Aid and Recovery in
Post-Earthquake Nepal
Independent Impacts and Recovery Monitoring Phase 2
Quantitative Survey: February and March 2016

Aid and Recovery in
Post-Earthquake Nepal
Independent Impacts and Recovery Monitoring Phase 2
Quantitative Survey: February and March 2016

July 2016

The Asia Foundation is a nonprofit international development organization committed to improving lives across a dynamic anddeveloping Asia. Informed by six decades of experience and deep local expertise, our programs address critical issues affecting Asia
in the 21st century—governance and law, economic development, women’s empowerment, environment, and regional cooperation. In
addition, our Books for Asia and professional exchange programs are among the ways we encourage Asia’s continued development
as a peaceful, just, and thriving region of the world. Headquartered in San Francisco, The Asia Foundation works through a network ofoffices in 18 Asian countries and in Washington, DC. Working with public and private partners, the Foundation receives funding from adiverse group of bilateral and multilateral development agencies, foundations, corporations, and individuals.

Independent Impacts and Recovery Monitoring Phase 2

Quantitative Survey: February and March 2016
© The Asia Foundation
All rights reserved. No part of this book may be reproduced
without written permission from The Asia Foundation

456 California Street, 9th Floor
San Francisco, CA U.S.A. 94104
www.asiafoundation.org
The project is funded by UK aid through the UK government and the Swiss Development Cooperation.
The views expressed in this report do not necessarily reflect the UK or the Swiss government’s official policies.
Cover photo: Alok Pokharel
Design: Deddeaw Laosinchai

Aid and Recovery in Post-Earthquake Nepal

PREFACEIn June 2015, The Asia Foundation began a longitudinal series of studies that seek to provide insights into the effectiveness of aid delivery and its
impact on recovery in the aftermath of the disastrous
earthquakes of April-May 2015 in Nepal. The studies
track changes over time through a mix of quantitative and qualitative research methods to assess andunderstand how local contextual factors interact
with state and non-state provision of aid. In doing so,
the series go beyond damage assessments that havetended to focus on the quantification of impacts andcosts. They focus also on social relations, cooperationand conflict, politics and leadership, and how they,
with current aid efforts, shape the coping strategiesof those affected. Combined with analysis of shifts ingovernment structure and policy over the course of
the series, the studies provide valid and reliable dataon the direction and magnitude of public sentiment
about state performance. They also enable a sharper
focus and more precise placement of recovery/reconstruction goods and services.
Field data collection for the first study was completed
two months after the quakes, with reports on findings
from in-depth fieldwork and from a large representative household survey released in parallel. At the time,
the Nepali government had completed a Post-Disaster
Needs Assessment and successfully organized a donors’ conference to help determine the overall level ofofficial development assistance and government funds
needed to recover from the disaster. Our first study
affirmed the magnitude of the earthquakes’ impacts.
Housing destruction was widespread in highly impacted districts. In many wards in medium and lower
impact districts, levels of destruction were higher thanaggregated district level data revealed. The study also
noted some crucial gaps in aid distribution. Many inhighly impacted wards in medium impact districts
missed out. There were vast differences across districts on how initial damage assessments were done

and how it was determined who was eligible for abeneficiary card.
The second round of research, the findings of which wereport here, involved fieldwork in February and March
2016, almost one year on from the earthquakes. The
Nepali government established a National Reconstruction Authority early this year and commissioned thedevelopment of a framework for recovery and reconstruction over the short, medium, and longer terms.
Around the same time, violent protests surrounding
the promulgation of the new constitution, and a debilitating five-month blockade along the Nepal-India
border, had petered out. Findings from the second
round of research thus provide a valuable snapshotof Nepali state performance over the course of a yearof political turmoil as well as a substantive baselinethat will allow for a future assessment of the NRA’s
performance.
Among the many interesting findings of the second
study, the following are emblematic:• Okhaldhunga district needs attention; only two
percent of people in this crisis hit district received
food aid in this round;• Borrowing has risen with the number of borrowers
doubling and average loan size increasing by over
400 percent in severely hit districts; there is greatrisk of a debt trap for the most vulnerable;• There is a need to focus livelihood support on
farming which is the main source of income ofmost people and which is recovering slower thanother livelihoods; and• Eighty percent of survivors in severely hit districts
are still in contemporary shelter.
 he third and fourth studies in the series are scheduledTfor September-October 2016 and March-April of 2017.

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Aid and Recovery in Post-Earthquake Nepal

We thank our research partners (Democracy Resource
Center Nepal and Interdisciplinary Analysts), ourdonor partners (UK Department for International
Development and Embassy of Switzerland), and

Nepali government officials in the NRA and theMinistry of Federal Affairs and Local Development
for their support.

George Varughese, Ph.D.
Nepal Country Representative
The Asia Foundation

Patrick Barron, Ph.D.
Regional Director for Conflict & Development
The Asia Foundationiv

Aid and Recovery in Post-Earthquake Nepal

AcknowledgementsB

oth the IRM-1 and IRM-2 surveys were implemented by a team from Interdisciplinary Analysts (IDA). IDA’s work was led by Sudhindra
Sharma. While Sudhindra provided overall guidance,
Hiranya Baral coordinated the survey fieldwork, Bal
Krishna Khadka provided essential support in thinking
through the implications of the technical aspects of thesurvey methodology, Chandra KC worked on getting
the dataset in a form ready for analysis and generated alarge set of initial tables, and Sandeep Thapa designed
the software for data entry. Kurt Burja of the World
Food Programme provided the NeKSAP food security
data which is used in Chapter 5.
Analysis of the data was done by Sangamitra Ramachander, Anup Phayal, and Patrick Barron, who
co-authored the report. Sasiwan Chingchit provided
research support and inputs throughout.
A number of people provided useful inputs at various
stages, including in the formation of the questionnaires, finalization of the sampling strategy, andanalysis of the data. They include George Varughese,Mark Koenig, and Lena Michaels (The Asia Founda-tion) and Sudip Pokharel and his team (Democracy
Resource Center Nepal). Patthiya Tongfueng provided
editorial assistance and Deddeaw Laosinchai designed
the report.
Many thanks to the people of the 14 affected districts
who spent time sharing their views with the researchteams. In 11 of these districts, many people wereinterviewed twice, once in IRM-1 and again in IRM-2.
We particularly value the time they have taken tocontribute to the research.
The project is funded by UK aid through the UK government and the Swiss Development Cooperation,with support from the UK Department for International Development’s Programme Partnership Arrangement with The Asia Foundation. Andy Murray(UK DFID) and Pia Haenni (SDC) have managed the
project from the donor side, and have given useful
inputs at every stage.
The views here do not necessarily reflect the UK or the
Swiss government’s official policies.

vAid and Recovery in Post-Earthquake Nepal

Executive SummaryOne year since two major earthquakes hit Nepal,
the situation in affected areas reveals the depthof impacts and the complex ways in which
recovery is occurring. This report provides data andfindings from a large household survey, conductedin 11 quake-impacted districts from February-March2016. It is part of a larger longitudinal mixed methods
study—the Independent Impacts and Recovery Monitoring for Accountability in Post-Earthquake Nepal(IRM)—which involves multiple waves of household
surveying and in-depth qualitative fieldwork. IRM isbased on the belief that the impacts of major disasters
do not only manifest immediately but play out in complicated and multidimensional ways over the longer
run. For the Government of Nepal and its partners todevelop and implement effective recovery programs,
there is a need to understand these deeper impacts andhow aid responses and coping mechanisms, as well associo-political context, shape the recovery. The firstround of IRM research was conducted in June 2015.
Findings in this report are from the second wave of thequantitative survey involving face-to-face interviews
with 4,850 respondents and 305 ward leaders.

Livelihoods
The earthquakes affected the livelihoods of aroundhalf of the people living in the earthquake zone.
Impacts were more prevalent in the districts where
the earthquake led to more infrastructure damage,with 76% affected in severely hit districts, 56% in crisis
hit districts, 34% in hit with heavy losses districts,
and less than 10% in the hit district. Businesses weremost likely to be totally or somewhat affected but
have recovered more than other livelihoods. The
impacts on livelihoods were more widespread in urban
areas than in rural ones. There is a high correlation
between the impacts of the earthquake on housing
and on livelihoods at the district level. This suggeststhat the PDNA earthquake impact categories arereflective of damage to sources of income in addition
to property. Sindhupalchowk, a severely hit district,saw the greatest damage in terms of both livelihoodsvi(97%) and homes (91%). In severely hit districts, those
who farm their own land and livestock farmers have
had the slowest rate of recovery while businesses
and wage labor performed better in these areas thanelsewhere. Lower caste groups have suffered slightly
lower impacts to livelihoods than higher caste groups
and Janajatis. Damage to wage work and livestock
rearing have tended to have a larger impact on lower
income segments. There are no discernible differencesin livelihoods affected between men and women or
between those with and without disabilities.

Coping strategies
Borrowing has massively increased since IRM-1 was
conducted in June 2015. More than twice as many
people have borrowed and average loan sizes haveincreased 240%. Increases in the number of peopleborrowing, and in loan sizes, are greatest in the severely
hit districts. People are predominantly borrowing forlivelihoods, food, and shelter. Borrowing appears to be
used as a coping strategy by a greater share of people
in poorer districts. However, major credit constraints,
such as smaller loans received and higher rates of loan
refusals, affect vulnerable groups such as the poorest,
women, lower castes, and the disabled. Formal loansources are becoming more important. But the largest
loans are still from moneylenders who charge thehighest interest rates. Moneylenders are particularly
active in poorer districts. Banks lend mainly in urban
areas. The proportion of people who say remittances
are a main income source has increased. But total
remittances from abroad are either staying at the samelevel (76%) or declining (13%). Six percent of people
have sold assets with most of these being livestock.
Migration levels have been low (6%) and most of those
who migrated returned home.

Earthquake relief
The nature of aid has changed since IRM-1. There has
been a decline in the distribution of tarps and foodAid and Recovery in Post-Earthquake Nepal

but a large increase in the provision of cash. Aid isstill concentrated in severely hit districts. However,there has been a move towards decreased coveragein more affected districts and increasing coverage inless affected districts, especially by INGOs and NGOs.
The overall amount of aid appears to have declined.
There has been an increase in the number of people
not receiving aid, in particular in Okhaldhunga
district, where only 2% now receive food aid andmany are borrowing for food. There appears to havebeen an over-distribution of tarps to those who may
not need them. The number of people receiving
CGI has increased but provision is insufficient tomeet needs. There has been little distribution ofreconstruction materials. Government cash aid has
been more widely distributed, and at higher volumes,
than non-government cash, which has tended to go
to lower impact districts and to people who have notreceived government cash. However, cash received
has been insufficient to meet needs. The government,
NGOs, INGOs, and the Red Cross remain the main
providers of aid and people are more satisfied withthese providers than in the past. Satisfaction levels
are highest in Solukhumbu, which has received
large quantities of assistance while sustaining lower
impact. Almost all people whose house was classified
as fully damaged in the damage assessments havereceived beneficiary cards. However, many others
have also received cards. Satisfaction with the damage
assessments is not fully determined by whether people
received a beneficiary card or not. Both government
and non-government cash has been targeted at people
who received beneficiary cards. The poorest and therichest are the least likely to have received aid sinceJune 2015. Non-government cash has been less likely
to go to poor people in rural areas. Janajatis are more
likely to have received aid than others. However, lowcaste people are more likely to be satisfied with aid
providers. Aid appears to have been well targeted byhousing damage. Of those who received aid, those
whose house was less damaged are more satisfied.
There are no major differences in the experience ofaid between men and women and the disabled andnon-disabled.

Needs and services
Cash, shelter, and food remain the priority needsof people. Over two-thirds of people identify cash
as a priority immediate need, over 85% in severely
hit districts. Eighty percent of people in severely hit
districts are still living in self-constructed temporary
shelters, with shelter needs highest in Nuwakot,
Ramechhap, and Sindhupalchowk districts. People
increasingly want reconstruction materials (or cash to
buy them) rather than CGI or tarps. Reconstruction
materials are in high demand amongst those whose
house was badly but not completely damaged. Thereis a need to ensure these people also receive assistanceto rebuild. Many people who are now staying in theirown or others’ houses say they need reconstruction
materials, suggesting that they are currently living in
unsafe houses. Where levels of food aid have declined,
food consumption has decreased. Food aid has beensuccessfully targeted at areas that are more severely
food insecure. Yet people in these areas are more likely
to say food consumption has decreased, suggesting
volumes of food aid are not sufficient. Dissatisfaction
with most public services has increased since IRM-1,
with schools being the exception. Dissatisfaction ishighest amongst the richest, the most educated, andthose from higher castes.

The fuel crisis
The crisis affected access to fuel for cooking for twothirds of people. Access to fuel for transportation wasless affected, in part because half of respondents said
they do not need transportation fuel. Rural, lower
income, and lower caste groups tend to have had theiraccess to fuel more affected than other groups. Thoseliving in temporary shelters are the most likely to have
had their access to fuel for cooking affected. Of thoseable to obtain fuel for cooking, the most importantsource by far is wood, which tends to be used moreamong people in temporary shelters and much morein rural areas. More people got fuel on the regular or
black market than from the government. In urban
areas, 14% bought fuel on the black market. Richer
people were more likely than poorer ones to buy on
the black market. The poorest were the least likely
to receive free fuel from the government. One-halfof people paid more for fuel during the crisis. Whilepeople who live in rural areas suffered more, with lessaccess to fuel, they were less likely to pay more because
they tend to use wood rather than buy fuel on the black
market. People with the lowest income were the most
likely to pay more for fuel during the crisis. Disability
and gender did not affect access to fuel. There were no
major differences in prices paid by caste, gender, ordisability. Ninety-five percent of people reported thatthe price of basic staples increased during the crisis.
One-quarter of people in severely hit districts said thataid completely stopped during the crisis; 41% said it
continued but at lower volumes than before.

Politics
Satisfaction with the aid response of political parties
has declined since IRM-1. The drop is greatest
in severely hit districts. Among those who weresatisfied in IRM-1, 72% now say they are dissatisfied.
Only 32% of all respondents are satisfied with local
political parties. There are indications that declines
in satisfaction are higher in areas where less aid wasvii

Aid and Recovery in Post-Earthquake Nepal

received. Those with higher incomes or education,and people who are either high caste or Janajati,
are more likely to report low levels of satisfaction.
Satisfaction with the aid response of the central
government has increased since IRM-1 but one-third
are still dissatisfied. Among those who were satisfied
in IRM-1, 32% now say they are dissatisfied. Patterns
are similar to those of attitudes towards local parties.
People in higher impact districts are more likely to bedissatisfied than those in lower impact districts. Most
people do not know who they will vote for in the next
election. Amongst those who have decided, there isincreased support for Nepali Congress and CPN-UML
with a slight drop in support for UCPN (Maoist). UCPN
(Maoist) voters tend to live in severely hit districts
and large share of them are uncertain about who tovote for in next election. Those in lower caste groupwere more likely to vote for UCPN (Maoist) but thedrop in planned voted for the party holds for all caste
groups. RPP-N has been most successful in recruiting
voters who chose other parties last time around. Visitsfrom Constituent Assembly members to earthquakeaffected wards are quite rare and have dropped slightly
since IRM-1, with only one-third reporting that visits
took place since the 2015 monsoon.

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Social relations and violence
Reported levels of violence are very low in the affectedareas; people’s feelings of safety have improved sinceIRM-1 and are at very high levels. Respondents inrural areas feel safer compared to those in urban
areas. More people living in self-constructed shelterson public land or who are renting feel unsafe thanpeople living in other accommodation. Most people
feel that community members can cooperate witheach other to deal with problems. However, people
in the most affected districts are less likely to agree.
Eighty-nine percent of respondents feel people ofevery caste, religion, and ethnicity are equally able toreceive aid according to their needs. Dalits, however,are far less likely than other groups to agree with this.
There is a large increase from IRM-1 in the share ofpeople who think Village Development Committees(VDCs)/municipalities are distributing aid fairly.
Those in severely hit districts are the most likely
to feel aid distribution has been fair while those incrisis hit districts, which have received much less aid
despite high needs, are the least likely to agree. Dalits
are more likely to feel that aid distribution by VDCs/
municipalities has been fair. This suggests that there
are other structural barriers to them receiving aid.
Those with low income and lower education are most
likely to feel aid distribution has been fair. People
whose house was destroyed or badly damaged are
more likely to feel aid distribution has been fair.

Aid and Recovery in Post-Earthquake Nepal

LIST OF ACRONYMS
CA

Constituent Assembly

CGI

Corrugated Galvanized Iron

CPN-UML

Communist Party of Nepal (Unified Marxist Leninist)

DRCN

Democracy Resource Center Nepal

EA

Enumeration area

IDA

Interdisciplinary Analysts

INGO

International non-governmental organization

IRM

I ndependent Impacts and Recovery Monitoring for Accountability
in Post-Earthquake Nepal project

LGCDP

Local Governance and Community Development Programme

NeKSAP

Nepal Food Security Monitoring ProgramNGO

Non-governmental organization

NMKP

Nepal Mazdoor Kizan Party

NPR

Nepali Rupees

PDNA

Post-Disaster Needs Assessment

RPP

Rastriya Prajatantra Party

RPP-N

Rastriya Prajatantra Party Nepal

UCPN (M)

Unified Communist Party of Nepal (Maoist)

UN

United Nations

VDC

Village Development Committeeix

Aid and Recovery in Post-Earthquake Nepal

TABLE OF CONTENTSPREFACEIII

ACKNOWLEDGEMENTS

V

EXECUTIVE SUMMARYVI

LIST OF ACRONYMS

IX

LIST OF FIGURESXIILIST OF TABLESXVxChapter 1. Introduction
1.1 Background
1.2 Methodology and approach113

Chapter 2. Livelihoods
2.1 People’s occupations and income in the earthquake zone
2.2 The impacts of the earthquakes on livelihoods and income
2.3 Links between housing and livelihoods damage
2.4 Recovery of livelihoods

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Chapter 3. Coping Strategies
3.1 Borrowing
3.2 Remittances
3.3 Migration
3.4 Asset sales

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38

Chapter 4. Earthquake Aid
4.1 What aid are people receiving?
4.2 Food, shelter, and cash
4.3 Providers of aid
4.4 Satisfaction with aid providers
4.5 Damage assessments and beneficiary cards
4.6 Experiences of aid among different population groups

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Chapter 5. Needs and Services
5.1 Overall needs
5.2 Shelter needs
5.3 Food needs
5.4 Need for other relief
5.5 Services

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Chapter 6. The Fuel Crisis
6.1 Access to fuel6.2 Sources of fuel
6.3 Price paid for fuel
6.4 Impacts on aid and recovery

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Chapter 7. Politics
7.1 Political party preferences
7.2 Satisfaction with local political parties
7.3 Satisfaction with the central government
7.4 Have Constituent Assembly members visited areas?

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113
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Aid and Recovery in Post-Earthquake Nepal

Chapter 8. Social Relations and Violence
8.1 Safety and security
8.2 Trust and social cohesion
8.3 Sense of fairness and grievances with aid distribution

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Chapter 9. Conclusions

129

Annex A: Methodology
Sampling frame and district selection
Selection of VDC/wards within districts and replacement of VDC/wards
Selection of enumeration areas within VDC/wards
Selection of households within EAs
Selection of respondents within households
Weighting data

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Annex B. Sample Characteristics
GenderCaste
Income brackets
Disability
Rural/urban areasLevel of housing damage
Current type of shelter

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LIST OF FIGURES
Figure 2.1:	Share of people with at least one income source affected by the earthquakes
10– by district impact (IRM-2)
Figure 2.2:	Share of people whose source of income was affected by the earthquakes –11
by source of income, all districts (IRM-2)
Figure 2.3:	Share of people within each income source whose income from that source11
was affected by the earthquakes – by source of income, severely hit districts (IRM-2)
Figure 2.4:	Share of people within each income source whose income from that source12
was affected by the earthquakes – by urban/rural (IRM-2)
Figure 2.5:	Housing damage and livelihood damage – by urban/rural (IRM-2) 
14
Figure 2.6:	Share of people within each income source whose income from that source
15
has improved in the past three months – by source of income, all districts (IRM-2)
Figure 2.7:	Share of people within each income source whose income from
16
that source has improved in the past three months – by occupation,
severely hit districts (IRM-2)
Figure 3.1:	Share of people who have borrowed since June 2015 – by district impact(IRM-1/IRM-2 comparison)
Figure 3.2:	Reasons for borrowing, share of those borrowing – by district impact (IRM-2)
Figure 3.3:	Sources of borrowing among those who borrowed (IRM-1/IRM-2 comparison)
Figure 3.4:	Unsuccessful borrowing attempts – by district (IRM-2)
Figure 3.5:	Monthly interest rates for different sources (IRM-1/IRM-2 comparison)
Figure 3.6:	Distribution of income – by urban/rural (IRM-2)
Figure 3.7:	Sources of borrowing among those who borrowed – by urban/rural (IRM-2)
Figure 3.8:	Monthly interest rates for different sources – by urban/rural (IRM-2)
Figure 3.9:	Distribution of income – by disabled/non-disabled (IRM-2)
Figure 3.10:	Distribution of income – by gender (IRM-2)
Figure 3.11:	Sources of borrowing among those who borrowed – by gender (IRM-2)
Figure 3.12:	Monthly interest rates for different sources – by gender (IRM-2)
Figure 3.13:	Distribution of income – by caste (IRM-2)
Figure 3.14:	Sources of borrowing among those who borrowed – by caste (IRM-2)
Figure 3.15:	Monthly interest rates for different sources – by caste (IRM-2)
Figure 3.16:	Share of people who have borrowed since June 2015 – by income band (IRM-2)
Figure 3.17:	Average amount borrowed per borrower (NPR) – by income band (IRM-2)
Figure 3.18:	Remittances as a main income source – by district impact(IRM-1/IRM-2 comparison)
Figure 3.19:	Changes in remittances received from abroad (IRM-2)
Figure 3.20:	Reasons for migration (IRM-2)
Figure 3.21:	Share of people migrating – by income band (IRM-2)
Figure 3.22:	Share of people selling assets – by district (IRM-2)
Figure 3.23:	Share of assets sold – by type of asset (IRM-2)
Figure 4.1:	Share of people receiving different types of aid – all districts(IRM-1/IRM-2 comparison)
Figure 4.2:	Share of people receiving different types of aid – severely hit districts (IRM1/IRM-2 comparison)
Figure 4.3:	Quantities of cash (NPR) among those who received cash,from all sources – by district impact (IRM-1/IRM-2 comparison)

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Aid and Recovery in Post-Earthquake Nepal

Figure 4.4:	Quantities of cash (NPR) among those who received cash – by district
impact and aid provider (IRM-2)
Figure 4.5:	Share of cash among top two needs now and in three months’ time (IRM-2)
Figure 4.6:	Source of aid – all districts (IRM-1/IRM-2 comparison)
Figure 4.7:	Source of aid – severely hit districts (IRM-1/IRM-2 comparisons)
Figure 4.8:	Share of people receiving government aid – by district impact(IRM-1/IRM-2 comparison)
Figure 4.9:	Share of people receiving NGO aid – by district impact(IRM-1/IRM-2 comparison)
Figure 4.10:	Share of people receiving INGO aid – by district impact(IRM-1/IRM-2 comparison)
Figure 4.11:	Satisfaction with aid providers – all districts, among those who received aid
only (IRM-1/IRM-2 comparison)
Figure 4.12:	Satisfaction with aid providers – severely hit districts, among those who
received aid only (IRM-1/IRM-2 comparison)
Figure 4.13:	Share of people who have received beneficiary card and whose house wasclassified as fully damaged – by district (IRM-2)
Figure 4.14:	Share of people who have received cash – by whether or not received
beneficiary cards (IRM-2)
Figure 4.15:	Share of people receiving different types of aid – by urban/rural (IRM-2)
Figure 4.16:	Sources of aid reported by those who received aid – by urban/rural (IRM-2)
Figure 4.17:	Satisfaction with aid providers among those who received aid –by urban/rural (IRM-2)
Figure 4.18:	Types of aid received – by gender (IRM-2)
Figure 4.19:	Sources of aid – by gender (IRM-2)
Figure 4.20:	Satisfaction with aid providers – by gender (IRM-2)
Figure 4.21:	Satisfaction with aid providers among those who received aid – by caste (IRM-2)
Figure 4.22:	Satisfaction with aid providers among those who received aid –by disabled/non-disabled (IRM-2)
Figure 5.1:	Priority needs (share in top two needs) – all districts (comparison IRM-1
immediate needs, IRM-1 three-month needs, IRM-2 immediate needs)
Figure 5.2:	Priority needs (share in top two needs) – severely hit districts (comparison
IRM-1 immediate needs, IRM-1 three-month needs, IRM-2 immediate needs)
Figure 5.3:	Where people are living now – by district impact (IRM-2)
Figure 5.4:	Type of temporary shelter – people living in temporary shelter only (IRM-2)
Figure 5.5:	Share of people reporting food as among their top two needs immediately
and in three months – by district (IRM-2)
Figure 5.6:	Food insecurity – by income band (IRM-2)
Figure 5.7:	Food insecurity – by urban/rural (IRM-2)
Figure 5.8:	Food insecurity – by caste (IRM-2)
Figure 5.9:	Food insecurity – by disabled/non-disabled (IRM-2)
Figure 5.10:	Did the fuel crisis affect the prices you pay for food types? (IRM-2)
Figure 5.11:	Did the fuel crisis affect the prices you pay for food types? – by foodsecurity (IRM-2)
Figure 5.12:	Satisfaction with public services (IRM-2)
Figure 5.13:	Dissatisfaction with services – individual panel data
(IRM-1/IRM-2 comparison)
Figure 5.14:	Dissatisfaction with public services – by urban/rural (IRM-2)
Figure 6.1:	Effect of the crisis on access to fuel for cooking and transportation (IRM-2)
Figure 6.2:	Effect of the crisis on access to fuel for cooking and transportation –by urban/rural (IRM-2)
Figure 6.3:	Effect of the crisis on access to fuel for cooking and transportation –by income band (IRM-2)
Figure 6.4:	Effect of the crisis on access to fuel for cooking and transportation –by disabled/non-disabled (IRM-2)

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Aid and Recovery in Post-Earthquake Nepal

Figure 6.5:	Effect of the crisis on access to fuel for cooking and transportation –by caste (IRM-2)
Figure 6.6:	Effect of the crisis on access to fuel for cooking and transportation –by current shelter (IRM-2)
Figure 6.7:	Sources of fuel during the crisis (IRM-2)
Figure 6.8:	Sources of fuel during the crisis – by urban/rural (IRM-2)
Figure 6.9:	Changes in fuel prices compared to before the crisis (IRM-2)
Figure 6.10:	Changes in fuel prices compared to before the crisis – by urban/rural (IRM-2)
Figure 6.11:	Changes in fuel prices compared to before the crisis – by income band (IRM-2)
Figure 6.12:	Changes in fuel prices compared to before the crisis – by current shelter(IRM-2)
Figure 6.13:	Did protests over the new Constitution and the fuel crisis affect aid
assistance in your ward – by district impact (IRM-2)

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Figure 7.1:	Who people voted for in the last election (IRM-2)
Figure 7.2:	Voting preference for next election (IRM-1/IRM-2 comparison)
Figure 7.3:	Satisfaction with local political parties – by district impact (IRM-2)
Figure 7.4:	Satisfaction with local political parties – by income band (IRM-2)
Figure 7.5:	Satisfaction with local political parties – by education level (IRM-2)
Figure 7.6:	Satisfaction with the central government – by district impact (IRM-2)
Figure 7.7:	Satisfaction with central government – by income (IRM-2)
Figure 7.8:	Satisfaction with central government – by education level (IRM-2)

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Figure 8.1:	How safe and secure do you feel now in your community? (IRM-2)
Figure 8.2:	How safe and secure do you feel now in your community? – by district
impact (IRM-2)
Figure 8.3:	How safe and secure do you feel now in your community? – by gender andurban/rural (IRM-2)
Figure 8.4:	Violence reported in community since the monsoon – by district impact(IRM-2)
Figure 8.5:	Do you think people of every caste, religion, and ethnicity are equallyable to receive aid according to their needs? – by district impact,
urban/rural, and gender (IRM-2)
Figure 8.6:	Do you think people of every caste, religion, and ethnicity are equally able
to receive aid according to their needs? – by ethnic group (IRM-2)
Figure 8.7:	Is aid being distributed by VDC/municipalities fairly? (IRM-2)
Figure 8.8:	Fair distribution by VDC/municipalities – by urban/rural (IRM-2)
Figure 8.9:	Fair distribution by VDC/municipalities – by ethnic group (IRM-2)
Figure 8.10:	Fair distribution by VDC/municipalities – by income band (IRM-2)
Figure 8.11:	Fair distribution by VDC/municipalities – by education level (IRM-2)
Figure 8.12:	Fair distribution by VDC/municipalities – by housing damage (IRM-2)

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Aid and Recovery in Post-Earthquake Nepal

LIST OF TABLES
Table 1.1:	Districts surveyed (IRM-2)

3

Table 2.1:	Main sources of income – by district impact and district (IRM-2)
Table 2.2:	Monthly income for each source of income (IRM-2)
Table 2.3:	Share of people within each income source whose income from that source
was affected by the earthquakes – by district impact and district (IRM-2)
Table 2.4:	Share of people within each income source whose income from that source
was affected – by income band and source of income (IRM-2)
Table 2.5:	Housing damage and livelihood damage – by district impact and district (IRM-2)
Table 2.6:	Share of people within each income source whose income from that source
has improved in the past three months – district impact and district (IRM-2)

8
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Table 3.1:	Reasons for borrowing, share of those borrowing – by district impact
19
and district (IRM-2)
Table 3.2:	Average amount borrowed (NPR) per borrower – by district impact
20(IRM-1/IRM-2 comparison)
Table 3.3:	Average amount borrowed (NPR) per borrower – by district impact and20
district (IRM-2)
Table 3.4:	Income distribution – by district impact and district (IRM-2)
21
Table 3.5:	Sources of borrowing among those who borrowed – by district impact
23(IRM-1/IRM-2 comparison)
Table 3.6:	Source of borrowing among those who borrowed – by district impact
23
and district (IRM-2)
Table 3.7:	Average loan amounts by source (NPR) – by district impact (IRM-2)
24
Table 3.8:	Intention to borrow in the next three months – by district impact
25
and district (IRM-2)
Table 3.9:	Monthly interest rates for different sources – by district impact and district (IRM-2) 26
Table 3.10:	Proportion borrowing and amount borrowed – by caste (IRM-2)
31
Table 3.11:	Sources of borrowing among those who borrowed – by income band (IRM-2)
34
Table 3.12:	Remittances as a share of main income source, impact on remittances, and35
recovery of remittances – by district impact and district (IRM-2)
Table 4.1:	Share of people not receiving aid – ​by district impact and district(IRM-1/IRM-2 comparison)
Table 4.2:	Average aid quantity among those who received that type of aid –by district impact (IRM-2)
Table 4.3:	Average aid quantity among those who received that type of aid –by district impact (IRM-1)
Table 4.4:	Average aid quantity among those who received that type of aid –by district (IRM-2)
Table 4.5:	Share of people who have received food aid – by district(IRM-1/IRM-2 comparison)
Table 4.6:	Share of people who have received shelter items – by district impact
and district (IRM-1/IRM-2 comparison)
Table 4.7:	Share of shelter items in top two current needs – by district impact
and district (IRM-2)

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Aid and Recovery in Post-Earthquake Nepal

Table 4.8:	Amount of cash received (NPR) and share who have received cash –by district impact, district, and source (IRM-2)
Table 4.9:	Source of aid – by district impact and district (IRM-2)
Table 4.10:	Satisfaction with aid providers, among those who received aid only –by district impact and district (IRM-2)
Table 4.11:	Damage assessment results and self-reported damage – by district impact
and district (IRM-2)
Table 4.12:	Share of people who have received beneficiary card – by classificationin damage assessment (IRM-2)
Table 4.13:	Satisfaction with damage assessment – by whether or not received
beneficiary cards (IRM-2)
Table 4.14:	Whether or not received beneficiary card – by satisfaction with damage
assessment (IRM-2)
Table 4.15:	Satisfaction with damage assessment – by how house was classified indamage assessment (IRM-2)
Table 4.16:	Types of aid received – by income band (IRM-2)
Table 4.17:	Share of people who have received cash and average amounts received(NPR) for those who received it – by government/non-government,rural/urban, and income band (IRM-2)
Table 4.18:	Sources of aid among those who received aid – by income band (IRM-2)
Table 4.19:	Satisfaction with aid providers among those who received aid –by income band (IRM-2)
Table 4.20:	Share of people who have received aid of different types –by caste groups (IRM-2)
Table 4.21:	Sources of aid – by caste groups (IRM-2)
Table 4.22:	Aid received – by disabled/non-disabled (IRM-2)
Table 4.23:	Aid received – by level of housing damage (IRM-2)
Table 4.24:	Volumes of government and non-government cash aid among those
who received it (IRM-2)
Table 4.25:	Share of people who have received aid from each source – by level of
housing damage (IRM-2)
Table 4.26:	Satisfaction with aid providers among those who received aid – by level of
housing damage (IRM-2)
Table 5.1:	Priority immediate needs (share in top two needs) – by district impact (IRM-2)
Table 5.2:	Priority three-month needs (share in top two needs) – by district impact (IRM-2)
Table 5.3:	No need for relief items and no aid received – by district impact(IRM-1/IRM-2 comparison)
Table 5.4:	Where people are living now – by district (IRM-2)
Table 5.5:	Were you able to make sufficient repairs to your shelter for the winter? –by district impact and district, those living in temporary shelters (IRM-2)
Table 5.6:	Share of people reporting shelter as priority immediate need –by district impact and district (IRM-2)
Table 5.7:	Share of people reporting shelter as priority three-month need –by district impact and district (IRM-2)
Table 5.8:	Share of people reporting shelter as priority immediate need –by level of housing damage (IRM-2)
Table 5.9:	Share of people reporting shelter as priority immediate need –by current shelter (IRM-2)
Table 5.10:	Share of people reporting shelter as priority immediate need –by urban/rural (IRM-2)
Table 5.11:	Share of people reporting shelter as priority immediate need –by disability (IRM-2)
Table 5.12:	Share of people reporting food as priority immediate need –by district impact (IRM-2)
Table 5.13:	Share of people reporting food as priority three-month need –by district impact (IRM-2)

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Aid and Recovery in Post-Earthquake Nepal

Table 5.14:	Food aid, borrowing, food consumption, and current need for food –by district impact and district (IRM-1 and IRM-2)
Table 5.15:	Food security sample (IRM-2)
Table 5.16:	Food aid, borrowing, consumption, and need for food – by level of foodinsecurity (IRM-2)
Table 5.17:	Priority immediate needs, besides food and shelter (share in top two needs)– by district impact and district (IRM-2)
Table 5.18:	Dissatisfaction with public services – by district impact and district (IRM-2)
Table 5.19:	Dissatisfaction with public services – by caste, gender, disability, andincome level (IRM-2)
Table 5.20:	Dissatisfaction with public services – by education level (IRM-2)
Table 6.1:	Effect of the crisis on access to fuel for cooking and transportation –by district impact and district (IRM-2)
Table 6.2:	Sources of fuel during the crisis – by district impact and district (IRM-2)
Table 6.3:	Sources of fuel during the crisis – by income band (IRM-2)
Table 6.4:	Sources of fuel during the crisis – by caste (IRM-2)
Table 6.5:	Sources of fuel – by current shelter (IRM-2)
Table 6.6:	Changes in fuel prices compared to before the crisis – by district impact anddistrict (IRM-2)
Table 6.7:	Did protests over the new Constitution and the fuel crisis affect aid
assistance in your ward – by district impact and district (IRM-2)
Table 7.1:	Who people voted for in the last election – by district impact,
urban/rural, and gender (IRM-2)
Table 7.2:	Who people voted for in the last election – by district impact and district (IRM-2)
Table 7.3:	Party preferences – by district impact and district (IRM-2)
Table 7.4:	Current political preferences – by past votes (IRM-2)
Table 7.5:	Changes in political preferences – by caste, selected responses (IRM-2)
Table 7.6:	Past political preferences – by intended future votes (IRM-2)
Table 7.7:	Who people will vote for based on current preferences – individual panel
data (IRM-1/IRM-2 comparison)
Table 7.8:	Who people will vote for based on past preferences – individual panel data
(IRM-1/IRM-2 comparison)
Table 7.9:	Satisfaction with local political parties – by district (IRM-2)
Table 7.10:	Satisfaction with local political parties – individual panel data
(IRM-1/IRM-2 comparison)
Table 7.11:	Satisfaction with the central government – by district (IRM-2)
Table 7.12:	Satisfaction with the central government – individual panel data
(IRM-1/IRM-2 comparison)
Table 7.13:	Share of people saying CA member has visited their area – by district
impact and district (IRM-2)
Table 8.1:	How safe and secure do you feel now in your community? – by district
impact and district (IRM-2)
Table 8.2:	How safe and secure do you feel now in your community? – by current
shelter (IRM-2)
Table 8.3:	Trust – by district impact, district, and urban/rural (IRM-2)
Table 8.4:	If public officials asked everyone to conserve water or share food becauseof some emergency, how likely is it that people in your community would
cooperate? – by district impact and district (IRM-2)
Table 8.5:	Trust – by income band (IRM-2)
Table 8.6:	Fair distribution by VDC/municipalities – by district impact
and district (IRM-2)
Table 8.7:	Fair distribution by VDC/municipalities – by district impact and district,individual panel data (IRM-1/IRM-2 comparison)

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Aid and Recovery in Post-Earthquake Nepal

Table A.1:	Distribution of sample and error margin for each of the districts

133

Table B.1:	Distribution of demographic and socio-economic characteristics – by
district impact and districts
Table B.2:	Distribution of types of disability – by district impact and district (IRM-2)
Table B.3:	Rural/urban and housing damage distribution – by district impact and district
Table B.4:	Distribution of current type of shelter – by district impact and district (IRM-2)

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Photo: Tenzing Paljor

<No data
Photo:
Tenzingfrom link>
PaljorAid and Recovery in Post-Earthquake Nepal

Chapter 1.
Introduction
Photo: Aruna Rana Thapa

1.1 BackgroundTwo major earthquakes hit Nepal in April and May
2015. The quakes are estimated to have affected thelives of eight million people—one-third of the country’s
population—with lives lost, houses destroyed anddamaged, and livelihoods impacted. One year on, how
are the earthquake-affected recovering? How effective
has the aid response been? And what needs remain?
This report provides data and findings from a large
household survey, conducted in 11 quake-impacted
districts from February-March 2016. It is part ofa larger longitudinal mixed methods study: theIndependent Impacts and Recovery Monitoring for
Accountability in Post-Earthquake Nepal (IRM, inshort). IRM involves multiple waves of household
surveying and in-depth qualitative fieldwork. This
report provides quantitative findings from the second
wave of surveying. It is published in parallel witha report outlining the qualitative data and a reportsynthesizing findings.1

1

The Asia Foundation and Democracy Resource Center Nepal(2016). Aid and Recovery in Post-Earthquake Nepal: Independent
Impacts and Recovery Monitoring Nepal Phase 2 – Qualitative
Field Monitoring (February and March 2016). Kathmandu andBangkok: The Asia Foundation; The Asia Foundation (2016).
Independent Impacts and Recovery Monitoring Nepal Phase 2(February and March 2016) – Synthesis Report. Kathmandu andBangkok: The Asia Foundation.

The IRM project was premised on the belief that the
impacts of major disasters such as the Nepal earthquakes do not only manifest immediately but play out
in complex and multidimensional ways over the longer
run. Many of the direct impacts—deaths and injuries,
decimated houses, and physical infrastructure—areimmediately apparent. A quantification of such damages and losses was conducted by the Nepal government and its partners in the form of a Post-Disaster
Needs Assessment (PDNA)2 and the first round of IRM
also provided information on the level of destruction.3
Yet disasters affect populations in many other waysthat often take time to appear. Social, economic, andpolitical structures and institutions can be affectedby such massive shocks. Understanding these deeper
impacts requires tracking levels of recovery, and things
that are supporting or hindering it, over time.
The extent to which earthquake impacts endure, andtheir seriousness, will be affected by two things. First,

2

Government of Nepal (2015). Nepal Earthquake 2015: PostDisaster Needs Assessment (Volume A: Key Findings), Kathmandu:
National Planning Commission (available at: http://www.npc.gov.
np/images/download/PDNA_Volume_A.pdf)

3

The Asia Foundation (2015). Aid and Recovery in Post-Earthquake Nepal: Independent Impacts and Recovery Monitoring
Nepal Phase 1 – Quantitative Survey (June 2015). Kathmandu andBangkok: The Asia Foundation.

1

Introduction

Photo: Kushal Puri

the aid response, which has aimed to address immediate emergency needs while helping people recover.
Second, the coping mechanisms and strategies employed by affected individuals. To understand why andhow recovery is occurring or not, and how this differs
across different population groups, it is necessary toassess what aid is going in to the earthquake zone,
and the extent to which it matches with needs. It isalso necessary to track what community members
are doing to recover. Such information allows for assessments to be made of current and potential futurechallenges and needs. It can also enhance the responseof the government, donors, and others aiming to support recovery.
A third factor has shaped recovery in post-earthquake
Nepal. Following protests over a new contested Constitution, promulgated in the wake of the earthquakes,
a fuel crisis hit Nepal. Between September 2015 andFebruary 2016, fuel imports from India fell dramatically, with groups blockading the border. This additional shock had multifold impacts in earthquake-affected areas as prices rose and aid delivery slowed.
This report aims to paint a picture of the situation
almost one year on from the earthquakes (IRM-2).
Throughout, comparison is made with data andfindings from the first wave of monitoring, conductedin June 2015 (referred to throughout this report as2

IRM-1). Future waves of surveying and fieldwork areplanned for August 2016 and February 2017.

Focus areas
The report focuses on a number of areas:• T he impact of the earthquakes on people’s
livelihoods and the extent to which livelihoods
are recovering (Chapter 2);• The coping strategies employed by the affected(Chapter 3);• The nature of the aid response since the beginning of the 2015 monsoon season and levels ofsatisfaction with aid providers (Chapter 4)• Current needs in earthquake-affected areas andsatisfaction with services (Chapter 5);• The impacts of the fuel crisis (Chapter 6);• Changes in local politics since the earthquake(Chapter 7);• Security and social relations in affected areas(Chapter 8).
The report concludes with a summary of the main
findings and a discussion of some of their implications.
Annexes provide more details on the methodology
employed.

Aid and Recovery in Post-Earthquake Nepal

1.2 Methodology and approach

Map 1.1: Location of surveyed districts

Sampling
The IRM-2 survey involved face-to-face interviews
with 4,850 respondents (plus surveys with 305 ward
leaders). These were conducted in 11 districts, all ofwhich were covered in the IRM-1 survey (Map 1.1).
The IRM-1 survey was conducted in 14 districts. Threeof these districts were dropped for IRM-2. IRM-1
was conducted before the PDNA was released andselection of districts was made from the 26 districts
initially deemed affected by the government. Three ofthe selected districts (Manang, Khotang, and Dang),surveyed in IRM-1, were subsequently not includedin the PDNA’s classification of earthquake-impacted

districts. As such, they were not part of the sample for
the IRM-2 survey.
The IRM-1 survey involved the stratification of districts
into three categories—high, medium and low impact—
based on initial government estimates of housing
damage. The PDNA later came up with new categories.
The eleven remaining IRM survey districts fall into
four of these categories (Table 1.1). Throughout thereport, we use these PDNA classifications whenpresenting the data. (Severely hit districts are thosedeemed most affected; moving towards the right inthe table, districts are less affected).

Table 1.1: Districts surveyed (IRM-2)
Severely hit

Crisis hit

Hit with heavy losses

Hit

Ramechhap
GorkhaSindhupalchowk
Nuwakot
DhadingOkhaldhunga
Bhaktapur
Kathmandu

Solukhumbu
LamjungSyangja3

Introduction

Photo: Amanda Gurung

IRM-1 involved stratified randomized identificationof respondents, ensuring that the survey results wererepresentative of the full population in earthquakeaffected areas. Because we aim to assess changes
since IRM-1, we sought to re-interview those people
who were interviewed in IRM-1. This was not always
possible: some people had moved away or were notavailable at the time the enumeration teams visited
selected wards. In total, 1,558 people were interviewed
in both IRM-1 and IRM-2.
When IRM-1 was released, it was clear there was anappetite amongst policy-makers and practitioners
for district-disaggregated data. IRM-1 allowed forthis. But sample sizes were not sufficient to generate
precise information on how districts compared with4each other. As a result, IRM-2 bolstered the sample
size within each district with a minimum of 350 peopleinterviewed per district. This involved selecting an
additional 67 wards, to complement the 238 surveyed
in IRM-1. This allows for a margin of error of +/5.2% for district-disaggregated analyses. Across thewhole sample, the error margin is +/- 1.4% at a 95%
confidence interval. It should be noted that the larger
sample size allows for more accurate estimates than
was possible in IRM-1, and that the margins of error
are very small compared to most surveys, in Nepal
and beyond.
Another concern of policy-makers was the food(in)security situation. The Nepal Food Security
Monitoring System (NeKSAP) collects monthly dataAid and Recovery in Post-Earthquake Nepal

from local leaders that allows them to track changes
in such insecurity. To help verify this, and to see
how food insecurity is linked to other measures ofvulnerability, IRM-2 boosted the sample in four
districts (Sindhupalchowk, Ramechhap, Gorkha, andOkhaldhunga) with NeKSAP data used to help selectedwards to be surveyed.
The boosting was done as follows. The 1,400
households in the main sample (350 per district foreach of the four districts) were first classified per
NeKSAP into four categories: minimally food insecure;moderately food insecure; highly food insecure;and severely food insecure. Following this, 250
households were added per district in order to create
a total food security sample of 600 households per
district, with an even representation across all relevant
NeKSAP classifications for the district. The additional
250 households were added using a random sampling
method, based on a list of households corresponding
to each NeKSAP classification within the district.
Analysis of this food security data is presented in
Chapter 5.

Analysis
The rich survey data is used in a number of ways
throughout this report.
First, for many analyses we compare the IRM-1 andIRM-2 data at the aggregate level, allowing for anassessment of changes over time. The IRM-2 survey
was deliberately designed to mirror the IRM-1
instrument, with many of the questions remaining
the same. This allows direct assessment to be madeof changes over time. The IRM-1 survey tracked
attitudes, perceptions, and experiences two months
after the disaster. Many of the IRM-2 questions ask
about changes and experiences since the IRM-1 survey
was conducted. Most of the IRM-2 questions record
information on what has happened since then, withthe beginning of the 2015 monsoon period (June2015) used as the time marker. For some areas, newquestions were added to the IRM-2 survey, to explore
further issues that came up in IRM-1 or to assess newissues, such as the fuel crisis. For these questions, datafrom IRM-2 is presented alone.
Second, because 1,558 households were interviewed
in both IRM-1 and IRM-2, we can assess with more
rigor how individuals’ perceptions and experiences
have changed over time. Some of the analyses in thereport draw on data from this household panel.
Third, many of the analyses and data breakdowns
compare aggregate responses from each of the PDNA
impact categories: severely hit district; crisis hit ones;
hit with heavy losses districts; and the hit district.

These analyses provide a broad-brush picture of thedifferences (and similarities) between districts withvarying degrees of earthquake impact.
Fourth, and as discussed above, most of the analyses
are also broken down by individual districts. Each
district has experienced the earthquake, and the aid
response, differently. These granular analyses allow
for an exploration of how districts vary, say in aid
received, in coping strategies employed, in attitudes
towards local leaders. This level of disaggregationmeans that, at times, the report gets into detailed
analysis of the situation in specific districts. While
this may somewhat disrupt the narrative flow, andpoint away from some of the ‘big picture messages’,
we believe the analyses will be useful, in particular for
those working in particular districts.
Fifth, analyses of the data are broken down by a hostof demographic and geographic variables. Different
groups of the population (men/women; people ofdifferent caste; people with different incomes; etc.)
will likely have experienced the earthquake in different
ways. And structural factors, related to these demographic factors, will also likely shape opportunities
and constraints for recovery. Similarly, impacts andaid will likely differ between urban and rural areas.
Disaggregating analyses by all these variables allows
for a much finer assessment of differing patterns ofimpacts and recovery. The analyses provide information on which groups of people are more vulnerable.

Limitations
The survey data presented here is a result of a careful
and methodical sampling design. The results are
representative of the full population of the 11 surveyed
districts. The survey was piloted to ensure that
respondents understood questions and adjustments
were made where necessary. As noted, the large
sample size means that the estimates in the report are

exceptionally accurate when compared to many othersurveys, meaning we can have strong confidence thatthe findings are true to reality.
However, and as with all surveys, caution should
be taken when interpreting findings. Quantitative
research has both strengths and weaknesses.

5Introduction

First, surveys provide useful information on thesituation of large numbers of people, selected suchthat findings can be generalized across the broader
population. However, surveys are less well equipped to
explain the underlying factors that determine different
situations and attitudes – for example, why people feelsafe or have not received aid.4
Second, information provided throughout the report isbased on the reports of those interviewed. People may
have incentives to over- or under-report the level ofimpact they experienced, whether or not they received
aid, and so on. Many may not have full knowledgeof the situation (e.g. who provided aid or whether

4

Throughout this report, we present correlations between outcome
variables and factors that may be associated with them (for example,
whether people received aid and the extent to which their house
was damaged by the earthquakes). But even where we find close
correlations, this does not mean that one causes the other.

6

politicians have visited their wards). The data andfindings should be read with this in mind.
Third, some questions, such as whether violence has
occurred or which party people plan to vote for, aresensitive and some may prefer not to answer them.
The IRM-2 synthesis report, published separately,
combines information from both the quantitative
survey and the in-depth qualitative fieldwork. This
allows for a triangulation of findings and a deeper
exploration of causal relationships – i.e. what is
driving recovery.

Aid and Recovery in Post-Earthquake Nepal

Chapter 2.
Livelihoods
Photo: Aneta Buraityte

The earthquakes had major impacts on people’s
livelihoods and sources of income. IRM-1, conductedin June 2015 shortly after the earthquakes hit, foundthat all occupational groups had suffered negative
effects. Business people and daily wage laborers wereparticularly affected but farmers were also impacted.
Almost one year on from the earthquakes, to what
extent have livelihoods recovered?

This chapter provides information on the level ofimpacts to different occupational groups and on thelikelihood that they have been able to recover. It also
considers how livelihood impacts have differed forgroups within the population and analyzes the extent
to which livelihoods impacts and housing impacts
are related.

Key findings:
Impacts of the earthquake on income• Fifty-seven percent of people report that theirlivelihoods were affected by the earthquake.
Seventy-six percent were totally or somewhat
affected in the severely hit districts, 56% in crisis
hit districts, 34% in hit with heavy losses districts,
and less than 10% in the hit district.

differences in livelihoods impacts between men
and women or between those with and without
disabilities.
Links between livelihoods and housing
impacts• T he livelihoods that were most likely to beaffected are businesses (72%), daily wages (59%),
and farming on own land (53%).

• There is a high correlation between the impactsof the earthquake on housing and on livelihoods
at the district level. This suggests that the earthquake impact categories are reflective of damage
to sources of income in addition to property.

• Livelihoods in urban areas were more likely to beaffected than in rural areas. Apart from farmers,a larger share of people within each occupation/
source of income have been negatively impacted
in urban areas.

• In the severely hit districts, housing damage
was more widespread than livelihoods damage.
In most other districts, a greater proportion ofpeople saw negative impacts on their livelihoods
than on their houses.

• Lower caste groups have suffered slightly lower
impacts to livelihoods relative to higher caste
groups and Janajatis. There are no discernible• S indhupalchowk saw the greatest damage interms of both livelihoods (97%) and homes (91%).

7

Livelihoods

Recovery• Remittances, although an income source for only
a very small share in severely hit districts, has
improved to the greatest extent (57%) in these
districts.

• B usinesses are recovering more than otherlivelihoods.
• Severely hit districts have the slowest rate ofrecovery for those who farm their own land andlivestock farmers compared to other districts.
However, business and wage labor, the next
two most common occupations, have seen morewidespread recovery in severely hit districts thanelsewhere.

• Damages to wage work and livestock rearing havetended to have a larger impact on lower income
segments.

2.1 People’s occupations and income in the earthquake zone
Farming is the predominant occupation in earthquakeaffected areas (Table 2.1). Overall, farming, primarilyon their own land, is a major source of income for 83%
of people.5 Eighteen percent generate income fromlivestock farming and 17% through business. Other
occupations all have much smaller shares (1-3%).
Farming accounts for a larger share of people’s sourcesof income in the severely hit districts. Nevertheless,the proportion of those whose generate income fromfarming is high and consistent across districts (86%and above), with the exceptions of Lamjung (74%), and
Bhaktapur (65%). Kathmandu is the major exception
with just 10% generating income from farming. In thenational capital, 63% gain income from business, 19%from salaried work, and 9% from daily wage labor.
Sixteen percent of households receive remittanceincomes, with the lowest shares in Kathmandu
and Bhaktapur. The highest shares are in Syangja,
Dhading, and Lamjung.

5

District

Farm own land

Farm another’s land

Daily wage

Business

Remittances

Private salary

Government
salaryPension

Rent

Livestock

Table 2.1: Main sources of income – by district impact and district (IRM-2)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

88%
86%
74%
93%
97%
90%
53%
56%
9%
95%
80%
70%
90%
91%
91%
77%

7%
3%
12%
1%
11%
6%
6%
9%
1%
9%
3%
4%
2%
3%
3%
6%

16%
6%
19%
10%
15%
30%
10%
7%
9%
12%
8%
12%
4%
13%
13%
13%

10%
11%
15%
4%
8%
13%
33%
29%
63%
6%
11%
11%
11%
9%
9%
17%

15%
24%
15%
13%
15%
10%
8%
6%
6%
13%
17%
23%
11%
41%
41%
16%

5%
10%
6%
1%
4%
6%
16%
23%
19%
7%
2%
4%
1%
3%
3%
8%

6%
8%
9%
4%
5%
7%
8%
12%
5%
7%
9%
12%
6%
9%
9%
8%

5%
7%
7%
1%
6%
3%
6%
10%
3%
4%
6%
11%
1%
10%
10%
6%

1%
0%
3%
0%
1%
1%
6%
9%
9%
0%
1%
1%
1%
0%
0%
2%

29%
3%
11%
14%
87%
33%
7%
10%
0%
11%
12%
24%
1%
3%
3%
18%

People could choose multiple sources of income.

8

Aid and Recovery in Post-Earthquake Nepal

Photo: Kushal Puri

Table 2.2 shows the share of people who generate
income from different sources with different levels of
income. Within each group there is a wide distribution
in the amount of income earned. Approximately 86%
of those who generate income from farming theirown land and 75% of wage workers, along with 60%
who are engaged in livestock rearing, had incomes in
the range of NPR 2,501-19,999 per month before theearthquakes hit.6 Large shares of those involved inlivestock farming, daily wage labor, or who generate
incomes from rent or pensions, are amongst thepoorest. In contrast, 68% of those who gain income
from business, 72% who work in government, and 69%
who are privately employed have incomes clustered in
the range NPR 10,000-39,000 at the higher end of theincome spectrum.

Table 2.2: Monthly income for each source of income (IRM-2)76

Main sources of income

<NPR
2,500

NPR
2,501
-9,999NPR
10,000-19,999

NPR
20,000 39,999>NPR
40,000Refused

Don’t
Know

Farming own land
Farming other's land
Daily wages
Own business
Remittance
Wages from private company
Wages from government service
Pension
Rent
Livestock farming
Total

3%
2%
12%
7%
5%
6%
4%
12%
12%
20%
4%

43%
31%
31%
13%
24%
12%
14%
32%
24%
20%
37%

43%
52%
44%
41%
39%
49%
51%
29%
41%
40%
43%

10%
8%
10%
27%
27%
20%
21%
21%
12%
20%
13%

1%
2%
0%
9%
4%
10%
8%
6%
12%
0%
2%

0%
0%
0%
1%
0%
0%
0%
0%
0%
0%
0%

1%
6%
4%
3%
1%
4%
1%
0%
0%
0%
1%

Throughout this report, incomes reported are those in the immediate period before the first earthquake. Clearly, incomes may havechanged for many since the earthquakes.

7

For some of the tables, numbers add up to 99% or 101%. This isa result of rounding.

9Livelihoods

2.2 The impacts of the earthquakes
on livelihoods and income
Overall, 57% said that their main sources of income
were affected by the earthquakes, with shares affected
declining with each successive impact category. 8
Seventy-six percent of people were affected in theseverely hit districts, 56% in the crisis hit districts,
34% in hit with heavy losses districts, and less than
10% in Syangja, the hit district (Figure 2.1).

Despite this, only 0.6% of the total population, and 1%
or less in each district, have changed their livelihoods
in response (see the in-depth analysis of coping
strategies in Chapter 3).

figure 2.1

Figure 2.1: Share of people with at least one income source affectedby the earthquakes – by district impact (IRM-2)

57%

All districts

Hit

9%

34%

Hit with heavy losses

56%

Crisis hit

76%

Severely hit
0%20%

As was the case two months after the earthquake(IRM-1), those who work in business or who are daily
wage labors are the most likely to say their income
was negatively affected (Figure 2.2). Given that many
wage laborers earned relatively little (Table 2.2 above),this suggests that this group may be particularly
vulnerable. Over half of those who make money from

8

This includes people who said their income was either severely
or somewhat affected.

10

40%

60%

80%

renting property or from farming their own land also
report that their income was negatively impacted.
Those who work for the government and, especially,
those for whom remittances is a primary source ofincome, were less likely to see their income negatively
affected. These people also tend to have more income
than many others.

Aid and Recovery in Post-Earthquake Nepal
figure 2.2

Figure 2.2: Share of people whose source of income was affectedby the earthquakes – by source of income, all districts (IRM-2)
40%

Livestock

56%

Rent

25%

Government service
Private company

41%
9%Remittance72%

Own business

59%

Daily wages

43%

Farming other land

53%

Farming own land
0%

20%

40%

The picture changes somewhat if we focus on the districts most affected by the earthquakes (the severely
hit districts). In these places, a much larger proportion of people saw their income negatively affected(Figure 2.3). In particular, farmers were much more
figure 2.3

60%

80%

likely to see negative impacts on their income than inmost other districts. Given that 89% of people gain
their income from farming in severely hit districts, it
is clear that the livelihoods impacts were extremely
widespread in these areas.

Figure 2.3: Share of people within each income source whose income from that source
was affected by the earthquakes – by source of income, severely hit districts (IRM-2)
43%

Livestock

71%

Rent

25%

Government service
Private company

37%
12%

Remittance74%

Own business

69%

Daily wages

56%

Farming other land

75%

Farming own land
0%20%

Livelihoods in a number of districts were particularly
affected. Impacts on farmers’ incomes were especially high in Gorkha, Nuwakot, and Sindhupalchowk(Table 2.3). Kathmandu, which saw less physicaldestruction from the earthquake, also has particularly high occupational impacts across many sources

40%

60%

80%

of income. Syangja, in the lowest earthquake impact
category, was relatively insulated from negative effects
on people’s incomes.

11Livelihoods

Farmingother's land

Daily wages

Own business

Remittances

Privatecompany

Government
service

Rent

Livestock

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Farming
own land

Table 2.3: Share of people within each income source whose income from that source
was affected by the earthquakes – by district impact and district (IRM-2)9

75%
57%
76%
85%
69%
90%
41%
50%
52%
20%
33%
7%
60%
9%
9%
53%

56%
11%
56%
40%
85%
86%
33%
21%
75%
3%
8%
15%
0%
0%
0%
43%

69%
38%
77%
75%
72%
83%
60%
65%
75%
40%
20%
9%
31%
11%
11%
59%

74%
72%
68%
64%
79%
87%
72%
86%
86%
43%
44%
23%
65%
6%
6%
72%

12%
8%
13%
9%
18%
12%
12%
14%
20%
2%
5%
3%
8%
6%
6%
9%

37%
44%
20%
20%
50%
50%
39%
48%
60%
9%
25%
0%
50%
0%
0%
41%

25%
36%
23%
15%
32%
22%
31%
47%
26%
20%
26%
7%
45%
3%
3%
25%

71%
100%
33%
50%
100%
59%
53%
66%
33%
0%
66%
57%

43%
20%
49%
27%
40%
77%
20%
44%
0%
16%
52%
4%
100%
17%
17%
40%

Note: Data on pensions affected unavailable from the survey, so are omitted.

Overall, livelihoods in urban areas were worse affected. The share of urban households reporting thatlivelihoods were somewhat or totally affected by theearthquake is 66% against 55% in rural areas. Farmers figureof all2.4
types were more likely to have seen negative

impacts on their income in rural areas (Figure 2.4).
However, for all other occupations, a larger share ofpeople within each occupation/source of income were
negatively impacted in urban areas.

Figure 2.4: Share of people within each income source whose income from that source
was affected by the earthquakes – by urban/rural (IRM-2)
Livestock

33%

40%
53%

Rent
34%

Private company
23%

Government service
Remittance

8%

59%

56%

35%

12%
65%

Own business
58%

Daily wages

82%
69%

44%
42%

Farming other's land
Farming own land

47%

0%

15%
Rural

30%

45%

54%

60%

75%

90%

Urban
9

The table shows the people within each occupation/source ofincome in each district whose income was negatively affected.

12Aid and Recovery in Post-Earthquake Nepal

Who suffered damage to their livelihoods?
Income. There are differences in whether the richestor poorest were more likely to have their incomesaffected across different occupations/sources ofincome (Table 2.4). In the category of business, where
72% reported their livelihoods were affected, thelikelihood of being affected is highest in the top-mostincome bracket (81%) and the lowest income bracket(79%), with fairly high impacts across other income

brackets (62%-76%). This suggests that the earthquake
affected both large and small businesses. A similar
pattern can be observed for daily wage laborers, with
the impacts most widely felt amongst the very poorest
and the richest. Within livestock rearing, more people
with relatively lower income before the earthquake(NPR 2,501-20,000) were affected.

Daily wages

Own business

Remittances

Government
salaryPrivate Salary

Rent

Livestock<NPR 2,500
NPR 2,501 - 9,999
NPR 10,000 - 19,999
NPR 20,000 - 39,999> NPR 40,000
Total

Farmingother's land

Monthly income

Farming
own land

Table 2.4: Share of people within each income source whose income from that source
was affected – by income band and source of income (IRM-2)

36%
53%
57%
43%
50%
53%

0%
40%
50%
35%
100%
43%

78%
57%
59%
60%
100%
59%

79%
62%
72%
76%
81%
72%

27%
6%
9%
11%
10%
9%

57%
36%
44%
36%
57%
41%

75%
25%
25%
15%
36%
25%

82%
82%
55%
28%
57%
56%

19%
47%
41%
24%
13%
40%

Disability. There is not much difference in the proportion of disabled and non-disabled people whose
occupations were negatively affected: 56% of theformer have seen adverse income impacts versus 58%
of the latter.

Gender. Men and women are equally likely to say
their occupation was negatively impacted (both 57%).
Caste. Janajatis and those belonging to a higher caste
are more likely to have had their livelihoods affected(59% and 56%, respectively) than lower castes (40%).

2.3 Links between housing and livelihoods damage
IRM-1 provided extensive information on the damage
the earthquakes did to people’s houses.10 To what extent were occupations and income sources affected in
districts that also saw high levels of housing damage?
Overall, there is a high correlation on the impact of
the earthquake on housing and livelihoods at the
district level (66%).11 This suggests that the earthquake
impact categories are reflective of damage to sourcesof income in addition to property.
Housing damage is reported by a larger share of the
population (66%) than livelihood damage (57%).
10

See Table 4.11 for an updated breakdown of housing damage by
district.
11

Housing damage refers to those homes that were reported by

There is variation, however, between districts in theextent to which they saw both housing and livelihoods
impacts (Table 2.5). In the severely hit districts, housing damage was more widespread (with 94% of houses
affected) compared to livelihoods damage (76%). This
was also true for Lamjung (in the hit with heavy losses
category), Okhaldhunga (crisis hit), and Syangja (hit).
For all other districts, a greater proportion of people
saw negative impacts on their livelihoods than on theirhouses. In these districts, many people whose houses
were not damaged have been impacted by the earthquake through negative effects on their livelihood.

respondents as fully or badly damaged; livelihood damage refers to
those livelihoods that were reported as being totally or somewhat
affected by the earthquake.

13

Livelihoods

Table 2.5: Housing damage and livelihood damage – by district impact and district (IRM-2)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Housing damage

Livelihood damage

94%
97%
89%
97%
90%
97%
49%
60%
26%
61%
44%
35%
52%
21%
21%
66%

76%
57%
71%
82%
76%
91%
55%
65%
75%
27%
34%
10%
59%
9%
9%
57%

Sindhupalchowk experienced the greatest damage
in terms of both livelihoods (97%) and homes (91%).
There has been a greater flow of aid to this district thanothers (see Chapter 4), consistent with greater needs.
Among the other severely hit districts, livelihoods in
Dhading were the least likely to be affected (57%),
followed by Gorkha (71%), despite these districts
showing a high degree of damage to housing (97% and89%, respectively). These districts have a much higher
share of non-agricultural income sources—business,government service, pensions, and remittance income—compared to the other severely hit districts; inthe case of Gorkha, there is also a higher share of wage
workers. Given that the likelihood of agriculture as anincome source being impacted was significantly higher
in the severely hit districts than in the overall sample,12
the greater diversity in income sources in these two
districts is likely to have mitigated the impacts.
In Kathmandu, far fewer households report that theirhouse was damaged than report that their sources ofincome were negatively impacted. Only 26% of homes
were damaged, compared to 75% of livelihoods affected. Within the same category of impact, 61% of houses
in Okhaldhunga were affected, compared to just 27%
of livelihoods. The sharply differing levels of housing
damage between Kathmandu and Okhaldhunga appears to be the result of more resilient materials being
used for home construction in Kathmandu. Fifty-five

percent of houses in Kathmandu use pillared walls
compared to none in Okhaldhunga; 96% of homes in
Okhaldhunga have walls constructed with mud mortar
compared to less than 1% in Kathmandu. Further, 78%
of homes have concrete roofs in Kathmandu versus
none in Okhaldhunga; and 49% of homes in Okhaldhunga have hay roofs compared to none in Kathmandu. There is strong suggestive evidence that thequality of housing materials, within the same categoryof impact, makes a significant difference to the overall
extent of damage to housing at the district-level.13
Figure 2.5: Housing damage andfigure2.5
livelihood
damage – by urban/rural (IRM-2)
80%
71%
66%

60%

40%

55%

38%

20%

0%

Urban

Rural

12

These are significant by a t-test, which examined the difference
in means of damage in agriculture versus damage in all othercategories of livelihoods, for the severely hit districts and for the
total sample.
13This confirms findings from IRM-1.

14

Housing damage

Livelihood damage

Aid and Recovery in Post-Earthquake Nepal

There is also a distinctly different livelihood profile between the two districts. Ninety-four percent of people
in Okhaldhunga are engaged in agriculture against 8%
in Kathmandu; 61% work in business in Kathmandu
against 1% in Okhaldhunga. Given businesses are themost likely to report livelihood damage overall, it is
not surprising that Kathmandu has more widespread
damage to livelihoods.
Overall, rural areas suffered a far greater share of
housing damage (71%) than urban areas (38%) –Figure 2.5. The evidence on the use of different
housing materials leading to different housing damage

outcomes is supported here. Eighty-four percent inrural areas had homes with walls made with mud
mortar against only 14% in urban areas; and 93%
in urban areas used either galvanized or zinc roofs
against only 63% in rural areas.
In contrast, urban areas experienced more widespread
livelihood damage than rural areas (66% versus 55%),
although the difference is less stark. The higher shareof damage to urban livelihoods is likely a result of more
people being engaged in businesses, private company
employment, and wage employment relative to rural
areas.

2.4 Recovery of livelihoods
Among the three major livelihoods—farming, livestock-rearing, and businesses—businesses have recovered the most in the last three months (Figure 2.6).14
Seventy percent of those in business whose occupation
was negatively affected report that their livelihood has
improved
over the past three months compared to 57%
figure 2.6

engaged in livestock rearing and 48% who farm theirown land. Even larger proportions of those who workin government or who are daily wage workers, whose
income was affected, have seen improvements in thelast three months. But far fewer people work in theseoccupations (2.3% and 2.7%, respectively).

Figure 2.6: Share of people within each income source whose income from that source
has improved in the past three months – by source of income, all districts (IRM-2)
57%

Livestock

65%

Rent

77%

Government service

65%

Private company

53%

Remittance70%

Own business

74%

Daily wages

41%

Farming other land

48%

Farming own land0%20%

Looking only at the districts most affected by theearthquake, it is clear that businesses and daily wage
labor are recovering more than farming and livestock
rearing, the dominant occupations in these districts(Figure 2.7). In the severely hit districts, 75% of people
working in business, and the same figure for daily wage
14

Time period: first quarter of 2016.

40%

60%

80%

laborers, saw negative impacts on their livelihood.
However, more than three-quarters of each grouphas seen improvements in the past three months.
In contrast, similar proportions of farmers were
negatively affected. But only 39% of those who farmtheir own land, and 38% of those who farm others,
have seen improvements in the last three months. Of
47% who rear livestock in these districts, just over halfreport improvements.

15

Livelihoods

figure 2.7

Figure 2.7: Share of people within each income source whose income from that source
has improved in the past three months – by occupation, severely hit districts (IRM-2)
Livestock

54%

47%

Rent

75%

53%

Government service

70%

26%

Private company

68%

40%

Remittance

59%

12%

Own business

78%
75%

Daily wages

79%
75%

Farming other's land

38%

Farming own land

39%

0%

15%

30%

Share improved (if affected)

Remittance income, which is reported as a mainincome source by only a very small share in theseverely hit districts (1.3%), has improved to a greater

68%
76%

45%

60%

75%

90%

Share of livelihood affected

extent (58%) than in other districts. Overall, the hit
with heavy losses districts are showing the mostwidespread recovery across most livelihoods.

16

Farmingother's land

Daily wages

Own business

RemittancePrivatecompany

Government
service

Rent

Livestock

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Farming
own land

Table 2.6: Share of people within each income source whose income from that source has
improved in the past three months – district impact and district (IRM-2)

39%
34%
43%
58%
36%
23%
64%
78%
75%
39%
82%
76%
87%
46%
46%
48%

61%
100%
39%
100%
34%
33%
52%
57%
100%
0%
50%
50%
.
.
.
41%

77%
75%
84%
70%
74%
80%
57%
88%
46%
38%
88%
75%
100%
60%
60%
74%

81%
75%
86%
89%
96%
59%
71%
74%
61%
78%
79%
89%
69%
100%
100%
70%

58%
86%
43%
75%
60%
25%
39%
67%
50%
0%
33%
0%
67%
38%
38%
53%

74%
60%
50%
100%
88%
70%
75%
90%
35%
100%
100%
.
100%
.
.
65%

66%
70%
86%
50%
67%
60%
67%
100%
20%
80%
78%
67%
89%
0%
0%
77%

67%
0%
100%
.
100%
67%
64%
94%
33%
.
100%
.
100%
.
.
65%

62%
50%
89%
69%
48%
54%
88%
93%
.
83%
83%
67%
100%
0%
0%
57%

Aid and Recovery in Post-Earthquake Nepal

Chapter 3.
Coping Strategies
Photo: Amanda GurungThis chapter examines the various coping strategies—such as borrowing, inward remittances, andmigration—being used in earthquake-affected areas.

It examines the strategies different population groups(particularly those that are likely to be more vulnerable) use to recover from the earthquake.

Key findings:
Borrowing• Borrowing has massively increased since IRM-1.
More than twice as many people have borrowed
and average loan sizes have increased 240%.
Increases in the number of people borrowing,
and in loan sizes, are greatest in the severely hit
districts. People are predominantly borrowing forlivelihoods, food, and shelter.
• Borrowing appears to be used as a coping strategy
by a greater share of people in poorer districts.
However, there is evidence of clear credit constraints in these places with lower loan amounts
and higher loan refusals.
• T here are major credit constraints for somegroups. Loans for men are twice the size of thosefor women; high caste people’s loans are fourtimes the size of others’. The poorest are least
likely to take loans and the size of loans for therichest is seven times those of the poorest. Loansto the disabled are half the size of those to thenon-disabled.

• Formal loan sources are becoming more important. But the largest loans are still from moneylenders who charge the highest interest rates.
Moneylenders are particularly active in poorer
districts. Banks and cooperatives lend mainly inurban areas and higher income districts.
Remittances• R emittances have become more common asa key source of income but absolute levels ofremittances do not appear to have increased since
the earthquake.
Migration• Migration levels have been low (6%) and most of
those who migrated returned home.
Asset sales• Six percent of people have sold assets with mostof these being livestock. One-third of livestock
farmers have sold livestock.

17

Coping Strategies

3.1 Borrowing
How has borrowing changed?
Borrowing has more than doubled since the immediate
post-earthquake period (Figure 3.1). Whereas in June
2015, 19% had borrowed since the earthquake, 42%
report taking loans since the beginning of the 2015
monsoon (June 2015). In severely hit districts, morethan half have borrowed money since the beginningof the 2015 monsoon compared to one-quarter in thefirst few months after the disaster. Borrowing has also
more than doubled in the crisis hit and hit with heavy
impact districts. In the hit district of Syangja (the least
affected of the sampled districts), borrowing has risen
ten-fold: from 4% to 43%.

figure 3.1

Figure 3.1: Share of people who have borrowed since June 2015 – by district impact(IRM-1/IRM-2 comparison)

42%

Total

19%
43%

Hit

4%
25%

Hit with heavy losses

11%
37%

Crisis hit

17%
52%

Severely hit

25%

0%

15%
IRM-2

30%

45%

60%

IRM-1

What are people borrowing for?
Borrowing is most common for livelihoods, food,and shelter. The share of the population who haveborrowed in IRM-2 is 42%. Of this group, the largest
share (60%) borrowed to support their livelihoods,
which typically refers to the repair and replacementof damaged assets (Figure 3.2). This is true for all four
categories of district impact (Table 3.1). Seventeen
percent borrowed for farming or business inputs,
investments also related to livelihoods. Borrowing
to purchase food is the second most common reason(35% of borrowers took loans for food), across alllevels of impact, including in the least affected districtof Syangja. The fact that borrowing for food is sohigh in Syangja is surprising given that only 10% inthat district report that sources of income have been
affected, with the share being slightly less (9%) for
agriculture on own farms, the occupation of over 90%
in the district. One-quarter of people who borrowed
did so to rebuild their home (24% in severely hit

18

districts) and 20% to finance temporary shelter (31%in severely hit districts).
The highest share of borrowing among borrowers forlivelihoods is in Gorkha, Lamjung, Sindhupalchowk,
and Okhaldhunga; for food it is in Okhaldhunga,
Ramechhap, and Dhading; for temporary shelters
it is in Ramechhap, Sindhupalchowk, Nuwakot, andDhading; and for rebuilding homes it is in Gorkha,
Kathmandu, Dhading, and Solukhumbu (Table 3.1).

Aid and Recovery in Post-Earthquake Nepal

60%

62%

70%

65%

Figure 3.2: Reasons for borrowing, share of those borrowing – by district impact (IRM-2)

24%

23%

18%

Livelihoods

Severely hit

Food

Farming/business
Crisis hit

Hit with heavy losses

Migrate

5%

5%

0%

1%

0%

3%

1%

1%

1%

1%

1%

Rebuild
house

4%

9%

Temporary
shelter

1%

0%

1%

2%

3%

6%

7%

9%

18%

18%

21%

25%

30%

30%

31%

35%

37%

35%

39%

40%

53%

Home repairs Home-monsoon repairs-winter

Misc

Hit

Food

Farming /business

Temporary
shelterRebuild house

MigrateHome repairs monsoon

Home repairs winterMiscellaneous

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Livelihoods

Table 3.1: Reasons for borrowing, share of those borrowing – by district impact and district (IRM-2)

62%
48%
73%
53%
67%
69%
60%
47%
49%
68%
65%
73%
59%
39%
39%
60%

35%
44%
18%
33%
49%
26%
40%
18%
15%
54%
21%
27%
16%
37%
37%
35%

18%
20%
13%
15%
19%
19%
18%
24%
26%
13%
9%
16%
3%
25%
25%
17%

31%
27%
16%
34%
39%
34%
7%
6%
9%
6%
3%
3%
3%
2%
2%
20%

24%
38%
43%
27%
7%
15%
23%
33%
42%
15%
30%
26%
33%
30%
30%
25%

1%
2%
1%
1%
2%
0%
1%
4%
3%
0%
6%
0%
10%
1%
1%
2%

4%
0%
2%
11%
2%
4%
1%
1%
2%
1%
1%
0%
1%
1%
1%
2%

3%
1%
9%
1%
4%
1%
1%
1%
0%
2%
1%
3%
0%
0%
0%
2%

5%
6%
1%
9%
3%
4%
0%
1%
0%
0%
5%
12%
0%
9%
9%
4%

19

Coping Strategies

How much are people borrowing?
Borrowing volumes have grown substantially since theimmediate aftermath of the earthquake. Average sums
borrowed have increased from NPR 61,440 per person
who borrowed in IRM-1 to NPR 208,749 in IRM-2, ajump of 240% (Table 3.2).
As with shares of borrowing, loan amounts appear to
be related to the level of earthquake impact. Borrowing

is higher in severely and crisis hit districts (NPR
225,827 and NPR 200,229 on average for each person
who borrowed) relative to the lower impact categories(NPR 172,592 and NPR 167,070, respectively).
Volumes have increased the most in the severely hit
districts (402%), suggesting that credit is being sought
in increasing amounts to cope with the impacts of theearthquake.

Table 3.2: Average amount borrowed (NPR) per borrower – by district impact(IRM-1/IRM-2 comparison)
ImpactSeverely hit
Crisis hit
Hit with heavy losses
Hit
All districts

IRM-1 (NPR)

IRM-2 (NPR)% Increase

44,94187,545110,959
34,37561,440225,827
200,229
172,592
167,070
208,749

402%
129%
56%
386%
240%

There is, however, large variation in the amounts
borrowed between districts within each category of
impact (Table 3.3). Amongst severely hit districts, forexample, borrowers in Dhading have taken loans ashigh as NPR 645,171 on average, the largest amountof any district. In contrast, borrowers in Ramechhap,
also in the severely hit category, have borrowed only

NPR 90,809 on average, the lowest across all districts.
Kathmandu follows Dhading as the district with thelargest average loan size among borrowers (NPR
528,477), while the other two crisis hit districts have
far lower average borrowing amounts. Lamjung in thethird category ranks third in terms of average loan size
among borrowers.

Table 3.3: Average amount borrowed (NPR) per borrower – by district impact
and district (IRM-2)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Proportion borrowing

Average borrowing among
borrowers (NPR)

52%
52%
47%
43%
68%
51%
37%
22%
19%
70%
25%
21%
29%
43%
43%
42%

225,827
645,171
159,561
153,287
90,809111,522200,229
213,808
528,477
103,698
172,592
228,662
131,100
167,070
167,070
208,749

Ramechhap (a severely hit district) and Okhaldhunga(a crisis hit district), which have the highest shares ofborrowing, have among the lowest average borrowing

20

amounts. This suggests lower income levels and coping
capacity among households in these districts withmany borrowing but only eligible for smaller loanAid and Recovery in Post-Earthquake Nepal

amounts. Table 3.4 confirms that these two districts
have among the largest share of poor households:
the share of the population in the lowest two income

brackets is 53% for Okhaldhunga and 51% for
Ramechhap.

NPR 2,501 9,999

NPR 10,000 19,999

NPR 20,000 39,999> NPR 40,000

RefusedDon’t know

Severely hit
Dhading
GorkhaOkhaldhunga
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Nuwakot
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts<NPR 2,500

Table 3.4: Income distribution – by district impact and district (IRM-2)

4%
4%
1%
4%
8%
2%
6%
1%
12%
6%
1%
1%
0%
1%
1%
4%

41%
48%
28%
49%
43%
40%
27%
29%
5%
48%
39%
18%
61%
40%
40%
37%

45%
41%
61%
39%
40%
45%
38%
45%
35%
36%
44%
54%
35%
44%
44%
43%

9%
7%
9%
7%
8%
13%
18%
15%
30%
10%
14%
24%
4%
14%
14%
13%

1%
0%
1%
0%
1%
1%
5%
2%
14%
0%
2%
3%
1%
1%
1%
2%

0%
0%
0%
0%
0%
0%
0%
0%
1%
0%
0%
0%
0%
0%
0%
0%

0%
1%
0%
1%
0%
0%
4%
7%
4%
1%
0%
0%
0%
0%
0%
1%

Ramechhap and Okhaldhunga also have among thehighest reported rates of loan refusals, providing
further evidence of lower coping capacity (Figure 3.4
below). This suggests that the low amounts borrowed
per person is not on account of a lack of need formore credit but due to credit constraints facedby households. Table 3.8 below also shows thatRamechhap (79%) and Okhaldhunga (54%) are among
the districts reporting the greatest share of people who
intend to borrow in the next three months, once again
pointing to the need for credit in these districts. Credit
constraints in these two districts are of particularconcern since current borrowing is the highest for foodand, in the case of Ramechhap, for temporary shelters,
and among the highest for livelihoods. The inability
to access credit might be preventing access to basic
needs and the restoration of livelihoods.
Borrowing appears to be used as a coping strategy bymore people in districts where there are greater sharesof poor households; but the large proportion of poor
people leads to lower loan amounts.15

15

At the district level, the share of the population who have low
incomes (with pre-earthquake monthly incomes below NPR 10,000)
has a fairly large, positive correlation with the share that borrows

A notable exception is Solukhumbu. The district
has the largest proportion of poor households (61%)
but a low borrowing share (29%). The district also
reports a low share who intend to borrow over the
next three months (21%) (Table 3.8 below). Thisis despite fairly high damage to homes (52%) andlivelihoods (59%) (see Table 2.5 in the last chapter).
One possible explanation is that the district isbeing well served across aid items relative to other
districts in the lower impact categories and has thelowest share among these districts reporting “no aid
received” (5%) – see Chapter 4. Solukhumbu also
received the largest volume of non-government cash
aid per household (NPR 22,125 per person comparedto the sample average of NPR 12,172, although witha very low reported reach (1%) – Table 4.8 below.
This suggests that the domestic and international
NGO sector (including the Red Cross)—the sourcesof non-government aid cited by households in this
district—have been responsive to the vulnerability
that exists in this district because of the large shareof poor households.

(61%). There is also a smaller, negative correlation between theshare of the population who have a low income and average loan
size (-28%).

21

Coping Strategies

Who do people borrow from?
Sources of borrowing have changed since IRM-1
with a larger share of people turning to formal andsemi-formal sources of credit and a lower share using
informal sources (Figure 3.3).16 Relatives and neighbors
continue to account for a large share of lenders. But
there have been notable drops in the proportion ofborrowers who take loans from moneylenders. There
has also been a rise in the relative prominence of
figure 3.3

banks, savings groups, and other financial institutions
as lenders. This is a positive development given thehigher rates of interest charged by informal sources(Figure 3.5 below). However, it should be noted that
because more people are borrowing than in the IRM-1
period, the actual number of people taking loans frommoneylenders has increased: from 3.6% of people inIRM-1; to 5.5% in IRM-2.

Figure 3.3: Sources of borrowing among those who borrowed (IRM-1/IRM-2 comparison)
30%

29%

23%

21%

21%

23%
19%
15%

15%

15%

13%

11%

8%

FriendIRM-1

Relative

Neighbor Individual

2%

5%
2%
0%

Bank

Savings group

Coop

Finance Moneylenderinstitution

0%

Govt

IRM-2

In several districts with higher shares of the poor,moneylenders appear to be more important as a borrowing source. Dhading, Solukhumbu, and Ramechhap have the highest relative shares who borrow frommoneylenders (Table 3.6). These districts also have thehighest shares of population in the lowest two income
brackets (approximately 50% in each case and 60%
for Solukhumbu). Nuwakot and Okhaldhunga (alsoFormal financial institutions include banks, development banks,
micro-credit development banks, finance companies, microfinance
NGOs and cooperatives that are licensed and supervised by the
Nepal Rastra Bank, the central bank. Semi-formal institutions referto microfinance NGOs and cooperatives that are registered under

22

13%

5%

This pattern of increased use of formal sources can beseen across levels of impact with the exception of thecrisis hit districts, where borrowing from neighbors
has risen, and the hit with heavy losses districts, where
borrowing from neighbors and relatives has risen.
Syangja (the least affected district) is the only place
where the proportion of borrowers who take loans
from moneylenders has risen (Table 3.5).

16

13%

7%

1%

0%

19%

districts with high shares of poor populations – over50%) are exceptions, with the relative share using
moneylenders very low (3%). In both these districts,
other informal lending sources (friends, relatives, andneighbors) and savings groups are used to a greater
extent vis-à-vis other sources. These informal lending sources and savings groups tend to have higher
relative use within districts with larger numbers ofpoor people.
Cooperatives and banks, part of the formal lending
sector, appear to be more important as borrowing
sources in districts with lower shares of poor populations. Gorkha, Kathmandu, and Lamjung have thehighest relative shares that borrow from cooperatives
and banks. These districts correspond to those with thelowest shares in the bottom two income brackets (29%,various non-financial legislation and not supervised by the Nepal
Rastra Bank. Informal sources refer to moneylenders, relatives andfriends. From ADB report: http://www.adb.org/sites/default/files/
linked-documents/36169-02-nep-oth03.pdf

Aid and Recovery in Post-Earthquake Nepal

17%, and 19%). An exception is Bhaktapur, where,
despite a fairly low share of poor people compared toother districts (31%), moneylenders are an importantborrowing source. Similarly, the relative share of bankborrowing is the highest in Syangja, despite a higher
share of lower income populations (41%) comparedto other districts.

Table 3.5: Sources of borrowing among those who borrowed – by district impact(IRM-1/IRM-2 comparison)

FriendRelative
Neighbor
IndividualBank
Savings group
Cooperative
Finance institution
Moneylender

Severely hit

Crisis hit

Hit with
heavy losses

Hit

Total

8%
7%
32%
22%
23%
19%
1%
15%
2%
12%
12%
19%
4%
14%
3%
6%
17%
12%

19%
7%
28%
19%
16%
23%
2%
7%
1%
7%
16%
24%
5%
16%
0%
4%
18%
17%

16%
4%
12%
23%
20%
28%
0%
20%
4%
19%
10%
10%
10%
22%
0%
2%
31%
14%

0%
4%
50%
15%
38%
38%
0%
26%
13%
26%
13%
15%
13%
7%
0%
2%
0%
7%

11%
7%
29%
21%
21%
23%
1%
15%
2%
13%
13%
19%
5%
15%
2%
5%
19%
13%

IRM-1
IRM-2
IRM-1
IRM-2
IRM-1
IRM-2
IRM-1
IRM-2
IRM-1
IRM-2
IRM-1
IRM-2
IRM-1
IRM-2
IRM-1
IRM-2
IRM-1
IRM-2

Note 1: data on borrowing from government not available for IRM-1
Note 2: red = smaller proportion of borrowers borrowed from this source in IRM-2 compared to IRM-1
green = increased proportion of borrowers borrowed from this source in IRM-2 compared to IRM-1

Neighbor

IndividualBank

Savings
group

Cooperative

Financeinstitution

MoneylenderSeverely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Relative

Districts

FriendTable 3.6: Source of borrowing among those who borrowed – by district impact and district (IRM-2)

7%
11%
1%
10%
9%
5%
7%
11%
1%
10%
4%
3%
5%
4%
4%
7%

22%
20%
15%
31%
19%
26%
19%
20%
15%
31%
23%
12%
31%
15%
15%
21%

19%
5%
29%
24%
21%
20%
23%
5%
29%
24%
28%
11%
41%
38%
38%
23%

15%
23%
21%
11%
13%
10%
7%
23%
21%
11%
20%
26%
15%
26%
26%
15%

12%
18%
20%
11%
4%
9%
7%
18%
20%
11%
19%
26%
14%
26%
26%
13%

19%
18%
16%
20%
17%
26%
24%
18%
16%
20%
10%
19%
3%
15%
15%
19%

14%
13%
20%
5%
13%
19%
16%
13%
20%
5%
22%
41%
9%
7%
7%
15%

6%
3%
6%
6%
7%
7%
4%
3%
6%
6%
2%
3%
1%
2%
2%
5%

12%
23%
5%
3%
19%
8%
1%
23%
5%
3%
0%
7%
19%
0%
7%
13%

23

Coping Strategies

How much do different sources lend?
While moneylenders are becoming less prominent inseverely hit districts, they continue to lend the largest
amounts per borrower (Table 3.7). In severely hit
districts, they lend on average NPR 109,326 to eachborrower. This is almost three times the amount thatbanks lend. They are followed by banks, neighbors,
relatives, cooperatives, and savings groups. Thisordering is also true overall across the whole sample.

Neighbor

IndividualBank

Savings group

Cooperative

Financeinstitution

Moneylender

Government

Severely hit
Crisis hit
Hit with heavy losses
Hit
All districts

Relative

ImpactFriendTable 3.7: Average loan amounts by source (NPR) – by district impact (IRM-2)

4,924
6,448
2.672
2,616
4,820

23,461
17,043
17,833
28,10921,80821,351
16,045
17,62644,70521,898

3,788
402
2,198

35,52978,49390,04655,57053,155

11,763
22,13816,092
12,844
14,723

11,771
28,41314,00013,742
16,036

3,915
5,867
632
5,676
4,177

109,326
25,696
13,287
3,808
69,91485
20

Unsuccessful borrowing
Prior loan refusals are low (6% of people who tried toborrow said they were unsuccesful). However, withthe exception of Syangja, a less affected district, loan
refusals appear to be higher in more affected districts(Figure 3.4). Severely hit districts on average havegreater shares of people who tried to borrow but
were unsuccessful (7%), suggesting greater credit
constraints. The greater economic losses suffered byhouseholds in these regions may make them less credit
worthy to lenders. Loan refusals are most frequently
reported from informal lending sources: neighbors(28%), moneylenders (26%), relatives (13%), andfriends (9%). But there are also high levels of refusals
from formal and semi-formal lending sources: banks(17%) and savings groups (8%).

figure 3.4

Figure 3.4: Unsuccessful borrowing attempts – by district (IRM-2)
12%
11%

9%

9%

9%

7%

7%
6%

6%
5%

5%

3%

3%
2%

0%

0%

Sy
ja

a

bu

um

kh

lu

g
an

So
ng

ju

ng

u

d
an

u
dh

m

al

m

kh

La

O

th
Ka

ur
ap

k

ow

ch

l
pa

hu

p
ha

h
ec

t
ak

nd

Bh

Si

m
Ra

ot

ak

uw
N

a

kh
or

G

ng

di
ha
D

24

Aid and Recovery in Post-Earthquake Nepal

The largest shares of unsuccessful borrowing attempts
are in Ramechhap and Okhaldhunga, as previously
noted, and also in Sindhupalchowk. The high rate ofrefusals in Sindhupalchowk is of concern given thatthe district suffered the greatest damage to both housing and livelihoods from the earthquake (Table 2.5
above). Although not among the top five districts interms of the proportion of people who are poor (41%),

credit constraints would be expected to hamper thepace of recovery given the levels of damage. Fifty-eightpercent of people in Sindhupalchowk intend to borrowin the next three months. This is despite the fact thatthe district has been relatively well served in terms ofaid in IRM-2, with high shares for nearly every relief
item (Chapter 4).

Intention to borrow
Overall, larger shares of the population in higher
impact districts intend to borrow in the next threemonths (Table 3.8). This points again to the continued
need for credit in these areas. The share is 57% inseverely hit districts, 31% in crisis hit districts, and
21% in the third and fourth categories. Districts withthe highest proportion of people intending to borrowin the next three months are the same as those withthe greatest share of past loan refusals (i.e. those withthe greatest credit constraints): Ramechhap (79%),
Sindhupalchowk (58%), and Okhaldhunga (54%).17
As previously noted, these districts have larger sharesof poor populations, with greater numbers of peopleborrowing smaller amounts, particularly for basic
needs, such as food, temporary shelter, and livelihoods
assistance.

Table 3.8: Intention to borrow in the next three months – by district impact and district (IRM-2)
District

Current borrowing

Borrow in next threemonthsSeverely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

52%
52%
47%
43%
68%
51%
37%
22%
19%
70%
25%
21%
29%
43%
43%
42%

57%
50%
50%
49%
79%
58%
31%
29%
10%
54%
21%
20%
21%
21%
21%
40%

What interest rates are lenders charging?
Interest rates charged on loans have risen slightly
for most lenders since IRM-1. The highest rates arecharged by individuals (on average, 2.51% per month),followed by moneylenders (2.44%) and neighbors(2.17%) – Figure 3.5.

Interest charged by formal lenders—such as banks,
cooperatives, and other financial institutions—arestill high relative to what might be expected fromcommercial banks.18 Reported bank lending rates haveremained consistent since the period immediately

17

common in rural areas and tend to have annual interest rates
of 14-16% for certain types of loans (as per published sources in2016). “Cooperatives”, a separate survey option, can refer to formalcooperative banks and informal savings groups. The informal kind

At the district level, we find that current borrowing is also highly
and positively correlated with future intention to borrow (87%).
18

The reported rates charged by banks are surprisingly high. Banks
referred to here are likely to be cooperative banks, which are more

25

Coping Strategies

following the earthquake, with cooperative interest
rates rising by 0.1%. Interest rates charged by informal
lending sources in IRM-2 are: friends (1.93%), relatives
figure 3.5(2.03%), neighbors (2.17%), individuals (2.51%) andmoneylenders (2.44%). These have also risen only
slightly since IRM-1 (in the range of 0.1-0.29%).

Figure 3.5: Monthly interest rates for different sources (IRM-1/IRM-2 comparison)
2.6%

2.51%

2.44%

2.33%

1.95%

1.93%
1.78%

2.03%

2.25%

2.17%
2.07%

2.01%

1.74%

1.61% 1.6%

1.68%1.71%

1.74%
1.68%

Savings group

Coop

1.67%

1.3%

0.65%

0%

FriendRelative

IRM-1

IRM-2

Neighbor

Individual

All informal lending sources—moneylenders, friends,
neighbors, relatives, and individuals—are associatedwith higher interest rates in the severely hit districts(Table 3.9). In contrast, formal lending sources—
banks, cooperatives, and financial institutions—Bank

Finance Moneylenderinstitution

charge, on average, lower interest rates in the severely
hit districts. Savings groups are charging lower interestin the two categories of greatest impact, although therate in the severely hit districts is slightly higher (1.7%)
compared to the crisis hit category (1.59%).

Neighbor

IndividualBank

Savings group

Coop

Financeinstitution

Moneylender

Severely hit
Dhading
GorkhaOkhaldhunga
Ramechhap
Sindhupalchowk
Crisis hit

Relative

Districts

FriendTable 3.9: Monthly interest rates for different sources – by district impact and district (IRM-2)

2.07%
1.60%
2.50%
2.37%
2.33%
1.94%
1.57%

2.23%
1.81%
2.38%
2.41%
2.37%
2.15%
1.72%

2.26%
2.00%
2.42%
2.46%
2.21%
2.01%
2.14%

2.53%
1.80%
2.00%
2.95%
2.00%
-

1.46%
1.54%
1.41%
1.45%
1.36%
1.45%
1.30%

1.70%
1.74%
1.74%
1.61%
1.70%
1.69%
1.59%

1.64%
1.62%
1.49%
1.34%
1.84%
1.67%
1.72%

1.62%
1.78%
2.00%
1.56%
1.59%
1.37%
1.78%

2.47%
1.93%
2.13%
2.70%
2.93%
2.63%
2.49%

are characterized by much higher interest rates, particularly for
micro-loans (up to 24% per annum as per published sources in2016). As such, interest rates reported for cooperatives appear to be
more in line with the informal types of cooperatives. The figures for
informal lending institutions, such as moneylenders, appear to beaccurate, gelling with evidence from the qualitative field research.

26

Neighbor

IndividualBank

Savings group

Coop

Financeinstitution

Moneylender

Bhaktapur
kathmandu
Nuwakot
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Relative

Districts

FriendAid and Recovery in Post-Earthquake Nepal

0.80%
2.31%
1.69%
1.77%
2.50%
1.48%
2.17%
2.17%
1.93%

0.42%
1.31%
2.17%
1.68%
2.33%
1.48%
1.92%
1.92%
2.03%

1.15%
0.00%
2.21%
2.12%
2.19%
2.11%
1.96%
1.96%
2.17%

2.00%
2.00%
.2.51%

1.03%
1.09%
1.72%
1.94%
2.18%
1.61%
1.90%
1.90%
1.60%

1.18%
2.25%
1.59%
2.03%
2.11%
1.67%
2.09%
2.09%
1.71%

1.41%
2.82%
1.62%
2.04%
2.14%
1.72%
1.89%
1.89%
1.74%

1.00%
2.67%
1.75%
1.83%
2.00%
1.50%
1.83%
1.83%
1.67%

1.50%
1.93%
2.53%
2.33%
2.00%
2.42%
2.01%
2.01%
2.44%

Borrowing across rural and urban areas
People in urban areas are less likely to borrow (20%against 46% in rural areas) but urban borrowing
amounts, for each person who borrows, are almost
twice those in rural areas: NPR 388,779 against NPR
195,796.
People in urban areas are likely able to secure loansof higher volumes due to them having higher incomes
and because of the availability of better financial

infrastructure. Data on the incomes of those in ruraland urban areas provides backing for the former
explanation. Figure 3.6 shows that urban areas havea relatively low share of population with incomes
below NPR 10,000 (27% compared to 43% in rural
areas); the share of the population in the top two
income brackets is much higher for urban areas (29%compared to 13% in rural areas).

figure 3.6

Figure 3.6: Distribution of income – by urban/rural (IRM-2)
50%
44%
40%

38%

38%

25%

23%
20%

13%

11%
7%

6%
3%

0%

2%

<NPR 2,500

Urban

6%

NPR 2,501- 9,999

NPR 10,000- 19,999

NPR 20,000- 39,999> NPR 40,000

1%

Don't know

Rural

The likelihood of past loan refusals is much higher
in rural areas (6%) compared to urban areas (2%),
indicating higher credit constraints, consistent
with the findings of lower income levels. It is thus

unsurprising that 44% in rural areas also intend toborrow in the next three months versus only 20% in
urban areas, although people may not always be able
to secure loans.

27

Coping Strategies

Both urban and rural households report borrowing
from a combination of formal and informal lending
sources but there are differences (Figure 3.7). Informal
lending sources (relatives, neighbors, moneylenders),
other than friends, are reported among a larger shareof rural borrowers; formal lending sources and savings
groupsare reported by larger shares in urban areas.
figure 3.7

Rural shares are the highest for neighbors (24%),
relatives (21%), and savings groups (19%), followed bymoneylenders (14%), and banks (12%); urban sharesare the highest for savings groups (23%), friends andrelatives (17% each), and banks, cooperatives, andindividuals (16% each), with very few taking loans
from moneylenders.

Figure 3.7: Sources of borrowing among those who borrowed – by urban/rural (IRM-2)
30%

24%

23%

23%

21%
19%

17%

17%

16%

15%

15%

16%

0%

15%

14%

12%

11%

8%

16%

6%

6%

5%

FriendRelative

Urban

Rural

Neighbor IndividualBank

Savings group

Coop

Financeinstitution

0%

0%

Govt.
loan

1%

Moneylenderfigure 3.8

Figure 3.8: Monthly interest rates for different sources – by urban/rural (IRM-2)
2.6%

2.51%

2%

1.95%
1.6%

2.06%

2.44%

2.18%
1.98%
1.63%

1.57%

1.68%1.71%

1.75%1.74%

1.71%1.67%
1.5%

1.32%

1.3%

0.65%

0%

FriendRelative

Urban

Rural

Neighbor

0%

IndividualMonthly reported interest rates are higher acrossnearly every formal and informal lending source inrural areas than in urban areas (Figure 3.8). This is

28

Bank

Savings group

Coop

Finance Moneylenderinstitution

especially concerning since rural areas have higher
shares of lower income households and greater credit
constraints. Higher demand for credit combined with

Aid and Recovery in Post-Earthquake Nepal

a greater perceived risk in lending (due to low incomes
and the consequent lack of collateral for loans) are
the likely explanations for this. Monthly interest
rates in rural and urban areas are the same only forcooperatives (1.7%) and financial institutions (1.7%).
There is less variation in monthly interest rates among

informal and formal lending sources in urban areas(none are above 2%). The lowest rates are charged
by banks (1.32%) and moneylenders (1.5%) and thehighest rates by neighbors (1.98%) and cooperatives(1.75%).

Borrowing across population groups
Disability. There are no differences in the likelihood of borrowing between those with and without
disabilities (42% of each have borrowed) and for any
particular borrowing purpose. However, there is alarge difference in borrowing amounts: NPR 250,748
for figure
those3.9without disabilities against NPR 143,506

for those with disabilities of any kind. One possible
explanation is that those with disabilities earn less,which would translate into lower eligibility for credit.
However, we find no major earning differences between those with and without disabilities (Figure 3.9).

Figure 3.9: Distribution of income – by disabled/non-disabled (IRM-2)
50%
44%
37%

38%

42%

37%

25%

12%

13%
3%

0%

13%

4%

<NPR 2,500

Without disability

3%

NPR 2,501- 9,999

NPR 10,000- 19,999

NPR 20,000- 39,999

2%> NPR 40,000

1%

2%

Don't knowWith disability

Despite no major differences in income, the incidenceof unsuccessful borrowing attempts for those withdisabilities is higher (7%) relative to those without(5%), which suggests credit constraints for the disabled, perhaps because of discrimination.19 Further theshare of those intending to borrow in the next threemonths is much higher for those with disabilities (45%against 37%). This is cause for concern as they may beless likely to receive the loans they want. There are no
major observable differences in sources of borrowing
reported by those with and without disabilities. There
are also no major differences in interest rates charged
for those with and without disabilities across borrowing sources. The greatest difference is in the caseof moneylenders who charge those with disabilities
slightly higher rates (2.53% against 2.38%).

19

This difference in past loan refusals between the two groups issignificant by a t-test at the 1% level.

29

Coping Strategies

Gender. There are no notable differences in the
proportion of men and of women who borrow (43%of women borrowed against 42% of men). However,of those who do borrow, men borrow on average morethan double the amount women borrow: NPR 288,206
versus NPR 131,606. This does not appear to be dueto the income distribution for the two groups, as there
are no major differences (Figure 3.10).

figure 3.10

Figure 3.10: Distribution of income – by gender (IRM-2)
50%
42%
37%

38%

44%

37%

25%

14%

13%
4%

0%

3%

2%

<NPR 2,500

Male

12%

NPR 2,501- 9,999

NPR 10,000- 19,999

NPR 20,000- 39,999

2%

2%

1%

> NPR 40,000

Don't know

FemaleThe share of women reporting unsuccessful loan
attempts in the past is 5%, not very different than the
6% for men, suggesting that they are not more credit
constrained than men. Women, however, report alower intention to borrow over the next three months(38%
against 42% for men).
figure 3.11

Borrowing by women from informal and semi-formal
sources is higher for women: relatives, neighbors, andsavings groups (which often are women-specific).
Men are more likely to borrow from cooperatives andmoneylenders (Figure 3.11).

Figure 3.11: Sources of borrowing among those who borrowed – by gender (IRM-2)
30%

23%

22%

22%

24%
20%

20%
18%
15% 15%

15%

8%

15%

14%

12% 13%

7%

16%

12%

6%

6%
3%

0%

30

FriendRelative

Male

FemaleNeighbor

IndividualBank

Savings group

Coop

Finance Moneylenderinstitution

Aid and Recovery in Post-Earthquake Nepal

Banks and cooperative—formal lending sources—
charge women slightly higher interest rates than they
do to men (Figure 3.12). This could be because men
are more typically the primary income earners andare therefore associated with stable income sources
whereas women might split their time between paid
jobs and household responsibilities.20

figure 3.12

Figure 3.12: Monthly interest rates for different sources – by gender (IRM-2)
3%

2.88%
2.52%

2.25%

2.34%
1.93%1.93%

2.01%2.06%

2.22%
2.12%

2.2%

1.6% 1.6%

1.5%

1.74%
1.67%

1.81%
1.6%

1.75%1.73%

0.75%

0%

FriendRelative

Male

FemaleNeighbor

Individual

Caste groups. Janajatis are less likely to borrow(40%) than low and high caste people (46% and45%, respectively) – Table 3.10. However, higher
caste people on average take the largest loans (NPRBank

Savings group

Coop

Finance Moneylenderinstitution

368,249), followed by Janajatis (NPR 117, 534), withlower caste people borrowing much less on average(NPR 86, 849).

Table 3.10: Proportion borrowing and amount borrowed – by caste (IRM-2)
Caste

Proportion borrowing

Average amount borrowed(NPR)

Low caste
Janajati
High caste

46%
40%
45%

86,849117,534
368,249

A much larger share of lower caste people can befound in the lowest two income brackets (52%)
relative to Janajatis and higher castes (44% and 33%,
respectively) – Figure 3.13. This would explain thelower borrowing amounts among lower castes. Lower
caste people also report a higher share of past loan

refusals (9% compared to 6% for Janajatis and 5% forhigh caste), indicating credit constraints. However,the lower caste group has a lower share reporting theintention to borrow in the next three months (33%versus 40% and 43%).

20

Although this does not explain why some lending sources(friends, individuals, financial institutions, and moneylenders)charge men more.

31

Coping Strategies
figure 3.13

Figure 3.13: Distribution of income – by caste (IRM-2)
50%
44%
40%

40%

42%

45%

38%
31%

25%
17%

13%
8%

8%
4%

0%

2%

0%

<NPR 2,500

Low

10%

NPR 2,501- 9,999

Janajati

NPR 10,000- 19,999

NPR 20,000- 39,999

1%

4%
0%

> NPR 40,000

2% 1%

Don't know

High

Lower caste people are the most likely to borrow frommoneylenders (23%) and neighbors (33%); higher
caste groups are the most likely to borrow fromindividuals(18%), cooperatives (16%), and banks
figure 3.14

(15%); and Janajatis are the most likely to borrow from
relatives (22%), savings groups (21%), and friends(7%) – Figure 3.14.

Figure 3.14: Sources of borrowing among those who borrowed – by caste (IRM-2)
40%
33%

30%

27%
22%

20%

20%

19%

14%

10%

7%
4%

0%

18%
13%13%

18%
15%
11% 11%

13%

12%

14%

16%
12%12%

6%

6%
2%

FriendRelative

Neighbor

Low

Janajati

High

Individual

Lower caste people are also charged higher interest
rates than Janajatis and higher caste people acrossevery type of lender, other than individuals andcooperatives (Figure 3.15). The interest reported by
Janajatis is similarly higher across all lenders relative
to higher caste people. This suggests that perceived

32

23%

21%

Bank

Savings group

Coop

4%

Finance Moneylenderinstitution

creditworthiness is linked to the caste hierarchy.
However, given average incomes also correlate withcaste hierarchy, it is difficult to assess whether caste initself is considered explicitly or implicitly by lenders in
the informal sector when determining interest rates.

Aid and Recovery in Post-Earthquake Nepal
figure 3.15

1.5%

2.68%
1.63%

2.12 %

2.25%
1.67%

1.79%

1.7%

1.66%

1.63%

1.76%

1.75%

1.7%

1.47%

1.89%

2.58%

2.5%

2%

1.98%

2.44%

2.28%

2.09%

1.89%

1.74%

2.25%

2%

2.4%

2.39%

3%

2.59%

Figure 3.15: Monthly interest rates for different sources – by caste (IRM-2)

0.75%

0%

FriendRelative

Neighbor

Low

Janajati

High

Individual

Income. Shares of borrowing are the highest in the
middle income groups (between NPR 10,000 andNPR
39,999) and lower in the highest and lowest
figure 3.16

Bank

Savings group

Coop

Finance Moneylenderinstitution

income groups (> NPR 40,000 and < NPR 2,500) –Figure 3.16.

Figure 3.16: Share of people who have borrowed since June 2015 – by income band (IRM-2)

42%

Total
Don't know

12%
33%

> NPR 40,000

41%

NPR 20,000 - 39,999

45%

NPR 10,000 - 19,999
42%

NPR 2,501 - 9,999
33%<NPR 2,500
0%

13%

25%

38%

50%

However, the average loan size is far larger (NPR
725,679) in the top-most income bracket relative tolower income brackets (Figure 3.17). For those whoborrow, the richest borrow more than twice as muchthan the next income bracket down. The lowest income
group has loan sizes that are higher than the second
lowest income group.

33

Coping Strategies
figure 3.17

Figure 3.17: Average amount borrowed per borrower (NPR) – by income band (IRM-2)

208,749

Total
59,000

Don't know

725,679> NPR 40,000
315,172

NPR 20,000 - 39,999

262,624

NPR 10,000 - 19,999
90,179NPR 2,501 - 9,999

107,378<NPR 2,5000200,000

In terms of sources of borrowing, the lowest income
group is the most likely to borrow from moneylenders;
and the highest income group is the most likely to

400,000

600,000

800,000

borrow from banks (36%), individuals (36%), andsavings groups (25%) – Table 3.11.

Neighbor

IndividualBank

Savings group

Cooperative

Financeinstitution

Moneylender<NPR 2,500
NPR 2,501 - 9,999
NPR 10,000 - 19,999
NPR 20,000 - 39,999> NPR 40,000
Total

Relative

Monthly income

FriendTable 3.11: Sources of borrowing among those who borrowed – by income band (IRM-2)

7%
8%
6%
5%
7%
7%

24%
20%
21%
20%
21%
21%

22%
27%
22%
17%
7%
23%

13%
12%
15%
22%
36%
15%

11%
9%
12%
21%
36%
13%

11%
19%
19%
19%
25%
19%

7%
11%
17%
21%
7%
15%

2%
4%
6%
4%
4%
5%

27%
14%
12%
14%
7%
13%

3.2 Remittances
Remittances as a main source of income
Remittances have increased in importance as anincome source. As discussed in Chapter 2, remittances
are a main source of income for 16% of the populationin IRM-2 (against 11% in IRM-1). As in IRM-1, Syangja
has the greatest proportion of people who report
remittances as a main income source (hit district:41%), followed by the severely hit impact districts

34(15%) – Figure 3.18. The share of households reporting
remittances as a main income source has grown sinceIRM-1 in all four categories of earthquake impact.
However, given that the growth is greatest in Syangja,
the least affected district, this is not necessarily linked
to disaster impacts.

Aid and Recovery in Post-Earthquake Nepal
figure 3.18

Figure 3.18: Remittances as a main income source – by district impact (IRM-1/IRM-2 comparison)
16%

Total

11%
41%

Hit

29%
17%

Hit with heavy losses

9%8%

Crisis hit

6%15%

Severely hit

11%

0%

13%
IRM-2

25%

38%

50%

IRM-1

The impact of the earthquakes on remittances
Nine percent of those who received remittances before
the earthquake report that the earthquake negatively
affected them (Table 3.12).21 The rate is highest in
Kathmandu (20%) and Bhaktapur (14%) along with anumber of severely hit districts (Gorkha, Ramechhap,
and Sindhulpalchowk). However, rates of recovery arealso highest in the severely hit districts as well as more
urban Kathmandu and Bhaktapur.

Table 3.12: Remittances as a share of main income source, impact on remittances,and recovery of remittances – by district impact and district (IRM-2)

Severely hit
Dhading
GorkhaOkhaldhunga
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Nuwakot
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

21

Remittance as a mainincome source (IRM-2)

Share of people whose
remittances were affectedby earthquakes (IRM-2)

Share of affected whose
remittances have improvedin the past three months(IRM-2)

15%
24%
15%
13%
15%
10%
8%
6%
6%
13%
17%
23%
11%
41%
41%
16%

12%
8%
13%
9%
18%
12%
12%
14%
20%
2%
5%
3%
8%
6%
6%
9%

58%
86%
43%
75%
60%
25%
39%
67%
50%
0%
33%
0%
67%
38%
38%
53%

The World Bank reports that remittances fell in 2015. This wasprimarily a result of a drop in global oil prices, which affected
the ability of people to send remittances. Changes in remittances

observed here are thus not necessarily linked to the earthquakes.
World Bank (2016). Nepal Development Update May 2016:
Remittances at Risk. Washington, D.C: World Bank.

35

Coping Strategies

Changes in remittances from abroad
Twenty-three percent of people say they have received
remittances from abroad, before or after the earthquake. A small amount of people (0.5%) say they havereceived remittances from within the country but notfrom abroad.22
While remittances have become more important as
an income source, absolute levels of remittances fromabroaddo not appear to have changed much. Of the
figure 3.19
23% who have received remittances from abroad,

9% report that these are new remittances that beganfollowing the earthquake, suggesting that the money isintended to cope with disaster impacts (Figure 3.19).
A further 76% who have received remittances fromabroad report that they received remittances before
the earthquake and continue to do so in similar
amounts. Thirteen percent say they continue toreceive remittances but at lower volumes than before
the earthquake, while 1% say that remittances haveincreased since the earthquake.

Figure 3.19: Changes in remittances received from abroad (IRM-2)

Don't know

1%

Received remittances before
the earthquake and receive
more since the earthquake

1%

Received remittances before
the earthquake and receive
similar level since the earthquake

76%

Received remittances before
the earthquake but receive
less since the earthquake
Didn’t receive remittances
before the earthquake
but have since the earthquake
0%

13%

9%

20%

Findings from the qualitative research suggest that thedecrease in remittances from abroad may be becausesome people overseas have returned home to help with22

The 23% who report receiving remittances from abroad may also
have received remittances from within the country. This is distinct
from those who report remittances as a main source of income,discussed above.

36

40%

60%

80%

recovery and reconstruction. A second possible reason
is that there has been damage to banking infrastructure that continues to affect the routing of payments.

Aid and Recovery in Post-Earthquake Nepal

3.3 Migration
Figure 3.20: Reasons for migration (IRM-2)

Migration levels since the earthquake are low at 6%.
Eighty-seven percent of those who migrated did soin the first three months after the earthquake, 8%
migrated during the 2015 monsoon, and 4% migrated
after. Of those who migrated, 60% had returned totheir homes by the time of the IRM-2 survey.

figure 3.20
70%

68%

53%

The most commonly cited reason for migration waslack of shelter (68%), followed by lack of livelihood
opportunities (22%), landslides caused by theearthquake (17%), and the risk of future landslides(10%) – Figure 3.20.

35%
22%

18%

17%
10%
6%
1%

0%

0%

D
se

ea

th

ea

is

D

oo

ih

r

te

el

liv

in

Ra

o

e

id

ke

ua

el

sl

sh

nd

o

N

N

La

hq

rt

Ea

Crisis hit districts, which include Kathmandu andBhaktapur, have more than double the rate of
migration on average (12%) relative to severely hit
districts (5%); the third and fourth categories ofimpact have lower rates (4% and 1%, respectively).
Overall, less than 1% borrowed to migrate.

d

The percentage of people who migrated amongst therural population is lower than in urban areas (5%against 9%). The likelihood of migration is higher
at higher income levels (NPR 10,000 and above) –Figure 3.21.

figure 3.21

Figure 3.21: Share of people migrating – by income band (IRM-2)

7%> NPR 40,000
8%

NPR 20,000 - 39,999
7%

NPR 10,000 - 19,999
4%

NPR 2,501 - 9,999
6%<NPR 2,5000%2%Migration rates are not notably different between
caste groups, although higher castes report slightly
lower migration rates (4% against 7% for Janajatis
and 8% for lower castes). Those with disabilities are

4%

6%

8%

more likely to have migrated (7%) compared to those
without (5%).

37Coping Strategies

3.4 Asset sales
Sale of assets since the earthquake has been low. Acrossall districts, 6% have sold assets since the beginningof the 2015 monsoon. This is higher in severely hit

districts (average 8%), especially Ramechhap and
Sindhupalchowk, and also in Okhaldhunga andSolukhumbu (Figure 3.22).

figure 3.22

Figure 3.22: Share of people selling assets – by district (IRM-2)
16%

16%

14%

12%

11%
10%

8%
6%

4%

4%
3%
2%

2%
1%

1%

0%

bu

um

a

gj

kh

lu

an

Sy

So
ng

ju

m
ga

un

dh

al

k

w

ho

du

an

r

lc

pa

pu

ta

m

ak

th

kh

La

O

Ka

Bh

hu

ap

hh

ec

m

nd

Si

Ra

ot

ak

uw

N

a

kh

ng

di

ha

or

G

D

The vast majority of asset sales have been of livestockwith land and household goods accounting for smaller
shares. Eighty-nine percent of those who sold assets
sold livestock, compared to 8% for land and 4% forhousehold goods (Figure 3.23).
Around one-third of livestock farmers sold livestock.
Given that 6% of all people sold assets, and 89% of
these sold livestock, this means that 5.3% of all people
across the whole sample have sold livestock. Across the
districts studied, 18% of people are livestock farmers(Table 2.1 above). It thus seems that around one-thirdof livestock farmers have sold some of their livestock.
Of those who sold livestock, 17% say they sold all, 28%
over half of the livestock they had, 30% sold between
one-quarter and one-half of their livestock, and 26%
under one-quarter.
People selling land account for 8% of those selling
assets. This means that around 0.5% of all respondents
have sold land. Of this group, 10% had sold all theirland, with 3% selling over half, 28% selling between
one-quarter and one-half, and 59% less than onequarter.

38

Less than 0.25% of people say they have soldhousehold goods.
figure 3.23
Figure 3.23: Share of assets sold –by type of asset (IRM-2)

90%

89%

68%

45%

23%
8%

0%

Livestock

Land

4%

Household goods

Aid and Recovery in Post-Earthquake Nepal

Chapter 4.
Earthquake Aid
Photo: Prajita Rana

IRM-1 was conducted while the emergency response
was coming to an end and recovery programs werebeginning. Eight months on, how has the spread andnature of aid changed? And how has this affectedsatisfaction amongst the earthquake-affected?

This chapter provides information on what aid people
are receiving, with a particular focus on the threedominant forms of assistance: food, shelter, andcash. It looks at the role of different aid providers,the damage assessment process, and at differingexperiences of aid across areas and population groups.

Key findings:
What aid are people receiving?
• The nature of aid has changed since IRM-1. There
has been a decline in the distribution of tarps
and food. There has been a large increase in theprovision of cash.
• Aid is still concentrated in severely hit districts
but there has been a move towards decreased
coverage in more affected districts and increasing
coverage in less affected districts.
• The overall amount of aid has declined. There
has been an increase in the proportion of people
not receiving aid, in particular in Okhaldhunga
district.
Food, shelter, and cash
• The food security situation is particularly grave in
Okhaldhunga. Only 2% now receive food aid anda large amount of people are borrowing for food.

• There appears to have been an over-distributionof tarps to those who may not need them. The
number of people receiving CGI has increased
but provision is insufficient to meet needs. There
has been little distribution of reconstruction
materials.
• G overnment cash aid has been more widelydistributed, and at higher volumes, than nongovernment cash. Non-government cash has
tended to go to lower impact districts, in generalto people who have not received government
cash. Cash received has been insufficient to meetneeds.
Providers of aid• The government, NGOs, INGOs, and the Red
Cross remain the main providers of aid. NGO andINGO aid has tended to refocus on lesser affecteddistricts since IRM-1.

39

Earthquake Aid

Satisfaction with aid

Different population groups• There has been a rise in satisfaction with most aid
providers since IRM-1. Satisfaction levels are thehighest in Solukhumbu, which has received large
quantities of assistance. People are least satisfied
with political parties as aid providers.

• The poorest and the richest are the least likely
to have received aid in IRM-2. Government cash
has reached poorer people but non-government
cash has been less likely to go to poor people inrural areas. Satisfaction with most aid providers
is lower for richer people.

Damage assessments• There are no major differences in the experienceof aid between men and women and between the
disabled and non-disabled. Janajatis are more
likely to have received aid than others. However,
low caste people are more likely to be satisfied
with aid providers.

• Almost all people whose house was classified as
fully damaged in the damage assessments havereceived beneficiary cards. However, many others
have also received cards.
• Satisfaction with the damage assessments is not
fully determined by whether people received abeneficiary card or not.

• Aid appears to have been well targeted by housing
damage. However, of those who received aid,
those whose house was less damaged are moresatisfied.

• Both government and non-government cash havebeen targeted at people who received beneficiarycards.

4.1 What aid are people receiving?
What types of aid are people receiving?
In June 2015, two months after the earthquake, themost common forms of aid received in earthquakeaffected districts were tarps, food, and cash. This
reflected the response being in the emergency stage.
Other relief items were received in smaller shares:
blankets, sanitation packages, corrugated iron sheets(CGI), and kitchen utensils.
The nature of aid has changed as time has passed.
Nearly one year on from the earthquake, respondents
were asked what types of aid they had received since
the beginning of the monsoon (June 2015). Figure 4.1
presents the proportion of people in surveyed districts
who received each type of aid within the two periods(IRM-1: April-June 2015; IRM-2: June 2015-February2016).
There has been a major decline in the share of people
receiving tarps (from 71% to 47%) and food (from 53%
to 37%). These types of aid are still more common thanmost others but they have been distributed much lesswidely since the 2015 monsoon than before it.
In contrast, there has been a large increase in the
proportion of people receiving cash. Whereas in IRM1, 28% had received cash, 64% report receiving cashfrom government and 11% from non-governmental
sources since the beginning of the monsoon.23 Since
the beginning of the 2015 monsoon, cash has been thedominant form of aid.
Other forms of aid have increased. Distribution ofblankets has nearly doubled, from 18% receiving
blankets in IRM-1 to 33% in IRM-2, reflecting
increased need during the winter. The number ofpeople receiving CGI has tripled from 7% to 21%. Other
items of relief have much lower shares in both IRM-1
and IRM-2. Clothes distribution has risen from 1%
to 9%; medical aid and kitchen items have had fairly
stable shares in both rounds.

23

IRM-1 did not disaggregate cash between that from the government and that from other sources.

40

Aid and Recovery in Post-Earthquake Nepal

figure 4.1

Figure 4.1: Share of people receiving different types of aid – all districts (IRM-1/IRM-2 comparison)
80%
71%
64%

60%
53%
47%

40%

37%

33%

30%

28%
18%
11%

7%

0%

24%

21%

20%

4% 4%

2% 1%

0%

0%

14%
10%

9%

6% 4%

1%

0%

N
d

en

s

tio
ta

ai

ni

o

Sa

h
tc

Ki

he
ot

Cl
t

v

go

n

n

v

ll)

no

go

(a

al

ke

sh

sh

sh

ic

an

Bl

Ca

Ca

Ca

ed
M

od

Fo

ps

I

CG

r
Ta

t

n
Te
IRM-1

IRM-2

Similar patterns can be seen when focusing only onseverely hit districts, the areas that were most affectedby the earthquakes (Figure 4.2). Cash, food, and tarps
have reached the most people in IRM-2, with cash
increasing in spread since IRM-1 and tarps and food
figure 4.2

declining. Cash has been received by almost everyonein the severely hit districts. There has also been a large
rise in the number of people receiving blankets, CGI,
clothes, and sanitation packages.

Figure 4.2: Share of people receiving different types of aid – severely hit districts(IRM-1/IRM-2 comparison)

100%

96%

93%

88%

75%
65%

65%

56%

50%

43%

38%

26%

25%
12%

0%

25%

17%

15%

8% 8%

2% 2%

0%

0%

2%

0%

11%

16%
8%
1%

5%

o

N
d
ai

v

go

n

n

tio
ta

n

he

tc

ni
Sa

Ki

s

he

ot

Cl

et

no

v

go

k
an

Bl

sh
Ca

sh
Ca

ll)
(a

al

ic
ed

sh
Ca

M

od

Fo

I

CG

s
rp

Ta

nt

Te
IRM-1

IRM-2

41

Earthquake Aid

There appears to be a mismatch between livelihoods
needs and assistance provided. Nine percent of people
in IRM-2 (14% in severely hit districts) report receiving farm implements. This is low given that farmingis the primary occupation (77% farm their own land,
88% do so in severely hit districts) and 53% of those
who farm their own land report that their livelihood
was affected by the earthquakes (75% in severely hit
districts) – see Chapter 2. There is no reported livelihood assistance for livestock rearing, despite thisbeing the second largest occupation (29% in severely
hit districts) and 40% of livestock farmers saying

their income was affected. Sixty percent of those whoborrowed in IRM-2 did so to support their livelihoods(Figure 3.2 above).
There has been very limited distribution of reconstruction materials since the beginning of the 2015
monsoon, with 6% receiving materials (8% in severely
hit districts).24 With 66% of houses completely or
badly damaged (94% in the severely hit category),
there is clearly a need for more support to help people
rebuild.25

How does aid coverage vary by districts?
Table 4.1: Share of people notreceiving aid – b​ y district impact and district(IRM-1/IRM-2 comparison)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja

IRM-1

IRM-2

1%
3%
3%
1%
0%
0%
40%
46%
70%
5%
31%
63%
4%
86%
86%

5%
8%
7%
2%
5%
3%
59%
58%
77%
42%
29%
53%
5%
70%
70%

Aid is still primarily concentrated in severely hit
districts but there has been a trend towards decreased

coverage in more affected districts and increased
coverage in less affected districts (Table 4.1). In IRM1, only 1% in severely hit districts had not received
any aid since the earthquake. But since June 2015,
the share has increased to 5%. Similarly, in crisis
hit districts, the proportion of people receiving no
aid increased from 40% (IRM-1) to 59% (IRM-2). In
contrast, aid coverage has increased in hit with heavy
losses districts, with those not receiving aid decreasingfrom 31% to 29%. In the hit district, there has been alarger decline in the proportion of people not receiving
aid: from 86% to 70%.
In all districts, with the exceptions of Lamjung andSyangja, there has been an increase in the share ofpeople who have not received aid. Whereas in IRM-1,
0-3% of people in each of the severely hit districts had
not received aid, this has risen to 2-8% for IRM-2.
There continues to be relatively wide distribution ofaid in Solukhumbu, even though this district is in thethird impact category.
Okhaldhunga has seen a particularly large decrease in
the share of people receiving aid: from 42% in IRM-1
to 5% in IRM-2.

What volumes of aid are people receiving?
For many types of aid, overall flows have decreased.
While amounts for each person who has received aid
have increased, reductions in the number of people
receiving assistance mean that there has been adecrease in absolute volumes disbursed.

Tables 4.2 and 4.3 show the number of units of aid
received for each person who received that type ofaid, for IRM-2 and IRM-1, respectively. (Units arein pieces for all items except for food, where it is thenumber of days for which food to feed all members of
the household was provided).

24

25

Data on the distribution of reconstruction material was not collected in IRM-1, so it not possible to formally make comparisons
between the two time periods. However, qualitative fieldworkfrom IRM-1 showed that there was next to no distribution of reconstruction materials (beyond CGI) in the first months after theearthquakes.

42

Cash can of course be used to purchase reconstruction materials.
However, the volumes of cash distributed are insufficient for any
significant reconstruction to occur.

Aid and Recovery in Post-Earthquake Nepal

Volumes of food for households who receive it haveincreased since IRM-1, from food for 24 days to foodfor 32 days, with this increase greater in severely hit
districts (from food for 26 days to food for 36 days).
However, the proportion of people receiving foodhas declined (see Figures 4.1 and 4.2). As such, theoverall volume of food distributed is lower in IRM-2
than IRM-1. It should also be noted that the IRM-2
time period is longer than the IRM-1 one, so the foodreceived in IRM-2 has to last for longer.26

There has been a slight increase in the number oftarps provided to each person who receives them.
However, again, the proportion of people receiving
tarps has declined since IRM-1, indicating that theoverall volume of tarps has declined.
In contrast, both volumes of CGI per person, and thenumber of people receiving CGI, have increased sinceIRM-1.

Tents

Tarps

CGI

Reconstruction
materials

Food

Blankets

Clothes

Kitchen

Sanitation

Farm
implements

Table 4.2: Average aid quantity among those who received that type of aid – by district impact (IRM-2)

1.0
1.0
1.8
1.1

2.3
1.4
1.6
1.2
2.0

13.1
12.3
15.0
6.6
13.2

4.5
4.3
3.9
4.4

35.9
15.8
12.2
16.2
31.8

2.4
2.1
2.4
1.0
2.4

2.6
2.3
2.9
3.3
2.6

1.3
1.0
1.0
1.3

11.3
7.4
10.1
4.0
10.9

6.4
3.8
6.2

ImpactSeverely hit
Crisis hit
Hit with heavy losses
Hit
All districts

Note: Items in the table are those for which quantity information is available in IRM-2

Table 4.3: Average aid quantity among those who received that type of aid – by district impact (IRM-1)
ImpactSeverely hit
Crisis hit
Hit with heavy losses
Hit
All districts

Tents

Tarps

CGI

Food

Blankets

Clothes

Kitchen

5.7
1.0
1.0
1.0
3.7

2.0
1.2
1.1
1.2
1.7

9.5
6.9
8.8
4.0
9.2

26.5
12.4
6.7
4.8
24.1

1.5
1.2
1.0
1.0
1.4

1.5
1.3
.
.
1.5

1.9
1.0
1.0
.
1.8

Note: Items in the table are those for which quantity information is available in IRM-1

How do volumes of aid vary by district?
Sindhupalchowk has received higher volumes ofalmost every type of aid compared to other districts(Table 4.4). In the crisis hit category, Okhaldhunga,
which has also seen a steep decline in the share ofpeople receiving aid, has received lower quantities of
all aid items than average, except for sanitation kits,although the proportion of people receiving this item
is very low (3%).

Of the hit with heavy losses districts, Solukhumbu andLamjung have quite similar average quantities perrecipient of most items except for CGI and sanitation
kits, where average volumes are higher in Lamjung.27
Volumes of aid per recipient in Syangja are lower thanother districts for all items except food and clothes.

26

Lamjung. Sanitation: 5% Solukhumbu; 9% Lamjung. Proportions
receiving most other types of aid are much higher in Solukhumbu
than Lamjung. For example, 92% of people in Solukhumbu received
tarps in IRM-2 compared to 41% in Lamjung. Thirty-three percent
received food compared to 15% in Lamjung. And 81% received cashfrom the government compared to 23% in Lamjung.

IRM-1 covered aid received for approximately two months (lateApril to June, 2015). IRM-2 covered aid received for over six months(late June 2015 to February 2016).
27

Larger shares of people received CGI and sanitation packages
in Lamjung than in Solukhumbu. CGI: 12% Solukhumbu; 16%

43

Earthquake Aid

CGI

Reconstruction
materials

Food

Blankets

Warm clothes

Sanitation

Kitchen items

Farm implements

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Tarps

Districts

Tents

Table 4.4: Average aid quantity among those who received that type of aid – by district (IRM-2)

1.1
1.0
2.0
1.1
1.0
1.0
1.0
1.0
1.0
1.8
2.0
1.5
1.1

2.3
2.1
1.8
1.8
1.6
3.8
1.4
1.5
1.8
1.1
1.6
1.5
1.7
1.2
1.23
2.0

13.1
12.6
12.7
13.3
7.9
13.9
12.3
11.3
12.9
13.0
15
18.0
11.2
6.6
6.6
13.2

4.5
3.1
4.5
4.8
4.0
4.7
4.3
5.0
2.0
3.9
4.0
3.9
4.4

35.9
29.1
31.5
28.8
10.9
69.7
15.8
14.9
19.3
3.8
12.2
11.5
12.5
16.2
16.2
31.8

2.4
2.0
2.2
1.8
2.5
3.0
2.0
1.7
3.5
1.7
2.4
1.8
2.7
1.0
1.0
2.4

2.6
2.7
2.9
1.7
1.6
4.0
2.3
1.8
3.0
2.2
2.9
2.0
3.7
3.3
3.3
2.6

11.3
5.3
13.8
6.7
11.8
10.8
7.4
6.7
6.5
11.5
10.1
11.2
8.2
4.0
4.0
10.9

1.3
1.4
1.0
1.0
.
1.7
1.0
1.0
1
1.0
1.0
1.3

6.4
7.4
6.5
1.7
5.2
6.5
3.8
3.7
3.8
6.2

4.2 Food, shelter, and cash
Food
Table 4.5: Share of people who have received
food aid – by district (IRM-1/IRM-2 comparison)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

44

IRM-1

IRM-2

93%
89%
96%
89%
100%

38%
50%
87%
67%
83%

34%
9%
34%

11%
9%
2%

6%
10%

15%
33%

3%
37%

4%
37%

In almost all districts there has been a drop in the
proportion of people receiving food aid (Lamjung,
Solukhumbu, Syangja, and Kathmandu are the exceptions) – Table 4.5. These drops are particularly notablein Dhading (from 93% in IRM-1 to 38% in IRM-2),
Ghorka (from 89% to 50%), Bhaktapur (from 34% to11%), and Okhaldhunga (from 34% to just 2%).
The food security situation appears particularly serious in Okhaldhunga. Only 2% have received foodaid since June 2015, far lower than any district. Thisis especially concerning as Okhaldhunga has a highshare of low income households (54% have a monthly income below NPR 10,000). The district has thehighest share of any district of people who borrowedin IRM-2 (70%), with the largest proportion of borrowers taking loans for food of any district (53%) (seeChapter 3). Volumes of food aid in Okhaldhunga arealso the lowest of any district in both IRM-2 (fourdays of stock for the family, against the average of 32days) and IRM-1 (five days of stock against the averageof 24 days). This indicates persistent food insecurity
for some at the household level. The proportion ofpeople in Okhaldungha who say food consumption

Aid and Recovery in Post-Earthquake Nepal

Photo: Aneta Buraityte

has decreased since the 2015 monsoon is the highest
outside of the severely hit districts (7%) and higher
than in Ramechhap or Nuwakot, both of which wereseverely hit (see Chapter 5.3).
In contrast, Ramechhap and Dhading, which are similar to Okhaldhunga in several respects, fare far betterin terms of food aid received, although both have seena drop in the proportion of people receiving food.
Shares of low income households are high in Ramechhap (51%) and Dhading (52%) and the proportionsof people borrowing in IRM-2 are the highest after
Okhaldhunga (68% and 52%, respectively). The shareof borrowers taking loans for food are also the highest after Okhaldhunga (49% and 44%, respectively).
However, 67% of people in Ramechhap, and 38% in
Dhading, received food aid in IRM-2, far higher thanin Okhaldhunga. Average food stocks provided arealso much higher: 11 days in Ramechhap and 29 daysin Dhading, compared to four days in Okhaldhunga.
These districts were also better served for food aid
in IRM-1 than Okhaldhunga: 89% and 93% received
food aid; and average stocks were 11 days and 23 days,
respectively. Fewer people say food consumption decreased in Ramechhap (6%) but 15% said it decreasedin Dhading (Chapter 5.4).
Nuwakot district has been even better served by foodaid, resulting in less insecurity. The district also hasa large share of low income households (54%), quite

similar to Okhaldhunga, Ramechhap, and Dhading.
However, the proportion of people borrowing is much
lower (43%) and the share of borrowers taking loans
for food is also less (33%). The lower borrowing forfood is not surprising given that the district has been
well served in terms of food aid: 87% received foodaid in IRM-2 and average food stocks provided are
relatively high (29 days). The district also received
high levels of food aid in IRM-1 (96% received foodaid, with average stocks of 31 days). Only 1% say thatthey consume less food than before the 2015 monsoon.
Solukhumbu has the largest share of low income
households of any district but has far lower sharesof overall borrowing (29%) and borrowing for foodamong borrowers (16%), compared to the other poor
districts. The share receiving food aid in this district
is 33%, a large increase from the 10% in IRM-1.
Average food stocks provided have also increased
from four days in IRM-1 to 13 days. Only 1% say foodconsumption has declined since the 2015 monsoon.
The evidence from these five low-income districts
suggests that where food aid does not arrive, people
are more likely to borrow for food. Where groups facecredit constraints that prevent them doing this, there
are risks of high levels of food insecurity.28
28

See Chapter 3 for a discussion of credit constraints.

45

Earthquake Aid

Shelter
It is likely that there has been an over-distributionof tarps with many households receiving tarps both
before the monsoon and during/after. The proportionof people receiving tarps has declined since thebeginning of the 2015 monsoon season (from 71%
in IRM-1 to 47% in IRM-2; from 96% to 65% in theseverely hit districts) – Table 4.6. However, the large
amount of people still receiving tarps is surprising
given that initial distribution was so widespread andthe fact that tarps offer little protection during thewinter.

Table 4.6: Share of people who have received shelter items – by district impact and district(IRM-1/IRM-2 comparison)
IRM-1

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Housing
Damage

Tent

Tarps

CGI

Tent

Tarps

CGI

Reconstruction
materials

94%
97%
89%
97%
90%
97%
49%
60%
26%
60%
44%
35%
52%
21%
21%
66%

2%
1%
1%
6%
1%
0%
2%
3%
1%
2%
0%
1%
0%
1%
1%
2%

96%
93%
96%
91%
100%
99%
48%
40%
11%
95%
66%
35%
93%
11%
11%
71%

12%
13%
13%
10%
6%
20%
3%
5%
3%
0%
3%
2%
3%
1%
1%
7%

2%
1%
0%
5%
1%
3%
1%
1%
3%
0%
1%
1%
1%
0%
0%
1%

65%
37%
53%
72%
82%
82%
13%
17%
8%
16%
66%
41%
92%
14%
14%
47%

38%
33%
27%
39%
9%
82%
3%
3%
3%
1%
14%
16%
12%
2%
2%
21%

8%
4%
4%
1%
1%
29%
0%
1%
0%
0%
2%
2%
3%
0%
0%
4%

Tarps are clearly not sufficient to address people’s
shelter needs. Sindhupalchowk, Ramechhap, Dhading,and Nuwakot, districts that have received particularlyhigh distribution of tarps in IRM-2, have the highest
share of borrowers taking loans for temporary shelter(Chapter 3). It is clear that people need more durable
forms of shelter.29
This is because the distribution of more robust materials
for shelter has been minimal. CGI distribution inseverely hit districts has gone up since IRM-1 but isstill low: 38% against 12% in IRM-1. Indeed, in IRM2, tarps have been much more widely distributed
than CGI. Besides Sindhupalchowk, where 82% havereceived CGI since the beginning of the 2015 monsoon,

29

Table 4.7 confirms that the largest shares who report that CGI isamong their top two current needs (18-20%) are in Sindhupalchowk,
Ramechhap, Nuwakot, and Dhading, the four districts where
borrowing for temporary shelter is the highest.

46

IRM-2

the proportion of people receiving CGI is much lower
than the share of people whose houses were damaged.
With the exception of Sindhupalchowk, very few
people have received reconstruction materials.
This lack of distribution of more sturdy building materials means that demand is high. Table 4.7 shows
the proportion of people who state that different
types of shelter item are one of their top two priority
immediate needs. Demand for CGI is high across theseverely hit districts and some others (Okhaldhunga
and Solukhumbu). Almost half of the people in theseverely hit districts point to the need for reconstruction materials.3030Note that some people may prefer cash that allows them to buy
reconstruction materials rather than the materials themselves. This
will explain why those prioritizing reconstruction materials or a
new house are lower than the number of people still in temporary
shelter. For a full analysis of needs, see Chapter 5.

Aid and Recovery in Post-Earthquake Nepal

Table 4.7: Share of shelter items in top two current needs – by district impact and district (IRM-2)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Tent

CGI

Reconstruction
materials‘A house’

4%
1%
2%
1%
14%
1%
2%
2%
0%
3%
3%
1%
6%
3%
3%
3%

18%
20%
15%
21%
18%
19%
11%
11%
3%
20%
22%
11%
33%
9%
9%
16%

47%
39%
35%
69%
59%
34%
24%
16%
17%
40%
22%
23%
21%
23%
23%
34%

11%
6%
11%
0%
1%
39%
1%
3%
0%
0%
0%
0%
0%
0%
0%
5%

The data show that provision of shelter items has notbeen sufficient to deal with people’s shelter needs inmany districts. Along with tarps, Sindhupalchowk
has the largest share of people who have received
CGI in IRM-1 and IRM-2. Reconstruction materials
distribution is also the highest in this district in IRM-2(29% against the average of 4%). Yet a large amountof people in the district say receiving reconstruction
materials or a house is a key need.
The two poorest districts, Solukhumbu and Okhaldhunga, have contrasting experiences with the provision of shelter. In the former, tarps distribution wasvery high in both time periods (93% and 92%, respectively), comparable to the severely hit districts. The
proportion of people receiving CGI also increased from
4% in IRM-1 to 12% in IRM-2, while reconstruction

materials distribution has only covered 3% of the population. In Okhaldhunga, however, tarps distribution
has fallen sharply since IRM-1 (from 95% to 16%),
CGI is also very low (0% in IRM-1 and 1% in IRM-2),
and there has been no distribution of reconstruction
materials.
Given that the distribution of reconstruction materials
is very low overall (4% have received them), including
in severely hit districts (8%), it should not be
surprising that this is identified as among the top twoneeds by 47% in severely hit districts, with people in
Nuwakot and Ramechhap most likely to prioritize
such materials. Among the non-severely hit districts,
people in Okhaldhunga, which fares worst in termsof shelter provided, are the most likely to prioritizereconstruction materials (40%).

47

Earthquake Aid

Cash
Cash has been much more widely distributed sincethe 2015 monsoon than before and in increasing
volumes. The share of people who have received cashfrom any source (government or non-government)
has risen from 28% in the immediate aftermath ofthe earthquake to 64% (for cash from government)
and 11% (from non-government sources) in IRM-2.
Amounts of cash per person have also increased. The

average total cash aid received per household thatreceived cash aid is NPR 23,066 in IRM-2, almost
double the IRM-1 average of NPR 12,179 (Figure 4.3).
The greatest increase has been in the severely hit
districts, with Syangja (the hit district) seeing a fallin the average amount of money provided to each
recipient.

figure 4.3

Figure 4.3: Quantities of cash (NPR) among those who received cash,from all sources – by district impact (IRM-1/IRM-2 comparison)

23,066

Total

12,179
7,616

Hit

11,42915,422Hit with heavy losses

13,204
21,414

Crisis hit

16,961
26,207

Severely hit

11,311

0

7,000
IRM-2

14,00021,00028,000

IRM-1

figure 4.4

23,09522,005
8,317

12,172

15,45015,4508,202

14,0008,389

11,717

13,75021,00014,732

21,694

19,95625,001

28,00026,140Figure 4.4: Quantities of cash (NPR) among those who received cash – by district impact
and aid provider (IRM-2)

7,000

0

Severely hit
Gov

48

Non–gov

Crisis hit
Total

Hit with heavy losses

Hit

Total

Aid and Recovery in Post-Earthquake Nepal

On average, recipients of cash from the government
receive more than recipients from non-government
providers (Figure 4.4). In severely hit districts, theamount provided to each recipient by the government
is more than double that provided from other sources.
In less affected districts, the average sum provided per
cash recipient is slightly higher from non-government
sources than from the government. Indeed, cash from
non-government sources is highest in the third impact
category (NPR 15,450), followed by the second (NPR
13,750). In contrast, the average amount of cash aid
from non-government sources in severely hit districts(NPR 11,717) is lower than the average amount provided to crisis hit and hit with heavy losses districts.31
This suggests that at lower levels of impact, where thereach of all items, including government cash aid, islower, non-government players are playing a bigger
role in relief efforts, albeit with a smaller reach.
That total amounts of cash received per person fromall sources are only slightly higher than the amounts
provided by the government suggests that nongovernment providers are targeting people who have

not received cash from the government. Indeed, inthe severely hit districts, there is a significant negative
correlation between the volume of cash aid fromgovernment and non-government sources, indicatingthat non-government cash aid is working to coverpeople who have not received government cash.32
Within the severely hit districts, the reach of cash
aid has risen from 44% in IRM-1 to 94% in IRM2 (inclusive of government and non-government
sources). However, there is variance between districts,
with fewer people receiving cash, especially fromthe government, in Gorkha (Table 4.8). In crisis hit
districts, 46% have received cash aid from any source;
the figures are 54% for hit with heavy losses districts
and 28% for the hit district.
The high level of non-governmental cash in hit withheavy losses districts is driven by the amounts received
in Solukhumbu, the district with the largest share of
low income households. The most frequently citedsources of such aid in Solukhumbu are NGOs, INGOs,
and the Red Cross.

Table 4.8: Amount of cash received (NPR) and share who have received cash – by district impact,
district, and source (IRM-2)

31

Cash aid –non-government

Cash aid - total

Cash aid government

Cash aid –non-government

Cash aid fromany source

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Proportion receiving cash

Cash aid government

Amount received (NPR)

25,001
24,552
19,66929,93324,906
24,51019,95624,22926,77814,22014,732
23,00012,4208,202
8,202
22,005

11,717
12,565
12,821
7,944
10,000
11,227
13,75015,365
11,318
8,111
15,45014,42322,1258,389
8,389
12,172

26,207
26,155
20,12630,74524,90628,54021,414
26,84827,327
14,882
15,422
23,67012,712
7,616
7,616
23,066

90%
93%
70%
96%
95%
97%
45%
50%
21%
65%
52%
23%
82%
24%
24%
65%

17%
14%
31%
9%
0%
32%
6%
14%
3%
2%
5%
8%
1%
5%
5%
11%

94%
93%
88%
96%
95%
97%
46%
51S%
21%
65%
54%
27%
82%
28%
28%
68%

The t-test of the difference in sample means is significant at the
1% level.

32

The correlation is -0.14.43 and significant at the 5% level.

49

Earthquake Aid

Despite increases in the number of people receiving
cash, and the rise in volumes of cash per person, it isclear that cash provided thus far is not enough. The
sums provided are insufficient for addressing key
needs such as building more robust housing. For thisreason, larger numbers of people, especially in severely
hit districts but also elsewhere, prioritize cash as oneof their priority immediate and medium-term needs(Figure 4.5).

figure 4.5

Figure 4.5: Share of cash among top two needs now and in three months’ time (IRM-2)

27%

Hit

36%

56%

Hit with heavy losses

45%

55%

Crisis hit

45%

62%

Severely hit
0%

70%

18%
Top need in three months

35%

53%

70%

Top current need

4.3 Providers of aid
Who are people receiving aid from?
Respondents in both IRM-1 and IRM-2 were asked toprovide information on the sources from whom theyreceived aid.33 It should be noted that responses are
based on people’s awareness; as such, results cannot
be taken to necessarily reflect the actual shares of aid
being provided by different providers.
The government, INGOs, and NGOs continue toprovide aid to the most people. In IRM-1, the mostcommon sources of aid were the government (61%),33‘Government’ includes the Nepal Government, Village Development Committees, and municipalities; ‘Citizen group’ includes Ward
Citizen Forums, Citizen Awareness Centers, Social Mobilizers, andthe LGCDP project; ‘Army/police’ includes the Nepal Army, Armed
Police Force, and the Police.

50

followed by INGOs (19%), NGOs (14%), individuals(11%), and the Red Cross (9%). These continue to be
the most dominant providers across districts, with the
exception of individuals, with only 4% receiving aid
from this source in IRM-2 (Figure 4.6). The proportionof people receiving aid from the government has risen
to 66%, from the Red Cross has increased to 11%, andfrom NGOs has increased to 22%. The proportion
receiving aid from INGOs has stayed the same (19%).
Other aid providers are much less prominent.

Aid and Recovery in Post-Earthquake Nepal

figure 4.6

Figure 4.6: Source of aid – all districts (IRM-1/IRM-2 comparison)
70%

66%
61%

53%

35%
22%

18%
9%

11%
4%

3%

1% 1%

2% 1%

1%

s

s

s

lic

s

al

ss

ie
tr

or

un

on

D

Co

O

G

IN

O

ne

du

os

s

Cr

vi

si

G

N

Bu

d

di

In

Re

tie

po

s

en

tiz

1% 1%

y/
m
Ar

Ci

ov

G

1% 1%

N
U

0%

1% 1%

r
Pa

3% 4%

11%

19%19%

14%

e

IRM-1

IRM-2

The same groups are the main sources of aid in theseverely hit districts (Figure 4.7), although they coverlarger shares of the population than in other districts.

Government aid has reached 90% of people in thesemost affected districts, while NGO aid has been
received by 33% and INGO aid by 31%.

figure 4.7

Figure 4.7: Source of aid – severely hit districts (IRM-1/IRM-2 comparisons)
90%

90%
82%

68%

45%
33%

23%

6%

2% 1%

4%

1%

2%

s

or

on

D
s

rie

t
un

Co

O

G

IN

O

G

N

ss
ne

si
Bu
s

l
ua

id

s

os

cr

v
di

In

d
Re

e

ic

ol

p
y/

s
ie

m

N

2% 1%

Ar

s

en

tiz

Ci

ov

G

2% 2%

U

0%

7%
2% 1%

rt
Pa

3% 4%

31%

21%

20%

15%16%

34%

IRM-1

IRM-2

51

Earthquake Aid

Aid from the government has been targeted at severely
hit districts but has also covered many people in lesser
affected districts (Figure 4.8). The sharpest rise incoverage between IRM-1 and IRM-2 has been in theleast affected hit district, Syangja. Almost all of the aid
going to Syangja comes from the government.

figure 4.8

Figure 4.8: Share of people receiving government aid – by district impact (IRM-1/IRM-2 comparison)

90%

90%
82%

68%

66%
61%

57%
47%

45%

45%

48%

29%

23%
13%

0%

Severely hit
IRM-1

Crisis hit

Hit with heavy losses

Hit

Total

IRM-2

NGO aid has also reached a larger amount of people
in severely hit districts than in others. However, there
has been a massive increase in the proportion ofpeople who are receiving such assistance in the hit withheavy losses districts (Figure 4.9). The two hit withheavy losses districts—Lamjung and Solukhumbu—
have very similar coverage of aid from NGOs.

figure 4.9

Figure 4.9: Share of people receiving NGO aid – by district impact (IRM-1/IRM-2 comparison)

40%
33%

30%
24%

20%

22%

21%

14%

10%

0%

7%

8%

1%

1%

Severely hit
IRM-1

52

7%

IRM-2

Crisis hit

Hit with heavy losses

Hit

Total

Aid and Recovery in Post-Earthquake Nepal

There has also been a large rise in INGO aid coveragein the hit with heavy losses districts, with coverageincreasing from 6% in IRM-1 to 19% (Figure 4.10).

In contrast to NGO aid, most of this is driven by aid
provided to Solukhumbu, where 35% have received aid
from INGOs in IRM-2 compared to 4% in Lamjung.

figure 4.10

Figure 4.10: Share of people receiving INGO aid – by district impact (IRM-1/IRM-2 comparison)

40%
34%
31%

30%

19%

20%

19%

19%

10%
6%

0%

6%

5%

0%

Severely hit
IRM-1

Crisis hit

Hit with heavy losses

0%

Hit

Total

IRM-2

Table 4.9 provides a full breakdown of the proportionof people in each district who have received aid fromeach source in IRM-2.

Citizen groups

Army/police

Political
partiesUN

Red Cross

Individuals

Businesses

NGOs

INGOs

Foreigngovernments

DonorsSeverely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Government

Table 4.9: Source of aid – by district impact and district (IRM-2)

90%
94%
73%
96%
98%
87%
45%
49%
20%
67%
57%
27%
88%
29%
29%
66%

4%
1%
2%
5%
9%
3%
2%
2%
3%
1%
6%
1%
10%
1%
1%
4%

1%
0%
1%
0%
1%
2%
1%
1%
1%
0%
2%
2%
1%
0%
0%
1%

2%
1%
1%
2%
5%
1%
0%
1%
0%
0%
0%
0%
0%
1%
1%
1%

1%
1%
0%
0%
4%
0%
0%
0%
0%
0%
4%
0%
7%
0%
0%
1%

16%
5%
28%
13%
15%
19%
5%
8%
4%
2%
11%
11%
10%
3%
3%
11%

7%
8%
12%
11%
2%
5%
1%
2%
1%
1%
4%
4%
3%
1%
1%
4%

1%
2%
1%
1%
1%
1%
0%
0%
0%
0%
0%
0%
1%
0%
0%
1%

33%
25%
40%
29%
15%
57%
7%
5%
6%
10%
24%
26%
23%
1%
1%
22%

31%
12%
45%
50%
10%
36%
5%
9%
3%
2%
19%
4%
35%
0%
0%
19%

1%
0%
1%
3%
1%
2%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1%

2%
0%
0%
1%
7%
1%
1%
1%
1%
0%
1%
0%
2%
0%
0%
1%

53

Earthquake Aid

4.4 Satisfaction with aid providers
There has been a rise in satisfaction with most aid
providers since IRM-1 amongst those who receivedaid (Figure 4.11). This increase has been greatest for
local community organizations. The army, police, andbusinesses have seen the greatest drops in satisfactionlevels. However, the two former organizations still see
the highest satisfaction levels among any aid provider.

The army and police have actually delivered very little
assistance: only 1% had received aid in both IRM-1 andIRM-2 from them (Figure 4.6 above). People may still
have positive memories of the rescue role they playedin the days following the earthquakes. Satisfaction
with both INGOs and NGOs has increased and issimilar to levels of satisfaction with the government.34

figure 4.11

Figure 4.11: Satisfaction with aid providers – all districts, among those who received aid only
(IRM-1/IRM-2 comparison)

90%

85%

83%
76%

68%

74%
69%

66%

71%
62%

59%

57%

66%

66%
59%

54%

59%

50%

45%

47%
39%

30%

33%

39%
32%

23%

0%

Re
us

io

lig

ou

ps

gr

ou

gr

v

ity

go

un

n

m

ig

m

re

Fo

Co
ss

ne

si

s

ps

54

Bu

ie

Okhaldhunga has the lowest satisfaction with most aid
providers among all districts. Aid has fallen sharplysrt

People are most satisfied with aid providers in the hit
with heavy losses districts (Table 4.10). In particular,
Solukhumbu has the highest satisfaction of any
district for nearly every aid provider. Satisfaction inthese districts has risen significantly since IRM-1. For
example, in June 2015, 66% of those who had received
aid in the hit with heavy losses districts expressedsatisfaction with the government; by February
2016, this had risen to 81%. Similarly, satisfaction
with NGOs rose from 74% to 92%; and satisfaction
with INGOs increased from 71% to 92%. One major
exception is satisfaction with political parties, which
has remained constant at 49%.

O

pa

IRM-2

Results are very similar when focusing solely on theseverely hit districts where the most people havereceived aid (Figure 4.12).

G

N
s

O

G

IN

al

in

ic

m

lit

Ad

Po

e

lic

Po

y

m

Ar

ov

G
IRM-1

in this district in IRM-2, particularly for essential
items (food and shelter). However, even those who
receive aid are less satisfied there, perhaps reflectingthat either volumes have not been sufficient or that
there is disillusionment as other needy people in the
district miss out.
Among the severely hit districts, satisfaction withthe government is highest in Ramechhap (83%)
and Dhading (70%); with the army in Dhading(83%) and Gorkha (78%); with the police in Gorkha(84%) and Dhading (82%); with political parties in
Ramechhap (49%); with the local administrationin Sindhupalchowk (68%) and Gorkha (67%); with
INGOs in Gorkha (88%) and Sindhupalchowk

34

Analysis of satisfaction with political parties is provided in
Chapter 7.

Aid and Recovery in Post-Earthquake Nepal(81%); with NGOs in Nuwakot (81%) and Gorkha(74%); with business groups in Gorkha (55%); with
community groups in Gorkha (72%) and Nuwakot(66%); with foreign governments in Gorkha (75%) and
Sindhupalchowk (65%); and with religious groups in
Nuwakot (74%).

figure 4.12

Figure 4.12: Satisfaction with aid providers – severely hit districts, among those who received aid only
(IRM-1/IRM-2 comparison)
90%

84%

82%
75%

68%

74%

71% 72%

66%

63%

58%

67%

66%
58%

57%
51%

56%
47%

47%

45%

38%

37%
31%

29%

30%

23%

0%

Re
lig
ou

gr
ps

ps

IRM-2

us

io

v

s

ou

ie

gr

rt

ity

go

un

n

m

ig

m

re

Fo

Co

pa

ss

ne

si

Bu

s

O

s

O

G

G

N

IN

al

in

ic

m

lit

Ad

Po

e

lic

Po

y

m

ov

Ar

G
IRM-1

Districts

Government

Army

PolicePolitical
partiesLocaladministration

INGOs

NGOs

Businesses

CommunitygroupsForeigngovernments

Religious
groupsTable 4.10: Satisfaction with aid providers, among those who received aid only –by district impact and district (IRM-2)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

66%
70%
53%
45%
83%
69%
56%
49%
44%
63%
81%
71%
94%
76%
76%
66%

75%
83%
78%
70%
77%
65%
66%
66%
100%
58%
93%
93%
94%
89%
89%
76%

74%
82%
84%
70%
75%
56%
62%
63%
89%
55%
93%
93%
94%
92%
92%
74%

31%
20%
26%
26%
49%
33%
22%
20 %
33%
20%
49%
36%
69%
59%
59%
33%

57%
49%
67%
42%
57%
68%
55%
35%
61%
69%
71%
64%
81%
76%
76%
59%

72%
60%
88%
79%
57%
81%
50%
46%
67%
49%
92%
89%
95%
78%
78%
71%

67%
64%
74%
81%
55%
64%
44%
35 %
67%
45%
92%
93%
92%
78%
78%
66%

37%
31%
55%
41%
30%
31%
26%
26 %
61%
19%
62%
57%
69%
70%
70%
39%

58%
52%
72%
66%
53%
48%
47%
57%
67%
36%
81%
80%
81%
89%
89%
59%

56%
31%
75%
56%
56%
65%
41%
37%
72%
38%
93%
93%
94%
86%
86%
59%

47%
34%
45%
74%
34%
56%
32%
43 %
61%
18%
64%
61%
67%
57%
57%
47%

55

Earthquake Aid

4.5 Damage assessments
and beneficiary cards
The government has conducted a number of assessments to damages to houses, using these as a basis
for deciding who will receive a beneficiary card. These
cards determine who should receive cash assistance,
including two payments made over the winter andlarger future sums of money to support housing re-construction. Only those whose house was classified
as fully damaged were to receive cards.35 To what extent were these beneficiary cards well targeted? And
how has this shaped satisfaction and the likelihood ofreceiving cash assistance.

Damage assessment results
Table 4.11 compares the results of the damage assessment with households’ self-reporting of the extent towhich their house was damaged. In general, there isa close correlation between being classified as fully
damaged in the assessment and households reportingthat their house was completely damaged. In Gorkha

and Ramechhap, however, far more people were classified as fully damaged than report that their house
was completely damaged. In contrast, 53% of people
in Dhading say their house was classified in the assessment as fully damaged while 77% say that their house
was completely damaged.

Table 4.11: Damage assessment results and self-reported damage –by district impact and district (IRM-2)

35

Partially
damagedNormal/not
damaged

Official did not
arriveRefused/don’t
knowCompletely
damagedBadly damaged(needs major
repairs)

Habitable(but needs
minor repairs)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

80%
53%
80%
92%
83%
92%
36%
50%
16%
43%
31%
27%
35%
11%
11%
53%

6%
5%
13%
3%
6%
3%
21%
11%
16%
37%
39%
19%
58%
22%
22%
17%

2%
3%
6%
1%
1%
1%
36%
31%
59%
19%
22%
38%
6%
66%
66%
21%

6%
28%
0%
2%
1%
1%
2%
1%
3%
0%
0%
0%
0%
0%
0%
3%

6%
11%
2%
3%
10%
4%
5%
7%
6%
2%
8%
16%
1%
1%
1%
6%

78%
77%
57%
92%
69%
94%
31%
46%
16%
32%
27%
24%
29%
8%
8%
50%

16%
20%
32%
5%
20%
3%
18%
14%
10%
29%
17%
11%
23%
13%
13%
16%

5%
3%
8%
3%
10%
2%
30%
15%
38%
37%
42%
39%
45%
62%
62%
24%

See Chapter 3.4 of the qualitative report for extensive analysis of
the damage assessments. The Asia Foundation and Democracy Resource Center Nepal (2016). Aid and Recovery in Post-Earthquake

56

Self-reported damage

Fully damaged

Damage assessment results

Nepal: Independent Impacts and Recovery Monitoring Nepal
Phase 2 – Qualitative Field Monitoring (February and March
2016).

Aid and Recovery in Post-Earthquake Nepal

Beneficiary cards and damage assessment results
In severely hit districts, most people have received
beneficiary cards. However, there is some degree ofvariation in this, with people in Gorkha less likely tohave been issued cards (Figure 4.13). In districts inother categories of earthquake impact, this variation
is even greater. Amongst crisis hit districts, people inOkhaldhunga and Bhaktapur are much more likely
to have received cards than people in Kathmandu.
Amongst the two hit with heavy losses districts, mostpeople have received cards in Solukhumbu while only
just over one-third got cards in Lamjung.

Beneficiary cards are meant to only be issued topeople whose houses are officially classified as being
fully damaged. The proportion of people receiving
beneficiary cards does not, however, always fit with
the number of people who say that the assessmentclassified their houses as such. In Dhading, forexample, 93% of people received beneficiary cards
even though only 53% report that the damage
assessment classified their house as fully damaged.
In Solukhumbu, the gap is even bigger with morethan twice as many people receiving cards as thosereporting that their house was fully damaged.

figure 4.13 Figure 4.13: Share of people who have received beneficiary card and whose house

was classified as fully damaged – by district (IRM-2)

100%

93%

94%

89%

92%

99%

96%

92%
85%

83%

80%

75%
60%

50%

53%

51% 50%
43%
35%

25%

35%
29%

27%

22%
16%

11%

0%

bu

um

a

gj

kh

lu

ga

un

ng

dh

ju

an

Sy

So

m

al

w
ho

du

an

r

lc

pa

pu

ta

m

ak

th

kh

La

O

Ka

Bh

hu

ap

hh

ec

m

nd

Si

Ra

ot

ak

uw

N

a

kh

or

G
ng

di

ha

D

k

Received beneficiary card

Fully damaged in damage assessment

Table 4.12: Share of people who have received beneficiary card – by classificationin damage assessment (IRM-2)
Has your household received a beneficiary identity card?

How was your houseclassified in the official
damage assessment?
Total

Fully damaged
Partially damaged
Normal/not damaged
Official did not arrive
RefusedDon’t know

Yes

No

RefusedDon’t know

97%
67%
5%
76%
0%
40%
69%

3%
32%
95%
24%
100%
54%
31%

0%
0%
0%
0%
0%
0%
0%

0%
1%
1%
0%
0%
6%
1%

57

Earthquake Aid

Looking at the household level, Table 4.12 showsthat almost all of those whose house was classified
in the damage assessment as being fully damaged
have received beneficiary cards. Two-thirds of those
whose houses were classified as partially damaged
have received cards. Five percent of those who said

that their houses were classified as not being damaged
have received beneficiary cards. Interestingly, threequarters of those who say their house did not receive
a classification because officials did not arrive for theassessment have also received beneficiary cards.

Satisfaction with the damage assessment
Those who received beneficiary cards tend to besatisfied with the classification of their house in thedamage assessment (61% are very satisfied, 27% aresomewhat satisfied) – Table 4.13. In contrast, there

are mixed levels of satisfaction amongst those who
did not receive cards. Two-thirds of this group are
still satisfied, but one-quarter say they are unsatisfied.

Table 4.13: Satisfaction with damage assessment – by whether
or not received beneficiary cards (IRM-2)
Has your household received a beneficiary
identity card?

Very satisfied
How satisfied wereSomewhat satisfied
you with the classifiSomewhat unsatisfied
cation of your house
Very unsatisfied
in the official damRefused
age assessment?
Don’t know
Total

Yes

No

RefusedDon’t know

61%
27%
3%
1%
0%
7%
100%

22%
43%
15%
10%
1%
9%
100%

0%
100%
0%
0%
0%
0%
100%

0%
23%
8%
19%
4%%
46%
100%

Levels of satisfaction with the damage assessment are
not fully determined by whether or not people received
beneficiary cards. Of people who are very satisfied,
86% received beneficiary cards (Table 4.14). Amongst
those who are somewhat satisfied with how their house
was classified, 58% received cards while 42% did not.

Total
49%
32%
7%
4%
0%
8%
100%

Furthermore, substantial shares of those who received
beneficiary cards were not satisfied with how theirhouse was classified. This suggests that some people
were not happy with the damage assessment process,even when they received a beneficiary card.

Table 4.14: Whether or not received beneficiary card – by satisfaction
with damage assessment (IRM-2)
Has your household received a beneficiary
identity card?

Very satisfied
How satisfied wereSomewhat satisfied
you with the classifiSomewhat unsatisfied
cation of your house
Very unsatisfied
in the official damRefused
age assessment?
Don’t know

Yes

No

RefusedDon’t know

86%
58%
32%
22%
0%
64%

14%
42%
67%
75%
90%
32%

0%
0%
0%
0%
0%
0%

0%
0%
1%
3%
10%
4%%

Of those whose house was categorized as fully damaged, almost everyone is satisfied. Satisfaction levels
are the lowest for those whose house was partially

58

Total
100%
100%
100%
100%
100%
100%

damaged, presumably because many felt they shouldhave received assistance but have not been issued with
beneficiary cards that help them access aid (Table 4.15).

Aid and Recovery in Post-Earthquake Nepal

Table 4.15: Satisfaction with damage assessment – by how house
was classified in damage assessment (IRM-2)

SomewhatsatisfiedSomewhat
unsatisfied

Veryunsatisfied

RefusedDon’t know

Fully damaged
Partially damaged
How was your house
Normal/not damaged
classified in the official
Official did not arrive
damage assessment?
RefusedDon’t know
Total

Very
satisfied

How satisfied were you with the classificationof your house in the official damage assessment?
Total

73%
28%
26%
3%
0%
1%
49%

25%
44%
50%
9%
33%
5%
32%

1%
16%
15%
2%
67%
8%
7%

0%
11%
6%
5%
0%
10%
4%

0%
0%
0%
1%
0%
2%
0%

1%
1%
3%
80%
0%
74%
8%

100%
100%
100%
100%
100%
100%
100%

Links between beneficiary cards and the distribution of cash
The government has targeted cash at those who
received beneficiary cards (Figure 4.14). Ninety-two
percent of those with a beneficiary card received cashfrom the government compared to 6% who do not havea card. Similarly, 15% of those with a card received
cash from non-government organizations compared
to 1% who did not have a card.

Figure 4.14: Share of people whohave received cash – by whether
figureor 2.5not received beneficiary cards (IRM-2)
80%
71%
66%

60%

40%

55%

38%

20%

0%

Urban
Housing damage

Rural
Livelihood damage

59

Earthquake Aid

Photo: Tenzing Paljor

4.6 Experiences of aid among
different population groups
Rural and urban areas
Aid provision in rural areas has been higher, with only
24% not receiving any aid compared to 67% in urban
areas (Figure 4.15). For most types of aid, a larger
proportion of people in rural areas received assistancefigure 4.15

than in urban areas, with the exception of tents, which
is an item of minimal importance (1% received tents).
This is unsurprising given the greater impacts fromthe earthquake in rural areas.36

Figure 4.15: Share of people receiving different types of aid – by urban/rural (IRM-2)
69%

70%

67%

52%

53%

41%

37%

35%

32%
24%

24%

17%

18%

0%

3%

1% 1%

Tent

Urban

Tarps

CGI

1%

Food

16%

12%

12%

8%

5%

Medical10%

2%Cash gov

Cash - Blanket
non gov

3%

Clothes

5%

5%

0%

Kitchen Sanitation No aid
items

Rural

36

Seventy-one percent of houses in rural areas were damaged by
the earthquake compared to 38% in urban areas. However, theimpacts on livelihoods were greater in urban areas. See Chapter 2.

60

Aid and Recovery in Post-Earthquake Nepal

Because the amount of aid that has gone to urban
and rural areas is so different, in large part becauseof different levels of needs, Figure 4.16 compares therelative role of different aid providers for those who
received aid only.

The most notable difference is that NGOs and INGOs
account for a much greater share of aid in rural areas
than in urban ones.

figure 4.16

Figure 4.16: Sources of aid reported by those who received aid – by urban/rural (IRM-2)
100%
90%90%

75%

50%
32%

28%

25%
13%

14%
8%

6% 6%

1% 2%

3% 2%

1% 1%

1% 1%

s

s

U

ie

n

rt

t

s a
or r th
on e
D th
(O
s
ie

tr

O

un

Co

G

IN

O

al

ss

ne

du

s

pa

os

al

ic

Cr

vi

si

G

N

Bu

d

di

In

Re

lit

N

U

Po

en

nm

en

y

tiz

er

m

3% 1%

Ci

ov

G

2% 1%

Ar

4% 5%

0%

15%

N

Rural

)

Urban

Satisfaction with aid providers is higher in rural
areas, with the exception of religious groups, where
the proportion of people who are satisfied in urban
areas is higher (51% against 47%) – Figure 4.17. On
the one hand, this is not surprising, given that more
4.17
aid figure
has flowed
to rural areas. On the other, needs arealso higher in these places. Dissatisfaction with thegovernment amongst those who received aid is notably
higher in urban areas than in rural ones and there isalso much lower satisfaction with political parties inurban places.

Figure 4.17: Satisfaction with aid providers among those who received aid – by urban/rural (IRM-2)
47%

Religious groups
Foreign gov

51%
60%

46%

60%
57%

Community groups
39%
37%

Business
NGOs

68%

49%

INGOs

72%

54%

Local administration

60%

40%

Political parties

21%

34%

Police

68%

Army

70%

Government
0%
Rural

40%

76%

67%

49%

20%

75%

60%

80%

Urban

61

Earthquake Aid

Income groups
The poorest and the richest are the most likely to havereceived no aid (Table 4.16). That the poor are missingout is worrying, given that the poorest were amongst
the most likely to have been negatively impacted by theearthquakes.37 Disaggregating by types of aid received,
the richest and poorest are the least likely to havereceived almost every kind of assistance.

CGI

Food

MedicalCash government

Cash – non-​government

BlanketClothes

Kitchen
items

Sanitation

No Aid<NPR 2,500
NPR 2,501 - 9,999
NPR 10,000 - 19,999
NPR 20,000 - 39,999> NPR 40,000
Total

Tarps

Monthly income

Tent

Table 4.16: Types of aid received – by income band (IRM-2)

0%
1%
1%
2%
5%
1%

40%
55%
46%
34%
28%
47%

10%
24%
22%
13%
6%
21%

38%
44%
36%
23%
10%
37%

1%
5%
4%
3%
1%
4%

56%
74%
64%
46%
31%
64%

3%
11%
13%
9%
9%
11%

30%
37%
33%
23%
19%
33%

3%
8%
11%
7%
6%
9%

1%
5%
5%
1%
1%
4%

7%
15%
15%
13%
7%
14%

40%
22%
29%
48%
60%
30%

Government cash has been more likely to reach thoselower down the income spectrum, except in urban
areas where the poorest are less likely to have received
support (Table 4.17). The richest in both urban andrural areas are the least likely to have received cashfrom the government. Volumes of government cash inrural areas are fairly similar across all income groups,
except for the richest who receive, on average, aroundhalf the amount per person that the poorer do. Averageamounts of government cash per person are moresimilar across income groups in urban areas.
In contrast, the very poorest in rural areas are substantially less likely to receive non-government cash. In urban areas, there are few differences between different
income groups. The volume of non-government cash
per person also tends to be higher for richer people.

Table 4.17: Share of people who have received cash and average amounts received (NPR) for those
who received it – by government/non-government, rural/urban, and income band (IRM-2)

Monthly income<NPR 2,500
NPR 2,501 - 9,999
NPR 10,000 - 19,999
NPR 20,000 - 39,999> NPR 40,000
Total

Proportion
receiving
non-government
cash

Proportion
receiving
government cash

Non-government
cash quantity(NPR)

Government cash
quantity (NPR)

Rural
3%
11%
14%
11%
13%
12%

Rural
67%
76%
69%
54%
42%
69%

Rural
12,833
11,54012,164
13,354
19,214
12,271

Rural
15,91016,793
15,034
13,758
8,904
12,368

Urban
3%
3%
1%
2%
3%
2%

Urban
25%
49%
31%
25%
15%
32%

37

Urban
7,000
5,667
5,000
12,50010,000
7,667

Urban
23,05821,852
21,511
24,97020,13021,979The Asia Foundation (2015). Aid and Recovery in PostEarthquake Nepal: Independent Impacts and Recovery Monitoring
Nepal Phase 1 – Quantitative Survey (June 2015). Kathmandu andBangkok: The Asia Foundation, pp. 10-11.

62

Aid and Recovery in Post-Earthquake Nepal

Table 4.18 reports the proportion of people receiving
aid from each aid provider by income band. Most
providers follow the common pattern, with the poorest
and the richest the least likely to receive aid. However,notably the poorest are more likely to receive aid frompolitical parties and citizen groups than those who
earn more. INGOs are particularly unlikely to provide
support to the poorest or the richest.

Monthly income

Government

Citizen groups

Army

Political
partiesUN

Red Cross

Individuals

Businesses

NGOs

INGOs

Other
countries

Donors (otherthan UN)

Table 4.18: Sources of aid among those who received aid – by income band (IRM-2)<NPR 2,500
NPR 2,501 - 9,999
NPR 10,000 - 19,999
NPR 20,000 - 39,999> NPR 40,000
Total

60%
76%
66%
48%
34%
66%

4%
4%
4%
2%
5%
4%

1%
0%
1%
2%
1%
1%

4%
1%
1%
0%
0%
1%

1%
1%
2%
0%
3%
1%

6%
11%
12%
8%
9%
11%

3%
4%
5%
4%
2%
4%

1%
1%
0%
0%
1%
1%

14%
21%
24%
21%
16%
22%

7%
23%
20%
11%
7%
19%

1%
1%
1%
0%
0%
1%

1%
1%
2%
0%
2%
1%

Satisfaction levels with all aid providers tend to belower among the top two income brackets, with the
exception of satisfaction with the army and police(Table 4.19). Satisfaction with INGOs, NGOs, andpolitical parties is also highest among the lowest
income bracket: INGOs (75% against the average of71%); NGOs (68% against 66%); political parties (45%against 33%).

Income Groups

Government

Army

PolicePolitical
partiesLocaladministration

INGOs

NGOs

Businesses

CommunitygroupsForeigngovernments

Religious
groupsTable 4.19: Satisfaction with aid providers among those who received aid – by income band (IRM-2)<NPR 2,500
NPR 2,501 - 9,999
NPR 10,000 - 19,999
NPR 20,000 - 39,999> NPR 40,000
Total

65%
72%
62%
59%
60%
66%

80%
75%
78%
71%
80%
76%

73%
72%
78%
67%
80%
74%

45%
32%
33%
32%
40%
33%

60%
55%
62%
55%
67%
59%

75%
70%
72%
70%
67%
71%

68%
67%
66%
63%
67%
66%

38%
40%
38%
44%
40%
39%

63%
58%
60%
60%
73%
59%

60%
59%
58%
59%
60%
59%

53%
50%
43%
51%
47%
47%

Gender
Women are slightly less likely to get most types of aid.
Thirty-one percent of women report receiving no aid
compared to 29% of men (Figure 4.18). Although thedifferences are minor, women have a slightly lower
likelihood of receiving cash from government (65%versus 62%) and non-government sources (11% versus10%). A slightly larger proportion of women than men
have received food aid (37% against 36%) and clothes(10% against 9%).

63

Earthquake Aidfigure 4.18

Figure 4.18: Types of aid received – by gender (IRM-2)
70%

65%

53%

48%

62%

46%
36% 37%35%

33% 33%29%

31%

21% 20%18%

14%14%

11% 10%

4% 5%

4% 4%

1% 2%

0%

9% 10%

Tent

Tarps

Male

CGI

Food

MedicalCash gov

Cash - Blanket
non gov

Clothes

Kitchen Sanitation No aid
items

Femalefigure 4.19

Figure 4.19: Sources of aid – by gender (IRM-2)
70%

67%

65%

53%

35%
22% 21%

18%
12%

1% 1%

1% 2%

s an
or th
on er
D th
(o
s
ie

tr

un

O

G

IN

O

G

N
ss

ls
ua

id

ne

si

Bu

v
di

N

U
)

There are also no major differences between men andwomen in satisfaction ratings amongst those whososThere are no notable differences between men andwomen in terms of sources of aid (Figure 4.19)

64

In

cr

s

ie

Female

1% 0%

Co

4% 5%

rt
pa

t

en
Male

18%

10%

d
Re

U
al

ic

y

lit

N

1% 1%

m

en
tiz

Ci

nm

er

ov

G

1% 1%

Po

0%

1% 1%

Ar

3% 4%

20%

receive aid across aid providers, although womenare slightly more likely to be satisfied with the main
aid providers: the government, INGOs, and NGOs
(Figure 4.20).

Aid and Recovery in Post-Earthquake Nepal
figure 4.20

Figure 4.20: Satisfaction with aid providers – by gender (IRM-2)
48%
46%

Religious groups
Foreign gov

56%

61%

59%
60%

Community groups
37%

Business

41%

67%
66%

NGOs

72%
70%

INGOs
58%
60%

Local administration
33%
33%

Political parties
Police

73%
76%

Army

76%
75%

Government
0%20%Female

40%

60%

64%

69%

80%

Male

Caste groups
Janajatis are more likely to have received aid than low
or high caste people (Table 4.20). This is particularly
the case for CGI, food, and government cash. However,there is little difference between the groups for nongovernment cash and lower caste people are almost as
likely to have received tarps as Janajatis.

Tarps

CGI

Food

MedicalCash government

Cash – non-​government

BlanketClothes

Kitchen items

Sanitation

No AidLow
Janajatis
High

Tent

Table 4.20: Share of people who have received aid of different types – by caste groups (IRM-2)

1%
1%
1%

49%
50%
42%

19%
25%
14%

31%
42%
28%

4%
5%
2%

57%
68%
59%

12%
11%
10%

33%
36%
27%

9%
9%
9%

6%
5%
3%

12%
16%
11%

35%
26%
36%

Of the main aid providers, the government and INGOs
are much more likely to have provided aid to Janajatis
than either low or high caste people (Table 4.21). In

contrast, there is little difference in the aid provided
by the Red Cross and NGOs.

Citizen groups

Army

Political
partiesUN

Red Cross

Individuals

Businesses

NGOs

INGOs

Other
countries

Donors (otherthan UN)

Low
Janajatis
High

Government

Table 4.21: Sources of aid – by caste groups (IRM-2)

58%
70%
61%

1%
3%
4%

1%
1%
1%

2%
1%
1%

1%
1%
1%

10%
11%
10%

4%
4%
5%

0%
1%
1%

22%
22%
20%

16%
23%
13%

0%
1%
0%

1%
2%
1%

65

Earthquake Aid

Despite being less likely to receive aid than Janajatis,
lower castes are much more likely to be satisfied withaid providers than others (Figure 4.21).
Figure 4.21: Satisfaction with aid providers among those who received aid – by caste (IRM-2)

figure 4.21

41%

Religious groups

50%
50%
53%

Foreign gov

61%

Community groups

57%

69%

60%
60%

39%
40%
36%

Business

62%

NGOs

68%
70%
66%

INGOs
54%

Local administration
30%

Political parties

34%

60%

73%

80%

70%

38%

Police

73%
74%

Army

75%
66%
65%

Government
0%

20%
High

Janajati

40%

78%
78%
77%

73%

60%

80%

Low

Disability
Those with disabilities are slightly more likely tohave received aid than those without, although thedifferences are not large (Table 4.22). This holds true
for most of the main types of aid.

Tarps

CGI

Food

MedicalCash government

Cash – non-​government

Blankets

Clothes

Kitchen items

Sanitation

No AidWithout Disability
With Disability

Tent

Table 4.22: Aid received – by disabled/non-disabled (IRM-2)

1%
2%

47%
46%

19%
24%

36%
38%

4%
5%

61%
68%

10%
12%

31%
35%

7%
12%

4%
4%

13%
16%

31%
28%

There are not substantial differences in the likelihoodof those with and without disabilities receiving aid
from most providers. Those with disabilities areslightly more likely than those without to get aid fromthe government (69% versus 65%) and NGOs (25%versus 20%). They are slightly less likely to get aid
from INGOs (18% versus 20%).

66

Across all aid providers, a smaller share of those withdisabilities report being satisfied relative to those
without disabilities, with the exception of religious
groups as aid providers, where the group withdisabilities has a slightly higher satisfaction rate (49%against 46%) – Figure 4.22.

Aid and Recovery in Post-Earthquake Nepal

figure 4.22

Figure 4.22: Satisfaction with aid providers among those who received aid –by disabled/non-disabled (IRM-2)
49%
46%

Religious groups
Foreign gov

55%

62%
60%
59%

Community groups
38%

Business

41%
64%

NGOs

68%
68%

INGOs

73%

58%
59%

Local administration
31%

Political parties

35%
74%
75%

Police

75%
77%

Army
64%

Government
0%20%With disability

40%

68%

60%

80%

Without disability

Housing damage
Those whose houses were destroyed are the most
likely to have received aid, with the likelihood ofreceiving aid reducing as housing impacts becomeless (Table 4.23). This is not just the case for shelterrelated aid but for most other types of assistance, too.

There are, however, some indications of mistargeting.
A small proportion of shelter-related assistance,
especially tarps, reached those who did not experience
any housing impact.

Tarps

CGI

Food

MedicalCash government

Cash – nongovernment

Blankets

Clothes

Kitchen items

Sanitation

No AidCompletely damaged
Badly damaged
Habitable
Not damaged

Tent

Table 4.23: Aid received – by level of housing damage (IRM-2)

2%
1%
0%
0%

63%
48%
30%
4%

36%
15%
2%
1%

58%
30%
12%
2%

7%
3%
0%
0%

91%
67%
32%
2%

18%
8%
2%
0%

54%
26%
7%
2%

13%
13%
2%
1%

6%
7%
0%
0%

21%
15%
4%
1%

6%
22%
57%
94%

The likelihood of receiving cash is the highest amongst
those whose houses were completely damaged anddeclines as housing impact reduces (Table 4.24).
Overall, less than 5% of those with completely
damaged homes report not having received cash aid
from any source. It is more concerning that over onequarter with badly damaged homes have not received
cash aid from any source. Two-thirds of those withdamaged but habitable homes have not received cash.

is little difference in the amount of cash received byeach person for non-government cash. Those withcompletely damaged homes receive the most cash
per person. Surprisingly, however, those whose
houses have not been damaged who have received
government cash report receiving NPR 21,429 on
average, although this is only a very small share (0.5%)of people who received cash across all the districts.

The volumes of cash received for each beneficiary
do not correlate closely with housing impact. There67

Earthquake Aid

Table 4.24: Volumes of government and non-government cash aid among those who received it (IRM-2)
Housing Damage

Non-government cash
(NPR)

Government cash (NPR)

No cash aid (Proportion)

12,44010,071
12,763
10,00012,172

24,60819,784
9,758
21,42922,005

4%
27%
66%
98%
32%

Completely damaged
Badly damaged
Habitable
Not damaged
Total

The government, NGOs, INGOs, and the Red Cross
are the dominant aid providers for people with all
categories of housing impact (Table 4.25).

Citizen groups

Army

Political
partiesUN

Red Cross

Individuals

Businesses

NGOs

INGOs

Other
countries

Donors (otherthan UN)

Completely damaged
Badly damaged
Habitable
Not damaged

Government

Table 4.25: Share of people who have received aid from each source – by level of housing damage (IRM-2)

91%
73%
37%
3%

5%
4%
1%
0%

1%
2%
0%
0%

2%
1%
0%
0%

1%
2%
1%
0%

16%
13%
2%
1%

6%
7%
2%
1%

1%
0%
0%
0%

32%
24%
7%
2%

29%
16%
8%
1%

1%
0%
0%
0%

1%
2%
1%
0%

Levels of satisfaction are higher amongst those
whose homes were less affected for most providers(Table 4.26). This may be because volumes of aid havebeen sufficient to help those who were less impacted
recover, while for those who experienced higher
impacts, aid has been insufficient.

Housing Damage

Government

Army

PolicePolitical
partiesLocaladministration

INGOs

NGOs

Businesses

CommunitygroupsForeigngovernments

Religious
groupsTable 4.26: Satisfaction with aid providers among those who received aid –by level of housing damage (IRM-2)

Completely damaged
Badly damaged
Habitable
Not damaged
Total

66%
63%
70%
88%
66%

77%
77%
69%
75%
76%

75%
77%
68%
75 %
74%

32%
27%
40%
75%
33%

59%
56%
62%
75%
59%

72%
68%
66%
75%
71%

67%
67%
63%
63%
66%

37%
45%
41%
63%
39%

59%
62%
56%
63%
59%

56%
64%
68%
63%
59%

49%
40%
46%
63%
47%

68

Aid and Recovery in Post-Earthquake Nepal

Chapter 5.
Needs and Services
Photo: Tenzing Paljor

Almost one year on from the earthquakes, what needs
do people have and how do these differ across areas
and between population groups? And how do peopleview public services?
This chapter tracks changes in needs since the earlypost-earthquake period. It looks at what people say

they need now and in the near future. It focuses inon needs related to shelter and food and assesses theextent to which the provision of aid in these areas hashelped. It also looks at people’s views of government
services.

Key findings:
Needs• Cash, shelter, and food remain the priority needsof people. Over two-third of people identify cash
as a priority immediate need (85% in severely
hit districts).
• Of the remaining needs, water is identified by thehighest amount of people (11%, 17% in severely
hit districts).
Shelter needs• Eighty percent of people in severely hit districts
are still living in self-constructed temporary
shelters.
• Shelter needs are highest in Nuwakot, Ramechhap,
and Sindhupalchowk. People increasingly wantreconstruction materials rather than tarps or CGI.
• Reconstruction materials are in high demand
amongst people whose house was badly but notcompletely damaged. There is a need to provide

assistance to these people as well as those whose
houses were completely destroyed.
• M any people who live in their own house orothers’ houses want reconstruction materials.
This suggests they are living in damaged andunsafe houses.
Food needs• Wherever food aid has fallen since IRM-1, people
report that food consumption has decreased.
In the five poorest districts, food aid appears to
reduce the need to borrow for food.
• Food aid has been successfully targeted at areasthat are more severely food insecure. However,people in these areas are still the most likely to
say that food consumption has decreased sinceIRM-1, suggesting volumes of food aid are notsufficient.
• The fuel crisis raised the price of food in all areas.

69

Earthquake Aid

Services• Levels of dissatisfaction with most public services
have increased slightly since IRM-1. The exception is education where satisfaction has increased.
• In the districts most affected by the earthquakes,
electricity and drinking water are the two services

needing the most attention. Dissatisfaction withservices is higher in urban areas (especially
Kathmandu and Bhaktapur) than in rural areas.
• Dissatisfaction levels are higher for those whoare richer, more educated, or high caste. Thereis little difference in satisfaction levels by gender
or disability.

5.1 Overall needs
What are current immediate needs?
Key needs in earthquake-affected areas relate to cash,
food, and shelter. Respondents were asked what theirpriority immediate needs were. Table 5.1 presents theshare of people who identified each aid item as one of
their top two immediate needs.
In districts of all levels of impact, cash is the mostfrequently voiced immediate need. Over-two thirdsof people identified this as a priority immediateneed. The proportion rises to 85% in the severely
hit districts. Next most common was reconstruction
materials in every category of impacts with 34% of
people prioritizing this (47% in severely hit districts).

This should not be surprising since households would
now want to have permanent housing, given that it has
been over a year since the earthquake. Food is also ahigh priority, although less so in the hit district. CGI
is prioritized by 16% of people, primarily people inseverely hit and hit with heavy losses districts.
Of the remaining potential priorities, 11% identify
water (7% drinking water and an additional 4% water
for the household). Respondents who prioritized water
are concentrated in severely hit districts, where 17%
said it was a top priority. Other potential needs areprioritized by far fewer respondents.

Table 5.1: Priority immediate needs (share in top two needs) – by district impact (IRM-2)

Cash
Reconstruction materials
Food
CGI
Drinking water
A houseWater for household
Farm Implements
Livestock
Blankets
Tarps
MedicalSanitation
Clothes
Tent
Fuel

70

Severely hit

Crisis hit

Hit with heavy
losses

Hit

Total

85%
47%
23%
18%
11%
11%
6%
5%
4%
2%
3%
2%
3%
1%
1%
0%

53%
24%
13%
9%
5%
1%
2%
2%
2%
1%
1%
2%
1%
2%
1%
2%

65%
22%
17%
22%
2%
0%
1%
2%
3%
6%
2%
1%
0%
1%
2%
0%

40%
23%
7%
9%
4%
0%
1%
1%
1%
2%
3%
3%
1%
1%
1%
0%

69%
34%
18%
16%
7%
5%
4%
3%
3%
2%
2%
2%
2%
1%
1%
0%

Aid and Recovery in Post-Earthquake Nepal

How have needs changed over time?
Cash and shelter have become higher priority needs
for affected people compared to the early months after
the earthquakes. Food, while still an important need,is prioritized less than in IRM-1.
In IRM-1, the largest share had indicated that theirmost important immediate need was for cash: 41% had
cited cash as among their top two immediate needs
and this was fairly consistent across levels of impact.
The next requirement was for food (30%), followed
by clean drinking water (5%). Prioritization of shelter
was low and spread across the various shelter options
fairly evenly: tents (6%), tarps (7%), CGI (5%), and“houses” (2%).
Respondents in IRM-1 were also asked to prioritize
their needs over the next three months (July-October,corresponding with the 2015 monsoon season). People
largely prioritized the same needs, but with more
people saying that cash and CGI would be needed (26%compared to 5% as an immediate need). The muchhigher share of CGI indicated as a future need relative
to a current need in IRM-1 reflected a recognition of
the importance of people getting in to more durable
shelters, even if they were temporary, before thewinter hit.
Figure 5.1 compares needs prioritized by respondents
across three time periods: needs in June 2015 (IRM-1
immediate needs); expected needs during the 2015
monsoon season (IRM-1 three-month needs); andneeds in February-March 2016 (IRM-2 immediateneeds).

Figure 5.1: Priority needs (share in top two needs) – all districts(comparison IRM-1 immediate needs, IRM-1 three-month needs, IRM-2 immediate needs)

figure 5.1

CashReconstruction
materials

34%

0%
0%
18%
17%

Food

16%

CGI

5%

Drinking water

5%
5%

House

2%
2%

69%

48%

30%
26%

7%

5%

4%
3%
2%

Water for household

3%5%

Farm implements
Livestock

41%

2%
0%
0%

Tarps
Sanitation
Medical
Tent
0%

3%

2%

4%

7%

2%
2%
1%
2%
2%

1%
2%

6%

6%

IRM-2 (immediate)

18%

35%
IRM-1 (3 months)

53%

70%

IRM-2 (immediate)* Data on reconstruction materials and livestock as needs were not collected in IRM-1. Blankets, clothes,and fuel are not shown in the figure, because shares prioritizing these (for both IRM-1 and IRM-2) are
negligible.
The comparison shows that cash is becoming an evermore important priority for people. Whereas 41% of
people identified it as an immediate need in IRM-1,
and 48% as a three-month need, 69% now prioritize it.

Over one-third identify reconstruction materials as
a top-two priority in IRM-2. (This was not included
as an option in IRM-1, but it is likely that at thattime people were more focused on ensuring their71

Earthquake Aid

temporary shelters were durable than thinking aboutreconstruction, especially given the monsoon was
about to start). A much larger share of people in IRM2 compared to IRM-1 say CGI is a priority immediateneed (16% compared to 5%), although this is less thanthe 26% of IRM-1 respondents who prioritized CGI asa three-month need. This reflects the fact that tarps
have been distributed in large shares to households(71% in IRM-1 and 47% in IRM-2, Figure 4.1 above),
while CGI distribution has been lower (7% in IRM-1
and 21% in IRM-2).

Food, while still an important priority in IRM-2, hasdeclined in importance since IRM-1. The importanceof water, both for drinking and for other household
uses, has increased since IRM-1.
Figure 5.2 presents the same analysis, looking onlyat the severely hit districts. While the proportion ofpeople prioritizing cash, food, and shelter is higher
in severely hit districts for all three time periods, theoverall pattern is the same.

Figure 5.2: Priority needs (share in top two needs) – severely hit districts(comparison IRM-1 immediate needs, IRM-1 three-month needs, IRM-2 immediate needs)

figure 5.2

CashReconstruction
materials

43%
0%
0%

23%

Food
18%

CGI

8%

Drinking water

7%
7%

House

3%
2%

Farm implements

2%
0%
0%

11%
11%

4%

5%

3%
4%
2%

Sanitation

2%

Medical

1%

1%
1%

0%

6%

7%

IRM-2 (immediate)

72

49%
34%

5%
7%

3%
1%

Tarps

Tent

47%

6%
4%
2%

Water for household

Livestock

28%

85%

61%

23%

45%
IRM-1 (3 months)

IRM-2 (immediate)

68%

90%

Aid and Recovery in Post-Earthquake Nepal

What are priority needs over the next three months?
The top expected needs in three months’ time (whichcorresponds to June 2016), are the same as the top
current needs (Table 5.2). Cash remains the top need,
although fewer people prioritize cash as a three-monthneed than as an immediate need: 51% compared to69%. Slightly more people prioritize food over the next
three months than do as an immediate need, perhaps
reflecting potential expected food shortages as the
monsoon approaches.

Table 5.2: Priority three-month needs (share in top two needs) – by district impact (IRM-2)

Cash
Food
Reconstruction materials
CGI
Drinking water
Farm implements
MedicalHouse
Water for household
Livestock
Warm clothes
Sanitation
Blankets
Tent
Tarps
Fuel

Severely hit

Crisis hit

Hit with heavy
lossesHit

All districts

62%
24%
21%
10%
9%
9%
7%
7%
4%
3%
2%
3%
1%
1%
1%
0%

45%
12%
14%
3%
3%
2%
3%
0%
1%
1%
1%
0%
0%
0%
1%
1%

44%
9%
11%
3%
1%
4%
1%
0%
0%
1%
1%
0%
5%
1%
0%
0%

27%
9%
9%
4%
2%
1%
6%
0%
1%
1%
3%
1%
1%
1%
0%
0%

51%
17%
16%
6%
5%
5%
5%
3%
2%
2%
2%
1%
1%
1%
1%
0%

Where there is no need for aid
Overall, the share of people who state that they do
not need any more aid has fallen slightly since IRM1 (from 24% to 21%) – Table 5.3. This suggests thatsome people have realized that the challenges of recovering have been greater than they expected when
IRM-1 was conducted in June 2015 and that they now
feel they need external support.
In severely hit districts, the share stating they haveno need for further aid is very low and there has been
virtually no change from IRM-1 (it is approximately
1.5%). Between IRM-1 and IRM-2, there have also
been small drops in the proportion of people in thecrisis hit and hit with heavy losses districts who say
they do not need relief. The greatest fall is seen in theleast affected hit district (from 86% to 55%).
As discussed in Chapter 4, and shown in the table,
the proportion of people receiving aid has declined
between IRM-1 and IRM-2, with the exception of thehit district. Overall, there is a high positive correlation
at the household level between not receiving aid andnot requiring further aid (63%), which suggests that
there has been fairly effective targeting of aid basedon needs at the household level.

Table 5.3: No need for relief items and no aid received – by district impact (IRM-1/IRM-2 comparison)
ImpactSeverely hit
Crisis hit
Hit with heavy losses
Hit
Total

IRM-1

IRM-2

Need nothing

No aid received

Need nothing

No aid received

1%
40%
31%
86%
24%

1%
39%
28%
55%
20%

2%
38%
29%
55%
21%

5%
59%
29%
70%
30%

73

Earthquake Aid

5.2 Shelter needs
Where are people living now?
The fact that shelter needs are high is not surprising
given the large amount of people who are still living
in temporary shelters. As of March 2016, 80% of
people in severely hit districts were living in selffigure 5.3

constructed temporary shelters (Figure 5.3). Smaller,
but still substantial portions of the population, werein temporary shelters in other districts, especially incrisis hit areas.

Figure 5.3: Where people are living now – by district impact (IRM-2)
100%

95%

91%
81%

80%

75%

50%

25%

19%

16%
8%
1%

0%

2%

0%

Severely Hit

1%

Crisis Hit

1%

1%

0%

Hit with heavy losses

Own house

Self-constructed shelter

Neighbor/friend

Rented house

4%

0%

Hit

People are most likely to be in temporary shelters in
Sindhulpalchowk and Nuwakot (Table 5.4).

74

Neighbor’s house

Friend’s house

Self-constructed
shelter on own
land

Self-constructed
shelter on otherpeople’s land

Self-constructed
shelter on public
landCommunity
shelter

Rentedaccommodation

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga

Own house

Table 5.4: Where people are living now – by district (IRM-2)

19%
20%
45%
9%
16%
6%
81%
79%
90%
76%

1%
1%
2%
0%
0%
0%
2%
2%
1%
2%

0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

74%
77%
50%
84%
80%
78%
14%
13%
8%
21%

6%
2%
3%
5%
4%
16%
2%
2%
0%
1%

0%
0%
0%
2%
0%
0%
0%
1%
1%
0%

0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

0%
0%
0%
0%
0%
0%
1%
3%
0%
0%

Neighbor’s house

Friend’s house

Self-constructed
shelter on own
land

Self-constructed
shelter on otherpeople’s land

Self-constructed
shelter on public
landCommunity
shelter

Rentedaccommodation

Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Own house

Aid and Recovery in Post-Earthquake Nepal

91%
92%
91%
95%
95%
56%

0%
0%
1%
1%
1%
1%

0%
0%
0%
0%
0%
0%

8%
8%
7%
3%
3%
39%

1%
0%
1%
1%
1%
3%

0%
0%
0%
0%
0%
0%

0%
0%
0%
0%
0%
0%

0%
0%
0%
0%
0%
1%

Figure 5.4: Type of temporary shelter – people
living in temporary shelter only (IRM-2)
figure 5.4

40%
47%

4% 1% 2%

4%

The vast majority of people living in temporary shelters are in shelters made from either CGI alone (40%)
or CGI with wood or bamboo (47%) – Figure 5.4. Four
percent are in shelters made entirely from bamboo, 3%
in wooden shelters, and 2% in cow sheds
Of those still living in temporary shelters, 72% said
they were able to make sufficient repairs to their
shelter to make it sufficient for the winter. Another21% said they made repairs but these were notsufficient for the winter. Five percent were unable to
make repairs at all (Table 5.5).
In general, those who are living in temporary sheltersin less affected areas were the least likely to havebeen able to make sufficient repairs. Within more
affected districts, people in Okhaldhunga, Bhaktapur,
Nuwakot, and Sindhulpachowk were the least likely tohave been able to make sufficient repairs.

3%

CGI shelter
Wood shelter

Tent
CGI + tarp

Bamboo shelter
Cow shed

CGI + wood/bamboo

Table 5.5: Were you able to make sufficient repairs to your shelter for the winter? –by district impact and district, those living in temporary shelters (IRM-2)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu

Housing
was not
damagedWas able tocompletely fix the
house

0%
0%
0%
0%
0%
0%
1%
0%
6%

1%
1%
1%
0%
1%
2%
5%
2%
16%

Was able
Was able toto repairmake repair
and made it
but not
sufficient for sufficient for
winterwinter
75%
98%
86%
60%
77%
64%
50%
45%
50%

21%
1%
12%
39%
21%
27%
22%
27%
16%

Was not
able torepair thehouse at all

Refused

2%
0%
2%
0%
1%
7%
21%
25%
9%

0%
0%
0%
0%
0%
0%
1%
0%
3%

75

Earthquake Aid

Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Housing
was not
damagedWas able tocompletely fix the
house

0%
0%
0%
0%
0%
0%
0%

3%
14%
0%
27%
8%
8%
2%

Was able
Was able toto repairmake repair
and made it
but not
sufficient for sufficient for
winterwinter
53%
47%
59%
37%
46%
46%
72%

21%
23%
30%
17%
0%
0%
21%

Was not
able torepair thehouse at all

Refused

24%
16%
11%
20%
46%
46%
5%

0%
0%
0%
0%
0%
0%
0%

Immediate need for shelter
The immediate reported need for shelter is highest
in Nuwakot, Ramechhap, and Sindhupalchowk(Table 5.6). These three districts saw amongst thehighest levels of housing damage from the earthquake.
Sindhupalchowk has received the highest level of shelter aid in both IRM-1 and IRM-2, yet still has among
the highest levels of immediate need for shelter aid.
This reflects the fact that most shelter aid so far has

been for temporary shelter (Table 4.6 above). While
29% of people in the district have received reconstruction materials, 34% say that such materials are an
immediate priority need and another 39% say that ahouse is top priority. Demand for reconstruction materials is particularly high in Nuwakot and Ramechhap,
both of which have received almost none of these sofar. Demand for reconstruction materials is also highin Okhaldhunga.

Table 5.6: Share of people reporting shelter as priority immediate need –by district impact and district (IRM-2)
Shelter as priority immediate need
District
Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Tent

Tarps

CGI

Reconstruction
materials

4%
1%
2%
1%
14%
1%
1%
1%
0%
3%
3%
1%
6%
3%
3%
3%

1%
0%
0%
0%
2%
1%
1%
1%
0%
2%
0%
0%
0%
2%
2%
1%

18%
20%
15%
21%
18%
19%
9%
5%
3%
20%
22%
11%
33%
9%
9%
16%

47%
39%
35%
69%
59%
34%
24%
16%
17%
40%
22%
23%
21%
23%
23%
34%

House

Shelter(total)

11%
6%
11%
0%
1%
39%
1%
3%
0%
0%
0%
0%
0%
0%
0%
5%

72%
61%
58%
84%
79%
79%
33%
24%
19%
57%
43%
33%
54%
32%
32%
53%

Housing
damage (%)
94%
97%
89%
97%
90%
97%
49%
60%
26%
60%
44%
35%
52%
21%
21%
66%

Three-month needs for shelter
Across districts, people are more likely to reportshelter as an immediate need than as a three-monthneed (Table 5.7). This suggests that households hope76

to have their housing situation resolved sooner rather
than later.

Aid and Recovery in Post-Earthquake Nepal

Table 5.7: Share of people reporting shelter as priority three-month need –by district impact and district (IRM-2)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Tent

Tents

CGI

Reconstruction
materials

House

Shelter(total)

1%
1%
2%
1%
3%
0%
0%
0%
0%
1%
1%
1%
1%
1%
1%
1%

1%
0%
0%
0%
2%
1%
1%
1%
0%
2%
0%
0%
0%
2%
2%
1%

10%
9%
8%
17%
7%
9%
3%
1%
1%
5%
3%
1%
4%
4%
4%
6%

21%
25%
14%
13%
39%
15%
14%
12%
13%
17%
11%
10%
12%
9%
9%
16%

7%
5%
8%
0%
0%
22%
0%
1%
0%
0%
0%
0%
0%
0%
0%
3%

38%
38%
32%
28%
49%
44%
18%
15%
14%
25%
14%
11%
17%
13%
13%
26%

Shelter needs by housing damage
Shelter aid overall, and in particular reconstruction
materials, are the most in demand among those
whose houses have been completely damaged(Table 5.8). However, demand for shelter, includingreconstruction materials and CGI, is also very highamong those whose houses were badly damaged. Given
that government support for housing reconstruction
may only go to those whose houses were completely
damaged, this suggests that the needs for this group
may not be met.

Table 5.8: Share of people reporting shelter as priority immediate need –by level of housing damage (IRM-2)
Housing damage

Tents

Tents

CGI

Reconstruction
materials

Shelter aid (total)

Completely damaged
Badly damaged
Habitable
Undamaged
Total

1%
1%
0%
0%
1%

3%
2%
2%
0%
2%

20%
21%
9%
1%
16%

46%
41%
19%
3%
34%

72%
62%
28%
3%
53%

Shelter needs by where people are currently living
Reconstruction materials for permanent homes are in
the greatest demand among those who continue to live
in self-constructed shelters, either on their own land oron land owned by others or the public (Table 5.9).38 The
share of people prioritizing reconstruction materials

who live in rented houses or their own house is lower
but still substantial. This indicates that many people
are living in houses that are still damaged and needreconstruction, with major implications for safety.

38

These populations comprise 39% and 4% of the sample, respectively. Fifty-six percent of the population lives in their own house.

77Earthquake Aid

Table 5.9: Share of people reporting shelter as priority immediate need – by current shelter (IRM-2)
Where living now

Tents

Tarps

CGI

Reconstruction
materials

Shelter aid(total)Own house
Friend/neighborSelf-constructed shelter (own land)
Self-constructed shelter (other’s land)
Rented house
Total

1%
3%
1%
1%
0%
1%

1%
3%
3%
3%
0%
2%

13%
27%
18%
22%
7%
16%

23%
35%
50%
45%
20%
34%

36%
73%
74%
76%
40%
53%

Shelter needs by population group
Rural/urban. All types of shelter aid are more in
demand in rural areas, with the greatest shelter needin these areas being reconstruction materials followed
by CGI (Table 5.10)

Table 5.10: Share of people reporting shelter as priority immediate need – by urban/rural (IRM-2)

Urban
Rural
Total

Tents

Tarps

CGI

Reconstruction
materials

Shelter aid(total)

0%
1%
1%

1%
2%
2%

4%
18%
16%

17%
37%
34%

21%
58%
53%

Caste. There are not substantial differences in
the proportion of people of different caste groups
prioritizing shelter assistance. Fifty-four percent
of Janajatis rank shelter as one of their two top
immediate needs while 51% of both low caste and highcaste people say the same.

Disability. Overall, those with disabilities have ahigher need for shelter aid (55% compared to 52%, forthose without) – Table 5.11. Those with disabilities are
more likely to say they need reconstruction materials
but less likely to say they need tents, tarps, or CGI. Thisshows the greater need for many within this group to
get into more robust housing.

Table 5.11: Share of people reporting shelter as priority immediate need – by disability (IRM-2)

Without disability
With disability
Total

Tents

Tarps

CGI

Reconstruction
materials

Shelter aid(total)

1%
0%
1%

2%
2%
2%

17%
14%
16%

33%
37%
34%

52%
55%
53%

Gender. Males are slightly more likely to list shelter
as a top immediate need (54% against 51%).

78

Aid and Recovery in Post-Earthquake Nepal

5.3 Food needs
The largest negative livelihoods impact of the Nepal
earthquake has been on farming (Chapter 2). As
such, we would expect there to have been a negative
impact on the food supply, especially because manyare involved in subsistence farming. Equally, withother sources of income, there is likely to have been an
adverse impact on purchasing power. This would bereflected in the need to borrow for food, particularlyin the severely hit districts.

While food has declined in importance as a priority
need since IRM-1, large amounts of people still say
it is one of their top two priority immediate needs:
23% in severely hit districts, 13% in crisis hit districts,
17% in hit with heavy losses districts, and 7% in thehit district.

Where is food needed?
Current food demand is the highest in the severely
hit districts. The only districts with comparable levelsof immediate food needs among the non-severely hit
districts are Okhaldhunga, where food aid has fallen
to very low levels, and Solukhumbu, which is thepoorest of the districts studied. The latter district has
the highest share reporting food as an immediate need,followed by Gorkha and Sindhupalchowk, the worst
affected district in terms of damage to homes andlivelihoods. Needs in three months are almost always
lower than articulated current needs, suggesting thatfigure 5.5households consider food an urgent item. The mostnotable exceptions are Nuwakot and Sindhupalchowk,
the two districts that have received the highest levelsof aid in IRM-1 and IRM-2.
Current and future food needs are the lowest in Lamjung, Kathmandu, and Syangja. These three districts
received the lowest volumes of food aid in IRM-1 andIRM-2, which suggests that the low volumes of foodaid received in these districts are consistent with low
requirements.

Figure 5.5: Share of people reporting food as among their top two needs
immediately and in three months – by district (IRM-2)
39%

40%

32%
29%

30%

24%

20%

10%

31%

28%

22%
19%

18%

21%

23%

18%
14%

12%

10%

8%

7%

7%

6%
2%

9%

3%

0%

ja

g
an
Sy
bu

um

ng

ju

m
a
ng

u
dh

al

Three months

k

w

ho

du

an

m

kh
lu

So

La

kh

O

th
Ka

ur
ap

t
ak
Bh

ap

lc
pa
hu

nd

Si

hh

ec

t

o
ak

uw

a

kh

m
Ra

N

or

G

ng
di

ha

D

Immediate

What type of food is needed?
Stapes (rice and wheat) are the greatest food needs,
both now and in three months (Tables 5.12 and 5.13).

The requirements for condiments is the second largest
immediate food need (5%) followed by readymade

79

Earthquake Aid

food (2%). The requirement for all types of food is
the highest in the severely hit districts followed by
the crisis hit districts, both now and in three months.

Readymadefood

Sugar, salt,etc.

Lentils

Vegetables

Meat

Food (total)

Severely hit
Crisis hit
Hit with heavy losses
Hit
All districts

Rice/wheat

Table 5.12: Share of people reporting food as priority immediate need – by district impact (IRM-2)

15%
11%
10%
5%
12%

2%
1%
5%
1%
2%

7%
4%
6%
0%
5%

0%
1%
0%
0%
0%

1%
1%
0%
0%
1%

1%
0%
0%
0%
0%

23%
13%
17%
7%
18%

Readymadefood

Sugar, salt,etc.

Lentils

Vegetables

Meat

Food (total)

Severely hit
Crisis hit
Hit with heavy losses
Hit
All districts

Rice/wheat

Table 5.13: Share of people reporting food as priority three-month need – by district impact (IRM-2)

17%
9%
5%
8%
12%

3%
1%
1%
1%
2%

5%
3%
4%
0%
4%

1%
1%
0%
1%
1%

1%
1%
0%
0%
1%

1%
0%
0%
0%
0%

24%
12%
9%
9%
17%

Food aid, borrowing for food, and food consumption
The number of people receiving food aid has fallen
across almost all districts (see Chapter 4.2 above).
Wherever food aid has fallen, a larger share of people
reports that consumption has slightly decreased
since the 2015 monsoon compared to other districts(Table 5.14). This suggests that food aid is affecting
overall consumption levels, despite borrowing for foodas a means to cope.
The share of people reporting that food consumption
has decreased a lot is fairly low (7%), although it ishigher in Dhading, Sindhupalchowk, Okhaldhunga,
Gorkha, and Ramechhap. In each of these districts,
with the exception of Sindhupalchowk, there arealso relatively larger decreases seen in shares offood aid compared to other districts. The fact that
Sindhupalchowk reports such large decreases infood consumption, despite receiving relatively highlevels of food aid, is probably a result of the level ofearthquake impacts in that district, which were higher
than elsewhere.
As discussed in Chapter 4, there is a negative relationship between level of food aid and level of borrowing

80(-23%) in the five poorest five districts (Dhading, Nuwakot, Ramechhap, Okhaldhunga, and Solukhumbu).
This suggests that food aid reduces the need to borrow.
There is also a very high negative correlation between
the proportion of borrowers who are taking loans forfood and the shares within the district who cite foodas a top current need in the poorest districts (- 86%).
This suggests that where there has been less borrowing
for food as a means to cope, there is also a greater
immediate need for food.
In order to verify the net effect of food aid on the immediate need for food, we check the direct correlation
between food aid provision at the district level andthose who identify food as an immediate priority. In
poorer districts there is a negative correlation (- 15%)
suggesting that higher food aid is leading to a lower
articulated immediate need for food. However, acrossthe whole sample, there is a positive correlation (60%).
This could mean that food is regarded as a consistently important need, even at higher levels of food aid
provision, given that the agricultural sector remains
badly affected.

Aid and Recovery in Post-Earthquake Nepal

In almost all districts, the share that has cited food as a
top current need is larger than the share that has citedfood as a top need in three months’ time, suggestingthat levels of food insecurity are fairly high. The two

exceptions are Sindhupalchowk and Nuwakot, thedistricts have received the most food aid coverage in
IRM-1 and IRM-2. Once again, this suggests a positive
influence of food aid on household food security.

Table 5.14: Food aid, borrowing, food consumption, and current need for food –by district impact and district (IRM-1 and IRM-2)

Decreasedslightly

35%
44%
18%
33%
49%
26%
40%
18%
15%
54%
21%
27%
16%
37%
37%
35%

Decreased
a lot

28%
55%
38%
10%
22%
17%
18%
23%
0%
32%
-16%
-9%
-23%
-2%
-2%
17%

No change

65%
38%
50%
87%
67%
83%
7%
11%
9%
2%
24%
15%
33%
4%
4%
37%

Increasedslightly

Difference(IRM-1 –IRM-2)

93%
93%
89%
96%
89%
100%
26%
34%
9%
34%
8%
6%
10%
3%
3%
53%

Borrowing
for food(IRM-2)

Change in food consumption
since monsoon (June 2015) (IRM-2)
Increased
a lot

IRM-2

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

IRM-1

Food aid

3%
1%
6%
2%
7%
1%
9%
7%
11%
8%
3%
3%
3%
1%
1%
5%

26%
10%
17%
44%
47%
14%
26%
24%
17%
35%
25%
22%
28%
39%
39%
27%

60%
73%
70%
53%
41%
62%
61%
64%
68%
50%
69%
69%
68%
58%
58%
60%

9%
15%
7%
1%
6%
14%
5%
5%
3%
7%
3%
4%
1%
3%
3%
7%

2%
1%
0%
0%
0%
8%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1%

Immediateneed now(IRM-2)

23%
18%
29%
22%
19%
28%
13%
12%
7%
21%
17%
2%
31%
7%
7%
18%

How do food aid, borrowing, consumption,and needs differ by levels of food insecurity?
In order to study food security in greater depth, 250
respondents were added to the 350 respondents inthe main sample in four districts: Sindhupalchowk,
Ramechhap, Gorkha, and Okhaldhunga. This resulted
in a sample of 2,400 respondents for an analysis offood security analysis. This allows for a breakdownof responses by places with different levels of foodinsecurity as identified by the Nepal Food Security
Monitoring System (NeKSAP) – Table 5.15.39

Table 5.15: Food security sample (IRM-2)
Districts

Minimally foodinsecure

Moderately
food insecure

Highly foodinsecure

Severely foodinsecure

Total

Okhaldhunga
Ramechhap
Sindhupalchowk
Gorkha
Total

200
96
0
210
506

200
252
200
195
847

200
252
202
195
849

0
0
200
0
200

600
600
602
600
2,402

39

See Chapter 1 and Annex A for a further discussion of themethodology used.

81

Earthquake Aid

Food aid has had a much larger reach at successively
higher levels of food insecurity (Table 5.16). In particular, severely food insecure areas have had near full coverage of food aid (96%). Volumes of food aid are also

larger at successively higher levels of food insecurity
and the highest volumes are in the severely insecurecategory (103 days of stock for the household), doublethat in the highly insecure category (52 days).

Table 5.16: Food aid, borrowing, consumption, and need for food – by level of food insecurity (IRM-2)

Increased a lot

Increasedslightly

Same asbeforeDecreased alot

Decreasedslightly

Borrowed for food

28%
55%
62%
96%
55%

22
41
52
103
52

11%
4%
4%
0%
5%

23%
27%
36%
17%
28%

60%
56%
52%
61%
56%

6%
9%
8%
15%
8%

0%
3%
1%
8%
2%

18%
19%
28%
14%
21%

Despite these levels of food aid, the severely insecurecategory has the largest share reporting that foodconsumption has decreased a lot (15%) and slightly(8%) since the monsoon. This suggests that while
food aid has been targeted effectively based on foodinsecurity, the more insecure regions remain at thegreatest likelihood of a fall in consumption levels.
figure 5.6

Three-month

Quantityof food aid(days of foodper household)Minimally food insecure
Moderately food insecure
Highly food insecure
Severely food insecure
Total

Food as need

Immediate

Share receiving
food aid

Change in food consumption

NeKSAP
food security
classification

Food aid

21%
19%
22%
33%
22%

21%
24%
28%
42%
26%

This is also reflected in the fact that food as a share of
the top two needs is the highest in severely insecure
regions now (33%) and in three months’ time (42%),
despite these places receiving the most food aid.
This suggests persistent food insecurity (as would be
expected in these places). In short, the data suggestthat food aid is being well targeted but is insufficient
to help those in the most food insecure areas.

Figure 5.6: Food insecurity – by income band (IRM-2)
100%

7%

8%

8%

38%

34%

12%

9%

19%

75%

50%

38%

35%

41%

37%

34%

29%

39%

25%
34%

0%

<NPR 2,500

20%

21%

22%

NPR 2,501 - 9,999

NPR 10,000 - 19,999

NPR 20,000 - 39,999

Minimally food insecure

Highly food insecure

Moderately food insecure

Severely food insecure

It is encouraging to note, however, that borrowing forfood is the lowest in the severely insecure category,
which again appears to be linked to much higher levels

82

17%

> NPR 40,000

of food aid in these areas (in terms of both the shareof the population receiving food aid and the quantities
they receive).

Aid and Recovery in Post-Earthquake Nepal

The low borrowing does not appear to be linked tolower income levels. As seen in Figure 5.6, poorer
households are as likely to be classified as severely foodinsecure as those households with incomes between
NPR 10,000 and NPR 19,999.

Food insecurity and population groups
Figure 5.7: Food insecurity – by urban/rural (IRM-2)
100%

8%

Figure 5.8: Food insecurity – by caste (IRM-2)
100%

1%

2%
12%

21%

75%

36%

28%

75%
41%

50%

48%

50%

100%

50%
36%

27%

25%

25%

29%

20%

0%

Urban

Rural

0%

21%

Low

Janajatis

Minimally food insecure

Minimally food insecure

Moderately food insecure
Highly food insecure
Severely food insecure

Moderately food insecure
Highly food insecure
Severely food insecure

Rural/urban. Urban areas fall only within theminimally food insecure category whereas rural
areas are distributed across the higher levels of foodinsecurity (Figure 5.7).
Caste. Janajatis are more likely to live in foodinsecure areas (highly or severely), whereas high andlow castes are more likely to live in moderately foodinsecure areas (Figure 5.8).
Gender. Unsurprisingly, given food insecurity isdetermined by areas rather than person, there are nodifferences in the gender split across food insecurity
levels.
Disability. Those with disabilities are twice as likely
to live in severely food insecure areas (12% against
6%) – Figure 5.9.

20%

High

Figure 5.9: Food insecurity – by disabled/
non-disabled (IRM-2)
100%

75%

6%

12%

36%

34%

50%
35%

36%

25%
22%

0%

Without disability

19%With disability

Minimally food insecure
Moderately food insecure
Highly food insecure
Severely food insecure83Earthquake Aid

Did the fuel crisis impact the price of food?
The fuel crisis increased the price of all types of food
figure 5.10
(Figure 5.10).
Figure 5.10: Did the fuel crisis affect the prices you pay for food types? (IRM-2)
100%

95%

98%

93%

98%

78%

75%

70%

65%

50%

25%

0%

Rice

Wheat

Lentils

Cooking oil

In general, those in the most food insecure areas werethe least likely to say that the fuel crisis had raised
prices for most types of food, presumably because

Vegetables

Sugar etc.

Meatpeople in these places either do not eat these types offood or because they receive it for free in the form offood aid (Figure 5.11).

figure 5.11

98%

98%

94%

86%
60%

68%

63%

74%

71%

78%

86%

98%

99%
91%

98%

98%

96%

71%
52%

74%

75%

70%

74%

82%

94%

92%

97%

97%

100%

97%

Figure 5.11: Did the fuel crisis affect the prices you pay for food types? – by food security (IRM-2)

50%

25%

0%

84

Rice

Wheat

Lentils

Cooking oil

Minimally food insecure

Highly food insecure

Moderately food insecure

Severely food insecure

Vegetables

Sugar etc.

Meat

Aid and Recovery in Post-Earthquake Nepal

Photo: Alok Pokharel

85

Earthquake Aid

5.4 Need for other relief
Cash, by far, is the top prioritized need, both in theimmediate term (69% overall and 85% in severely hit
districts, Table 5.1) and in three months’ (51% and 62%
overall, Table 5.2). Cash is consistently prioritized in
all the severely hit districts as well as in Okhaldhunga
and Solukhumbu (Table 5.17).
Among other relief items, besides food and shelter,
drinking water features as the most important, with
7% citing it as an immediate need (11% in severely
hit districts) and water for the household is cited by
4% (6% in severely hit districts). The districts withthe highest immediate requirement for drinking
water (over 10%) are Nuwakot, Ramechhap, andSindhupalchowk. The need for water for household
use is also the highest in Sindhupalchowk (14%).
The immediate need for farm implements is higher
in Nuwakot (10%) and for blankets in Solukhumbu(11%); Gorkha and Nuwakot have a higher demand
for sanitation (7% and 6%, respectively); Nuwakot,
Ramechhap, and Lamjung have a higher demand forlivestock (5% in the first two districts and 4% in thelatter).

86

Sanitation

Medical aid

BlanketsWarmclothes

Fuel

Farm
implements

Livestock

Cash

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Water forhouseholdDistricts

Drinking
water

Table 5.17: Priority immediate needs, besides food and shelter (share in top two needs) –by district impact and district (IRM-2)

11%
6%
10%
14%
16%
11%
5%
4%
2%
8%
2%
1%
4%
4%
4%
7%

6%
3%
7%
5%
2%
14%
2%
3%
1%
2%
1%
1%
2%
1%
1%
4%

3%
0%
7%
6%
0%
1%
1%
0%
1%
0%
0%
0%
1%
1%
1%
2%

2%
1%
3%
4%
2%
2%
2%
2%
1%
2%
1%
1%
1%
3%
3%
2%

2%
0%
3%
3%
1%
1%
1%
2%
0%
1%
6%
2%
11%
2%
2%
2%

1%
0%
1%
0%
2%
1%
2%
4%
0%
2%
1%
0%
2%
1%
1%
1%

0%
0%
0%
0%
0%
0%
1%
5%
0%
0%
0%
0%
0%
0%
0%
0%

5%
1%
3%
10%
5%
4%
2%
2%
1%
2%
2%
1%
3%
1%
1%
3%

4%
2%
3%
5%
5%
3%
2%
2%
1%
3%
3%
4%
1%
1%
1%
3%

85%
87%
86%
85%
90%
76%
54%
51%
28%
83%
65%
52%
78%
40%
40%
69%

Aid and Recovery in Post-Earthquake Nepal

5.5 Services
Beyond household needs, the earthquakes also impacted public services. Among various services, schoolingsuffered the most significant setbacks from the earthquake. One-third of respondents in IRM-1 stated thataccess to schools worsened a lot because of the earthquake and 36% stated that it somewhat worsened.40
Other public services were relatively less affected but
respondents still noted negative impacts. While 45%of the IRM-1 sample responded that electricity had
worsened (either a lot or somewhat), a significant
number of people reported that other services also
suffered similar setbacks: 35% for drinking water, 31%
5.12 roads, and 28% for medical facilities.
for figure
motorable

In addition to asking how much the earthquake
affected these services, IRM-2 respondents were also
asked about their satisfaction level with electricity,drinking water, medical facilities, schools, and roads
in both waves of the survey. Figure 5.12 shows level ofdissatisfaction expressed by respondents in IRM-2.41
Respondents in IRM-2 were most satisfied with schools(85%). The greatest level of dissatisfaction was with
two services: drinking water (21% dissatisfied) andelectricity (17%). Only 11% expressed dissatisfaction
with motorable roads and 9% with medical facilities.

Figure 5.12: Satisfaction with public services (IRM-2)
100%

9%

17%

6%

21%

4%

75%

9%

11%

6%

5%

85%

84%

6%

50%
85%

79%

73%

25%

0%

Electricity
Satisfied

MedicalNeutral

Water

SchoolRoad

Dissatisfied

How has satisfaction with services changed?
Overall, 1,558 individuals across the 11 districts weresurveyed in both IRM-1 and IRM-2. Figure 5.13
compares responses across the two surveys using thepanel data.41
Levels of dissatisfaction have increased for all services,
except for schools. The greatest rise in dissatisfaction
is for water with 21% dissatisfied compared to 11% in

IRM-1. The dissatisfaction level has increased by three
percentage points for electricity, three percentage
points for medical facilities, and two percentage
points for motorable roads. This suggests growing
frustration as these services have not fully recovered or
improved. In contrast, IRM-2 respondents who werealso interviewed in IRM-1 were more likely to express
satisfaction with schools than before.

40

42

The Asia Foundation (2015). Aid and Recovery in Post-Earthquake Nepal: Independent Impacts and Recovery Monitoring
Nepal Phase 1 – Quantitative Survey (June 2015). Kathmandu andBangkok: The Asia Foundation, p. 17.

Note that the panel data is based on the sub-set of surveyed
respondents (1,558), who were interviewed in both the waves, while
the satisfaction levels reported in Figure 5.12 are based on the IRM-2
sample of 3,853 individuals.

41

The figure combines very satisfied and somewhat satisfied as satisfied, and very dissatisfied and somewhat dissatisfied as dissatisfied.

87

Earthquake Aidfigure 5.13

Figure 5.13: Dissatisfaction with services – individual panel data (IRM-1/IRM-2 comparison)

11%

Motorable roads

9%5%

Schools8%9%Medical facilities

6%
21%

Water

11%
14%

Electricity

11%

0%

6%
IRM-2

12%

18%

24%

IRM-1

How does earthquake impact affect satisfaction with services?
Table 5.18 shows levels of dissatisfaction with eachservice by district. A few points stand out.
First, levels of dissatisfaction with most services arehighest in Kathmandu and Bhaktapur. These districts
were less affected by the earthquake than many others. However, they are both predominantly urban. As
Figure 5.14 shows, dissatisfaction is much higher in urban areas than rural ones, in part because expectations
are higher, in part because there are greater needs
for things like electricity in these areas. The fact that
dissatisfaction is so high with medical facilities androads in Kathmandu shows that earthquake impacts
are but one determinant of satisfaction.

Second, in general dissatisfaction is higher in more
severely affected districts. Besides Kathmandu andBhaktapur, dissatisfaction with electricity, water,
medical facilities, schools, and roads is highest in theseverely hit districts. Electricity and drinking water inseverely hit and crisis hit districts are the two public
services that need the most attention.
Third, districts vary by which service people are mostdissatisfied with. In Ramechhap, for example, dissatisfaction with drinking water, medical facilities,
and roads is high, but is very low for electricity. In
contrast, dissatisfaction with electricity is relatively
high in Gorkha and Okhaldhunga. In Sindhupalchowk,
drinking water and roads are the services people are
more dissatisfied with.

Table 5.18: Dissatisfaction with public services – by district impact and district (IRM-2)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses

88

Electricity

Drinking water

Medicalfacilities

Schools

Motorable
roads

7%
8%
18%
1%
1%
5%
51%
59%
64%
19%
1%

22%
19%
27%
4%
37%
20%
36%
39%
49%
19%
2%

11%
13%
10%
0%
23%
6%
9%
1%
21%
5%
5%

6%
4%
9%
1%
12%
3%
7%
0%
18%
2%
5%

13%
10%
12%
4%
25%
15%
11%
3%
22%
8%
7%

Aid and Recovery in Post-Earthquake Nepal

Lamjung
Solukhumbu
Hit
Syangja

Electricity

Drinking water

Medicalfacilities

Schools

Motorable
roads

1%
1%
0%
0%

3%
1%
11%
11%

7%
3%
8%
8%

8%
1%
3%
3%

10%
1%
3%
3%

figure 5.14

Figure 5.14: Dissatisfaction with public services – by urban/rural (IRM-2)

11%

Motorable roads

Schools

11%
9%
5%
11%

Medical facilities

9%43%

Drinking water

17%
50%

Electricity

11%

0%

13%
Urban

25%

38%

50%

Rural

Photo: Ashray Pande89

Earthquake Aid

How does satisfaction with services differ by population groups?
Table 5.19 shows rates of dissatisfaction by caste,
gender, disability, and income level.
Gender. There are almost no differences in levels of
dissatisfaction with services between men and women.
Disability. Those without disabilities are moredissatisfied with electricity, medical facilities, schools,
and motorable roads than those with disabilities.

Caste. High castes and Janajatis are more dissatisfied
with electricity (by 7% or more compared to low castes)and drinking water (by 2% or more compared to low
castes).
Income. The highest degree of variation is observed
across income bands. People with higher income are
more dissatisfied with public services than those withlower income.

Table 5.19: Dissatisfaction with public services – by caste, gender, disability, and income level (IRM-2)

GenderDisability
Caste
Income level

FemaleMale
No disability
Disability
High caste
Janajati
Low caste
Low
MediumHigh

Electricity

Drinking
water

MedicalSchoosl

Motorable
roads

16%
18%
17%
13%
18%
17%
10%
10%
17%
30%

20%
22%
21%
22%
19%
22%
17%
17%
21%
28%

9%
9%
9%
6%
8%
10%
10%
8%
9%
12%

6%
5%
6%
2%
5%
6%
6%
5%
6%
8%

10%
11%
11%
6%
9%
12%
10%
10%
11%
14%

Education. From the above, it seems like dissatisfaction with public services is a function of individual’ssocio-economic situation. The results suggest that people born in high caste and higher income households
are more expressive about dissatisfaction with public

services. This implies that people’s education levelshould also be correlated with their level of dissatisfaction with public services. Table 5.20 confirms this.
People with higher education more readily express
dissatisfaction with public services for all services.

Table 5.20: Dissatisfaction with public services – by education level (IRM-2)

Education of
respondents

90

Illiterate
Literate
Primary level
Lower secondary level
Secondary level
SLC Pass+2/Intermediate pass
Bachelor pass
Master & above

Electricity

Drinking
water

MedicalSchools

10%
16%
15%
19%
25%
17%
31%
37%
55%

18%
19%
21%
20%
24%
33%
28%
32%
45%

9%
10%
10%
8%
7%
33%
10%
12%
15%

5%
6%
6%
5%
5%
9%
7%
10%

Motorable
roads
10%
12%
11%
11%
10%
17%
12%
13%
25%

Aid and Recovery in Post-Earthquake Nepal

Chapter 6.
The Fuel Crisis
Photo: Kushal Puri

This chapter examines how the fuel crisis, which
occurred in the midst of on-going earthquake relief
efforts, affected different regions and populationgroups. Following the passage of a controversial andcontested Constitution, protests occurred across someareas of Nepal, although largely not in the earthquake
zone. One consequence was the blockading of theborder with India. During the crisis of September
2015-February 2016, fuel imports from India fell

dramatically. This affected many areas of the economy
at a time when the country was trying to recover fromthe earthquake.43
The chapter focuses on the impacts of the crisison access to and prices of fuel, and the impact onaid. Impacts on the most vulnerable sections of the
population are explored.

Key findings:
Access to fuelSources of fuel• Almost two-thirds said that the fuel crisis had
negatively affected their access to fuel for cooking.
Access to fuel for transportation has been lessaffected, in part because half of respondents said
they do not need fuel for transportation.

• Of those able to obtain fuel, the most importantsource by far was wood, used for cooking. Morepeople got fuel on the regular or black market
than from the government.

• More people’s access to fuel was affected in ruralthan urban areas. The richest, and higher caste
people, were the least likely to say access to fuelhas been affected.
• Those living in temporary shelters were the most
likely to have had their access to fuel for cooking
affected. Disability and gender do not affectreported access to fuel.

• The use of wood for fuel was much higher inrural than urban areas. Those in self-constructed
shelters are much more likely to have used wood
as fuel than those in other types of housing.
• In urban areas, 14% bought fuel on the black
market. Richer people were more likely thanpoorer ones to buy on the black market. The
poorest were the least likely to receive free fuelfrom the government.
43
Nepal depends primarily on India for fuel (in particular,
petroleum and gas), essential for cooking and transportation. The
fuel shortage therefore had impacts across the country.

91The Fuel Crisis

Price of fuel• Those in the lowest income bracket were themost likely to say they paid significantly more
for fuel than before the crisis. There are no majordifferences in prices paid by caste, gender, ordisability.

• O ne-half of people said they paid more forfuel during the crisis. There is extremely large
variation across districts.

Impacts on aid and recovery• While more people’s access to fuel was affectedin rural areas, people there were less likely to pay
more. This is because people in rural areas weremore likely to use wood for fuel and less likely tobuy on the black market.

• One-quarter of people in severely hit districts
said that aid completely stopped during the crisis;
41% said it continued but at lower volumes than
before.

6.1 Access to fuel
The fuel crisis had substantial effects on people’s accessto fuel. Almost two-thirds of respondents said that thefuel crisis negatively affected access to fuel for cooking(Figure 6.1). Access to fuel for transportation appears
to have been less affected. Around half of respondents
said they do not need fuel for transportation in their
day-to-day lives. Of the remaining 51% of people whoneed fuel for transportation, only around 12% said
the crisis negatively affected this (6% of the wholesample).
Figure 6.1: Effect of the crisis on access to fuel forcooking and transportation (IRM-2)
4%
10%

35%

Both affected
Cooking affected, transport unaffected
Cooking affected, transport not required
Transport affected, cooking unaffected
Both unaffected

Table 6.1 presents the breakdown of responses by
district and district impact category. A number ofpoints stand out.
First, there is very wide variation in the extent to whichpeople said access to fuel for both uses was unaffected.
Amongst severely hit districts, only 4% of people in
Sindhupalchowk and 6% in Nuwakot said this was thecase. (Solukhumbu, a hit with heavy losses district,is the other place where very few people say accessto both was unaffected). In contrast, 73% in Gorkha
said that access to fuel for cooking and transportation
was unaffected. As discussed below, the black market
functioned in Gorkha to a greater extent than in mostother districts.
Second, there is also large variation in the extent towhich the crisis affected access to fuel for cooking. In
Nuwakot, reduced access to cooking fuel affected 94%
people, in Sindhupalchowk the figure was 96%, and
93% in Solukhumbu saw negative impacts on theiraccess to cooking fuel.44

49%

2%

Geographic variation in access to fuelThird, there is large variation in the extent to which
fuel for transportation is not required. In four
districts, over three-quarters of people said fuel fortransportation was not needed and hence restricted
access to it was not important: Nuwakot (77%),
Sindhupalchowk (90%), Okhaldhunga (82%), andSolukhumbu (90%).
There are not clear systematic links between theimpacts of the earthquake and the extent to which
the crisis affected access to fuel. However, in manyof the more impacted districts, the crisis did severely
restrict access to fuel.
44

92This is calculated by adding the first three columns in Table 6.1.

Aid and Recovery in Post-Earthquake Nepal

Cooking
affected,transport
unaffected

Cooking
affected,transport
not required

Transport
affected,cooking
unaffectedBothunaffected

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Bothaffected

Table 6.1: Effect of the crisis on access to fuel for cooking and transportation –by district impact and district (IRM-2)

1%
2%
1%
2%
1%
2%
10%
10%
19%
2%
1%
0%
0%
2%
2%
4%

6%
1%
9%
15%
3%
4%
12%
5%
30%
0%
2%
1%
3%
32%
32%
9%

54%
50%
16%
77%
40%
90%
38%
18%
16%
82%
66%
41%
90%
25%
25%
49%

0%
0%
0%
0%
1%
0%
2%
5%
1%
0%
5%
10%
0%
4%
4%
2%

37%
47%
73%
6%
56%
4%
37%
61%
35%
16%
26%
46%
5%
36%
36%
35%

More people’s access to fuel was affected in rural areas
than urban ones (Figure 6.2). Almost half of urban
respondents said the crisis had not affected theiraccess for either use; the figure for rural respondents

was one-third. A far higher proportion of people inrural areas said access to cooking fuel was affectedcompared to those in urban areas.

Figure 6.2: Effect of the crisis on access to fuel for cooking and transportation – by urban/rural (IRM-2)

2% 8%

55%

2%

33%

Rural
14%

18%

17%

3%

48%

50%

75%

Urban

0%

25%

Both affected
Cooking affected, transport unaffected
Cooking affected, transport not required

100%

Both unaffectedTransport fuel affected, cooking unaffected

What was the effect on access fordifferent population groups?
Income. The richest, those who have an incomeof more than NPR 40,000/month, were the most
likely report that access to fuel for both cooking
and transportation was not affected (Figure 6.3).
The richest primarily live in urban areas, which, asshown in Figure 6.2, were less affected in terms ofaccess. Transportation fuel is also more likely to be arequirement for higher income households. However,this group was also the second most likely to reportthat access was unaffected. This may be because they
were more able than those with lower incomes to pay
higher prices for scarce fuel if they needed it.

93

The Fuel Crisis

Figure 6.3: Effect of the crisis on access to fuel for cooking and transportation – by income band (IRM-2)
17%

NPR 40,000 >7%

NPR 20,000 - 39,999

20%

13%

4%

NPR 10,000 - 19,999

16%

40%

9%

2% 4%

< NPR 2,500

0%

45%

5%

36%

1% 9%

NPR 2,501- 9,999

1%

2%

60%

40%

38%

1%

1%

25%

36%

52%

50%

Both affected
Cooking affected, transport unaffected

29%

75%

100%

Transport affected, cooking unaffected
Both unaffected

Cooking affected, transport not required

Disability. There is little discernible difference
between those with and without disabilities in theability to access fuel (Figure 6.4). A larger share ofthose with disabilities reported being completely

unaffected (37%) by the fuel crisis in terms of accessto cooking and transport fuel. The same share reported
that transport was affected and not cooking (2%).

Figure 6.4: Effect of the crisis on access to fuel for cooking and transportation –by disabled/non-disabled (IRM-2)

With disability

Without disability

2%

5%

0%

9%

49%

10%

2%

49%

25%

2%

50%

Both affected
Cooking affected, transport unaffected
Cooking affected, transport not required

Caste. Higher caste groups seem to be have beenslightly more insulated from impacts. Forty-three
percent reported no impact on access to fuel fortransportation and cooking compared to the sample

94

37%

34%

75%

100%

Transport affected, cooking unaffected
Both unaffected

average of 35% (Figure 6.5). Higher caste groups werealso less likely to report that transport fuel was not arequirement, indicating greater transport use (37%against the sample average of 49%).

Aid and Recovery in Post-Earthquake Nepal

Figure 6.5: Effect of the crisis on access to fuel for cooking and transportation – by caste (IRM-2)

1%

16%

37%

4%

8%

52%

4%

10%

46%

3%

43%

High caste
1%

34%

Janajati
3%37%

Low caste
0%

25%

50%

75%

Both affected
Cooking affected, transport unaffected
Cooking affected, transport not required

Gender. There are no gender-based differences interms of access to transport and cooking fuel. Nearly
the same proportion reported that fuel for both
purposes was unaffected (35% women, 36% men) andthe largest share of both reported that cooking wasaffected and transport was not required (49/50%).
Both cooking and transport are household rather than
individual expenditures. As such, the gender of thesurvey respondent should not have affected responses.

100%

Transport affected, cooking unaffected
Both unaffected

Current type of shelter. Those who continue tolive in self-constructed shelters (on their own landor land belonging to others) were more likely to have
faced problems with respect to cooking fuel access, andthey were also more likely to report that transport is
not required (Figure 6.6). This is a concern, as thosein shelters are among the most vulnerable following
the earthquake.

Figure 6.6: Effect of the crisis on access to fuel for cooking and transportation – by current shelter (IRM-2)

Neighbor/friend
Self-constructed shelter(other's land)
Self-constructed shelter(own land)

3%

16%

1% 8%

1% 6%

0%

1%

29%

57%

36%

33%

6%

43%

61%

Rented house

Own house

38%

27%

12%

44%

25%

40%

3%

50%

Both affected
Cooking affected, transport unaffected
Cooking affected, transport not required

35%

75%

100%

Transport affected, cooking unaffected
Both unaffected95

The Fuel Crisis

6.2 Sources of fuelSixty-four percent of respondents had access to fuelduring the crisis: for cooking, transport, or for both.
Of those able to obtain fuel, the most important source
by far was wood (Figure 6.7).45 Wood can be used forcooking but not for transportation. The second andthird most important sources of fuel were oil and gas
bought from the black market and the regular market,
both of which are used for transportation. More people
got fuel on the market than from the government.

Figure 6.7: Sources of fuel during the crisis (IRM-2)

Buy oil/gas black market

4.8%

Buy oil/gas market

4.1%

Buy oil/gas
from government

0.9%Free oil/gas
from government

0.8%

Wood

57.2%

0%

15%

30%

45%

60%

Geographic variation in sources of fuelTable 6.2 shows where people got fuel from in each
district. People were most likely to be able to buy oil or
gas on the regular market in Bhaktapur, Kathmandu,
and Gorkha. These same districts, along with Syangja,
are where black market oil and gas were most readily
available.

Reliance on the government for access to fuel (paid andunpaid) was very low, with free fuel distribution noted
only in Dhading, Nuwakot, and Kathmandu at levelsof over 1% of the district populations; and sales of fuelby government at over 1% of the district populations
occurred only in Bhaktapur and Kathmandu.

Table 6.2: Sources of fuel during the crisis – by district impact and district (IRM-2)
District

Wood

Free oil/gas
government

Buy oil/gas
government

Buy oil/gas
marketBuy oil/gas
black market

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit

59%
51%
18%
91%
43%
93%
45%

1%
1%
0%
3%
0%
1%
1%

1%
0%
1%
1%
1%
0%
2%

2%
1%
7%
2%
0%
2%
9%

2%
2%
5%
1%
3%
3%
10%

45

Respondents could pick multiple answers. Throughout thissection, reported percentages are of the full sample, not of only
those who could access fuel.

96

Aid and Recovery in Post-Earthquake Nepal

District

Wood

Free oil/gas
government

Buy oil/gas
government

Buy oil/gas
marketBuy oil/gas
black market

Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

15%
36%
84%
70%
52%
89%
59%
59%
57%

1%
2%
0%
0%
0%
1%
0%
0%
1%

2%
3%
0%
1%
0%
1%
1%
1%
1%

19%
7%
1%
2%
2%
2%
2%
2%
4%

13%
17%
0%
1%
1%
1%
9%
9%
5%

The use of wood for fuel during the crisis was strikingly
higher in rural areas (63% compared to 23% in urban
places) – Figure 6.8. Reliance on all other sources offuel was much higher in urban areas – particularly

buying oil and gas from the black market (16.3%
against 2.9% in rural areas) and from the regularmarket (14.1% against 2.4% in rural areas).46

Figure 6.8: Sources of fuel during the crisis – by urban/rural (IRM-2)

62.9%

0.6% 0.6% 2.4% 2.9%

Rural
23.3%

2% 2.2%

14.1%

16.3%

Urban

0%

20%
Wood
Free oil/gas from government
Buy oil/gas from government

40%

60%

80%

Buy oil/gas market
Buy oil/gas black market

Where did different population groups
get their fuel from?
Income. The highest share of those who used wood
as a source of fuel (68%) is those in one of the lower
income brackets (NPR 2,501-9,999/month). The very
poorest were among the least likely to access any formof fuel. However, there was also relatively high use ofwood among higher income segments, although there

is a declining share for each successive income bracket.
Richer people were more likely than poorer ones to gettheir fuel from other sources, especially from the black
market. Notably, the poorest were the least likely toreceive free fuel from the government.

46

Percentages in the figure do not add up to 100% because noteveryone could access fuel.

97

The Fuel Crisis

Table 6.3: Sources of fuel during the crisis – by income band (IRM-2)
Monthly income

Wood

Free oil/gas
government

Buy oil/gas
government

Buy oil/gas
marketBuy oil/gas
black market<NPR 2,500
NPR 2,501- 9,999
NPR 10,000 - 19,999
NPR 20,000 - 39,999
NPR 40,000 >
Total

44.9%
68.2%
53.1%
48.6%
39.5%
57.2%

0.0%
0.3%
1.3%
1.0%
1.2%
0.8%

0.7%
0.5%
0.8%
1.4%
1.2%
0.9%

0.7%
1.8%
5.2%
6.9%
5.8%
4.1%

1.5%
2.0%
5.2%
10.2%
16.3%
4.8%

Disability. There are no discernible differences interms of sources from which fuel was obtained during
the crisis, between those with disabilities and those
without.
Caste. Janajatis are slightly more likely to haveused wood than lower and higher caste groups (58%compared to 56% and 54%) – Table 6.4. Surprisingly,
lower caste people were more likely to have purchased
oil and gas from the black market (6% compared to 4%
for Janajatis and for high caste), while Janajatis weremore likely to have bought oil or gas on the regularmarket. No high caste people received fuel from thegovernment.

Table 6.4: Sources of fuel during the crisis – by caste (IRM-2)
Caste

Wood

Free oil/gas
government

Buy oil/gas
government

Buy oil/gas
marketBuy oil/gas
black market

Low caste
Janajati
High caste
Total

55.8%
58.4%
53.8%
57.2%

1.1%
0.7%
0.0%
0.8%

0.9%
1.0%
0.0%
0.9%

3.7%
4.4%
2.4%
4.1%

6.0%
4.3%
3.6%
4.8%

Gender. There are no significant gender-based
differences in terms of sources of fuel during the crisis
period.
Current type of shelter. Those in self-constructed
shelters were much more likely to have used wood
(63% and 65%) relative to those in other forms of
housing (the sample average is 57%) – Table 6.5.
Although in extremely small shares, those in self-constructed shelters on their own land were also the most

likely to have received free oil or gas supplies fromthe government (1%); and those in self-constructed
shelters on other people’s land were the most likely
to have purchased oil and gas from the government(2%). Those in shelters (whether on their own land oron land owned by others/community) were also lesslikely to have purchased oil and gas from the regularor black market. This would follow as these sources
are likely to be more expensive.

Table 6.5: Sources of fuel – by current shelter (IRM-2)
Current shelter

Wood

Free oil/gas
government

Buy oil/gas
government

Buy oil/gas
marketBuy oil/gas
black market

Own house
Rented house
Self-constructed shelter(own land)
Self-constructed shelter(other's land and community
owned)
Neighbor/friend
Total

53.4%
20.0%

0.7%
0.0%

1.2%
0.0%

5.9%
26.7%

7.3%
20.0%

62.6%

1.0%

0.3%

1.1%

1.4%

65.3%

0.0%

2.1%

3.5%

2.8%

43.2%
57.2%

0.0%
0.8%

0.0%
0.9%

13.5%
4.1%

2.7%
4.8%

98

Aid and Recovery in Post-Earthquake Nepal

6.3 Price paid for fuelFigure 6.9: Changes in fuel prices compared tobefore the crisis (IRM-2)

Forty-six percent of people said they paid more forfuel—if they could get it—compared to before thecrisis (Figure 6.9). Of this group, the vast majority
said they paid slightly more rather than significantly
more. Interestingly, 29% said that they paid less than
before the crisis. This may be because they limited
their fuel consumption.

8%
17%

Unlike other questions on the ability to access fueland alternate sources used during the crisis, where thepercentage of those who answered that they did notknow was less than 1%, a much larger share (17%) wereunable or unwilling to provide responses regarding theprice they paid for fuel.

7%

29%

39%

Pay the same
Pay less
Pay slightly more

Pay significantly more
Refused/don’t know

Geographic variation in changes in fuel prices
A large share of people in the comparatively lessearthquake-affected district of Syangja report thatprices they paid for fuel increased during the crisis(Table 6.6). This might be associated with greaterlevels of economic activity in that district and, as aresult, a greater demand for fuel for industrial andtransport use. This is substantiated by the fact that
Syangja has the lowest share of households (25%, from
Table 6.1 above) reporting that transport fuel was nota requirement during the fuel crisis. Prior findings onhigher levels of borrowing in this district (including
for investment in farming and business), the highest
rate of recovery among businesses over the past threemonths, and the highest incidence of bank borrowing
in this district, which is generally associated with
non-consumption requirements, also point to greater
overall economic activity in Syangja.

is perhaps unsurprising given that the majority ofhouseholds appear to have used wood, which was
not directly affected by the crisis, as a source of fuel.
The greatest share of those reporting paying a muchhigher price is in the crisis hit districts (9%), and, inparticular, in Kathmandu (18%). This keeps with theearlier finding that the greatest negative impacts onaccess were in these districts for both cooking andtransport (10%, from Table 6.1 above, relative to thesample average of 4%). This indicates that fuel supply
likely fell far short of demand. Further, the highest
share in Bhaktapur and Kathmandu reported buying
oil and gas from the regular market (19% and 7%,
respectively, compared to the sample average of 4%)
and from the black market (13% and 17%, respectively,
compared to the sample average of 5%). Prices can be
expected to be higher from these sources.

There is extremely large variation across districts. In
Dhading, for example, 93% of people said they paid
more, while in Nuwakot, which was also severely hit
by the earthquake, only 18% said they paid more.47
Overall, only 7% of respondents reported that they
paid significantly more for fuel during the crisis. This

47

Gorkha has the lowest rate of people saying they paid more (16%).
But 72% of respondents in Gorkha refused to answer the questionor said they did not know.

99

The Fuel Crisis

Table 6.6: Changes in fuel prices compared to before the crisis –by district impact and district (IRM-2)
District

Pay the same

Pay less

Pay slightly
more

Pay
significantly
more

Refused/Don’t know

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

13%
4%
3%
36%
13%
9%
9%
4%
18%
5%
2%
3%
0%
2.%
2%
8%

26%
2%
9%
43%
29%
48%
42%
29%
29%
67%
51%
63%
38%
32%
32%
29%

40%
83%
10%
16%
49%
42%
34%
50%
35%
18%
26%
25%
26%
52%
52%
40%

4%
10%
6%
2%
3%
0%
9%
5%
18%
5%
3%
3%
2%
5%
5%
7%

16%
1%
72%
2%
8%
0%
6%
11%
0%
5%
19%
5%
3%
8%
8%
17%

Rural areas appear to have been relatively insulated
from the impacts of the crisis on the price of fuel,with only 6% reporting paying significantly higher
prices, against 10% in urban areas, and 38% reporting
paying slightly more, compared to 45% in urban areas(Figure 6.10). This is in keeping with the earlier findingthat 62% of the rural population used wood during thecrisis (Figure 6.8 above) relative to 23% in urban areas.
Urban areas also had a significantly higher share thatbought oil and gas from the regular and black market.

Figure 6.10: Changes in fuel prices compared to before the crisis – by urban/rural (IRM-2)

6%

30%

38%

6%

20%

Rural
13%

25%

45%

10%

8%

Urban

0%

25%
Same as before (Sept.)
Pay less
Pay slightly more

50%

100%

Pay significantly more
Refused/don’t know

Income. It is concerning that those in the lowest
income bracket are the most likely to say they paid
significantly more for fuel than before the crisis(41%) – Figure 6.11. This share is far higher than thesample average (7%). The second poorest group werethe most likely to say they paid slightly more. With
the caveat that price information is self-reported,the data suggest that the impact of the crisis on fuel

100

75%

prices was borne disproportionately by those withlower incomes.48

48

This caveat is worth noting since the question on price resulted ina much larger share of respondents stating that they did not know
the answer, compared with other questions, pointing to a potential
reluctance to share this information.

Aid and Recovery in Post-Earthquake Nepal

Figure 6.11: Changes in fuel prices compared to before the crisis – by income band (IRM-2)
12%

NPR 40,000 >

25%

9%

NPR 20,000 - 39,999

0%

4%

44%

17%

Same as before (Sept.)
Pay less
Pay slightly more

Disability. There does not appear to be a conclusive
association between disability and the price paid for
fuel. Those with disabilities were more likely to say
they paid less for fuel during the crisis (27% against
30% for those without) but also comprise a larger
share of those paying slightly more (44% against 37%
for those without).
Caste. A higher share of lower caste people reported
paying slightly more (43%) compared to the sample
average (40%) but there are no other notable associ-

11%

21%

7%

26%

25%

3%

4%

38%

25%

14%

< NPR 2,500

42%

30%

7%

NPR 2,501- 9,999

22%

36%

7%

NPR 10,000 - 19,999

38%

17%

41%

50%

3%

75%

100%

Pay significantly more
Refused/don’t know

ations between caste groups and the reported pricepaid for fuel during the crisis.
Gender. There are not significant differences inreported prices men and women paid.
Current type of shelter. Those in their own houses
or in rented houses were the most likely to report thatthey paid more than before the crisis (47% and 62%)
– Figure 6.12. However, those in shelters on their own
land or others’ land also frequently said they had paid
more (46% and 42%).

Figure 6.12: Changes in fuel prices compared to before the crisis – by current shelter (IRM-2)
Neighbor/friend

5%

Self-constructed shelter(other's land)
Self-constructed shelter(own land)

41%

9%

8%

Own house
0%

7%

29%

19%

15%

Rented house

23%

38%

42%

20%

27%

62%

34%

Same as before (Sept.)
Pay less
Pay slightly more

18%

4%

4%

23%

25%

14%

38%

50%

9%

75%

12%

100%

Pay significantly more
Refused/don’t know

101

The Fuel Crisis

6.4 Impacts on aid and recovery
The fuel crisis had an impact on recovery in two ways.
First, it increased the prices people paid for staple
goods, reducing the money they had for other things(see Figure 5.10 above). Second, it directly impacted
the distribution of aid.

Fifty-four percent of people said that the fuel crisis
led to either the stopping, or a reduction, of aid in
their wards. In severely hit districts, 66% said thesame, with one-quarter reporting that aid completely
stopped (Figure 6.13). There is variation between
districts in the perceived impact of the fuel crisis on
aid distribution (Table 6.7).

Figure 6.13: Did protests over the new Constitution and the fuel crisis affect aid assistancein your ward – by district impact (IRM-2)
50%
41%
38%

38%

25%

25%

15%

13%

11%
8%

7%

0%

19%

19%

16%

0

Yes, aid
completely stopped

Yes, less aid
than before

Severely hit

All districts

1%

Yes, more aid
than beforeNo

Never received
any aid assistance

Don’t know

Table 6.7: Did protests over the new Constitution and the fuel crisis affect aid assistancein your ward – by district impact and district (IRM-2)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

102

Yes, aid
completely
stoppedYes, less aid
than before

Yes, more
aid thanbeforeNo change

25%
37%
25%
9%
24%
30%
14%
5%
19%
17%
4%
1%
6%
5%
5%
16%

41%
19%
57%
42%
45%
42%
25%
5%
52%
19%
43%
25%
62%
47%
47%
38%

0%
0%
1%
1%
0%
0%
1%
0%
4%
0%
0%
0%
1%
1%
1%
1%

7%
10%
11%
8%
2%
6%
10%
6%
4%
21%
5%
8%
3%
10%
10%
8%

Never
received
any aidassistance
11%
19%
2%
14%
15%
6%
32%
59%
12%
25%
21%
43%
9%
9%
19%

Don’t know
15%
15%
5%
26%
14%
16%
17%
25%
9%
17%
26%
24%
29%
28%
28%
19%

Aid and Recovery in Post-Earthquake Nepal

Chapter 7.
Politics
Photo: Binu Sharma

In the early post-earthquake period, the disasterhad little observable impact on people’s politicalpreferences. Has this changed over time? This chapter

analyses attitudes towards political parties and thestate and how these have changed as the recovery
process has continued.

Key findings:
Political party preferences

Satisfaction with political parties• Most people say they do not know who they will
vote for in the election. Amongst those who havedecided, there is increased support for Nepali
Congress and CPN-UML with a slight drop in
support for UCPN (Maoist).

• Satisfaction with the aid response of politicalparties has declined since IRM-1. The drop is
highest in severely hit districts. Among those
who were satisfied in IRM-1, 72% now say they
are dissatisfied. Only 32% of all respondents are
satisfied with local political parties.

• Past UCPN (Maoist) voters are more likely to live
in severely hit districts than other areas. A larger
share of these voters say they are uncertain who
they will vote for next time and some have moved
to support other parties.
• People belonging to higher caste and Janajatis
tended to vote for Nepali Congress and CPN-UML
in the last election, whereas those in the lower
caste group were more likely to vote for UCPN
(Maoist). However, the drop in planned voted for
UCPN (Maoist) holds for all caste groups.

• There are indications that declines in satisfaction
are higher in areas where less aid was received.
• There are not substantive differences in satisfaction levels across most demographic characteristics. However, those with higher incomes or
education, and people who are either high caste
or Janajati, are more likely to report low levelsof satisfaction.

• RPP-N has been most successful in recruiting
voters who chose other parties last time around.

103

Politics

Satisfaction with central government

Responsiveness of Constituent Assembly(CA) members• Satisfaction with the aid response of the central
government has increased since IRM-1 but onethird are still dissatisfied. Among those who
were satisfied in IRM-1, 32% now say they are
dissatisfied.

• T wo-thirds of respondents report that CA
members have not visited their areas since thebeginning of the 2015 monsoon. Visits from CA
members to earthquake-affected wards have
dropped slightly since IRM-1.

• Patterns are similar to those of attitudes towards
local parties when analyzing by earthquake
impact levels and demographic characteristics.
People in higher impact districts are more likely
to be dissatisfied than those in lower impact
districts.

7.1 Political party preferences
Who did people vote for in the last election?
IRM-2 asked people which party they voted for in
the last election. As Figure 7.1 shows, most voted for
Nepali Congress or CPN-UML, with large numbers

saying they did not vote or did not know who they
voted for.49

Figure 7.1: Who people voted for in the last election (IRM-2)
40%

30%

30%

22%

20%
14%

0%

12%

10%

10%

NepaliCongress

CPN-UMLUCPN(Maoist)

8%

1%

1%

RPP-N

RPP

1%

Did not vote

NMKP

RefusedDon’t know

Districts that were less affected have higher shares of
Nepali Congress voters and severely hit districts havea much larger proportion of voters (19%) who chose
UCPN (Maoist). Voters in rural areas are more likely

to disclose who they voted for than those in urban
areas. There are not large differences in the past voting
preferences of men and women (Table 7.1).50

49

50

Similar to IRM-1 in February 2015, respondents were asked whichparty they voted for in the last elections. In both surveys, the electionreferred to is the 2013 national parliamentary elections. We do notfind much difference in responses across the two periods.

104

One significant change from the last survey is that respondents
were more open this time. Only 8% refused to respond, which isseven percentage points less than in IRM-1.

Aid and Recovery in Post-Earthquake Nepal

Table 7.1: Who people voted for in the last election – by district impact, urban/rural, and gender (IRM-2)

Hit with
heavy losses

Hit

Rural area

Urban area

FemaleMale

GenderCrisis hit

Nepali Congress
CPN-UMLUCPN (Maoist)
RPP-N
RPP
NMKP
Did not vote
RefusedDon’t know
Total

Urban/rural

Severely hit

District earthquake impact

28%
21%
19%
0%
2%
0%
14%
5%
11%
100%

24%
20%
4%
1%
0%
5%
14%
16%
16%
100%

43%
25%
4%
1%
0%
0%
11%
5%
11%
100%

36%
27%
2%
0%
0%
0%
17%
11%
8%
100%

32%
24%
11%
1%
1%
0%
14%
6%
10%
100%

20%
12%
7%
0%
1%
6%
13%
22%
20%
100%

29%
21%
9%
0%
1%
1%
16%
8%
15%
100%

32%
23%
11%
1%
1%
1%
12%
9%
9%
100%

Solukhumbu, Nuwakot, and Okhaldhunga have thehighest number of respondents who voted for Nepal
Congress. Lamjung, Sindhulpalchowk, and Dhading have the most CPN-UML voters. Gorkha andAll
districts

30%
22%
10%
1%
1%
1%
14%
8%
12%
100%

Ramechhap have the highest number of UCPN-Maoists voters – Table 7.2. Significant numbers of voters in
Kathmandu and Bhaktapur either refused to respondor chose the don’t know option.

CPN-UMLUCPN (Maoist)

RPP-N

RPP

MJF-Nepal

Did not vote

NMKP

RefusedDon’t know

Total

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja

Nepali Congress

Table 7.2: Who people voted for in the last election – by district impact and district (IRM-2)

28%
25%
25%
45%
29%
16%
24%
13%
20%
40%
43%
35%
51%
36%
36%

21%
32%
7%
13%
23%
32%
20%
13%
17%
31%
25%
35%
16%
27%
27%

19%
9%
46%
9%
20%
10%
4%
4%
4%
3%
4%
4%
3%
2%
2%

0%
1%
1%
0%
0%
0%
1%
1%
1%
0%
1%
1%
2%
0%
0%

2%
1%
0%
1%
0%
8%
0%
0%
1%
0%
0%
1%
0%
0%
0%

0%
0%
0%
0%
0%
0%
0%
0%
0%
1%
0%
0%
0%
0%
0%

14%
21%
13%
13%
11%
13%
14%
11%
16%
13%
11%
17%
6%
17%
17%

0%
0%
0%
0%
0%
0%
5%
13%
0%
0%
0%
0%
0%
0%
0%

5%
3%
4%
7%
2%
8%
16%
24%
21%
4%
5%
3%
6%
11%
11%

11%
9%
5%
12%
15%
13%
16%
21%
19%
8%
11%
6%
15%
8%
8%

100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%

For most demographic breakdowns, there is not muchvariation in support for political parties. Women
expressed slightly higher level of uncertainty (6% more
women report they didn’t know compared to men) andindividuals in higher income groups were more likely
to refuse to respond. However, when breaking down
by caste, people belonging to high caste groups weremore likely to vote for Nepal Congress or CPN-UML,

while low caste respondents were relatively more likely
to have chosen UCPN (Maoist) – data is provided inTable 7.5 below.

105

Politics

Have political preferences changed since the earthquake?
When asked which party they would vote for if anelection was to be held, most respondents either statedthat they did not know or refused to answer. This pattern of non-response is similar to IRM-1 (Figure 7.2).
The main difference is the increased support for thetwo mainstream parties, by four percentage points for
Nepali Congress and three percentage points for CPNUML. There has been a slight decrease in support for
UCPN (Maoist) by one percentage point.

Figure 7.2: Voting preference for next election (IRM-1/IRM-2 comparison)
60%
55%

57%

45%

30%

15%

16%
14%

12%

11%
8%

8%
2%

0%

Nepali Congress

CPN-UML

5%

3%
1%

UCPN (Maoist)

1%

RPP-N
IRM-2

There is substantial variation in current party preferences between districts. In general, support for
UCPN (Maoist) is highest in severely hit districts, inparticular in Gorkha (Table 7.3). Support for Nepali
Congress is highest in the hit with heavy losses districts: Lamjung and Solukhumbu.

5%Will not vote

RefusedDon’t know

IRM-1

There has been a significant decrease in support for
Nepali Congress in Okhaldhunga. In that district, 40%of people said they voted for Nepali Congress in thelast election (Table 7.2) while only 16% said they will
do so in the next election (Table 7.3). Okhaldhunga
has been the district least served by aid since IRM-1(see Chapter 4). There has also been a sharp decline insupport for UCPN (Maoist) in Gorkha and Ramechhap.

106

CPN-UMLUCPN(Maoist)

RPP-N

RPP

MJF-Nepal

Will not vote

NMKP

RefusedDon’t know

Total

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk

NepaliCongress

Table 7.3: Party preferences – by district impact and district (IRM-2)

14%
13%
20%
19%
10%
7%

9%
13%
7%
6%
9%
8%

4%
3%
10%
1%
5%
2%

0%
0%
1%
0%
0%
1%

1%
0%
0%
0%
0%
3%

0%
0%
0%
0%
0%
0%

4%
4%
3%
6%
1%
4%

0%
0%
0%
0%
0%
0%

7%
3%
9%
11%
1%
12%

61%
63%
49%
56%
74%
62%

100%
100%
100%
100%
100%
100%

CPN-UML

UCPN(Maoist)

RPP-N

RPP

MJF-Nepal

Will not vote

NMKP

RefusedDon’t know

Total

Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja

NepaliCongress

Aid and Recovery in Post-Earthquake Nepal

10%
6%
9%
16%
30%
28%
31%
21%
21%

9%
3%
8%
14%
19%
26%
12%
18%
18%

0%
0%
0%
1%
2%
3%
1%
0%
0%

1%
1%
1%
0%
1%
1%
1%
0%
0%

0%
0%
0%
0%
0%
1%
0%
0%
0%

0%
0%
0%
1%
0%
0%
0%
0%
0%

6%
6%
8%
5%
8%
15%
1%
3%
3%

1%
4%
0%
0%
0%
0%
0%
0%
0%

17%
15%
22%
13%
2%
2%
1%
2%
2%

56%
65%
51%
51%
39%
26%
53%
56%
56%

100%
100%
100%
100%
100%
100%
100%
100%
100%

Where are the changes?
UCPN (Maoist) appears to have lost the most support.
Table 7.4 combines information on who people voted
for before and who they say they will vote for at the next
election. As with IRM-1, of those who have decided
who to support in the next election, most intend tovote for the same party as before. For example, only
1% of people who voted Nepali Congress in the lastelection say they will vote for a different party nexttime round. The figure is also 1% for CPN-UML. As

shown above, past UCPN (Maoist) supporters makeup a larger share of voters in severely hit districts. But
only 20% of those who said they chose UCPN (Maoist)at the last election state that they would vote for theparty if an election were held. Indeed, those who voted
for UCPN (Maoist) in the last election are more likely
to have shifted their allegiance, with 8% saying theywould now vote for Nepal Congress or CPN-UML and
61% saying they still have to make up their mind.

Table 7.4: Current political preferences – by past votes (IRM-2)

CPN-UMLUCPN (M)

RPP-N

RPP

MJF-Nepal

Will not vote

NMKP

RefusedDon’t know

Total

Voted forin lastelection(IRM-2)

Nepal Congress
CPN-UMLUCPN (Maoist)
RPP-N
RPP
MJF-Nepal
Did not vote
NMKP
RefusedDon’t know

NepaliCongress

If an election was to be held soon, which party would you vote for?
(IRM-2)

49%
1%
5%
0%
5%
0%
5%
0%
0%
0%

1%
44%
3%
8%
3%
0%
3%
0%
1%
0%

0%
0%
20%
0%
0%
0%
1%
0%
0%
0%

0%
0%
0%
52%
0%
0%
1%
0%
0%
0%

0%
0%
0%
0%
40%
0%
0%
0%
0%
0%

0%
0%
0%
0%
0%
75%
0%
0%
0%
0%

3%
4%
4%
0%
3%
25%
18%
6%
2%
1%

0%
0%
0%
0%
0%
0%
0%
26%
0%
0%

4%
6%
6%
4%
3%
0%
4%
4%
53%
1%

42%
45%
61%
36%
48%
0%
68%
64%
44%
98%

100%
100%
100%
100%
100%
100%
100%
100%
100%
100%

The decline in support for UCPN (Maoist) is seen
across all caste groups. Amongst high caste voters,
9% said they voted for them in the last election while
only 2% say they will do so next time, a decline of 78%(Table 7.5). The decline in planned votes for UCPN
(Maoist) is 82% for Janajatis and 81% for lower caste
voters.51 These drops are substantively larger whencompared to those for Nepal Congress and CPN-

UML. For both parties, the decline in votes has been
larger amongst Janajatis than high caste voters and,especially, lower caste voters.

51

There is a drop in recorded support for all parties at the next
election, in large part because more than half of people say they
have not decided yet who they will vote for.

107Politics

Table 7.5: Changes in political preferences – by caste, selected responses (IRM-2)
High caste

Janajati

Lower caste

Vote
Vote
Vote
Vote
Vote
Vote
DeclineDeclineDecline
last
next
last
next
last
next
in votesin votesin votes
election election
election election
election election
Nepali Congress
CPN-UMLUCPN (Maoist)
Total

32%
26%
9%
100%

18%
14%
2%
100%

44%
46%
78%

30%
20%
11%
100%

RPP-N has been the most successful in recruiting
voters who chose other parties last time around. Only
65% of those who say they will vote for RPP-N in thenext election said they voted for them last time with
20% saying they voted for other parties (Table 7.6).
Comparing this result to IRM-1, RPP-N appears to be

15%
9%
2%
100%

50%
55%
82%

26%
23%
16%
100%

17%
14%
3%
100%

35%
39%
81%

gaining in popularity. Seven percent of those who say
they will vote for CPN-UML voted for other parties
in the last election. The figure is 4% for future Nepali
Congress voters, 5% for RPP voters, 8% for NMKP
voters, and 2% for UCPN (Maoist) voters.

Table 7.6: Past political preferences – by intended future votes (IRM-2)

Voted for in lastelection (IRM-2)

Total

Don’t know

Refused

NMKPWill notvote

MJF-Nepal

RPP

Nepali Congress 91% 4%
1%5%
0%
0% 19% 0% 13% 23% 30%
CPN-UML
1% 88% 1% 10% 5%
0% 17% 8% 15% 18% 22%
UCPN (Maoist)
3%
3% 90% 5%
0%
0%
8%
0%
8% 11% 10%
RPP-N
0%
0%
0% 65% 0%
0%
0%
0%
0%
0%
1%
RPP
0%
0%
0%
0% 84% 0%
1%
0%
0%
1%
1%
MJF-Nepal
0%
0%
0%
0%
0% 100% 1%
0%
0%
0%
0%
Did not vote
4%
4%
5% 15% 5%
0% 49% 0%
7% 17% 14%
NMKP
0%
0%
0%
0%
0%
0%
2% 92% 1%1%1%
Refused
0%
0%
0%
0%
5%
0%
3%
0% 53% 7%
8%
Don’t know
0%
0%
2%
0%
0%
0%
2%
0%
2% 21% 12%
Total
100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

While the above tables provide aggregate level changes, results from the panel data, which track responsesof the same individuals over different time periods,
better capture the change in preferences. Table 7.7 presents a cross tabulation of responses to the question
about which political party people would choose if anelection were held for the two time periods. The tableis based on 1,558 individuals across the 11 districts who
were surveyed in both IRM-1 and IRM-2.
The analysis provides further evidence that people
who claimed to support the Maoist party in IRM-1 areskeptical in IRM-2. Fifty-seven percent of respondents
who said in IRM-1 that they would support the Maoist

108

RPP-N

UCPN
(Maoist)CPN-UML

NepaliCongress

If an election was to be held soon, which party would you
vote for? (IRM-2)

party in the next election now say they don’t know who
they would vote for. Next in line are voters supporting
NMKP and the Nepali Congress, although the sample
size for NMKP supporters is extremely small (only two
individuals).

Aid and Recovery in Post-Earthquake Nepal

Table 7.7: Who people will vote for based on current preferences – individual panel data
(IRM-1/IRM-2 comparison)

CPN-UMLUCPN(Maoist)

RPP-N

RPP

MJF-Nepal

Will not
vote

NMKP

RefusedDon’t know

Total

Nepali Congress
CPN-UMLUCPN (Maoist)
If an election was
RPP-N
to be held soon,
RPP
which party would
NMKP
you vote for?
Will not vote(IRM-1)
RefusedDon’t know
Total

NepaliCongress

If an election was to be held soon, which party would you
vote for? (IRM-2)

42%
5%
4%
10%
17%
0%
8%
16%
13%
16%

4%
46%
4%
0%
0%
0%
3%
12%
9%
11%

1%
3%
22%
0%
8%
0%
0%
4%
2%
3%

0%
0%
2%
40%
0%
0%
2%
0%
1%
1%

0%
0%
0%
0%
25%
0%
0%
0%
1%
1%

0%
0%
0%
10%
0%
0%
0%
0%
0%
0%

4%
3%
7%
0%
8%
0%
14%
6%
5%
5%

0%
0%
0%
0%
0%
50%
0%
1%
0%
0%

4%
7%
4%
0%
8%
0%
10%
10%
9%
8%

45%
37%
57%
40%
33%
50%
63%
50%
60%
55%

100%
100%
100%
100%
100%
100%
100%
100%
100%
100%

Is there evidence in the panel data that people haveshifted their support from the big three parties to
RPP-N as the earlier findings suggest? Table 7.8
presents the column total cross-tabulation of the samequestion: who would the respondent vote for if anelection were to be held soon. Looking at the RPP-N
column in the table, we find that 9% of respondents

had preferred UCPN (Maoists) in IRM-1, 9% had
stated they would not vote, and 45% had responded
they did not know. In contrast to UCPN (Maoist)
voters, there has not been a move to RPP-N fromthose who previously said they would vote for Nepali
Congress or CPN-UML.

Table 7.8: Who people will vote for based on past preferences – individual panel data
(IRM-1/IRM-2 comparison)

Nepali Congress
CPN-UMLUCPN (Maoist)
If an election was
RPP-N
to be held soon,
RPP
which party would
Will not voteyou vote for?
NMKP
(IRM-1)
RefusedDon’t know
Total

Don’t know

Refused

NMKPWill notvote

MJF-Nepal

RPP

RPP-N

UCPN
(Maoist)CPN-UML

NepaliCongress

If an election was to be held soon, which party would you vote for?
(IRM-2)

33%
5%
4%
0%
0%
0%
9%
0%
5%
10%
2%
30%
6%
0%
0%
0%
4%
0%
6%
5%
1%
1%
24%
9%
0%
0%
5%
0%
2%
4%
0%
0%
0%
36%
0%
50%
0%
0%
0%
0%
1%
0%
2%
0%
27%
0%
1%
0%
1%
0%
2%
1%
0%
9%
0%
0%
11%
0%
5%
5%
0%
0%
0%
0%
0%
0%
0%
14%
0%
0%
17% 17% 22%0%9%0%18% 29% 19% 15%
44% 46% 42% 45% 64% 50% 52% 57% 62% 61%
100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

109

Politics

7.2 Satisfaction with local political parties
Satisfaction with the role of political parties in the aid
response is low. Only 32% of respondents in IRM2 are satisfied with local political parties with 61%
dissatisfied.
People in more affected districts are less likely to besatisfied with political parties than those in areas
which did not see as large impacts. Whereas 57% of
people in Syangja (the hit district) and 40% of people
in the hit with heavy losses districts (Lamjung and

Solukhumbu) say they are satisfied, the figure drops
to 30% for severely hit districts and 19% for crisis hit
districts (Figure 7.3). This increasing dissatisfaction
in higher impact areas highlights the challenge facedby parties in addressing mass expectations in anenvironment where aid flows have diminished whileurgent needs remain. It also reflects a positive tendency
among people to hold political parties accountable forproviding and managing disaster relief.

Figure 7.3: Satisfaction with local political parties – by district impact (IRM-2)
8%

Hit

16%

Hit with heavy losses

Crisis hit

Severely hit

49%

2%

24%

17%

3%

14%

25%

26%

25%
Very satisfied
Somewhat satisfied
Somewhat unsatisfied

There is variation between districts within impact
categories in satisfaction with political parties’ rolein aid delivery (Table 7.9). Amongst the severely hit
districts, there is less dissatisfaction in Ramechhap
and Nuwakot than in the other districts. For hit withheavy losses districts, people are much more likely
to be satisfied in Solukhumbu, which has received
significant amounts of aid, than in Lamjung.

5%

28%

8%

47%

25%

27%

0%

24%

8%

38%

50%

7%

75%

100%

Very unsatisfied
Don’t know

Indeed, the level of aid provided appears to be an
important determinant of levels of satisfaction. Morepeople have received aid in Ramechhap and Nuwakot compared to most other severely hit districts,
although aid coverage was also wide in Sindhupalchowk (Table 4.1 above) where satisfaction is lower.
In Solukhumbu, 95% of people have received aid
compared to 47% in Lamjung.

Table 7.9: Satisfaction with local political parties – by district (IRM-2)
How satisfied or unsatisfied you are with local political parties?

Severely hit
Dhading
Gorkha*110

Very satisfied

SomewhatsatisfiedSomewhat
unsatisfied

Veryunsatisfied

Don’t know

Total

3%
1%
3%

27%
20%
21%

25%
11%
23%

38%
66%
49%

7%
2%
4%

100%
100%
100%

Aid and Recovery in Post-Earthquake Nepal

How satisfied or unsatisfied you are with local political parties?

Nuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu*
Okhaldhunga
Hit with heavy losses
Solukhumbu
Lamjung
Hit
Syangja

Very satisfied

SomewhatsatisfiedSomewhat
unsatisfied

Veryunsatisfied

Don’t know

Total

3%
4%
5%
2%
1%
2%
2%
16%
30%
1%
8%
8%

26%
39%
28%
17%
17%
16%
17%
24%
21%
26%
49%
49%

31%
33%
25%
26%
14%
23%
43%
25%
15%
34%
14%
14%

25%
18%
36%
47%
57%
53%
29%
28%
23%
33%
24%
24%

15%
5%
7%
8%
10%
5%
9%
8%
11%
5%
5%
5%

100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%

*1% refused to respond

How are levels of satisfaction with political parties changing?
Dissatisfaction with political parties has increased
over the past year. Comparing responses for those
who were interviewed in both IRM-1 and IRM-2, it isclear that people are less happy with parties. Among
those who were very satisfied in IRM-1, 72% now state

dissatisfaction; 57% who said they were somewhat
satisfied in IRM-1 now express dissatisfaction.
Only 30% or less of respondents who expressed
dissatisfaction earlier mention that they are now
satisfied with local political parties (Table 7.10).

Table 7.10: Satisfaction with local political parties – individual panel data (IRM-1/IRM-2 comparison)
How satisfied or unsatisfied are you with local politicalparties? (IRM-2)
Verysatisfied
How satisfied or
unsatisfied are
you with local
political parties?
(IRM-1)

Very satisfied
Somewhat satisfied
Somewhat unsatisfied
Very unsatisfied
RefusedDon’t know

11%
6%
3%
4%
17%
2%

Somewhat Somewhat
Verysatisfied unsatisfied unsatisfied
24%
32%
23%
26%
17%
24%

38%
23%
30%
22%
17%
20%

24%
34%
39%
41%
50%
37%

Don’t
know
3%
4%
5%
6%
16%

Total
100%
100%
100%
100%
100%
100%

Who is less likely to be satisfied with political parties?
Urban/rural. Levels of satisfaction with politicalparties are much lower in urban areas than rural ones.
Seventy-two percent of people in urban areas say they
are unsatisfied (with 52% of people saying they arevery unsatisfied) compared to 57% in rural areas.
Gender. The difference by gender is negligible.
Disability. There are also not substantive differencesin levels of satisfaction between those with disabilities
and the non-disabled. Although 32% of respondents
with no disability report satisfaction with local parties,
compared to only 26% respondents with disabilities,
the dissatisfaction proportion is similar. However, thelevel of uncertainty among those with disabilities is 5
percentage points higher.
Caste. Lower caste respondents are more satisfied
with political parties than high caste or Janajatis.
Sixty-six percent of high caste respondents and 61%
of Janajatis express dissatisfaction with local politicalparties, compared to 53% of lower caste respondents.
Income. Richer people are more likely to be dissatisfied with local political parties than poorer ones.
Each respondent’s pre-earthquake monthly income
was coded as being either low (less than NPR 10,000),
medium (NPR 10,000-19,999), or high (NPR 20,000

111Politics

or more). Overall, 41% of respondents are in the low
income group, 43% in the medium income group, andthe 14% in the high income group.≈52 Figure 7.4 showsthat people in the medium and high income groups areslightly more dissatisfied (65% in both).

Figure 7.4: Satisfaction with local political parties – by income band (IRM-2)
7%

Low

Medium

High

25%

2%

25%

28%

25%

26%

6%

19%

Refuse/don’t know

35%

40%

5%

28%

6%

0%

8%

37%

44%

25%

31%

50%

Very satisfied
Somewhat satisfied
Somewhat unsatisfied

2%

75%

100%

Very unsatisfied
Don’t know

Education level. Those who are dissatisfied are more
likely to have higher levels of education (Figure 7.5).
Over 80% of those with a post-graduate education

report being dissatisfied with local political parties andalmost 60% of the same group report dissatisfaction
with the central government’s aid response.

Figure 7.5: Satisfaction with local political parties – by education level (IRM-2)
18%

Master & above
Bachelor pass+2/intermediate pass

4%

23%
25%

4%

59%
13%

19%

52%

24%

53%

4%

Literate
Illiterate

27%

6%

Lower secondary level
Primary level

24%

6%

30%

6%

28%

6%

0%

40%

4%

25%

40%

5%

23%

24%

25%

36%
36%

25%

1% of respondents refused to state their income level.

112

25%

24%

Very satisfied
Somewhat satisfied
Somewhat unsatisfied

52

1%

100%

SLC pass
Secondary level

6%

34%

50%
Very unsatisfied
Don’t know

75%

5%
8%
10%

100%

Aid and Recovery in Post-Earthquake Nepal

7.3 Satisfaction with the central government
In contrast to attitudes towards political parties,
more people express satisfaction with the central
government’s aid response. Fifty-three percent ofpeople say they are satisfied while 34% are dissatisfied.

likely to be dissatisfied than those in lower impact
districts (Figure 7.6). Thirty-two percent of respondents in severely hit areas and 51% in crisis hit areas
report dissatisfaction with the central government. In
contrast, only 21% in hit with heavy losses districts and
20% in the hit district report dissatisfaction with thecentral government

However, patterns are similar to those of attitudes towards local parties when analyzing by earthquake impact levels. People in higher impact districts are more

Figure 7.6: Satisfaction with the central government – by district impact (IRM-2)
21%

Hit

27%

Hit with heavy losses

Crisis hit

Severely hit
0%

53%

8%

10%

50%

35%

10%

14%

22%

55%

25%
Very satisfied
Somewhat satisfied
Somewhat unsatisfied

Again, there is substantial variation in satisfactionlevels between districts within impact categories(Table 7.11). Amongst severely hit districts, only 14%
in Ramechhap are unsatisfied compared with 46% in
both Nuwakot and Gorkha. Dissatisfaction in Lamjung
is far higher than in Solukhumbu. Ramechhap also saw
lower levels of unhappiness with the aid role of politicalparties than other severely hit districts and received
high levels of aid coverage. Similarly, Solukhumbu
received far wider coverage of aid than Lamjung.

75%

6%

7% 2%

29%

17%

50%

10%

6%

15%

3%

100%

Very unsatisfied
Don’t know

However, aid coverage was wide in Nuwakot (98%),
showing that other factors also matter in determining
people’s perceptions of the central government.
Indeed, Nuwakot was more likely to get aid from thegovernment than all other severely hit districts except
Ramechhap (see Table 4.9 above). The highest levelof dissatisfaction with the central government is in
Kathmandu, perhaps unsurprising given that people
are more exposed to national politics there.

113

Politics

Table 7.11: Satisfaction with the central government – by district (IRM-2)
How satisfied or unsatisfied are you with the central government?

Severely hit
Nuwakot
Ramechhap
Sindhupalchowk
Dhading
GorkhaCrisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Solukhumbu
Lamjung
Hit
Syangja

Very satisfied

SomewhatsatisfiedSomewhat
unsatisfied

Veryunsatisfied

Don’t know

Total

10%
3%
16%
16%
6%
9%
8%
2%
4%
19%
27%
40%
13%
21%
21%

55%
48%
63%
51%
65%
46%
35%
39%
29%
38%
50%
53%
47%
53%
53%

17%
23%
10%
19%
11%
21%
22%
23%
19%
23%
14%
4%
25%
10%
10%

15%
23%
4%
12%
16%
24%
29%
26%
46%
13%
7%
1%
12%
10%
10%

3%
3%
7%
2%
1%
1%
6%
10%
2%
7%
2%
1%
3%
6%
6%

100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%

How are levels of satisfaction with the central government changing?
In contrast to attitudes towards political parties, satisfaction with the central government seems to be increasing one year after the earthquake. Using the panel
data, which only includes respondents interviewed inboth rounds, we see slight improvements. Fifty-five
percent or more of respondents who reported they

were either somewhat or very unsatisfied with thecentral government in IRM-1 expressed satisfaction
with the central government in the IRM-2 (Table 7.12).
Thirty-two percent of respondents in the satisfied category in IRM-1 are now somewhat or very dissatisfied.

Table 7.12: Satisfaction with the central government – individual panel data (IRM-1/IRM-2 comparison)
How satisfied or unsatisfied are you with central government? (IRM-2)
Verysatisfied
How satisfied
or unsatisfied are youwith central
government?
(IRM-2)

Very satisfied
Somewhat satisfied
Somewhat unsatisfied
Very unsatisfied
RefusedDon’t know

Somewhat Somewhat
VeryDon’t know
satisfied unsatisfied unsatisfied

14%
15%
13%
7%
0%
16%

53%
50%
45%
48%
100%
49%

16%
17%
19%
21%
0%
12%

16%
14%
19%
22%
0%
16%

1%
3%
3%
2%
0%
8%

Total

100%
100%
100%
100%
100%
100%

Who is less likely to be satisfied with the central government?
Urban/rural. As with political parties, levels ofsatisfaction with the central government are lower inurban areas than rural ones. Forty-three percent in theformer are satisfied, compared with 65% in the latter.

Disability. There are no real differences in satisfaction levels between those who have disabilities andthose who do not: 61% of the former are satisfied,compared to 62% of those without disabilities.

Gender. There is almost no difference in levels ofsatisfaction between men and women.

Caste. As with political parties, lower caste respondents are more satisfied. While 35% of high caste and114

Aid and Recovery in Post-Earthquake Nepal

Income. Again, people with higher income levels
report a higher degree of dissatisfaction (Figure 7.7).
In the low income group, 58% are dissatisfied, compared to 63% with medium income and 68% withhigh income.

34% of Janajatis report dissatisfaction with the central
government, only 26% of lower caste people say thatthey are dissatisfied.

Figure 7.7: Satisfaction with central government – by income (IRM-2)
7%

Low

Medium

High

25%

4%

27%

5%

24%

13%

Refuse/don’t know

23%

24%

28%

11%

0%

35%

9%

39%

5%

40%

3%

44%

25%

31%

50%

Very satisfied
Somewhat satisfied
Somewhat unsatisfied

75%

100%

Very unsatisfied
Don’t know

Education level. As with political parties, those withhigher levels of education are less likely to be satisfied
with the central government (Figure 7.8).
Figure 7.8: Satisfaction with central government – by education level (IRM-2)
41%

Master & above
Bachelor pass+2/intermediate pass10%
8%Lower secondary level
Primary level
Literate
Illiterate
0%

43%

41%
12%

41%

31%

23%

50%

SLC pass
Secondary level

18%

10%

22%
48%

16%

18%

50%

17%

14%

50%

17%

15%

51%

15%

25%
Very satisfied
Somewhat satisfied
Somewhat unsatisfied

28%

1%

23%

2%

50%
43%

14%

4%

50%

75%

19%

1%

13%

3%

16%

3%

13%

7%

100%

Very unsatisfied
Don’t know

115

Politics

7.4 Have Constituent Assembly
members visited areas?
Political attitudes are shaped not only by socioeconomic factors and people’s political awareness,but also by the responsiveness and actions of politicalparties and representatives. In IRM-1, more than
70% of people reported that Constituent Assembly(CA) members had not visited their areas since theearthquakes.
IRM-2 followed up by asking if CA members had
visited their area since the beginning of the 2015
monsoon. Seventy-four percent of people in bothseverely hit areas and crisis hit areas reported thatthey had not visited (Table 7.13). This figure is higher
than that in IRM-1. Those in less affected areas are
more likely to have received a visit. Only 51% in hit
with heavy losses and 44% respondents in hit areas
reported that politicians had not visited their area.
The fact that CA members are not visiting those areas
most affected may be a reason for rising frustrations
with political leadership amongst people in the more
severely impacted areas.

Table 7.13: Share of people saying CA member has visited their area –by district impact and district (IRM-2)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Solukhumbu
Lamjung
Hit
Syangja

116

Visited a lot

Visited onceor twiceHave notvisitedDon’t know

Total

1%
0%
1%
0%
2%
0%
1%
1%
1%
1%
2%
2%
1%
5%
5%

15%
8%
25%
12%
16%
12%
14%
9%
10%
24%
27%
46%
8%
41%
41%

74%
86%
63%
66%
73%
81%
74%
76%
80%
65%
51%
28%
74%
44%
44%

11%
5%
11%
21%
9%
6%
11%
14%
8%
10%
21%
24%
17%
10%
10%

100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%

Aid and Recovery in Post-Earthquake Nepal

Chapter 8.
Social Relations
and Violence
Photo: Chiran Manandhar

In IRM-1, conducted shortly after the disaster, 83% of
respondents said they felt safe in their communities
and only 3% reported violent incidents in theircommunities. However, despite the high perceptionsof security and low levels of violence, the study noted
the potential for a rise in social conflict and tensions in
the longer run. This could occur if dissatisfaction andgrievances with political institutions hardened in theabsence of sufficient aid.53 It could also occur if a lackof equitable distribution, or ineffective targeting, led toheightened perceptions of unfairness, in particular inless affected districts which received less aid. One year
on from the earthquake, do we continue to witness
dissatisfaction among people? How does this affectsocial cohesion?

Key findings:
Safety and security

Fairness and grievances with aid
distribution• Feelings of safety have improved since IRM-1 andreported levels of violence are very low.

• E ighty-nine percent of respondents feel thatpeople of every caste, religion, and ethnicity are
equally able to receive aid according to theirneeds. Dalits are far less likely than other groups
to agree with this.

• Respondents in rural areas feel safer comparedto those in urban areas. People living in selfconstructed shelters on public land or who are
renting feel the most unsafe.

• There is a large increase from IRM-1 in the shareof people who think that VDCs/municipalities
are distributing aid fairly. Those in severely hit
districts are the most likely to feel aid distribution
has been fair. Those in crisis hit districts, whichhave received much less aid despite high needs,
are the least likely to agree.

Trust and social cohesion• People tend to trust their friends, neighbors andrelatives, along with those of the same religion,
the most. Very few people say that most people
can be trusted.
• Most people feel that community members cancooperate with each other to deal with problems.
However, people in the most affected districts areless likely to agree.

53

The Asia Foundation (2015). Aid and Recovery in Post-Earthquake Nepal: Independent Impacts and Recovery Monitoring
Nepal Phase 1 – Quantitative Survey (June 2015). Kathmandu andBangkok: The Asia Foundation, p. 77.

117

Social Relations and Violence• Dalits are more likely to feel aid distribution by
VDCs/municipalities has been fair. This suggeststhat there are other structural barriers to themreceiving aid. Those with low income and lower
education are the most likely to feel aid distribution has been fair.

• Those whose house was fully or partially damaged are more likely to feel aid distribution has
been fair.

8.1 Safety and security
Figure 8.1: How safe and secure do you feel now
in your community? (IRM-2)
2%

How safe and secure do you feel now
in your community?
Feelings of safety have improved since IRM-1.
Whereas 83% of respondents in IRM-1 reported
feeling safe in their communities, this has increased
to 97% of IRM-2 respondents. Only 2% report feelingunsafe (Figure 8.1).

Variation in perceptions of safety

40%

58%

People in severely hit districts report feeling safer
compared to other areas (Figure 8.2). However, thedifference across different categories of earthquake
impact is minimal.

Somewhat unsafe

Very safe
Somewhat safe

Figure 8.2: How safe and secure do you feel now in your community? – by district impact (IRM-2)
48%

Hit

49%

Hit with heavy losses

0%Very safe

1%

41%

64%

Severely hit

118

50%

56%

Crisis hit

2%

49%

3%

34%

25%

50%

Somewhat safe

Somewhat unsafe

75%

2%

100%

Aid and Recovery in Post-Earthquake Nepal

While almost all people in every district feel safe,
there is variation in the extent to which people feelsafe (Table 8.1). The highest proportions (4%) of

respondents reporting feeling unsafe are in Kathmandu, Bhaktapur, Okhaldhunga, and Sindhupalchowk.

Table 8.1: How safe and secure do you feel now in your community? –by district impact and district (IRM-2)
How safe and secure do you feel now in your community?

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja

Very safe

Somewhatsafe

SomewhatunsafeVery unsafe

Don’t know

64%
75%
59%
42%
67%
76%
55%
45%
50%
72%
49%
44%
54%
48%
48%

34%
23%
39%
55%
32%
20%
40%
50%
46%
25%
50%
54%
45%
49%
49%

2%
1%
3%
3%
1%
3%
3%
4%
4%
3%
1%
1%
0%
2%
2%

0%
1%
0%
0%
0%
1%
1%
0%
0%
1%
0%
1%
0%
0%
0%

0%
0%
0%
0%
0%
0%
0%
1%
0%
0%
0%
0%
1%
1%
1%

Urban/rural. Respondents living in rural areas
are more likely to feel safe than those in urban areas(Figure 8.3). Perceptions of safety do not vary muchby level of remoteness. Three percent of people living
less than one hour away from the district headquarter
felt unsafe. For all other categories of remoteness (1-3
hours away; 3-6 hours away; more than 6 hours away),
2% felt unsafe.

Total
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%

Gender, caste, and disability. As with IRM-1,
responses on safety and security do not vary between
men and women (Figure 8.3). There is also no substantive variation when disaggregating perceptionsof safety by caste or disability.

Figure 8.3: How safe and secure do you feel now in your community? – by gender and urban/rural (IRM-2)

Male

Female

57%

40%

2% 1%

58%

40%

2%

53%

Urban area

43%

58%

Rural area0%
Very safe

4%

39%

25%

50%

Somewhat safe

Somewhat unsafe

75%

2% 1%

100%

Very unsafe

119Social Relations and Violence

Current shelter. Concerns about safety and security
are more closely linked to the type of shelter people areliving in now (Table 8.2). Those living in temporary

shelters, especially those on public land, are more likely to feel unsafe, along with those renting their homes.

Table 8.2: How safe and secure do you feel now in your community? – by current shelter (IRM-2)
Where are you living now?

Very safe

Somewhatsafe

SomewhatunsafeVeryunsafeTotal

Own house
Neighbor’s house
Friend’s house
Self-constructed shelter on own land
Self-constructed shelter on other people’s land
Self-constructed shelter on public land
Community shelter
Rent

53%
44%
67%
64%
68%
58%
67%
60%

44%
53%
33%
34%
28%
33%
33%
27%

2%
0%
0%
2%
4%
8%
0%
7%

0%
3%
0%
0%
0%
0%
0%
7%

100%
100%
100%
100%
100%
100%
100%
100%

How much violence has occurred?
There has been an absence of violence in earthquakeaffected areas. Ninety-nine percent of people reportthat there have been no violent incidents in theircommunity since the beginning of the 2015 monsoon.
Violence is most likely to be reported in the least
affected (hit) district of Syangja. In contrast, only 0.5%
in severely hit districts report that a violent incident
has occurred (Figure 8.4). There is little variation
across districts.
These figures are much smaller compared to thefindings in IRM-1, but the pattern is similar. In IRM1, respondents in lower impact districts also reported
more violence in their community. However, unlike
the previous study, the proportions of people reporting
violence in urban and rural areas have converged inthis survey (0.6% in rural areas and 0.7% in urban
areas). In IRM-1, 4% of respondents in urban areas
reported violence, compared to 2% in rural areas.
Where people report that violence has occurred, they
were asked whether the crime rate has changed in theirmunicipality. Forty-six percent said it has increased,
one-quarter that it has stayed the same, and 17% thatit had fallen. However, it should be emphasized that
this data is only for the one percent who said violence
had occurred.

120

Figure 8.4: Violence reported in community since
the monsoon – by district impact (IRM-2)
1.5%

1.4%

1.0%
0.8%

0.5%

0.0%

0.5%

Severely
hit

0.4%

Crisis hit

Hit with
heavy losses

HitAid and Recovery in Post-Earthquake Nepal

8.2 Trust and social cohesion
What is the level of trust in affected areas?
Respondents were asked about the level of trust
within their communities.54 They were asked whether
they trusted everyone or just some groups within
the community. Only 9% of respondents report thatmost people can be trusted (Table 8.3). Just over half
say they trust their family members, friends, andneighbors, while 25% show trust along religious lines.
The lower levels of trust are along ethnic (3%) andcaste (9%) lines.
This distribution varies distinctly when disaggregating
by impact levels. People in the least-affected hit district

have the lowest level of trust (1%) for most people. In
higher impact districts, trust is predominantly either
along family and friends or religious lines. Trust along
ethnic lines is low. It is likely that caste and religion
are more defining feature for people in Nepal thanethnicity. When analyzing by district, the two districts
that have the highest level of trust along religious lines
are Bhaktapur and Dhading. People in urban areas
trust along religious lines more. In contrast, trust isslightly higher along caste lines among those in rural
areas.

Peoplethat I know(friends,
family,neighbors)

People who
belong to
my caste

People who
belong to
my ethnicity

People who
belong to
my religion

Don’t know

Refused

Total

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Solukhumbu
Lamjung
Hit
Syangja
All districts
Rural area
Urban area

Most people
can betrustedTable 8.3: Trust – by district impact, district, and urban/rural (IRM-2)

7%
15%
8%
1%
5%
4%
10%
2%
8%
19%
16%
29%
3%
1%
1%
9%
9%
4%

53%
35%
54%
60%
65%
51%
50%
48%
63%
40%
45%
42%
47%
70%
70%
52%
52%
56%

10%
5%
12%
15%
4%
13%
4%
3%
3%
6%
17%
15%
18%
2%
2%
9%
10%
3%

4%
2%
1%
3%
1%
14%
2%
3%
1%
4%
2%
1%
3%
2%
2%
3%
3%
2%

25%
41%
25%
21%
23%
14%
31%
40%
24%
30%
18%
10%
25%
23%
23%
25%
24%
32%

2%
3%
0%
0%
2%
4%
2%
4%
1%
2%
3%
2%
4%
1%
1%
2%
2%
2%

0%
0%
0%
0%
0%
0%
0%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1%

100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%

Are people willing to cooperate in their community?
To measure the level of cooperation, respondents wereasked the following question: If public officials asked
everyone to conserve water or share food because ofsome emergency, how likely is it that people in yourcommunity would cooperate?55

Table 8.4 reports responses. Analyzing the first two
positive response categories (very likely and likely),
the data show fairly high levels of cooperation. However, there are some exceptions. Three districts where cooperation levels are much lower are Bhaktapur (20%),

54

55

These questions were not asked in IRM-1, hence assessment ofchanges over time cannot be made.

This question was not included in IRM-1.

121

Social Relations and Violence

Ramechhap (18%), and Sindhupalchowk (15%), all inthe top two earthquake impact categories.
Respondents in severely and crisis hit districts are lesscooperative. Twelve and 13% of people in severely andcrisis hit districts report that cooperation is unlikely,
compared to 5% in the lower two impact category
districts. The difference in cooperation levels acrossgender and education levels is negligible.

Table 8.4: If public officials asked everyone to conserve water or share food because of some emergency,
how likely is it that people in your community would cooperate? – by district impact and district (IRM-2)
Very likely

LikelyUnlikely

Very unlikely

Don’t know

Total

21%
30%
28%
8%
3%
39%
24%
26%
20%
27%
16%
22%
10%
40%
40%

65%
63%
59%
80%
78%
45%
57%
49%
59%
64%
77%
73%
80%
54%
54%

11%
5%
11%
11%
18%
12%
11%
17%
10%
7%
4%
2%
6%
5%
5%

1%
0%
0%
1%
0%
3%
2%
3%
3%
1%
1%
0%
1%
0%
0%

1%
1%
1%
1%
1%
1%
5%
6%
9%
1%
3%
3%
2%
0%
0%

100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Solukhumbu
Lamjung
Hit
Syangja

How does income level affect trust and cooperation?
Income level tends to affect the types of trust people
have but not whether they think people in their village
would cooperate or not. Those with the lowest andhighest level of income are more likely to say thatmost people can be trusted (Table 8.5). Those withthe lowest income are more likely to say those of theirown caste can be trusted; as incomes get higher, thisdeclines. In contrast, as income levels increase, people
are more likely to say those of the same religion canbe trusted.

Monthly income

Most people canbe trusted

People that I
know (friends,
family, neighbors)

People who
belong to my
caste

People who
belong to myethnicity

People who
belong to myreligion

RefusedDon’t know

Total

Table 8.5: Trust – by income band (IRM-2)< NPR 2,500/month
NPR 2,501 - 9,999/ month
NPR 10,000 - 19,999/ month
NPR 20,000 - 39,999/ month> NPR 40,000 / month
RefusedDon’t know

17%
9%
8%
8%
13%
0%
2%

57%
55%
51%
49%
47%
100%
45%

12%
10%
9%
5%
7%
0%
2%

4%
3%
3%
3%
0%
0%
0%

7%
21%
27%
33%
33%
0%
41%

0%
0%
0%
0%
0%
0%
2%

4%
2%
2%
2%
1%
0%
8%

100%
100%
100%
100%
100%
100%
100%

There is no significant association between income
level and perceptions about societal cooperation.
This is unsurprising given that cooperation involves

122

many people and hence the characteristics of any one
individual are less important.

Aid and Recovery in Post-Earthquake Nepal

8.3 Sense of fairness andgrievances with aid distribution
Sense of unfairness and grievances can be drivers ofunrest and conflict. The survey asked whether people
felt that people of every caste, religion, and ethnicity

were equally able to receive aid according to theirneeds,56 and whether aid was being distributed fairly
by VDCs/municiplaities.

Equal access to aid
Overall, 89% agree that people of every caste, religion,and ethnicity are equally able to receive aid according
to their needs. Across all areas, only 8% disagree.
However, when disaggregated by impact levels, there
is more substantial variation (Figure 8.5). Ten percent
of respondents in hit and crisis hit districts say they

disagree, compared to 6% in hit with heavy losses
districts and severely hit districts. There is relatively
little difference in the responses of people in urban
and rural areas, although more people in urban areas
say they do not know. There is little difference in the
responses of men and women.

Figure 8.5: Do you think people of every caste, religion, and ethnicity are equallyable to receive aid according to their needs? – by district impact, urban/rural, and gender (IRM-2)
20%

Severely hit

21%

Crisis hit
Hit with heavy losses

73%
60%

10%

Male
0%

8%

2%

5% 2%

25%

65%

17%

10%

73%
27%

Urban area
Female

9%

83%

Hit
Rural area

6% 1%

7% 2%

55%

17%

7% 2%

72%

20%

7%

70%

25%

50%

Strongly agree
Somewhat agree
Somewhat disagree

Strongly disagree
Don’t know

There are substantive differences in perceived fairness
amongst different castes. The highest proportion of
respondents who do not agree are Dalits (21%) –Figure 8.6. In contrast, only 9% of Newars, 8% of hill

9%4%

6% 1% 3%

75%

100%

castes, and 4% of hill ethnic groups think that peopleof every caste, religion and ethnicity have not received
aid equally according to their needs.

56

The question was not asked in IRM-1.

123

Social Relations and Violence

Figure 8.6: Do you think people of every caste, religion, and ethnicity are equally able to receive aid
according to their needs? – by ethnic group (IRM-2)
Madhesi ethnic

6%

Madhesi caste

94%

11%

24%

Newar
Hill dalit
Hill ethnic
Hill caste
0%

78%

11%

59%

14%

8% 1% 8%

64%

21%

1%

18%

75%

4% 1% 2%18%

72%

6% 1% 3%25%

50%

Strongly agree
Somewhat agree
Somewhat disagree

Strongly disagree
Don’t know

75%

100%

Is aid being distributed fairly by VDCs/municipalities?
Figure 8.7: Is aid being distributed by
VDC/municipalities fairly? (IRM-2)
6%

27%

15%

Those in severely hit districts were the most likely tofeel aid is being distributed fairly while those in thecrisis hit districts are the least likely (Table 8.6). Thisis likely because aid distribution has been very wide inseverely hit districts (reaching 95% of people) whereas
only around one-half of people in crisis hit districts
have received aid, despite high levels of need there(see Chapter 4).

13%

39%

Strongly agree
Somewhat agree
Somewhat disagree

57

Strongly disagree
Don’t know

The Asia Foundation (2016). Aid and Recovery in Post-Earthquake Nepal: Independent Impacts and Recovery Monitoring
Nepal Phase 1 – Quantitative Survey (June 2015). Kathmandu andBangkok: The Asia Foundation, p. 44.

124

There is a large increase in the share of people who
feel that aid is being distributed fairly by VDCs/
municipalities. In IRM-1, 55% of people felt aid was
being fairly distributed.57 When asked if this was thecase since the beginning of the 2015 monsoon, 66%
agreed and 28% disagreed (Figure 8.7).

Among districts, respondents in Lamjung expressed
the highest level of dissatisfaction, with 45% sayingthat aid was not distributed fairly.58 Aid distribution
has been particularly low in Lamjung, with 53% saying
they have not received aid (Table 4.1 above).

58

Lamjung is the district that borders with Gorkha, the epicenterof the first earthquake.

Aid and Recovery in Post-Earthquake Nepal

Table 8.6: Fair distribution by VDC/municipalities – by district impact and district (IRM-2)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja

Strongly
agree

Somewhatagree

SomewhatdisagreeStrongly
disagreeRefusedDon’t know

Total

34%
34%
33%
35%
27%
39%
14%
12%
4%
27%
30%
10%
51%
23%
23%

42%
38%
43%
45%
49%
35%
35%
28%
47%
30%
38%
40%
37%
41%
41%

12%
13%
9%
11%
15%
11%
14%
13%
16%
13%
13%
20%
5%
12%
12%

9%
10%
14%
6%
6%
10%
25%
21%
28%
26%
15%
25%
4%
13%
13%

0%
0%
0%
0%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3%
5%
2%
2%
3%
5%
12%
25%
5%
5%
4%
5%
3%
11%
11%

100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%

How has satisfaction with VDC/municipalities evolved over time?
To compare how the views of respondents havechanged since IRM-1, Table 8.7 uses panel data forthe 1,558 individuals who were asked about theirsatisfaction with VDC/municipalities in both rounds.
Reported figures on satisfaction and dissatisfaction
are aggregated figures (the category agree, for example, combines strongly agree and somewhat agree
responses).
The data show that there have been greater improvements in perceptions of VDCs/municipalities in lessaffected districts. The views of respondents in theseverely hit districts have remained the same (with
73% now saying VDCs/municipalities have performedwell) and there is just a slight improvement in crisis
hit districts. In contrast, lesser affected districts, especially Syangja, have seen more substantive increases inpositive views. Only in Gorkha and, to a lesser extent
Sindhupalchowk, Okhaldhunga, and Lamjung, has
dissatisfaction with VDCs/municipalities increased.

Table 8.7: Fair distribution by VDC/municipalities – by district impact and district,individual panel data (IRM-1/IRM-2 comparison)
IRM-1

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja

IRM-2

Agree

DisagreeRefused/Don’t know

Agree

DisagreeRefused/Don’t know

72%
69%
81%
66%
73%
73%
47%
36%
43%
60%
58%
56%
63%
48%
48%

24%
28%
15%
30%
25%
20%
45%
54%
48%
33%
35%
37%
31%
27%
27%

4%
3%
3%
4%
3%
7%
8%
9%
8%
8%
7%
7%
6%
25%
25%

73%
68%
73%
81%
74%
74%
51%
42%
52%
61%
63%
55%
87%
72%
72%

23%
26%
25%
18%
23%
23%
36%
31%
47%
36%
31%
38%
12%
17%
17%

3%
6%
2%
1%
3%
4%
13%
27%
2%
3%
6%
7%
2%
11%
11%

125

Social Relations and Violence

Photo: Amanda Gurung

What determines perceptions of whether aid isbeing distributed fairly by VDCs/municipalities?
Urban/rural. People in urban areas are less satisfied
with aid distribution by VDCs and municipalities thantheir counterparts in rural areas (Figure 8.8).
Gender. There is no difference in how men andwomen respond on how aid is being distributed.
Disability. Respondents with disabilities expressa higher rate of uncertainty (12% said they did notknow) compared to respondents with no disability(6%). Otherwise, there are not substantial differences.

Caste. There is substantive variation of perceived
unfairness among caste and ethnic groups.59 Dalits
and hill ethnic groups are most likely to agree thataid was distributed fairly (Figure 8.9). In contrast,
only 36% of Madhesi ethnic, 56% Madhesi caste, 58%
Newars, and 60% hill caste report that they agree thataid was distributed fairly after the monsoon. Given
that lower caste groups are the least likely to feel all
people can access aid, irrespective of their identity(Figure 8.6), this suggests that other factors beyond
the VDC/municipality are responsible for perceptionsof unequal access.

Figure 8.8: Fair distribution by VDC/municipalities – by urban/rural (IRM-2)

Urban area
Rural area

0%

59

10%

41%

16%

30%

39%

12%

25%

50%

75%

Strongly agree
Somewhat agree
Somewhat disagree

Strongly disagree
Don’t know

There are five broad ethnic group categories: Madhesi ethnic,
Madhesi caste, Newar, Hill Dalits, Hill ethnic, and Hill castes.
Since all affected districts in the samples are hill districts, Madhesi
respondents are low in number. For instance, there are only 17
respondents that are Hill ethnic (Tharu, Kumal, and Darai), nine
respondents in the sample that are Hill caste category (Yadav, Bania,
Hajam, Kanu), and three Muslim respondents (not reported in the

126

18%

15%

14%

5%

100%

figure). Among the hill category respondents, 1,352 respondents are
Hill caste, which are the privileged caste groups (Bahun, Chhetri,
Thakuri and Sanyasi), 1,544 respondents are Hill ethnic groups thatare the indigenous castes or the Janajatis (Sherpa, Bhote, Thakali,
Magar, Tamang, Rai, Gurung, Limbu, Gharti/Bhujel, Sunuwar,
Chepang, Thami, Pahari, Chantyal, and Dura), 287 are Hill Dalits,
and 641 are Newars.

Aid and Recovery in Post-Earthquake Nepal

Figure 8.9: Fair distribution by VDC/municipalities – by ethnic group (IRM-2)
18%

Madhesi ethnic

18%

18%

41%

56%

Madhesi caste
19%

Newar

22%

39%

39%

32%

Hill ethnic

12%

11%

15%

10%

36%

0%

16%

43%

24%

Hill caste

22%

14%

29%

Hill dalit

6%

15%

25%

50%

Strongly agree
Somewhat agree
Somewhat disagree

Strongly disagree
Don’t know

10%

20%

6%

5%

5%

75%

100%

Income. Respondents with low income are themost likely to feel that aid has been distributed fairly(Figure 8.10).
Figure 8.10: Fair distribution by VDC/municipalities – by income band (IRM-2)
33%

12%

39%

11%

6%

Low
26%

41%

13%

15%

6%

Medium
16%

39%

16%

25%

5%

High
4%

13%

33%

13%

4%

33%

Refused/don't know
0%

25%

50%

Strongly agree
Somewhat agree
Somewhat disagree

Strongly disagree
Refused75%

100%

Don’t know

Education. As with attitudes towards the aid response of political parties and the central government(Chapter 7), those with higher levels of education

are less likely to feel aid has been distributed fairly(Figure 8.11).

127

Social Relations and Violence

Figure 8.11: Fair distribution by VDC/municipalities – by education level (IRM-2)

Master & above

5%

36%

18%

23%

Bachelor pass

30%

22%

+2/intermediate pass

16%

40%

13%

40%

28%

Primary level

20%

6%

37%

7%

13%

16%

6%

12%

29%

40%

12%

25%

50%

75%

Strongly agree
Somewhat agree
Somewhat disagree

Strongly disagree

0%

1% 5%

15%

40%

Illiterate

22%

15%

29%

Literate

10%

50%

23%

Lower secondary level

24%

33%

21%

Secondary level

13%

34%

17%

SLC pass

41%

14%

5%

11%

8%

100%

RefusedDon’t know

Housing damage. There is also significant variation
when disaggregating responses by individuals’ level of
housing damage (Figure 8.12). On average, respondents whose houses were fully or partially damaged
report higher level of satisfaction with aid distribution
by VDCs/municipalities, compared to those living inhouses with minor or no damage. This pattern is acontinuation of that observed in the last survey.60 It
suggests that assessments of housing damage havebeen the major way in which aid has been targeted
and that some people with other needs may be missingout as a result.

Figure 8.12: Fair distribution by VDC/municipalities – by housing damage (IRM-2)
35%

Fully damaged

23%

Partially damaged

0%

60

8%

10%

18%

36%

13%

38%

19%

25%

50%

Strongly agree
Somewhat agree
Somewhat disagree

Strongly disagree
RefusedDon’t know

The Asia Foundation (2015). Aid and Recovery in Post-Earthquake Nepal: Independent Impacts and Recovery Monitoring

128

10%

39%

22%

Minor damage

No damage

41%

16%

4%

21%

19%

75%

1%

4%

8%

15%

100%

Nepal Phase 1 – Quantitative Survey (June 2015). Kathmandu andBangkok: The Asia Foundation, p. 48.

Aid and Recovery in Post-Earthquake Nepal

Chapter 9.
Conclusions
Photo: Alok Pokharel

The first IRM survey, conducted in June 2015, foundthat the physical impacts had been immense. In
many areas, almost all houses were fully or partiallydestroyed; most public facilities in the earthquake
zone had been damaged; and affected communities
were trying to come up with strategies to cope andrecover. Emergency aid was flowing and was helping
out, including in remote areas, but there was a highdegree of mistargeting and people were identifying
new forms of assistance that would be needed,
especially cash and support to build more sturdy
shelters or to reconstruct their houses.
Almost one year on from the Nepal earthquakes, theIRM-2 survey provides evidence of how conditions
and needs have evolved since the emergency period.
The vast majority of those whose houses were badly orcompletely damaged are still living in self-constructedtemporary shelters. Volumes of cash to the earthquake-affected have risen but have been insufficient
to allow for rebuilding. In March 2016, the demandfor cash as a priority need has risen exponentially andpeople also prioritize receiving reconstruction materials that will allow them to move out of shelters into
more secure homes.
Livelihoods have rebounded, with most people backat work.≈ Most businesses, in particular, have madeprogress in recovery. Farmers, most of the populationin affected areas, have gone back to their fields, buthave seen slower recovery in severely hit districts thanhas been the case for business or daily wage workers.

The most important coping strategy has been borrowing, which has risen significantly since IRM-1, both in
the number of people borrowing and the size of loans.
There has been a partial move towards borrowing fromformal sources who charge lower rates of interest.
Yet informal sources still predominate, especially inrural areas, and the absolute number of people taking
loans from moneylenders has increased. Borrowing
is particularly high in poorer districts, yet loan sizes
are also smaller there and there is evidence that thepoor face major credit constraints. Other vulnerable
groups—such as women and lower caste people—areoften having trouble accessing larger loans. Remittances, migration, and asset sales have been a lesscommonly used coping strategies.
There have been two problems with aid. First, overall
volumes have declined since the early post-earthquake
months. Aid now primarily comes in the form ofcash, at low volumes. Some districts, in particularOkhaldhunga, have seen especially large declines inaid. Sindhupalchowk, which saw the largest physicalimpacts from the earthquake, has had by far thehighest levels of aid – but this has not been sufficient
to meet key needs such as shelter or food.
Second, emergency types of aid, such as tarps, continued to be provided with little distribution of materials
for reconstruction. There is a mismatch between thetypes of aid people need and what they are receiving.

129

Conclusions

Food assistance appears to have been well targeted,reaching most in severely food insecure areas and atlarger volumes than elsewhere. Yet food aid seems to
be insufficient in preventing people from decreasing
their food consumption.
The survey provides quantitative evidence on theimpact of the fuel crisis that hit Nepal in September
2015. Around two-thirds of people said the crisis had
negatively impacted their access to fuel for cooking;fuel for transportation has been less affected, in part
because people in many earthquake-affected areas do
not use motorized transport. People’s use of wood as
cooking fuel somewhat dampened the amount they
had to pay for fuel during the crisis. But there was anincrease in the price of staple foods. And the crisis
was associated with a reduction in the volumes of aid
arriving in affected areas.

IRM tracks changes in local politics. The first round ofresearch found relatively few changes in the politicalparties that people were supporting. By March 2016,
there were still not clear changes in party affiliation,
but there is some evidence that UCPN-Maoist may belosing some voters, even as most remain uncertain
about who they will vote for in the future. Satisfaction
with political parties has declined since IRM-1 andthe responsiveness of Constituent Assembly members
appears to have done so, too. This may lead to futurechanges in support in the future.
IRM-1 found a broadly positive picture in terms ofsocial cohesion in the earthquake zone. One year on
from the disaster social relations are still strong. There
has been almost no violence and people feel moresecure than they did in IRM-1.

Key focus areas
The findings point to areas where an increased focus is needed:61
Temporary shelter
The extremely high proportion of the population who
are still living in temporary shelter in districts thatwere severely hit by the quakes is of concern. In largepart, this is because there has been little reconstruction
materials delivered and because volumes of cash perperson have been too small to allow for rebuilding. The
beginning of the formal reconstruction period, which
will see the disbursement of larger sums of cash, will
help some people begin the task of rebuilding. Yet
it is unrealistic to expect that the majority of people
currently in temporary shelters will be able to rebuildin the near future, especially given the fact that many
people whose houses were partially damaged will notreceive large-scale government funds. There is thus aneed to develop short-to-medium term strategies toimprove the living conditions of people who will likely
remain in shelters for the foreseeable future.
Debt and borrowing
Borrowing is the most-used coping strategy. However,with livelihoods having not fully recovered, and capitalneeded for rebuilding, it is likely that many will facedifficulties in paying back the loans they take. The
high levels of interest rates charged by many of thedominant providers of loans increases this risk. To
date, there have been few sales of assets beyond
livestock. But there is a risk that people will be forced
to sell assets such as land if they get stuck in debt

61

This report does not provide recommendations for policy-makers,
donors, or other organizations delivering aid. These will be provided

130

traps. There is a need to ensure that credit is provided
with reasonable interest rates, to provide additional
cash to limit borrowing, and to provide protections
to ensure that vital assets such as land are not lost if
people default.
The fit of aid with needs
The survey data shows that the overwhelming needsof the earthquake-affected are for cash, housing, andfood. People prefer the former because it is liquid –people can choose ways to spend money in ways that fit
with the distinctive needs they have. Despite the needfor robust housing, little assistance has been provided
in this area, with shelter aid focusing more on shortterm assistance such as the provision of tarps. There isa need for aid providers to recalibrate the types of aid
they provide so that it focuses on the big three needs.
Focus in on districts that are missing out
The survey data shows that there have been vastdifferences in experiences between districts withsimilar levels of earthquake impact. Solukhumbu, forexample, has been well relatively served by aid while
assistance has plummeted in Okhaldhunga. In thelatter, there is evidence that some people face foodsecurity issues and that needs are great. The data in thereport highlights specific issues in different districts.
This can allow aid providers to adjust their responses
in ways that lead to more effective responses.

in the IRM-2 synthesis report, which combines data and findings
from both the survey and the qualitative fieldwork.

Photo: Tenzing Paljor

Photo: Ashray Pande

Aid and Recovery in Post-Earthquake Nepal

Annex A:

Methodology

The IRM-1 survey, conducted in June 2015, involved
face-to-face interviews with 2,980 respondents in 14
districts alongside 298 ward leaders. The second IRM
survey was conducted in 11 of the 14 districts, with thesame households interviewed (where possible) but
additional households also selected. This resulted ina sample size of 4,850 (plus surveys with 305 ward
leaders). The household sample was distributed

equally among men and women. Respondents wereindividuals over 18 who are involved in decisionmaking in the household. Findings are representativeof the population of the 11 districts studied.
A careful sampling strategy was employed that gives
us confidence that the results reported the broader
situation and views in earthquake-affected areas.62

Sampling frame and district selection
The survey for IRM-2 was conducted in 11 districts,
all of which were covered under the IRM-1 survey.
Three districts—Manang, Khotang and Dang—were
dropped from the sample because they do not appear
in the PDNA’s list of affected districts.
The new 11-district sample includes districts acrossfour of the PDNA impact categories (see Table 1.1
above) with only the least affected areas missing.
Based on feedback on IRM-1, the sampling frame for
IRM-2 and future waves of surveying also takes into
account the classification of VDCs/municipalities on

the basis of the five categories of food security as developed by the Nepal Food Security Monitoring System(NeKSAP). The five categories are: (1) minimally foodinsecure; (2) moderately food insecure; (3) highly foodinsecure; (4) severely food insecure; and (5) humanitarian emergency. The sample was boosted in four
districts to allow for robust disaggregation of data bythese food insecurity categories.63
Table A.1 lists the districts, the basic sample size, theadditional sample in selected districts for assessing
correlations with food security, the total sample ineach district, and the margin of error per district.

Table A.1: Distribution of sample and error margin for each of the districts
Districts

Basic district
sampleAdditional sample for foodsecurity analysis

Total sample
per district

Error margin at the
district level (+/- %)Nuwakot
Sindhupalchok
Ramechhap
GorkhaSolukhumbu
Dhading
Bhaktapur

350
350
350
350
350
350
350

250
250
250
-

350
600
600
600
350
350
350

5.2
4.0
4.0
4.0
5.2
5.2
5.2

62

For a summary of the construction of the initial IRM-1 survey, see:
The Asia Foundation (2015). Aid and Recovery in Post-Earthquake
Nepal: Independent Impacts and Recovery Monitoring Nepal
Phase 1 – Quantitative Survey: June 2015. Annex A.

63

NeKSAP produces an Integrated Food Security Classificationfor each VDC/MC every four months, based on meetings at the
district level. The NeKSAP data used came from meetings held
15-30 November 2015.

133

Conclusions

Districts

Basic district
sampleAdditional sample for foodsecurity analysis

Total sample
per district

Error margin at the
district level (+/- %)Okhaldhunga
Lamjung
Kathmandu
Syangja
Total

350
350
350
350
3,850

250
1,000

600
350
350

4.0
5.2
5.2
5.2

The sample produces results with +/- 1.4% error
margin at a 95% confidence level at the aggregate level.
The sample size for each district, with four exceptions,is 350; this sample produces an error margin of +/5.2% for district-disaggregated analyses.
In addition to the basic sample size of 350, for the four
districts where correlations with the food insecurity
situation were assessed in detail, an additional sample

4,850

of 250 has been allocated, which means that each ofthese districts will have a sample size of 600. This
allows for generalizing at an error margin of +/- 4% in
these districts. Across food security categories in thefour districts as a whole, the error margin is +/- 4%
at a 95% confidence level. Across the food security
categories within each district, the error margin is+/-7% at a 95% confidence level.

Selection of VDC/wards within districts and replacement of VDC/wards
Within districts, multistage random sampling (PPS)
was adopted in the selection of sample wards. In
the earlier survey (IRM-1), 238 sample wards wereselected from the 11 districts that remain in the sample
using PPS. These same 238 wards were selected for
IRM-2.
In addition to the 238 previously sampled wards, 67 newwards were selected in the districts of Sindhupalchok,

Ramechhap, Gorkha, and Okhaldhunga using PPS.
These 67 new sample wards in the four districts
were uniformly distributed across the food security
categories within the districts.64 For the four districts
where additional wards will be selected, the sampling
method at the VDC/ward level is stratified random
sampling since the additional VDCs/wards selectedin these districts were based on the food security
categories.

Selection of enumeration areas within VDC/wards
For the 3,850 sample, the same enumeration areas(EAs) that were sampled during IRM-1 were visitedin IRM-2. The number of interviews per EA, however,has increased since IRM-1. On average, 16 interviews
were conducted in each EA. In IRM-1, there had beenten interviews per EA. The reason for more interviews
per EA is to decrease the margins of error for analyses.
For the additional 1,000 respondents in the newly
sampled wards (in the four districts where analysis

disaggregated by food security category was conducted)
broadly the same procedure as used in IRM-1 wasfollowed. Within the sampled ward, there could bevarious settlements. These various settlements wereregarded as EAs. The various EAs within a ward wereidentified and listed once the survey team reachedthe locality. From this list, one EA was randomly
selected using simple random sampling. On average,
16 interviews were conducted in each EA within these
new 67 wards.

64

Examination of the distribution of sampled VDCs in IRM-1
across NeKSAP’s five food security categories reveals that the VDCs
sampled were more or less evenly distributed across the categories.
This further justifies continuing with the same wards as in IRM-1.

134

Aid and Recovery in Post-Earthquake Nepal

Selection of households within EAs
To the extent possible, the households surveyed in
IRM-1 were identified for interviews for IRM-2. In
total, it was possible to reinterview 1,558 households.
The extra six households in each EA, who were notinterviewed in IRM-1 were selected using the same
protocols as employed in the first survey. Households
were randomly selected using the household lists
generated for each EA during IRM-1.

For the new 67 wards, the same procedure as IRM-1
was followed. Before selecting the households in thesampled EA, the supervisor listed the households inthe sampled EA. Then, systematic random sampling
was used to select the households from the list ofhouseholds. In this way, a total of 4,850 households
were selected using systematic random sampling.

Selection of respondents within households
The same respondents as surveyed in IRM-1 wereselected where possible. The IRM-1 survey obtained
the names and mobile numbers of the interviewees.
This was used to identify the respondent in thehousehold to be interviewed for each survey.
For the extra six respondents in each previously
selected EA, plus those in the new 67 wards, the sameprocedure as in IRM-1 was followed. Once a household

was selected for the interview, the next task was toselect the respondent from within the household.
Only those who play some role in decision-making in
the household could be interviewed. From within thislist within the household, respondents were randomly
selected but steps were taken to ensure gender balanceof respondents. The names and mobile numbers of
new respondents were collected, allowing for their
inclusion in the household panel in the future.

Weighting data
In IRM-1, weighted statistics were reported for the
cross-sectional study. In IRM-2, the analysis reports
comparisons between the first and second waves ofthe survey. Given the fairly sizeable sample attrition
between the two rounds (35% of respondents), andthe fact that an analysis of the drivers of such attritionand correction for potential bias was beyond thescope of this paper, all statistics in IRM-1 and IRM2 are reported in terms of their unweighted sample
means in this report. A comparison of the unweighted
and weighted figures in several cases shows that the
numbers are very similar.

135

Photo: Amanda GurungAid and Recovery in Post-Earthquake Nepal

Annex B.

Sample Characteristics
Throughout this report, the various outcomes of interest—livelihoods, coping strategies, aid, needs, accessto basic services, impacts of the fuel crisis, politicalpreferences, and social cohesion and violence—are
analyzed in terms of their variation across areas andpopulation sub-groups.
• By area, the analysis is by district and rural andurban areas.
• Within population groups, differences are studied
on the basis of gender, caste, income bracket, anddisability.

• Further, in some cases, outcomes are studied
within groups that have been the most affectedby the earthquake, in terms of the level of
housing damage and the type of shelter where
respondents currently live (given that those who
continue to live in temporary shelter are, overall,more vulnerable).
Analysis was carried out primarily by comparing
the average value of the variables of interest acrossbetween different groups. This annex presents how
these geographic and population characteristics are
distributed in the overall sample and across the elevensample districts.

Table B.1: Distribution of demographic and socio-economic characteristics –by district impact and districts
Disability

Disability

50.00% 12.14% 54.14% 33.71% 0.57% 39.29% 44.14% 14.00% 2.00%

0.00%

26.29%

50.00% 18.00% 42.29% 39.71% 1.14% 18.00% 53.71% 24.29%
50.00% 6.29% 66.00% 27.71% 0.00% 60.57% 34.57% 3.71%
49.86% 15.38% 38.18% 46.44% 0.85% 39.89% 43.59% 14.25%
49.86% 15.38% 38.18% 46.44% 0.85% 39.89% 43.59% 14.25%
49.94% 7.53% 57.15% 35.32% 3.53% 37.04% 43.03% 12.77%

0.00%
0.00%
0.28%
0.28%
1.32%

38.86%
13.71%
47.29%
47.29%
39.45%High caste

44.07%
41.43%
36.86%
48.00%
39.84%
54.24%
37.82%
55.14%
20.00%
38.32%

Janajatis

0.40%
0.57%
0.29%
1.14%
0.00%
0.00%
4.11%
7.43%
4.00%
0.90%

Low caste

Don’t Know

> NPR 40,000

NPR 20,000 39,999

NPR 10,000 19,999

NPR 2,501 9,999

Income bracket

49.94% 6.22% 61.42% 32.36% 3.53% 41.40% 45.23% 8.76% 0.67%
50.00% 4.86% 52.86% 42.29% 3.71% 47.71% 41.14% 6.57% 0.29%
50.00% 10.00% 56.29% 33.71% 0.86% 28.29% 60.57% 9.14% 0.86%
49.71% 4.29% 67.43% 28.29% 3.71% 48.57% 39.14% 7.14% 0.29%
49.73% 7.14% 59.07% 33.79% 7.69% 42.86% 40.38% 7.97% 1.10%
50.28% 4.80% 71.47% 23.73% 1.69% 39.55% 44.92% 12.99% 0.85%
49.91% 4.04% 58.22% 37.74% 6.37% 27.40% 38.35% 18.15% 5.34%
49.43% 0.29% 74.00% 25.71% 1.14% 29.43% 44.86% 15.14% 1.71%
50.00% 2.86% 48.57% 48.57% 12.29% 4.86% 34.57% 29.71% 14.00%
50.30% 8.98% 52.10% 38.92% 5.69% 47.90% 35.63% 9.58% 0.30%

FemaleSeverely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy
losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Caste<NPR 2,500

Gender

2.86%
1.14%
1.14%
1.14%
2.23%

137

Conclusions

GenderGiven that the sample was designed to ensure equal
representation of men and women, this is reflectedacross all districts and earthquake impact categories.

Caste
Janajatis have the highest representation in the sample overall (57%), followed by high caste groups (35%),
and low caste groups (8%). The share of Janajatis is
the highest in all categories of impact, except for the
hit category, where higher castes have the dominant

share (46% against 35% for Janajatis). Lower castes
have much higher shares in the third and fourth impact categories (12% and 15%, respectively) than in
the first and second categories of impact (6% and 4%,
respectively).

Income brackets
Most people in the sample earned between NPR
2,501-19,999 per month before the earthquake. Muchsmaller shares have very high or very low incomes.
Those with low incomes are more concentrated inhigher impact districts and, especially, Kathmandu.
Interestingly, Kathmandu, the districts with thehighest share in the lowest income bracket (12%),also has the highest share in the top income bracket(14%), suggesting high levels of inequity. In contrast,
Solukhumbu, which has the highest share in the bottom two income brackets combined (61%), does nothave any households in the very lowest income bracket(< NPR 2,500) and has only about 5% in the upper
two income brackets combined, suggesting greater
homogeneity in incomes. The higher disparities in
Kathmandu are also evidently an urban phenomenon
as the capital city is the most urban among all districts(64% urban, from Table B.3 below). However, Bhaktapur, which ranks second in terms of urban share(59% urban), is not characterized by similar extremes
in income distribution (only 2% of the population isin the highest income bracket and 1% in the lowest).

Disability
Forty percent of the total sample reported some kindof disability. The disability measure is a composite ofa set of six questions on different types of disability.
These come from the Washington Group on Disability
Statistics, a United Nations-sponsored group commissioned to improve the quality and international
methods used to measure disability.65 The questions
measure whether people have difficulty seeing,
hearing, walking or climbing steps, remembering orconcentrating, caring for themselves, and commu-

nicating. If people said they had difficult with any ofthese, they were classified as disabled for the purposeof the analysis.

65

Available at: https://www.cdc.gov/nchs/data/washington_group/
meeting8/nso_report.pdf

See Washington Group on Disability Statistics. “The Developmentof an Internationally Comparable Disability Measure for Censuses.”

138

Table B.2 shows the percentage of people classified as
disabled (using the binary variable) for each districts,
as well as the breakdowns of types of disability withineach district. The most common ailments in thesample are problems with sight (27%), followed bywalking 22%, and remembering (20%).

Aid and Recovery in Post-Earthquake Nepal

Table B.2: Distribution of types of disability – by district impact and district (IRM-2)

Severely hit
Dhading
GorkhaNuwakot
Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

SeeingHearing

Walking

Remembering

Self-Care

Language

Disability

28.64%
29.71%
22.29%
22.57%
29.67%
38.98%
24.21%
36.86%
9.43%
26.35%
23.00%
36.00%
10.00%
32.19%
32.19%
26.76%

14.55%
12.57%
14.29%
18.00%
13.19%
14.69%
8.96%
11.71%
2.29%
12.87%
12.43%
18.29%
6.57%
14.53%
14.53%
12.64%

26.09%
26.00%
20.57%
30.00%
22.80%
31.07%
20.59%
30.86%
10.86%
20.06%
9.71%
12.29%
7.14%
28.21%
28.21%
21.83%

23.23%
22.00%
14.29%
38.29%
17.31%
24.29%
18.73%
32.86%
6.86%
16.47%
10.71%
11.71%
9.71%
26.78%
26.78%
20.06%

11.27%
14.29%
6.57%
15.14%
9.34%
11.02%
6.98%
10.86%
2.00%
8.08%
7.00%
8.86%
5.14%
16.81%
16.81%
9.84%

14.71%
16.00%
6.86%
30.00%
7.14%
13.56%
5.35%
6.86%
1.71%
7.49%
3.71%
6.29%
1.14%
21.94%
21.94%
10.82%

44.07%
41.43%
36.86%
48.00%
39.84%
54.24%
37.82%
55.14%
20.00%
38.32%
26.29%
38.86%
13.71%
47.29%
47.29%
39.45%The shares of those disabled are higher in the severely
hit and hit districts (44% and 47%, respectively). The
survey questions do not allow us to infer whether these
medical conditions have either arisen or worsened
following the earthquakes. However, the fact that

there is nearly an equal representation of those withdisabilities in the most affected and least affectedcategories in the sample suggests that these conditions
are not attributable to the earthquake.

Rural/urban areas
The majority of the sample (86%) lives in rural areas
and the share is upwards of 86% in all districts withthe exception of Kathmandu (36%) and Bhaktapur(59%) – Table B.3. Of the five poorest districts (definedin terms of the shares of people in the bottom two
income brackets)—Dhading, Nuwakot, Ramechhap,

Okhaldunga, and Solukhumbu—all are entirely ruralin sample composition except for Nuwakot, whichis 91% rural. Sindhulpalchowk, the worst affecteddistrict in terms of livelihoods and housing damage(Chapter 2), is also entirely rural.

Table B.3: Rural/urban and housing damage distribution – by district impact and district
Area

Level of housing damage
Rural

Completely
damagedBadly
damagedHabitable

Undamaged

Severely hit
Dhading
GorkhaNuwakot

95.77%
100.00%88.00%
90.86%

77.86%
76.86%
57.14%
92.00%

15.95%
19.71%
31.71%
4.86%

5.23%
3.14%
8.29%
2.86%

0.96%
0.29%
2.86%
0.29%

Sindhupalchowk
Crisis hit
Bhaktapur

100.00%58.95%
40.57%

93.79%
31.24%
46.00%

3.39%
17.78%
14.29%

2.26%
30.09%
14.86%

0.56%
20.89%
24.86%

Ramechhap

100.00%69.51%

20.05%

9.62%

0.82%

139

Conclusions

Area

Level of housing damage
Rural

Completely
damagedBadly
damagedHabitable

Undamaged

Kathmandu

36.29%

16.29%

10.00%

38.29%

35.43%

Okhaldhunga

100.00%31.44%

29.04%

37.13%

2.40%

Hit with heavy losses

100.00%26.71%

16.86%

42.29%

14.14%

Lamjung100.00%24.00%

11.14%

39.14%

25.71%

Solukhumbu

100.00%29.43%

22.57%

45.43%

2.57%

Hit
Syangja
All districts

86.32%
86.32%
85.65%

7.69%
7.69%
49.65%

13.39%
13.39%
16.32%

62.39%
62.39%
23.83%

16.52%
16.52%
10.20%

Level of housing damage
Given that the four levels of earthquake impact havebeen classified on the basis of housing damage, it is
not surprising that the self-reported damage levels
for housing are much higher in severely hit districts(Table B.3). The share of completely damaged homes
is 78% in the severely hit districts, which is more thandouble that in the crisis hit districts (31%). There isless variation in the share of badly damaged homes
across the four categories of impact. The share ofhomes in the habitable and undamaged categories ismuch lower in the severely hit districts (5% and 1%,
respectively), as would be expected, compared to lower
levels of impact.

Current type of shelter
Just over half of the sample (55%) lived in their own
house as of February 2016 and less than 1% lived inrented houses (Table B.4). Forty-three percent of thesample continues to live in self-constructed shelterson their own land or others’ land. In the severely
hit districts, the share that continues to live in selfconstructed shelters is as high as 80%, compared
to only 16% in crisis hit districts and 8% in hit withheavy losses districts. The severely hit category also
has the highest share of those in the lowest two income
brackets pre-earthquake (45%). The fact that poor
households tend to live in homes constructed withless resilient materials appears to be a contributing
factor to the higher level of damages in these districts(Chapter 2).

Table B.4: Distribution of current type of shelter – by district impact and district (IRM-2)
Type of current shelter

Severely hit
Dhading
GorkhaNuwakot140

Own house

Friend/neighbor

19.06%
19.71%
44.57%
8.57%

0.69%
0.86%
2.00%
0.29%

Self-constructed
Self-constructed
shelter (others’
shelter (own land)
land)
73.53%
76.86%
49.71%
84.29%

6.61%
2.29%
3.43%
6.86%

Rented house
0.11%
0.29%
0.29%
0.00%

Aid and Recovery in Post-Earthquake Nepal

Type of current shelter

Ramechhap
Sindhupalchowk
Crisis hit
Bhaktapur
Kathmandu
Okhaldhunga
Hit with heavy
losses
Lamjung
Solukhumbu
Hit
Syangja
All districts

Self-constructed
Self-constructed
shelter (others’
shelter (own land)
land)

Own house

Friend/neighbor

Rented house

16.21%
6.21%
81.24%
78.86%
89.43%
75.45%

0.00%
0.28%
1.65%
2.00%
1.14%
1.80%

79.40%
77.40%
13.94%
12.57%
8.00%
21.26%

4.40%
16.10%
1.93%
3.14%
1.14%
1.50%

0.00%
0.00%
1.24%
3.43%
0.29%
0.00%

91.29%

0.57%

7.57%

0.57%

0.00%

91.71%
90.86%
95.16%
95.16%
55.80%

0.57%
0.57%
1.14%
1.14%
0.96%

7.71%
7.43%
2.85%
2.85%
39.11%

0.00%
1.14%
0.85%
0.85%
3.74%

0.00%
0.00%
0.00%
0.00%
0.39%

141