Error in Night Lights Variability

Let xd be the true incidence of outages and xd the observed variability in night lights in

subnational unit d that may be a distribution company, district, or the lowest level at which

such data may be available. Suppose the relationship between true outages and lights variability

is contaminated by measurement error ud with constant variance σ2u.

xd = xd + ud (1.2)

The error term in the in equation 1.2 is due to noise in the way measured lights variability

reflects the true incidence of outages. For example, weather and seasonal variations in cloud

cover can reduce the number of usable satellite observations which adds more noise to the

yearly averaged percent frequency of observation. Variability or persistence in lighting may be

underestimated due to pixel saturation and blooming effects. It is not possible to recover the

true frequency of light observation at top coded pixels in the average and normalized lights

data. Blooming effects, that is bleeding of light from bright pixels into neighbouring pixels can

also lead to us to over estimate the frequency of light observation.

Differences in economic activity and geographic variables may also lead to noise in the way

persistence of observed lighting reflects actual outages. For example, agricultural districts emit

less light, may receive more rainfall and have fewer usable observations due to cloud cover.

Another potential source of noise is adoption of generators or other types of off grid electricity

supply technologies in urban areas that can partially restore supply during outages.7 Both of

these sources of noise will cause us to under estimate true outages.

Night lights variability will also capture the true incidence of outages with noise if electricity

outages are not evenly spread across day and night. There is some evidence from urban house-

hold surveys to suggest that scheduled outages take place more frequently during the day than

7Results from a survey carried out in the fourteen largest cities of the country in 2012, to measure generatorownership suggest that less than 17 percent of the households with average income in large cities own generatorsand less than 26 percent have access to backup storage devices (Appendix Table B.1).


night time in the cities of Pakistan (IPP, 2013). However, in terms of unplanned interruptions

that coincide with peaks in electricity demand, there is no evidence to suggest that unplanned

outages take place more frequently during the day as compared to the night.8

What are the consequences of the measurement error if we plan to use night lights variability

as a proxy for outages in empirical work? If night lights variability is used as the left hand

side variable in a regression, then we should expect large standard errors as the regression error

term now includes additional noise. If instead, we wish to estimate the effect of outages on an

outcome of interest yd using night lights variability as a proxy for outages, then we estimate:

yd = βxd + vd

yd = βxd + vd = βxd + vd − βud (1.3)

If the measurement error in variability is uncorrelated with variability or the outcome of

interest, then it is well known that any effect of variability on the outcome will be attenuated

towards zero. Using the standard OLS formula it can be shown that if we regress yd on xd, we

find an attenuation factor that is proportional to the variance of the noise to signal ratio σ2u

σ2x.

plim(β) = βσ2

x

σ2x + σ2

u

= β( 1

1 + σ2u

σ2x

)(1.4)

If night lights variability also suffers from non classical measurement error, then there may

be an additional source of bias apart from attenuation. From the previous discussion, this may

arise if agricultural districts have more noisy estimates of variability and the noise is correlated

with unobserved determinants of outcomes. Since measurement error is correlated with the

outcome variable (σuv 6= 0), then there will be an additional source of bias in β. If the true

effect of outages was negative, σuv > 0 will cause us to further underestimate β and may even

cause a reversal of sign. If σuv < 0, the additional bias will offset the attenuation in β.

plim(β) = βσ2

x

σ2x + σ2

u

+σuv

σ2x + σ2

u

(1.5)

If the measurement error is correlated with true outages (σux 6= 0), for example, if variability

under estimates outages more in urban areas due to saturation and bleeding of lights, and urban

8The typical intra-day demand pattern in Pakistan (Appendix Figure B.1) shows peak demand occurs duringthe day as well as night. In the summer and winter demand peaks after sunset and remains high in the evening. Inthe winter there is an additional period of peak demand in the afternoon. Given this pattern it seems reasonableto expect that unplanned outages can occur during night or day time.


areas had a lower incidence of outages then we estimate:

plim(β) = βσ2

x + σux

σ2x + σ2

u + 2σux(1.6)

β is still attenuated in this case, as the numerator is smaller than the denominator. It is easy

to show that the attenuation factor will be larger than in levels specification if the variance of

the noise σ2u is less than the variance of the true outage incidence σ2

x and vice versa.

Some of these sources of measurement error may be invariant or on average equal over time.

Therefore, even if the cross sectional variation in night lights variability does not fully capture

the true incidence of outages, the within district variation in variability should capture the

change in outages. If we had panel data on variability and outcomes by subnational units,

then differencing or removing subnational unit fixed effects can solve the attenuation problem

when the measurement error in night lights variability is constant across time. Suppose that

we estimate the first differenced specification,

ydt − ydt−1 = β(xdt + udt − xdt−1 − udt−1) + (vdt − vdt−1) (1.7)

In general, when ud is time varying, the attenuation problem is not removed. The OLS

estimate is now given by:

plim(βFD) = βσ2

Δx

σ2Δx + σ2

Δu

(1.8)

Denote the first order autocorrelation between outages and the measurement error in lights

variability over time by ρ and r respectively, then σ2Δx = 2σ2

x(1 − ρ) and σ2Δu = 2σ2

u(1 − r).

Then the OLS estimate of β is given by:

plim(βFD) = βσ2

x(1 − ρ)σ2

x(1 − ρ) + σ2u(1 − r)

(1.9)

If the true incidence of outages and the error in lights variability is not serially correlated or if

ρ = r, then the attenuation factor is identical to 1.4. When ρ > r or outages are more serially

correlated than the measurement error in the lights variability, differencing enlarges the noise

to signal ratio. Only in the case when ρ < r and measurement error is more correlated than

outages, the attenuation factor will be reduced by differencing. In the presence of non classical

and time varying measurement error, we can also obtain the expressions for the OLS estimate


of βFD analogous to equations 1.5 and 1.6.9

1.3.2 Measurement Error in Reported Outages

As in the previous section, let xd be the true incidence of outages, wd the reported duration

of outages, and xd the observed variability in night lights in subnational unit d. Assume that

there is classical measurement error (ed) in reported duration of outages with a variance of σ2e .

wd = xd + ed (1.10)

In order to estimate an elasticity between observed lights variability and recorded outages,

we can run the following regression:

xd = θwd + εd (1.11)

In the absence of measurement error in reported outage duration and when the measurement

error in night lights variability is uncorrelated with true outages, we know from equations 1.2

and 1.9 that θ = 1. If the measurement error in reported duration is non zero then, the OLS

estimate of θ = cov(x,w)var(w) , and its probability limit is given by:

plim(θ) =σ2

x

σ2x + σ2

e

=( 1

1 + σ2e

σ2x

)(1.12)

The expression above shows that OLS estimated elasticity between night lights variability

and reported outage duration is also attenuated since plim(θ) < 1. In general, there is greater

attenuation the larger is the variance of the noise in reported outages to actual outages σ2e

σ2x.

The attenuation problem can be addressed if we had an exogenous variable zd, that shifts

reported duration and variability, but is otherwise uncorrelated with measurement error in

these variables. This is because,

plim(θIV ) =cov(xd, zd)cov(wd, zd)

=cov(xd + ud, zd)cov(xd + ed, zd)

= 1 (1.13)

If we have panel data on lights variability and reported duration of outages by subnational

units, then we can run the following regression to eliminate the subnational unit fixed effects.

xdt − ˜xdt−1 = θ(wdt − wdt−1) + (edt − edt−1) (1.14)

9When σuv 6= 0, then plim(βFD) =βσ2

x(1−ρ)+σuv

σ2x(1−ρ)+σ2

u(1−r). Similarly, when σxu 6= 0, then plim(βFD) =

β(σ2x(1−ρ)+σux)

σ2x(1−ρ)+σ2

u(1−r)+2σux.


The attenuation bias in a differenced specification will depend on the relative autocorrelation

between the true incidence of outages (ρ) and autocorrelation between the measurement error

in reported duration say q. Using the OLS formula,

plim(θFD) =σ2

x(1 − ρ)σ2

x(1 − ρ) + σ2e(1 − q)

=1

1 + σ2e(1−q)

σ2x(1−ρ)

(1.15)

If the measurement error in recorded outages was fixed, that is edt = ed then differencing

removes it completely and there is no attenuation in the estimated elasticity θ. If the true

incidence of outages and the error in reported duration was not serially correlated, or ρ = q ,

then the attenuation factor is the same as before and increases with the relative variance of noise

to signal ratio. When q < ρ or true outages are more correlated over time but the measurement

error in recorded outages displays very little or no serial correlation, then differencing enlarges

the noise to signal ratio. This will cause the attenuation factor to go up. Only in the case when

q > ρ, then the attenuation bias in the differenced specification is smaller as compared to the

levels specification.

Therefore, in order to estimate θ, or the elasticity of variability in night lights to reported

duration, we must be aware that the classical measurement error in reported duration will also

lead to an attenuation of the OLS estimates. When true outages are highly correlated over

time but the measurement error in recorded outages displays little or no serial correlation, the

elasticity estimated from a differenced specification will be attenuated further.

1.4 Does Night Lights Variability Capture Outages?

1.4.1 Night Lights Variability and Reported Outages

In this section, I estimate θ or the elasticity of variability in night lights to reported duration.

Table 1.2 shows the results from estimating a reduced form correlation between log of lights

variability and log of reported duration using distribution company-year data. The sample size

is small and after standard errors are large due to measurement error in lights variability. If

there was no noise in the reported duration data and measurement error in lights variability

is uncorrelated with outages, then we should expect θ = 1. Column 1 and 2 shows that the

coefficient is attenuated to zero which suggests the presence of classical measurement error with

a substantial noise to signal ratio. In column 3, after adding distribution company fixed effects,

the estimates reverse in sign but are insignificant. A negative coefficient can be due to omitted

variables that are negatively correlated with reported duration and positively correlated with

lights variability within districts. It may also suggest the presence of non classical measurement

error in reported duration, for example if changes in reported duration are lower in places with


greater increase in outages and variability.

To address the problem of measurement error in reported outage duration, in columns 4

to 6, I instrument it using the two electricity interruption indices. The indices of SAIFI and

SAIDI capture the average number of electricity interruptions and the average duration of

interruptions experienced by customers served by the distribution company. This is positively

correlated with the reported duration and the F-stat on the excluded instruments in the first

stage regression is close to 10 despite the small sample size. The exclusion restriction is that

SAIFI and SAIDI should only affect variability through reported outages.

The IV estimate in columns 4 and 5 imply that a 1 percent change in outage duration is

associated with a 0.65 to 0.54 percent change in observed lights variability. Equivalently a

18 percent change in outage duration which is an additional hour of outages each day of the

year from the mean duration, leads to approximately 10 to 12 percent change in variability.

Another way to understand the magnitude of the elasticity is that a one standard deviation

change in reported duration (2.75 hours or 50 percent change from mean duration) results in

28 to 33 percent change in observed variability. Lastly, in column 6, I find that after adding

distribution company fixed effects the elasticity is once more attenuated to zero and standard

error of the estimate also increases substantially. This is what we would expect if true outages

are correlated over time but the measurement error in recorded outages displays very little or

no serial correlation.

To sum up, the results of Table 1.2 show that observed variability in night lights increases

as the incidence of outages increases. Measurement error in duration leads to attenuation bias

in θ and measurement error in variability increases the standard errors of the estimates. After

instrumenting for outage duration with other measures of electricity interruption, I find that a

18 percent change in reported duration which is approximately equal to an additional hour of

outages per day increases lights variability by 10 to 12 percent. These estimates are economically

meaningful and they suggest a plausible elasticity between night lights variability and outages

that can be used in future empirical work using variability as a proxy for subnational outages.

1.4.2 Night Lights Variability and Household Electricity Consumption

As a further check to validate night lights variability as a measure of outages, I use the house-

hold consumption data to estimate the the response of electricity expenditures to changes in

variability. In particular, I estimate the following expenditure equation for household i observed

in district d in year y :

log(elecidy) = γ0 + γ1log(Outagesdy) + γ2Xidy + uidy (1.16)


Lights variability is measured at the district-year level which is the lowest identifiable sub-

national unit in the household data. In column 1 of Table 1.3, I find that in a simple log

linear specification without any controls, a 10 percent increase in variability (approximately

1 additional hour of outages per day) reduces electricity consumption by 5.3 percent. After

adding household controls, district and year fixed effects and a district specific time trend, the

estimate drops to 4.2 percent in column 4. Using the average monthly electricity expenditure

of Rs. 520 and electricity price of Rs. 4.3 per kWh, a 4.2 percent reduction is equivalent to 0.17

kilowatt (kw) in usage each day of the month in response to an additional hour of outages per

day. This magnitude seems plausible as typical appliances commonly owned by the household

such as a ceiling fan and incandescent light bulb require 0.05 to 0.10 kw each per hour. Larger

appliances such as a television requires 0.15 kw of electricity, refrigerator requires 0.40 kw, and

a washing machine requires 0.50 kw per hour.

Another way to check whether variability reduces electricity consumption is to estimate

household electricity demand omitting variability. If variability does indeed capture outages,

then it should be correlated with the residuals predicted from the household demand equation.

Specifically, if demand is low due to outages, and expenditures are below what is predicted

by the demand equation, then variability should be correlated with this unexplained drop in

demand.

To implement this check, in Table 1.4, I estimate electricity demand using a rich set of

household controls but omitting lights variability. In addition to monthly per capita expendi-

tures and households size, I control for the household composition in different age categories

(0–4 years, 5-9 years, 10–14 years, 15–65 and above 65 years) defined separately for males and

females, indicators for five commonly owned appliances namely refrigerator, fan, washing ma-

chine, television, and air conditioner, district monthly rainfall, and mean monthly temperature.

Since prices are not observed in the data, I use published data on national electricity prices and

include the price charged for the most common consumption band in the regression.10 Model

1 reports the coefficients with all the household controls and Model 2 adds district and year

fixed effects and a district specific time trend.

The residuals predicted from both models displayed in Figure 1.5 show a long left tail, that

is there is a significant number of households with consumption falling below what would be

predicted by the demand model. To examine the correlation between variability and residuals,

I carry out a validation check that is more conservative than simply looking at the reduced form

correlation between the residuals and variability. Specifically, for each district-year observed

in the sample, I obtain the mean fraction of households for which consumption falls below

10Electricity prices for households depend on the consumption band. The most common consumption bandis 101–300 kilowatt hours per month.


predicted consumption by a given threshold as follows:

Rdy =∑

i∈dy

(

I (uidy < t)

)

Table 1.5 shows the correlation between night lights variability and Rdy using a threshold of 0

percent, 10 percent, 20 percent, and 30 percent. In Panel A, I find that a 10 percent increase in

variability increases the mean fraction of households reporting unusually low consumption by

roughly 1.2 to 1.1 percent. In Panel B, using the residuals from Model 2, a 10 percent increase

in variability increases the mean fraction of households with unusually low consumption by 0.9

percent. Changing the threshold does not matter significantly in Panel A. In Panel B, raising

the threshold increases both the precision and the magnitude of the correlation.

The results of Table 1.4 and Table 1.5 jointly suggest that changes in night lights variabil-

ity capture changes in outages within districts. An increase in night lights variability by 10

percent or an additional hour of outages reduces monthly household electricity expenditures

by 4.2 percent within districts. This is a plausible magnitude given electricity requirements of

typical household appliances. As a further validation check, I also find that changes in night

lights variability also explain unpredicted drops in household electricity expenditures across

and within districts over time.

1.5 Conclusion

In this chapter I create a subnational measure of outages for Pakistan, using the intra-annual

variability in night lights observed from space. The measure coded on a scale of 0 to 1 increases

as outages go up. In particular, it captures the observed change in incidence of outages in the

country across space and over time. Despite the problem of measurement error, night lights

variability is significantly correlated with reported outage duration and household electricity

consumption. The results of the reduced form regression between variability and reported

outage duration measured at the distribution company-year level suggest that an additional

hour of outages per day, generates a 10 to 12 percent change in observed night lights variability.

A 10 percent increase in lights variability is associated with a 4.2 percent decrease in monthly

electricity consumption, after controlling for district and year fixed effects and district time

trends. Variability also explains unusually large drops in household electricity expenditures

that are otherwise unexplained by other determinants of electricity demand. Thus, the results

of this chapter show that night lights variability is a valid measure of subnational outages and

can be used in empirical applications where subnational data on outages is not available.


Table 1.1: Summary Statistics

Mean StandardDeviation

Panel A – Night Lights Data (2000–2012)

Variability ( 0–1) 0.15 0.07

Average lights (DN) 11.8 10.6

Panel B – Distribution company data (2010–2012)

Average outage duration (hrs. per day) 5.39 2.75

SAIFI (times per year) 232 400

SAIDI (minutes per year) 8,149 8,736

Transmission and distribution losses (%) 19.8 9.65

Recovery rate (%) 85.5 20.2

Number of faults per km 6.20 9.03

Connection delays (%) 16.4 9.72

Panel C – Household data (2004–2011)

Monthly electricity expenditure (Rs.) 520 744

Monthly per capital expenditures (Rs.) 12,287 7,959

Household size 7.13 3.36

Refrigerator 0.383 0.486

Air conditioner 0.083 0.276

Fan 0.919 0.272

Washing machine 0.441 0.497

Television 0.542 0.498

Mean monthly temperature (◦C) 0.467 0.673

Total monthly rainfall (cm) 23.0 8.24

Notes: In Panel A, variability is measured at the district-year level in 117 districtsover a period of 2000 to 2012. In Panel B, the sample consists of nine distributioncompanies reporting data from 2010-11 to 2012-13. In Panel C, the sample consistsof 47,281 households observed in 117 districts from 2004 to 2011.


Table 1.2: Night Lights Variability and Outage Duration

Dependent variable: Log variability

(1) (2) (3) (4) (5) (6)

Log duration 0.071 0.126 -0.214 0.648*** 0.543*** 0.117(0.150) (0.134) (0.134) (0.141) (0.045) (0.446)

Year F.E N Y N N Y NDistribution company F.E. N N Y N N YF-stat 8.83 9.31 0.53N 27 27 27 27 27 27R2 0.54 0.77 0.81 0.63 0.67 0.85

Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors clustered by distribution company reported in parentheses.Log variability is the log of night lights variability in the distribution company in a given year. Log duration is the averageduration of outages in hours per day reported by distribution companies. All regressions include distribution company timevarying controls. The instruments for reported duration in column 4 to 6 are the system average interruption durationindex and system average frequency of interruption index.

Table 1.3: Correlation between Lights Variability and Household Electricity Demand

Dependent variable - Log monthly electricity expenditure

(1) (2) (3) (4)

Log variability -0.528*** -0.398*** -0.401*** -0.419***(0.060) (0.054) (0.087) (0.098)

Log per capita expenditures 1.62*** 1.35*** 1.33***(0.072) (0.049) (0.049)

Log household size 1.12*** 1.14*** 0.13***(0.059) (0.035) (0.032)

Year F.E. N N Y YDistrict F.E. N N Y YDistrict specific time trend N N N YN 47,281 47,281 47,281 47,281R2 0.037 0.180 0.360 0.374

Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors clustered by district reported in parentheses.The dependent variable is the log of household’s monthly electricity expenditures. Log outages is the logof night lights variability measured at the district year level. Log per capita expenditures is the log ofhousehold’s monthly expenditures on food and non durable consumption.


Table 1.4: Household Electricity Demand Excluding Outages

Model 1 Model 2βk se(βk) βk se(βk)

Per capita expenditure 0.741*** (0.024) 0.799*** (0.020)Log household size 0.722*** (0.032) 0.778*** (0.029)Refrigerator 0.219*** (0.012) 0.164 *** (0.012)Air conditioner 0.088 (0.055) 0.064 (0.038)Fan 0.209*** (0.042) 0.108*** (0.025)Washing machine 0.116*** (0.020) 0.059*** (0.010)Television 0.124*** (0.014) 0.040*** (0.008)Log price 0.612*** (0.043) 0.281*** (0.057)Monthly rainfall (cm) 0.061*** (0.019) -0.018 (0.021)Monthly temperature (◦C) 0.005*** (0.002) 0.001 (0.007)

N 47,281 47,281R2 0.44 0.59

Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors clustered by district. Thedependent variable is log monthly household electricity expenditures. Both models in-clude controls for household age-gender distribution. Model 2 includes district fixed, yearfixed effects and a district specific time trend. Log price is the marginal price charged forconsumption between 100–300 kw.

Table 1.5: Correlation between Unpredicted Drop in Demand and Lights Variability

Dependent variable – Rdy

Panel A – Model 1 (1) (2) (3) (4)

Log variability 0.121*** 0.121*** 0.121*** 0.119***(0.015) (0.014) (0.014) (0.014)

Threshold 0.00 -0.10 -0.20 -0.30N 793 793 793 793R2 0.14 0.14 0.14 0.14

Panel B – Model 2 (1) (2) (3) (4)

Log variability 0.067* 0.089** 0.095** 0.099**(0.036) (0.037) (0.037) (0.036)

Threshold 0.00 -0.10 -0.20 -0.30N 793 793 793 793R2 0.08 0.09 0.15 0.17

Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors bootstrap clustered by district reported inparentheses. The dependent variable is mean fraction of households with observed expenditure below pre-dicted expenditures in district-year cells. Variability is measured at the district-year level. Model 2 includesdistrict fixed effects, year fixed effects and district specific time trend.


Figure 1.1: Measuring Night Lights Variability in Districts

Notes: From left to right, each figure shows the distribution of average lights, normalized lights and variability

within districts in 2010 measured using the DMSP-OLS night lights data. The top panel shows rural areas

comprising Rahimyar Khan district, the middle panel shows the city of Sialkot and the bottom panel shows

urban areas comprising Peshawar division.


Fig

ure

1.2:

Nig

htLig

hts

Var

iabi

lity

inD

istr

icts

ofPak

ista

n

Not

es:

Out

ages

are

mea

sure

das

vari

abili

tyin

nigh

tlig

hts

ona

scal

eof

0to

1.Lig

htco

lour

edbou

ndar

ies

dem

arca

teth

ear

eas

serv

edby

differ

ent

dist

ribu

tion

com

pani

es.


Figure 1.3: Distribution of Lights Variability Pre and Post 2006

05

1015

20D

ensi

ty

0 .1 .2 .3 .4 .5 .6Variability

Urban Pre 2006 Urban Post 2006 Rural Pre 2006 Rural Post 2006

Notes: The graph shows the smoothed distribution of night lights variability measured on a scale of 0 to 1 in

rural and urban districts of Pakistan.

Figure 1.4: Correlation between Growth in Average Lights and Lights Variability

0.1

.2.3

.4M

ean

annu

al c

hang

e in

Var

iabi

lity

post

200

6

-.04 -.02 0 .02 .04 .06Mean annual change in AL pre 2006

beta=1.65, R-squared= 0.15

Notes: The graph shows the correlation between district mean yearly change in Average Lights pre 2006 and the

mean yearly change in Variability post 2006, measured in logs.


Figure 1.5: Density of Residuals from Electricity Demand Equation

0.2

.4.6

.81

-6-5

.7-5

.4-5

.1-4

.8-4

.5-4

.2-3

.9-3

.6-3

.3 -3-2

.7-2

.4-2

.1-1

.8-1

.5-1

.2 -.9 -.6 -.3 0 .3 .6 .9 1.2

1.5

1.8

2.1

2.4

2.7 3

Demand residuals

Model 1 Model 2

Notes: The graph shows the kernel density of residuals estimated from electricity demand equations (Model 1

and Model 2) using household expenditure data.

Chapter 2

Energy Prices, Electricity Outages

and Employment

2.1 Introduction

Electricity is an important input in many productive activities taking place in the factory, shop

or at home. Frequent interruption in electricity supply due to outages can have a negative

impact on the extent to which individuals and businesses can utilize their resources for income

generation.1 Recent work on the impact of outages on manufacturing firms, for example, Allcott,

Collard-Wexler and O’Connell (2016), Fisher-Vanden, Mansur and Wang (2015), Abeberese

(2013) and Alam (2013) suggests that big firms are able to cope with outages by investing in self

generation or adjusting their production processes which mitigates the impact on productivity.

However, in many developing countries, a large number of income generation activities are

carried out in small establishments often operating within the home. These establishments

may not be able to fully insure themselves against electricity outages. Therefore, by focussing

on manufacturing firms, we will not be able to fully capture the effect of unreliable electricity

supply on economic activity.

In this chapter, I analyze the effect of outages on labour market outcomes using district

and household data from Pakistan over the period 2004 to 2011. In Pakistan, the aggregate

electricity demand and supply gap rose sharply after 2006, coinciding with a period of high

world oil prices which created a negative supply shock in electricity generation (Figure 2.1). I

use the variability in night lights constructed using high resolution satellite data, as a measure

of outages to examine the impact of outages on employment and earnings of adult males in

Pakistan. The previous chapter shows that the variability in night lights is a valid measure

1Firms surveyed in the World Bank enterprise surveys report losing on average 4.7 percent of their outputdue to 28 hours of electricity outages in a typical month.

27

Chapter 2. Energy Prices, Electricity Outages and Employment 28

of the incidence of outages at the subnational level, as it is correlated with average reported

duration of outages by electricity distribution companies and household electricity consumption.

I utilize exogenous variation in energy input prices of thermal power plants due to the global

oil price shock combined with spatial variation in the location of the plants, to identify the effect

of outages on employment outcomes. Using detailed data on plant inputs, plant capacity, and

distance to the districts, I construct a shift-share instrument that measures the district’s yearly

average price of the energy mix that is used to generate electricity. The instrument acts a local

electricity supply shifter, as plants near a district using expensive oil based fuels experience a

drop in generation. I find that districts with a greater share of plants using oil based fuels are

not systematically different from other districts in measures of prosperity, living standards and

other economic characteristics at baseline. The change in the district’s price of energy mix in

the post period is also not correlated with pre period labour market outcomes.

I find that electricity outages have a significant negative effect on employment and earnings

of adult males in the labour force. An increase of 10 percent in night lights variability or

approximately 26 additional hour of outages per month, reduced the probability of employment

in the past month by 2.1 percent, days worked by 8 percent, labour productivity by 11 percent

and total earnings by 18 percent. I find that the effect is larger in districts that are more

reliant on electricity intensive industries and districts with a greater fraction of more educated

workers. This suggests labour demand driven reduction in productivity and incomes, as the

marginal product of labour falls due to erratic supply of complementary electricity inputs. Part

of this productivity loss is also due to reduction in labour efficiency due to reduced physical

or cognitive ability of workers to work without electricity in high temperatures. I also find

evidence that suggests a larger effect in districts located close to national roads, which may

be growing faster, and thus input price shocks result in a larger gap in electricity supply and

potential demand in these districts.

My research is part of a growing literature on estimating the effects of electricity outages.

The current research in this area has found small impact of outages on productivity, costs

and revenues of large manufacturing firms, as these firms adjust in different ways to substitute

away from grid electricity.2 In contrast, I estimate the effect of unreliable electricity supply

on labour market outcomes, and my findings show a large and negative effect on incomes

and labour productivity. Utilizing natural and spatial variation in energy input prices and

2Fisher-Vanden et. al (2015) find that in China, production costs increased due to outages but firms are ableto avoid productivity losses. Allcott et al. (2016) using data from Indian firms find a very small negative effect(less than one percent) on productivity. Smaller plants and those without generators are affected more negatively.There is also significant inter-industry heterogeneity in impact, and firms in industries where the productionprocesses can not be easily adjusted to deal with disruptions in electricity supply are likely to experience loss inoutput and profits (Alam, 2013).


location of plants, I am able to overcome the omitted variable bias that would lead us to

under-estimate the negative impact of outages, as unplanned outages are likely to be higher

in prosperous places. My empirical strategy is closest to Allcotta et. al (2016) who use state

level hydroelectric power availability to instrument for electricity shortages in India. However,

instead of relying on weather induced fluctuations in availability of hydro power, I am able to

utilize supply shocks induced by large variations in energy input prices in a market where end

user prices are subsidized and do not adjust completely. This is a common feature of electricity

markets in many developing countries.

My work is also related to the literature on infrastructure and development. A number

of recent papers have shown that access to new infrastructure such as roads, railways, and

electricity has a large effect on growth and other measures of development.3 Electrification

programs, in particular have very positive effects on labour force participation (Dinkelman, 2011

and Lewis, 2013), income, poverty reduction and productivity (Lipscomb, Mobarak, Barham,

2013 and Rud, 2012). The findings of my research emphasize that even though access to

electricity infrastructure is important, the reliability (quality) of service also matters. I focus on

one aspect of quality of electricity infrastructure, and find a significant income and productivity

loss due to frequent interruption in service. The findings of my paper suggest that these effects

should not be ignored when comparing the costs and benefits of different policy options, such as

pricing and subsidy regimes, governance reforms, or investments in modern grid infrastructure,

to improve the reliability of electricity supply.

The chapter is organized as follows. Section 2.2 discusses the background on electricity

market in Pakistan and the electricity outages crisis which was triggered by the rising oil

prices. Section 2.3 describes the plants and energy input prices database, construction of the

outages measure from satellite images of night lights, and the labour market outcomes data.

Section 3.4 describes the empirical strategy that I use to estimate the effect of outages on

labour market outcomes. Section 3.5 reports the main results and section 3.6 presents evidence

on the mechanisms underlying the results. Section 3.7 presents robustness checks and section

3.8 concludes with a summary of main findings.

2.2 Electricity Sector in Pakistan

2.2.1 Electricity Generation

The electricity sector in Pakistan is served by a combination of public and private generation

companies. The total installed generation capacity increased from 19.6 GW to 23.4 GW from

3For example Faber (2014), Banarjee, et al. (2012) and Baum-Snow et al. (2012) study the impact of roadsand Donaldson (2012) and Donaldson and Hornbeck (2014) estimate the impact of railroads on development.


2004 to 2011.4 During this period, around 65 percent of the electricity was generated from

thermal energy sources, 30 percent was generated from hydropower, and the remaining from

nuclear and renewable energy sources. 55 percent of the electricity generated at thermal power

plants was from oil based fuels such as furnace oil or diesel, 44 percent was generated from

natural gas, and less than 1 percent was generated by coal fired power plants. Around 60

percent of the thermal capacity is owned by the private generation companies (NEPRA, 2012).

Historically, Pakistan relied on hydropower to meet two thirds of its electricity demand,

with the remaining being fulfilled by publicly owned thermal power plants. As electricity

demand increased, there was a need for investment in new generation capacity. It was difficult

to operationalize new hydropower proposals due to lack of political consensus on where to

build new dams. In 1994, the government allowed private generation companies to set up new

thermal power plants. Most of the new private plants that were set up relied on oil based fuel

to generate electricity. Oil plants have a short lead time of around three years and require lower

capital investments in comparison to coal, hydro and renewable energy plants.5 Eventually, the

energy mix used to generate electricity in Pakistan shifted disproportionately towards oil based

plants (Akbar, 2015).

2.2.2 Electricity Transmission

The private and the public generation companies supply electricity to the national grid com-

pany called the National Transmission and Dispatch Company (NTDC). The NTDC acts as

the central power purchase company and is responsible for scheduling electricity generation at

different plants based on plant availability, costs, and historical demand patterns. The NTDC

uses high voltage lines to transmit electricity from producers to the main grid and the distri-

bution companies that are supplying electricity to end consumers. Transmission losses in the

NTDC system are low and typically less 3 percent of electricity is lost in transmission (NEPRA,

2011).

The producers sell electricity to the NTDC at a price that depends on two components: (1)

an energy purchase price and (2) the capacity purchase price. The energy purchase component

consists of fuel cost per unit generated and other variable costs of production such as labour

and operational costs. It is adjusted quarterly to reflect the changes in energy prices. Thus,

there is a complete pass through of any increase in energy prices to the NTDC and in turn

the distribution companies. End user prices are determined by the government and are usually

4The installed capacity is approximately two-third of the generation capacity of the province of Ontario,one-third of the capacity in the state of California, and only one-tenth of the capacity in India.

5Natural gas provides a cheaper and cleaner source of fuel for thermal plants in comparison to oil. However,Pakistan has been facing a steady depletion of domestic natural gas reserves and has not been able to build anyinfrastructure to import natural gas from neighbouring countries due to regional political instability.


subsidized for residential and agricultural consumers (see below). The capacity purchase price

depends on fixed costs of the producer such as land, construction, taxes, administration, debt

financing, and return on equity. It is determined at the time the plant is approved and remains

fixed for a contracted term.

2.2.3 Electricity Distribution and the Subsidy Regime

Electricity distribution was decentralized in 2002, when regional distribution companies were

created to serve distinct geographic areas shown in Figure 1.2. The distribution companies

are responsible supplying electricity to the end consumers, metering, collection of bills, and

maintenance of the distribution infrastructure. The distribution infrastructure consisting of

overhead supply lines, grid stations, electricity feeders, and power transformers, has grown

slowly, despite the rapid increase in demand and urbanization. Overloading and tripping at

various points in the distribution network is common occurrence. Average line losses in the

distribution companies are in the range of 20 – 25 percent (NEPRA, 2011), which partly reflects

the poor state of distribution infrastructure. Another reason for the high line losses is theft

and illegal connections. As a result, distribution companies are not able to recover the cost of

a significant amount of electricity lost or used illegally within the system.

An important feature of the electricity distribution system is the subsidy regime of the gov-

ernment. Distribution companies can not set prices independently. The federal price regulator,

National Electric Power Regulatory Authority (NEPRA) recommends a price for residential,

commercial, agricultural and industrial consumers on a quarterly basis. NEPRA has the man-

date to recommend prices based on the cost of delivering electricity. However, the government

has the final authority to notify prices and it has kept electricity prices below the level recom-

mended by NEPRA for popular support.6 Since the cost of is much higher than the revenue

per unit of electricity sold, the government pays the distribution companies a “tariff differen-

tial subsidy’, which is the difference between the price determined by NEPRA and the actual

prices notified by the government. Although, the subsidy regime is meant to make electricity

affordable, it actually creates a disincentive for distribution companies to improve their opera-

tional efficiency. They have no incentive to tackle difficult problems of theft and institutional

accountability when they expect to receive payments from the government to cover their losses.

6The government has typically announced prices 20 to 30 percent below the price determined by NEPRAon the basis of cost of service formula (NEPRA, 2010). Pakistan is not unusual in this respect, as Brownand Mobarak (2009) show that in many developing countries, electricity prices for large constituencies such ashouseholds and agricultural consumers are often subsidized by governments for popular support.


2.2.4 Oil Prices and the Electricity Shortage Crisis

The electricity shortage crisis which appeared in 2006-07 was triggered by a period of high

global oil prices. Figure 2.1 shows the national supply and peak demand and how it evolved

with the prices of main energy inputs used to generate electricity in the country. After 2006,

the gap between supply and demand continued to rise sharply coinciding with the upward trend

in oil prices. As world oil prices increased, there was a steady increase in the cost of generating

electricity, which increased the energy component of the price generation companies charged the

national grid company. Electricity prices for consumers were also revised periodically, but the

government decided not to raise prices fully. Instead the government pledged to compensate the

distribution companies with “tariff differential subsidies” to cover the gap between cost based

price and the notified price.

The fluctuation in the world oil prices after 2006 was unexpectedly large and the government

was unable to disburse subsidy payments to the distribution companies in a timely manner

(NEPRA, 2010). The distribution companies defaulted on paying for electricity purchased

from the national grid company, which was in turn unable to pay the power producers for

electricity generated. The power producers were unable to pay the companies that supplied

generation fuel to the plants. The fuel suppliers in turn defaulted on payments to oil refineries

and international suppliers. As a result of this liquidity crunch that ultimately affected all the

players in the electricity sector, the existing capacity at thermal power plants especially those

using oil based fuels could not be utilized fully. Plants were forced to remain shut and operate

fewer days in the year due to delayed payment of receivables and inability to purchase fuel

inputs for generation (Ali and Badar, 2010 and Sethi, 2015).

At the beginning of the crisis, outages were largely unplanned and took place whenever the

electricity demand at the local level was in excess of the supply available from the national grid.

The new government that came into power in 2008 promised to end outages popularly termed

as loadshedding by 2009.7 However, as it became clear that the supply and demand gap would

persist, the government committed to an equal loadshedding policy. Under this policy electricity

available in the national grid would be allocated across distribution companies proportional

to the anticipated demand. Distribution companies were directed to publish load-shedding

schedules for feeders in cities, towns, and villages, cutting down the number of hours electricity

was available proportionally, based on historical demand patterns. Despite these commitments,

media reports suggest that actual duration of outages usually exceeded the planned schedule.

Unplanned outages were more likely to occur during the summer season, at peak times in the

afternoon and evening, and in areas with high electricity demand such as commercial centers.

7Dawn (2009). “Raja Parvez vows to end loadshedding in 2009.”Dawn, June 25, 2009.


Additionally, political economy concerns may have played a role, to the extent interruptions

were minimized in areas with government, military installations or connected with influential

politicians.

In summary, the oil price shock combined with institutional distortions in the electricity

market, acted as a catalyst to the liquidity crisis in the electricity sector, and increased outages

by creating idle capacity in thermal power plants. Since the electricity shortage crisis in Pakistan

was driven in part by global oil price movements, which is plausibly uncorrelated with local

economic conditions, I am able to utilize the variation in energy prices combined with location

of thermal power plants to identify the effect of outages on employment outcomes.

2.3 Data

2.3.1 Thermal Power Plants and Energy Prices

In order to correlate the spatial and temporal variation in outages across Pakistan with energy

input prices, I construct a database of thermal power plants operating in the country. The

annual reports of federal regulator NEPRA are the main source of data that I utilize for this

database. These reports contain data on plant capacity, fuel type, annual generation and fuel

cost per kilowatt generated for each fiscal year from 2004-05 onwards. Additionally, I use the

Power Systems Statistics of the NTDC that provide more detailed data on the individual units

installed at power plants, the capacity of each unit, date of commissioning, and fuel type by

unit. The Power System Statistics also report the location of plants, so I am able to geo-code

the location of plants.

To the plant database, I add data on the yearly national prices of furnace oil, high speed

diesel, natural gas, and coal which are the four types of energy inputs used by thermal power

plants. The prices of furnace oil, high speed diesel and natural gas are determined by the

national Oil and Gas Regulatory Authority (OGRA). Price of oil products closely follow the

world price of oil, as the pricing formula used by OGRA is based on import parity pricing.

The price of natural gas depends on local supply and demand conditions as there is negligible

international trade in natural gas during this period. I use the published gas prices for industrial

consumers. Although plants may be procuring fuels at different rates, these prices are expected

to track the national prices, therefore, I use the national prices in my empirical work to measure

plant energy input prices. Data Appendix A.1 provides more details on data sources and

construction of the plant variables and energy prices.

Table 2.1 shows the descriptive statistics of thermal power plants operating in Pakistan

from 2004 to 2011. At the beginning of the sample period there were 28 thermal plants with

an average capacity of 277 MW. The total share of the installed capacity operating only on


oil based energy sources was 23 percent. The share using only gas was 18 percent, while 59

percent of the capacity installed at plants used natural gas as the primary fuel with oil or diesel

as a secondary fuel.8 The sample consists of 41 plants in total, but at the end of the sample

period there were 39 plants. Two small gas plants were commissioned on a rental basis by the

government and later these plants were shut down. Two-thirds of the new capacity that was

commissioned after 2004 used oil based fuels while the remaining capacity could be operated

on natural gas or a secondary fuel such as oil or diesel. Out of the 13 plants that enter during

the sample period, 10 were proposed prior to the year 2007 before the electricity shortage crisis

became fully apparent.

2.3.2 Night Lights Variability and Outages

Disaggregated data on outages is not reliably and consistently recorded by the distribution com-

panies during the period under study. Most distribution companies were using manual records

of outages at the electricity feeders managed by the company.9 Therefore, I use a measure of

outages based on the intra-annual variability in night lights observed in meteorological satellite

data. Chapter 1 describes the construction of night lights variability at the district-year level

and different validation checks that confirm that this measure captures the actual incidence

of outages within districts over time. Chapter 1 also shows that if reported outage duration

increases by an additional hour per day, or 18 percent, then night lights variability should go

up by 10 to 12 percent. Due to different sources of measurement error discussed earlier, we

can expect night lights variability to under estimate the true incidence and changes in outages

within districts. If the measurement error in night lights variability is random and uncorrelated

with changes in outcomes within districts then it will create an attenuation of any effect of

outages on outcomes. If the measurement error is correlated with changes in outcomes, then it

can also introduce an omitted variable bias that can be addressed using exogenous variation in

outages.

Figure 2.2 shows the yearly average of outages as measured by district level variability in

night lights, together with a 95 percent confidence interval. Night lights variability is coded on a

scale between 0 to 1, with higher number indicating greater variability and incidence of outages.

The increase in night lights variability coincided closely with the rise in energy prices faced by

furnace oil and diesel electricity generating plants. Figure 2.3 shows the location of thermal

8In my empirical work, I assume that plants or units within plants that can operate on multiple fuels usethe less expensive fuel two thirds of the time and the more the expensive fuels the remaining time. These ratioscorrespond roughly to historical generation patterns at the plants using multiple fuels.

9These records do not follow a common standard, are susceptible to errors, omissions and manipulationby officials. The distribution companies performance evaluation reports published the federal regulator NEPRAindicate the concern that distribution companies may be misreporting electricity outages in order to meet targetsset by the regulator (NEPRA, 2014).


power plants as well as the geographic variation in night lights variability across districts in

2004 and 2011. Outages increase the variability of night lights and districts appear darker by

the end of the sample period. This is especially true for districts located close to the oil based

thermal plants.

2.3.3 Labour Market Outcomes and District Characteristics

In order to analyze the effect of outages on employment, I construct a district level panel data

set from repeated cross sections of males of working age (20–65 years) observed from 2004

to 2011. This data was collected under the national Household Income Expenditure Survey

(HIES) which was carried out in four rounds spanning 2004 to 2011.10

I use the employment section of the surveys that record data on employment, days worked,

and labour market earnings of individuals in the household in the past month. Labour market

earnings include income earned from paid work or profit from a business but excludes in-kind

income and transfers. The survey also contains information on individual age and schooling,

household composition, monthly expenditures on non-durable goods including electricity con-

sumption, as well as other household characteristics. The lowest geographic unit identified in

the HIES is the district for rural households, and city or urban division for urban households.

There are 14 large cities, 25 urban divisions and 78 rural districts in my sample. For ease of

notation, I refer to the cities, urban divisions and rural districts as districts in my empirical

work. Data Appendix A.2 describes the construction of variables measuring labour market

outcomes, household living standards, and demographic characteristics of the workforce.

To this district panel, I also add data on weather and infrastructure. I use total monthly

rainfall data from the Climate Research Unit (CRU) and daily temperature data produced by

the European Centre for Medium-Range Weather Forecasts (ECMWF) to create population

weighted average of the average monthly precipitation and monthly temperature distribution

for each district-year observed in my sample. I also measure the district’s distance to the

Karachi port and the nearest highway or national road. Data Appendix A.3 describes in detail

the data sources that I use to construct these district level controls.

Table 2.2 shows average labour market outcomes of adult males observed over this period.

On average 84 percent of the individuals report working during the last month. This includes

work for pay or profit in the labour market or own business.11 The average individual earns an

income that is approximately 70 percent of the total household monthly expenditures. House-

10In addition to these four rounds, I also use HIES data collected in 2001–02, prior to the electricity shortagecrisis to compare baseline correlations, trends, and perform placebo checks discussed later.

11Unpaid family workers are excluded when constructing the district yearly average employment and earningsvariables. The income generated from unpaid labour should already be accounted for in income from profitearned by other household members.


holds are fairly large (7 or 8 members) and 1.3 male members report working in the past month.

The average years of schooling is less than 7 years. Furthermore, 25 percent of those observed

in the workforce are employed in the agriculture sector, 10 percent in manufacturing, 60 percent

in services and the remaining in smaller sectors such as mining, construction and other sectors.

62 percent of these individuals are engaged in paid employment, 32 percent are self employed

(including self-cultivators), and the remaining are working as contract cultivators.

2.4 Empirical Strategy

2.4.1 Conceptual Design

In an ordinary least squares fixed effects (OLS-FE) regression framework, the average partial

effect of outages on labour market outcomes, can be estimated using equation 2.1:

ydy = β0 + β1ln(Outagesdy) + β2Xdy + γd + γy + γdt + edy (2.1)

In the above equation, ydy is the average outcome of interest such as fraction working, days

worked or earnings in district d in year y. Outagesdy is the measure of outages based on

variability in night lights. In order to control for time invariant district characteristics, yearly

national shocks, and district specific trends that are correlated with outages and outcomes, I

control for district fixed effects (γd), year fixed effects (γy), and a district specific linear time

trend (γdt).

Since outages are not randomly assigned across districts, I also control for district specific

time varying variables (Xdy) that cause outages to be higher certain types of districts and may

be correlated with labour market outcomes. For example, districts with more economic activity

or districts with hot and dry weather can experience larger fluctuations in peak demand caus-

ing unplanned shutdowns. Specifically, Xdy includes controls for district annual average light

intensity as a proxy for economic prosperity, average monthly precipitation, and the average

monthly temperature distribution in the district in a given year. It also includes household

living standards variables such as average per capita monthly expenditures, household size, the

number of rooms in dwelling, fraction with access to gas, piped water, and phone connection.

Finally, I also include demographic controls, such as average years of schooling among males,

age and age squared as a proxy for skill and experience of the workforce.

Even with these controls, there can be other omitted variables that can bias the OLS

estimates of β1. For example, one can argue that average lights do not perfectly control for

local economic prosperity, because of top coding of pixels, or because certain types of economic

activity such as agriculture or service sector activities do not necessarily produce lights in


proportion to income generated. To the extent that electricity demand and outages go up

faster in places with faster local economic growth, omitting this variable from equation 2.1

will attenuate any negative effect of outages on employment outcomes. However, if the change

in outages is larger in less developed districts with high unemployment and poor electricity

infrastructure, then OLS estimates will be larger in magnitude and we will erroneously attribute

the negative effects of confounding variables to outages. There can also be other idiosyncratic

district level events such as floods that cause damage to infrastructure, increase outages, and

also affect labour markets.

Since I am using variability in night lights which measures the actual incidence of outages

with error, OLS estimates of β1 are likely to suffer from attenuation bias. If the measurement

error is random and uncorrelated with changes in labour market outcomes within districts,

it will simply lead to an attenuation of any potential effect of outages on outcomes. If the

measurement error is correlated with changes in outcomes, this introduces an omitted variable

problem which can be addressed using exogenous variation coming from an instrument for

outages. Due to the concerns about confounding variables and measurement error in night

lights variability, I turn to an instrumental variables strategy to estimate β1 or the average

causal effect of outages on labour market outcomes.

2.4.2 The Instrument – Price of Energy Mix

The instrument (Zdy) that I use for outages using is a weighted price of different thermal energy

fuels that are used at plants generating electricity near a district. There are four different types

of fuels used by thermal energy plants, namely furnace oil, diesel, natural gas and coal. I

use the plant level database described above, together with information on energy prices and

distance of plants from the districts to create a district level price of energy mix. This is a

shift-share instrument that affects the district’s electricity supply but is plausibly uncorrelated

with district level changes in economic trends and electricity demand as discussed later. Below,

I outline the steps used to construct the instrument.

(1) Construct district specific plant weights (skfdy): For each plant k and fuel type f that

it can operate on, I assign a weight proportional to the capacity and inverse of square distance

to the plant from the district centre. For plants or units that can operate on multiple fuels, I

assume that two-thirds of the time the plant is operated on the cheaper fuel that is natural gas,

and the remaining time on the expensive fuel that is furnace oil or diesel. Given the centralized

nature of the electricity distribution, I allow all plants to supply electricity to the district. If

the plant is large but located farther away it still gets weight as it can supply electricity to

districts within a large radius. However, the weights decline sharply as distance of the plant


from the district increases.

skfdy =capacityk

distance2kd

(2) Construct district specific energy weights (Sfdy): For each district and fuel type f that

is furnace oil, diesel, natural gas or coal, I add up the weights attributable to all the plants that

supply electricity using that fuel type. To get the total sum of weights, I sum across all plants

supplying to the district. This gives district specific energy weights Sfdy for each fuel type.

Sfdy =

∑k∈fy skfdy∑

k skfdy

(3) Construct district level price of energy mix: Finally, I use the district specific energy

weights together with annual prices of furnace oil, diesel, natural gas and coal to construct the

instrument which is a district level price of energy mix that combines the temporal variation in

energy prices with the spatial variation in plant location and energy sources used to generate

electricity close to a district.

Zdy =∑

f

Sfdyln(pricefy)

Figure 2.4 shows the relationship between the price of energy used by a plant, the plant

specific variable cost of generation, and the plant level utilization factors over time. As the

price of energy source that is used in generating electricity goes up, the fuel cost of generating

each kilowatt hour of electricity at that plant goes up. There is a one to one relationship

between energy prices and the variable component of generation cost that depends on energy

prices, which raises the cost of electricity for distribution companies. For instrument validity,

I also need that the increase in energy prices reduces generation. To check this, I look at the

correlation between plant utilization factors and energy prices after removing plant level fixed

effects. Since energy prices from different sources are normalized to a common energy unit,

they measure the cost of purchasing a fixed energy content from a given source over time.12

The utilization factor is the ratio of total generation during the year and the total potential

generation based on capacity. The utilization factors can depend on the position of the plant

in the dispatch order, therefore, I look at the within plant variation in utilization factors and

energy prices or generation costs. Figure 2.4 shows that on average a 1 percent increase in

energy prices reduced utilization of the same plant by 0.46 percent over time. Thus, energy

prices act as a negative supply shock, reducing electricity generated at plants that experienced

a large increase in input prices over the sample period.

For the instrument to be valid, the district’s price of energy mix should not affect labour

12The prices are measured in Rupees per millions of british thermal unit (mbtu). An mbtu is standard unitof measurement used to denote the energy content of fuels. (1mbtu= 106 btu and 1 mbtu = 293 kWh).


market outcomes except through its effect on outages conditional on the control variables.

Energy prices are set nationally by federal regulators and and we should not expect them to

be systematically correlated with any particular district’s outcomes. However, energy prices

can have a direct effect on economic outcomes due to household energy usage.13 Energy prices

can also have an effect due to transport costs that affect local prices and economic growth.

Increase in oil prices raises transport costs and the effect is likely to be larger in districts that

are not well connected to the national infrastructure such as the port and highways. As a

result of this shock, these places may experience slower growth that is not necessarily due to

electricity outages.14 Finally, since plant location is not randomly assigned and in particular,

newer oil based plants are likely to be located farther from the port, so the effect of energy

price mix on outages can be biased, if we exclude the direct effect of energy prices on outages

in these districts. For these reasons, I control for energy prices interacted with distance from

the highway and port to allow energy prices to have a direct effect on outages and outcomes.

The first stage equation that I estimate is:

ln(Outagesdy) = λ0 + λ1Zdy + λ2Xdy +∑

f

δf ln(pricefy)X port−highway−distd

+γd + γy + γdt + wdy (2.2)

In addition to energy prices, the instrument utilizes also information about capacity and

distance of plants from the district centre. In Table 2.3, I check if plants using oil based fuels

(furnace oil and diesel) which experience a larger input price increase, are located systematically

closer to more prosperous or faster growing districts which could violate the exclusion restriction.

Table 2.3 shows the baseline partial correlation between district level economic characteristics

and the share of capacity (Sfdy) from oil and diesel plants in 2004–05. Column 1 shows that at

the start of the sample period, districts appear balanced in terms of economic prosperity and

household living standards. There are also no significant differences in terms of geography or

proximity to the port or national roads.

Column 2 repeats the same test for correlation between baseline characteristics and district’s

change in generation capacity from oil based plants, due to entry of new plants during the sample

13Energy is also used in household activities apart from electricity usage, for example natural gas and coal isused for cooking or heating. Rich households may also use gas or diesel as a fuel for self-generation. Generatorownership is not recorded in the HIES data. See Appendix Table B.1 for generator ownership rates from a smallindependent survey carried out in 2012.

14A number of papers using quasi experimental variation in expansion of infrastructure within countries suchas Donaldson (2013), Donaldson and Hornbeck (2014), Faber (2014) find that transport costs have a causal effecton growth and income. In a different approach, Storeygard (2015) using exogenous annual changes in transportcosts induced by world oil price fluctuations, finds that cities that are farther away from the port in Sub-SaharanAfrica, experience smaller increase in incomes when oil prices go up.


period. There is a weak positive correlation with household monthly expenditures. Looking

at other characteristics of district development such as average lights, schooling and access to

infrastructure, the differences in economic prosperity are not jointly significant. New plants

are more likely to be located farther away from the port in districts with lower temperature

and higher precipitation. Apart from these variables which I am able to control for, there is no

systematic pattern in economic characteristics across districts, where entry of oil based plants

takes place.

Since the empirical strategy relies on within district variation in outcomes and explanatory

variables, in columns 3 and 4, I repeat the same tests for district level baseline trends in economic

prosperity, household living standards and access to infrastructure. The baseline trend in each

characteristic is measured using the change in the variable from 2001 to 2005. I use HIES data

collected in 2001–02 together with the data from 2004–05 round, to measure changes in average

characteristics at the district level. The results of the last two columns of Table 2.3 show that

districts with a greater share of oil based plants or the districts that experienced entry of oil

based plants were not systematically different in baseline trends.

Overall, the results in Table 2.3 suggest that oil based plants which experience a larger

input price shock are not systematically located closer to faster (or slower) growing districts at

baseline. Once oil prices go up and there is a negative shock to electricity generation, outages

should go up in districts located close to these plants. Conditional on instrument validity, βIV1

estimates the causal impact of outages on employment outcomes.

ydy = α0 + βIV1 ln(Outagesdy) + β2Xdy +

∑

f

πf ln(pricefy)X port−highway−distd

+γd + γy + γdt + εdy (2.3)

Since I am using annual night lights variability which does not perfectly captures outages,

I also expect attenuation in the OLS fixed effects estimates due to random measurement error

which should be corrected in the IV specification. I also expect the IV estimates to be larger in

magnitude than the OLS estimates if outages are more likely to occur in prosperous districts

with higher electricity demand. However, if outages are higher in economically backward dis-

tricts with poor infrastructure, then we will be attributing the negative effect of these omitted

variables on labour market outcomes to outages. If this is the case then IV estimates should

be smaller in magnitude.


2.5 Results

2.5.1 Outages and Price of Energy Mix

Table 2.4 shows the results from estimating the first stage. In column 1, which controls for

district and year fixed effects and a district specific time trend, the first stage estimate implies

that a 1 percent increase in the district’s price of energy mix increases outages as captured by

night lights variability by 0.16 percent. In column 2, I control for the price of natural gas and

coal interacted with the district’s combined distance from highway and the port. Natural gas

and coal are common household energy sources used for cooking and heating. However, adding

these variables does not change the first stage estimate.

Next, I control for furnace oil and diesel prices interacted with the district’s combined

distance from highway and the port. This increases the first stage estimate of the relationship

between energy price index and outages significantly. The direct effect of the interaction term

is also positive, which shows that oil prices increase variability in districts farther away from

the port and highway. But the effect of oil prices on the energy price mix is smaller in these

places relative to the places closer to the port and highway, and omitting this variable causes

the first stage estimate to be underestimated. Price of diesel has a smaller effect on outages in

districts located farther from the port and highway but a larger effect on the price of energy mix

in these districts so adding this variable also increases the first stage estimates. Furthermore,

if oil prices decrease local growth by increasing transport costs and unplanned outages are less

frequent in such places, then we should expect the first stage coefficient to be under-estimated,

if this variable is omitted from the first stage.

In columns 4 and 5, I add district time varying controls for average lights, temperature, rain-

fall, household living standards and demographic characteristics which improves the precision

of estimates but does not change the magnitude of the coefficient significantly.15 The estimate

in column 5 with all the controls implies that a 1 standard deviation change in the within

district price of energy mix (0.50) increases outages as measured by night lights variability by

19.5 percent. Using the distribution company data on reported duration of outages and the

elasticity found in chapter 1, this implies an increase of in duration of outages by approximately

1.6 hours per day.

15I also estimate columns 2 to 5 with each price interacted separately with distance from highway and port.There is no significant change in the first stage coefficients on price of energy mix or the IV results reported inTable 2.5 onwards.


2.5.2 Outages, Employment and Earnings – OLS and IV Results

Table 2.5 shows the OLS and IV results of the effect of outages on employment outcomes,

using an indicator for work and log days worked in the past month as the dependent variable.

Columns 1 and 3 report the OLS and IV results, controlling for district and year fixed effects,

district time trends, and district time varying controls. Columns 2 and 4 report the results after

controlling for household living standards and demographic characteristics. The OLS estimates

are negative and imply that a 10 percent change in outages (or 1 additional hour per day)

reduces probability of work by 0.2 to 0.3 percentage points.

The IV estimates are also negative, significant and much larger than the OLS estimates.

The difference in the magnitude of the OLS and IV suggests attenuation of the negative effect

of outages due to measurement error. It can also be due to omitted variables that are positively

correlated with within district changes in local labour market outcomes and outages, such as

economic growth. After adding all the controls, I find that a 10 percent change in night lights

variability decreases the probability of working by 1.8 percentage points which is a reduction

of 2.1 percent in male employment.

In Panel B, I estimate the effect of outages on labour supply, with days worked in the past

month as the dependent variable. I find that a 10 percent increase in variability leads to a 8.3

percent decline in days worked in the past month. The mean number of days worked in the

past month is 23 so this implies that on average 1.9 days of work are lost due to approximately

one additional hour of outages per day. With a 8 hour work day and 26 work days in the

month, this is equivalent to 16 hours of lost work due to 26 hours of outages.16 These estimates

imply that outages have a significant effect in disrupting the ability of workers to engage in

productive activities. Even when people are employed, the time spent at work falls due to

electricity outages.

In order to further explore whether this change is occurring due to movement out of the

workforce or increase in temporary jobs, I use an indicator for working more than 20 days

as the dependent variable. The results in Appendix Table B.2 show that the probability of

working more than 20 days falls by 2.4 percentage points or 2.9 percent which is larger (but not

significantly different) than the effect on working. There is also a larger effect on probability of

engaging in paid employment (7 percent) as compared to self employment. This suggests that

there may be some movement into self employment as most of the extensive margin effect is

due to people experiencing loss of work in paid jobs.

Table 2.6 shows the estimates of the effect of outages on labour market earnings and pro-

16There is only one official holiday (Sunday) in the week, and most businesses are open six days from Mondayto Saturday.


ductivity of adult males in the sample. In Panel A, the dependent variable is total monthly

earnings from labour market activities in the past month, including paid work and income

from business. As with employment, the OLS estimates are negative but attenuated, suggest-

ing measurement error and omitted variables that are positively correlated with outages and

labour market outcomes within districts. The IV estimate in column 4 with all the controls

implies that a 10 percent increase in night lights variability or 26 additional hour of outages in

the past month, decrease total monthly earnings by 18 percent.

In order to decompose the loss of earnings into days worked and labour productivity, I

use individual earnings per day which is one measure of labour productivity, as the dependent

variable. Panel B of Table 2.6 reports the OLS and IV results. The IV specification in columns

4 with all the controls shows that outages reduces individual earnings per day by 11 percent.

Therefore, these estimates suggest that approximately 55 to 60 percent of the loss in earnings

is due to loss in productivity and the remaining is due to loss in days worked. 17

The effect on earnings and productivity is large, and it can arise due to a number of reasons.

If outage duration is under reported by distribution companies, then an 18.5 percent change

from mean duration or equivalently a 10 percent change in variability actually represents a much

larger change in actual outage duration.18 If earnings per hour were independently distributed

then we should expect a reduction of roughly 13 percent in earnings if there is complete loss of

production due to one hour of outages in a 8 hour work day. However, if there is complementarity

in the tasks that are required to produce a good or a service, then a disruption of electricity

for even one hour can result in a larger loss of earnings due to spill-over effects. The large

negative effect can also be due to general equilibrium effects, as electricity outages could create

a downturn in economic activity. Specifically, a decline in one sector or businesses can reduce

demand for products and services for other businesses which can multiply the negative effect of

outages. In section 2.6, I show that there is a larger impact on districts that are more reliant

on electricity intensive industries.

Furthermore, it is likely that the IV estimates are identifying the effect of outages due to the

variation in unplanned outages which are expected to coincide with times of peak demand in

business activity. Since 40 percent of the workforce in the sample is engaged in wholesale, retail

and public and commercial service sector activities where demand is essentially variable and

there is not much scope for inter temporal adjustment, loss of electricity during peak business

time can have a substantial negative effect. Workers and businesses could be unprepared for

17The coefficients estimates provide an approximate decomposition of the total change in earnings (18 percent)due to change in days (8.3 percent) and earnings per day (11 percent) as earnings data is missing for someindividuals reporting positive number of days worked.

18For example, if the true average duration is 8 hours, then an 18.5 percent change in duration (10 percentchange in variability) is an increase of 1.5 hours.


disruption in production which can amplify the negative effect of outages.

Loss of electricity during the day when production is taking place or at night when people

need sleep, can also have a negative effect on labour efficiency by reducing the availability of

electricity for indoor cooling. Research from field and laboratory experiments shows that an

increase in ambient temperature above a certain threshold can reduce the ability of workers in

factories and indoor offices to perform cognitive and physical tasks (see Seppanen, Fisk, and

Lei, 2006 for a meta analysis). Section 2.6 also shows that when outages occur during periods

of very high temperatures there is a larger effect on employment and earning outcomes.

The large effect also suggests that the ability of households and small businesses to cope

with outages using generators or backup storage is limited. A back of the envelope calculation

shows that the average cost of operating a small 1 KVA generator that can supply up to 1

kW of electricity per hour is less than the earning loss averted by having electricity during

outages.19 However, generators are costly and the purchase price, which was upwards of Rs.

20,000, exceeds the average household monthly expenditures in this sample (Rs. 12,287). The

price of a backup storage device with similar power was also high (Rs. 10,000 or more). These

large fixed costs can explain why only a small fraction of the households own a generator or

backup storage device (Appendix Table B.1).

2.6 Mechanisms

The results described in the previous section show that there is a significant negative impact

of electricity outages on employment and earnings. Around 40 to 45 percent of the drop in

earnings is due to reduction in days worked. An extra hour of outages every working day of the

month (24 hours) results in approximately 2 days (16 hours) of lost work. This seems plausible

if offices, shops, and factories remain shut when electricity is unavailable or they operate fewer

hours or days during the month. The reduction in days could may also be due to a government

policy that mandated early closure of markets and reduced the number of working days from

six to five per week, to conserve electricity during the months when electricity shortage was

very large.20 If people are not well rested due to electricity interruptions at night, outages

may also result in increased absenteeism. Anecdotal evidence from media and news reports

from Pakistan also suggest that people often complain being tired and sleep deprived due to

1918 percent reduction in average monthly earnings implies a monthly loss of Rs. 1,552 due to an extra hourof outages every day of the month. The operating cost of a generating 1 kw of electricity for each day of themonth ranges between Rs. 750 to Rs. 1,250, using an average diesel price of Rs. 100 per litre and efficiencyfactor of 0.30 - 0.52 litres per 1 kw of electricity generated. In addition to the running cost, there can also beadditional maintenance costs.

20Dawn (2010). “Power Conservation Measures Proposed.” Dawn, April 20, 2010.


electricity disruptions especially during the long summer season.21

Approximately 55 to 60 percent of the total effect on earnings is driven by the decline in

earnings per day or productivity. This can arise due to disruption of electricity when workers

need it as a complementary input into production, a reduction in labour efficiency, or a decline

in demand for goods and services driven by general equilibrium effects. All of these mechanisms

would lead to a downward shift in labour demand especially in districts where a greater fraction

of the workforce is engaged in electricity intensive industries and occupations.

In order to understand how much of the negative effect on employment and earnings is

due to differential effect of outages on labour demand, I allow the effect of outages to differ in

electricity intensive districts. Specifically, I obtain the mean fraction of the district’s workforce

observed in electricity intensive industries over the entire sample period. Apart from manufac-

turing this also includes service industries that make greater use of electricity such as retail,

restaurants, information and communication, financial sector and business services, education,

health and personal services. The remaining industries such as agriculture, forestry, fishing,

mining, construction, handicrafts, transport, wholesale trade, sanitary, recreational services are

classified as low electricity intensive.

Table 2.7 shows the effect of outages in electricity intensive districts. In columns 1 and

3, I find a positive interaction effect, that is districts with a greater fraction of the workforce

engaged in more electricity intensive industries experienced a smaller negative effect of outages.

This may reflect faster growth of electricity intensive sectors within districts that experience

greater outages over time. A smaller effect can also be due to higher adoption of generators and

other coping mechanisms, since the marginal benefit of adoption is high in electricity intensive

industries. To control for this effect, in columns 2 and 4, I add the district mean fraction

observed in electricity intensive industries interacted with year fixed effects. The interaction

effect that is now negative albeit imprecise. This suggests that outages have a larger negative

effect on labour demand in districts that rely more on electricity intensive industries, conditional

on differential changes in industrial growth and coping mechanisms.

In order to explore this impact of negative labour demand shocks further, I allow the effect

of outages to differ by the fraction of the district’s work force with more than median years

of schooling (6 years). Education is also a good marker of the extent to which income may

be generated from electricity intensive production. Using the industry and occupation codes

reported by individuals who work, I find that 62 percent of those with more than median

years of schooling are engaged in the service sector, 10 percent in manufacturing sector and 13

percent in agriculture. 26 percent of these individuals are involved in professional occupations

21See for example, Declan Walsh (2011). “Power Cuts Leave Pakistan Hot and Bothered.”The Guardian, July3rd, 2011 and Economist (2014).“The Urdu rate of growth.” Economist, Feb 15, 2014.


in management, education, and health sectors. In comparison, 35 percent of those with below

median schooling are involved in agriculture sector, 9 percent in manufacturing and 36 percent

in service sector activities. 40 percent of the less educated workers are employed as street

vendors, domestic workers, or as labourers in agriculture, mining, transport, manufacturing, or

other elementary occupations. These numbers suggest that more educated workers are likely to

be employed in industries and occupations that require greater complementary use of electricity,

so we should expect outages to have a larger effect in districts with a greater fraction of educated

workers.

In columns 1 and 3 of Table 2.8, I do not find a significant additional effect of outages on

districts with more educated workers. The additional effect on employment variables is positive

and on income and productivity is negative, which may arise if labour supply is inelastic as

education increases. In columns 2 and 4, I also control for the mean fraction employed in

electricity intensive industries interacted with year fixed effects. Now the effect of education is

estimated conditional on district specific time varying factors related to electricity intensity of

industries. With these controls, there is a larger effect on labour market outcomes in districts

with more educated workforce. The interaction effects are negative and precisely estimated.

This suggests that conditional on differential industrial trends, outages cause larger demand

shocks in districts with more educated workforce, driving down the marginal product of labour.

Labour productivity can also be reduced due to outages as a result of disruption in sleep

at night and high indoor temperatures in the workplace during the day. In order to identify

potential effect on labour productivity operating through heat, I construct a temperature index

which is the cumulative number of degrees above 30 Celsius (CDD30).22 This type of tempera-

ture index, also used by Burgess, Donaldson, Deschenes and Greenstone (2013) to examine the

effect of temperature on mortality in India, has the advantage of combining information about

the duration and intensity of high temperatures into a single index.23

Table 2.9 shows that outages have a larger negative effect on labour market outcomes in

districts with higher temperatures. The interaction effect of CDD30 with outages implies an

additional 2.5 percent reduction in earnings due to approximately equal percent reduction

in days worked and labour productivity. The negative effect of high temperature is smaller

than labour demand driven reduction in marginal product found in Table 2.8, as it is due

to a reduction in labour efficiency. However, it is significant and does not change even after

22For example, if temperature exceeded 30 Celsius on two days in the past month, and was 35 and 40 Celsius,then CDD30 is (35-30) + (40-30) =15. I calculate CDD30 by district-month-year using daily temperature dataand obtain average over all months observed in a district-year cell. Note that this measure is similar to thecooling degree days measure used in the energy literature, which is the number of days with temperature above18 Celsius.

23The effect when using only duration, that is the number of days above 30 Celsius is similar but less preciselyestimated.


controlling for the electricity intensive industrial composition of districts interacted with year

fixed effects.

Finally, in Table 2.10, I explore heterogeneity in the effect of outages in more remote districts

that are located farther away from a national road. In more remote districts labour supply may

be more inelastic as workers have fewer outside options which can amplify the effect of any

negative demand shocks on income and productivity. Another potential mechanism for a larger

effect of outages is higher line losses, as electricity is carried over long distances within the

distribution system. Thus, any given supply shock can lead to a larger increase in outages in

remote districts.

However, neither of these mechanisms seems to be at work as columns 1 and 3 show that

the effect of outages is actually lower in districts located farther away from national roads.

In columns 2 and 4, I also control for distance interacted with year fixed effects to flexibly

control for any omitted district time varying factors correlated with distance that may bias

the estimated effects, such as smaller labour demand shocks. This specification also shows

a smaller effect in remote districts, or conversely a larger effect in districts located closer to

national roads. The offsetting effect of distance also persists even after controlling for fraction

employed in electricity intensive industries interacted with year fixed effects.

A possible explanation is that energy price shocks have a heterogenous effect on outages

in remote districts as compared to districts located closer to the highway. The heterogenous

effect may arise due to overburdening of distribution infrastructure in faster growing districts,

which results in more line losses and thus more outages. It may also arise as an unintended

consequence of government policy that cut down the electricity allocated from the central grid in

an equal proportion manner using historical demand. For example, if the national shortage was

estimated to be 20 percent, supply was cut down by 20 percent of estimated demand. As a result

districts located closer to national infrastructure that were growing faster may actually have

experienced larger effective electricity cuts, if growth in electricity demand was underestimated

in determining the allocation of electricity. The negative impact in districts located close to

the highways can also be multiplied due to negative spill-over effects from declining growth in

neighboring districts.

2.7 Robustness Checks

In this section I assess the sensitivity of the main results to potential threats to the IV strategy,

additional controls and different specifications.

For instrument validity, I require that that district’s price of energy mix does not affect

labour market outcomes except through its effect on outages. If prices were rising faster in


districts that were already on a downward trend in employment or prices were rising faster in

prosperous districts where employment is growing faster, then this exclusion restriction would

be violated. This could happen if oil based thermal plants locate disproportionately closer to

the backward districts, to avoid high land and construction costs, or if they choose to locate

close to faster growing districts with large markets. The balance test in Table 2.3 shows that

districts with a greater share of oil based plants are not systematically different in baseline

characteristics or pre-period trends.

In Table 2.11, I check the validity of the exclusion restriction indirectly using the correlation

between pre period labour market outcomes and the post crisis changes in the price of energy

mix. Using the household survey data from 2001 to 2005 and the district’s energy price mix

from 2007 to 2011, I estimate the reduced form correlation between individual labour market

outcomes and district’s energy price mix using the specification which includes all the standard

control variables. Columns 1 to 4 in Panel A show a positive and imprecise correlation between

probability of work, earnings and productivity and a negative correlation with labour supply.

These results suggest that districts that experienced the largest shock due to rise in oil prices

were not systematically doing worse or better before the price shock hit the electricity sector.

In Panel B, I estimate the reduced form correlation between changes in electricity demand

and ownership of appliances with energy price mix. The results in columns 1 to 4, show that

the correlation is not significant. This suggests that districts that experienced a larger increase

in energy price mix used to generate electricity, did not experience faster change in electricity

consumption or durable goods ownership in the period prior to the oil price shock. Taken

together, these placebo checks suggest that the district’s price of energy mix is uncorrelated

with other factors that could directly affect the outcomes of interest in the period prior to the

oil price shock.

In Table 2.12, I assess the sensitivity of the main results to different controls. In Panel A, I

control for the mean fraction of district workforce in energy related industries interacted with

energy prices. I define energy related industries as mining, refining, chemical processing and

electricity, water and gas distribution. Districts in which there is a larger fraction of labour

force employed in these industries may directly experience changes in employment apart from

the effect of outages on employment, following large shocks to energy prices. The results are

not significantly different, although the effects are somewhat larger which indicates that the

effect of outages in non energy related industries is larger.

In Panel B, I find that the estimates are smaller but not statistically different without con-

trolling for district average light intensity which is a proxy for district prosperity and economic

activity. This suggests that districts with more outages that experience larger drop in earnings

and employment, also experience a larger drop in average light intensity, and omitting this effect


leads to smaller estimates of the outages on outcomes. The reduction in brightness of night

lights may be due to energy conservation measures that were put in place by the government

such as early closure of marketplaces.

In the last panel, I check the robustness of the results to various plant characteristics.

Specifically, I control for the distance of the district from the nearest 100 MW plant, mean

age of plants, the mean capacity of plants, and the mean fraction using modern combined

cycle generation technology within a 400 km radius which is the average plant distance to

district center. The results show that the effect of outages on employment are not affected by

characteristics of plants located close to the district.

Next, I check the robustness of the estimates to entry decisions of plants. In Table 2.13,

I control for an indicator for entry of oil based plants within 50 km radius of district center.

The estimates are larger but not significantly different from the main results. In Panel B, I

exclude the distribution company with the highest geographic concentration of oil based plants.

The estimates are smaller but not significantly different from the main results which confirms

that the results are not driven by unobserved correlation between entry decisions of plants and

labour market conditions. Finally, in Panel C, I report the results when using global oil prices

instead of local oil prices to construct the instrument. There is a concern that the government

may have strategically kept the prices low to benefit certain types of districts that may be

located close to the oil based plants, which would violate the exclusion restriction.24 However,

the results show that the estimates are robust to this concern.

Finally, Appendix Table B.3 shows the results obtained from estimating the main equations

using individual observations rather than district-year observations and using survey sampling

weights. The estimates are smaller in magnitude (and not statistically different) in the indi-

vidual regressions. As outages are measured the district-year level, it is preferable to collapse

the data and estimate the effects at district-year level. This is a more conservative approach

that takes into account the effective sample size are reduces concerns about serial correlation

between observations within the same district-year. The results from the weighted regressions

show that the effects are larger (and not statistically different) but less precise when using

sampling weights. The advantage of using weights is that they can correct for under or over

sampling of specific types of populations within districts, which may cause biased estimates if

the sample heterogeneity is correlated with outages. For example, if less educated workers are

24I prefer to use the national oil prices in the main results, as the timing and the changes of these pricesreflect more closely the changes in the fuel prices for the generation plants and ultimately the variation in mymeasure of outages. As shown in Figure 1 and 2, the local price of furnace oil and diesel closely tracks the globalcrude oil price. Between 2006 and 2008, there was a much larger gap between the global and national price asthe government was approaching the next election cycle and prices were not adjusted fully by the incumbentgovernment prior to the elections.


less represented, and the effect of outages on these workers is larger, then we may underestimate

the average effect of outages. However, as discussed in Solon, Haider and Wooldridge (2013)

and Dickens (1990), weighting can also inflate standard errors when sample size within groups

(in this case districts) are different.

2.8 Conclusion

In this chapter, I use the electricity shortage crisis in Pakistan, to estimate the causal effect

of electricity outages on employment and earnings of adult males in the labour force over the

period 2004 to 2011 and provide evidence on the mechanisms underlying the observed impact.

In order to address concerns about measurement error in outages and potential endogeneity in

occurrence of outages due to correlation with local economic conditions, I use the variation in

price of fuels used by thermal energy plants combined with information on location of plants

to construct a district level price of energy mix as an instrument for outages. As the world oil

prices went up substantially during this period, the cost of generation at plants using furnace

oil and diesel went up. Price of electricity did not adjust to reflect the new costs, and outages

went up as generation and distribution companies were unable to meet electricity demand.

I find that as the district’s price of energy mix goes up, outages as measured by variability

in night lights also go up. I show that the variation in the district’s energy price mix is plausibly

uncorrelated with other factors that affect outcomes, and thus it can be used to identify the

effect of outages on labour market outcomes. My results show that outages have a significant

negative effect on employment and earnings of adult males in the labour force. A 10 percent

increase in night lights variability or approximately 26 additional hour of outages per month,

reduces probability of employment by 2.1 percent, total earnings by 18 percent, days worked

by 8 percent and labour productivity by 11 percent.

The larger effect in electricity intensive and more educated districts implies that labour

productivity declines due to negative labour demand shocks. Part of this effect also arises due

to reduction in labour efficiency when electricity outages coincide with high temperatures. I also

find evidence that the effect of outages is smaller in remote districts located farther away from

the highway even after allowing for differential impact of omitted factors such as smaller labour

demand shocks. Conversely, a larger negative effect on districts located close to the highways

suggest that energy price shocks may have resulted in a larger gap in electricity supply and

potential demand in these districts.

My research provides causal evidence on the impact of outages beyond firms in the organized

manufacturing sector. I find a large negative effect of electricity outages on the ability of

individuals to use their labour market resources for production activities. This is in contrast to


the finding that large firms are able to cope fairly well by investing in self-generation. These

large effects suggest that the high fixed costs of purchasing generators limit the ability of average

income households and small businesses to adjust to outages.

The estimates of the effect of outages on earned income found in this research partly capture

the costs of outages to a growing developing country. Outages can also lead to changes in female

labour force participation, the levels of and investment in health and education, as well as overall

well being of the population. Developing country governments seeking to improve electricity

reliability through pricing and governance reforms, or investments in modern infrastructure,

need to fully understand their impact on a range of development outcomes. These remain

important topics for future research.


Table 2.1: Thermal Power Plants and Energy Prices

2004 2011 After 2004 All

Panel A – Plant variables

Plant generation capacity (MW) 277 228 137 254(271) (197) (49.9) (235)

Distance to district (km) 385 394 415 391(237) (201) (43.3) (204)

Fraction private 0.46 0.59 1.00 0.51(0.50) (0.49) (0.00) (0.50)

Share of installed capacity using only oil 0.23 0.29 0.62 0.24(0.02)

Share of installed capacity using only gas 0.18 0.18 0.11 0.18(0.08)

Share of installed capacity using multiple fuels 0.59 0.53 0.27 0.58(0.02)

Number of plants 28 39 13 41Number of new plants proposed prior to 2007 10 10 10Number of plant-fuel types 67 81 16 83

Panel B – Energy prices

Furnace oil (Rs. per mbtu) 289 1,718 835(520)

Diesel oil (Rs. per mbtu) 475 1,786 972(477)

Natural gas (Rs. per mbtu) 177 397 274(86)

Coal (Rs. per mbtu) 115 362 201(110)

Notes: Mean of the variable is reported with standard deviation in parentheses. The sample is all public and privatethermal plants that generate electricity for national grid including KESC plants. A plant can have multiple units whichmay use different fuels or the same unit may be operated on different fuel. Units using multiple fuels use gas as primaryfuel and oil or diesel as secondary fuel. Energy prices are measured yearly, in Rupees per millions of british thermalunits (mbtu).



2004–05 2010–11 All

Panel A – District variables

Night lights variability (0–1) 0.10 0.17 0.13(0.06) (0.07) (0.07)

Average lights (DN) 15.3 16.2 14.4(10.7) (15.2) (12.4)

Price of energy mix Rs. per mbtu (log) 5.50 6.65 5.99(0.18) (0.38) (0.52)

Mean monthly precipitation (cm) 0.43 0.61 0.44(0.60) (0.93) (0.63)

Mean monthly temperature (◦C) 20.8 23.5 22.9(8.07) (8.93) (8.35)

Distance to port (km) 711(369)

Distance to national highway (km) 19.2(20.2)

Panel B – Labour market variables

Indicator for work 0.828 0.832 0.840(0.377) (0.374) (0.366)

Days worked (log) 3.37 3.31 3.36(1.44) (1.49) (1.46)

Monthly earnings (log) 7.96 8.06 8.08(3.77) (3.70) (3.60)

Earnings per day (log) 4.74 5.37 5.04(2.31) (2.50) (2.32)

Panel C – Household and demographic variables

Age (years) 37.0 37.0 37.0(12.9) (12.9) (12.9)

Years of schooling 6.29 7.36 6.56(5.60) (6.16) (5.88)

Monthly per capita monthly expenditures (log) 7.39 7.55 7.41(0.54) (0.46) (0.48)

Household size (persons) 8.12 7.78 8.18(4.01) (3.67) (4.12)

Number of rooms in dwelling 2.77 2.70 2.72(1.53) (1.50) (1.54)

Phone connection 0.27 0.82 0.73(0.44) (0.38) (0.44)

Piped water 0.43 0.37 0.37(0.49) (0.48) (0.48)

Gas connection 0.31 0.42 0.32(0.46) (0.49) (0.47)

Notes: Mean of the variable is reported with standard deviation in parentheses. The total number ofdistricts is 117, number of households is 47,281, and the number of males of age 20 –65 years observed inthe sample is 70,697. The mean number of days worked is 23, the mean monthly earnings are Rs. 8,602,the mean earnings per day are Rs. 333 and the mean household monthly expenditure is Rs. 12,287.


Table 2.3: Correlation between Baseline District Characteristics and Share of Oil Capacity

Characteristic in 2004–05 Δ in Characteristic 2005–2001

Share of oilcapacity in2004–05

Δ share ofoil capacity2004-2011

Share of oilcapacity in2004–05

Δ share ofoil capacity2004–2011

(1) (2) (3) (4)

Average lights 0.07 1.13 -0.07 -0.09(0.86) (0.27) (0.15) (0.17)

Log per capita expenditures 0.04 0.26* 0.02 -0.06(0.62) (0.06) (0.82) (0.63)

Log household size -0.05 -0.29** 0.05 -0.06(0.39) (0.02) (0.36) (0.66)

Years of schooling -0.00 0.11 0.03 -0.43*(0.99) (0.92) (0.83) (0.07)

Phone connection 0.05 0.14 0.04 0.32***(0.22) (0.12) (0.38) (0.00)

Piped water -0.06 -0.37 -0.03 -0.09(0.62) (0.13) (0.74) (0.62)

Gas connection -0.08 -0.21 0.03 -0.12(0.46) (0.31) (0.64) (0.21)

Mean monthly temperature 0.96 -5.57(0.61) (0.19)

Mean monthly rainfall -0.09 0.98***(0.56) (0.00)

Distance to the port 0.85* 0.83**(0.09) (0.02)

Distance to the nearest highway 0.33 -0.39(0.58) (0.75)

Notes: Column 1 reports the coefficient of a regression of each district economic characteristic in 2004–05 on thedistrict’s share of electricity generation capacity from oil based plants (using furnace oil and diesel), controlling for allother characteristics. Column 2 repeats the same test for the change in district’s share of oil capacity from 2004 to2011. Columns 3 and 4 repeat the test using the change in each characteristic from 2001 to 2005 as the dependentvariable. Share is constructed using plant capacity and inverse of square distance to district center. P-values fromdistrict clustered standard errors are reported in parentheses. * p<0.10, ** p<0.05, *** p<0.01. For the Bonferronitest of joint significance of q coefficients, p-value must be less than (0.05/q) to reject the null of all q coefficients zeroat 5 percent significance level.


Table 2.4: Outages and Price of Energy Mix

Dependent variable: Log outages

(1) (2) (3) (4) (5)

Log price of energy mix 0.166 0.164 0.403*** 0.399*** 0.386***(0.116) (0.128) (0.130) (0.115) (0.099)

Price of gas x distance to 0.020 0.050 0.135 0.122highway and port (0.056) (0.299) (0.347) (0.325)Price of coal x distance to -0.012 -0.212*** -0.267*** -0.243***highway and port (0.100) (0.064) (0.099) (0.087)Price of furnace oil x distance to 0.574*** 0.667*** 0.642***highway and port (0.167) (0.172) (0.144)Price of diesel x distance to -0.479* -0.469* -0.385highway and port (0.292) (0.285) (0.275)

District time varying controls N N N Y YHousehold and demographic con-trols

N N N N Y

F-statistic 2.04 1.64 9.54 12.0 15.2N 793 793 793 793 793R2 0.95 0.95 0.95 0.95 0.95

Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors two way clustered by district and round reported inparentheses. Each observation is a district-year. Log price of energy mix is the price of energy used by thermal plantsweighted by capacity and inverse of squared distance to district centre. All regressions include district fixed effects, yearfixed effects and district specific time trend. District time varying controls are average light intensity, average monthlyprecipitation, and monthly temperature distribution; household and demographic controls are log monthly per capitaexpenditures, log household size, number of rooms in dwelling, access to piped water, gas, and phone connection, age,age squared, and years of schooling.


Table 2.5: Effect of Outages on Employment

Panel A – Dependent variable: Fraction working in past month

OLS OLS IV IV(1) (2) (3) (4)

Log outages -0.030** -0.022*** -0.189*** -0.192***(0.014) (0.008) (0.070) (0.049)

First stage F-stat 12.0 15.2N 793 793 793 793Predicted Δ -0.003** -0.002*** -0.018** -0.018***

(0.001) (0.001) (0.007) (0.005)

Panel B – Dependent variable: Log days worked in past month

OLS OLS IV IV(1) (2) (3) (4)

Log outages -0.121** -0.079** -0.855*** -0.869***(0.056) (0.033) (0.286) (0.208)

First stage F-stat 12.0 15.2N 793 793 793 793Predicted Δ -0.012** -0.008** -0.081*** -0.083***

(0.005) (0.003) (0.027) (0.020)

Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors two way clustered by district and round reportedin parentheses. Columns 1 and 3 include district time varying controls, district fixed effects, year fixed effectsand a district specific linear time trend, and columns 2 and 4 include all controls. The predicted change is thechange in dependent variable due to 10 percent increase in night lights variability. The mean fraction of maleswho report working in the past month is 0.84 and the mean days worked in the past month is 23.


Table 2.6: Effect of Outages on Earnings

Panel A – Dependent variable : Log monthly earnings

OLS OLS IV IV(1) (2) (3) (4)

Log outages -0.296*** -0.184*** -1.97*** -1.87***(0.090) (0.064) (0.662) (0.483)

First stage F-stat 11.9 15.2N 793 793 793 793Predicted Δ -0.028*** -0.017*** -0.187*** -0.178**

(0.009) (0.006) (0.063) (0.046)

Panel B – Dependent variable: Log earnings per day

OLS OLS IV IV(1) (2) (3) (4)

Log outages -0.208*** -0.129*** -1.35*** -1.22***(0.051) (0.040) (0.429) (0.316)

First stage F-stat 11.9 15.2N 793 793 793 793Predicted Δ -0.020*** -0.012*** -0.120*** -0.109***

(0.005) (0.004) (0.040) (0.030)

Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors two way clustered by district and roundreported in parentheses. Columns 1 and 3 include district time varying controls, district fixed effects, yearfixed effects and a district specific linear time trend, and columns 2 and 4 include all controls. The predictedchange is the change in dependent variable due to 10 percent increase in night lights variability. The realmean monthly earnings are Rs. 8,602 and mean earnings per day are Rs. 333.


Table 2.7: Effect of Outages in Electricity Intensive Districts

Panel A – Dependent variable: Work Log days

(1) (2) (3) (4)

Log outages -0.027** -0.020** -0.128** -0.102**(0.014) (0.010) (0.053) (0.042)

Log outages x Fraction in 0.014 -0.016 0.071* -0.038electricity intensive industries (0.010) (0.014) (0.041) (0.054)

Cragg-Donald F-statistic 6.88 5.86 6.88 5.86N 793 793 793 793Predicted Δ -0.013** -0.036** -0.057** -0.141**

(0.005) (0.016) (0.020) (0.062)

Panel B – Dependent variable: Log earnings Log earnings per day

(1) (2) (3) (4)

Log outages -0.282** -0.217** -0.184** -0.145**(0.144) (0.109) (0.093) (0.070)

Log outages x Fraction in 0.150 -0.179 0.094 -0.114electricity intensive industries (0.118) (0.162) (0.075) (0.101)

Cragg-Donald F-statistic 6.88 5.86 6.88 5.86N 793 793 793 793Predicted Δ -0.132*** -0.396** -0.090*** -0.259***

(0.042) (0.163) (0.026) (0.100)

Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors two way clustered by district and round reported inparentheses. Coefficients, standard errors, and predicted change are reported for increase in night lights variability by10 percent and one standard deviation increase in the fraction employed in electricity intensive industries. The mean(standard deviation) of the fraction in electricity intensive industries is 0.25 (0.09). The instruments are district’senergy price mix and its interaction with mean fraction employed in electricity intensive industries. All regressionsinclude district time varying controls, fixed effects and district time trends. Columns 2 and 4 also control for meanfraction employed in electricity intensive industries interacted with year fixed effects. For 2 endogenous regressors and2 excluded instruments, the Stock-Yogo critical value for a weak instrument size test of maximal size 10 (15) percentis 7.03 (4.58).


Table 2.8: Effect of Outages in Districts with More Educated Workforce

Panel A –Dependent variable: Work Log days

(1) (2) (3) (4)

Log outages -0.020* -0.011 -0.091*** -0.055(0.011) (0.013) (0.033) (0.051)

Log outages x Fraction with 0.002 -0.014*** 0.011 -0.059***above median schooling (0.008) (0.005) (0.024) (0.023)

Cragg-Donald F-statistic 4.44 4.80 4.44 4.80N 793 793 793 793Predicted Δ -0.018*** -0.026** -0.080*** -0.115***

(0.004) (0.017) (0.019) (0.042)

Panel B– Dependent variable: Log earnings Log earnings per day

(1) (2) (3) (4)

Log outages -0.161 -0.073 -0.098 -0.046(0.123) (0.150) (0.078) (0.096)

Log outages x Fraction with -0.013 -0.174*** -0.011 -0.106***above median schooling (0.09) (0.059) (0.062) (0.037)

Cragg-Donald F-statistic 4.44 4.80 4.44 4.80N 793 793 793 793Predicted Δ -0.174*** -0.247** -0.109*** -0.152**

(0.047) (0.116) (0.029) (0.073)

Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors two way clustered by district and round reported inparentheses. Coefficients, standard errors, and predicted change are reported for increase in night lights variabilityby 10 percent and one standard deviation increase in the fraction with above median schooling. The mean (standarddeviation) of the fraction with above median schooling is 0.46 (0.14).The instruments are district’s energy price mixand its interaction with fraction with above median schooling. All regressions include district time varying controls,fixed effects and district time trends. Columns 2 and 4 also control for mean fraction employed in electricity intensiveindustries interacted with year fixed effects. For 2 endogenous regressors and 2 excluded instruments, the Stock-Yogocritical value for a weak instrument size test of maximal size 10 (15) percent is 7.03 (4.58).


Table 2.9: Effect of Outages in High Temperatures

Dependent variable: Work Log days Log earnings Log earningsper day

IV IV IV IV(1) (2) (3) (4)

Log outages -0.017*** -0.077*** -0.173*** -0.110***(0.007) (0.028) (0.055) (0.035)

Log outages x CDD30 -0.004*** -0.014*** -0.025*** -0.014***(0.001) (0.008) (0.009) (0.007)

Cragg-Donald F-statistic 4.67 4.67 4.67 4.67N 793 793 793 793Predicted Δ -0.021*** -0.092*** -0.198*** -0.124***

(0.005) (0.018) (0.050) (0.032)

Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors two way clustered by district and round reported inparentheses. CDD30 is the cumulative number of degrees daily average temperature is above 30 C in a month.Coefficients and standard errors are reported for increase in night lights variability by 10 percent and one standarddeviation change in within district CDD30 (19 degree days). Regressions include district time varying controls, fixedeffects, and district time trends. The instruments are district’s energy price mix and its interaction with CDD30. For2 endogenous regressors and 2 excluded instruments, the Stock-Yogo critical value for a weak instrument size test ofmaximal size 10 (15) percent is 7.03 (4.58).


Table 2.10: Effect of Outages in Remote Districts

Panel A– Dependent variable: Work Log days

(1) (2) (3) (4)

Log outages -0.023*** -0.026*** -0.102*** -0.122***(0.048) (0.079) (0.020) (0.036)

Log outages x Distance 0.010 0.015 0.046 0.074*from national highway (0.008) (0.010) (0.033) (0.045)

Cragg-Donald F-statistic 6.02 5.89 6.02 5.89N 793 793 793 793Predicted Δ -0.013 -0.011 -0.057 -0.048

(0.010) (0.010) (0.039) (0.040)

Panel B – Dependent variable Log earnings Log earnings per day

(1) (2) (3) (4)

Log outages -0.227*** -0.296*** -0.150*** -0.206***(0.039) (0.090) (0.022) (0.061)

Log outages x Distance from 0.097 0.184* 0.058 0.126*national highway (0.088) (0.109) (0.062) (0.074)

Cragg-Donald F-statistic 6.02 5.89 6.02 5.89N 793 793 793 793Predicted Δ -0.131 -0.112 -0.092 -0.079

(0.109) (0.105) (0.075) (0.070)

Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors two way clustered by district and round reported inparentheses. Coefficients, standard errors, and predicted change are reported for increase in night lights variabilityby 10 percent and one standard deviation increase in distance from the national highway (20 km). The instrumentsare district’s energy price mix and its interaction with distance from the national highway. All regressions includedistrict time varying controls, fixed effects and district time trends. Columns 2 and 4 also control for distance from thehighway interacted with year fixed effects. For 2 endogenous regressors and 2 excluded instruments, the Stock-Yogocritical value for a weak instrument size test of maximal size 10 (15) percent is 7.03 (4.58).


Table 2.11: Placebo Check – Correlation between Energy Prices and Prior Outcomes

Panel A – Labour market outcomes (2001-2005)

Work Log daysworked

Log earnings Log earningsper day

(1) (2) (3) (4)

Log price of energy mix 0.087 -0.068 0.440 0.991(2007-2011) (0.384) (1.33) (4.83) (3.39)

N 355 355 355 355R2 0.86 0.86 0.83 0.83

Panel B – Household electricity and durable goods consumption (2001-2005)

Log electricityexpenditures

Number of re-frigerators

Number of airconditioners

Durable goodsindex

(1) (2) (3) (4)

Log price of energy mix 1.49 -0.213 -0.213 -2.67(2007-2011) (1.15) (1.28) (0.413) (6.71)

N 355 352 352 351R2 0.89 0.94 0.90 0.93

Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors clustered by district and round reported inparentheses. Districts observed in 2001–02 and 2004–05 are assigned the district’s energy price mix in 2007-08and 2010–11 respectively. The regressions controls for all district time varying time controls, household, anddemographic variables measured in 2001–02 and 2004–05, district and year fixed effects and a district specifictime trend. Durable goods index is the first principal component of indicators for durable goods owned by thehousehold.


Table 2.12: Robustness checks – I

Dependent variable: Work Days Earnings Earnings per day

Panel A – Energy allied workforce

(1) (2) (3) (4)

Log outages -0.023*** -0.095*** -0.205*** -0.132***(0.006) (0.028) (0.061) (0.039)

First stage F-stat 10.1 10.1 10.1 10.1N 793 793 793 793

Panel B – Excluding average lights

(1) (2) (3) (4)

Log outages -0.017*** -0.073*** -0.158*** -0.102***(0.005) (0.021) (0.051) (0.032)


Panel C – Plant characteristics

(1) (2) (3) (4)

Log outages -0.021*** -0.096*** -0.200*** -0.130***(0.005) (0.021) (0.044) (0.028)


Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors clustered two way by district and round reportedin parentheses. Coefficients and standard errors are reported for increase in night lights variability by 10 percent.Regressions include all district time varying controls, fixed effects and district time trends. Log outages is instru-mented by the district’s weighted average price of energy mix. In Panel A, the regressions include controls for thedistrict’s mean fraction of work force in energy related industries interacted with yearly energy prices. Panel Bexcludes average lights control. In Panel C, plant controls are distance to the nearest 100 MW thermal plant, meancapacity, mean age, mean fraction using combined cycle technology in 400 km radius.


Table 2.13: Robustness checks – II

Dependent variable: Work Days Earnings Earnings per day

Panel A – Plant entry in 50 km radius

(1) (2) (3) (4)

Log outages -0.020*** -0.090*** -0.194*** -0.124***(0.005) (0.021) (0.048) (0.032)


Panel B – Excluding LESCO

(1) (2) (3) (4)

Log outages -0.013** -0.062*** -0.126** -0.082***(0.004) (0.015) (0.038) (0.026)


Panel C –Global oil price instrument

(1) (2) (3) (4)

Log outages -0.017*** -0.073*** -0.16*** -0.103***(0.004) (0.017) (0.041) (0.027)


Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors clustered two way by district and round reportedin parentheses. Coefficients and standard errors are reported for increase in night lights variability by 10 percent.Regressions include all district time varying controls, fixed effects and district time trends. Log outages is instru-mented by the district’s weighted average price of energy mix. In Panel A, the regressions control for indicator forentry within 50 km of district center. In Panel B, the distribution company with the highest geographic concen-tration of oil based plants in its neighbourhood is excluded from the sample. In Panel C, global crude oil price isused to construct the instrument.


Figure 2.1: Aggregate Demand-Supply Gap and Oil Prices

050

010

0015

0020

00

1400

016

000

1800

020

000

2200

0D

eman

d, S

uppl

y (M

W)

2004 2005 2006 2007 2008 2009 2010 2011 2012Year

Est. Peak Demand SupplyWorld Crude Oil Furnace Oil

Notes: Data on peak demand and supply is from NEPRA State of Industry Reports. Peak demand is the maximumnational demand on any given day of the year estimated using demand patterns. Supply is the maximum electricity thatwas generated with the existing capacity. Price of crude oil and furnace oil are measured in Rs. per millions of britishthermal units (mbtu).


Figure 2.2: Energy Prices and Outages Measured by Variability in Night Lights

050

010

0015

0020

00R

s. p

er m

btu

.1.1

5.2

.25

Out

ages

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012Year

Outages World Crude Oil Furnace OilDiesel Gas Coal

Notes: Outages are measured as variability in night lights on a scale of 0 to 1 and energy prices are measured in a commonenergy unit of Rs. per millions of british thermal units (mbtu).


Fig

ure

2.3:

The

rmal

Pla

nts

and

Out

ages

Mea

sure

dby

Var

iabi

lity

inN

ight

Lig

hts

Note

s:O

uta

ges

are

mea

sure

das

vari

ability

innig

ht

lights

on

asc

ale

of0

to1.

The

red

mark

ers

indic

ate

loca

tion

ofpla

nts

usi

ng

oil

or

die

selfu

elto

gen

erate

elec

tric

ity.

The

gre

enm

ark

ers

show

pla

nts

only

usi

ng

natu

ralgas

as

afu

el.

The

bro

wn

mark

ers

show

pla

nts

that

use

natu

ralgas

as

the

pri

mary

fuel

wit

hoil

or

die

selas

ase

condary

fuel

.


Figure 2.4: Energy Prices, Plant Cost and Plant Utilization Factor

01

23

4Lo

g fu

el c

ost p

er k

wh

4.5 5 5.5 6 6.5 7 7.5Log price

beta =1.05 R-squared =0.80

-4-2

02

Log

utili

zatio

n fa

ctor

|pla

nt f.

e.

-1 -.5 0 .5 1 1.5Log price|plant f.e.

beta=-0.46 R-squared 0.42

Notes: The first graph shows the correlation between price of energy and the plant’s fuel cost of generating one kWh. Thesecond graph shows the correlation between price of energy and plant utilization factor after partialling out plant fixedeffects. Plant utilization factor is the ratio actual yearly generation to potential yearly generation at full capacity. Dataon plant cost and total yearly generation is from 2004-2011 compiled from NEPRA State of Industry Reports.

Chapter 3

The Welfare Effect of Electricity

Outages on Households

3.1 Introduction

The household sector is a large consumer of electricity in many growing developing countries

where electricity outages are an every day phenomenon. Electricity is used by residential

consumers for a range of services such as cooking, laundry, information and communication,

lighting, indoor cooling or heating, and in many cases for running a home business. Understand-

ing the willingness to pay for reliable electricity is important for governments and electricity

providers in order to allocate scarce resources effectively across competing investment proposals.

From the perspective of achieving efficiency, this implies that the marginal cost of decreasing

outages or improving other aspects of quality of electricity supply should be set equal to the

marginal benefit (or the reduction in welfare costs) accruing to consumers following the inter-

vention. Measuring the welfare cost of electricity outages on consumers in the business sector

is usually possible since these consumers use electricity to produce output with a market value.

However, measuring the welfare cost for households is challenging as they use electricity to

produce outputs that may not be observed or easy to monetize.

In this chapter, I present a model of household optimization under outages and derive

an expression for the total welfare effect of outages on households. The total welfare effect

is composed of the change in utility arising from change in home produced services disrupted

from electricity interruptions and the change in time devoted to performing these services valued

at the opportunity cost of household time. Other components of the welfare effect arise due

to adjustments made by the household to its electricity usage. There is a reduction in grid

electricity expenditures and an increase in electricity expenditures due to utilization of off-grid

technologies such as generators or backup storage devices.

69

Chapter 3. The Welfare Effect of Electricity Outages on Households 70

Although the data requirements do not permit me to estimate the total welfare effect, I

use the observed adjustments in grid electricity expenditures in a sample of urban households

from Pakistan to examine how this component of the welfare effect varies across income groups

and during periods of unusually hot weather. Income and temperature are important shifters of

electricity demand. Figure 3.1 shows the relationship between per capita electricity consumption

and per capita income in a cross section of developing countries in 2011. Electricity consumption

follows a S-shaped pattern, rising at a faster rate in the income range of $2000 to $5,000 per

capita. Figure 3.2 shows the cross country correlation between the average annual growth rate

of electricity consumed per capita between 2000 and 2011 and the average annual temperature.

The relationship is also positive as expected exhibiting a somewhat sharper upward trend

for temperatures above 20 Celsius. These reduced form relationships suggest that electricity

demand should increase with both temperature and income. As demand increases, the welfare

cost of unserved electricity should also go up.

The household model predicts that the smaller is the observed change in grid electricity

expenditures, the larger is the willingness to pay for a reduction in outages. Intuitively this

is because households with a high value for electricity will reschedule their electricity using

activities to non-outage hours or try to mitigate the non availability of electricity using off-grid

technology. Using data from urban households, I find that electricity consumption of middle

and high income households is less sensitive to outages as compared to low income households.

Electricity consumption is also less sensitive to outages during periods of hot weather.

The rest of the chapter is proceeds as follows. In section 3.2, I develop a model of household

optimization under outages that can be used to arrive at an expression for the welfare effect or

the willingness to pay for a reduction in outages. In section 3.3, I survey the previous literature

on measuring the welfare effect of outages on households. In section 3.4, I describe the data and

estimation strategy that can be used to estimate how the adjustment in observed electricity

expenditures varies with income and temperature. Section 3.5 presents the results. Finally,

section 3.6 concludes with a summary of findings.

3.2 Model

In this section, I develop a model of household utility maximization in the presence of outages,

and derive an expression for the willingness to pay for a decrease in the incidence of outages.

The model is based on earlier work by Harrington and Portney (1985) and Deschenes and

Greenstone (2011) to measure the welfare impact of environmental changes on households.


3.2.1 The Experience of Outages By Households

The household’s per period utility depends on consumption of market goods (X), leisure (L),

and home produced services (S). Electricity consumption is not valued directly by the house-

hold, rather its utility depends on the production of S, which includes home produced services

that rely on electricity as an input such as laundry, housekeeping, cooking, and water pumping.

It also includes the use of electricity to supply lighting, indoor cooling (or heating), and for

information and communication services such as internet usage and mobile phone charging.

In this model the probability of an electricity outage per day is q, which is observed from ex-

perience. The electricity utility publishes a schedule of outages which is known to the household.

However, each day there is a positive probability of a forced or unplanned outage. q represents

the total outage probability that includes scheduled outages as well as the household’s expec-

tation of unplanned outages learnt from prior experience. Since the timing of interruption is

partially known, the household can reschedule some of the electricity using activities to non-

outage hours. However, there are some services such as lighting and indoor cooling that can not

be substituted over time. During an electricity interruption the household can utilize off-grid

electricity through self-generation or backup storage devices to partially restore electricity sup-

ply to produce these services. Assume that the production of services is concave and increasing

in household’s time input (Ts), total electricity input (E), and decreasing in outages.

S = S(Ts, E, q) (3.1)

STs > 0, STsTs ≤ 0, SE > 0, SEE ≤ 0, Sq < 0

In the simplest formulation we can think of electricity consumption as a function of the time

devoted to production (Ts) and a transformation parameter (κ) that determines the rate at

which electricity is converted into services. The transformation parameter will depend on the

stock and power requirements of the appliances owned by the household. Assume that κ is

fixed and does not change in response to outages in the short run. When outages occur with a

probability q, household services are produced under the following electricity input constraints,

where f(.) and g(.) are functions that are non decreasing in Ts and increasing in κ:

E = Eg + Eo = (1 − q)f(Ts, κ) + qg(Ts, κ)

Eo ≤ Eo

(3.2)

Electricity used from the grid (Eg) declines as q or the rate of interruption increases, while elec-

tricity used from off-grid sources (Eo) increases in response to increase in q as the household

undertakes mitigation measures to partially restore the supply of electricity for its activities.


However, electricity from off-grid sources can be constrained by the type and capacity of alter-

native electricity source available to the household.

Household income depends on time spent in the labour market (Tw) earning a wage of w. It

also earns non-labour income I from household business or other sources. The household also

enjoys leisure from non-electricity using activities using leisure time TL and other fixed inputs

according to the function L. For simplicity, assume that the electricity using leisure activities

are not a large component of leisure or that these activities can be rescheduled without any

cost so that interruptions do not disrupt leisure:1

L = L(TL) (3.3)

3.2.2 Household Optimization

The household’s problem per period is to choose consumption of the market good, home services,

grid and off-grid electricity, and the allocation of time given the electricity interruption rate,

prices, budget constraint, time constraint, and the electricity availability constraints.

max{X,TL,Ts,Tw,Eg ,Eo} U(X,L, S)

s.t. X + pgEg + poEo ≤ I + wTw

Tw + TL + Ts = T

E = Eg + Eo = (1 − q)f(Ts, κ) + qg(Ts, κ)

Eo ≤ Eo

(3.4)

Assuming the household does not waste any resources, the first order conditions of the house-

hold’s choice problem depend on prices as well as the multiplier on the budget constraint (λ)

and the off-grid electricity constraints (μ) facing the household.

UX = λ (3.5)

UTL

∂L

∂TL= λw (3.6)

UTS

∂S

∂Ts= λw (3.7)

US∂S

∂Eg= λpg (3.8)

1Television and electronic social media are the primary form of leisure activities that rely on electricity. Itis easy to show that the welfare effect will be higher if we extend the model to include disruption of electricityusing leisure activities. The additional component of the welfare cost is the utility loss due to leisure disruptedfrom outages.


US∂S

∂Eo= λpo + μ (3.9)

The first order conditions for electricity show that at the margin, household sets the disutility

arising from disruption of electricity inputs in production of services (Us

∂S∂Eg

λ ), to the shadow

price of an additional unit of electricity that depends on the market price. In the case of off-

grid electricity, the shadow price also includes the value of relaxing the constraint on electricity

supply (μλ ). Similarly, the marginal value of time devoted to producing services (

UTs∂STs

λ ) and

time devoted to leisure (UTL

∂LTL

λ ) is set equal to the marginal cost of time (w).

3.2.3 Willingness To Pay For a Change in Outages

In order to derive an expression for the household’s willingness to pay for a change in outage

probability, we can use the first order conditions and the indirect utility function from the op-

timization problem. Suppose that non-labour income, wages, and prices are fixed, and outages

decrease from qo to q1 and as a result utility changes from Vo to V1. Define a transfer func-

tion I∗(q) which is the amount that must be transferred to the household such that its utility

remains constant at Vo following the change. If there is a decrease in outages, I∗(q) < 0 as

utility increases and the household should be willing to pay a positive amount to stay at the

initial utility level. If outages go up, I∗(q) > 0 as utility decreases and the household must be

paid to bring it back to the original utility level. In other words, I∗(q) is a transfer that allows

the total derivative of V with respect to q to be set equal to zero along the indifference curve

V = V0. Then we have,

V (I∗(q), q) = V0

dV

dq= VI

dI∗(q)dq

+ Vq = 0

dI∗(q)dq

= −Vq

VI

(3.10)

In the above expression, dI∗(q)dq measures the welfare effect of a change in outages on the

household. Since utility is kept fixed at the initial level this is also the compensating variation.

The terms VI and Vq are easy to compute as many terms drop out when the first order conditions

hold.

VI = λ (3.11)

Vq = Us∂S

∂q+ λ

∂I

∂q(3.12)

Using 3.11, 3.12, and the total derivative dSdq , the expression for willingness to pay can be written


as:

dI∗(q)dq

= −Us

λ(dS

dq−

∂S

∂Ts

∂Ts

∂q−

∂S

∂Eg

∂Eg

∂q−

∂S

∂Eo

∂Eo

∂q) −

∂I

∂q(3.13)

Next using the first order conditions 3.7 to 3.9:

dI∗(q)dq

= −Us

λ(dS

dq) + pg

∂Eg

∂q+ (po +

μ

λ)∂Eo

∂q+ w

∂Ts

∂q−

∂I

∂q(3.14)

The first term in the expression for willingness to pay for a reduction in outages is due

to the utility gain arising from change in home produced services which depends on the total

or reduced form change in home produced services due to outages. From an empirical point

of view this is attractive as we do not have to worry about unobserved heterogeneity in the

household’s observed choice of home produced services. The second term is the change in time

devoted to production of services valued at the opportunity cost of time or the wage rate, which

is expected to decrease or stay the same if all activities were rescheduled prior to the change.

The next two terms in the expression for willingness to pay arise due to adjustments in

electricity usage. The smaller is the observed adjustment in grid electricity and the larger

is the observed adjustment in off-grid usage, the larger is the welfare effect. Intuitively, this

comes about due to partial rescheduling of electricity using activities, that can be brought about

by greater utilization of appliances during non outage hours and larger off-grid expenditures.

Finally, the last term is the effect of outages on non-labour income of the household. This term

is also predicted to increase the total willingness to pay depending on how sensitive non-labour

income is to outages.

3.2.4 Backup Storage

One of the most common form of off-grid technology used by households to mitigate the effect

of outages are backup power storage units. These units can be charged up using grid electricity

and can partially restore supply during outages. They are used more widely than generators

due to lower fixed cost (price) for the household. The above setup can easily be modified

to this commonly observed case. Suppose that the household now chooses Z which is storage

battery capacity per period at a cost of pZ per unit. Also suppose for simplicity that the battery

capacity depreciates completely at the end of the period. The battery can be charged up during

non outage hours and the maximum electricity it can supply during outage hours depends on

the transformation function h(.) which is increasing in battery capacity, grid electricity, and

decreasing in outage probability:

Eo = h(Eg, Z, q)


The new budget and off-grid electricity constraints are given by:

X + pgEg + pZZ ≤ I + w(T − TL − TS)

Eo ≤ h(Eg, Z, q)(3.15)

Denoting the multipliers on the budget and off-grid electricity constraint by λ and μ respectively,

the first order conditions with respect to Eg, Eo, and Z given by:

US∂S

∂Eg= λpg − μ

∂h

∂Eg

US∂S

∂Eo= μ

∂h

∂Z=

λ

μpZ

(3.16)

The expression for Vq is now given by:

Vq = US∂S

∂q+ λ

∂I

∂q+ λpz

∂Z

∂q+ μ

∂h

∂q(3.17)

Using the total derivative dSdq and the first order conditions as before, the willingness to pay

now reduces to:

dI∗(q)dq

= −Us

λ(dS

dq) + pg

∂Eg

∂q+ (pz +

μ

λ

∂h

∂Z)∂Z

∂q+ w

∂Ts

∂q+ −

∂I

∂q(3.18)

The welfare effect depends on the change in grid electricity expenditures and the change in

expenditures on backup capacity, valued at the shadow price of backup capacity that depends

on the market price pZ and the additional value of having electricity during outage hours μλ .

If ∂h∂Z > 0, that is the availability of off-grid electricity increases in battery capacity, then the

shadow price is greater than the market price of battery capacity. Thus, the larger the observed

change in backup expenditures, the larger should be the willingness to pay. Also note that the

change in ∂Eg

∂q should be smaller than before as the household is able to store up electricity

during non outage hours for use during outage hours.

3.2.5 Model Predictions

For a given change in outages and with standard assumptions on utility function, all the terms

in 3.14 or 3.18 have the same sign except ∂Eg

∂q or the observed change in grid electricity ex-

penditures. For example, when outages decrease, ∂Eg

∂q > 0, while all the other terms are non

positive. This can be used to derive the following predictions about the willingness to pay.

Prediction 1: The larger is the difference in the utility change due to disruption of elec-


tricity using services and the change in grid electricity expenditures, the larger is the willingness

to pay.

Proof: Assume that outages have no effect on off grid electricity usage, time allocation,

non-labour income, and other terms in the expression for willingness to pay. Then dI∗(q)dq =

−Usλ (dS

dq ) + (pg)∂Eg

∂q . For a given change in outages and with standard assumptions on utility

function, the two terms will have opposite signs. For example, when outages decrease, ∂Eg

∂q > 0,

while −Usλ (dS

dq ) < 0. We can compare the magnitude of the terms below to determine which

one dominates the total effect:Us

λ(dS

dq) ≷ pg

∂Eg

∂q

Household optimization implies Us = λpg/∂S∂Eg

. Using this expression for Us the above

expression can be simplified to:dS

dq≷

∂S

∂Eg

∂Eg

∂q

But the total change in services due to outages should be larger than the partial effect due to

change in grid electricity expenditures only that is dSdq > ∂S

∂Eg

∂Eg

∂q . Therefore,

Us

λ(dS

dq) > pg

∂Eg

∂q

Since the two terms have opposite signs for any given change in q, the larger is the difference

|−Usλ (dS

dq )+(pg)∂Eg

∂q |, the larger is the willingness to pay |dI∗(q)dq |. If other terms in 3.14 or 3.18 are

non zero, the inequality is easier to satisfy as |(pg)∂Eg

∂q | is the only term that reduces |dI∗(q)dq |.

Intuitively, we can imagine that households reschedule their electricity usage to non outage

hours, which leads to a smaller observed change in total grid electricity expenditures. However,

rescheduling of usage also reduces the utility loss due to reduction of services. The larger the

difference between these two terms, the larger is the willingness to pay for reliable electricity.

Prediction 2: Suppose that the total disruption in service production is larger when

demand for electricity is high. Then the difference in the change in electricity expenditures

when demand is high versus low can be used to put a lower bound on the difference in the

welfare impact across the two scenarios.

Proof: When demand for electricity is shifted outward, for any given change in outages

|Usλ (dS

dq )| will be larger. This can be shown as follows.

Household optimization implies Us = λpg/∂S∂Eg

. Using this expression for Us we can compareUsλ (dS

dq ) in the high and low demand scenarios:

pg

∂S∂Eg

(dS

dq)High ≷

pg

∂S∂Eg

(dS

dq)Low


In the high demand scenario, price paid for electricity may be higher if electricity prices

increase with consumption. The term ∂S∂Eg

is decreasing for a concave to linear production

function of services in electricity (SE > 0 , SEE ≤ 0).

Suppose that for any given change in outages there is a larger total change in production

of services under the high demand scenario |dSdq |

High > |dSdq |

Low. This is likely to occur when

demand for electricity using services is high, there is limited scope for rescheduling, and the

choice of off-grid technology is fixed. Under these conditions,

|Us

λ(dS

dq)|High > |

Us

λ(dS

dq)|Low

Suppose all other terms in 3.14 or 3.18 are zero, and if |(pg)∂Eg

∂q |High < |(pg)∂Eg

∂q |Low, then

by the same arguments as for Prediction 1, |dI∗(q)dq |High > |dI∗(q)

dq |Low. If |(pg)∂Eg

∂q |High =

|(pg)∂Eg

∂q |Low then the difference in these two terms is exactly equal to the difference in the

willingness to pay across the high versus the low demand scenarios which is also the difference

in Usλ (dS

dq ). The difference is a lower bound if the other terms in the expression 3.14 or 3.18 are

larger (or equal) in the high versus low demand scenario. This should be easy to satisfy if the

disruption in time devoted to services is larger and electricity usage is more constrained in the

high demand scenario (larger μ).

If however, |(pg)∂Eg

∂q |High > |(pg)∂Eg

∂q |Low, then the difference in utility change due to services

and other parts of the willingness to pay must be at least as large as the observed difference

between electricity expenditures for the total willingness to pay to be larger in the high demand

scenario.

3.3 Measuring Willingness To Pay

Stated preference methods such as contingent valuation (CV) surveys or discrete choice ex-

periments are commonly used to measure the willingness to pay for new and better quality

goods and services related to health, infrastructure, and the environment. Some of the recent

work eliciting household valuations for improvements in electricity services include Munasinghe

(1980) for Brazil, Billinton and Pandey (1999) for Nepal, Carlsson and Martinsson (2007, 2008)

for Sweden, Kateregga (2009) for Uganda, Abdullah and Mariel (2010) for Kenya, and Ozbfali

and Jenkins (2015) for North Cyprus.2 The willingness to pay estimates obtained in these

studies is typically very low. Ozbfali and Jenkins (2015) report that the willingness to pay

2Note that asking willingness to pay to avoid or accept a change in outages is different from asking aboutactual monetary cost imposed by the outages. As shown in the previous section, willingness to pay is the totalwelfare effect which includes monetary as well as non-monetary costs incurred by the household as a result ofthe change in outages.


ranges from $ 0.10 to $ 1.15 per hour of unserved electricity in developing countries.3

In most contingent valuation studies, respondents are asked to report their willingness to

pay (open ended format) or to choose from a list the amount that they are willing to pay

(closed ended format), to ensure a hypothetical scenario such as reliable electricity supply

without any outages. Both of these methods are widely used but prone to behavioral problems

(Carson and Hanemann, 2005). Open ended questions are problematic as respondents have

no incentive to report their true valuation and typically report zero or a very small number.

Closed ended questions may create anchoring and reference point dependency. Other behavioral

issues may also come into play with both formats, such as the free rider problem due to public

good aspect of infrastructure improvements or entitlement effects due to subsidized electricity

provision. Another important concern is that consumers may understate their marginal value

of electricity when they do not have confidence in the ability of the electricity utility to improve

quality of supply. If in the past consumers have not experienced improvements in quality after

a price increase or infrastructure upgrades, then they are likely to undervalue proposed future

improvements (Ozbfali and Jenkins, 2015).

The second approach is to use revealed preference data such as electricity consumption and

estimate the welfare effect using the demand curve for electricity. For example, if there is suffi-

cient cross sectional variation in prices, and we can collect data on household consumption and

other determinants of demand, then we can directly estimate the demand function for electric-

ity under different reliability levels. With this demand function in hand we can calculate the

change in welfare from improved reliability using the change in consumer surplus as a measure

of the welfare effect (Dias-Bandaranaike and Munasinghe, 1983, and Westley, 1984). The will-

ingness to pay estimates from the consumer approach are generally high, ranging between 3

percent to 10 percent of average household income (Westley, 1984).

Despite the attractiveness of this approach, there are a number of econometric challenges in

consistently estimating counterfactual electricity demand under different outage scenarios. In

most institutional contexts outages are not randomly assigned and it is difficult to implement

random variation in outages since electricity allocation is a politically sensitive issue in electricity

deficient countries. Identification of demand can be complicated by unobserved determinants

that jointly affect quality of electricity supply in the household’s locality, electricity demand,

and the price paid. For example, McRae (2015) shows that in Colombia price subsidies reduce

infrastructure investments leading to higher outages, lower appliance ownership and electricity

usage amongst households.

Given the constraints of each approach, a combination of revealed preference and stated

3In OECD countries willingness to pay for improvements in electricity services ranges from $ 0.29 to $ 57.99.


preference methods can be useful to estimate the value of reliable electricity. This would

involve utilizing experimental or quasi experimental variation in outages, together with data

on household electricity consumption, time use, and other observed adjustments to put a dollar

value on the observed components of the welfare cost of outages. The components of the

willingness to pay that can be estimated directly using observed household choices include

the change in grid electricity demand ( ∂Eg

∂q ), the change in off-grid electricity demand ( ∂Eo∂q ),

the change in time devoted to home produced services ( ∂Ts∂q ), and the change in mitigation

expenditures ( ∂Z∂q ) due to outages. The willingness to pay also includes intangible components,

such as the utility change due to the total change in home produced services ( Usλ (dS

dq )) or the

value of a small change in electricity availability during outage hours (μ) which can not be

observed directly. To arrive at a total welfare cost, we would need stated valuations of these

unobserved components, that can be elicited using contingent valuation surveys.

3.4 Estimation

As discussed above, data, research design and econometric constraints can make it difficult to

estimate the marginal willingness to pay for a reduction in outages. In the rest of the paper, I

provide evidence on the testable predictions of the model that are useful from a policy as well

as future research design perspective. In this section, I describe the data and methods that

can be used to estimate the demand for electricity, ∂Eg

∂q , and how it varies with income and

temperature using household consumption surveys.

3.4.1 Data

In many developing countries subnational data on electricity outages is not available due to

non digital grid infrastructure and absence of reporting standards. Therefore, I use a measure

of outages based on meteorological satellite data used in the first two chapters, to measure

outages at the district level. The measure captures the intra-annual variability in night lights

at the district level. Coded on a scale of 0 to 1, the higher the night lights variability, the lower

is the frequency of observation, and the higher is the probability of outages or q in the model.

As discussed in chapter 1, in the absence of data on actual outages, night lights variability is a

good proxy of outages in Pakistan.

In the urban areas of Pakistan, which is the sample used in this chapter for demand analysis,

night lights variability increased from 0.04 to 0.16 from 2004 to 2011, as this was the period

in which outages escalated sharply due to higher energy input prices. Table 3.1 shows that

the mean variability in this sample was 0.073 with a standard deviation of 0.050. From 2007

to the end of the sample period, when the country was facing an electricity crisis, the mean


variability in urban areas increased to 0.093, with a standard deviation of 0.059. A one standard

deviation reduction or equivalently a 63 percent decrease in variability from the mean during the

electricity shortage crisis in urban areas translates into almost 100 percent decrease in average

reported duration outages (approximately 5.4 hours using the elasticity of reported duration

and variability found in chapter 1).

In order to measure electricity consumption and other determinants of electricity demand, I

use data from the Household Income and Expenditure Surveys from Pakistan. I use data from

2004 to 2011 that was collected under four rounds. These surveys are carried out in different

quarters of the year to capture seasonal patterns in consumption. I use data from urban areas

where 96 percent of the households use electricity for lighting to estimate demand. Electricity

is likely to be the most important energy source for indoor cooling and other household services

such as laundry, water supply, and communication for these households.4 There are a total of

14 large cities and 28 urban divisions in the sample. For ease of notation, I refer to the cities

and urban divisions as districts in the rest of the chapter.

Residential electricity prices are obtained from the Economic Survey of Pakistan. Electricity

prices follow a block structure whereby the price charged for an additional kilowatt hour (kWh)

of electricity increase with usage depending on the household’s consumption band. For example,

the residential electricity price (per kWh) in 2011 were as follows: Rs. 1.87 for consumption

below 50 units; for consumption exceeding 50 units the price was Rs. 4.45 for the first 100 units,

Rs. 6.73 for the next 200 units (101 to 300), Rs. 10.65 for the next 400 units (301 to 700), and

Rs. 13.29 for consumption above 700 units. Since the household surveys do not record data

on electricity consumed in kilowatt hours, I use the reported expenditures and the published

electricity prices to calculate the electricity used by the household in the last month in kWh.

Table 3.1 shows that the mean electricity consumption of urban households in the sample was

160 kWh and the mean price per unit consumed was Rs. 4.49.

In order to measure household’s per capita expenditures, I use data on food, electricity,

and other non-durable expenditures reported for the past month. I use the published consumer

price index to deflate the expenditures to a common base year. The mean monthly per capita

expenditures are approximately Rs. 1,900 for a household of seven persons. Figure 3.3 shows

a smoothed plot of the relationship between electricity demand and per capita expenditures

for households in the sample. The elasticity is approximately 0.50 and the slope of the curve

suggests that electricity usage increases almost linearly with income. The elasticity increases

4Pakistan Standards of Living Measurement Survey (2004-05). Natural gas is the main energy source usedin cooking and only 0.07 percent of the households in urban areas use electricity for cooking. In rural areas 74percent of the households use electricity for lighting and 25 percent use gas. Modelling electricity demand inrural areas requires a model fuel choice that is not well captured in the household consumption data. Therefore,I only use data from urban areas in this chapter.


somewhat with income beyond a threshold, but this is not estimated precisely.

In addition to household expenditures in the past month, the surveys also contain informa-

tion on ownership of common electrical appliances. Table 3.1 reports the mean and standard

deviation of indicator variables measuring appliance ownership. 52 percent of the households

own a refrigerator, 71 percent own a television (TV), 62 percent own a washing machine, 96

percent own a fan, and only 7 percent own an air conditioner. Figure 3.4 shows the a smoothed

plot of the indicator for each appliance owned and household per capita income. The relation-

ship between ownership of a TV and washing machine is almost linear and the elasticity declines

somewhat with income. However, the probability of owning a refrigerator or air conditioner

rises with income following a S-shaped pattern. This suggests that there could be threshold

effects in adoption of these appliances as discussed in Gertler, Shelef, and Fuchs (2013), that

can lead to a sharper increase in electricity consumption with income as the adoption of these

appliances crosses the threshold.

The other main determinant of residential electricity usage is temperature. Households in

Pakistan are exposed to a long summer season and indoor cooling is commonly used by urban

households to cope with the with the heat. To measure the effect of temperature on electricity

demand, I use daily temperature data produced by the European Centre for Medium-Range

Weather Forecasts (ECMWF) available at a resolution of 0.5 degrees to create a population

weighted average of daily temperature. I use the daily average district temperature to construct

the number of days falling within a temperature bin in the past month. Specifically, I create

the number of days with temperature between 15 – 30 C and above 30 C, excluding the number

of days blow 15 C as the base category. Since temperature is also correlated with other weather

conditions such as rainfall, I also use the district’s total monthly rainfall, constructed from the

Climate Research Unit (CRU) data as a control variable.

3.4.2 Electricity Demand Equations

I estimate electricity demand using a log-linear model,5 and exploit the within district variation

in outages to estimate the elasticity of grid electricity consumption with respect to outages. The

main equation for estimating electricity demand is:

5A model in which electricity demand depends only on income and own price arises from a Cobb-Douglasutility function or the assumption of separability and two stage budgeting across electricity and all other non-durable expenditures. Another approach is to jointly estimate a model of appliance ownership and electricityusage by maximum likelihood with structural assumptions on the error terms e.g. Dubin and McFadden(1984),Reiss and White (2005) and McRae (2015). This approach address the concern of unobserved household charac-teristics that jointly determine electricity demand and appliance ownership. Due to absence of detailed data onappliances attributes I adopt the simpler log linear approach.


log(Eg,idtm) = α + α1Xi + βlog(qdy) +∑

j

θjTjdtm + γt + γd + γdt + εidtm (3.19)

where log(Eg,idtm) is the log of household’s monthly grid electricity consumption measured

in kilowatt hours, Xi is a set of explanatory variables which affect household electricity demand

including income, price, and other controls such as household size, the age composition of the

males and females in the households, and the number of rooms in the dwelling. To measure the

effect of temperature, I use a non-linear specification used in Deschenes and Greenstone (2011).

Specifically, Tjdtm is the number of days with temperature falling between 15 – 30 C and above

30 C. The effects θj are measured relative to the omitted category of number of days below

15 C. If electricity demand increases with temperature we should expect θj to be positive and

increasing.

The district fixed effect (γd) removes the effect of time invariant factors such as district

geography, infrastructure quality, that can affect outages and electricity consumption. Year

fixed effects (γt) capture any national or macro events that affect electricity demand and can

be correlated with outages and consumption such as institutional reforms, elections, or macroe-

conomic crises. Since I do not observe other district time varying variables such as price of

appliances and development programs in the electricity sector that may affect electricity de-

mand and outages, I add a district specific trend γdt that controls for linear trends that can bias

the estimate of β. Finally, εidtm is a an idiosyncratic shock to household’s electricity demand.

Suppose that the true effect of outages on electricity demand varies by income. To examine

this, I divide households into three categories using the per capita income distribution. First,

the low income households with income below the median, next the middle income households

with income above the median but below 90th percentile, and finally high income households

with income above the 90th percentile. Then we can estimate 3.20 to obtain the heterogenous

effect on electricity consumption by household income:

log(Eg,idtm) = α + α1Xi + β1log(qdy) +∑

i=M,H

βilog(qdy) ∗ I(HH)i +∑

i

δiI(HH)i

+∑

j


In this equation, β1, β1 + βM and β1 + βH , is the elasticity of electricity demand with

respect to outages for low, middle, and high income consumers, respectively. The identifying

assumption is that conditional on household characteristics, district and year fixed effects and

district time trends, the residual variation in electricity consumption adjusts in response to


outages reflects the heterogenous response of households.

Since outages are not randomly assigned, there can be confounding omitted variables that

lead to bias in the estimates. One potential omitted variable is theft and illegal connections.

While un billed electricity implies that levels of household grid electricity consumption are

under estimated, if it is the case that theft is increasing in districts with increasing outages,

then we should expect β1 to over estimate the true effect of outages. Another potential threat to

identification is unobserved changes in the household’s appliance portfolio, which cause a shift

in electricity demand. If households in districts with increasing outages sell their appliances

which reduces their consumption, we will over estimate the true effect of outages. With these

caveats, we can use the total estimated elasticity for the high and low groups, together with

electricity usage for the mean household in each group, the grid electricity price, and the mean

outage level, to calculate the contribution of the term pg∂Eg

∂q to the willingness to pay for a

reduction in outages.

Similarly, we can also test the heterogeneity in response to periods of hot weather. If

households use electricity as a coping mechanism then we should expect a positive interaction

between hot weather and outages, after controlling for the main effect of outages. As discussed

in chapter 2, during the electricity shortage, the official policy was to cut down the electricity

supply to the distribution companies by an equal proportion of estimated electricity demand.

If distribution companies follow a similar rule in allocating electricity to districts, then districts

with hotter weather and higher estimated demand receive more electricity supply. As a result

total hours of outages and night lights variability should be lower in hotter districts as compared

to those with cooler weather. We should expect that the change in night lights variability to be

smaller in districts with hotter weather if demand is expected to grow faster and the allocation

policy takes this into account.6

To address this problem, instead of using the actual number of days in the hot temperature

bin, that will be correlated with past weather and electricity allocation rules, I use temperature

shocks to estimate the heterogenous effect of outages on electricity demand during hot weather.

Define a temperature shock as the deviation of the number of days above 30 C from the long

term district specific mean number of days above 30 C in a given month. In order to obtain

the long term district-month mean number of days above 30 C, I use all available temperature

data from ECMWF ERA-Interim database starting from 1979. The equation that I estimate

6Using the within district variation in night lights variability and the number of days in the 15–30 C binand above 30 C bin, I find a negative correlation between variability and temperature. This suggests thathotter districts experienced smaller change in outages. Estimating equation 3.19 with interaction terms betweentemperature bins and outages, I also find negative interaction effects, which suggests an omitted variable such asthe allocation policy which was positively correlated with within district variation in temperature, demand, andnegatively correlated with the within district variation in outages. Omitting the effect of this policy can lead toan underestimate of the interaction coefficient between outages and temperature.


with the household data is:

log(Eg,idtm) = α + α1Xi + β1log(qdy) + β2log(qdy) ∗ Shockdtm + β3Shockdtm

+∑

j


Using this equation, β1 and β1+β2, are the elasticity of demand to outages with and without

temperature shock, where these coefficients are identified using the within district variation in

temperature shocks. If weather shocks can not be forecasted prior to determination of electricity

allocation rules then it is plausible to assume that there is no unobserved correlation between

shocks, changes in electricity allocation rules, and unobserved trends in electricity demand. As

before, using electricity consumption for the mean household with and without a shock, the grid

electricity price, and outage levels we can estimate the contribution of pg∂Eg

∂q to the willingness

to pay for a reduction in outages.

3.5 Results

In this section, I present the results from estimating the electricity demand equations 3.19 to

3.21, allowing the effect of outages to differ for high income consumers and during periods of

unusually hot weather. The estimated total elasticity with respect to outages can be used to

calculate the change in grid electricity expenditures across these groups following a reduction

in outages.

3.5.1 Electricity Demand in Urban Areas

Table 3.2 reports the results from estimating the electricity demand equation 3.19. Since

the dependent variable is log electricity consumption measured in kWh, the coefficients of

explanatory variables represent elasticities. For example, the expenditure elasticity of electricity

demand ranges from 0.76 to 0.79. The price elasticity is approximately -0.10 when exploiting

the time variation in prices without any district fixed effects or trends. These estimates are

comparable to the short run elasticity estimates from a number of developing countries surveyed

in Ahmad and Jamil (2011). However, within districts after controlling for year fixed effects and

district time trends, demand is more price elastic (-0.23 to -0.43). This may be due to omitted

variables, such as development programs implemented in the later part of the sample, that are

positively correlated with rising prices and demand (or omitted variables that are negatively

correlated with changes in prices and demand).

Among the appliances, the largest effect on electricity demand is associated with owning a

refrigerator (17 percent to 14 percent) followed by fan (14 percent to 10 percent) and washing


machine (9 percent to 8 percent). The effect of air conditioners is large in the cross-section but

drops after adding the fixed effects and time trends, which suggests changes in air conditioner

ownership are positively correlated with changes in electricity demand within districts. TV

ownership is associated with an additional increase of 4 to 6 percent in electricity usage. The

effect of temperature is positive and precisely estimated. In the specification with all the

controls, the effect of an extra day between 15–30 C is 0.6 percent, and the effect of an extra

day above 30 C is approximately 0.8 percent. The coefficients for the two bins are statistically

different from each other at the 95 percent confidence level. Using the within district standard

deviation in the number of days in each bin (approximately 10 days), the effect is approximately

equal to a 6 to 8 percent increase in electricity demand.

The effect of outages on electricity consumption is negative as expected, but it is attenuated

after adding the fixed effects and trends which suggests the presence of measurement error in

night lights variability that is exacerbated due to differencing. In the last column, I add the

district’s yearly average night lights as an additional control, which increases the estimate.

The effect of average lights on electricity consumption within districts is negative but not

precisely estimated. This suggests that districts where average lights were falling experienced

larger increase in outages and electricity demand. Omitting this effect reduces the elasticity of

demand with respect to outages.

Using the estimate in column 4, a 10 percent decrease in night lights variability or equiv-

alently a one hour reduction in outages is associated with a 2.2 percent increase in monthly

electricity demand. This is the average effect of a reduction in outages on electricity demand in

urban areas during the sample period. Using the mean electricity consumption this implies an

increase of approximately 0.12 kW per day for every hour of outages reduced during the month.

This magnitude seems plausible as typical appliances commonly owned by the household such

as a ceiling fan and incandescent light bulb require 0.05 to 0.10 kW each per hour. Larger

appliances such as a television requires 0.15 kW of electricity, refrigerator requires 0.40 kW,

and a washing machine requires 0.50 kW of electricity per hour.

3.5.2 Heterogeneity by Income and Appliance Type

Table 3.3 reports the effect of outages on electricity consumption allowing the effect to vary for

households in the middle and high income categories relative to households below the median

in the per capita expenditure distribution. Middle income is defined as households above the

median but below the 90th percentile of the per capita expenditure distribution and high income

households are those for which per capita expenditures exceed the 90th percentile. Column 1

shows that the effect of a 10 percent decrease in lights variability is to increase electricity

consumption by around 2.9 percent for households below the median. For middle income and


high income groups the effect of outages is lower as the interaction terms have the opposite

sign. Adding up the main and interaction effects, the total effect on households above median

but below the 90th percentile is 2.2 percent, and for those above the 90th percentile it is 1.2

percent.

The consumption surveys do not report ownership of generators or backup storage devices.

However, a small survey carried out in 2012 in fourteen cities included in the estimation sample,

shows that only 2 to 4 percent of the households with income below Rs. 15,000 (or Rs. 12,295

in 2010 Rupees) own a generator and a backup device respectively (see Appendix Table B.1).

The average household monthly expenditures in this sample are Rs. 13,379 in 2010 Rupees.

The fixed cost (purchase price) of acquiring a small 1 KVA generator that can supply a load

of up to 1 kWh was approximately Rs. 20,000 and that of a backup device was approximately

Rs. 10,000. This is much larger than household monthly average per capita expenditures which

can explain why the observed rates of ownership are very low. Ownership rates also increase

sharply with income. For the average household in the middle and high income groups (as

defined above), it is expected that around 17 percent own a generator and around 26 percent

own a backup device. Therefore, it seems likely that as we move up the income distribution,

there is a greater likelihood that households will have access to backup storage devices and

their grid electricity usage does not change as much in response to outages as compared to the

lower income households. Another likely mechanism that could be responsible for the lower

elasticity even in the absence of off-grid technology is that middle and high income households

reschedule their electricity using activities to non outage hours.

In order to understand the extent to which services from different electricity using appliances

are rescheduled over time, I allow the effect of outages to vary by appliance type. In column

2, I repeat the main regression adding interaction of outages with each appliance indicator.

The interaction term captures the differential effect of appliance usage in response to outages.

The interaction terms are positive but not significant. This is not surprising since most of

the households do not have any off-grid technology and it is usually not possible to reschedule

use of services from these appliances to non outage hours. Therefore, outages will cause a

loss of services from these appliances that is equal to the average or main effect of outages.

The interaction effects of outages with washing machine is significant, which suggests that

households are likely to compensate the use of electricity for laundry by rescheduling it to non

outage hours.

In column 3, I report the results allowing the effect of outages to differ by income categories

and by appliance type. The total effect of reduction in outages on electricity consumption of

households below median is now 3.1 percent. The effect on middle and high income households

is 2.1 percent and 1 percent respectively. The effects are not significantly different from column


1. Finally, in column 4, I also estimate the differential effect of outages jointly by appliance type

and income category including all two way and three way interactions of the five appliances and

two income categories with outages. The coefficient of the three way interaction of outages and

ownership of refrigerator and TV are negative for middle and high income groups but generally

estimated with large standard errors. The three way effect is also negative for air conditioners

and fans for the high income group. This is likely due to usage of generators during outages

which will reduce grid electricity consumption more as compared to low income households. The

three way interactions for owning a washing machine are positive for all groups which indicates

households find it easy to reschedule laundry to non outage hours. Overall, the total predicted

effect of a decrease in outages on electricity consumption for a households in the low, middle

and high income group owning the median appliance portfolio in that group is 3.1 percent, 2.2

percent, and 1.6 percent, respectively.7

3.5.3 Heterogeneity by Temperature

In Table 3.4, I estimate how the elasticity of grid electricity usage to outages varies with heat

shocks by estimating equation 3.21. In column 1, using the standard specification with all

controls, I find that the temperature shock itself has no significant effect on electricity demand.

In column 2, after allowing the effect of outages to vary by temperature shocks, temperature

shocks on their own do increase electricity demand. The effect of a one standard deviation

positive shock (3.5 days above 30 C), is an increase in electricity demand by 9 percent. This is

an additional effect of unusually hot temperatures, as it is estimated conditional on the current

weather or the number of days in different temperature bins. (The direct effect of the number

of days above 30 C in the current month is 8 percent which is the same as in Table 3.2).

The differential effect of outages by temperature shock is also positive which implies that

outages lead to a smaller decrease in electricity demand when they coincide with periods of

high temperature. Conversely, when outages go down by one hour, the change in electricity

demand due to outages is only 1.5 percent when it is unusually hot as compared to 2.4 percent

in normal periods. This smaller adjustment in electricity usage likely occurs because it is an

important mechanism by households to cope with hot temperatures.

In column 3, I also control for the heterogenous effect of outages by income groups. Outages

have a smaller effect on demand of middle and higher income groups, which is possible due to

greater reliance on off-grid technologies. Finally, in column 4, I allow the effect of outages and

7The total effect of a change in outages for each group is the sum of the main effect of outages, the two wayinteraction between outages and income group indicator, the two way interaction between outages and applianceownership indicator, and the three way interaction between outages, income group indicator, and applianceownership indicator. The median appliance portfolio of low income group is a fan and TV, middle and highincome group is refrigerator, fan, TV and washing machine.


temperature shocks to vary by income groups by including all three way interactions. There

differential effect of outages during temperature shocks is smaller for high and middle income

groups possibly due to the use of generators. The total effect of a reduction of outages by one

hour is to increase electricity demand by 2.6 percent for the low income group, 2.2 percent for

the middle income and 1.2 percent for the high income group.

3.5.4 Discussion

The results of Table 3.3 and Table 3.4 suggest that the observed adjustments in electricity

expenditures in response to a decrease in outages will vary with income and temperature. The

total predicted effect for each group found in the previous results can be used to compute the

total change in grid electricity expenditures pg∂Eg

∂q in response to a reduction in outages. Since

this is the only term with a sign that offsets the total willingness to pay, under the condition

that the total disruption in services under the high demand scenario is larger, these estimates

can be used to compare how the welfare effect of outages varies with income and temperature.

If consumers in each group (high versus low income or hot versus normal temperature)

used the same amount of electricity, then a smaller elasticity implies that |pg∂Eg

∂q | should be

smaller. However, when comparing groups with different electricity demand, we can only use

the observed |pg∂Eg

∂q | to put a lower bound on the difference in the willingness to pay across

the high and low demand scenarios if |pg∂Eg

∂q |High ≤ |pg∂Eg

∂q |Low. If |pg∂Eg

∂q |High > |pg∂Eg

∂q |Low,

then the difference in the change in utility due to total change in utility from electricity using

home services across these two scenarios |Usλ

dSdq |

High −|Usλ

dSdq |

Low must be greater than observed

difference in the change in electricity expenditures |pg∂Eg

∂q |High − |pg∂Eg

∂q |Low, for willingness to

pay to be higher in the high demand scenario.

In Table 3.5, I use the total predicted elasticities of demand with respect to outages for each

group to calculate the term pg∂Eg

∂q or the change in grid electricity expenditures for a reduction

in outages by one hour. Using the standard elasticity formula, the total increase in electricity

expenditures for the average household in each group can be found as follows:

pg∂Eg

∂q= pg

∂ln(Eg)ln(q)

Eg

q

Panel A of Table 3.5 reports the estimates for low, middle and high income groups using

predicted elasticities found in Table 3.3. I find that low and middle income households would

experience approximately an equal increase in total electricity expenditures of Rs. 239 per

month if outages were to decline by 1 hour each day of the month. This is equivalent to 2.4

percent and 1.7 percent of the average monthly expenditures in the low an middle income

category. For high income households, total monthly expenditures are expected to increase by


Rs. 405 for each additional hour of electricity available from the grid, equivalent to 1.7 percent

of average monthly expenditures. Using the observed standard deviation of lights variability in

the urban areas which approximately translates into 2.8 hours, these estimates imply a total

change of 6.72 percent and 4.76 percent in monthly expenditures for low, middle and high

income households respectively. Based on some of the plausible omitted variables such as theft

and demand shifts due to changes in appliance ownership, discussed in section 3.4.2, we should

expect these numbers to overestimate the true change in grid electricity expenditures.

As the observed change in electricity expenditures is the same for low and middle income

households, as long as electricity demand is higher for middle income households, if the total

change in services is larger for middle income households, we can conclude that middle income

households have a higher willingness to pay for reduction in outages as compared to the low

income households. When comparing the high income households to low or middle income, we

find that the observed change in electricity expenditures is larger for high income households.

This implies that the utility gain for high income households as compared to the low income

households |Usλ

dSdq |

High − |Usλ

dSdq |

Low must be at least as large as the observed difference in

electricity expenditures which is Rs. 167 found using the estimates in Panel A. In other words,

the additional value of being able to enjoy electricity using services experienced by the high

income households as compared to other groups must exceed Rs. 167, for their willingness

to pay to be larger. This is approximately equal to $1.95 using the market exchange rate in

2010.8 This is larger than the stated preference estimates found in previous studies described

in Ozbfali and Jenkins (2015) who report that the total willingness to pay ranges from $0.10 to

$1.15 per hour of unserved electricity in developing countries. My estimate implies that utility

gain from increase in home services alone must exceed $1.95 per hour of outages reduced for

the high income households.

In Panel B of Table 3.5, I repeat the exercise using the total predicted elasticities of demand

with respect to temperature found in Table 3.4 for the entire sample and for each income group.

Using the mean electricity consumption, outages, and prices for each group, I find that in the

months with a heat shock, a reduction in outages leads to a smaller increase in electricity

expenditures. The average difference in the willingness to pay is Rs. 90 or approximately $

1.05. Within income categories, low income households spend Rs. 200 per hour of outages

reduced as compared to Rs. 230 in months with no heat shocks. The estimates imply that the

willingness to pay for an additional hour of electricity supplied from the grid must be at least

Rs. 30 ($0.35) more in a month with a heat shock. For the middle income and high income

households, the willingness to pay for an additional hour of electricity supplied from the grid

8The average market exchange rate in 2010 was Rs. 85.5 per US dollar.


must be at least Rs. 21 ($0.25) and Rs. 76 ($0.89) more respectively, in a month with a heat

shock. These estimates are a lower bound as they ignore the other components of the welfare

effect that add to the willingness to pay for electricity.

3.6 Limitations of Welfare Analysis

The results from the the welfare analysis discussed above are partial and subject to several lim-

itations. From a conceptual viewpoint, the model makes a number of simplifying assumptions

to characterize how households are affected by disruptions in electricity supply. In this model

outages occur intermittently and the actual timing is partially known. Households experience

unreliable electricity and the total impact is summarized in the outage probability, q. The

higher this number, the larger is the reduction in grid electricity available for household ser-

vices, which reduces household welfare. However, there can be other ways unreliable electricity

affects households. For example, households may incur additional non-pecuniary costs due to

uncertainty in when electricity will be available. This cost can vary across households and de-

pend on the task or service being considered. In order to understand separately the welfare cost

of the reduction in available electricity and welfare cost of unreliable electricity, we need real

time data that tracks electricity usage, household time use, and production of home services

at a household level. Additionally, we also need to elicit stated valuations to fully quantify the

cost of outages and unreliable electricity.

The simplified framework presented above treats the household as a single unit, ignoring the

issues of intra-household allocation. In particular, the choice between home (TS) and market

work (TW ) is treated as a single household decision. To the extent that home produced services

in developing countries are usually performed by female members of the household, we should

expect a change in outages to affect the female home and market labour supply tradeoff. It is

well known that households do not behave as a single unit and income earned by females will

increase expenditures on food, health and education more than income earned by males. The

model also ignores human capital choices which are affected in the long run by the availability

(or lack) of electricity. Incorporating these features into the model will lead to additional welfare

consequences of outages.

From an empirical viewpoint, the estimated elasticities can be confounded by unobserved

trends in electricity usage that are correlated with changes in outages and may also have a

heterogenous effect on households in different income groups. Theft and illegal connections may

be a bigger problem for low income households. Therefore, we should expect that the estimated

elasticity is bigger than the actual elasticity, if outages go up in districts with increasing trends

in unaccounted electricity usage. The model has also assumed that household’s appliance stock


is fixed. Any changes in this stock due to acquisition or disposal of electricity using assets

will shift demand. If household’s sell of appliances when outages increase, then the we will

over estimate the change in electricity expenditures due to outages. However, by ignoring the

changes in household assets we also fail to account for the welfare consequences of selling of

assets as an additional outage cost.

3.7 Conclusion

In this chapter, I develop a model of household optimization under outages and use it to derive

an expression for the welfare effect of a change in outages. The welfare effect or the willingness

to pay increases with the total change in home produced services affected by outages and the

additional time devoted to performing these services valued at the opportunity cost of household

time. It is also larger the smaller is the observed change in grid electricity expenditures.

Intuitively this is because households that are more likely to be impacted, will reschedule

home productions activities by utilizing appliances more intensively during non outage hours

or by utilizing off-grid technologies such as generators or backup storage devices to partially

compensate for absence of electricity during outages. In contrast to the previous literature that

used either stated preferences or consumer surplus approach to measure the welfare effect, my

work shows that the welfare effect should be measured using a combination of revealed and

stated preference data.

I use household electricity consumption data to estimate the heterogeneity in adjustment

in electricity expenditures by income and temperature. The results show that the elasticity

of electricity consumption to outages is smaller the more outward shifted is the demand for

electricity due to higher income or higher temperatures. The implied change in electricity

expenditures for low and middle income groups is equal, which suggests that willingness to

pay is higher for the middle income households, if the change in electricity produced services

is larger for these households. For high income households to have a larger willingness to pay,

the results imply that the utility gain due to reduction in outages must exceed the utility

gain to low or middle income households by at least $1.95 for each hour of outages reduced.

The change in electricity expenditures during months with temperature shocks imply that the

difference in the willingness to pay should be at least $0.25 to $0.89 more across months with

and without temperature shocks. Despite the conceptual and empirical challenges in measuring

the total welfare effect, the results of this chapter suggest that welfare cost of outages borne by

households is significant and changes with income and temperature.



Mean Standard Deviation

Lights variability (0 –1) 0.073 0.050

Average lights 26.9 11.4

Electricity consumption (kWh) 160 114

Price (Rs. per kWh) 4.49 1.34

Per capita expenditures 13,118 7,634

Household size (persons) 6.97 3.18

Number of rooms 2.55 1.39

Refrigerator 0.52 0.50

Air conditioner 0.07 0.26

Fan 0.96 0.19

TV 0.71 0.46

Washing machine 0.62 0.49

Monthly rainfall (cm) 0.479 0.685

Number days 15 – 30 ◦C 17.6 11.7

Number days > 30 ◦C 7.28 10.8

Temperature shock 0.508 3.45

Notes: The sample consists of 18,190 households observed in 39 urban districts from2004 to 2011. Lights variability, average lights and weather variables are measured atthe district year level. Temperature shock is the deviation of number of days above30 ◦C from the district-month long run mean. All other variables are measured atthe household level. Per capita expenditures are deflated to 2010 using the consumerprice index. Price is the price for additional unit in the 101 - 300 kWh consumptionband.


Table 3.2: Electricity Demand in Urban Areas

Dependent variable: Log electricity consumption

(1) (2) (3) (4)

Log per capita expenditures 0.759*** 0.750*** 0.790*** 0.790***(0.036) (0.037) (0.032) (0.032)

Log outages -0.177*** -0.159* -0.224**(0.042) (0.082) (0.091)

Log price -0.095 0.012 -0.231 -0.426*(0.061) (0.070) (0.153) (0.249)

Number days 15 – 30 ◦C 0.009*** 0.007*** 0.006** 0.006***(0.003) (0.003) (0.002) (0.002)

Number days > 30 ◦C 0.015*** 0.013*** 0.009*** 0.008***(0.005) (0.004) (0.004) (0.003)

Refrigerator 0.173*** 0.156*** 0.138*** 0.138***(0.018) (0.017) (0.015) (0.012)

Air conditioner 0.089** 0.077** 0.004 0.004(0.035) (0.031) (0.026) (0.026)

Fan 0.136*** 0.162*** 0.095** 0.095**(0.044) (0.049) (0.045) (0.045)

TV 0.062*** 0.042** 0.043** 0.043**(0.020) (0.019) (0.016) (0.017)

Washing machine 0.091*** 0.090*** 0.083*** 0.084***(0.020) (0.020) (0.018) (0.018)

Log average lights -0.477(0.441)

District fixed effects N N Y YYear fixed effects N N Y YDistrict time trend N N Y YN 18,190 18,190 18,190 18,190R2 0.27 0.28 0.32 0.32

Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors clustered by district reported in parentheses.The dependent variable is the log of electricity consumption in kWh. Log outages is the log of night lightsvariability in the district in a given year. Log price is the price per kWh for consumption between 101 –300kW. All regressions include log household size, the age and gender composition of the household, number ofrooms in the dwelling, month of survey fixed effects and district monthly rainfall.


Table 3.3: Effect of Outages on Demand by Income and Appliances


(1) (2) (3) (4)


Log outages -0.269*** -0.331*** -0.349*** -0.355***(0.093) (0.098) (0.099) (0.097)

Log outages X 0.060*** 0.052*** 0.01450th–90th percentile (0.019) (0.019) (0.085)Log outages X 0.148*** 0.154*** 0.484**Above 90th percentile (0.032) (0.035) (0.200)

Log outages X 0.017 -0.005 0.025Refrigerator (0.022) (0.022) (0.028)Log outages X -0.030Refrigerator X 50th–90th percentile (0.032)Log outages X -0.325***Refrigerator X Above 90th percentile (0.115)

Log outages X 0.003 -0.061* -0.025Air-conditioner (0.030) (0.032) (0.102)Log outages X 0.016Air-conditioner X 50th–90th percentile (0.123)Log outages X -0.003Air-conditioner X Above 90th percentile (0.120)

Log outages X 0.027 0.029 0.00Fan (0.042) (0.043) (0.062)Log outages X 0.096Fan X 50th–90th percentile (0.078)Log outages X -0.030Fan X Above 90th percentile (0.124)

Log outages X 0.026 0.021 0.063**TV (0.023) (0.023) (0.025)Log outages X -0.084**TV X 50th–90th percentile (0.038)Log outages X -0.119TV X Above 90th percentile (0.146)

Log outages X 0.054** 0.050** 0.035Washing machine (0.024) (0.024) (0.031)Log outages X 0.027Washing machine X 50th–90th percentile (0.044)Log outages X 0.074Washing machine X Above 90th per-centile

(0.076)

N 18,190 18,190 18,190 18,190R2 0.32 0.32 0.32 0.32Predicted EffectsBelow 50th percentile 0.029*** 0.031*** 0.031***

(0.010) (0.010) (0.010)50th - 90th percentile 0.022** 0.021*** 0.022**

(0.010) (0.009) (0.010)Above 90th percentile 0.012 0.010 0.016*

(0.009) (0.009) (0.009)Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors way clustered by district reported in parentheses. Thedependent variable is the log of electricity consumption in kWh. Predicted effects are for a one standard deviationdecrease in outages and median portfolio of appliances in each income category. Column 4 includes the main effectsand all two way interaction effects of outages, income categories and appliance indicators.


Table 3.4: Effect of Outages on Demand by Temperature and Income


(1) (2) (3) (4)


Log outages -0.225** -0.171* -0.285*** -0.282***(0.092) (0.085) (0.095) (0.096)

Log outages X 0.148*** 0.154***50th – 90th percentile (0.033) (0.033)Log outages X 0.060*** 0.056***Above 90th percentile (0.019) (0.019)

Temperature shock 0.003 0.026* 0.025* 0.038**(0.004) (0.013) (0.014) (0.018)

Log outages X 0.007* 0.007* 0.011**Temperature shock (0.004) (0.004) (0.005)Log outages X -0.008Temperature shock X 50th-90th per-centile

(0.007)

Log outages X -0.003Temperature shock X Above 90th per-centile

(0.007)

N 18,190 18,190 18,190 18,190R2 0.32 0.32 0.32 0.32Predicted Effectsa. No temperature shockAll sample 0.024**

(0.010)Below 50th percentile 0.030*** 0.030***

(0.010) (0.010)50th - 90th percentile 0.024** 0.024**

(0.010) (0.010)Above 90th percentile 0.014 0.013

(0.010) (0.009)b. Positive temperature shockAll sample 0.015**

(0.010)Below 50th percentile 0.027*** 0.026***

(0.011) (0.010)50th - 90th percentile 0.021** 0.022**

(0.010) (0.010)Above 90th percentile 0.012 0.010

(0.010) (0.009)

Notes: * p<0.10, ** p<0.05, *** p<0.01 and standard errors way clustered by district reported in parentheses. Thedependent variable is the log of electricity consumption in kWh. Temperature shock is the deviation of number ofdays above 30 degrees from the long term district monthly mean. Predicted effects are for a one standard deviationdecrease in outages for different income categories with and without one standard deviation of temperature shock (3.5days). Column 4 includes the main effect and all two way interactions of outages, temperature shocks and incomecategories.


Table 3.5: Estimates of |pg∂Eg

∂q |

Predictedeffect

Eg |dEg

dq | pg |pg∂Eg

∂q |

Panel A – Heterogenous effect by Income and Appliance Usage

Below 50th percentile 0.031 121 51.4 4.63 238

50th – 90th percentile 0.022 171 51.5 4.63 239

Above 90th percentile 0.016 280 61.4 6.60 405

Panel B – Heterogenous effect by Income and Temperature

All sample

No shock 0.024 157 51.6 4.63 239

Temperature shock 0.015 157 32.3 4.63 149

Below 50th percentile

No shock 0.030 121 49.7 4.63 230


50th – 90th percentile

No shock 0.024 171 56.2 4.63 260


Above 90th percentile

No shock 0.013 280 50.0 6.60 329


Source: Elasticities in panel A and B are total predicted effects for each group from Table 3 column 4 and

Table 4 columns 1 and 4, respectively. |∂Eg

∂q| = |Pred.effect| ∗

Eg

q. Eg is the mean electricity consumption

for each group in kWh, q is the mean variability (0.073), and pg is the marginal price of consumption ineach group.


Figure 3.1: Electricity Consumption and Income Per Capita in Developing Countries

ALBARM

AZE

BEN

BLR

BOL

BRA

CHN

COL

DOMECU

EGY

ERI

GAB

GHA

IND

IRQ

JAM

JOR

KEN

MEX

NAM

NIC

PAK

PAN

PER

SEN

SLV

TUN

TUR

UKR

UZB

YEM

AGO

BGD

BGR

BIH

BWA

CMR

COG

CRI

DZA

ETH

GEO

GTM

HND

HTI

IDN

IRN

KAZ

KGZ

KHM

KSVLBN

LBY

LKA

MAR

MDA

MKD

MNE

MNG

MOZ

MUS

MYS

NGA

NPL

PHL

PRY

ROM

SDN

SRB

TGO

THA

TJK

TZA

VNM

ZAF

ZAR

ZMBZWE

45

67

89

Log

elec

tric

ity c

onsu

mpt

ion

per

capi

ta, 2

011

6 7 8 9 10Log income per capita, 2011 (PPP)

Notes: The graph shows the relationship between electricity consumption measured in kWh per capita and GDP per capita(PPP) measured in 2011. Both variables are from the World Development Indicators.

Figure 3.2: Electricity Consumption and Temperature in Developing Countries

AGO

ALB

ARE

ARG

ARM

AUS

AUT

AZE

BEL

BGD

BGR

BIH

BLR

BOL

BRA

BRN

BWA

CANCHE

CHL

CHN

CIV

CMR

COG

COL

CRICUB

CYP

CZEDEU

DNK

DOM

DZA

ECU

EGY

ERIESP

EST

ETH

FINFRA

GAB

GBR

GEO

GHA

GRC

GTM

HNDHRV

HTI

HUN

IDN

IND

IRL

IRN

ISL

ISRITA

JAM

JOR

JPN

KAZ

KEN

KGZ

KHM

KOR

KWT

LBN

LBYLKA

LTU

LUX

LVA

MAR

MDA

MEX

MKD

MMR

MNG

MOZ

MYS

NAM

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POLPRT

PRYRUS

SAU

SDN

SEN

SLV

SVKSVN

SWE

SYRTHA

TJK

TKM

TTO

TUN

TUR

TZA

UKRURY

USA

UZB

VEN

VNM

YEM

ZAF ZMBZWE

-50

510

15A

vg. a

nnua

l gro

wth

in e

lect

ricity

con

sum

ptio

n (2

000-

2011

)

0 5 10 15 20 25 30Temperature (deg. C)

Notes: The graph shows the relationship between average annual growth in electricity consumption per capita in kWhfrom 2000-2011 and temperature in degrees Celsius. Electricity consumption is from World Development Indicators.Temperature is population weighted annual temperature in 2000 obtained from Dell, Jones and Olken (2012).


Figure 3.3: Electricity Consumption and Household Per Capita Expenditures

44.

55

5.5

6Lo

g el

ectr

icity

con

sum

ed k

wh

6.5 7 7.5 8 8.5 9Log per capita expenditure

Notes: The graph shows a smoothed local polynomial prediction of electricity consumption in kilowatt hours as a functionof per capita expenditures. The sample is urban households surveyed in Pakistan from 2004 to 2011.

Figure 3.4: Appliance Ownership and Household Per Capita Expenditures

0.2

.4.6

.81

Pro

babi

lity

of o

wne

rshi

p

6.5 7 7.5 8 8.5 9Log per capita expenditures

Refrigerator Air-conditioner FanTV Washing machine

Notes: The graph shows a smoothed local polynomial prediction of owning an appliance as a function of per capitaexpenditures. The sample is urban households surveyed in Pakistan from 2004 to 2011.

Bibliography

[1] Abdullah, Sabbah and Mariel, Petr (2010).“Choice Experiment Study on the Willingness

To Pay to Improve Electricity Services.” Energy Policy, 38: 4570-4581.

[2] Abeberese, Ama Baafra (2013). “The Effects of Electricity Shortages on Firm Investment:

Evidence from Ghana.” Working Paper, Columbia University (November).

[3] Ahmad, Eatzaz and Faisal Jamil (2011), “Income and price elasticities of electricity de-

mand:Aggregate and sector-wise analyses.” Energy Policy, 39: 5519-5527.

[4] Ali, Syed Sajid and Sadia Badar (2010). “Dynamics of Circular Debt in Pakistan and Its

Resolution.” The Lahore Journal of Economics, 15 SE: 61-74.

[5] Akbar, Javed (2015). “Addressing the Present Energy Crisis by Avoiding Mistakes of the

Past.” in Pakistan’s Interminable Energy Crisis – Is there Any Way Out? ed. Michael

Kugelman, Wilson Center: Washington DC.

[6] Alam, Muneeza (2013). “Coping with Blackouts: Power Outages and Firm Choices.” Work-

ing Paper, Yale University (November).

[7] Allcott, Hunt, Allan Collard-Wexler and Stephen D. O’Connell (2016). “How Do Electricity

Shortages Affect Industry? Evidence from India.” American Economic Review, 106(3):

587-624.

[8] Banerjee, Abhijit, Esther Duflo and Nancy Qian (2012). “On the Road: Access to Trans-

portation Infrastructure and Economic Growth in China.” Working Paper, Yale University

(February).

[9] Baskaran,Thushyanthan, Brian Min and Yogesh Uppal (2015). “Election Cycles and Elec-

tricity Provision: Evidence from a Quasi-experiment with Indian Special Elections.” Jour-

nal of Public Economics, 126: 64-73

99

Bibliography 100

[10] Baum-Snow, Nathaniel, Loren Brandt, J. Vernon Henderson, Matthew Turner and

Qinghua Zhang (2013). “Roads, Railroads, and the Decentralization of Chinese Cities.”

Working Paper, Brown University (October).

[11] Billinton, R., Pandey, M. (1999). “Reliability Worth Assessment in a Developing Country

– Residential Survey Results.” IEEE Transactions on Power System, 14(4): 1226-1231.

[12] Brown, David and Mushfiq Mobarak (2009). “The Transforming Power of Democracy:

Regime Type and the Distribution of Electricity.”American Political Science Review,

103(2): 193-213.

[13] Burgess, Robin, Dave Donaldson, Olivier Deschenes and Michael Greenstone (2013). “The

Unequal Effects of Weather and Climate Change: Evidence from Mortality in India.” MIT

Working Paper (October).

[14] Carson, Richard and W. Michael Hanemann (2005). “Contingent Valuation.” Handbook of

Environmental Economics, Volume 2. ed. K.G. Maler and J.R. Vincent, pp. 822-936.

[15] Carlsson, Frederik and Peter Martinsson (2007).“Willingness to Pay Among Swedish

Households to Avoid Power Outages a Random Parameter Tobit Model Approach.” En-

ergy Journal, 28(1): 75-90.

[16] Carlsson, Frederik and Peter Martinsson (2008).“Does it Matter When a Power Outage

Occurs? A Choice Experiment Study on the Willingness to Pay to Avoid Power Out-

ages.”Energy Economics, 30(3): 1232-1245.

[17] Chand, T. R. Kiran, K. V. S. Badarinath, C. D. Elvidge and B. T. Tuttle (2009). “Spatial

Characterization of Electrical Power Consumption Patterns over India Using Temporal

DMSP-OLS Night-time Satellite Data,” International Journal of Remote Sensing, 30(3):

647-661.

[18] Chen, Xi and William Nordhaus (2014). “A Sharper Image? Estimates of the Precision

of Nighttime Lights as a Proxy for Economic Statistics.” Journal of Economic Geography

15(1): 217-246.

[19] Dawn (2009). “Raja Parvez Vows to End Loadshedding in 2009.”Dawn, June 25, 2009.

[20] Dawn (2010). “Power Conservation Measures Proposed.” Dawn, April 20, 2010.

[21] Declan Walsh (2011). “Power Cuts Leave Pakistan Hot and Bothered.”The Guardian, July

3rd, 2011.

Bibliography 101

[22] Declan Walsh and Imran Masood (2013). “Pakistan Faces Struggle to Keep Its Lights On.”

New York Times, May 27, 2013.

[23] Dell, Melissa, Ben Olken and Ben Jones (2012). “Temperature Shocks and Economic

Growth: Evidence from the Last Half Century.” American Economic Journal: Macroe-

conomics, 4(3): 66-95.

[24] Deschenes, Oliver and Michael Greenstone (2011). “Climate Change, Mortality, and Adap-

tation: Evidence from Annual Fluctuations in Weather in the US.” American Economic

Journal: Applied Economics, 3(4): 152-185.

[25] Dias-Bandaranaike, Romesh and Mohan Munasinghe (1983). “The Demand for Electricity

Services and the Quality of Supply.” The Energy Journal, 4(2): 49-71.

[26] Dickens, William T (1990). “Error Components in Grouped Data: Is It Ever Worth Weight-

ing? Review of Economics and Statistics 72(2):328-333.

[27] Dinkelman, Taryn (2011). “The Effects of Rural Electrification on Employment: New

Evidence from South Africa.” American Economic Review, 101(7): 3078-3108.

[28] Donaldson, Dave (2012). “Railroads of the Raj: Estimating the Impact of Transportation

Infrastructure.” forthcoming American Economic Review.

[29] Donaldson, Dave and Richard Hornbeck (2014). “Railroads and American Economic

Growth: A Market Access Approach.” forthcoming Quarterly Journal of Economics.

[30] Dubin, Jeffrey A. and Daniel L. McFadden (1984). “An Econometric Analysis of Residential

Electric Appliance Holdings and Consumption.” Econometrica, 52(2): 345-362.

[31] Economist (2014).“The Urdu rate of growth.” Economist, February 15, 2014.

[32] Faber, Ben (2014). “Trade Integration, Market Size, and Industrialization: Evidence from

China’s National Trunk Highway System.” forthcoming Review of Economic Studies.

[33] Elvidge, Chris, Ziskin, Daniel, Kimberely E. Baugh, Benjamin T. Tuttle, Tilottama Ghosh,

Dee W. Pack, Edward H. Erwin and Mikhail Zhizhin (2009). “A Fifteen Year Record of

Global Natural Gas Flaring Derived from Satellite Data.” Energies, 2(3): 595-622.

[34] Elvidge, Chris, Feng-Chi Hsu, Kimberly E. Baugh, and Tilottama Ghosh (2014), “Na-

tional Trends in Satellite-Observed Lighting 1992-2012.” Global Urban Monitoring and

Assessment through Earth Observation ed. Qihao Weng, CRC Press.

Bibliography 102

[35] Fisher-Vanden, Karen, Erin Mansur, and Qiong (Juliana) Wang (2015). “Electricity Short-

ages and Firm Productivity: Evidence from China’s Industrial Firms.” Journal of Devel-

opment Economics, 114: 172-188.

[36] Gertler, Paul, Orie Shelf and Catherine Wolfram (2012). “How will Energy Demand De-

velop in the Developing World?” Journal of Economic Perspectives, 26(1): 119-138.

[37] Government of Pakistan (2007 -2011). “Economic Survey of Pakistan.” Ministry of Finance:

Islamabad.

[38] Greenstone, Michael (2014). “Energy, Growth and Development.” International Growth

Center Evidence Paper.

[39] Harrington, Winston and Paul R. Portney (1987). “Valuing the Benefits of Health and

Safety Regulation.” Journal of Urban Economics, 22(1): 101-112.

[40] He Chunyang, Qun Ma, Zhifeng Liu and Qiaofeng Zhang (2014).“Modeling the Spatio-

temporal Dynamics of Electric Power Consumption in Mainland China Using Saturation

Corrected DMSP/OLS Nighttime Stable Light Data”, International Journal of Digital

Earth, 7(12): 993-1014.

[41] Henderson, James. V., Adam. Storeygard and David Weil (2012). “Measuring Growth from

Outer Space.” American Economic Review, 102(2): 994-1028.

[42] International Energy Agency (2010). Electricity Information, International Energy Agency:

Paris.

[43] Institute of Public Policy (2013). “Economic Costs of Power Loadshedding in Pakistan.”

IPP BNU: Lahore.

[44] Kateregga, Eseza. (2009). “The Welfare Costs of Electricity Outages: A Contingent Valu-

ation Analysis of Households in the Suburbs of Kampala, Jinja and Entebbe.” Journal of

Development and Agricultural Economics, 1(1): 1-11.

[45] Lewis, Joshua (2014). “Short-run and Long-run Effects of Household Electrification.”

Working Paper, University of Montreal (April).

[46] Lipscomb, Molly, Ahmed Mushfiq Mobarak and Tania Barham (2013). “Development Ef-

fects of Electrification: Evidence from the Topographic Placement of Hydropower Plants

in Brazil.” American Economic Journal: Applied Economics, 5(2): 200-231.

[47] McRae, Shaun (2015). “Infrastructure Quality and the Subsidy Trap.” American Economic

Review, 105(1): 35-66.

Bibliography 103

[48] Michalopoulos, Stelios and Elias Papaioannou (2013). “Pre-Colonial Ethnic Institutions

and Contemporary African Development.” Econometrica, 81(1): 113-152.

[49] Min, Brian and Kwawu Mensan Gaba (2014). “Tracking Electrification in Vietnam using

Nighttime Lights.” Remote Sensing 6(10): 9511-9529.

[50] Munasinghe, Mohan (1980).“Costs Incurred by Residential Electricity Consumers Due to

Power Failures.” Journal of Consumer Research, 6(4): 361-369.

[51] National Electric Power Regulatory Authority (2006-2014). “State of Industry Report.”

NEPRA: Islamabad.

[52] National Electric Power Regulatory Authority (2014). “Performance Evaluation Report of

Distribution Companies and K-Electric.” NEPRA: Islamabad.

[53] National Transmission and Dispatch Company (2013). “Power System Statistics 38th Edi-

tion.” NTDC: Islamabad.

[54] Ozbafli, Aygul and Glenn P. Jenkins (2015). “The Willingness to Pay by Households for

Improved Reliability of Electricity Service in North Cyprus.” Energy Policy, 87: 359-369.

[55] Pence, Karen (2006). “The Role of Wealth Transformations: An Application to Estimating

the Effect of Tax Incentives on Saving.” Contributions to Economic Analysis and Policy

5(1): Article 20.

[56] Reiss, Peter, C. and Mathew W. Wright (2005). “Household Electricity Demand, Revis-

ited.” The Review of Economic Studies, 72(3): 853-883

[57] Rud, Juan P. (2012). ‘Electricity Provision and Industrial Development: Evidence from

India.”Journal of Development Economics, 97: 352-367.

[58] Seppanen, Olli, William J. Fisk and Q H Lei (2006). “Effect of Temperature on Task Per-

formance in Office Environment.” Ernest Orlando Lawrence Berkeley National Laboratory,

Berkeley, CA (US).

[59] Sethi, Nargis (2015). “Power Sector Reforms: Pakistans Energy Crisis and Ways Forward.”

in Pakistan’s Interminable Energy Crisis – Is there Any Way Out? ed. Michael Kugelman,

Wilson Center: Washington DC.

[60] Solon, Gary, Steven J. Haider and Jeffery M. Wooldridge (2015). “What are We Weighting

For?” Journal of Human Resources, 50(2):302 -316

Bibliography 104

[61] Storeygard, Adam (2015). “Farther on Down the Road: Transport Costs, Trade and Urban

Growth in Sub-Saharan Africa.” Tufts University Working Paper (November).

[62] Westley, G. D. (1984). “Electricity Demand in a Developing Country.” Review of Economics

and Statistics, 66(3): 459-467.

Appendix A

Data Appendix

A.1 Electricity Sector, Power Plants and Energy Prices Data

The main source of data on the electricity sector is the annual State of Industry Report of

National Electric Power Regulatory Authority (NEPRA) which has been published every year

since 2006 and contains data on plant capacity, fuel type, fuel cost per kilowatt hour, and plant

generation from 2004 onwards. Data is reported by fiscal years, for example, 2004-05, 2005-06,

and so on. The second source of data is National Transmission and Dispatch Center’s (NTDC)

Power Systems Statistics which provides detailed information on plant location, names of units

within plants, fuel type by plant and unit, and date of commissioning. Finally, I also use the

Pakistan Power Infrastructure Board (PPIB) website which provides dates and years of the

proposals of new power plants that enter during the sample period.

I construct a database of thermal plant capacity and fuel type at the plant-unit level to which

I add yearly data on energy prices described below. There are some plants that can operate on

natural gas or a secondary fuel such as furnace oil or diesel. To construct the weights in the

instrument, I assume that the plants run on the cheaper fuel two-thirds of the time and the

remaining time on the expensive fuel. This approximately corresponds to historical generation

patterns which show that most plants have been using oil or diesel roughly one third of the

time. In fact some of the mixed plants have been increasingly relying on oil due to natural gas

shortage towards the end of the sample period.

To construct the district level weights used in the energy price index, I measure the distance

of each plant from district centroid. The weights utilize information from all the public and

private thermal plants that are part of the NTDC system. For the districts served by the Karachi

Electric Supply Company (KESC) that is vertically integrated, I use only plants that are owned

by the company to calculate the price of energy mix. KESC also purchases electricity from

NTDC. However, the contract between NTDC and KESC specifies the amount of electricity

105

Appendix A. Data Appendix 106

that NTDC is obliged to supply. The contracted electricity was delivered by NTDC, and

therefore it is reasonable to assume that outages in the KESC area are not affected by supply

shocks in the NTDC system.

The price of world crude oil is obtained from the World Development Indicators. I convert

it from US dollars to Pakistani Rupees using the average yearly market exchange rate. The

price of furnace oil is obtained in Rupees from the archives of Pakistan State Oil (PSO). I use

the import parity ex-depot price of high sulphur furnace oil at Zulfiqarabad Oil Terminal in

Karachi. The price of gas for industrial consumers and high speed diesel is reported in the

yearly publication Economic Survey of Pakistan. Local price of coal is not available so I use

the world price of coal retrieved from Index Mundi. I convert all fuel prices to yearly averages

and british thermal units (btu) which is a common common unit of energy content. An btu is

standard unit of measurement used to denote the energy content of fuels. (1 mbtu= 106 btu,

3412 btu = 1 kWh or alternatively 1 mbtu = 293 kWh ). This normalization allows me to

create a weighted energy price index for each district using prices of different energy sources.

A.2 Household Data

The Pakistan Bureau of Statistics (PBS) carries out the national Household Income and Em-

ployment Surveys (HIES) to measure consumption, living standards and income generating

activities of the household. I have access to five rounds of the surveys carried out in 2001-02,

2004-05, 2005-06, 2007-08, and 2010-11. Since the data on power plants and energy prices starts

in 2004 so I am able to use the last four rounds of the survey for my empirical analysis. The

lowest geographic unit identified in the HIES is the district for rural households, and city or

urban division for urban households. There are 14 large cities, 25 urban divisions and 78 rural

districts in my sample. For ease of notation, I collectively refer to cities, urban divisions and

rural districts as districts in my empirical work.

Within the districts, I identify the primary sampling units (villages and urban neighbour-

hoods), that had access to electricity using household response to the question on whether it

has an electricity connection. Specifically, I deem the primary sampling unit to be connected to

the national grid if the median household in the primary sampling unit has access to electricity.

In the entire sample of households collected by PBS, 85 percent of the households had access

to electricity at the start of the sample period which increased to 93 percent by the end of the

sample period. The district-year labour market and household and demographic variables are

constructed using primary sampling units that had access to electricity as defined above.

I use the employment section of the survey which records labour market activities in the

past 30 days for each member above age 10. The respondents are asked if they worked for pay,

profit or family business in the last month, the number of days worked in the past month, and


the total monthly earnings. Respondents also have the option to report their earnings on a

yearly basis in which case they report the number of months worked. For these individuals, I

divide yearly earnings by number of months to get monthly earnings. To create the variable

total monthly earnings, for each individual I add up the earnings from labour market work

(pay, profit, or family business), excluding income from non-labour sources.

I use the consumption section of the survey to construct the total monthly expenditures

on non-durable goods such as food, energy, transportation, personal goods, and miscellaneous

items. Other variables that I construct from the HIES data are household size which is the

number of members residing in the home, number of rooms in the dwelling, and indicators for

having a gas connection, piped water and phone connection.

Inverse Hyperbolic Sine Transformation

The variables log days worked, log earnings, and log earnings per day are constructed at the

individual level using the inverse hyperbolic sine (IHS) transformation ln(y +√

(y2 + 1)) and

then averaged at the district-year level. A comparison of means of log transformed data is

actually a comparison of the geometric means. Since monthly earnings has a long right tail,

using geometric means is preferred as geometric means are not overly influenced by very large

values in a skewed distribution. The IHS is a useful transformation for wealth and income data

that have long right hand tails and also contain large number of zero values (Pence, 2006).

The IHS has an additional advantage over the simple natural log transformation that it maps

zero values to zero. This means that we can retain observations in the left tail which would be

otherwise dropped from the sample using a natural log transformation.

The IHS has many useful properties which make it attractive for interpreting results. At

reasonably large values of y, the transformation is close to ln(2y). Therefore, when considering

changes in the dependent variable at the mean due to changes in explanatory variables, we can

interpret coefficients the way standard log transformed coefficients are interpreted. For example,

in the following regression where the outcome is transformed using IHS, a percent change in

the explanatory variable keeping all other variables constant, implies a percent change in the

dependent variable.

f(y) = a + bln(x)

f(y2) − f(y1) = b(ln(x2) − ln(x1))

ln(2y2

2y1) ≈ bln(

x2

x1)

The IHS also allows an approximate decomposition of changes in transformed dependent

variables, when the untransformed dependent variable is a product of two variables. Using the


notation y = a ∗ b , when y, a, and b are sufficiently large, the change in IHS transformed y is

equal to the change in IHS transformed a and b as shown below.

f(y2) − f(y1) ≈ ln(2y2

2y1)

= ln(a2 ∗ b2

a1 ∗ b1) = ln(

a2

a1) + ln(

b2

b1)

= ln(2a2

2a1) + ln(

2b2

2b1) ≈ (f(a2) − f(a1)) + (f(b2) − f(b1))

A.3 District Infrastructure and Weather Data

The distance to the Karachi port and nearest national road or highway are measured from the

district centroids. I use the map of roads available in the online GIS data depository of the

UN Pakistan’s Office for the Coordination of Humanitarian Affairs (UN OCHA) to identify the

national trunk roads and motorways (pakresponse.info).

Temperature data is obtained from the European Center for Medium-Range Weather Fore-

cast (ECMWF) ERA-Interim database that contains daily temperature and precipitation data.

The daily data, produced using weather station data and climatic models, is also termed as

reanalysis data. I retrieve air temperature measured at 2 meters from surface at a resolution of

0.5 degrees. Monthly rainfall data are from the Center for Research Unit’s (CRU) Time Series

3.21 also available at a resolution of 0.5 degrees. I use the population counts data produced by

GRUMP v1 (at a resolution of 30 arc seconds), which is based on the 1998 population census,

and the district boundaries map to create a population weighted average of daily temperatures

and total monthly rainfall over the sample period for the districts observed in my sample. I

use the daily temperature data to create variables that measure the number of days the tem-

perature fell in a specified range, for example, 15 – 30 or above 30 degrees Celsius for each

district-month-year cell observed in the household data.

A.4 Standard Errors Clustering

All empirical results in chapter 2 are reported using standard errors clustered two way by

district and survey round which also corresponds to fiscal years. Although outages and energy

prices are defined by year, plant characteristics such as capacity is defined by fiscal years that

correspond to rounds. Rounds straddle across years, therefore, clustering by round is more

conservative than clustering by year alone. Since the year 2005 occurs across two rounds, I

define a pseudo variable that combines 2004–05 and 2005–06 into one round and cluster by

district and this pseudo variable.

Appendix B

Appendix Tables and Figures

Table B.1: Generator and Backup Supply Ownership Rates

Monthly expenditures(Rs.)

Households withGenerator (%)

Households withBackup Supply (%)

0 – 15,000 2 4

15,000 – 30,000 17 26

35,000 – 70,000 45 47

Above 70,000 75 43

Source: Reproduced from Institute of Public Policy (2013). The sample is 500 householdsdrawn from 14 large cities of Pakistan. The deflated income brackets using the nationalconsumer price index to 2010 are 0–12,295; 12,295–28,688; 28,688–57,377, and above 57,377.The mean income for the sample of urban households used in chapter 3 is: Rs.10,165 for thosebelow 50th percentile, Rs. 14,204 for those between 50th–90th percentile and Rs.23,534 forthose above 90th percentile, and Rs. 13,379 for the entire urban sample.

109

Appendix B. Appendix Tables and Figures 110

Table B.2: Outages and Type of Employment

Dependent variable: Work > 20days

Self-employed

Paid worker

OLS OLS OLS(1) (2) (3)

Log outages -0.0024*** -0.0009 -0.004***(0.0008) (0.002) (0.002)

N 793 793 793R2 0.19 0.10 0.20

IV IV IV(1) (2) (3)

Log outages -0.024*** 0.013 -0.040***(0.006) (0.009) (0.009)

N 793 793 793F-statistic 15.2 15.2 15.2

Notes: * p<0.10, ** p<0.05, *** p<0.01. Coefficients and two way clustered standard errorsare reported for increase in night lights variability by 10 percent. Regressions include standardcontrols. Mean fraction of labour force working more than 20 days is 0.81, self-employed is0.18 and paid workers is 0.54.


Table B.3: Effect of Outages – Additional Specification Checks

Dependent variable Work Days Earnings Earnings per day

Panel A – Un-weighted regressions (individual-district-year)

(1) (2) (3) (4)

Log outages -0.015*** -0.079*** -0.144*** -0.095***(0.005) (0.018) (0.050) (0.030)

F-statistic 30.1 30.1 30.1 30.1N 70,697 70,447 69,962 69,962

Panel B – Weighted regressions (individual-district-year)

(1) (2) (3) (4)

Log outages -0.020** -0.095*** -0.191** -0.120***(0.008) (0.030) (0.077) (0.045)

F-statistic 15.2 15.2 15.2 15.2N 70,697 70,447 69,962 69,962

Panel C – Weighted regressions (district-year)

(1) (2) (3) (4)

Log outages -0.021** -0.082*** -0.214** -0.144***(0.007) (0.032) (0.075) (0.048)

F-statistic 10.1 10.1 10.1 10.1N 793 793 793 793

Notes: * p<0.10, ** p<0.05, *** p<0.01. Coefficients and two way clustered standard errors are reported for a 10percent increase in night lights variability.


Figure B.1: Variation in Electricity Demand During the Day and Night

Notes: Data on typical hourly demand is from NEPRA Stata of Industry Report (2010).

the impact of electricity outages on households...the smaller is the observed change in grid...

Documents