FOOD AND NUTRITION SECURITY IN NEPAL: A STATUS REPORT · FOOD AND NUTRITION SECURITY IN NEPAL:...

FOOD AND NUTRITION SECURITY IN NEPAL:

A STATUS REPORTBaitadi

Surkhet

Banke

Dang

Palpa

Sarlahi

Makwanpur

SindhupalchokDolakha

Ramechhap

Okhaldhunga

Khotang

TaplejungKavrepalanchok

SirahaDhan

usha

Rautahat

Mahotari

Rupandehi

Kanchanpur

Kailali

Bardiya

Doti Achham Kalikot

Darchula

Bajhang

Bajura

Dailekh Jajarkot

Dolpa

RukumMyagdi

Baglung

Parbat

Lamjung

Parsa

Rasuwa

Nuwakot

KtmBhaktapur

Solukhumbu

Udaypur

Saptari

Bhojpur

Dhankuta

Terathum

Pancht

har

Morang

Lalitpur

Manang

Syangja

Rolpa

Pyuthan

Kapilbastu

Arghakhanchi

Salyan

Mugu

Mustang

Kaski

Gorkha

Dhading

Gulmi

Tanahu

NawalparasiChitwan

Bara

Sindhuli

Sunsari

Ilam

Jhapa

Sankhuwa

shabha

Humla

JumlaDadeldhura

FOOD AND NUTRITION SECURITY IN NEPAL:

A STATUS REPORT

Report byMinistry of Agricultural Development and Central Bureau of Statistics for

the Nepal component of the FAO Project“Building statistical capacity for quality food security and nutrition information

in support of better informed policies TCP/RAS/3409”

January 2016Kathmandu, Nepal

Foreword

It gives me a great pleasure to see the publication of the Food and Nutrition Security in Nepal:

a Status Report,prepared jointly by the Ministry of Agricultural Development (MOAD), Central

Bureau of Statistics (CBS) and the Food and Agriculture Organization (FAO). Let me express at

the outset my thanks to all who contributed to the project and the preparation of this report.

The report is largely based on statistics and information produced by a joint MoAD/CBS/FAO

project designed to strengthen national capacity in generating and analyzing food and nutrition

security information. The two main statistical frameworks focused by the project and utilized by

this report are the freshly compiled Supply Utilization Accounts/Food Balance Sheets(SUA/FBS) for Nepal for 2008 to 2013, and the Nepal Living Standards Survey 2010-11 OtrLSSIII) which provided valuable food and nutrition statistics. Compiling the FBS and extracting and

processing the data from the NLSS III were valuable contributions of the project.

Based on these databases, the study finds that the overall aggregate level of food availability in

Nepal is higher than previously assumed. This also implies lower rates of prevalence of

undernutrition and food inadequacy, which are welcome news. On the other hand, the cross-

sectional analyses based on the NLSS III data show that there are marked gaps in food and

nutrition security status across Nepal's regions and income levels of households. These findings

point to the need for paying as much attention to addressing these gaps in food insecurity as to

enhancing food production at the national level. The study also points to some serious

deficiencies as regards nutrition indicators for under-five children.

The MoAD assigns high priority to enhancing food security through a range of programs that

encompass all the four dimensions of food insecurity. The MoAD is also one of the main

partners of the Multi-sector Nutrition Plan of the Government of Nepal. Availability of high

quality statistics covering all the dimensions of food security, and on a timely basis, is essential

for policy deliberations and fine-tuning of national programmes and projects. The outputs of the

project are valuable in this regard. Let me once again express my appreciation to all who

implemented the project successfully as well as produced this Status Report. Finally, I would

also like to express my thanks to FAO for its technical assistance and overall coordination of this

important work.

December,2015Government of Nepal

Uttam K BhattaraiSecretary

Ministry of Agricultural Development

Table of Contents

I. INTRODUCTION ...................................................................................................................... 1

II. FOOD AND NUTRITION SECURITY STATUS REPORT – PART 1: TRENDS AND

EVOLVING PICTURE .................................................................................................................. 3

2.1 The availability dimension of food security ......................................................................... 3

2.1.1 Trends in cereals production .......................................................................................... 3

2.1.2 Food availability and adequacy of the DES relative to requirements ............................ 5

2.1.3 Availability of food energy, proteins and fats based on the new FBS ........................... 6

2.1.4 Trends in food import dependency based on the new FBS ........................................... 8

2.2 The access dimension of food security ............................................................................... 11

2.2.1 Prevalence of undernourishment and food inadequacy ............................................... 11

2.3 The utilization dimension of food security ......................................................................... 12

2.4 The stability dimension of food security ............................................................................ 14

2.4.1 Trends in instability in selected food security indicators............................................. 15

2.4.2 Climate change risks to food security .......................................................................... 16

2.5 Summary of the main findings ............................................................................................ 17

III. FOOD AND NUTRITION SECURITY STATUS REPORT – PART 2: VARIATIONS IN

HOUSEHOLD FOOD SECURITY, 2010-11 .......................................................................... 20

3.1 Food expenditure as a share of total expenditure................................................................ 20

3.2 Variations in food nutrient intakes by income and regions ................................................ 22

3.3 Food consumption pattern by major food sub-groups ........................................................ 24

3.4 Variations in the cost of food nutrients ............................................................................... 26

3.5 Extent of the reliance on own food production versus market ........................................... 28

3.6 Variation in market price of foodstuffs ............................................................................... 30

3.7 Indicators of malnutrition of children ................................................................................. 31

3.8 Severity of food insecurity based on Integrated Food Security Phase Classification ......... 33

3.9 Summary of the main findings ............................................................................................ 35

IV. SUMMARY AND CONCLUDING REMARKS .................................................................. 37

References ……………………………………………………………………………………… 41

Annexes ………………………………………………………………………………………….44

List of figures, tables and annexes

List of figures

Figure 2.1: Trends in per capita cereals production in Nepal, 2000-2013 (in kg/capita/year)

Figure 2.2: Trends in the DES and their adequacy levels for Nepal and other South Asian countries

Figure 2.3: Import dependency rates for food energy, protein and fat derived from cereals and non-

cereals

Figure 2.4: Prevalence of undernourishment in Nepal and South Asia (%)

Figure 3.1: Engle ratio – expenditure on food to total consumption expenditure (in %) by deciles

Figure 3.2: Income elasticity of demand for food by decile

Figure 3.3: Variation in caloric intake levels by TCE decile, 2010/11

Figure 3.4: Variation in intakes of proteins, fats and calories by TCE quintiles, 2010/11

Figure 3.5: Household food expenditure patterns in rural and urban areas, 2010-11

Figure 3.6: Variation in expenditures on cereals and non-cereals by income levels

Figure 3.7: Sources of food consumption, 2010-11

Figure 3.8: Sources of food from markets and own production by income decile, 2010-11

Figure 3.9: Food prices paid by urban and rural households (Rs/kg)

List of tables

Table 2.1: Trends in cereals production in Nepal on a per capita basis, 2000-13

Table 2.2: Trends in the availability of food energy (calories/per capita/day), 2008-13

Table 2.3: Trends in the availability of proteins (grams/per capita/per day), 2008-13

Table 2.4: Trends in availability of fats (grams/per capita/per day), 2008-13

Table 2.5: Import dependency rates (%) measured in terms of food energies supplied by various food

products (average 2008-13)

Table 2.6: Progress made in reducing child undernutrition in Nepal and some South Asian countries

Table 2.7: Six indicators of the stability dimension of food security in Nepal

Table 3.1: Food consumption patterns in Bangladesh, India and Nepal

Table 3.2: Cost of nutrients from various food sub-groups by place of residence and income

Table 3.3: Prevalence rates of stunting, underweight and wasting among under-5 children, 2010/11

Table 3.4: Food insecurity prevalence rates for 13 sub-regions as per the IPC classification, 2014

List of Annexes

Annex 2: A note of the discrepancy on aggregate food supplies in the FAO database and the new

SUA/FBS

Annex 3.1: NLSS III and ADePT-FSM for extracting food consumption statistics

Annex 3.2: Selected key statistics extracted from NLSS III with Adept-FSM software

Annex 4.1: Project activities on capacity building through training and workshop

Annex 4.2: Nepal‘s Multi-sector Nutrition Plan for Accelerating the Reduction of Maternal and

Child Under-nutrition, 2013-2017

Annex 4.3: Food and nutrition policy, legislations and institutions

Annex 4.4: Food security and nutrition: An Approach Paper to the Thirteenth Plan

Annex 4.5: Report of the final workshop on sharing of the outputs of the Project

Acronyms

ADePT-FSM ADePT-Food Security Module

ADER Average dietary energy requirement

ADSA Average dietary supply adequacy

CBS Central Bureau of Statistics, Nepal

CEF Consumption expenditure on foods

CFI Chronic Food Insecurity

D1-D10 Decile (income groups in NLSS)

DES Dietary energy supplies

FBS Food Balance Sheets

FNS Food and nutrition security

GoB Government of Bangladesh

GoI Government of India

GoN Government of Nepal

HBS Household budget survey

IDR Import dependency ratio

IPC Integrated Food Security Phase Classification

Kcal Kilocalorie

MDER Minimum dietary energy requirement

MT Metric tonne

NDHS Nepal Demographic Health Survey

NLSS Nepal Living Standards Survey

NPC National Planning Commission, Nepal

p.a. Per annum

PoFI Prevalence of food inadequacy

PoU Prevalence of undernourishment

Q1-Q5 Quintiles (income groups in NLSS)

SUA Supply Utilization Account

TCE Total consumption expenditure

WFP World Food Programme of the United Nations

1

I. INTRODUCTION

This report on Nepal‘s food and nutrition security is one of the three main outputs of an

analytical and capacity building regional project coordinated by the FAO Regional Office for

Asia and the Pacific. Besides Nepal, the project is also implemented in Bangladesh, Myanmar, Lao

PDR, the Philippines and Timor-Leste. The main objective of the project is to strengthen national

capacity for generating quality indicators of food and nutrition security (FNS) and analyzing them

for improving policies and programmes. The project was a response by FAO to requests made by

Member countries at the 31st Asia Pacific Regional Conference (APRC) in 2012 for the provision

of technical assistance in strengthening national FNS information systems.

FAO has a long tradition of undertaking analytical and statistical works aimed at improving the

capacity of Member countries to generate essential statistics and indicators, and to use them for

improving food and nutrition policies and programmes. As part of this work, FAO collates a wide

range of indicators on the FNS information system and makes them available in public domain.

One crucial statistical framework that provides a range of FNS indicators is Food Balance Sheets

(FBS). FAO collaborates with its Members in compiling, updating and maintaining national

FBS, currently for about 180 countries. The FBS also provide the core information for

monitoring global trends in hunger, one of the MDG1 target. The FBS is also widely used by

national and international researchers and policy makers. For this reason, FAO devotes a

considerable amount of resources for improving the database.

National consumption, expenditure or income surveys, also called household budget surveys

(HBSs), are also being increasingly used to collect statistics for the FNS information system.

Across the world, the coverage of food and nutrition aspects is being expanded considerably in

recent HBSs. Given the wealth of information available in a HBS, FAO has been promoting its

use for expanding the range of indicators beyond those that are available in the FBS. In order to

facilitate this task, FAO and the World Bank collaborated and produced a software for extracting

FNS statistics from the HBS database. The software is called ADePT-Food Security Module

(ADePT-FSM), which evolved from the Food Security Statistics Module (FSSM) used earlier by

the FAO.

Accordingly, the FAO regional project focused its capacity building activities around these two

statistical frameworks. The project provisioned a number of activities to strengthen national

capacity in utilizing these two sources of statistics. Thus, the three main components of the

project, which correspond to the three project outputs, are:

- Compiling/updating SUA and FBS using FAO harmonized methodological framework;

- Deriving FNS statistics from HBS using the ADePT food security module; and

- Analysing these and other relevant statistics to prepare for Nepal a Status Report on FNS.

2

The rest of this report is structured as follows. Sections II and III present the Status Report,

which is split into two sections considering the different nature of the statistical and analytical

inputs. The analyses are based on two broad categories of statistics. One is time-series data and

related information for documenting the evolution of the food security status over time along its

four dimensions, i.e. availability, access, utilization and stability. The FBS is the principle source

of information for this, along with other time-series indicators for the four dimensions. This

study also makes the most use of the wide range of these statistics available in the FAO database

on food security. The FAO data also helps assessing Nepal‘s progress on FNS in a comparative

manner, e.g. by also comparing the progress made by some neighbouring South Asian countries.

This is the kind of analyses reported in Section II.

The other source of statistics is the NLSS III data as extracted with the Adept software as part of

this project. This data is valuable for understanding cross-sectional variations in food and

nutrition status in Nepal for the survey year, 2010/11 in this report.

From an analytical standpoint, it made a good sense analysing these two sources of statistics

separately, and draw the inferences from both assessments. This is the main reason for splitting

the status report into two sections. Having done this, Section IV summarizes the main findings

and conclusions. A number of Annexes provide further information on the project activities and

supplementary information.

A final workshop was held in Kathmandu on 28 December for sharing the outputs of the Project. It

was attended by members of the task force who directly and indirectly supported the

implementation of the project as well as other government officials and members of the media.

Participants commented on the project activities and main results of various analyses and their

implications. Annex 4.5 presents the report of the workshop.

3

II. FOOD AND NUTRITION SECURITY STATUS REPORT – PART 1: TRENDS

AND EVOLVING PICTURE

As said at the end of the previous section, this section presents the status report using time-series

data and related analysis. The assessment is structured along the four dimensions of food

security, namely availability, access, utilization and stability. The analysis makes maximum use

of the wide range of statistics on FNS indicators made available by FAO. It also makes use of the

newly compiled SUA/FBS as part of the FAO project. The newly compiled FBS provide data for

2008 to 2013 while FAO indicators begin in 1990. In addition, national sources are also used

where necessary. As said in Section I, while the focus is on Nepal, several tables and graphs also

show similar statistics for some South Asian countries in order to provide a comparative picture

on the progress being made. The section is organized along the four dimensions of food security,

namely food availability, access, utilization and stability, with the last sub-section summarizing

the main findings. The next section continues with the status report based on the NLSS III data

as extracted with the Adept software.

2.1 The availability dimension of food security

Food availability, the first pillar of food security, refers to the physical availability of adequate

levels of food in a particular location, including the country as a whole. For most countries,

production is the main source of availability, with imports augmenting the shortfalls. The same is

true at the household level, with own production supplemented by market purchases. FAO‘s

suite of food security indicators provides time-series data for five indicators: food production,

levels of the Dietary Energy Supplies (DES) and adequacy, protein supply, share of the DES

from cereals, and share of protein of animal origin. This sub-section reviews availability for

Nepal based on two sets of indicators: i) trends in per capita production of cereals and some

other foods; and ii) availability and adequacy of the DES.

2.1.1 Trends in cereals production

Despite the decade-long insurgency, political instabilities and large-scale outmigration of youths

from rural areas, cereal production does not seem to have suffered as much as many in Nepal

perceived or speculated. Total cereal production (paddy, maize and wheat, with paddy in rice

equivalent) in 2011-13 was 33% higher than in 1999-01 (6.9 versus 5.2 million MT). The growth

rate of 2.3% p.a. recorded for the 1990s was largely sustained during the 2000s also (with 2%

p.a. growth rate during 2000-13).1 In the 2000s, cereal output performed even better in the later

years, with 3% p.a. growth rate during 2007-13. So, overall, the performance was not as bad as

often perceived given the adverse environment as noted above.

1 Unless otherwise stated, all annual growth rates reported in this study are trend growth rates estimated by

regressing the log of the variable concerned on time trend, including all the years covered. In other words, these are

not averages of annual percentage changes.

4

The largely sustained performance of the cereals sub-sector in the 2000s was mainly due to

markedly improved performance of maize and sustained growth rate in wheat production. As for

paddy, the growth rate during the 2000s decelerated to one-third of the rate attained in the 1990s

(0.8% p.a. versus 2.4% p.a.). However, paddy production has picked up from 2007. Yet another

positive development was that for all three cereals, it was mostly the growth in yield rather than

area that was behind the growth in output.

For the sake of comparability with the overall food supply trends reviewed in the next sub-

section, Figure 2.1 shows trends in cereals production on a per capita basis, separately for the

three main cereals and their sum, with paddy in rice equivalent. Table 2.1 summarizes related

statistics.

Figure 2.1: Trends in per capita cereals production in Nepal, 2000-2013 (in kg/capita/year)

Note: Cereals total is the sum of paddy, maize and wheat, with paddy in rice equivalent (with a conversion rate of

0.65).

Source: Based on the production data from FAOSTAT.

The figure shows that the overall trend in cereal production on a per capita basis during 2000-13

has been positive for maize, wheat and cereals as a whole but negative for paddy until around

2008 after which the trend has been positive and fairly strong (1.9% p.a. during 2007-13). Paddy

output per person at 174 kg in 2011-13 was actually lower than in 2001-03 (177 kg). The table

also shows good performance for potatoes, soybeans and lentils. The other impression from the

figures is that fluctuations of production around the trends seem to be increasing in more recent

years. The assessment in the following sub-section shows that the overall food supply, i.e.

counting all foods consumed in Nepal, has been improving. In part this was due to growing

imports but the share of imports in total food supply is small, and so most of the credit for

improved aggregate food situation in the country should go to domestic production.

y = 0.1585x2 - 0.4726x + 234.75

210

230

250

270

2001 2003 2005 2007 2009 2011 2013

Cereals (paddy in rice equivalent), kg/capita/year

y = 0.3172x2 - 5.201x + 187.79

140

160

180

200

2001 2003 2005 2007 2009 2011 2013

Rice, kg/capita/year

y = -0.0529x2 + 1.9281x + 61.027

60

65

70

75

80

85

2001 2003 2005 2007 2009 2011 2013

Maize, kg/capita/year

y = 0.0052x2 + 0.9799x + 51.656

50

55

60

65

70

2001 2003 2005 2007 2009 2011 2013

Wheat, kg/capita/year

5

Table 2.1: Trends in cereals production in Nepal on a per capita basis, 2000-13

1/ Cereals total is the sum of paddy, maize and wheat, with paddy in rice equivalent (with a conversion rate of 0.65).

Source: Production data from FAOSTAT.

2.1.2 Food availability and adequacy of the DES relative to requirements

The dietary energy supplies (DES) is the most popular metric for measuring the sum total of all

food available for human consumption in a country, after deduction of all other uses (exports,

animal feed, industrial use, seed and wastage). It is derived from a FBS and is expressed in

kilocalories per person per day (kcal/person/day). The level of the DES tends to be around 3,000

kcal in countries with virtually zero or very low prevalence of hunger, e.g. in developed

countries as well as in many countries in Latin America. The average for the ASEAN region, for

example, was 2,764 kcal in 2012-14 and 2,769 kcal in developing countries as a whole. The

DES, together with the parameter expressing the distribution of the DES among the population

and a normative minimum requirement, determines the prevalence of undernourishment or of

food inadequacy for a country. FAO‘s food security indicators provide these prevalence rates.

Figure 2.2 shows the levels and growth rates of the DES for Nepal (the lower panel in the figure,

the adequacy rate, is discussed next). Also shown are the data for some other South Asian

countries for comparing the status. The average level of the DES in South Asia-5 was 2,270 in

1991-93, which rose to 2,350 by 1999-01 and to 2,450 by 2011-13. Nepal‘s average DES was

mostly lower than those of the other South Asian countries during 1990-01and 1999-01. But

during 2001-2013, Nepal‘s DES grew the fastest among the five countries and, as a result, was

highest at 2,544 kcal in 2011-13.

As said, the level of the DES needs to be judged against the requirements. FAO provides

indicators for assessing the adequacy of the DES against two levels of requirements. One of them

is Average Dietary Supply Adequacy (ADSA) which expresses the DES as a percentage of the

Average Dietary Energy Requirement (ADER). The ADER covers food requirements associated

with normal physical activity. It is about 25% higher (depends on country factors) than another

level of food requirement, called Minimum Dietary Energy Requirement (MDER) which

indicates the amount of energy needed for light activity. The MDER is the one used for

computing the traditional FAO prevalence of undernourishment. FAO added the ADER in 2012.

2013 over 2013 over 2001 to 2001 to 2007 to

2001-03 2005-07 2011-13 2001 2006 2013 2006 2013

Paddy 177 164 174 -1 6 -0.5 -0.7 1.9

Maize 65 71 78 20 9 1.7 2.0 1.0

Wheat 53 59 66 24 13 1.8 2.6 2.1

Potatoes 61 76 97 57 27 4.7 6.1 3.9

Lentils 6 7 8 27 22 1.7 0.9 5.1

Mustard 6 6 6 3 3 0.0 0.0 0.7

Soybeans 1 1 1 38 29 2.8 1.9 5.3

Cereals total 1/ 233 236 257 10 9 0.7 0.8 1.7

Average values (kg) Percent change Growth rate, % p.a.

6

Figure 2.2: Trends in the DES and their adequacy levels for Nepal and other South Asian

countries

Source: Based on the FAO data.

A value of ADSA greater than 100 indicates that, on average, the total DES available in a

country is more than enough to meet the needs of the population for a healthy and active life. An

important proviso, of course, is how these supplies are distributed among the population. A level

of ADSA of 100 or more would ensure adequate food for all only if the available food is

perfectly equally distributed to all, which hardly happens in real life. In reality, ADSA levels

have to be higher than 100% for lower levels of hunger (discussed below).

The lower panel of Figure 2.2 shows the levels of the ADSA and their trends during the 1990s

and 2000s. The average ADSA for Nepal was 118% in 2011-13 which means that, on average,

the available DES was 18% more than the average requirement (ADER). Nepal‘s ADSA levels

in 1991-93 and 1998-01 were similar to those in other South Asian countries, but surged in the

2000s, growing at the rate of 0.80% p.a., the highest rate among the five South Asian countries.

An ADSA of 118% is indeed similar to that for the developing countries as a whole and thus it

could be said to be on the higher side for a country at Nepal‘s level of development.

2.1.3 Availability of food energy, proteins and fats based on the new FBS

One substantive work under the FAO regional project was the compilation of the SUA/FBS for

six years, 2008 to 2013, based on updated FAO methodology. It was a comprehensive exercise

that required, inter alia, putting together a wide range of statistics from production to trade as

well as various technical coefficients. Nepal‘s FBS maintained by FAO provided the initial basis

for this work. However, the new FBS is much more comprehensive in its coverage of food

products as well as in its use of better statistics and technical coefficients. Hence the indicators

Average DES, kcal/caput/day

Growth rate, % p.a.

1991-93 1999-01 2011-13 1991-00 2001-13

Bangladesh 2,077 2,272 2,454 1.00 0.53

India 2,291 2,359 2,453 0.52 0.73

Nepal 2,218 2,279 2,544 0.38 1.01

Pakistan 2,318 2,365 2,442 0.42 0.58

Sri Lanka 2,165 2,343 2,492 1.05 0.67

SA-5 2,270 2,349 2,454 0.55 0.69

Adequacy of the DES, %

Growth rate, % p.a.

1991-93 1999-01 2011-13 1991-00 2001-13

Bangladesh 97 103 108 0.70 0.22

India 106 107 108 0.28 0.52Nepal 106 108 118 0.24 0.80

Pakistan 109 109 108 0.17 0.28

Sri Lanka 96 102 110 0.82 0.76

SA-5 105 107 108 0.31 0.47

Average values

Average values

0

20

40

60

80

100

120

Bangladesh India Nepal Pakistan Sri Lanka SA-5

Adequacy of the DES, %

Avg1991-93 Avg1999-01 Avg2011-13

0

500

1000

1500

2000

2500

3000


Average DES, kcal/caput/day

Avg1991-93 Avg1999-01 Avg2011-13

7

from the new FBS should reflect Nepal‘s FNS status more realistically than the picture that

emerges from the current FAO database.

Food energy (DES) – Similar to the trends shown by the FAO database, the new FBS data also

shows steady, albeit modest, growth in aggregate DES during 2008-13, from 2,772 Kcal in 2008

to 2,922 Kcal in 2013, the growth rate being 1.4% p.a. (Table 2.2). Unlike with the FAO

database, the new FBS database is highly disaggregated and so permits reviewing the sources of

the DES from various foods. It shows that the contribution of cereals in the total DES was 65%

during 2008-13 (30% by rice, 20% by maize, 12% by wheat and 3% by other cereals). The

database also shows that not only is the aggregate DES high but also that there have been marked

improvements in the quality of the diet as well. For example, the share of cereals has been

declining, from 68% in 2008 to 61% in 2013, while the growth rate of the share of non-cereals in

total DES has been impressively high (3.4% p.a.). The non-cereals with relatively large

contributions include edible oils (with a 4.8% p.a. growth in its DES), potatoes/yams (5.6% pa),

milk/dairy products (6.1% p.a.) and pulses/beans (9% p.a.). There have been increases also for

sugar and vegetables. Overall, thus, the FBS data show significant positive trends in living

standards.

Table 2.2: Trends in the availability of food energy (calories/per capita/day), 2008-13

Note: Growth rates are computed from log-linear trend regressions with data for 2008-13.

Source: Based on newly compiled FBS for 2008 to 2013.

Protein - Table 2.3 shows similar trends for protein as above for the DES. The national average

level of protein availability increased from 52 gm in 2008 to 60 gm in 2013, equivalent to a

growth rate of 3.3% p.a. Eight products made up 90% of the total during 2008-13, with cereals

Grw. rate

S.N. Product groups 2008 2009 2010 2011 2012 2013 Av08-13 % Cum % % p.a.

1 Rice 891 837 818 819 831 878 846 30 30 -0.3

2 Maize 525 535 537 604 608 507 553 20 49 1.0

3 Wheat 386 326 406 340 318 321 350 12 62 -3.4

4 Edible oils/oilseeds 217 197 242 253 254 259 237 8 70 4.8

5 Potatoes/yams 126 151 156 158 166 176 155 5 76 5.6

6 Milk/dairy products 118 122 147 142 155 158 140 5 81 6.1

7 Pulses/beans 77 79 72 89 84 134 89 3 84 9.0

8 Spices 65 79 87 88 105 86 85 3 87 6.4

9 Other cereals 90 81 81 81 80 89 84 3 90 -0.3

10 Sugar 75 75 84 82 80 92 81 3 93 3.3

11 Vegetables 64 66 69 74 77 77 71 3 95 4.2

12 Meats 60 69 61 59 59 65 62 2 97 -0.4

13 Fruits 55 57 33 34 49 48 46 2 99 -3.2

14 Nuts 7 7 27 17 11 15 14 0.5 99 12.2

15 Alchol. beverages 10 11 13 13 13 12 12 0.4 100 3.9

16 Fish 3 3 3 3 3 3 3 0.1 100 -1.5

17 Coffee/tea 1 1 1 0 1 1 1 0.0 100 6.8

All total 2,772 2,698 2,837 2,855 2,894 2,922 2,830 100 - 1.4

Cereals total 1,892 1,779 1,842 1,844 1,837 1,795 1,832 65 - -0.5

- cereals share % 68 66 65 65 63 61 65 - - -1.8

Non-cereals total 879 919 995 1,012 1,057 1,126 998 35 - 4.8

- non-cereals share % 32 34 35 35 37 39 35 - - 3.4

Food energy -kcal/capita/day

8

contributing 60%. As with the DES, the share of cereals has been declining (at the rate of

negative 2.5% p.a.) while that of the non-cereals increased at the rate of 3.7% p.a., from 25 gm in

2008 to 34 gm in 2013. The food products whose contributions increased markedly are pulses,

milk and dairy products, and potatoes/yams. So, overall, the outcome has been positive for food

security.

Table 2.3: Trends in the availability of proteins (grams/per capita/per day), 2008-13

1/ Sum of fruits, fish, nuts, alcoholic beverages, sugar and tea/coffee.



Fats - There has been a marked increase in the national average supply of fats, from 52 gm in

2008 to 60 gm in 2013 (annual growth rate of 3.3%) (Table 2.4). Six groups of foodstuffs

contributed to 90% of the total, with edible oils accounting for 46%, followed by milk/dairy

(18%), maize (9%), meat (8%), rice (5%) and wheat (4%). The data show some notable shifts

taking place, the most prominent being the contribution of milk/dairy, with a trend growth rate of

4.7% p.a. While fats supplied by edible oils increased at the rate of 3.3% p.a., its share in the

total has remained flat. What is somewhat surprising is the negative growth rate for meats despite

the popular perception in Nepal that meat consumption has been growing rapidly.

2.1.4 Trends in food import dependency based on the new FBS

This sub-section highlights trends in food import dependency based on the data from the newly

compiled FBS. One emerging picture of food supply in Nepal is that aggregate availability has

increased considerably during the 2000s but this is also associated with increasing dependency

on imports. The FBS provides highly disaggregated data for reviewing trends in self-sufficiency

rates, or its inverse import dependency rates (IDRs), for individual food products. By converting

Grw. rate


1 Rice 18 16 16 16 16 17 17 23 23 -0.3

2 Maize 13 13 13 15 15 12 14 19 42 1.0

3 Wheat 11 10 12 10 9 9 10 14 56 -3.3

4 Pulses/beans 5 5 5 6 6 9 6 8 64 9.0

5 Meat 5 5 5 5 5 6 5 7 72 1.0

6 Milk/dairy products 5 5 5 5 5 5 5 7 79 2.5

7 Vegetables 4 4 4 4 4 4 4 6 84 1.6

8 Potatoes/yams 3 4 4 4 4 4 4 5 89 5.6

9 Spices 2 2 3 2 3 2 2 3 93 6.5

10 Other cereals 3 2 2 2 2 3 2 3 96 -0.2

11 Edible oils/oilseeds 0 0 1 2 2 2 1 1 97 65.4

12 Other 6 groups 1/ 2 2 2 2 2 2 2 3 100 1.9

All total 70 68 71 72 74 76 72 100 - 2.0

Cereals total 44 41 43 43 43 42 43 60 - -0.6

- cereals share % 64 61 61 59 58 55 60 - - -2.5

Non-cereals total 25 27 27 29 31 34 29 40 - 5.7


Proteins - per capita per day

9

Table 2.4: Trends in availability of fats (grams/per capita/per day), 2008-13

1/ Sum of fruits, potatoes/yams, sugar, fish, coffee/tea and alcoholic beverages.



the volume data for individual food items into common metrics, such as calories and protein,

IDRs can be reviewed at the aggregate sub-groups of foods. For example, there are 15 lines of

individual products in the sub-category called ―other cereals‖, for which an IDR can be

computed by first converting the individual foods into the common metrics and aggregating them

to the sub-group.

Figure 2.3 shows, first, the results for the IDRs of three aggregates: cereals, non-cereals and all

foods, and, second, Table 2.5 shows the same results for major food products. The figure shows

that during 2008-13, the IDR for food energy sourced from all foods taken together was 11%, but

only 4% for energy derived from cereals and 25% from non-cereals. The IDR is much higher for

fats, at 37%, due largely to 45% IDR for fats that come from non-cereals. This is also an

indication that as household incomes rise and more nutrients are sourced from non-cereals, the

IDR is likely to increase, unless of course the production of non-cereals expands proportionately.

The results in Table 2.5 show strong positive trends in the IDRs for wheat and rice, with the

value of the IDR being as high as 11% for rice in 2013 and 13% for wheat in 2010. Among other

major products, the IDR for edible oils is very high, at 71%, although the IDR has been stable

during 2008-13. Interestingly, Nepal has been experiencing high IDRs for potatoes, a product for

which Nepal is considered to have natural comparative advantage for production and trade.

Fruits, another important food product and considered to have immense production potentials in

Nepal, also stand out in terms of a strong positive trend in the IDR. For some products with zero

values for 2011 (rows 12 to 16 in the table) the data seem suspect and need to be rechecked

carefully while updating the FBS.

Grw. rate


1 Edible oils/oilseeds 24.4 22.2 26.7 27.0 26.8 27.4 26 46 46 3.3

2 Milk/dairy products 8.2 8.5 11.3 10.8 11.8 12.0 10 18 64 8.0

3 Maize 4.9 5.1 5.0 5.6 5.6 4.7 5 9 73 0.8

4 Meat 4.2 5.2 4.2 4.1 4.1 4.5 4 8 81 -1.1

5 Rice 3.2 3.0 2.9 2.9 3.0 3.1 3 5 86 -0.6

6 Wheat 2.0 1.8 3.5 2.1 1.8 1.8 2 4 90 -3.1

7 Spices 1.6 1.8 1.8 1.7 2.2 1.7 2 3 93 2.8

8 Nuts 0.3 0.3 2.5 1.5 1.0 1.2 1 2 95 28.1

9 Other cereals 0.8 0.7 0.7 0.7 0.7 0.8 1 1 96 -0.6

10 Vegetables 0.5 0.6 0.5 0.5 0.6 0.6 1 1 97 1.3

11 Pulses/beans 0.4 0.5 0.4 0.5 0.5 0.8 1 1 98 9.4

12 Sum of 6 groups 1/ 0.6 0.6 0.6 0.6 0.7 0.7 1 1 100 5.7

All total 52 51 60 58 59 60 57 100 - 3.3

Cereals total 11 11 12 11 11 10 11 19 - -0.4

- cereals share % 21 21 20 19 19 17 20 - - -3.7

Non-cereals total 41 40 48 47 48 49 46 81 - 4.1


Fat - per capita per day

10

Figure 2.3: Import dependency rates for food energy, protein and fat derived from cereals

and non-cereals (average values for 2008-13)

Note: The numbers indicate what percentage of food energy, protein or fats is derived from cereals, non-cereals and

all foods.


Table 2.5: Import dependency rates (%) measured in terms of food energies supplied by

various food products (average 2008-13)

Note: A dash (-) for some growth rates indicates that these could not be computed using logs as some numbers

(IDRs) are zero. Annual growth rates are for 2008 to 2013.

Source: Computed from newly compiled FBS for 2008-13.

4 36

25

11

45

11

7

37

0

10

20

30

40

50

Energy Protein Fats

Import dependency rates (%), 2008-13 average

From cereals From non-cereals From all products

Grw.rate

2008 2009 2010 2011 2012 2013 2008-13 % p.a.

1 Wheat 0.6 0.7 12.5 6.7 1.9 3.2 4.5 30

2 Rice 3.8 4.0 3.6 4.7 7.6 10.6 5.8 21

3 Maize 0.1 0.3 0.1 0.1 0.1 0.1 0.1 -11

4 Other cereals 1.7 1.6 2.3 2.1 2.8 3.3 2.3 15

5 Potaoes/yams 2.8 2.7 4.6 3.9 6.8 9.0 5.2 24

6 Sugar 18.7 24.8 26.3 21.7 12.9 20.8 20.9 -5

7 Pulses 1.5 42.6 22.5 30.8 24.1 20.4 23.5 34

8 Nuts 66.1 63.4 98.2 107.1 94.7 78.6 90.5 6

9 Edible oils 73.5 75.0 73.5 67.9 68.3 69.7 71.1 -2

10 Vegetables 7.2 5.1 6.0 6.8 8.2 8.2 7.0 6

11 Fruits 6.9 2.8 13.4 2.9 14.0 17.2 9.4 23

12 Tea/coffee 58.8 69.2 72.3 0.0 69.4 68.1 64.7 -

13 Spices 8.5 12.6 7.4 0.0 8.5 18.6 9.2 -

14 Meat 0.8 11.3 0.2 0.0 0.1 0.2 2.3 -

15 Milk/dairy products 1.6 2.0 3.1 0.0 2.9 2.7 2.1 -

16 Fish 0.9 0.9 0.0 0.0 0.0 0.0 0.3 -

17 Alcoh. drinks 16.1 34.3 34.1 27.1 30.2 45.3 31.5 13

Cereals 2.0 2.2 4.5 3.4 3.9 6.0 3.7 20

Non-cereals 22.9 26.0 27.6 24.8 24.3 26.3 25.4 1

All total 8.7 10.3 12.6 11.0 11.4 13.8 11.3 7

Import dependency rate , %

11

2.2 The access dimension of food security

Access, the second pillar of food security, is crucial for ensuring food security. Even if food is

physically available in a region or country, it may not be accessible if households lack

purchasing power. Thus, access is essentially a problem for low-income households not being

able to buy adequate food that is physically available in the country or local markets. Typical

determinants of access would thus include income levels of the poor, overall distribution of

income in the country, food inflation, employment programmes and social safety nets for the

poor.

FAO provides time-series data for 9 indicators under the access dimension. These include

indicators of the state of transport and market (paved road, road density, rail lines), income

growth and food price index and share of food expenditure of the poor. Two of the other three

indicators are prevalence rates (of undernourishment and of food inadequacy) while the third is

also a related indicator, the depth of food deficit.

This section reviews the state of access to food based on the two prevalence rates, of

undernourishment and of food inadequacy. The former is the main indicator of hunger monitored

by FAO for decades. Note that although these appear as outcomes and hence seem to belong to

the utilization dimension of food security, in reality the prevalence rates are indicators of access

as they tell what percentage of the population, obviously the poorest ones, fail to access

minimum or adequate foods. Besides this sub-section, most of the analysis reported in Section III

is about access, based as it is on household income and expenditure survey data.

2.2.1 Prevalence of undernourishment and food inadequacy

The prevalence of undernourishment (PoU) is the traditional FAO indicator of hunger, as well as

one of the MDG1 indicators. The PoU measures the percentage of population that is at risk of not

covering the food requirements associated with normal physical activity, also the level associated

with a ―hunger‖ threshold (the MDER). FAO also provides since 2012 a new prevalence rate

based on a higher level of food requirement (the ADER) (but using the same level of the DES).

This is called the prevalence of food inadequacy (PoFI).

Figure 2.4 shows the evolutions of the PoFI and PoU for Nepal as well as for four other South

Asian countries. The figures and the associated statistics show the following two main results.

First, the prevalence rates are lowest for Nepal, with 19% for the PoFI and 13% for PoU in 2011-

13, versus 24% and 15% for India and 25% and 16% for South Asia-5 as a whole, respectively.

Second, Nepal achieved sharp reductions in the prevalence rates in the 2000s, not in the 1990s.

The reduction rates for Nepal in the 2000s were 4.5% p.a. for the PoFI and 5.7% p.a. for the

PoU. These rates are several times higher than those attained by other countries in South Asia. In

contrast to Nepal, other countries attained steeper reductions in the 1990s.

The depth of food deficit is yet another useful indicator of food deprivation among the suite of

FAO indicators. It indicates the amount of calories that would be needed to lift the

12

Figure 2.4: Prevalence of undernourishment in Nepal and South Asia (%)

Source: Based on the data from FAO Food Security Indicators.

undernourished from their status, other things being the same. The data show that the depth of

food deficit, at 79 kcal/capita/day in 2012-14, is lowest for Nepal, versus 110 for India and 118

for South Asia-5. It means that Nepal needs to raise food supply on average equivalent to 79

kcal/capita/day so as to lift the remaining population just above the hunger line.

Thus, overall, Nepal‘s food security situation based on the indicators of food supply appears

much better than those of other South Asian countries. Note that the main reason for Nepal‘s

relatively lower levels of the PoFI and PoU is the food adequacy rate of 118% (the ADSA), as

reviewed above. Distribution remaining the same, food adequacy level has to be raised further to

reduce the prevalence rates to even lower levels.2 Even at the current level of food adequacy,

Nepal fares better than many other developing countries with higher levels of per capita GDP.

2.3 The utilization dimension of food security

The reason utilization is considered to be a separate dimension of food security is that even if the

quantity of the dietary energy intake is adequate, human body may not properly utilize the food

due to factors such as illness and infection, prolonged inadequacy of food intake, repeated

episodes of infections, repeated episodes of acute undernutrition etc. Other factors that affect

utilization include micronutrient deficiencies, food quality and safety during preparation and

access to safe water and hygienic conditions. The two prevalence rates – the PoU and PoFI -

measure food shortages in terms of caloric deficits but do not tell the full story of malnutrition. In

2 For a sample of 123 pairs of the ADSA and PoFI for 41 sub-Saharan African countries, the estimated regression

relationship was found to be as follows: % PoFI = 161.5 - 1.2 * % ADSA (Konandreas et al. 2015). Thus, an ADSA

of 118% (Nepal‘s) gives a PoFI of 20%. An ADSA of 126% is required to reduce the PoFI to 10% and of 130% for

a PoFI of 5%.

Prevalence of food inadequacy (%)

Growth rate, p.a.

1991-93 1999-01 2011-13 1991-00 2001-13

Bangladesh 42 34 27 -1.9 -0.8

India 32 26 24 -2.9 -1.8

Nepal 32 31 19 -0.4 -4.5

Pakistan 33 30 30 -1.4 -1.0

Sri Lanka 43 39 33 -1.2 -1.6

SA-5 33 28 25 -2.5 -1.7

Prevalence of undernourishment(%)

Growth rate, p.a.

1991-93 1999-01 2011-13 1991-00 2001-13

Bangladesh 34 24 17 -3.3 -1.2

India 23 17 15 -3.8 -2.5

Nepal 23 22 13 -0.3 -5.7

Pakistan 25 23 22 -1.6 -1.4

Sri Lanka 31 30 25 -0.3 -1.8

SA-5 24 19 16 -3.4 -2.3

Average values

Average values

0

10

20

30

40

50


Prevalence of food inadequacy (%)

Avg1991-93 Avg1999-01 Avg2011-13

0

10

20

30

40


Prevalence of undernorishment (%)

Avg1991-93 Avg1999-01 Avg2011-13

13

diets consisting mainly of staple cereals or root crops, it is possible to consume enough calories

without consuming enough micronutrients.

FAO database provide statistics for 10 indicators related to utilization. These include access to

improved water sources and sanitation facilities, prevalence rates for wasting, stunting and

underweight among under-5 children, underweight among adults, prevalence of anaemia among

pregnant women and under-5 children, and deficiencies in vitamin A and iodine. The assessment

below focuses on the three most prominent indicators of utilization - prevalence of wasting,

stunting and underweight among the children under five years. Nepal‘s most recent

comprehensive household survey (NLSS III) also provides data on these and other indicators

based on anthropometric measurements (NPC/CBS/WFP 2013). These data are reviewed in

Section III.

Stunting (low height for age) refers to an outcome when the height-for-age of a child under 5

years of age falls below 2 standard deviations below WHO‘s 2006 child growth standards.

Wasting is measured similarly for low weight for height. Underweight refers to low weight for

age and is a composite indicator based on stunting and wasting. Table 2.6 shows the progress

made by Nepal in reducing child undernutrition. Also shown are the outcomes for some South

Asian countries for a comparative picture.

Table 2.6: Progress made in reducing child undernutrition in Nepal and some South Asian

countries

Note: A dash "-" indicates missing data for that (or nearby) year. For India, the data is for 1999 (not 2001); and for

Sri Lanka for 2000 (not 2001) and 2009 (not 2011).

Source: Prevalence rates from FAO food security indicators database; annualized rates computed.

2001 to 2006 to 2001 to

2001 2006 2011 2006 2011 2011

-------- Stunting --------- ------------ Stunting --------------

Bangladesh 55.4 47.0 41.4 -3.3 -2.5 -2.9

India 51.0 47.9 - -0.9 - -

Nepal 57.1 49.3 40.5 -2.9 -3.9 -3.4

Pakistan 41.5 - 43.0 - - 0.4

Sri Lanka 18.4 - 19.2 - - 0.5

--------- Wasting --------- ------------ Wasting --------------

Bangladesh 13.2 12.4 15.7 -1.3 4.7 1.7

India 19.8 20.0 - 0.1 - -

Nepal 11.3 12.7 11.2 2.3 -2.5 -0.1

Pakistan 14.2 - 14.8 - - 0.4

Sri Lanka 15.5 - 11.8 - - -3.4

------- Underweight ------- ------------ Underweight -----------

Bangladesh 45.4 39.8 36.8 -2.6 -1.6 -2.1

India 44.4 43.5 - -0.3 - -

Nepal 43.0 38.8 29.1 -2.1 -5.8 -3.9

Pakistan 31.3 - 30.9 - - -0.1

Sri Lanka 22.8 - 21.6 - - -0.7

Prevalence rates (%) Annualized growth rates (% p.a.)

14

In cross-country comparisons, Nepal is considered to be one of the top ten countries in the world

with the highest prevalence of stunting. The data in the table show high levels of stunting for

Nepal as well as other South Asian countries with the exception of Sri Lanka. Nepal‘s prevalence

of stunting was higher than that in Bangladesh, India and Pakistan in 2001 and 2006 but declined

to below the levels of Bangladesh and Pakistan by 2011. The annualized growth rates show that

the rate of decline was highest for Nepal among the five countries. Most notably, Nepal‘s

prevalence of stunting declined rather impressively by 3.9% p.a. during 2006-11.

Nepal had the lowest prevalence of wasting in 2001 among the five South Asian countries. The

rate increased slightly in 2006 but came down again in 2011 when it was lowest again. As with

stunting, the rate of reduction was high during 2006-11. In contrast, Bangladesh has experienced

an increase in the prevalence of wasting.

Nepal had relatively high rate of prevalence of underweight in 2001 but the rate of reduction has

been impressive and as a result the prevalence rate fell to low levels, lower than in Bangladesh

and Pakistan. In 2009, Nepal‘s prevalence rate was also lower than India‘s by 5 percentage

points.

While the overall outcome for Nepal as a whole has been positive, there is a wide variation in

undernutrition rates across the country, as is the case with other indicators. Nepal Demographic

and Health Survey shows that stunting among rural children is 15% points higher than among

urban children (42% versus 27%). In contrast, the prevalence rates in the hills and the Tarai

regions are not that different: in the Tarai, 37% stunting, 11% wasting and 29% underweight and

in the hills 42% stunting, 11% wasting and 27% underweight. However, the prevalence rate is

very high (53%) in the mountains.

2.4 The stability dimension of food security

Stability is a cross-cutting concern in that stability is sought across all the three other dimensions

of food security, including in the outcomes and the inputs that contribute to the outcomes. One

could thus say that stability is ensured when food availability, access and utilization remain

secure throughout the year and for the long-term. The indicators of stability should in principle

include all the main key drivers of availability, access and utilization, e.g. stable food production,

ensuring that food import capacity is stable and that household incomes and market prices are

not subject to large fluctuations.

For the stability dimension, FAO provides, in its suite of food security indicators, time-series

statistics for the following seven indicators. Two are related to production shocks - percent of

arable land equipped for irrigation and per capita food production variability. Two others

concern with food imports - cereal import dependency ratio and value of food imports over total

merchandise exports. The fifth is domestic food price volatility while the sixth is about the

stability of the overall food supply – the per capita food supply variability. The indicators also

include political stability and absence of violence and terrorism.

15

Besides these traditional indicators of stability, climate change as a source of production shocks

is also pertinent to stability concerns of food security. A great deal of interest is being taken in

Nepal on the subject of climate risk and agriculture/food security, as evidenced by fresh

publications and field projects.

In view of this, this sub-section has two parts. The first reviews the traditional indicators of

stability, using the FAO data. The second highlights some issues on climate risks and food

security.

2.4.1 Trends in instability in selected food security indicators

Table 2.7 shows for Nepal, as well as for two other South Asian countries, the progress being

made on six of the seven FAO indicators of stability. The two trade indicators (1 and 2) together

show a worsening of the situation for Nepal. Indicator 1 tells that cereal import dependency has

been increasing since the early 2000s (while declining for Bangladesh and Sri Lanka, for

example). This is a common knowledge in Nepal as food imports have been surging in recent

years. While import dependency increased, the capacity to import foods (indicator 2) has

worsened, with the ratio of food imports to merchandise exports surging from 31% in the early

2000s to 52% in the recent years, amounting to a 7.5% p.a. growth rate of the ratio. Nepal‘s

exports have been faltering in recent years and that partly explains the worsening of the food

import capacity. The status would appear somewhat better if exports also included services as

Nepal‘s situation on this is better.

Indicator 3 shows some improvement, i.e. an increase in irrigated area. While irrigation

contributes to some stability for water-intensive crops, indicator 5 shows that there has been an

increase in the volatility of per capita food production as a whole. Indicator 4 shows an

improvement, with stable or slightly negative trend in food price volatility, in contrast to the

situation in Bangladesh and Sri Lanka for example, where volatility has been growing. But note

that the level of price volatility is higher for Nepal. The positive outcome for Nepal owes, most

presumably, to food price stability in India. The trend rate of the volatility index for India (-0.3%

p.a.) was almost the same as for Nepal (-0.2% p.a.) although the level of the volatility for India is

about half the level for Nepal (4.8 for India versus 9.7 for Nepal).

Finally, indicator 6 shows some slight improvement in the volatility of the overall food (DES)

supply. In contrast, marked declines were recorded for Bangladesh and Sri Lanka. One could

argue that Nepal‘s status on this indicator could have been worse given the slight increase in the

variability of food production. Most likely, domestic production shortfalls were offset by surging

food imports, in turn financed by large increases in remittances. Thus, it could be argued that

while trade indicators in themselves appeared negative, they could be responsible for the positive

outcome on overall food supply.

16

Table 2.7: Six indicators of the stability dimension of food security in Nepal

Note: Exports in Indicator 2 refer to merchandise exports. Indicator 5 measures production in constant 2004-06

international USD prices. Indicator 6 measures supply in terms of kcal/caput/day, i.e. the DES values.

Growth rates are percentage per annum, estimated from a log-linear regression on time trend using all the

observations available.

Source: Based on the FAO data on food security indicators.

Trends in variability in annual harvests of paddy, maize and wheat as well as cereals can be

visually appreciated from graphs in Figure 2.1 earlier. The figures show that fluctuations around

the trend lines were much lower during 2000-06 but were magnified after 2006, besides

becoming more frequent. For example, paddy output slumped by 10% in 2007 and, after

recovering for two seasons, by 12% in 2010 and again by 12% for the third time in 2013. Maize

suffered largest slump of 16% in 2009 but again a decline of 8% in 2013. Because of these recent

events, the link between climate risks and food security is attracting a great deal of attention -

discussed next.

2.4.2 Climate change risks to food security

How climate change impacts agriculture and livelihoods in Nepal has become an increasingly

researched subject in recent years. There are several recent studies on this subject, some of them

focused on food security. The later include WFP (2014) on climate risks and agriculture and

Krishnamurthy et al. (2013) published by the CGIAR Research Program on Climate Change,

Agriculture and Food Security (CCAFS). NPC/CBS/WFP (2013), which was the study that

analysed the NLSS III data for food security and nutrition, also has a section devoted to climate

change and food security. GoN (2011) is Nepal‘s National Framework on Local Adaptation

Plans for Action, prepared by all LDCs as part of a work programme of the UN framework for

climate change. FAO has also an ongoing project in Nepal on this subject. What follows

highlights some aspects of climate change and food security drawn from some of these studies.

2. Food imports to exports ratio (%)

Growth Growth

2000-02 Latest 3 yrs rate % p.a. 2000-02 Latest 3 yrs rate % p.a.

Nepal 1.4 3.6 13.4 Nepal 31.0 52.0 7.5

Bangladesh 10.5 9.4 -1.4 Bangladesh 23.0 21.3 -1.3

Sri Lanka 38.0 33.3 -1.8 Sri Lanka 13.3 17.7 4.4

4. Volatility of domestic prices (index) Growth

Growth 2000-02 Latest 3 yrs rate % p.a.

2000-02 Latest 3 yrs rate % p.a.

Nepal 50.0 59.3 2.1 Nepal 10.3 9.7 -0.2

Bangladesh 55.3 66.8 2.4 Bangladesh 4.1 7.9 5.5

Sri Lanka 61.4 47.3 -3.2 Sri Lanka 7.3 8.7 3.0

5. Per capita food production volatility (index) 6. Per capita food supply volatility (index)

Growth Growth

2000-02 Latest 3 yrs rate % p.a. 2000-02 Latest 3 yrs rate % p.a.

Nepal 2.6 3.2 3.1 Nepal 26.7 25.3 -0.6

Bangladesh 4.4 3.8 -0.3 Bangladesh 57.0 22.7 -9.9

Sri Lanka 3.6 4.1 2.8 Sri Lanka 40.7 24.7 -7.7

1. Cereal import dependency ratio (%)

Average values

Average values

Average values

Average values Average values

Average values

3. Arable land irrigated (%)

17

There is a consensus that Nepalese agriculture, and farm livelihoods as a whole, is highly

vulnerable to climate risks and that this risk will continue to elevate in the coming years. It has

been claimed based on an analysis of the historical data that almost 50% of the variability in crop

yields in Nepal during 1965-2005 could be explained by variations in temperature and

precipitation. Further, this correlation is much higher than those found in global analyses,

highlighting the high sensitivity of food production in Nepal to climatic variability. Over the last

decade, the data reveal that around 30,845 hectares of land owned by almost 5% of households

became uncultivable due to climate-related hazards. Moreover, climate-related disasters such as

inundations, landslides and droughts have increased and had a significant impact on livelihoods

and food security. For example, the 2008 floods affected over 6 million people (30% of the

population) and the 2008/2009 drought resulted in over a 15% decline in winter crop production.

It is noted in particular that under a climate change scenario, food access in the mountain areas

would suffer more, not only through significant declines in winter crops due to low precipitation

but also through disruptions to the functioning of markets from events such as landslides and

floods. The disaster reports from the OCHA suggest that over the last 25 years the impacts of

floods occurring during the monsoon months have increased.

One main official document of the GoN on response measures to climate risk is National

Framework on Local Adaptation Plans for Action. Many programmes and activities on climate

change adaptation are being implemented by the government as well as other agencies, with

enthusiastic support from development partners. FAO has also an ongoing project in Nepal on

this subject. Climate risks have now been recognized as one of the core elements of the stability

dimension of food security.

2.5 Summary of the main findings

This section presented a status report on food and nutrition security (FNS) in Nepal based on an

analysis of the evolution of the FNS indicators since the 1990s. The analysis was largely based

on FNS indicators from FAO, supplemented by data from the newly compiled SUA/FBS and

other national sources. The assessment was structured along the four dimensions of food

security, namely availability, access, utilization and stability. In order to provide a comparative

picture, Nepal‘s rate of progress was also compared to those for some other South Asian

countries, which was insightful. This status report continues to the next section with analysis

based on the NLSS III data.

Trends in food availability were assessed based on two indicators, cereals production and dietary

energy supplies (DES). First, the overall growth rate of per capita cereal production during

2000-13 has been positive for maize, wheat and cereals as a whole despite being negative for

paddy. But paddy production also picked up since 2008. The production growth was mainly due

to yield rather than area expansion. There were notable increases in the outputs of potatoes,

soybeans and lentils. Second, the FAO data on the availability of all foods in terms of the DES

averaged 2,544 kcal in 2011-13, which is markedly higher than the average level for other four

18

South Asian countries. Nepal had lagged behind others in the 1990s but made good progress in

raising the level of the DES during the 2000s.

While this picture is impressive in both the absolute and relative sense, the new data on food

availability from the freshly compiled SUA/FBS for 2008-13 shows a superior performance. The

new FBS estimates give an average DES level for 2008-13 of 2,830 kcal, higher by 297 kcal or

12% over the FAO number (also higher levels of protein and fats). This is indeed a large

difference. The DES level estimated from the NLSS III data (2,725 kcal) lies in-between the

above two estimates.

One important output of the Project is the compilation of fresh SUA/FBS for 2008-13. This work

was valuable in identifying several shortcomings in the FBS maintained by the FAO. A number

of important discrepancies were also noted. Annex 2 provides a brief on the reasons for these

discrepancies. One source was obvious – 20% of the discrepancy in the estimated DES levels (as

well as protein, fats etc.) was due to population numbers used, with FAO using higher numbers

(and hence lower DES level) and the revised SUA/FBS using lower numbers (hence higher DES

level). Other discrepancies were related to differences in production data, coverage of food

products, differences in trade data and trade coverage, assumptions on various uses of food,

conversion rates, and so on.

In the traditional FAO method, prevalence rate for hunger is estimated based on three statistics:

the DES, a parameter for the distribution of the DES, and food energy requirement. Higher the

DES, lower is the prevalence rate, holding the other two parameters constant. Using the same

level of energy requirement (2,200 kcal) and distribution parameter, the resulting prevalence

rates for 2010-11 are as follows: 17% using the DES from the new FBS, 22% using the DES

level from the NLSS III and 31% using the DES level in the FAO database. The prevalence rates

for undernourishment (PoU), similarly computed based on 1,724 kcal as the requirement, are

2.5%, 3.8% and 6.8% with the three DES estimates, respectively. These differences are

substantive.

Even with the lower DES level in the FAO data, Nepal stands out among four other South Asian

countries in having the lowest prevalence rate during 2011-13. Nepal attained this position by

significantly lowering the prevalence rates during the 2000s, not in the decade of the 1990s,

whereas the rates of hunger reduction for the other countries were slower during the 2000s than

in the 1990s.

The three indicators for the utilization dimension of food security assessed in this section were

stunting, wasting and underweight. On these, Nepal is generally considered to have relatively

high prevalence rates compared to countries at similar levels of development. While the levels

may be high in an absolute sense, the data for 2001, 2006 and 2011 (the survey years) show that

Nepal attained impressive rates of reduction of stunting and wasting, with even higher rates of

reduction during 2006-11. While the overall outcome has thus been positive, there is a wide

19

variation in these indicators across the country with some regions suffering from very high rates

(discussed further in Section III).

Finally, the stability dimension of food security was assessed based on seven indicators. These

pointed to different outcomes. Two trade indicators showed worsening trends - cereal import

dependency has been rising, while Nepal‘s capacity to import food (in terms of export earnings

from goods) has been falling. The data show some progress with the extension of the irrigated

area, which is a positive factor for stability. In the meantime, the volatility of food production

has increased in recent years, with climate shocks increasingly seen as the main reason.

However, on a positive note, the volatility of the overall food availability (the DES) has not

increased over time despite an increase in the volatility of food production, most presumably

because imports offset the supply shortfalls quickly and efficiently. It seems that one price Nepal

has been paying for higher aggregate food supplies is increasing dependency on food imports.

This is despite the positive trends in the production of cereals and several other foods –

obviously, the supply response is inadequate to meet the surging demand for food in Nepal.

20

III. FOOD AND NUTRITION SECURITY STATUS REPORT – PART 2: VARIATIONS

IN HOUSEHOLD FOOD SECURITY, 2010-11

Nepal‘s third comprehensive survey of living standards, the Nepal Living Standards Survey

(NLSS III), is a unique source of information on various aspects of food consumption by

households living in different parts of the country and belonging to different income groups. For

this reason, one core activity under the FAO regional project was to utilize this information for

profiling food and nutrition security (FNS) status. This was done with the help of a software

specifically designed for this purpose, the ADePT-Food Security Module (ADePT-FSM), jointly

developed by FAO and the World Bank (see Annex 3.1 for an introduction to the NLSS III and

Adept-FSM module). The documentation of the FNS status in this section is mostly based on the

NLSS III data extracted with the Adept-FSM. Annex 3.2 presents most of the statistics used in

this section.

The following six topics are discussed in this section: i) food expenditure as a share of total

expenditure; ii) variations in food nutrient intakes by regions and income; iii) food consumption

pattern by major food sub-groups; iv) variation in the cost of macro food nutrients; v) extent of

the reliance on market versus own production; and vi) variation in market price of food products.

In addition, the results of the 2014 survey of chronic food insecurity undertaken with the IPC

tool are also summarized in one sub-section. In terms of the four dimensions of food security,

most of the topics discussed below belong to the access dimension, specifically household access

to food.3

3.1 Food expenditure as a share of total expenditure

The share of total income or total consumption expenditure (TCE) on food, also called the Engle

ratio, is one of the closely monitored indicators of household wellbeing. This is also included

among the FAO suite of indicators for the access dimension of food security. An established

pattern throughout the world is the decline in the Engle ratio as income rises – the main

empirical questions asked are how high is the ratio for the low income groups and how rapidly

does the ratio decline with rises in income.4

The Engel ratio for Nepal as a whole in the 2003/2004 NLSS was 39% for urban and 63% for

rural areas. Seven years later, in 2010/11 (NLSS III), the ratio has barely changed – slightly

worsening for urban areas (to 40%) and slightly improving for the rural areas (to 61%). By

3 Having similar dataset for the previous NLSS would have considerably enriched the analyses, as this would allow

an assessment of the progress made between the two survey years, as well as the drivers of the changes. There are

however some written works that provide some information on changes between the two surveys – that literature is

consulted for the writeup here. 4 Note that Engle ratio is traditionally associated with income. Income, however, is relatively difficult to measure

accurately, and so most household surveys use consumption expenditure for analyses, as a proxy for income. Income

and total expenditure are often used interchangeably in studies based on such surveys, including in this section, but

the correct variable is total consumption expenditure and not income.

21

development region, the ratio for 2010/11 was lowest for the central region (49%) and around

60% for the other four regions. It is obvious that the figure for the central region is heavily

influenced by the weight of Kathmandu valley, where the ratio is lower on account of higher

incomes.

The main source of variation in the Engle ratios is TCE. The NLSS III provides data on the TCE

as well as consumption expenditure on foods (CEF) by 10 income groups (deciles) which

provides the shape of the Engle curve. This is shown in Figure 3.1 (also shown for comparison

the corresponding trend for Bangladesh based on 2010 household survey). For Nepal, the Engle

ratio drops from 72% for the poorest 10% of the population (first decile, called D1) (by a

coincidence, the ratio is the same for both the urban and rural areas) to 32% and 41% for the

richest 10% population (called D10) in the urban and rural areas, respectively. The following

points may be noted.

Figure 3.1: Engle ratio – expenditure on food to total consumption expenditure (in %) by

deciles (2010-11 for Nepal, 2010 for Bangladesh)

Source: NLSS III as generated by Adept-FSM for Nepal and GoB (2010) for Bangladesh.

First, while the ratio was the same, 72%, for D1 in both areas, it falls more rapidly for urban

households. For example, the ratio for D4 was 61% in urban but 68% in rural areas, with the

rural households reaching that level only when the TCE rises to a level between D7 and D8.

Second, the graphs show that the declines in the Engle ratios are not linear. For rural households

in particular, the declines are slow until D7 after which the ratio falls rapidly. For urban area, the

declines are much more linear with rises in TCE, notably from D3 to D9. An inspection of the

TCE and CEF data shows that while the TCE changed similarly by deciles, the CEF drops faster

for urban household. This could be due to several reasons, such as the availability of various

foods, food preferences as well as prices. Such an analysis requires finely disaggregated data and

a suitable analytical framework, something worth undertaking as a follow up work.

Comparing Nepal with Bangladesh, the graphs show superior food access status for the urban

households in Nepal, with Nepal‘s Engle ratios being consistently smaller, by 5-8 percentage

points, than Bangladesh‘s. The opposite is the case for the rural households, with the exception

of those in the D9 and D10 deciles, but the differences are small. Note also that for Bangladesh

too, the rate of decline in the Engle ratios is much slower for the rural than for the urban

households, similar to Nepal‘s case.

30

40

50

60

70

80

D 1 D 2 D 3 D 4 D 5 D 6 D 7 D 8 D 9 D 10

Engle ratio (%) - urban

Nepal Bangladesh

30

40

50

60

70

80

D 1 D 2 D 3 D 4 D 5 D 6 D 7 D 8 D 9 D 10

Engle ratio - rural

Nepal Bangladesh

22

The Adept software also generates income elasticity of demand for food by income deciles.

Figure 3.2 shows that, as expected, the values of the elastcities fall with increases in the TCE.

The elasticity dropped from 0.46 for D1 to 0.23 for D10 for rural households and from 0.30 to

0.18 for the urban households. Note that the differences in the values of the elasticities for urban

and rural households belonging to the same decile are marked, by about 0.15 for lower deciles

and by about 0.08 for higher deciles.

Figure 3.2: Income elasticity of demand for food by decile

Source: NLSS III data as extracted with Adept-FSM.

3.2 Variations in food nutrient intakes by income and regions

The Adept-FSM provides data from the NLSS III that enables an analysis of the relationship

between the levels of food energy intake and TCE, graphed in Figure 3.3. The graph shows

positive relations between TCE and kcal, as expected, but the relationship is not linear

throughout the income levels (note that the levels corresponding to Q10 are very high, clearly so

for rural households (4,151 kcal), and so the data are highly suspect and could be ignored until

this matter is re-checked and resolved). For urban areas, the kcal rises markedly up to Q3 and

then increases only slowly. For the rural areas, the relationship is stronger but with variations at

different levels of the TCE.

One way to summarize the response of the kcal to changes in the TCE is to compute anelasticity.

Although somewhat crude, a simple way is to regress the log of the kcal on the log of the TCE.

The results are as follows:

ln (kcal) = 6.94 + 0.19 ln (TCE) (for urban area)

ln (kcal) = 6.48 + 0.34 ln (TCE) (for rural area)

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

D 1 D 2 D 3 D 4 D 5 D 6 D 7 D 8 D 9 D 10

Income elasticity of demand for food concumption

Nepal

Urban

Rural

23

Figure 3.3: Variation in caloric intake levels by TCE decile, 2010/11

Source: NLSS III data as extracted with Adept-FSM. Note that the levels corresponding to Q10 are very high,

clearly so for rural households (4,151 kcal), and so the data are highly suspect and could be ignored until the matter

is re-checked and resolved.

The implied elasticity values are 0.19 and 0.34. This means that for every 10% increase in the

TCE, the kcal level rises by 1.9% for the urban and 3.4% for the rural areas. The higher elasticity

for the rural area is obvious from the graph also. While these values are similar to those from

other studies in South Asia and elsewhere, why the response is almost double for rural

households is not as clear. The difference in the sources of the calories obtained from different

foods may explain this to some extent, but available data do not allow further analysis of this

matter. It is also possible that there could be some bias in the kcal data as the gaps between the

rural and urban intakes are too wide to be explained by similar levels of the TCE for various

deciles.

As regards other macro nutrients (proteins and fats), data are available for income quintiles only.

The relationships are shown in Figure 3.4 (also shown is the graph for calories by quintiles, for

comparison sake). The graphs show that for all three nutrients, consumption levels increase with

income or TCE. For proteins, the level for Q5 (95 gm) is 72% higher than that for Q1, for fats

this is three times (over 200%) but the gap is lower for calories (55%). This indicates that the

demand for fats is most sensitive to income while this is weakest for calories. The data also show

that the relationships, while being positive, are not linear. Thus, for calories and proteins, intakes

increase by lower rates while moving from Q2 to Q4, while the relationship is linear for fats until

Q4. The intakes of all three nutrients surge from Q4 to Q5. There is a limit to explaining the

variation in nutrient intakes by income alone as other factors also play a role. One requires a

multiple regression approach to understand the relationship between nutrient intakes and income

while controlling the effects of a host of other socio-economic explanatory variables. This could

be part of a follow-up work in this area.

The NLSS III also provides data on nutrient intakes by rural and urban households and

development region. By place of residence, the differences are small for calories and protein

(differences of 50 kcal and 0.3 gm, respectively) but larger for fats (difference of 13 gm). By

1,500

2,000

2,500

3,000

3,500

4,000

4,500

D 1 D 2 D 3 D 4 D 5 D 6 D 7 D 8 D 9 D 10

Calories (kcal)

Urban households

Rural households

24

Figure 3.4: Variation in intakes of proteins, fats and calories by TCE quintiles, 2010/11

Source: Based on NLSS III data extracted with Adept-FSM.

development region, there are marked variations in calorie intake levels. Compared to the highest

level for the central region (2,813 kcal), intakes are lower by 47 kcal for the eastern region, by

158 kcal for the western, 205 kcal for the mid-western and 222 kcal for the far- western region.

For proteins too, intake is highest for the central region and lower by 2-6 gms for the other

regions.

The NPC/CBS/WFP (2013) study provides additional information on this subject based on the

NLSS data. It notes that compared to 2003/04, households in the poorer quintiles were spending

lower shares of their incomeon food in 2010/11, which is an improvement, but, on the other

hand, the rich were found to be spending a larger share of TPE on foods than in 2003/04. For the

latter, the reasons could presumably be the rich consuming more expensive foods than in

2003/04 and the marked rises in food prices since 2007.

3.3 Food consumption pattern by major food sub-groups

The NLSS III provides detailed statistics on household food consumption pattern. Its survey

questionnaire on food consumption included about 70 individual food items which can also be

aggregated into several sub-groups of foods. Figure 3.5 shows consumption pattern for nine such

sub-groups, measured in terms of food expenditure shares.5 The figure shows that cereals sub-

group accounts for 33% of the total food expenditure in the rural and 23% in urban areas, with

the remaining 67% and 77% being spent on eight other sub-groups. Among non-cereals, meat,

fish and eggs as a group stands out (with about 16% share), followed by vegetables (10%) and a

miscellaneous category that includes meals away from home and beverages (10% share in rural

and 16% in urban area). Expenditure shares differ markedly for the rural and urban households

mainly for two food sub-groups, cereals (10 percentage points higher in rural area) and meals

away from home and others (6 percentage points lower in rural area). For the rest, the differences

are small.

5 Food consumption shares for these nine sub-groups were also aggregated in terms of food energy or calories; the

differences with expenditure shares in monetary metric are noted briefly below.

2,000

2,500

3,000

3,500

Q 1 Q 2 Q 3 Q 4 Q 5

Calories (kcal/capita/day) by

TCE quntiles

20

40

60

80

100

Q 1 Q 2 Q 3 Q 4 Q 5

Proteins and fats by TCE quntiles

(gm/capita/day), 2010/11

Proteins/capita/day

Fats/capita/day

25

Figure 3.5: Household food expenditure patterns in rural and urban areas, 2010-11

(percentage share in total food expenditure)

Source: Based on NLSS III data extracted with Adept-FSM.

Not having similar data for the previous surveys (the NLSS of 1995/96 and 2003/04) is a

handicap for a thorough analysis of the changing consumption patterns in Nepal. However, the

NPC/CBS/WFP (2013) study provides some analysis comparing food consumption patterns in

NLSS II and NLSS III. It notes that households in 2010/11 are consuming a much greater

quantity of protein and micronutrient rich food items compared to that in 2003/04, e.g.

consumption has tripled for vegetables, more than doubled for meat and fish, increased by 40%

for potatoes, 33% for beans and lentil and over 50% for fruits, oil/ghee and eggs. Overall, the

increases in the consumption of meats, fish, and poultry have been significant in the seven years

since the 2003/04 survey. In rural areas, the largest increases have been in the consumption of

chickens (an increase of 180% since 2003/04) and fish (an increase of 105% since 2003/04). The

consumption of potatoes, considered as a staple in that report, also increased significantly since

2003/04. The most dramatic increase in potato consumption has been in the Mountain areas,

where average per capita consumption increased by 38% since 2003/04.

Both for making up for the lack of data on consumption pattern from the previous NLSS and for

having a comparative perspective, similar data were reviewed for Bangladesh and India, both for

a recent period and some years back. The results are shown in Table 3.1. How are consumption

patterns changing in Bangladesh and India over the past 6-7 years and how do they compare with

Nepal?

Comparing Nepal with India for 2011, marked differences may be noted for 4 of the 9 sub-

groups. First, for cereals, Nepal‘s expenditure shares are higher by 13 percentage points for rural

and 7 percentage points for urban households. This could reflect both Nepal‘s lower per capita

income and higher prices of non-cereal foods. Second, expenditure on milk and milk products is

markedly higher in India. Third, in contrast, expenditure share for meats is higher in Nepal by 6-

8 percentage points. Fourth, the share of meals away from home and beverages is higher in India

0

5

10

15

20

25

30

35% share of expenses on food sub-groups, 2010-11

Rural

Urban

26

Table 3.1: Food consumption patterns in Bangladesh, India and Nepal

(food expenditure shares, %)

Source: NLSS III for Nepal, 2010 survey for Bangladesh (GoB 2010) and results of the 68

th NSS Round for India

(GoI 2014).

by 5-6 percentage points. For the rest of the sub-groups, the differences are small, perhaps with

some exception for higher shares for vegetables in India.

Comparing Nepal‘s consumption pattern in 2010-11 with India‘s in 2004-05, it is striking that

Nepal‘s expenditure share in cereals is identical to India‘s (33% for rural and 24% for urban).

Other differences are smaller. Thus it seems that on the whole Nepal‘s 2010-11 consumption

patterns resemble India‘s in 2004-05. In contrast, Nepal‘s consumption pattern looks more

advanced than Bangladesh‘s, even with the 2004-05 patterns, in that the share of cereals in the

total is lower for Nepal. In contrast, the share of expenditure on meats and fish is much higher in

Bangladesh (of the total, about half is fish).

It is more often the income than the place of residence or taste that plays a dominant role in

determining consumption patterns. So in closing this sub-section on consumption patterns,

Figure 3.6 shows variations for the aggregates of cereals and non-cereals by income level or

TCE. It shows strong negative relationship between TCE and expenditures on cereals while the

relationship is positive for TCE and expenditures on non-cereals. The spread between Q1 and Q5

is striking - a difference of 28 percentage points, with the richest 20% households spending 81%

of their total food expenditure on non-cereals while the poorest 20% spending only 53%.

3.4 Variations in the cost of food nutrients

Information on variations in the unit cost of food nutrients by various categories of households

can be useful for policy formulation as well as for food and nutrition security interventions. The

sources of the variation could be differences in market prices themselves, e.g. between the urban

and rural areas, or the extent of the dependency on own production versus market. The NLSS III

Food sub-groups Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban

Cereals 33 24 20 16 42 31 40 28 33 23

Pulses 6 5 6 5 2 3 2 3 6 6

Milk and milk products 15 19 15 16 3 4 3 4 8 9

Edible oils 8 8 7 6 4 5 4 5 7 7

Meat, fish, egg 6 6 9 9 19 25 21 30 15 17

Vegetables 11 11 13 11 8 8 8 7 10 10

Fruits 3 5 5 8 3 4 3 5 3 5

Sugar, salt, spices 9 8 10 8 9 10 12 10 8 8

Beverages, away meal 8 15 15 21 9 9 7 8 10 16

Share - cereals 33 24 20 16 42 31 40 28 33 23

Share - non-cereals 67 76 80 84 58 69 60 72 67 77

Total all foods 100 100 100 100 100 100 100 100 100 100

India 2004-05 India 2011-12 Nepal 2010-11Bangladesh, 2005 Bangladesh, 2010

27

Figure 3.6: Variation in expenditures on cereals and non-cereals by income levels

Source: Based on NLSS III data extracted by Adept-FSM.

provides statistics for computing the unit cost of macro nutrients (or ―prices‖) as paid or

consumed by various categories of households. The unit costs were computed as follows. The

NLSS data provides for each food sub-group household consumption expenditures (e.g. Rs

15/capita/day for cereals) as well as total food nutrient derived from that sub-group (e.g. 1,351

kcal from cereals). Dividing the expenditure by nutrient level gives the unit cost of the nutrient,

e.g. Rs 1.10 for 100 kcal.

Table 3.2 presents statistics on unit cost of macro-nutrients obtained from various sub-groups of

foods by various categories of households. The data show that the cost of the nutrients is higher

for the urban than for rural households for almost all food sub-groups, the three exceptions being

sugar (for protein) and sugar and meats (for fats). The differences are often marked. For example

for nutrients from cereals, the differences are 49% for calories, 55% for protein and 82% for fats.

For non-cereals, the differences are 64%, 53% and 39% respectively for the three nutrients. As

for individual food sub-groups, urban households pay 25% or more price for calories from fruits,

spices and meals away from home, for proteins from vegetables, fruits, spices and meal away

from home, and for proteins from cereals and fruits. For the rest, price differences are below

25%.

The lower part of the table shows the same information for households belonging to the poorest

(Q1) and richest (Q2) quintiles. The differences are larger than were the case for the rural-urban

divide, as shown by the cost for the two food aggregates (cereals and non-cereals – last rows in

the table). For nutrients derived from cereals, the differences by quintiles are 64%, 53% and 39%

for calories, protein and fats respectively. Among individual food sub-groups, the cost for the

richest households exceeds that for the poorest for 6 of the 10 food sub-groups in the case of

calories (cereals, vegetables, meats, fruits, spices and meal away from home), for 5 of the 10

food sub-groups for proteins (cereals, meats, fruits, spices and meal away from home), but only

for 2 of the 10 food sub-groups for fats (cereals and fruits).

That the rich pay substantially more for the same quantity of the nutrients is well established for

other countries as well. This is due to both the differences in consumption patterns – the rich

deriving their nutrients from more expensive foods – and the place of residence. For example, a

0

20

40

60

80

100

Q 1 Q 2 Q 3 Q 4 Q 5 Urban Rural Nepal

Expenditures on cereals and non-cereals (% of food total)

Cereals Non-cereals

28

Table 3.2: Cost of nutrients from various food sub-groups by place of residence and income

Source: Based on data from NLSS III as extracted with Adept-FSM.

majority of the Q5 households reside in urban areas where nutrient costs are higher, as noted

earlier. Similar statistics for households in the Q 2 to Q4 quintiles are given in Annex 3.2, which

show a progression of costs from Q1 to Q4. Much of the progression should be due to pure

income effect, i.e. the more expensive consumption pattern associated with income, while the

rest – presumably of lesser significance – should be the result of the place of residence (rural and

urban) and so has to do with price differential as well. Overall, Nepal‘s experience seems to be

consistent with that of other countries at similar level of development, which is that the trend in

dietary diversity is associated with rising cost of nutrients.

3.5 Extent of the reliance on own food production versus market

The NLSS III also provides data on the sources of the food consumed. Four such sources were

identified - own production, market purchases, food consumed away from home and other

I. Cost of macro-nutrient by urban-rural groups

Urban Rural % Diff. Urban Rural % Diff. Urban Rural % Diff.

Cereals 1.1 0.9 20 0.5 0.4 24 3.6 2.5 47

Pulses 3.1 2.9 7 0.4 0.4 10 6.2 5.4 15

Vegetables 5.9 4.8 23 1.5 1.2 25 9.6 7.8 22

Meat, fish, eggs 14.9 14.6 2 1.4 1.3 6 2.5 2.6 -4

Milk and products 4.6 3.7 23 1.0 0.8 22 0.7 0.6 24

Fruits 7.6 5.8 33 4.9 3.3 47 4.2 2.3 81

Ghee/Oil and Fat 1.9 1.9 0 - - - 0.2 0.2 0

Spices and salt 2.6 1.9 34 0.9 0.7 30 1.2 1.2 2

Sugars and sweet 2.4 2.1 15 6.4 6.8 -7 1.7 1.8 -7

Meals away fr home 2.6 1.9 39 0.9 0.7 35 1.1 1.1 2

Cereals 1.1 0.9 20 0.5 0.4 24 3.6 2.5 47

All non-cereals 3.8 3.2 18 1.2 1.0 20 1.0 0.9 12

All foods 2.5 1.8 39 0.9 0.7 36 1.1 1.1 6

II. Cost of macro-nutrient by income quintiles (Q1 and Q5)

Q 1 Q 5 % Diff. Q 1 Q 5 % Diff. Q 1 Q 5 % Diff.

Cereals 0.8 1.2 49 0.3 0.5 55 2.0 3.7 82

Pulses 2.8 3.1 11 0.4 0.4 15 5.3 5.8 8

Vegetables 4.2 6.1 45 1.1 1.5 39 7.2 9.7 35

Meat, fish, eggs 11.8 16.8 42 1.0 1.5 54 2.3 2.8 24

Milk and products 3.4 4.3 26 0.8 0.9 20 0.5 0.7 25

Fruits 4.5 7.6 67 2.7 4.7 76 2.3 3.5 54

Ghee/Oil and Fat 1.6 2.1 31 - - - 0.1 0.2 31

Spices and salt 1.4 2.9 101 0.6 1.0 80 1.1 1.2 2

Sugars and sweet 1.9 2.5 26 5.7 6.4 13 1.5 1.7 12

Meals away fr home 1.3 2.7 116 0.5 1.0 96 1.0 1.1 12

Cereals 0.8 1.2 49 0.3 0.5 55 2.0 3.7 82

All non-cereals 2.5 4.0 64 0.8 1.2 53 0.7 1.0 39

All foods 1.3 2.8 122 0.5 1.0 100 1.0 1.1 12

Kcal: Rs per 100 kcal Protein: Rs per gram Fats: Rs per gram

Kcal: Rs per 100 kcal Protein: Rs per gram Fats: Rs per gram

29

sources (e.g. in-kind acquisitions). As pointed to in the NPC/CBS/WFP (2013) study,

understanding the source of food consumption helps determine which households in what region

are most reliant on market purchases and which ones are self-sufficient. This helps to bring the

topics of production shocks as well as trade and markets into the discussion of food and nutrition

security, notably in identifying households and regions that are most vulnerable to market and

production shocks.

Figure 3.7 shows that 51% of the value of total food consumed by households in Nepal was from

markets, followed by own production (31%), food away from home (15%) and other sources

(3%). As expected, households in urban areas acquire substantially more food (66%) from

markets than in rural areas (46%), where 37% of the foods come from own production. What

seems to be a somewhat surprising result is the fairly high expenditure share on foods consumed

away from home, even for rural households (14%). As most foods consumed away from home

are also from markets, the total share of the markets (adding both from markets and food away

from home) should be as high as 86% for urban and 60% for rural areas, and 66% for Nepal as a

whole. This is an important evidence for policy makers as this means that markets and trade

aspects should receive high prominence in government policies and programmes on food

security and agricultural growth.

Figure 3.7: Sources of food consumption, 2010-11

Note: The shares are computed based on the market value of the food commodities. An alternative way to compute

the same would be in terms of food nutrients, e.g. calorie. This was also reviewed as part of the study.

Source: NLSS III data as extracted with the Adept module.

The NLSS data on the sources of food can also be tabulated by consumption expenditure. This

helps assess the relationship between expenditure level and market dependency, among other

things. Figure 3.8 shows this information for the 10 expenditure deciles. The left panel shows,

for rural households, a fairly flat share from markets until D5 after which the importance of the

markets rises. For urban households, the shares essentially fluctuate in the 60-70% range for all

expenditure classes, i.e. the shares do not vary with income levels. The right panel shows that for

rural households the importance of own production wanes from around D7, i.e. own production

0

10

20

30

40

50

60

70

Urban Rural Nepal

Sources of food consumption (%), 2010-11

Purchased Own production Away from home Other sources

30

Figure 3.8: Sources of food from markets and own production by income decile, 2010-11

Source: NLSS III data as extracted with the Adept module.

becomes less important, and markets more, from around the middle of the income ranges (the

shares of own production average 41% for D1-D3 and 29% for D8-D10). Thus, as said above,

the state of markets and trade is of paramount importance for food security in Nepal, even in

rural areas and for both the poor and rich.

The NPC/CBS/WFP (2013) study provides additional insights on this topic. Correlating the

sources of food with the prevalence of hunger, it notes that the regions most reliant on their own

production, in both the absolute and relative terms, tend to be the most food insecure, including

the Mountains, Eastern Rural Hills, Mid and Far Western Rural Hills, Western Rural Tarai and

Mid and Far Western Rural Tarai. The study further remarks that this finding contradicts a

commonly held perception that food insecure regions in Nepal generally produce less and

therefore consume less of their own production than in other areas. Instead, the difference in the

overall food adequacy among regions is largely a result of households in more food secure

regions purchasing more food and more diverse foods than those in less food secure areas where

poor market access means households fail to supplement their diets with purchased food items to

the same extent as households in other areas. This study also documents that across Nepal,

households are most reliant on self-production of milk, staples, vegetables and pulses while

usually purchasing sugars, oils, and meats.

3.6 Variation in market price of foodstuffs

The NLSS III also provides statistics for comparing the price of foodstuffs paid by households

living in various regions and belonging to different income levels. Figure 3.8 above had shown

that even rural households, including those in the poorest income deciles, acquired over 40% of

their foods from the market. The Nepal Rastra Bank data on food prices show that during

2005/06-2011/11, food prices (the food index that includes all foods) increased by 82% in the

hills and 77% in the Tarai (average inflation rate of about 15% per year), with larger increases

for vegetables, sugar, spices and meats. It is interesting to note that Nepal made good progress in

poverty reduction during 2003/04 and 2010/11 (NLSS II and NLSS III years) despite the fact

that food prices surged in this period; obviously, progress would have been better if not for the

high food prices.

40

50

60

70

80

D 1 D 2 D 3 D 4 D 5 D 6 D 7 D 8 D 9 D 10

From markets (%)

Urban households Rural households

0

10

20

30

40

50

D 1 D 2 D 3 D 4 D 5 D 6 D 7 D 8 D 9 D 10

From own production (%)

Urban households Rural households

31

Figure 3.9 shows the prices of selected food products facing the urban and rural households. Of

the 21 food prices shown in the figure, prices in urban areas were higher than in rural areas for

all except two products, mustard oil and chicken. This result is consistent with the argument that

as foods are produced in rural prices, prices there should be lower than in the urban markets. A

counter argument would be that for many food items the main source is imports and so prices in

rural areas should be higher because of the cost of transport and marketing. In any case,

explaining the reasons for the price gaps goes beyond the scope of this study and the NLSS III

data themselves do not help explain the gaps. While prices in urban areas are higher, the gaps are

mostly small. Price gaps are 10% or more only for the following products: fine rice, maize, milk,

mutton, buffalo meat and gur.

Figure 3.9: Food prices paid by urban and rural households (Rs/kg)

Source: Source: NLSS III data as extracted with the Adept module.

3.7 Indicators of malnutrition of children

In addition to the general household survey, the NLSS III also collected anthropometric data

from approximately 2,500 preschool under-5 children. Thus the survey provided a range of

indicators of malnutrition for under-5 children. The most prominent indicators are stunting

(height-for-age), wasting (weight-for-height) and underweight (weight-for-age). Note that these

three indicators were also reviewed in Section 2.3, assessing the progress made over time. This

0

20

40

60

80

100

120

140

Fine rice Coarserice

Maize Wheatflour

Blackgram

Lentil Red gram Horsegram

Greengram,

Beans

Food prices - cereals and pulses (Rs/kg)

Urban areas

Rural areas

0

100

200

300

400

500

600

Milk Ghee Mustardoil

Potatoes Onions Fish Mutton Buffalomeat

Chicken Sugar Gur(Sakhar)

Food prices - other foods (Rs/kg)

Urban areas

Rural areas

32

sub-section complements that analysis by presenting a cross-sectional picture for the year

2010/11 based exclusively on the NLSS III data. Table 3.3 presents the status on stunting,

wasting and underweight.

Table 3.3: Prevalence rates of stunting, underweight and wasting among under-5 children,

2010/11

Note: The first columns (e.g. underweight) indicate the proportion of children whose indicator (e.g. weight-for-age)

falls below -2 S.D. from the median of the reference population and the second columns (e.g. severe underweight)

for below - 3 S.D.

Source: Table 19.1, 19.2 and 19.3 of Volume II report of CBS‘s analysis of the NLSS III data, CBS (2011).

The NLSS III data had shown that the variations in food energy intake were fairly narrow across

the analytical domains, e.g. a difference of only 50 kcal between the urban and rural areas. In

contrast, the under-5 indicators seem to vary markedly across the analytical domains, including

for income levels. Stunting and underweight rates are much higher for rural than for urban areas

although the difference is small for wasting. The stunting and severe stunting rates are more

pronounced in the mountains (56%) compared to the hills and Tarai regions (42% and 39%

respectively). The data (not shown in the table) also show stunting rates increasing sharply by

age - from 13% among under-1 children to 42% among those between 12 and 23 months, and

about 50% among those between 2 to 3 years. Stunting is also slightly higher among females

than males (43% and 40%). Even in Kathmandu valley, where the food deficits are low, stunting

is 19%.

Severe Under- Severe Severe

Stunting stunting weight underwt. Wasting wasting

------------- % of children fallinng below standard norms -------------

Ecological zone

Mountains 56.0 20.2 37.3 7.2 9.0 1.1

Hills 42.3 15.3 25.2 5.0 6.9 1.5

Tarai 38.6 13.9 35.2 10.1 20.4 5.0

Kathmandu valley 19.3 5.1 9.0 2.8 6.6 2.4

Urban/Rural

Urban 27.9 8.3 18.9 6.5 11.0 3.6

Rural 43.8 16.1 33.1 7.9 14.2 3.1

Consumption Quintiles

Poorest 51.9 22.3 40.8 13.0 17.5 4.4

Second 46.1 16.9 33.8 7.5 12.4 2.3

Third 39.7 13.0 30.6 7.3 13.3 3.0

Fourth 32.3 8.2 21.6 2.1 12.5 3.6

Richest 21.5 5.3 14.7 3.1 9.4 1.7

Nepal 41.5 15.0 31.1 7.7 13.7 3.2

33

Underweight is also markedly higher in rural areas (33% versus 19% in urban areas). But unlike

stunting, underweight prevalence is much lower in the hills but fairly similar for both the

mountains and Tarai. As with stunting, the rates increase sharply by age – from 17% among

under-1 year children to 32% for those in 24-35 months group and 38% for 48-59 months age

group. The rates are higher for females than for males. As regards wasting, the rate is 14% for

Nepal, with 3% as severely wasted. Although the gap between the rural and urban areas is small,

the Tarai region stands out with 20% prevalence rate, which is twice the rate in the Mountains

and three times that in the hills. The highest prevalence rate is in rural central Tarai and the

lowest in the urban hills.

Under-5 malnutrition indicators are strongly correlated with income. The prevalence rate for the

first quintile (poorest) is over two times the rate for the fifth in the case of stunting, almost 3

times for underweight and about 2 times for wasting. The gaps are even wider (about 4 times) for

severe stunting and severe underweight and close to 3 times for wasting, as the burden of acute

undernutrition magnifies among the poorest households. Besides income and food availability,

under-5 malnutrition indicators are also influenced by other factors. For this reason, one finds

higher prevalence rates for these indicators even in regions that do not appear to suffer from food

availability.

3.8 Severity of food insecurity based on Integrated Food Security Phase

Classification

The Integrated Food Security Phase Classification (IPC) is a global, multi-partner initiative to

inform food security policy and programming. It promotes a common approach for classifying

severity and underlying factors of both acute and chronic food insecurity, thus improving the

rigour, transparency, relevance, and comparability of food security analysis for decision makers.

The IPC provides information at the sub-regional and lower levels and therefore is a valuable

supplement to the cross-sectional assessment of this section based on NLSS III data. The IPC has

undertaken an assessment for Nepal as recently as December 2014 (IPC 2014). This sub-section

highlights the key findings.

Table 3.4 summarizes the prevalence rates for Chronic Food Insecurity (CFI) for 13 sub-regions,

as well as information on under-5 mortality rates and number of years with acute food insecurity

crisis in the region. Under the IPC classification system, CFI is classified into four levels in term

of the severity of the problem: Level 1 - no CFI; Level 2 – Mild CFI; Level 3 – Moderate CFI;

and Level 4 – Severe CFI. Hence, the sum of the prevalence rates for Levels 2 to 4 provides the

total prevalence rate for the CFI (as Level 1 implies no CFI). The under-5 mortality rates in the

table are from the Nepal Demographic Health Survey (NDHS) 2011; as this data was available

only at the regional level, the same numbers were used for all sub-regions within a given region.

The number of years of IPC acute phase 3 or higher is based on Acute Analyses previously

conducted in Nepal (NeKSAP).

34

Table 3.4: Food insecurity prevalence rates for 13 sub-regions as per the IPC classification,

2014

Source: IPC study for Nepal (IPC 2014). Note that the prevalence rate for CFI as a whole would be the sum of the

prevalence rates for Levels 2 to 4 as Level 1 implies no CFI (thus for Nepal as a whole, total CFI is 54%).

The last row of the table shows that about half (54%) of the population in Nepal faced CFI in

2014 (34% mild CFI, 13% moderate and 7% severe). Using 10% or more as an arbitrary cut-off

point for level 4 (severe CFI) prevalence rates, four sub-regions stand out: Eastern Mountain,

Far-western and Mid-Western Hills, and Western Mountain. Similarly, using 20% or more as the

cut-off point for level 3 (moderate CFI), four sub-regions stand out: Eastern Mountain, Central

Mountain, Far-western Hill and Western Mountain. Western Mountain stands out on all

indicators. Besides the CFI levels, this region also suffers from high rates of chronic malnutrition

with 29% of the under-5 children moderately stunted and 27% severely stunted. The analysis by

NeKSAP had also found the Western Mountain sub-region to have frequent recurrence of acute

crises in the past decade.

The IPC study also provides recommendations for responses. Highlights include the following

(the study provides response measures by sub-region). For vulnerable households living in the

Western Mountains, immediate interventions are required. The IPC study also recommends the

provision of safety net programmes for this region. For the 8 sub-regions facing high Level 2

prevalence rates, the study found that the CFI was reflected in terms of poor quality of household

food consumption and nutritional status. Even sub-regions/areas with fairly high production of

food crops and livestock, e.g. Mid-Western Tarai, were found to have relatively high chronic

malnutrition rates. Appropriate interventions suggested include the promotion of behavioural

change, focusing on food and nutrition and improved Infant and Young Child Feeding practices.

The areas found to be in mild CFI (level 2) tend to have adequate food availability and food

access but are affected by poor nutrition, as reflected in relatively high stunting prevalence.

Areas with level 3 were found to have problems in food access and utilization and those in level

4 were limited in food availability, access, utilization and stability related factors.

# years of

Total Under-5 Phase 3 or

Sub-region population Level 1 Level 2 Level 3 Level 4 mortality rate higher in

in 000 % % % % % 10 years

Central hill 4,894 48 35 13 5 60 <3

Central mountain 512 38 35 20 8 60 <3

Central Tarai 5,056 45 38 10 8 60 <3

Eastern hill 1,621 50 33 13 5 55 <3

Eastern mountain 393 25 35 28 13 55 <3

Eastern Tarai 4,000 55 28 10 8 55 <3

Far-western hill 885 33 33 22 13 82 <3

Far-western Tarai 1,298 45 38 13 5 82 <3

Mid-western hill 1,801 35 35 18 13 73 <3

Mid-western Tarai 1,571 50 31 14 5 73 <3

Western hill 2,857 45 36 13 7 57 <3

Western mountain 937 30 25 25 20 73 3

Western Tarai 2,218 55 30 10 5 57 <3

Total 28,045 46 33 13 7 - -

% of population falling under Levels 1 to 4 of

Chronic Food Insecurity (CFI)

35

It was also determined that in all sub-regions, food insecurity is also associated with inadequate

food utilization, which needs to be addressed through interventions such as inducing behavioral

change, improved food storage and access to improved cooking fuel. Food insecurity is also

associated with seasonality, with March-May and August-October being the periods during

which households in level 3 and level 4 face the greatest challenges in terms of both quantity and

quality of food. Social factors, including traditional practices, the caste system, taboos and

gender discrimination also have to be looked into when implementing food security

interventions. Finally, it is suggested that a food and nutrition security policy should be

formulated, institutionalized and operationalised as soon as possible.

3.9 Summary of the main findings

This section of the status report was fully based on statistics from the NLSS III as extracted with

the Adept-FSM. Thus it supplements the assessment of trends in Section II by providing a

picture for 2010/11 of the cross-sectional variations across the geographic space and income

levels. The various themes assessed included variations in total consumption expenditure (TCE),

also a proxy for income, food consumption patterns in the aggregate and by major sub-groups,

cost of macro-nutrients by type of food, sources of food and the reliance on markets versus own

production. Also highlighted were the main results of the 2014 assessment of chronic food

insecurity undertaken with the IPC tools.

The data show that the share of TCE on foods, also called the Engle ratio, behaves as predicted

by theory, i.e. the ratios decline with rising income. The ratio was 49% for the central region of

Nepal and around 60% for other regions – but 72% for the poorest 10% population. The rate of

decline of the ratio with rising incomes was found to be slower for the rural than for the urban

households. One study that compared the Engle ratios for 2003/04 and 2010/11 had found that

the ratio barely changed over the seven years.

The relationship between the levels of food energy intake and TCE is positive, as expected, but

the relationship is non-linear, with the implied elasticity of 0.19 for urban households and 0.34

for rural households. The difference in the sources of the calories obtained from different foods

should partly explain the different relationships. The demand for fats was found to be most

sensitive to changes in the TCE, followed by protein and lowest for calories.

The data on expenditure pattern on major food sub-groups show that cereals account for 33% of

the total food expenditure in the rural and 23% in the urban areas, the rest being non-cereal

foods. Among non-cereals, meat, fish and eggs as a group stands out in expenditure, followed by

vegetables and meals away from home and beverages. Between the rural and urban households,

expenditure shares differ markedly for cereals and meals away from home, while for the rest of

the foods the differences are small.

36

Comparing Nepal‘s consumption pattern in 2010-11 with India‘s in 2004-05, it is striking that

Nepal‘s expenditure share in cereals in 2010/11 is identical to India‘s in 2004/05 (33% for rural

and 24% for urban). Other differences are smaller. On the other hand, Nepal‘s consumption

pattern looks superior to Bangladesh‘s, even with the 2004-05 pattern, in that the share of the

cereals in the total is lower for Nepal. Variations on expenditure shares on cereals and non-

cereals are strongly associated with income level, both in the rural and urban areas. The spread

between the poorest 20% and the richest 20% of households is large and striking.

Not having similar data from the previous NLSS survey (NLSS II of 2003/04) limited the

analysis of the household survey data because having data sets for two surveys would have

enabled identifying changes over time, and also the likely drivers of the change. There is a merit

in following up this work with a comparative analysis based on data from the two surveys.

The unit cost of macro-nutrients was found to be higher for the urban than rural households for

almost all food sub-groups, with the differences often being marked, e.g. the cost of nutrients

from non-cereals varied by as much as 64%, 53% and 39% for calories, protein and fats

respectively. The differences are even higher for households belonging to different income

groups, with the rich paying more as their food nutrients come from expensive foods. Overall,

Nepal‘s experience seems to be consistent with that of other countries at similar level of

development, which is that the trend in dietary diversity is associated with rising cost of

nutrients.

Adding together the cost of foods from markets and away from home, the total share of foods

from markets turn out to be as high as 86% for urban and 60% for rural areas, and 66% for Nepal

as a whole (the rest is from own production). This implies that efficient markets and trade is

crucial for food security in Nepal. Markets are important even for lower income households in

rural areas.

The data on under-5 malnutrition indicators (stunting, wasting and underweight) show that

despite good progress made on food availability and intake, the situation on malnutrition of the

children is serious, with high levels of stunting, wasting and underweight. The prevalence rates

of these indicators are also strongly correlated with income, which means that the rates are very

high for households belonging to lower income brackets.

Finally, the assessment of the situation on Chronic Food Insecurity (CFI) using the tools of the

Integrated Food Security Phase Classification (IPC) show that about half (54%) of Nepal‘s

population faced CFI in 2014 (34% mild CFI, 13% moderate and 7% severe). The situation was

most severe in the Western Mountain region, but also the Eastern Mountain, Far-western and

Mid-Western Hills stand out on some indicators. This analysis also points to the importance of

addressing the poor state of caring, health and hygiene, education etc., besides food.

37

IV. SUMMARY AND CONCLUDING REMARKS

The Project background

This Status Report is one of the three main outputs of the Nepal component of the FAO regional

project on building capacity for generating and analyzing quality food and nutrition security

(FNS) information. The project was also simultaneously implemented in Bangladesh, Lao PDR,

Myanmar, the Philippines and Timor-Leste. The project focussed its capacity building activities on

two statistical frameworks that are valuable for a FNS information system – i) Supply Utilization

Accounts/Food Balance Sheets (SUA/FBS); and ii) national household budget surveys.

Accordingly, two of the three main outputs of the project were: i) fully updated SUA/FBS for

Nepal for six years, 2008 to 2013, using FAO harmonized methodological framework; and ii)

extraction of food and nutrition statistics from the Nepal Living Standards Survey 2010-11

(NLSS III) using ADePT food security module. The third output of the project is this Status

Report, which presents analyses based on these and other information.

Undertaking all these activities involved considerable statistical and analytical works. Many

national officials participated in training and workshops as well as learning-by-doing sessions,

which were the core activities provisioned by the Project for building national capacity. Annex

4.1 highlights these activities on capacity building.

This Report was split into two sections for the following reason. The analyses involved two

categories of statistics and coverage of the issues. One was the use of mainly time-series data to

document the evolution of food security over time and along the four dimensions of food

security, namely availability, access, utilization and stability. The second was an analysis of the

cross-sectional variations in food insecurity in Nepal using the NLSS III data. Because the

analyses entailed these two different approaches and statistics, it was considered prudent to split

the Report into two sections, although, as was seen, they are very much complementary.

Main findings

A summary of the main findings of the analyses in Section II was presented at the end of that

section and this summary could be succinct. That analysis looked into progress along the four

dimensions of food security and was focussed on understanding the evolution of the food

security status over time to recent periods. One major finding on food availability was that the

new SUA/FBS compiled for 2008-13 show higher levels of aggregate national food supplies

(food energy, proteins, fats etc.) than those in the FAO database. As a result, the prevalence of

hunger based on the FBS is much lower, which is good news for Nepal. Discrepancies in

statistics, assumptions etc. between the two sources were identified and steps already taken to

make necessary corrections in the FAO database. One source of the discrepancy was the use of

different population numbers but there were others also such as differences in production levels,

coverage of food products, coverage and accuracy of the trade data, assumptions on various uses

of food, conversion rates, and so on.

38

An analysis of trends in per capita cereal production during 2000-13 showed positive growth

rates for cereals as a whole despite being negative for paddy which was more than offset by

maize and wheat. For paddy too, production has picked up since around 2008. Overall, the data

show Nepal doing well in improving food availability, including relative to some other countries

in South Asia. It also appears that this increased availability is being associated with rising food

import dependency, despite the growth in food production. The supply response is simply

inadequate to meet the surging food demand. The analysis also showed that despite increasing

volatility in cereal/food production, the volatility of the overall food availability has not

increased. This means that imports fill up the shortfalls quickly and efficiently.

The one area where the overall situation is not as good is with the indicators of utilization,

notably under-5 malnutrition (stunting, wasting and underweight). Nepal is considered to have

relatively high prevalence rates among countries at similar levels of development, despite the

good progress being made at reducing these rates in the 2000s, especially since 2006. Some

regions of Nepal suffer from disproportionately higher prevalence rates.

Section III supplemented the analysis of the four dimensions of food security by providing a

cross-sectional picture of household food security for 2010/11, the NLSS III survey year. Most

analyses in Section III were focused on variations in food consumption patterns and nutritional

outcomes across geographical regions and income levels. Also highlighted were the results of the

2014 assessment of the chronic food insecurity undertaken with the Integrated Food Security

Phase Classification (IPC) methods and tools (IPC 2014).

A summary of the main results were presented at the end of Section III. It was observed that

while average national food supply was high, there were wide variations by income levels and, to

a lesser extent, geographical regions. For example, while the share of total expenditure on food

was 49% for the central region of Nepal, it was 72% for the poorest 10% of the population. But

income alone does not fully explain the variations. Food consumption patterns in terms of the

sources of the macro nutrients obtained from different foods vary across the regions and income

levels. Variations in expenditure shares on cereals and non-cereals are strongly associated with

income level, both in the rural and urban areas, with a large spread between the poorest 20% and

the richest 20% households.

The under-5 malnutrition indicators (prevalence rates for stunting, wasting and underweight)

showed that despite good progress being made on food availability, Nepal continues to suffer

from high levels of stunting, wasting and underweight. There is also a large disparity among

regions and income levels. This analysis, as well as on Chronic Food Insecurity using the IPC

tools, points to the importance of addressing the poor state of caring, health and hygiene,

education etc., besides food intake.

The analyses could have been more thorough had there been similar data set from a previous

NLSS survey (e.g. NLSS II of 2003/04). Having the data for two periods would have enabled to

39

document not only the changes over time but also to identify the drivers of the changes. Thus,

one area for a follow-up work would be using data from two rounds of NLSSs.

Concluding remarks

Food security is a broad concept with at least four dimensions of availability, access, utilization

and stability. These dimensions themselves are broad enough in terms of the indicators to

characterize them. For this reason, studies focussed on food security are often handicapped when

it comes to making recommendations because sufficient analysis on so many issues is simply not

feasible. The scope of the project under which this report has been prepared was largely limited

to capacity building in utilizing two statistical frameworks and in utilizing the data, and not to in-

depth analyses of the underlying FNS issues in Nepal. For this reason, it is not appropriate for

this report to try to offer a comprehensive set of recommendations on such diverse topics as the

four dimensions of food security.

A FBS is a crucial source of information for many FNS indicators and therefore it is important

that a regularly updated FBS is maintained and used for this and other purposes. The efforts made

under this project not only led to the compilation of fresh FBS for Nepal for six years (2008 to

2013) but also helped document a number of weaknesses on statistics used, including production

and trade, as well as parameters and assumptions (listed in Annex 2). Food imports from India are

playing an increasingly significant role in food supply (Sharma 2015) and therefore more attention

needs to be paid to incorporate trade data in the FBS, including informal trade (Karmacharya 2010

provides estimates for cross-border informal trade at the commodity level which could be useful

for fine-tuning the FBS). Indeed, the role of cross-border informal trade in accurately estimating

food availability in Nepal attracted considerable attention of participants during the final workshop

on sharing this Report, held on 28 December 2015 (the report of the workshop in Annex 4.1).

The value of household surveys is being increasingly recognized for food security analysis but

also in improving the accuracy of the national FBS. Several studies have been conducted on this

synergy and provide valuable suggestions (e.g. Jacobs and Sumner, 2002; FAO 2008; Njeru 2011;

Grünberger 2014; and Smith et al. 2014). For continuing this line of work in Nepal, three

suggestions could be made here. One, as said above, there is scope for further improving the FBS

using surveys – the above studies provided many valuable tips. Two, having disaggregated

statistics from a previous NLSS, namely NLSS II of 2003/04, would have considerably improved

the analysis on FNS, not only in documenting the changes over time but also in identifying the

drivers of the change, at disaggregated levels such as regions. So a follow up study could

consider undertaking that. Three, a comprehensive study for Nepal on the demand for foods as

well as projections into the future would be highly useful. The study should be based on statistics

from two household surveys (namely 2003/04 and 2010/11).

The data from the new FBS show that aggregate food supply (DES) in Nepal is already 2,922

kcal in 2013, which is the highest level among the five South Asian countries, as well as very

much on the higher side for a country at Nepal‘s level of development. The NLSS III data also

40

show higher level of the DES. This implies that the primary issue on food supply is not so much

the aggregate availability but the large gaps that exist across the regions and income levels. This

implies in turn a food production strategy that targets poorer areas and lower income households,

rather than a focus on high potential areas. Also, the ongoing shifts in food consumption pattern

show that the future demand growth will be in non-cereals. Both these indications have

implications for policies, notably in prioritizing (resource allocation) production programmes.

This study was not designed to analyse such fundamental issues and so what was said above are

not suggestions based on sound analysis but the results from this study do call for some

discussion on a food production strategy.

IFPRI-IIDS (2010) is an example of a study on FNS with some fresh analyses of a number of

related topics, including agricultural growth and food production. There are also other studies on

agricultural growth, including the Agricultural Development Strategy (ADS). Many

recommendations made in these and other studies on agriculture are similar. These include, for

example, the importance of expanding irrigation and increasing efficiency, raising productivity by

allocating more public resources to research rather than to subsidies on inputs and outputs,

strengthening rural infrastructures including markets and credit, expanding programmes on

adaptation to climate change, some form of a safety net programme targeted on the poorest

households and areas, and so on. In the same way, the IPC (2014) analysis of December 2014,

reviewed in Section III, contains many useful suggestions.

On under-5 malnutrition, World Bank (2012) is a comprehensive analysis with many

recommendations, while the Multi-sector Nutrition Plan (MSNP) is the GoN‘s main programme

for improving maternal and child nutrition and reducing chronic malnutrition (GoN 2011). In view

of its importance, Annex 4.2 presents a brief on the MSNP. On agriculture itself, the GoN has

formulated many sectoral and sub-sectoral policies, as well as legislations. For the sake of

completeness, Annex 4.3 provides a listing of agricultural policies, legislations and institutions

related to FNS, while Annex 4.4 is a brief on the approach paper to the 13th

Plan (FY 2013/14 to

2015/16) on food security and nutrition.

A final workshop was held in Kathmandu on 28 December for sharing the outputs of the Project. It

was attended by the project task force members who directly and indirectly supported the

implementation of the project as well as other government officials and members of the media.

Participants commented on the project activities and the results from various analyses and their

implications. Annex 4.5 presents the report of the workshop.

41

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44

Annex 2: A note of the discrepancy on aggregate food supplies in the FAO database and

the new SUA/FBS

The new SUA/FBS compiled for 2008-13 as part of the FAO project show higher levels of aggregate

national food supplies than estimated by the FAO. As a result, the new SUA/FBS gives markedly

lower prevalence rate of undernourishment. This is an important result and so deserves some

discussion on the sources of the discrepancy.

Figure A2 shows discrepancies in the levels of food availability in terms of three macro nutrients

while Table A2.1 presents related statistics. The graphs show that the new SUA/FBS gives higher

levels of food supplies for all three nutrients. For food energy, the difference between the two

estimates averages 297 kcal/capita/day for 2008-13, a difference of 12%. The discrepancy widens

every year from 282 kcal in 2008 to 384 kcal in 2013 (difference of 11 to 15%). The average

difference for 2008-13 is 5.7 gm/capita/day for protein (or 9%) and 7.5 gm/capita/day for fat (or

16%).

Figure A2: A comparison of the estimates of food supplies in the FAO database and the new

SUA/FBS

Source: FAO food security indicators and new SUA/FBS.

The12% difference in caloric supply leads to a marked difference in the estimated prevalence rates of

food insecurity. For example, assuming 1,724 kcal as the minimum and 2,200 as the adequate

requirement (see Section 2.1.2 for details), the standard FAO methodology gives the following

prevalence rates: 7.1% for undernourishment (the PoU) and 32% for food inadequacy (the PoFI) with

the lower DES in the FAO database (2,533 kcal), but only 2.7% of PoU and 18% of PoFI with the

higher DES (2,830) from the new SUA/FBS. The differences are large and potentially significant,

e.g. they could completely change conclusions about Nepal meeting or not meeting the MDG1 or

global rankings on a variety of socio-economic aspects.

Accounting for the discrepancy in the two estimates

What might explain the discrepancy in the estimated DES? One source of discrepancy is easily

identifiable. Table A2.2 shows the discrepancy in the population numbers used, higher population in

the FAO database and lower in the FBS. The CBS has released fresh estimates of Nepal‘s population

following the 2011 Census, including for the years prior to 2011. The SUA/FBS used these numbers.

Apparently, FAO did not update population numbers in its database. Assuming the same level of the

DES as in the FAO database, the use of the higher population (as in FAO database) reduces the DES

by 60 kcal/capita/day. In other words, the DES would have been higher by 60 kcal in the FAO

database if the correct (lower) population size was used. Table A2.1 shows that the discrepancy

2000

2200

2400

2600

2800

3000

2001 2003 2005 2007 2009 2011 2013

Dietary energy (kcal/capita/day)

FAO data

New FBS estimates

50

60

70

80

2001 2003 2005 2007 2009 2011 2013

Proteins (gm/capita/day)

FAO data

New FBS estimates

30

40

50

60

70

2001 2003 2005 2007 2009 2011 2013

Fats (gm/capita/day)

FAO data

New FBS estimates

45

between the two estimates amounted to 297 kcal during 2008-13, which means that 20% of the

difference between the FBS and FAO source was due to the population numbers used.

Table A2.1: A comparison of the estimates of food supplies in the FAO database and the new

SUA/FBS


Table A2.2: Population (in 000) used in the FAO estimates and the new SUA/FB


While 20% of the discrepancy was due to population, it is not easy to be as precise on the sources of

the other 80% discrepancy. A number of such sources were identified during the course of compiling

and updating the SUA/FBS. Highlights of these discrepancies and adjustments include the following.

For wheat and maize flour, the main difference was due to the extraction rate used (96% and

97%, instead of 80%).

For maize, the feed ratios used were different 15% instead of 20-30%.

The nutritional factors updated in the 2014 FAO workshop (organized under the FAO

project) were revised as these markedly impacted on total DES. A decision was made to use

the old factors. A further fine-tuning of the nutritional factors to be used in compiling the

Nepal SUA would be necessary.

2008 2009 2010 2011 2012 2013 Avgerage

kcal/capita/day

FAO database 2,490 2,522 2,553 2,547 2,546 2,538 2,533

New FBS estimates 2,772 2,698 2,837 2,855 2,894 2,922 2,830

Difference 282 176 284 308 348 384 297

% difference 11.3 7.0 11.1 12.1 13.7 15.1 11.7

Protein/capita/day

FAO database 63 64 65 n.a. n.a. n.a. 64

New FBS estimates 70 68 71 72 74 76 70

Difference 7.0 4.0 6.0 - - - 5.7

% difference 11.1 6.3 9.2 - - - 8.9

Fats/capita/day

FAO database 44 47 49 51 n.a. n.a. 48

New FBS estimates 52 51 60 58 59 60 55

Difference 8.0 4.0 11.0 7.0 - - 7.5

% difference 18.2 8.5 22.4 13.7 - - 15.7

2008 2009 2010 2011 2012 2013 Avgerage

FAO database 26,249 26,545 26,846 27,156 27,474 27,797 27,011

SUA/FBS 25,533 25,890 26,252 26,495 26,873 27,257 26,383

Difference 716 655 594 661 601 540 628

% difference 2.8 2.5 2.3 2.5 2.2 2.0 2.4

46

For all years covered (2008 to 2013), there is a need for adjusting stock variations (element

071) as the balancing element for paddy/rice and millet. These should be estimated as there

are no official data on stock variations.

Also, many cases were found where trade data were incomplete and product coverage

inadequate for some years. These include:

- Imports data for 2011 did not seem to be complete in coverage of products relative to

other years. In this regard, trade data were re-checked and confirmed for a number of

products, namely tomato juice concentrated, tomato paste, pineapples canned, dates,

fruit juice nes, fruit prep nes, water, coffee roasted, coffee substitutes, coffee extracts,

chocolates, pepper, cinnamon, essential oils, butter, dry skim cow milk, meat nes and

food prep nes. (code 1232).

- As above, trade data for 2012 had to be re-checked for the following products: bran

cereals, cake rapeseed, lettuce, tomato juice concentrated, fruit juice nes, coffee subst.

cinnamon, butter, pork, turkey meat exp, and meat dried nes.

- For 2013, trade data were re-checked and verified for a number of products: oil olive

residues, lettuce, tomato juice concentrated, grapefruit juice single strength, fruit juice

nes, coffee subst, cinnamon, hey cheese and bacon ham pigs.

For ginger, utilization assumptions were revised. While national FBS does not have waste,

other utilization (e.g. medicines) and stocks changes, FAO created, reportedly to reduce

calories and eliminate stock entries, entries for waste and other utilization based on some

articles from the web.

For mustard seed, there was a difference in production data between FAO and national FBS;

for 2010, area sown was higher than harvest area. Also, the element ―stock variation‖ has

been excluded from the national FBS.

For pastry (code 22), import volumes for 2010 were found out to be different and so

corrections were made.

For fruit fresh nes (code 619), marked statistical discrepancies were noted and corrected.

47

Annex 3.1: NLSS III and ADePT-FSM for extracting food consumption statistics

Nepal Living Standards Survey - The first Nepal Living Standards Survey (NLSS I) was conducted

by the CBS in 1995/96. It was based on the methodology developed and promoted by the World

Bank for Living Standards Measurement Survey (LSMS) worldwide. The second survey (NLSS II)

was conducted in 2003/04. Seven years later, the third survey (NLSS III) was implemented in 2010-

11.

The NLSS III was a nation-wide household survey conducted year round through February 2010 to

February 2011. It enumerated 7,020 households, of which 5,988 households were from the cross-

section sample and the remaining 1,032 from the panel sample. The NLSS II had enumerated 3,912

households while the NLSS I had 3,373 households surveyed. The NLSS III also tracked changes for

the previously enumerated households by NLSS I and NLSS II.

Consistent with the basic survey objectives, the NLSS III collected statistics on a range of topics

related to living standards: demography, housing, access to facilities, migration, consumer

expenditure, education, health, marriage and maternity history, work and time use, employment and

unemployment, wage and salary, agriculture, non-agricultural activities, credit and savings, absentees

population, remittances and transfers, social assistances, adequacy of consumption and government

services/facilities, anthropometry and tracking of households enumerated (in the previous surveys).

NLSS III‘s survey questionnaire on consumption included about 70 food items, which could be sub-

grouped into broader products such as cereals, vegetables, pulses, fruits, meat, milk etc.

ADePT-Food Security Module (ADePT-FSM) - The ADePT-FSM is a user-friendly software

designed to capture food security statistics collected by national household surveys such as the

NLSS. It evolved from the Food Security Statistics Module (FSSM) that was developed and used by

FAO for many years. The FSSM was designed to derive a comprehensive set of indicators on various

aspects of food security at national and subnational levels. In December 2011, FAO collaborated

with the World Bank to develop stand-alone software with user-friendly interface – the result was the

ADePT-FSM. A comprehensive publication dedicated to using the software for extracting and

analyzing food security statistics from surveys has been published by the World Bank (Moltedo et al.

2014).

The task of extracting food security statistics from household surveys in standardized ways has been

greatly facilitated by ADePT-FSM. It has been used in many countries by national statistical offices

or institutions involved in food security analysis. One core activity under the FAO regional project

which led to this report was to use ADePT-FSM to extract information from the NLSS 2010/11. With

the software, a large number of tables were generated on food consumption data by region and

income groups, as well as indices and summary statistics.

48

Annex 3.2: Selected key statistics extracted from NLSS III with Adept-FSM software

(page 1 of 3)

(continued ….)

Region/ House- Family Energy Protein Fat Min. DES Avg. DES

quintle/ holds size Populatn. (DEC) reqrmnt. reqrmnt.

deciles

# # 000 kcal/cap/day gm/cap/day gm/cap/day kcal/cap/day kcal/cap/day

Nepal all 5,988 4.9 28,024 2,725 73.5 47.0 1,724 2,175

Urban 2,088 4.4 5,329 2,685 73.7 57.5 1,788 2,280

Rural 3,900 5.0 22,694 2,735 73.5 44.6 1,709 2,150

Q 1 (poorest) 1,044 6.1 - 2,154 55.3 26.8 1636 -

Q 2 1,068 5.5 - 2,588 68.4 36.8 1686 -

Q 3 1,079 4.8 - 2,889 77.8 46.8 1736 -

Q 4 1,207 4.2 - 3,019 83.0 59.2 1761 -

Q 5 (richest) 1,588 3.7 - 3,332 95.1 82.2 1821 -

Eastern 1,272 4.8 6,525 2,766 73.9 45.6 1,732 2,186

Centeral 2,280 4.8 9,999 2,813 76.3 49.7 1,734 2,194

Western 1,152 4.6 5,367 2,654 70.5 50.2 1,727 2,180

Mid-western 756 5.2 3,653 2,608 71.8 42.6 1,695 2,123

Far-western 528 5.0 2,480 2,591 70.2 39.5 1,699 2,136

D 1 - Nepal 526 6.4 3,679,102 1,948 - - - -

D 2 - Nepal 518 5.9 3,389,366 2,377 - - - -

D 3 - Nepal 537 5.7 3,281,933 2,485 - - - -

D 4 - Nepal 531 5.3 3,051,084 2,698 - - - -

D 5 - Nepal 530 4.9 2,854,001 2,785 - - - -

D 6 - Nepal 549 4.6 2,650,725 3,001 - - - -

D 7 - Nepal 571 4.3 2,487,375 2,982 - - - -

D 8 - Nepal 636 4.1 2,357,639 3,057 - - - -

D 9 - Nepal 714 3.9 2,268,348 3,111 - - - -

D 10 - Nepal 874 3.5 2,002,976 3,582 - - - -

D 1 - Urban - - - 1,909 - - - -

D 2 - Urban - - - 2,066 - - - -

D 3 - Urban - - - 2,361 - - - -

D 4 - Urban - - - 2,308 - - - -

D 5 - Urban - - - 2,414 - - - -

D 6 - Urban - - - 2,556 - - - -

D 7 - Urban - - - 2,571 - - - -

D 8 - Urban - - - 2,532 - - - -

D 9 - Urban - - - 2,640 - - - -

D 10 - Urban - - - 3,304 - - - -

D 1 - Rural - - - 1,949 - - - -

D 2 - Rural - - - 2,399 - - - -

D 3 - Rural - - - 2,499 - - - -

D 4 - Rural - - - 2,746 - - - -

D 5 - Rural - - - 2,825 - - - -

D 6 - Rural - - - 3,086 - - - -

D 7 - Rural - - - 3,084 - - - -

D 8 - Rural - - - 3,313 - - - -

D 9 - Rural - - - 3,470 - - - -

D 10 - Rural - - - 4,151 - - - -

Estimates of food consumption & requirements ------------ Demographics ---------

49

Annex 3.2 ,,,,,,,,… continued (page 2 of 3)

(continued ….)

Region/ CoV of Skewness PoU Depth of Total Food Ratio: food

quintle/ DEC of DEC (undernrsh.) food deficit consmptn. consmptn. to total

deciles expndtr. expndtr. cons. expnd

% % kcal/cap/day Rs/cap/day Rs/cap/day %

Nepal all 24.58 0.75 3.1 18.0 95.42 51.86 54.4

Urban 24.46 0.75 5.0 32.5 164.73 66.13 40.1

Rural 26.16 0.80 3.4 19.6 79.14 48.52 61.3

Q 1 (poorest) - - - - 38.41 27.24 70.9

Q 2 - - - - 58.48 39.87 68.2

Q 3 - - - - 80.94 52.07 64.3

Q 4 - - - - 116.12 66.36 57.1

Q 5 (richest) - - - - 239.71 93.69 39.1

Eastern 24.91 0.76 2.9 17.2 85.73 51.79 60.4

Centeral 24.06 0.74 2.1 12.6 117.93 57.25 48.5

Western 23.10 0.71 3.2 19.0 96.24 53.27 55.3

Mid-western 26.99 0.83 5.6 31.8 70.72 44.09 62.3

Far-western 27.63 0.85 6.6 38.1 64.78 38.79 59.9

D 1 - Nepal - - - - 32.44 23.39 72.1

D 2 - Nepal - - - - 44.89 31.42 70.0

D 3 - Nepal - - - - 53.58 37.28 69.6

D 4 - Nepal - - - - 63.75 42.65 66.9

D 5 - Nepal - - - - 74.99 49.08 65.4

D 6 - Nepal - - - - 87.34 55.30 63.3

D 7 - Nepal - - - - 103.72 63.13 60.9

D 8 - Nepal - - - - 129.19 69.77 54.0

D 9 - Nepal - - - - 170.32 79.88 46.9

D 10 - Nepal - - - - 318.29 109.34 34.4

D 1 - Urban - - - - 32.86 23.74 72.2

D 2 - Urban - - - - 44.99 28.31 62.9

D 3 - Urban - - - - 53.61 35.10 65.5

D 4 - Urban - - - - 63.52 38.64 60.8

D 5 - Urban - - - - 75.39 44.27 58.7

D 6 - Urban - - - - 87.88 49.54 56.4

D 7 - Urban - - - - 104.78 55.42 52.9

D 8 - Urban - - - - 128.75 59.79 46.4

D 9 - Urban - - - - 171.28 71.29 41.6

D 10 - Urban - - - - 331.69 104.94 31.6

D 1 - Rural - - - - 32.42 23.37 72.1

D 2 - Rural - - - - 44.88 31.64 70.5

D 3 - Rural - - - - 53.58 37.52 70.0

D 4 - Rural - - - - 63.78 43.15 67.7

D 5 - Rural - - - - 74.95 49.60 66.2

D 6 - Rural - - - - 87.24 56.39 64.6

D 7 - Rural - - - - 103.46 65.02 62.8

D 8 - Rural - - - - 129.41 74.61 57.7

D 9 - Rural - - - - 169.59 86.43 51.0

D 10 - Rural - - - - 290.82 118.37 40.7

Food consumption/requirements ….. contnitued…. ------- Consmption expenditure -------

50

Annex 3.2 ,,,,,,,,… continued (page 3 of 3)

Source: NLSS III as extracted with Adept-FSM software.

Region/ From Own Away Other From Own Away Other Protein Fat

quintle/ market prodtn. from home sources market prodtn. from home sources

deciles

% % % % % % % % % %

Nepal all 50.66 30.97 15.42 2.95 43.52 39.33 14.63 2.52 10.78 15.52

Urban 66.44 11.48 19.59 2.49 62.95 16.06 18.79 2.20 10.96 19.24

Rural 45.62 37.20 14.09 3.10 39.04 44.70 13.67 2.59 10.74 14.67

Q 1 (poorest) 44.28 40.00 11.39 4.33 39.68 45.38 11.39 3.55 10.28 11.20

Q 2 46.69 38.21 12.11 2.99 41.02 44.42 12.12 2.44 10.57 12.81

Q 3 46.11 38.06 13.06 2.77 40.11 44.60 13.15 2.14 10.77 14.58

Q 4 50.99 31.36 15.05 2.60 45.10 37.81 15.21 1.88 10.98 17.64

Q 5 (richest) 59.25 16.65 21.44 2.67 52.69 22.68 22.02 2.61 11.38 22.16

Eastern 49.27 34.51 14.29 1.93 44.17 39.90 14.21 1.72 10.68 14.84

Centeral 56.14 22.28 18.64 2.94 47.46 31.96 17.54 3.04 10.85 15.90

Western 51.13 32.27 13.96 2.64 44.92 39.84 13.53 1.71 10.62 17.01

Mid-western 40.14 42.94 11.83 5.09 34.56 50.60 11.38 3.45 11.01 14.70

Far-western 39.20 46.31 10.57 3.91 34.62 52.16 10.31 2.92 10.83 13.72

D 1 - Nepal 43.10 40.54 10.98 5.38 38.64 46.56 10.45 4.35 - -

D 2 - Nepal 45.23 39.56 11.73 3.48 40.60 44.33 12.24 2.83 - -

D 3 - Nepal 47.25 38.00 11.52 3.24 42.74 43.66 11.24 2.36 - -

D 4 - Nepal 46.16 38.40 12.68 2.76 39.32 45.18 12.99 2.51 - -

D 5 - Nepal 44.64 40.42 12.52 2.42 38.71 47.15 12.34 1.80 - -

D 6 - Nepal 47.53 35.79 13.58 3.10 41.51 42.06 13.96 2.48 - -

D 7 - Nepal 48.29 34.77 13.85 3.09 43.80 40.56 13.56 2.08 - -

D 8 - Nepal 53.57 28.11 16.19 2.13 46.44 34.98 16.91 1.67 - -

D 9 - Nepal 55.67 23.62 18.16 2.55 49.37 30.76 17.53 2.35 - -

D 10 - Nepal 62.21 10.88 24.15 2.77 55.96 14.74 26.44 2.86 - -

D 1 - Urban 63.50 20.04 10.13 6.33 61.05 25.80 9.72 3.42 - -

D 2 - Urban 62.66 24.33 11.97 1.04 60.11 26.52 12.45 0.92 - -

D 3 - Urban 69.99 15.85 12.73 1.43 69.22 18.15 12.29 0.35 - -

D 4 - Urban 65.40 19.30 13.66 1.63 60.85 23.23 14.33 1.60 - -

D 5 - Urban 62.10 22.65 13.45 1.79 60.57 25.29 13.18 0.97 - -

D 6 - Urban 65.35 18.49 14.77 1.39 60.63 23.67 14.94 0.76 - -

D 7 - Urban 65.51 17.99 14.17 2.33 64.44 20.98 13.41 1.17 - -

D 8 - Urban 67.59 13.94 15.99 2.49 63.02 18.40 16.29 2.28 - -

D 9 - Urban 66.62 12.12 18.70 2.56 64.57 15.38 17.30 2.74 - -

D 10 - Urban 66.75 5.22 25.18 2.84 62.16 7.47 27.20 3.17 - -

D 1 - Rural 42.27 41.37 11.01 5.35 37.77 47.37 10.47 4.39 - -

D 2 - Rural 44.11 40.54 11.72 3.64 39.39 45.44 12.22 2.95 - -

D 3 - Rural 44.88 40.30 11.39 3.42 39.96 46.34 11.13 2.58 - -

D 4 - Rural 44.02 40.53 12.57 2.89 37.08 47.47 12.85 2.60 - -

D 5 - Rural 42.93 42.16 12.42 2.48 36.67 49.19 12.26 1.88 - -

D 6 - Rural 44.55 38.69 13.38 3.38 38.50 44.95 13.80 2.75 - -

D 7 - Rural 44.68 38.28 13.78 3.25 39.58 44.57 13.59 2.27 - -

D 8 - Rural 48.11 33.63 16.27 1.99 40.28 41.13 17.15 1.44 - -

D 9 - Rural 48.78 30.87 17.82 2.54 40.53 39.68 17.67 2.12 - -

D 10 - Rural 53.94 21.17 22.26 2.63 45.82 26.63 25.19 2.36 - -

Share of DEC from -------- Sources of food (in monetary terms) -------- ------ Sources of food (in DES/kcal terms) ------

51

Annex 4.1: Project activities on capacity building through training and workshop

The main objective of the FAO regional project under which this report has been prepared was, inter

alia, strengthening national capacity for generating quality food and nutrition security

information essential for informed policies and programmes. Accordingly, the project

provisioned several activities on training, national and regional workshops as well as some

learning-by-doing sessions, as summarizes below.

Upon the initiation of the project work in July 2013, the following activities were accomplished.

• An Inception Workshop was held on 24 September 2013 in Kathmandu.

• Two focal persons from MoAD and CBS participated in a Regional Training Workshop on

SUA/FBS, held in Pattya Thailand during 3-7 march 2014.

• About 30 participants from the Nepal multi-sectoral task force participated in a Training

Workshop on SUA and FBS held in Kathmandu during 26-30 May 2014.

• About 15 participants from Nepal participated in a Training Workshop on SUA and FBS,

focused on data entry and validation, held in Kathmandu during 10-13 March 2015.

• Four participants from the MoAD and CBS attended a Regional Training Workshop on

Validating SUA/FBS 2008-13, along with participants from Lao PDR and the Philippines,

held in Bangkok during 20-24 April 2015.

• National coordinator of the Project attended a training cum learning-by doing programme on

Food Security Statistics focused on ADePT and Food Balance Sheet, in Rome during 11-21

May 2015.

• Focal persons from MoAD and CBS and resource person from MoAD attended a Regional

Training Workshop held in Bangkok during 21-24 September 2015.

A number of tasks were accomplished in the course of the preparation of the new SUA/FBS, and

many lessons learnt. These include the following.

• The training session in May 2014 in Rome was utilized to finalize the SUA for 2009-2012,

but based on the various trainings received, the national focal persons were able to update the

SUA to 2013 as well as preparing FBS for 2013.

• Data on crop production (cereals, pulses, vegetables, fruits, oilseeds and so on) were updated

for 2009 to 2012 and incorporated in the SUA/FBS; in addition, fresh data was compiled for

2013 and SUA/FBS prepared.

• Likewise, data on livestock production and products were updated for the period 2009 to

2012, and further extended to 2013.

• Trade data available from Trade and Export Promotion Centre were acquired, processed and

incorporated in the SUA, including for 2013.

• Data and parameters on nutrition factors were updated and used as needed.

• The ultimate substantive output was the preparation of FBS for six years (2008 to 2013) from

newly compiled SUA.

52

Annex 4.2: Nepal’s Multi-sector Nutrition Plan for Accelerating the Reduction of Maternal and

Child Under-nutrition, 2013-2017

The Multi-sector Nutrition Plan (MSNP) for improving maternal and child nutrition and reducing

chronic malnutrition, approved by the GoN‘s cabinet on 15 May 2012, was prepared by five key

government agencies, led by the NPC. It claims to offer a package of activities and interventions with

priority strategic objectives by sector that, over a period of five years, should contribute to a

reduction by one third the current prevalence rates of chronic malnutrition, and within the next ten

years to ensure that malnutrition no longer becomes an impeding factor for enhancing Nepal‘s human

capital and socio-economic development.

The need for a framework such as the MSNP was felt for a long time but it was the Nutrition

Assessment and Gap Analysis (NAGA) in 2009 that specifically called for formulating the MSNP as

the national nutrition architecture as well as to mobilize all the key sectors to tackle the high rates of

malnutrition in a sustained manner through a multi-sector approach. The NPC led the process by

revitalizing the national nutrition steering committee and rolling the process that finally formulated

the MSNP. The MSNP document includes a detailed plan of action, institutional arrangements, the

costs and the updated monitoring and evaluation framework.

The MSNP set three major Outcomes: i) policies, plans and multi-sector coordination improved at

national and local levels; ii) practices that promote optimal use of nutrition ‗specific‘ and nutrition

‗sensitive‘ services improved, ultimately leading to enhanced maternal and child nutritional status;

and iii) strengthened capacity of central and local governments on nutrition to

provide basic services in an inclusive and equitable manner.

The MSNP focuses on the narrow window of growth falter, the first 1,000 days of life, with

an urgent set of essential interventions. It will complement other relevant sector policies

and strategies, such as the health sector‘s National Nutrition Policy and Strategy (2004/8) and

agriculture sector‘s upcoming Food and Nutrition Security Plan (FNSP) as part of Agriculture

Development Strategy (ADS). The MSNP has identified eight outputs (results) with a set of

indicative activities. Outputs 1 and 2 will contributes towards achievement of Outcome 1, outputs 3-6

will help attain Outcome 2, and outputs 7-8 will contribute towards attaining Outcome 3.

Implementation of the MSNP will be guided by the High Level Nutrition and Food Security

Steering Committee (HLNFSSC), chaired by the Vice Chairperson of the NPC. The HLFNSSC will

be responsible for policy direction, guidance and oversight functions. A technical multi-sector

nutrition committee will be formed at the national level to provide technical guidance.

Source: GoN (2012), Multi-sector Nutrition Plan for Accelerating the Reduction of Maternal and Child Under-

nutrition in Nepal: 2013-2017. May 2012, NPC, Kathmandu, Nepal.



53

Annex 4.3: Food and nutrition policy, legislations and institutions

Key agricultural policies related to food and nutrition security

National Agriculture Policy, 2061 (2004)

Agro-business Promotion Policy, 2064 (2006)

National Fertilizer Policy, 2058 (2002)

Irrigation Policy, 2060 (2003)

Commercial Agriculture Policy, 2064 (2007)

National Seed Policy, 2056 (2000)

National Tea Policy, 2057 (2000)

Policy and Institutional arrangement for the Effective Implementation of Agricultural

Perspective Plan, 2057 (2000)

National Coffee Policy, 2060

Dairy Development Policy, 2064 (2007)

Agriculture Bio-diversity Policy, 2063 (2007)

Trade Policy, 2009

Key Acts and Regulations related to food and nutrition security

The following are the major Acts related to food and nutrition security:

Agriculture and Forestry University Act, 2067 (2010)

Animal Health and Livestock Service Act 1998 / Rules 2000

Animal Slaughterhouse and Meat Inspection Act, 2055 (1999)

Aquatic Animal Protection Act, 2017 (1960)

Breast feed Substitution Substance Act (Sales, Distribution and Control) 1992,

Consumer Protection Act 1998/ Regulation 2000,

Contract Act, 2056 (2000)

Cooperatives Act, 2048 (1992)

Drugs Act, 2035, (1978)

Essential Commodities Control Act 1960,

Feed Act, 2033 (1976)

Food Act 1966/Food Regulation 1970 (Amendment 1998),

Forest Act, 2049 (1993)

Iodized Salt (Production and Sales) Act 1998

Irrigation Rules, 2056 (2000)

Local Self Governance Act 2055/ Rules 2056,

National Cooperatives Development Board Act, 2049 (1992)

National Dairy Development Board Act, 2048 (1992)

National Tea and Coffee Development Board Act, 2049 (1993)

Nepal Agricultural Research Council Act, 2048 (1992)

Nepal Veterinary Council Act, 2055 (1999)

Plant Protection Act, 2064 (2007)

Seeds Act, 2045 (1988)

54

Slaughterhouse and Meat Inspection Act 1998 / Rules 2000

The Pesticides Act, 2048 (1991)

Water Resources Act 2049 (1992)

Key institutions related to food and nutrition security

In absence of one umbrella institution to deal with food and nutritional issues, many institutions are

involved in one or the other way. The key ones are as follows:

National Nutrition and Food Security Steering Committee: Chair: Vice-chairman of

NPC, and Member-secretary: NPC Secretary, Joint Secretary, and Secretaries of relevant

Ministries.

National Nutrition and Food Security Coordination Committees: Chair: Member of NPC,

and Member-Secretary: Joint Secretary, Social Development Division/NPC, Joint Secretary

of Relevant Ministries, and Experts.

Food Security and Environment Division: Under the Ministry of Agricultural

Development, headed by Joint Secretary with separate food and nutrition section.

Food Security Monitoring and Analysis System (NeKSAP): Unit established in MoAD,

Agribusiness Promotion and Statistical Division.

National Nutrition Program: Under Department of Food Technology and Quality Control

(DFTQC), MoAD - deals with food-based nutrition activities.

Nutrition Section: Under Child Health Division, DoHS, MoHP– deals with clinical part of

nutrition.

Donor Food and Nutrition Co-ordination Committee

District Level Food and Nutrition Security Network: District Level Nutrition and Food

Security Steering Committee chaired by Chief District Officer (CDO) and District

Development Committee (DDC) Chairperson.

Municipality/VDC Level Nutrition and Food Security Steering Coordination

Committee: Chaired by Mayor/VDC chairperson.

District Food Management Committee: Chair, Chief Districts Officer, Member-Secretary:

Local Development Officer, and Nepal Food Corporation (NFC).

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Annex 4.4: Food security and nutrition: An Approach Paper to the Thirteenth Plan

(FY 2013/14 to 2015/16)

Background

Several past policy and other measures have had positive outcomes in the area of food security and

nutrition, but in order to achieve the expected results in the four pillars of food security--availability,

access, utilization and stability -- contextual reforms are needed. The fact that the majority of Nepal‘s

75 districts are reeling under food deficiency conditions and that nearly 41% of the population

consumes less than the minimum dietary requirements is a cause for alarm.

Objectives

To improve the consumption of food in sufficient quantity and nutrient value, and

To identify areas and communities vulnerable to food insecurity and increase their access to

nutritious foodstuffs.

Strategies

Make necessary arrangements in food-and-nutrition-insecure areas and communities for

solving the food crisis in the short and the long term.

Implement targeted programmes for raising the production of suitable high-value

commodities in those areas most vulnerable to food insecurity.

Ensure food security through the protection, promotion and efficient use of agro-biodiversity

and the development and expansion of climate change-adaptive technologies, and

Create an efficient regulatory mechanism to ensure that foodstuff is clean and healthy.

Operating policies

A Food and Nutrition Security Policy, Food Sovereignty Act, and National Food and

Nutrition Security Action Plan will be formulated and implemented.

Access to nutritious foodstuff will be enhanced by providing special facilities for increasing

livestock production and productivity

The Nepal Food Security Monitoring System will be institutionalized.

Expected outcomes

The status of food-and-nutrition security in vulnerable areas will have improved, market

infrastructures such as storage facilities and collection centers will have been enhanced, modern

technology and equipment will be used in food-related research, and standards for the import and

export of foodstuff will have been established.

Source: An approach paper to the Thirteenth Plan (FY 2013/14 – 2015/16), National Planning Commission,

Kathmandu.

56

Annex 4.5: Report of the final workshop on sharing of the outputs of the Project

As the final activity of the implementation of the MoAD/FAO project, Building statistical capacity

for quality food security and nutrition information in support of better informed policies

(TCP/RAS/3409), a workshop was held in Kathmandu on 28 December 2015 for sharing the outputs

of the project. Attachment 1 shows the Agenda, which consisted of: i) opening remarks by invited

dignitaries from the Government of Nepal (GoN) and FAO; ii) two substantive presentations on the

project; iii) floor discussion; and iv) closing remarks by senior officials from the GoN. The workshop

was attended by ** persons representing several agencies of the GoN, FAO and the media

(Attachment 2).

Remarks made during the opening and closing sessions

Senior officers from the GoN and FAO were invited for statements during the opening and closing

sessions. Their statements covered several themes related to the project, notably food and nutrition

security issues facing Nepal, the state of the Nepalese agriculture and economy, regional disparity in

poverty and food insecurity, importance of quality statistics for decision making, and the need for

continued efforts in capacity building for generating quality statistics and using them for various

analysis. Their statements also highlighted the GoN‘s policies, programmes and projects in these

areas. Furthermore, they expressed appreciation for FAO technical assistance provided through the

project.

Summary of the two substantive presentations

In the first substantive presentation, Mr Hem Raj Regmi, National Coordinator of the MoAD/FAO

Project, made a power point presentation introducing the project and its activities, as well as the main

outcomes and their significance for Nepal. He informed the workshop of the various capacity

building activities under the project, which included two training workshops attended by about 30

participants from the Nepal multi-sectoral task force, participation by national experts in training-

cum-workshop programmes in Bangkok, and one working session with FAO counterparts in Rome.

He highlighted how the project was implemented in its two core statistical activities: the compilation

of the SUA/FBS for Nepal for 2008-13 and the extraction of food and nutrition security information

from Nepal‘s NLSS III using the ADePT-Food Security Module (ADePT-FSM). He explained why

the new SUA/FBS for 2008-13 compiled using FAO harmonized methodological framework was a

substantive improvement over what currently existed, both with the MoAD in Nepal and the FBS for

Nepal maintained by FAO. In his presentation, he also stressed on the excellent collaboration among

the key agencies involved, notably the MoAD, CBS and TPEC.

The second substantive presentation was made by Mr Ramesh Sharma, FAO consultant. He

introduced the study - Food and Nutrition Security in Nepal: A Status Report, which was the third

substantive output of the MoAD/FAO project. He explained that the Status Report was presented in

two substantive sections, one presenting the evolution of food and nutrition situation using the data

from the new FBS and other national and FAO sources, and the second providing a cross-sectional

variation across Nepal based on the NLSS III data. Two main findings of the analysis were as

follows. One, the new FBS shows markedly higher level of aggregate food availability for Nepal,

57

thus lowering the prevalence of hunger and food inadequacy relative to what FAO estimates had

shown. Two, while the new estimates show an impressive progress on aggregate food availability,

the status as regards under-5 malnutrition has not been encouraging. The NLSS III data also show a

similar picture – aggregare food availability is higher than commonly held to be, but there were

significant variatons in food and nutrition indicators across Nepal‘s regions and income groups. Such

a finding points to the importance of addressing non-food deficiencies as well such as caring, health

and hygiene and education. The data also point to the need for a targeted approach to food production

and food supply and safety net programmes.

Summary of comments from the floor and discussions

The Status Report was made available to participants prior to the workshop which helped effective

participation and discussion on substantive issues.

On the whole, participants were appreciative of the activities implemented under the Project as well

as of the main outputs delivered. They took note of various capacity building activities and some of

them called for continuing this work. The overall assessment of the Report was positive, with

participants finding it to be comprehensive, up-to-date and analytical. Many participants also

appreciated that the analyses in the Report utilized data from both the SUA/FBS and NLSS III, some

also adding that the use of the time-series and cross-section statistics provided fresh perspectives on

the issues.

Many comments, and subsequent discussions, were focussed on one of the main findings reported

that the new SUA/FBS (and also, to a smaller extent, the NLSS III data) showed markedly higher

level of aggregate average availability of food in Nepal (expresed in terms of calories, proteins and

fats), relative to what was commonly held based on the FAO databse. This also meant that the

prevalence rates of hunger and food inadequacy were lower in Nepal than was generally held.

Therefore, many comments were also related to the accuracy of the data used and their coverage of

food products while estimating availability.

On this, one topic that attracted considerable discussion was the issue of unrecorded trade, i.e. cross-

border informal trade (CBIT) with India. Thus it was said that if food crosses the border without

being recorded, availability as computed for Nepal would be higher because while production is

recorded under supply, exports would not deducted from the FBS. Paddy/rice was given as an

example. Some participants reported that they had personally observed or were aware that

substantive amounts of paddy are exported to the Indian side for milling but not recorded as exports,

while what it is not clear if the milled rice is imported back or sold in India. Several participants had

the feeling that this may also be the case for several other products. Vegetables were mentioned by

some participants as products where informal exports could be substantial. Thus, unrecorded exports

were considered to be one possible reason for the relatively high availability of food in Nepal. Some

participants, however, were also of the view that while that may be the case, unrecorded trade could

not be that substantive to make a big difference at the aggregate national level.

Also on this subject, another insight provided by one participant was that an unknown but potentially

significant level of food production along Nepal‘s border with India could be taking place on a

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contract basis, with most contracts received by farmers from across the border. In such cases, most of

the produce under the tenant‘s share is taken across the border unrecorded, thus overestimating food

availability in Nepal.

Some participants also wondered about the drivers or policies behind the CBIT. For paddy for

example, the reasons considered were lower cost of milling across the border, even after taking into

account transport and other costs, as well as India‘s much higher minimum support price for paddy.

For several other products such as potatoes, cold storage facilities across the border in India could

also be the reason.

One participant suggested that it is unlikely that informal trade could be that high to make a big

difference in food availability because all trade in agricultural products to and from India is duty free,

with very low tariffs on imports from elsewhere. One response made to this argument was that tariffs

alone do not explain the volume of CBIT as NTBs also drive trade to informal channels.

In view of the above, it was suggested that a comprehensive study on CBIT would be highly useful

not just for updating the SUA/FBS but also for a variety of other purposes, including for food and

trade policy. The meeting was informed that the last time a study on CBIT covering many

agricultural products was conducted by the GoN, with FAO technical assistance, was in 2008.

Besides CBIT, some participants asked if the new FBS has taken into account food consumption by

tourists as well as the use of food for liquor. The answer was that all these uses have been

incorporated.

Some discussion also took place on the topic of increased food production in Nepal, as the Report

showed for cereals in particular, as yet another important reason for higher food availability. One

participant asked which GoN policies and programmes could have been the drivers of increased food

production. He added that the Report is weak in identifying such policies and programmes. In

response, it was said that an analysis of the effectiveness of food policies and programmes was

beyond the scope of the current work, but such analysis is pertinent and should be undertaken.

A number of interventions made were of the nature of suggestions made for further analyses of

various questions and issues related to the themes of the Project and the results discussed, or missing,

in the Report. They are summarized as follows.

Analyse changes in under-5 malnutrition rates and food availability for two periods and

Nepal‘s 12 analytical domains with a view to understanding the drivers of the changes in

both these prevalence rates. The results of such a study would be useful for targeting

programmes and policies.

Analyse the NLSS III data to enhance our understanding of the relative significance of farm

and no-farm incomes, as well as of other sources of income (e.g. remittances) in improving

food security. One question to ask would be whether farm income contributes more to food

security than non-farm income? An answer to this helps appreciate the importance of own

food and agricultural production versus exogenous sources of income, e.g. remittance.

On the suggestion made in the Report that there is a need for a comprehensive food demand

study including projections into the future, one participant said that a similar work was

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commissioned by FAO and undertaken some years back, using NLSS II data. So the study

could be updated with the NLSS III data.

Reacting to the positive picture on cereal and food production in Nepal in the Report, as well

as the fact that Nepal‘s agriculture GDP has been growing at the fairly high rate of 3-4% p.a.,

one participant wondered how was all this possible when half of the youths have been living

outside the country? Was this due to increased farm mechanization? In any case, a suggestion

was made that the impact of the youths on agriculture needs to be understood with a view to

formulating policies on mechanization and land management.

Some participants suggested that the minimum food requirements (in kcal) in urban areas

need to be reviewed, given that even 2,200 kcal looks like to be on the higher side given the

sedentary nature of job for a large segment of the urban population. Indeed, for this

population, food consumption as high as about 2,700-2,900 kcal would lead to obesity, a

form of malnutrition.

One participant held that the reason why there was so much variation in food availability

across Nepal‘s region was poor mobility of goods within the country. This most likely

reflects lack of infrastructures in rural areas but also variations in income. A suggestion was

made for a study focused on understanding the flow of food products across Nepal, including

its drivers.

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