Income Disparities and Poverty in Nepal: Regional and Ethnic Dimensions

Ram C. Acharya1 Department of Industry, Canada

Prem Sangraula The World Bank

April 2009

Abstract Using household survey data on Nepal, the paper examines income disparities by 13 regions and 12 ethnic, language and caste (ELC) groups that are identified as building blocks for a federal Nepal in Acharya (2007). Results show that there are massive between-region (across regions) and between-ELC group inequalities. The within-region (across individuals in the same region) and within-ELC group disparities are even wider. The inequalities are so alarming that to bring Nepal in par with one of the countries in the Nordic region (Denmark, Finland, Norway and Sweden), the 20% richest people in Nepal should pay additional taxes of 46% from their income. This will raise the share of tax in GDP from the present level of 9% to 26%, the level comparable to government budgetary expenditure. The replica of Nordic equality will also generate additional social welfare gain of 33% to the society. This tax policy can eliminate poverty and excessive disparities structurally if complemented with (1) land reform, (2) compulsory, standard and free education up to high school and (3) budgetary allocation in the region in proportion to the average share of population and poor. All these policies should be based on class/endowment divides; policies that aim for positive discrimination for particular groups are not justified and will be counterproductive.

Keyword Poverty, inequalities, welfare, regions, groups, GDP, tax

1 Ram Acharya, 36 Rodeo Drive, Ottawa, Ontario, K2J 4Z5 Canada; email address: rc_acharya@hotmail.com Prem Sangraula, email address: psangraula@worldbank.org The views expressed are those of the authors and should not be attributed to their affiliated organizations.

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I. Introduction

In 2007, Nepal’s gross domestic product (GDP)—the value of final goods and services produced

in a country in a year—was 720 billion Nepali Rupee (Rs), which at the official exchange rate,

amounts to about US$10 billion. Nepal is one of the poorest countries in the world with a per

capita GDP of US$ 234 (in 2000 prices), ranking 163rd out of 179 countries ranked in the world

while all 16 laggards are sub-Saharan countries. In comparison to its neighbours, Nepal’s per

capita GDP is only 36% of India’s per capita GDP of US$ 634 and 15% of China’s per

US$1,595. This disappointing performance has been the result of extremely sluggish growth

rates. In the last 25 years, the average per capita real income growth in Nepal has been only

about 1.8%, compared to 4.2% in India and 8.8% in China. With such growth rates, to double the

per capita income, it will take 40 years for Nepal, compared to 17 years for India and less than 9

years for China. Nepal is a laggard not only compared to the globally fastest growing two

neighbours, India and China, but also compared to many countries in the world.

Equally appalling is how asymmetrically economic opportunities are distributed across

regions and across different ethnic, language and caste (ELC) groups. These inequalities—the

direct outcome of wrong economic policies—have inhibited economic growth and have

substantially reduced the welfare of the society. The major policy mistakes have been pouring

budgetary resources into a handful of urban centres, deliberately ignoring tax on income and

wealth and relying on regressive indirect taxes and foreign aid to finance "grandiose" programs.

Moreover, the negative effects of growth-reducing and inequality-enhancing economic policies

are magnified by neglect of education and health services for the ordinary mass and adoption of

urban and elite centric education and health programs.

3

Just to give an example of, how the government is creating "elite" urban centers, research

shows that between 1999 and 2003, annual per capita government expenditure in Kathmandu

valley (three districts), with population of 7.2%, was Rs 28,000 compared to Rs 1,774 for the

region of Sagarmatha (eight districts) which houses a population of similar size (see Acharya,

2008 for fiscal disparities by regions).2 In foreign aid, the respective amount for the Kathmandu

and Sagarmatha regions were Rs 7,235 and Rs 245. This is just an example of how lopsided the

budgetary measures have been, and with such type of government policies, it is no surprise that

the economy stagnated and conflicts arose.

If there is any lesson to learn from the past, it is that for Nepal to be a peaceful, stable and

prosperous society, economic policies must unleash the forces of growth and at the same time

allow the benefits to be distributed in a more equitable manner. At present, Nepal is at the

crossroad of charting its political and economic restructuring, going from a unitary system to a

federal system. However, the debate on how to restructure has been rather cursory, as the focus

of political parties and ethnic representatives has been mainly confined on whether federalism

should be based on ethnic divide. No doubt, this is an important issue, but the actors seem to

have forgotten the fact that political entity is a mechanism or means to obtain political,

economic, social and cultural ends. Hence, the debate should revolve around federalism based on

what foundations (ethnicity, geography, culture, distribution of natural resources, market

accessibility, and services delivery) would provide the most efficient state machinery in

developing inclusive democracy, accelerating economic growth and ensuring equality of

opportunity (see Acharya, 2007, on how to restructure federal Nepal).

2 Kathmandu region includes districts of Kathmandu, Bhkatapur and Lalitpur. The Sagrmatha region includes eight districts: Ilam, Sankhuwasabha, Solukhumbu, Bhojpur, Khotang, Okhaldhunga, Dharan, and Udayapur.

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Worse, there is hardly any serious discussion on what different economic and social

policies are necessary for a federal Nepal. It seems likely that most of the political parties are

going to continue the same old polices, which will be a recipe for deepening crisis with further

disparities in socio-economic opportunities among different groups and regions. These policies

can neither maintain peace nor unleash the forces of growth in Nepal, no matter how "rightly"

the political restructuring is done and how politically inclusive the system is. Conducive political

restructuring is necessary, but that alone is not sufficient for economic growth and equitable

opportunity. Rather, what is required is a set of companion complementary economic policies.

Such economic policies should address the root causes of poverty, inequality and stagnation.

For that, we need to understand the state of the economy at the national and at the

regional level. Also since the inequalities have ethnic, caste and other dimensions (as recognized

by Sen (1997), we have to understand these aspects as well. As far as we are aware, there are no

papers in this line of research. Against this backdrop, the objective of this paper is to provide

answers to the following questions: How deep and widespread is poverty in Nepal? How sharp

are regional, ethnic disparities in Nepal? What is the root cause of the miserable state of the

nation—is it the outcome of regional, ethnic and gender disparities or the poverty across the

board? And, what policy responses are required to turn the nation from a state of gloom to a state

of bloom?

The regional and the ELC groups considered in the paper are the same 13 regions and

groups that are identified as building blocks for a federal Nepal in Acharya (2007). We have

used data on Population Census of 2001 and Nepal Living Standard Survey II of 2003/2004 for

the study. We agree with Sen's (1997) notion that the relevant dimension of inequality should

include not only income (or expenditure) but also literacy, health achievements, self-esteem, and

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other aspects of well-being—the basic capabilities in his terminology. Notwithstanding the

shortcoming of relying only on income (or expenditure) as a measure of inequalities, we do so in

the paper for two reasons and with two caveats. First, the inequalities in basic capabilities

correlate directly with income inequalities across regions and ELC groups in Nepal. Second, the

dealing of education and health by regions and ELC groups, which is the focus of this paper,

deserves separate study each. The related caveats are that the inequalities measured in the paper

are vital part of inequalities but they would have been more pronounced had we used the

capabilities approach. Furthermore, had there been data on wealth and assets, their inclusion

would have also sharpened the inequalities. So the inequality measures that we present based on

expenditure only will underestimate the reality.

The main findings of the paper are as follows. There are wide disparities in income (or

consumption expenditure) both by regions and by ELC groups. The richest region and group is

about four-times richer than the poorest counterparts. For example, the per capita expenditure of

the richest region Kathmandu is more than four times higher than that of Khaptad, the poorest

region. Similarly, the richest ELC group Newar has four-time higher income than that of poorest

Awadhi speaking group. The per capita expenditure of the second richest group, Gurung, is only

68% of Newar's and that of the third richest group, BC, (Brahman, Chhetri, Thakuri and Sanyasi)

is only 60% of Newar's. The disparities across regions can be termed as between-region or inter-

region disparities (similarly, across ELC groups disparities can be termed as between-ELC or

inter-ELC group disparities).

As a result of income disparities, about one-third of Nepal’s population live below the

poverty line defined at expenditure of Rs 7,696 per person per year (a very conservative

threshold). As in per capita expenditure by region and ELC groups, there are wide differences in

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the percentage of people that are below poverty line, ranging from 4% in Kathmandu region to

48% in Khaptad region and from 14% in Newar to 70% in Awadhi speaking group. The largest

numbers of poor are in BC groups, the third richest group after Newar and Gurung. We compute

the amount that is required to bring every poor who are below poverty line at the poverty level

amount by regions and groups, a roadmap for any government who is sincere in eliminating

poverty in Nepal.

The cost of inequality cut across all ELC groups. The plight of the poor in the three

richest groups is equally precarious as poor of any other groups. To cite a few results, out of 7.2

million poor in Nepal in 2003, 1.2 million are BCs. Even though on average Newar are richest by

wide margin than any other group, and they have less number of people under poverty, those

who are poor are desperately so. For example, to bring a single poor Newar at the poverty line

amount it would cost almost Rs 2,000 (almost one-quarter of poverty level amount). The same is

true for poor in Gurung community.

Not only are inter-region and inter-group disparities high as explained above, but even

more alarming are the intra-region (among people within a region) and intra-group disparities.

The richer regions are more unequal, and richer groups are more unequal among its residents

than their poorer counterparts. The richest region Kathmandu and Annapurna are the most

unequal regions. The poorest region Khaptad is the most egalitarian one. Similarly, the richest

group Newar, Gurung and BC are the most unequal ones. As a result of these within inequalities

the rich regions and rich groups are not as well off as they would have with the same level of

income and less inequality. The richness has not translated into higher social welfare; it has been

wasted as a cost of inequality.

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Comparison of inter-region (between) and intra-region (within) disparities shows that at

the regional level, about 25% of Nepal’s inequalities is explained by regional differences (inter-

region) and the remaining by differences among individuals within a region (intra-region). These

within region inequalities are driven mainly by three regions (Annapurna, Kathmandu and

Lumbini). Again, this is the outcome of government budgetary spending in some urban centers,

while neglecting regional balances. In terms of ELC groups, the share of within-group (in total

inequalities) is even higher than that of within-region. The high within group inequalities is

driven mainly by massive inequalities among the Newar and Gurung communities and to some

extent among BC and Rai communities.

Our results show that if there is a government committed to bringing Nepal on a growth

path, it is plausible. By adopting the model of one of the Nordic countries, Finland for example,

Nepal would benefit immensely. Our thought experiment shows that if we could bring Nepal to

the level of Finland in terms of equality index, Nepal should impose additional tax rates of 46%

in income to its richest 20% population. By doing this, Nepal would collect additional resources

Rs 88 billion and the share of tax revenue in GDP would increase from the present level of 9% to

26%. With this changes, it would take only 14% of tax revenue to eliminate poverty. The rest of

the resources could be used for public education and health services and to reduce taxes on

international trade and goods and services. Furthermore, by adopting the Finland level of

inequality, the social welfare of Nepal (the welfare of all Nepalese) would increase by 33%, from

208 billion to Rs 278 billion (calculation based on 2003 as base year). If you find these numbers

large, yes, indeed, they are. But what they show is that it is possible to bring the country out of

poverty, and to break the stagnation caused by inequalities. What are needed are consolidated

growth-enhancing and inequality-reducing policies.

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The rest of the paper is organized as follows. In Section II, we describe the data followed

by groundwork on how the regions (that are used for the study) are formed and which ethnic,

language and cultural groups are considered for comparison in Section III. The theoretical

methodology for welfare, inequalities and poverty is provided in Section IV. Based on this

literature, we compute and discuss poverty by regions and groups in Section V. Similar analysis

on inequalities is carried out in Section VI. The welfare analysis is covered in Section VII

followed by conclusion and recommendations in Section VIII.

II. Data

The paper uses macro level data from 2001 Nepal’s Population Census and household level data

from second Nepal’s Living Standards Survey (NLSS II), conducted in 2003/2004. The

Population Census data are used to identify the regional blocks and ethnic, language and caste

(ELC) groups that become the units of analysis for the paper. Once the regions and groups are

identified, we use the household level data from NLSS (II), a follow up survey of NLSS (I)

conducted in 1995/1996 by Central Bureau of Statistics, and based on the methodology of Living

Standards Measurement Survey methodology developed at the World Bank and applied in more

than 50 developing countries (for detail on the survey, see Central Bureau of Statistics (2004).

The survey used a two-stage stratified sampling approach: a nationally representative

cross-section survey to estimate trends and levels of socio-economic indicators in the country

and its different geographic regions; and the second component was a panel survey to track exact

changes experienced by those previously enumerated households during last eight years. In the

paper, we will use the data based on cross-section part of the survey only.

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Using 2001 Population Census of Nepal, NLSS II survey took the size of each ward

across Nepal (as measured by number of households) as a unit of sample frame, where bigger

wards were divided and smaller one were appended into neighbouring wards. The resulting

sampling frame consisted of 36,067 enumeration areas (wards or sub-wards) covering all 3,914

Village Development Committees (the smallest political entities) of the country. For the cross-

section part of the sample, first 334 Primary Sampling Units (PSU) were selected from six

explicit strata (Mountains, Kathmandu valley urban area, Other Urban areas in the Hills, Rural

Hills, Urban Terai and Rural Terai) of the country. Then 12 households in each of 334 PSU were

selected randomly, with total of 4008 households.3 We use NLSS II extensively because this is

the representative sample survey for all regions and population groups which provide

information on income and expenditure.

Besides, data are also taken from World Development Index database compiled by the

World Bank and website maintained by different institutions.

III. Unit of Analysis

Since the focus of the paper is to investigate the region-wise and group-wise comparisons of

economic condition, the selection of regions and groups for unit of analysis is an important issue.

So far, the regional analysis studies have used ecological belts and/or (Himalaya, Hill and Terai),

rural-urban dichotomy (see Pradhan and Shrestha, 2005; World Bank, 2006a; World Bank

3 The allocation of cross section PSUs by districts were as follows. Taplejung 4; Morang 16; Sunsari 11; Solukhumbu 3; Panchthar 3; Dhankuta 4; Bhojpur 4; Okhaldhunga 2; Ilam 5; Tehrathum 2; Khotang 3; Siraha 8; Jhapa 13; Sankhuwasabha 5; Saptari 9; Dhanusa 11; Udayapur 6; Makwanpur 8; Ramechhap 3; Lalitpur 10; Mahottari 8; Rautahat 7; Dolakha 5; Bhaktapur 6; Sarlahi 9; Bara 8; Sindhupalchok 10; Kathmandu 6; Sindhuli 5; Parsa 7; Kavrepalanchok 7; Nuwakot 5; Dhading 6 Kathmandu 35; Chitwan 9; Tanahun 5; Syangja 7; Gorkha 5; Kaski 14; Gulmi 5; Lamjung 4; Myagdi 3; Palpa 4; Manang 1; Parbat 2; Rupandehi 10; Nawalparasi 8; Baglung 4; Arghakhanchi 4; Kapilbastu 8; Banke 7; Bardiya 5; Doti 4; Pyuthan 4; Surkhet 7; Kalikot 3; Kailali 8; Rolpa 3; Dailekh 4; Mugu 2; Kanchanpur 5; Rukum 2; Jajarkot 1; Bajura 3; Dandeldhura 2; Salyan 3; Dolpa 1; Bajhang 4; Baitadi 4; Dang 6; Jumla 1; Achham 4; Darchula 3; Humla 1.

10

2006b). For groups, the existing studies use rather aggregate ethnic groups by lumping several of

them into a few groups. These types of studies are not very helpful in designing policies that

require regional and/or group-specific dimensions. Here, we follow the approach taken by

Acharya (2007), where regions or groups are formed based on common characteristics, the brief

discussion of which is provided below.

According to population Census 2001, there are 23 groups, which constitute at least one

percent of Nepal’s total population. Based on common threads such as ELC, Acharya (2007) has

combined them into distinct 13 groups as given in Table 1 (not counting “others” which

comprises several small groups).

Table 1. Nepal’s population composition by broader caste/ethnicity in 2001 Number of districts with

Ethnic, language and caste groups Share in total population Majority (&

plurality) 2nd place

1. Brahman, Chhetri, Thakuri, Sanyasi 30.9 19 (46) 19 2. Maithali speaking 12.3 5 (5) 1 3. Bhojpuri speaking 7.5 3 (4) - 4. Awadhi speaking 2.5 1 (2) - 5. Hill Dalit 7.2 0 (0) 15 6. Magar 7.1 1 (3) 9 7. Tharu 6.7 1 (2) 5 8. Tamang 5.6 1 (4) 5 9. Newar 5.5 1 (2) 1 10. Muslim 4.3 0 (0) 6 11. Rai 2.8 0 (3) 4 12. Gurung 2.4 1 (2) 4 13. Limbu 1.6 0 (2) 1 14. Others 3.3 0 (0) 3 Total 100 33 (75) 72 In column 4, we have total of 72 districts at the last row because due to lack of data on language, Acharya (2007), was not able to identify which group is in second position in the district of Jhapa, Morang and Siraha. na: not available

Brahman, Chhetri, Thakuri and Sanyasi (BC hereafter), is the largest group with 30.9%

(column 2, population groups presented in descending order of their shares) of Nepal’s

population. They are in majority (more than 50%) in 19 districts and in plurality (with highest

11

fraction but less than 50% of population) in 46 districts (column 3). Column 4 shows in how

many districts each group is in second place.

In an effort to maximize the concentration of ELC groups in a region other than BC,

Acharya (2007) identifies the focus districts for each of these groups—a district is considered a

focus district of an ELC group if that group is at least the second largest fraction after BC in the

district. Hence, if that group is in majority, or in plurality, or second largest after BC in the

district population, then the district will be the focus district of that group. Then all districts that

qualify to be focus ones for a given ELC group are combined to make a focus region for that

group. By this mechanism, 11 regions were identified as focus regions for 11 groups except for

BC, Muslim and "others". Since BC group is either in majority or in second place in most of the

districts, the exercise of identifying focus regions was not applied to BC.4

These 11 ELC groups and their respective focus regions are: Limbu (Kanchenjunga), Rai

(Sagarmatha), Tamang (Gaurishanker), Newar (Kathmandu), Maithali speaking (Mithila),

Gurung (Annapurna), Magar (Ridi) Bhojpuri speaking (central terai), Dalit (Khaptad), Awadhi

speaking (Lumbini) and Tharu (western terai). Sixty-eight districts were considered as focus

districts and were allocated that way. Of the remaining seven districts, in three districts in the

eastern Terai (Jhapa, Morang and Sunsari), majority or plurality of people seems to speak Nepali

and it was not obvious at what criteria these could be called focus districts for any of ELC group.

Hence these three districts compose a region without any focus group. The remaining four

districts are located in the most remote area of Nepal and they will be considered separately,

thereby making 13 regions, for the analysis. Based on this criterion, Acharya (2007) has Table

showing the number of districts, population of its focus groups and all other groups which is

12

reproduced as Table A1 in the appendix. We have also included the map of Nepal based on those

13 regions in the Appendix as Figure A1.

Our regional analysis will be based on 13 regions, whereas ELC analysis will be based on

12 groups, all the groups shown in Table 1 except Muslim and "others". The Muslims will be

included in the linguistic groups of Terai (where most of their sample is from), and we have left

the "others" category out of study in ELC comparison as it contains several groups and is not

very useful for policymaking. Each region has its own focus groups (except BC, which can be

considered as focus group for Rara special region), but the region east Terai has no focus group,

hence one unit less than in the regional analysis. With this established unit of analysis, next we

discuss the methodology we will be using for measuring poverty, inequality and social welfare.

IV. Methodology on Welfare, Inequality and Poverty Measurements: An Overview As discussed above, the objective of this paper is to look at the economic well-being of Nepalese

by regions and major ELC groups. In doing so, we concentrate on poverty, income distribution

and welfare of the defined regions and ELC groups. We follow the emphasis given by

researchers in integrating poverty and income distribution analysis in a coherent single

framework (Atkinson and Brandolini, 2008; see Deaton (1997) for lucid integrated expositions

of three concepts, welfare, poverty and inequality). We start with a social welfare function for

the government which is the welfare of all individuals/households in the economy.

(1) , ( )1 2, ,..., nW v y y y=

4 Understandably the latter category could have a natural homeland because it combines several very small groups. The Muslim group, however, represents 4% of the population and does not have a focus district because they are quite scattered. One of other groups given in Table 1 is higher in number than Muslims in all districts.

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where W is the social welfare and y's are some measure of living standard and n is size of

population in the country.5 We know that neither the living standard can be measured by one

variable nor the government actually selects level of individual's living standard as implied by

(1) but this type of representation is common in economics and allows us to focus on two most

important issues in economics: efficiency and equity, and hence on poverty, inequality and

welfare. Under the condition that proportional changes in all y's have the same proportional

effect on the aggregate social welfare, (1) can be written as:

(2) 1 2, ,..., nyy yW vμμ μ μ

⎛ ⎞= ⎜ ⎟

⎝ ⎠,

where μ is the mean of all y’s. If there is perfect equality, everyone would have mean level, μ, of

welfare. Then by the principle of transfers, first expounded by Dalton (1920).—the concept that

welfare will increase by any transfer of y from a richer to a poorer person, provided that the

transfer is not sufficiently large to change the relative position of these two individuals—social

welfare for any unequal allocation cannot be greater than the mean of the distribution, μ. Hence

(3) ( )1W I σμ= − ,

where I is the measure of inequality, taking the value of zero when the y’s are equally distributed

and increasing with disequalizing transfer; σ ranges from 0 to 1, can be considered a ethical

value that one would provide to the cost of inequality. The value of 1 implies that any degree of

inequality is a cost and is given by Iμ , meaning that the cost of inequality is equal to the amount

by which social welfare falls short of maximum that would be attained under perfect equality.

When σ takes the value of 0, then W = μ, inequality is taken out from the consideration of social

welfare function. Some argue that equity and efficiency are separate components of welfare, and

5 Y could be vector of basic capabilities such as income, education achievement, health provision, gender equality

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by allowing the value of σ to vary, one is giving an opportunity to balance the mix of efficiency

and equity (see Graff, 1977 and Atkinson, 1992). In any case, the choice of value for σ

represents ethical judgment on how costly the inequalities are for welfare. One thing is clear

though, whatever value is given to σ in (3), we cannot interpret measures of inequality as

measure of welfare. In our work below, we will use the value of 1 which is more common in

literature (Sen, 1976a; Deaton, 1997) and also 0.5.

Now, the next issue is how to measure inequality, I is social welfare function (3). One

can generate measures of inequality by specifying a social welfare function and solving for the

inequality measure, or starting from a measure of inequality and inquire with a social welfare

functions. We follow the first route and discuss Atkinson’s inequality measure which starts with

additive social welfare function6:

(4a) ( )1

1

1 11

ni

i

yW

n

ε

εε

−

==

−∑ ≠ and (4b) ( )

1

1ln ln 1n

ii

W yn

ε=

= =∑

where 0ε ≥ is “inequality aversion”, the higher value of ε, the more society is concerned about

inequality (Atkinson, 1970). The inequality measure associated with (4) is then

(5a)

11 1

1

11 1n

iA

i

yIn

ε ε

εμ

− −

=

⎡ ⎤⎛ ⎞= − ≠⎢ ⎥⎜ ⎟

⎢ ⎥⎝ ⎠⎣ ⎦∑ and (5b)

1

11 1

n ni

Ai

yI εμ=

⎛ ⎞= − =⎜ ⎟

⎝ ⎠∏

The Atkinson measure of inequalities, AI , range from 0 to 1, with zero representing no

inequality. The Atkinson measure in (5) is a special case of Generalized Entropy class of

inequality, EI , which we introduce below, as we will be using this measure in decomposing total

inequality into "within" and "between" inequalities.

and so on. 6 Note that the expression in (1) is a general form of social welfare function, but there are different specific functional forms in the literature, and Atkinson’s is one of them.

15

(6) ( ) 21

1 1 1n

iE

i

yIn

α

αα α μ=

⎡ ⎤⎛ ⎞= −⎢ ⎥⎜ ⎟− ⎢ ⎥⎝ ⎠⎣ ⎦

∑

EI ranges from 0 to ∝, with zero representing an equal distribution (all incomes identical) and

higher values representing higher levels of inequality. The parameter α represents the weight

given to distances between incomes at different parts of the income distribution. The lower value

of α is more sensitive to changes in lower tail of the distribution, and the higher value of α is

more sensitive to changes that affect the upper tail.7 The EI measure with parameters 0 and 1

become, with l’Hopital’s rule, two of Theil’s measures of inequality (Thiel , 1976). They are

called the mean log deviation and Theil index respectively and are given as follows:

(7a) ( )1

10 lnn

Ei i

In y

μ=

⎛ ⎞= ⎜

⎝ ⎠∑ ⎟ , and (7b) ( )

1

11 lnn

iE

i i

yIn y

μμ=

⎛ ⎞⎛ ⎞= ⎜ ⎟⎜ ⎟

⎝ ⎠ ⎝ ⎠∑

Setting α = 1- ε, the EI class becomes ordinarily equivalent to the Atkinson measure (Litchfield,

1999).

The other measure used for inequality is Gini coefficient which is closely associated with

Lorenz curve.8 Gini, GI , can also be measured using direct formula, and the more convenient

one is:

(8) ( )

1 21 1G i

i

niI y

n n nρ

μ+

= −− −

∑

7 The values that are used in the literature are 0, 1 and 2. A value of α = 0 gives more weight to distances between income in the lower tail; α = 1 applies equal weight across the distribution, while a value of α = 2 gives proportionately more weight to gaps in the upper tail. With α = 2, the With α = 2, the E measures becomes half the squared coefficient of variation EI measures becomes half the squared coefficient of variation 8 The Lorenz curve gives the distribution of income or consumption, measuring cumulative distribution of people on the x-axis (starting from the poorest) and cumulative distribution of income or other resources on the y-axis. If resources are equally distributed everyone receiving the same, the Lorenz curve will be a 45-degree line—a line of perfect equality. Gini index is the area between the Lorenz curve and the 45-degree line as a fraction of total area under the 45-degree line. If the area between the line of perfect equality and Lorenz curve is A, and the area under Lorenz curve is B, then Gini coefficient is equal to A/(A+B). Since A+B = 0.5, Gini = 2A = 1 - 2B.

16

where iρ is the rank of individual i in the y-distribution, counting from the top so that the richest

person has rank of 1. Note that the Gini lies between zero and one; when everyone has same μ,

the Gini coefficient is zero; while if one person has nμ and everyone else has zeros, then there

are n – 1 distinct nonzero absolute differences, each of which is nμ, so that the Gini is 1. The

associated social welfare function with Gini coefficient is one in which the y’s are weighted by

the ranks of each individual in the distribution, with the weights larger for the poor.

Now by plugging the value of inequality given by any of the expressions in (5a), (5b),

(7a), (7b) and (8), into (3), we can compute the value of social welfare. Sen (1976a) uses Gini

coefficient and measures social welfare, for Indian states using functional form ( )1 GIμ − .

Finally, we deal with poverty in the context of social welfare function. In terms of

measurement, what separates the social welfare from poverty is that the latter is defined as a

poverty line, below which people are defined as poor and above which they are nonpoor. In other

words, in the social welfare it is like giving zero weight to marginal benefits that accrue to the

nonpoor. In inequality literature, it is hardly the case that zero weight is given to nonpoor. In that

sense, poverty measures are special cases of social welfare measures. For the measurement

purpose, we start with Foster, Greer and Thorbecke concepts (1984), which can be reduced to a

couple of poverty measures.

(9) 1

1 1h

i

i

ypn z

α

α=

⎛ ⎞= −⎜ ⎟⎝ ⎠

∑

where p is the indicator for poverty measure; z is an agreed upon poverty line which looks like a

replacement of the mean income, μ, in inequality index.9 The number of poor people at or below

z in the economy is given by h and α is a “sensitivity” parameter. If α is low, then the p metric

9 1$ a day adjusted for purchasing power parity is the most common poverty lien used by World Bank.

17

weights all the individuals with incomes below z roughly the same. If α is high, those with the

lowest incomes (farthest below z) are given more weight in the measure. The higher value of p,

the more poverty there is in an economy. For α = 0, the formula is

(10) 0hpn

=

This is called the poverty headcount ratio, which gives the fraction of population who are

below the poverty line, people in poverty. See Deaton (1997) on how (10) it can be represented

in a social welfare function. One of the drawbacks of this measure is that it takes no account of

degree of poverty, and it would be unaffected even from a policy that makes the poor even

poorer. This measure yields the same number whether the poor are just below the poverty line

are very far below from the poverty line. According to this measure social welfare can be

increased by taking money from the very poor to lift some better off poor out of poverty (Deaton,

1997). The poverty gap measure could address this limitation which is the limiting case of (10)

if α = 1. In this case, the formula is

(11) 11

1 1h

i

i

ypn z=

⎛ ⎞= −⎜ ⎟⎝ ⎠

∑ .

According to (11), the contribution of individual i to poverty measure is larger, the poorer

is i. It can be interpreted as a per capita measure of total shortfall of individual welfare levels

below the poverty line. The poverty gap measure (11) has number of advantage over headcount

measure. This measure has the feature that social welfare is not altered if transferred among the

poor or among the nonpoor, it is no longer possible to increase social welfare by redistributing

resources from poor to the rich. The poverty gap measure will be increased by transfers from

poor to nonpoor, or from poor to less poor who thereby become nonpoor. But transfers among

the poor have no effect on the measure of poverty. Sen (1976b) developed poverty measure as

18

the average of the headcount and poverty gap measures weighted by the Gini coefficient of the

poor.

V. Poverty: Regional and ELC Dimensions

Using methodology developed in the previous section, we analyze poverty, inequality and

welfare among regions and ELC groups describe in Section II. First, we discuss the results based

on regions and then move to results based on ELC groups. In contrast to the methodology section

where our discussion proceeded from social welfare to inequality to poverty, here we follow the

reverse order and start our analysis from poverty. As is done in poverty measurement literature,

we use expenditure per capita rather than income per capita as the basis of our analysis. 10 The

exclusive use of only expenditure to measure inequality in the paper will somewhat

underestimates it for three reasons (showing poor people relatively better off than they actually

are).

First, in general, relatively poor people have higher expenditure than their income.

Second, the factors that are not complemented but are major part of economic well-being, wealth

and asset distribution, are more skewed towards the wealthier section of the population than

expenditure. Third, the expenditure inequality and inequalities of other basic capabilities (such as

education and heath) correlates positively thereby making poor in expenditure even poorer on

capabilities. Despite these shortcomings of our measure based on expenditure, our analysis is

very useful for policymaking. Even though we are not making full measure of inequalities, the

expenditure ranking captures the major part of it and the relative ranking of regions and ELC

10 In developing countries, a measure based on expenditure is considered more accurate than that based on income, as income tends to fluctuate more in these countries. This is true for Nepal as well where income depends, among other unstable factors, on volatile whether conditions.

19

groups, which is the center piece for policymaking, would not have been affected even if we

were able to include all factors mentioned above.

The first set of results are provided in Table A2, where average annual per capita

expenditure for all 13 regions, the poverty measures and cost for bringing all poor into poverty

line amount are shown. Column 2 shows the number of sample households that were covered in

the survey, the total of 3,912 (last row). Column 3 is the annual mean per capita expenditure,

obtained by dividing each household's expenditure by its number of household members. At the

national level, per capita mean annual expenditure is Rs 15,707.11 The median per capita annual

expenditure, however, is only Rs 10,427, implying highly skewed distribution of expenditure

toward very rich individuals. In other words, relatively a small number of high income people

have pulled up the mean value. Since our focus is to look at the relative position of the regions

and the groups (rather than absolute values), that does not change whether we use mean or

median, without any loss of generality, we base our analysis on mean expenditure.

The regional disparities are alarming. The magnitude of annual mean per capita

expenditure ranges from nine thousands in Khaptad (Dalit focus) region to, four times higher, 37

thousands in Kathmandu (Newar focus) region. The average per capita income of Kathmandu is

more than double, and that of Annapurna (the second richest and Gurung focus region) is almost

double the national average. Kathmandu is the only region whose per capita income is

comparable to India’s average GDP per capita. The other two regions with higher than national

average per capita are Eastern Terai and Central Terai. The second poorest region is

Gaurishanker (Tamang focus), followed by Kanchenjunga (Limbu focus) and Sagarmatha (Rai

11 At the conversion rate of Rs 75 per US$ (for average of years 2003 and 2004, years that the survey was conducted), this amount is equal to US$209 (US$ 0.57 for a day). At PPP conversion rate of 0.16 of the official exchange rate, this amount is equal to PPP international $1,306 ($3.6 per day).

20

focus) region. Western Terai (Tharu focus) is in the middle of the pack, neighbouring with

Mithial as slightly poorer region and Ridi as slightly richer region.

Column 4 (computed using equation 10) shows the percentage of people who are below

poverty line (defined at annual per capita income of Rs 7,696). The poverty line income is

defined on the basis of what is the

minimum calorie one would require

physically, a amount needed to purchase

bare minimum for subsistence. And people

falling below this line, which we call poor

in this report, are those who are at the state

of complete deprivation. Unfortunately, at

the national level, staggering 31% of people are below poverty line. Looking at the regions,

about half of the people in three regions (Khaptad (48%), Sagarmatha (47%) and Gaurishanker

(47%)) are below poverty line. The three richest regions, eastern Terai, Annapurna and

Kathmandu have less proportion of poor. For example, in Kathmandu, only 4% are poor, the

lowest percentage across regions. In Figure 1, we show the headcount and per capital

expenditure by regions.

Column 5 looks at the poverty gap ratio (equation 11), measuring the distribution of

people that falls below poverty line. When y is blow z, it contributes to poverty gap, if y is equal

to and above z its contribution to poverty gap is zero. Poverty gap can be interpreted as a per

capita measure of shortfall of individual (those below poverty line) income levels below the

poverty line. If the value of 1p = 0.1, 10% of the poverty level income has to be borne on per

0

0 .1

0 .2

0 .3

0 .4

0 .5

So

Kh

apta

d

Rar

a

Gau

rish

ank

er

Kan

chen

jun

ga

Sag

arm

ath

a

Mit

hila

Wes

tern

Ter

ai

Rid

i

Lu

mb

ini

Cen

ter

Ter

ai

Eas

tern

Ter

ai

An

nap

urn

a

Kat

hm

and

u 5 000

10000

15000

20000

25000

30000

35000

40000

urce: Nepal Living Standard Survey II

Figure 1. Annual per capita expenditure and poverty headcount by regions

Poverty headcount ratio (left scale)

Expenditure (NR, right scale)

0

0 .1

0 .2

0 .3

0 .4

0 .5

So

Kh

apta

d

Rar

a

Gau

rish

ank

er

Kan

chen

jun

ga

Sag

arm

ath

a

Mit

hila

Wes

tern

Ter

ai

Rid

i

Lu

mb

ini

Cen

ter

Ter

ai

Eas

tern

Ter

ai

An

nap

urn

a

Kat

hm

and

u 5 000

10000

15000

20000

25000

30000

35000

40000

urce: Nepal Living Standard Survey II

Figure 1. Annual per capita expenditure and poverty headcount by regions

Poverty headcount ratio (left scale)

Expenditure (NR, right scale)

21

capita basis to bring every poor at the poverty line income. When multiplied this fraction by z

( 1p z× ), we get per capita cost that needs to be borne to bring all poor at the poverty line.

If lump sum taxes and subsidies were possible without and inefficiency and

administrative cost, then 1p z× can be considered as a measure of the per capita cost to be

incurred in eliminating poverty. Again, when we multiply this expression by population

( 1p z× ×n ), we get total cost required to bring all poor at the poverty line amount (henceforth

this amount is referred as PLA, not to confuse with Maoist’s Peoples Liberation Army!). This is

the amount shown in Column 7 and should be read as it will cost Rs 2.754 billion to bring all

poor in Gaurishanker region at poverty line expenditure. In last column, we show PLA per poor

(amount needed to bring a single person at poverty line), and given by ( ) ( )1 1 0p z n h p p z× × ≡ .

Leaving aside the details of what these data represent, one thing is quite noticeable. At

the national level, the average per poor PLA is Rs 1,881 which about a-quarter less than the even

narrowly defined PLA. It means that not only the poor are in large number they are also

extremely poor. The average per capita PLA is as high as Rs 2,449 for Sagarmatha and Rs 2,356

for Gaurishanker region.

Looking from the national perspective, with national estimate of poverty gap of 0.076,

poverty line of Rs 7,696 and total number of population of 23.56 million, the PLA for the nation

is equal to about Rs 13,694 million (= 0.076 x 7696 x 23.56). So we can eliminate the defined

level of poverty with Rs 14 billion (rounded for simplicity). How big is Rs 14 billion at 2003

prices? It is 2.8% of Nepal’s 2003 nominal GDP of Rs 492 billion. Was it possible to cover this

amount by tax instrument or by achieving higher growth? Let us discuss mobilizing tax first.

Take an example of direct tax. In 2003, Nepal’s tax on income, profits, capital gains and

property was 2% of GDP at about Rs 8.5 billion (all figures in this example will be rounded to

22

make comprehensible). The additional tax of Rs 14 billon would make the total revenue from

this tax equal to Rs 22.5 billion. Then the share of this tax in total tax revenue in GDP would

have been 4.5%. How high is this ratio compared to our neighboring country India? India’s

income, profit and capital tax in 2003 was about 4.6% of GDP. So, it is completely plausible to

finance Nepal's poverty by mobilizing additional income, profit and capital gain taxes which are

very low presently and its negligible rates have distorted other economic activities negatively.

Indeed, later in the paper we will show that it is actually desirable to raise the share of income,

profit and capital gain and property tax in GDP to a very high level so that the tax revenue share

in GDP rises from present level of 9% to almost 30% in order to have equitable and efficient

economy.

Second, let us look from the growth aspect. Had Nepal’s GDP been grown at the rate of

India’s GDP, about three-quarters of that additional growth (India’s growth minus Nepal’s

growth) were enough to fill the poverty gap.12 Had Nepal achieved China's growth pace, it

would not have taken even half of the growth differential to eliminate the poverty. So looking

from either redistribution (tax) aspect or growth aspect, it's quite feasible to eliminate pove

However, we argue that unless ownership structure and other economic and social policies that

generated this poverty are addressed first, it is unlikely that Nepal could accelerate its GDP

growth rates in a substantial way. However, mobilizing higher income and profit taxes is not

only possible but also highly desirable from both efficiency and equity aspects.

rty.

Was there any attempt from the government in mitigating this deprivation? One could

argue the deprivation that started with asymmetric economic endowments, government

aggravated it through wrong budgetary priorities over time. In Table 2 we provide some

12 Note that Nepal’s GDP grew by about 3.7% whereas that of India and China grew by 7 and 9.5% annually from 1997 to 2007.

23

evidence on that line. Take Gaurishanker region. This region has about 11% of Nepal’s

population, requires 20% share of PLA to eliminate poverty but has received only 6% of

government expenditure annually, in the last five years. This region receives half of what it is

entitled on population proportionality and not even one-third of what is required to cover

poverty. In other words, the region needs double the share of resources than its population share

to cover poverty but the government is putting half the share of resources than its population

share. In Mithila region, the case is worse. The story repeats to several other regions.

Table 2. Summary of regional shares (in percent) Regions

Population share

Share of PLA Share government expenditure

Kanchenjunga 3.5 3.5 1.2 Sagarmatha 7.5 15.0 4.3 Eastern Terai 9.6 3.6 4.6 Gaurishanker 10.7 20.1 6.0 Kathmandu 7.8 0.9 50.0 Mithila 14.0 10.4 4.8 Annapurna 3.5 2.1 3.2 Ridi 13.2 11.1 7.7 Center Terai 9.7 6.6 3.7 Khaptad 6.0 9.4 6.0 Lumbini 4.9 6.7 3.5 Western Terai 8.3 9.3 3.7 Rara Territory 1.0 1.3 1.3 National 100 100.0 100 Note: the first column is the population share of region based on Population Census 2001 and Taken from Table Annex A1; the second column is the share of PLA, calculated from second last column of Table A1. The third column is taken from Acharya (2008) and shows the annual average share of government expenditure based on four years (1999-2003).

All resources are poured in Kathmandu (50%) to serve 7% of the country population.

This mismatch of population settlement and resources use has been the major reasons for Nepal's

socio-economic problems. This attempt by the government to "develop" urban "elite" centers has

not only made other parts of the country deprived of public services, it also has created enormous

distortions in the resource allocation. Hardly any investor in Nepal wants to invest in other

24

activities rather than buying real estates in these urban centers. The reason is that the price of real

estates in these centers are skyrocketing, as people throughout the country who can afford flux to

these centers to get schooling, hospitals and other facilities provided by government treasury.

Hence, the real benefits of the government resources are translated into private gains to the minor

fraction of the population who can afford to be in these centers by imposing huge costs to the

society. The poverty and inequalities are the cumulative outcome of these wrong policies.

It is important to allocate government expenditure in such a way that the regions which

are poorer should get higher shares than their population shares. This, we see as a minimum

requirement for building a efficient, peaceful and just society (on how to allocate resources in

federal Nepal, see Acharya (2009a)).

Before leaving this section, it should be noted that the average income of the poor in all

regions is very similar around 5-6 thousands as shown in column 5 (mean of the lowest 30%) of

Table A2. This is not surprising given the fact that the poverty line is defined at such a meager

amount and must of them might fall below that amount (note that 31% is the national cutoff for

poor and nonpoor), there is not much room for bigger discrepancy, as this is the barely

subsistence level. However, this no matter which part of the country they live, the 30% people

have almost the same level of deprivation. The only difference is that some regions have higher

proportion of people under poverty.

Next we examine the poverty measures by major ethnic, language and caste groups. A

question may arise what different results we can expect when the regions that were the basis of

analysis above were also formed using the ELC settlement clustering. It is true that the regions

are formed in a way that the share of ELC group in its focus region's population is maximized.

Despite this mechanism, as is clear from Table A1, only two groups are in majority in their focus

25

regions’ population, other nine groups are second place after BC. Furthermore, it is the case that

significant portion of ELC members live outside of their own focus region. Therefore, the results

based on regions may not collaborate with those based on ELC groups. Furthermore for a

policymaking purpose, it is important to know poverty and inequality measures based on ELC

groups. More so in Nepal today, where the class divide and ethnic divide seem to be

complementing each other, sometime creating confusion whether appropriate policy should be

class based or ethnicity based.

We have provided the poverty measures by 12 ELC groups in Annex Table A3. Again,

the inequality is equally alarming as was the case across regions. The per capita expenditure of

richest group (Newar, with Rs 32,696) is four times higher than that of poorest group (Rs 7,754

of Awadhi speaking group—level, not very different from poverty line). To put it another way,

with average family of four, an average Awadhi speaking family has the same level of

consumption as that of an average member in the Newar community. This is a startling level of

inequality. Newar’s per capita expenditure is more than double the income of national average.

Gurung, which has the second highest per capita expenditure, has only 68% per capita

expenditure of Newar. The BC's has per capita expenditure is Rs 19,370, less than 60% of that of

Newar.

In Awadhi speaking community 70% are below poverty followed by Tamang (61%), Rai

(49%) and Limbu (41%). There are only

three groups, the same three groups that

have the highest expenditure, that have

less headcount ratio than national average

of 30.9%. Among them, the lowest 0

0 .1

0 .2

0 .3

0 .4

0 .5

0 .6

0 .7

0 .8

Aw

adh

i sp

eak

ing

Tam

ang

Dal

it

Bh

ojp

uri

sp

eak

ing

Rai

Th

aru

Mai

thili

sp

eak

ing

Lim

bu

Mag

ar

BC

Gu

run

g

New

ar

Kat

hm

and

u 5 000

10000

15000

20000

25000

30000

35000

40000

Source: Nepal Living Standard Survey II

Figure 2. Annual per capita expenditure and poverty headcount by ELC groups

Poverty headcount ratio (left scale) Expenditure

(NR, right scale)

0

0 .1

0 .2

0 .3

0 .4

0 .5

0 .6

0 .7

0 .8

Aw

adh

i sp

eak

ing

Tam

ang

Dal

it

Bh

ojp

uri

sp

eak

ing

Rai

Th

aru

Mai

thili

sp

eak

ing

Lim

bu

Mag

ar

BC

Gu

run

g

New

ar

Kat

hm

and

u 5 000

10000

15000

20000

25000

30000

35000

40000

Source: Nepal Living Standard Survey II

Figure 2. Annual per capita expenditure and poverty headcount by ELC groups

Poverty headcount ratio (left scale) Expenditure

(NR, right scale)

26

poverty headcount is in Newar (14%), with similar ranking for BC and Gurung at 18%. In Figure

2, we plot the per capita expenditure and poverty headcount by ELC groups.

In terms of per capita PLA, the highest amount required is for Tamang at Rs 2,776 (36%

of poverty line income). The second poorest member is from Rai group who requires Rs 2,320

per capita PLA. One interesting point is that a poor Newar, whose group has such a high per

capita expenditure and so low headcount ratio, requires quite a large amount to get out of poverty

(Rs 1,936). It reflects that those Newar who are in poverty are a lot poorer than own ethnic

members, more so than poor from other ethnic groups while compared to their counterparts. This

also is a clear indication that if social or economic programs are designed based on ethnic, caste

or language groups, rather than on income/economic aspects, we are going to miss the target at

best and aggravate the situation at worst. By targeting ethnicity, we might be making already rich

person a super rich in that group, whereas neglecting the poor from other non-targeted

population section in perpetual deprivation.

Altogether there were about 7.3 million people under poverty in 2003, whose

distribution by ELC groups is given in Figure 3. The largest numbers of poor are BC, 1.19

million. The second largest number of poor

are Tamangs and the smallest number are

Gurungs. From poverty alleviation point of

view, what is important is the number of

people that are in poverty not the share of

poor in any particular group. As a result,

we have presented the absolute number of

poor in this diagram. One point that clearly

94143

247354

429503

570666672

767791

8541190

0 200 400 600 800 1000 1200

Gurung

Limbu

Newar

Awadhi speaking

Rai

Magar

Bhojpuri speaking

Others

Tharu

Maithili speaking

Dalit

Tamang

BC

Source: Nepal Living Standard Survey II

Figure 3. Number of poor (in thousands) by major groups

94143

247354

429503

570666672

767791

8541190

0 200 400 600 800 1000 1200

Gurung

Limbu

Newar

Awadhi speaking

Rai

Magar

Bhojpuri speaking

Others

Tharu

Maithili speaking

Dalit

Tamang

BC

Source: Nepal Living Standard Survey II

Figure 3. Number of poor (in thousands) by major groups

27

emerges from this figure is that although BC group ranks third after Newar and Gurung in per

capita expenditure, there are largest numbers of poor in this group. This is further evidence that

the poverty alleviation programs should not be targeted for some section of the population; they

should be very general and should target the poor based on class division. Any positive

discrimination in terms of ELC could restrict the participation of these poor from BC group,

making the policy counterproductive both from equity and efficiency points of view.

VI. Inequality: Regional and ELC Dimensions

The poverty measure provided in Tables A2 and A3 capture the incidence of poverty (percent of

poor), and inter-region and inter-group comparisons of poverty and income distribution. But it

does not tell us about how unequally distributed the expenditure is across members within a

region and across members within a group. In other words, these measures do not tell us about

within region and within ELC group inequalities. That will be dealt in this section. In Table A4,

we provide three measures, Gini coefficient (equation 8b), Generalized Entropy with α = 0

(given by equation 7a and denoted by E(0) in the table) and Atkinson’s measure with ε = 1

(given by equation 5b and denoted by A(1) in the table). Note that the value of α = 0 is

consistent with having ε = 1, as discussed in Section III. These inequality measures for regions

are within region and for ELC groups within group inequalities.

Whichever three measures is used, the ranking of regions based on inequality coefficients

is the same. Annapurna is the region with most unequal distribution with Gini of 54%, E(0) of

51% and A(1) of 40%. The second most unequal region is Lumbini with Gini of 46%, followed

by Kathmandu, Sagarmatha and Central Terai. Leaving aside the Rara with lowest Gini of 26%,

the most equal regions are Khaptad (Gini of 26%) and Kanchanjunga (with Gini of 29%).

28

Annapurna, Lumbini and Kathmandu are the regions that have higher Gini coefficients than that

for the nation (more unequal than the nation). Kathamandu and Annapurna regions are the

richest ones and the high within inequalities indicate that the richness is not distributed across.

The similar measures for 13 ELC groups are given in Table A5.13 As in region, group-

wise ranking of inequality measures is the same whichever of three measures is used. The most

unequal group is Newar (with Gini of 53%), followed by Gurung (Gini of 48%), others rich and

BC (with Gini of 44%, same as for the nation). Hence, the richest three groups (Newar, Gurung

and BC) are also the most unequal ones. The poorest Dalits are also the most egalitarian ones. As

a result in terms of welfare measure, the richest three groups are not as much better off as in

terms of per capita income; they have higher cost of inequality to incur which we will discuss

later.

Next, we want to complement these within measure of inequalities with between

measures. The methodology for decomposing the aggregate inequality into two components,

within and between, started with Bourguignon (1979) and there have been several papers after

(see, Shorrocks and Wan, 2004; Cowell and Jenkins,1995). Among the measures of inequality

that are consistent with decomposition are Generalized Entropy members including Atkinson’s

measure. Gini can be decomposed but it will have residual, which does not have a straight

forward interpretation.

We provide the detail decomposition in Annex B for Generalized Entropy when α = 0,

E(0), which shows that the final formula turns out to be as follows (given as equation B4 in

Annex B):

13 Note that here we have put three language groups (Maithili speaking, Bhojpuri speaking and Awadhi speaking, into one as Medhesi group). And we included two “others" categories. "Others" includes the mostly marginalized groups and "others rich" includes relatively affluent group (Bengali Marwari, Punjabi, and Thakali).

29

(12) ( )13 13

01 1

0 lE r rr r r

I E n μυ υμ= =

⎛ ⎞= + ⎜ ⎟

⎝ ⎠∑ ∑ ,

where the sum is over 13 regions (we discuss it for regions, but same is the case for ELC

groups); rμ is regional mean incomes, and rυ regional population shares in the country. The

first component on the right hand side is the weighted average of the 13 regions’ inequality

values, weighted by regional population shares. It is considered “within region” inequality. And

the second component is “between region” contribution to inequality. This is obtained by

replacing the income of each person by mean income of the region the person resides. Thus the

overall level of inequality of a country is the exact sum of average inequality within region and

inequality due purely to differences in average income between regions. Inequality falls if the

relative income across all individuals in region r are equalized (within component) and aggregate

inequality falls if regional mean income differences are eliminated, holding constant relative

incomes within region

Using formula in 12 for the results given in Table A4 (for regions) and A5 (for ELC

groups), we can compute the within component inequality for Nepal. And using the mean

expenditure from Table A2 and A3 we can compute the between component for Nepal. The

results are given in Table 3.

Table 3. Within and between decomposition of inequality measures, 2003-2004 Region ELC groups

E(0) A(1) E(0) A(1) Within inequality 0.240 0.229 0.251 0.244 Between inequality 0.076 0.054 0.064 0.035 Total inequality 0.316 0.283 0.315 0.279 Share of within in total inequality 75.9 80.9 79.7 87.5 Share of between in total inequality 24.1 19.1 20.3 12.5 Source: Authors' calculation based on Nepal Living Standard Survey, II 2003/2004 Taking regional aspect first, about 76 to 81% of inequalities is within the people in the region

and remaining 19 to 24% is between regions. Our range includes the 23.7% share of between

30

inequality measured by Elber et al (2007) with 10 groups in Nepal).14 By international standard

between-region inequality of this magnitude (close to one-quarter of total inequality) is high,

indicating that Nepal has a lot of regional disparities. The results show that about three-quarter of

inequalities in Nepal are across people within the same region. The within group inequalities (in

the range of 80 to 88%) are even higher than within region inequalities. The members of the

same groups are more unequal than members of the same region. The results mean that at least

80% of inequality is spread across members of the same ELC group. This finding intra group

disparity suggests that any inequality reducing programs should be class based not particular

group based.

VII. Welfare Measure

Finally, we compute the welfare and cost of inequality using the expression given in

equation (3) and taking Gini coefficients as measure of inequality. In most part of the analysis,

we use σ = 1, meaning that each Rs of inequality generates equivalent amount of cost. This may

sounds radical, but this is the value frequently used in the literature. Sen (1976) uses this concept

in ranking Indian states and Deaton (1997) uses this concept to see the welfare differences

between black and white during Apartheid regime in South Africa. Moreover, those who are

following Nepal’s stagnation, loss of lives, illiteracy, and strike-crippling economy, assuming

value of σ at unity is somewhat realistic. These all social evils are the costs associated with, to a

greater extent, inequalities. We also use σ = 0.5 towards the end. But the logic of the analysis

and the policy prescription provided using σ = 1 is equally valid for any values of σ, only the

magnitude of effect differs.

14 For more recent on how the between group measure is sensitive to number of partition, see Elber et al. (2007) and

31

The cost of inequality varies by regions as do their Gini coefficients. For Kathmandu

which has very high Gini coefficient, with per capita expenditure of Rs 37,031, the welfare is

equal to just Rs 20,784 and the inequality cost is Rs 16,247 per dweller in Kathmandu region.

The per person cost of inequality in Annapurna region is Rs 15,857. Because of this inequality,

Kathmandu region, which has four times higher per capita expenditure than that in the poorest

region Khaptad, is only three-times better-off than Khaptad in terms of welfare. Note that what

matters for the living standard is the welfare not per capita income or expenditure.

At the aggregate level for Nepal, we show the welfare and cost measures in Table 4 (row

1 with Nepal’s inequality). With national per capita expenditure of Rs 15,707 (Table A2), the

welfare will be equal to Rs 8,864, and the welfare cost is equal to Rs 6,864. If we multiply this

per capita numbers by total population of 23.56 million, we get the total welfare of Rs 208

billion and whopping cost of Rs 162 billion. Although the economy has total expenditure of Rs

370 billion (shown in last column), 44% has been wasted as cost in terms of social welfare.

Table 4. Welfare and cost of inequalities Per person (Rs) Total (millions of Rs)

Use of Welfare Welfare cost Welfare Welfare cost

Welfare plus cost (millions

of Rs)

Nepal’s inequality 8,843 6,864 208,342 161,715 370,057 Finland’s inequality 11,561 4,146 272,384 97,673 370,057 Difference (Nepal – Finland) -2,718 2,718 -64,042 64,042 0

Source: Authors' calculation based on Nepal Living Standard Survey, II 2003/2004

Next, we do a thought experiment, the sketch of which is as follows. Looking at Nepal’s

poverty and inequalities we feel that the best model for Nepal to adopt is that of Nordic

(Denmark, Finland, Iceland, Norway, and Sweden) regions. On that premises, we want to choose

average Gini of the Nordic (developed capitalist) countries in terms of inequality and using their

indicators we want to compute two things: (1) what would be the welfare gain for Nepal in table

Rao and Lanjouw (2008).

32

4 by adopting the representative Nordic country's equity preference, and (2) what should be the

tax rate that Nepal should impose to attain that level of equity.

In Nordic countries, the median Gini seems to be that of Iceland (0.25) but we take that

of Finland (0.26), which is second highest among five Nordic countries (Wikipedia, website).15

The region in Nepal with Gini that is closer to that of Finland is Khaptad which, interestingly,

has the same Gini (0.264 to be precise). The welfare and cost calculation for Nepal based on Gini

of Finland is given in Table 4 (row 2). The cost of inequality with actual Gini of Nepal falls from

162 billion to 98 billion with Finland's Gini. Consequently, the welfare rises from Rs 208 billion

to Rs 272 billion. So if we maintain income distribution like in Finland in Nepal, it would

transfer Rs 64 billion cost of inequality into social welfare gain. The increase in welfare from

Nepal’s Gini to Finland’s Gini is 31% (=64/208).

Even if one would like to use smaller value of σ, the trend in the benefit is the same, only

magnitude changes. So far we have used ( )11 GW Iμ= −

)0.5I

and used Nepal’s Gini and Finland’s

Gini. Even if we consider , being very tolerant to inequality (meaning that only

half of the inequality is considered as costs), still Nepal’s welfare would increase by 15% from

Rs 277 billion to Rs 317 billion, if inequality was eliminated.

(1 GW μ= −

Next, we calculate the tax rate that Nepal needs to adopt if it wants to be as equal as

Nordic countries. For that purpose, we take the income share of the richest 20% and poorest 20%

in these Nordic countries and calculate their ratio. We do the same for Nepal and then compare

what should be the tax rate in Nepal for the richest 20% people if we want the rich in Nepal to be

in the same footing as the rich in Nordic regions, compared to their poorest 20% compatriots.

15 In 2005, the Gini coefficients for Sweden was 0.23, for Denmark 0.24, for Iceland 0.25, for Finland 0.26 and for Norway 0.28. For India the Gini was 0.36, and for Canada 0.32. In Nepal's case we have used o.437 which is based

33

The income shares of these two groups for different countries are given in Table 5. Among the

countries listed, the level of disparities in Nepal is mind boggling. The richest 20% people

receive 54.6%, more than half, of Nepal's income. Their income is nine-times the income of

poorest 20% in Nepal, whereas in Nordic countries the income level of the richest to the poorest

20% is at factor of 4.

These income shares are based on before-tax income. If we consider after-tax income

(which is more relevant concept in inequality measure) the poor of Nepal vis-à-vis their richer

counterpart compared to Nordic region’s poor vis-à-vis their richer counterpart ranks even

worse. Because of highly progressive tax system in Nordic region (pay according to ability), the

after-tax income factor of richest to poorest 20% will be substantially smaller than 4. But in

Nepal, as the tax system is almost regressive (as the share of direct tax is low most of the tax

coming from goods and services including necessities) the after-tax income factor will only

slightly lower than 9.16

Next we ask the following question. What percent of income has to be taken out from the

richest 20% people in Nepal to make them richer by factor of 4 compared to poorest 20% in

Nepal so that the income ratio of these two groups in Nepal is the same as in Nordic countries? It

works that we have to take out 55% of income from the richest 20% people in Nepal (=(9 – 4) /

9). If we tax the richest 20% people by 55%, or in other words, take the extra income of richest

20% that is in excess to Nordic's average, then the collected tax revenue would be about Rs 103

billion (mean income of Rs 40 thousand for the richest 20% people multiplied by 4.7 million

on expenditure, however, in income basis, the inequality was even higher at 0.47 (WDI database). Hence, income based Gini would indicate even higher cost of inequality for Nepal. 16 There is another reason, why the poorest 20% in Nepal fares even worse than those in Nordic region compared to their respective 20% richest compatriots. As income from all sources in Nepal are not under government scrutiny and there is a tendency to underreport the income in surveys (especially from the richer section of the population), the income gap between the richest and poorest 20% would have been even higher than given by factor of 9.

34

20% richest people multiplied by 0.55 tax rate). The 55% is the total tax rate per capita (not per

household) that need to levy and to compute additional tax rate, we have to deduct the tax rates

that the richest 20% are paying from the 55%. As data on tax revenue by income strata are not

available in Nepal, we have no way of knowing precisely what tax rates they are paying.

Therefore we need to make a logical calculation, which is as follows.

Table 5. Income share for selected quartile population for selected countries Income share held by Canada Denmark Sweden Norway Finland India Nepal Lowest 20% 7.2 8.3 9.1 9.6 9.6 8.1 6.0 Highest 20% 39.9 35.8 36.6 37.2 36.7 45.3 54.6 Highest to lowest 20% (factor) 5.5 4.3 4.0 3.9 3.8 5.6 9.1 Source: World Bank, World Development Index (WDI) database Note: the data for all countries are for year 2000 except that for Denmark it is for 1997, for India for 2004 and for Nepal for 2003 (based on the same NLSS II data that we have used in this paper).

In 2003, the total tax revenue in Nepal was Rs 42 billion. If we assume that about half of

the tax was paid by the richest 20%, then they were contributing Rs 21 billion (in reality

however, the 20% will be paying lot less than 50% as tax system is not that progressive in

Nepal). In that case, out of would be tax revenue of Rs 103 billlion at 55% tax rate that we

computed above, we need to collect only additional of Rs 83 billion (= 103 – 21). To raise this

Rs 83 billion, the richest 20% has to be imposed additional tax rates of about 46%.17 Note that

this additional tax rate is somewhat under-rated as the richest 20% in Nepal are not paying 50%

of total tax that we made the assumption to reach at this conclusion.

With this tax rates, Nepal’s total tax in 2003 would have been, instead of Rs 42 billion,

Rs 130 billion ( = 42 + 88), which is 26% of GDP of Rs 492 billion. How does this 26% share of

tax revenue in GDP compare? It is lower to that of Norway and Denmark levels but higher than

that of Sweden and Finland. Interestingly, the share of government budget in GDP is also about

However, in Nordic countries, the income of all groups is under government scrutiny because of the well developed tax network and the data resembles the reality.

35

26%. Hence, if government was not getting foreign aid and loan, it would have to raise that much

tax to finance its budget anyway.

Table 6. Share of different taxes on GDP for selected countries Tax category Canada Denmark Sweden Norway Finland India Nepal Total tax 14.1 31.6 20.8 28.7 22.3 10.3 9.0 Income, profits, capital gains & property 10.6 15.3 7.8 16.6 8.8 4.7 2.1 Goods and services 3.3 16.2 13.0 12.1 13.5 3.8 4.3 International trade 0.2 - - 0.1 - 1.8 2.6 Exports - - - - 0.1 Source: World Bank, World Development Index (WDI) database Note: the data for all countries are average of three years, 2004-2006. The dashes (-) indicate that the shares of these taxes for corresponding countries were either zeros or near zeros.

Since the purpose of this paper is not to develop a tax proposal, we restrain from detail

discussion on which taxes to use to raise this revenue (that is done in more detail in Acharya

(2008)). Here we just reiterate the fact that (1) Nepal is under-taxed (2) the role of direct tax

(income, property, capital gains and property—IPCP) is even lower creating all sort of distortion

in the country, (3) the taxes and goods and services are regressive as they are levied even on the

necessities and (4) there is too much tax on international trade (except the luxury items taxes on

other items should be minimized). With this, we argue that most of the additional tax should

come from IPCP source, and some of it from taxes on goods and services, with exception for

necessities.

We are aware that, as Sen (1999) mentions, there is a distinction between income as a

unit in which to measure inequality and income as the measure of vehicle of inequality reduction.

The income gaps must not be taken as suggestion that the corresponding income transfers would

remedy the disparities. But what this “what if” exercise tells us is that if we can make Nepal like

one of Nordic countries in terms of income distribution, we can eliminate poverty by spending

17 This is obtained by using the formula that tax rate = the additional amount that needs to be raised (Rs 88 billion) divided by the product of average per capita income of the richest 20% people (Rs 40,000) and the number of richest people (4.7 million).

36

14% of money that is collected from taxes. The additional tax money that could be raised from

this process could be used to finance development projects, education and health services and to

increase other aspects of human capabilities. Besides, there is additional benefit of this reduced

inequality; the social welfare of the country would increase by almost one-third from what is

today.

This exercise also shows that one does not need to be a socialist to ask for action in

income distribution in Nepal. Any serious economist who cares about Nepal's development

would agree that the reduction of inequality is a precondition for Nepal to take off. Even though

the tax of 55% to the richest 20% people sounds radical, this will still leave the richer in Nepal as

rich as in Nordic country compared to their respective poorer fellow-citizens. A balancing

between equity and efficiency as done by a Nordic country, a developed capitalist economy,

would allow Nepal to mobilize tremendous amount of resources, enabling it to finance the

government budget without foreign assistance, with much more equitable society. From

country's perspective we have shown that social welfare increases by reducing inequality through

strong tax system (Table 5). We also argue that despite the short run cost of taxes to them, the

richest 20% will also be better off in the long run by making contribution for building a peaceful

and prosperous Nepal.

VIII. Conclusions and Policy Recommendations

Using household level data in Nepal, we have provided a novel analysis based on the major

population groups (that are more than 1% of the total population) and their settlement regions.

This analysis will be a vital piece of information for policymakers in Nepal who are genuinely

37

concerned with tackling the poverty, stagnation, regional and ethnic, language and caste (ELC)

group disparities.

We took the minimalist approach in measuring inequalities by looking only at income (or

expenditure). Considering the coupling of poverty with other capability indicators in Nepal (the

poor being less educated, having shorter life expectancy, owning less assets and wealth), one

could easily guess that the inequalities that we have measured in the paper are underestimated.

Economic inequalities or inequalities in capabilities across regions (ELC groups) and within

region (within groups) would have been even much more intensified than recorded in this

research. However, despite this limitation in terms of measuring the magnitude of inequalities

correctly, we are convinced that the inequalities that we have measured in the paper in terms of

region, ELC groups (both within and between) alone could provide a sufficient guideline in

making right policy decisions on how to correct the inequalities and release the forces of

development in Nepal.

At first sight, the major factors of inequalities may seem like, caste and ethnicity. But

deeper analysis shows that while all these factors have sharpened the disparities, the major factor

is endowment (class) related. The class factor has intensified the disadvantages related to the

other form of disparities. Poverty is in each region and group. Inequalities, both within region

and between region and within group or between group, are alarming.

Simple calculation shows that if we can attain the direct tax rate of that of India, we

would be able to bring the people above the poverty line. In 2003, the cost of poverty elimination

in Nepalese Rupees (Rs) would be 14 billion. Using a comparable tax rate as India, on income,

profit and capital gains, Nepal could finance all the poverty gap resources. Or, if Nepal could

attain even half of the growth rates that China and India are achieving, that amount would have

38

been more than enough to address poverty. However, as we argue in the paper, to remove

poverty structurally, Nepal needs more well thought-out multi-faceted policies.

There is a debate that reducing economic inequality creates conflict with economic

efficiency, a trade-off between higher growth and more equality. For some other countries,

which have more egalitarian distribution of ownership (as in Nordic region), it could reasonable

be the case that efficiency and equity are in conflict. But in Nepal's case, looking at the rate of

growth of the Nepalese economy and the level of inequalities across groups and regions, it is

hard not to conclude that the distributional concerns are congruent with economic efficiency.

Any policy that intends to promote development in Nepal should focus on distributional aspects.

Unless Nepal addresses the poverty and massive inequalities, it will not be able to accelerate its

growth process in a noticeable way; the economy will continue to stagnate.

Economic evidence suggests that the roots of deprivation and economic inequality in a

private-ownership country like Nepal are unequal ownership of resources. Hence, any policy

measures that do not alter the distribution of resources cannot be very effective in addressing

inequalities. The damning impact of highly skewed ownership pattern is aggravated by neglect of

public education and health services. As the role of direct taxation based on income, profit,

capital gains and property is negligible, this inequality has perpetuated over time. Nepal’s

poverty and income inequalities are not the outcome of market exchange per se but the outcome

of very unequal distribution of opportunities—to use Sen's terminology—in terms of human

capabilities (income, education, health, wealth and other social opportunities).

To put Nepal in growth trajectory and in development path from the state of stagnation and

massive inequalities, Nepal needs to adopt four complimentary set of policies together and soon.

First policy recommendation is for comprehensive land reform so that the land is in the hands of

39

actual farmers. Even if there is not much land to distribute, a well-thought-out land reform

program will unleash development related positive chain effects. Once the farmers are the

owners of the land, not only would the productivity of the land increase, it will also provide

much needed positive ripple effects by boosting self-esteem of the disadvantage people and by

promoting local democracy, the two paramount conditional factors for Nepal's development.

While land is perhaps the most obvious asset to redistribute, its effects to raise standard

of living for all Nepalese would be limited. For that to realize, the distribution of land should be

accompanied by three other policies. They are (1) massive investment in decent, compulsory and

free public education till grade 10 for all school age children, (2) progressive income tax that

transfer resources from the rich to the poor and more so from unproductive investment such as

real estate to other productive investment and (3) government budgetary resource allocation in

the region in proportion to the average share of population and poor population in the region.

In this context, we argue that the policies in Nepal should follow the model of one of

Nordic countries. In doing so, the richest 20% people have to pay additional tax of 46% from

their income. Consequently, Nepal’ share of share of tax revenue in GDP would increase from

present meager level of 9% to about 26%. We recommend that this increase should come mainly

from taxes on income, profit, capital gains and property. This increase in direct taxes will

eliminate most of the distortions that are crippling Nepal's economy today, from corruption, to

massive illiteracy, to unwarranted protests, to frequent demonstrations, to disrespect for

handwork, to being a millionaire overnight by any means, to lawlessness. Moreover, by lowering

Nepal's inequality to the level of Finland, Nepal would be able to finance the above-proposed

education, health services and also raise social welfare by 33%. If we can have all four policies

together, then it will initiate a responsible government which would concentrate on how to

40

effectively use its own resources. The strong internal resource mobilization will change the

dynamics of the government; it would have to start prioritizing on which sectors to mobilize

foreign investment and how to make conducive environment for it rather than wasting time on

determining how it should be able to get more foreign aid.

In Nepal there is widespread discussion and persuasive arguments in favour of positive

discrimination in employment and other economic policies, favoring ethnic and caste groups.

However, our results suggest that the basis of economic policies should be class and endowment

based, not ELC group based. Consider some of our findings: (1) Brahman and Chhetri have the

largest number of poor, despite being the mainstream groups (2) Poor Newars are desperately

poor, not favoring better than the poor of other groups, even though Newar is the richest group in

Nepal, (3) there are massive intra-group inequalities (larger inter-group inequalities) and (4) the

past policies have systematically created regional imbalance through elite enclave and favored a

handful of rich rather than being for or against any ELC groups. The evidence of this finding is

that the richest three groups Newar, Gurung and Brahman and Chhetri have the largest

inequalities. If government were favoring any one of these three groups but not the richest, then

one should find income distribution within members of these groups more egalitarian.

These all findings suggest that positive discrimination favoring some groups is the wrong

way to address inequalities and it would be counter-productive18. If remedies are wrongly

devised, there will be regressive consequences. The target should be to uplift the downtrodden

(residing in any region and member of any ELC group) and allow them be a part of the

production process by providing them resources rather than simply focusing on caste, ethnicity

and language groups. If economic and social policies are ELC group based, there is a possibility

41

of friendly fire (terminology used by Sen (2005) for India's situation)—whereby an army is hit

by its own firing rather than by enemy shelling—and the affirmative action that was supposed to

help the downtrodden helps the affluent rather.

References Acharya, Ram C. “A Model for Political Restructuring and Electoral System of Federal Nepal:

Building on the Strength of Ethnic Diversities and Regional Complementarities”, Draft, February 2007.

____________. “Nepal's Fiscal Policy: In Need of a Colossal Change”, Draft, July 2008. ____________. “A Model of Fiscal Federalism in Nepal” (2009a, in progress). ____________. “Education Sector in Nepal: What Should Be the Master Plan? (2009b, in progress). Atkinson, Anthony. B. “On the Measurement of Inequality”, Journal of Economic Theory,

1970, 2, pp. 244-63. ____________. “Measuring Inequality and Differing Social Judgements”, Research on

Economic Inequality, 3, 1992, pp. 29-56. Atkinson, Anthony B. and Brandolini, Andrea. “On Analysing the World Distribution of

Income”, ECINEQ Working Paper 2008-97, October 2008. Bourguignon, Francois. “Decomposable Income Inequality Measures.” Econometrica 47(4), 1979, pp. 901-920. Central Bureau of Statistics, Nepal. “Poverty Trends in Nepal 1995-96 and 2003-04”,

September 2005. ____________. “Nepal Living Standard Survey 2003/2004, Statistical Report, Volume 1”,

December 2004. Cowell, Frank A. and Jenkins Stephen P. “How Much Inequality Can We Explain? A

Methodology and an Application to the United States”, Economic Journal, 105, March 1995, pp. 421-430.

Dalton, H. “The Measurement of Inequality of Incomes”, Economic Journal, 1920, 30, pp. 348-61.

Deaton, A. "The Analysis of Household Surveys: A Microeconomic Approach to Development Policy. John Hopkins University Press, Baltimore, 1997.

Elbers Chris; Lanjouw, Peter; Mistiaen, Johan A and Ozler, Berk. “Reinterpreting Between-Group Inequality”, Journal of Economic Inequality, 2007.

Foster, James; Greer, J. and Thorbecke. Eric. "A Class of Decomposable Poverty Measures", Economerica, 52, 1984, pp. 761-65.

Graff, Jan de-Villers. “Equity and Efficiency as Components of the General Welfare”, South African Journal of Economics, 45, 1977, pp. 362-75.

Lanjouw, Peter and Rao, Vijaendra. “Revisiting Between-Group Inequality Measurement: An Application to the Dynamics of Caste Inequality in Two Indian Villages, World Bank, 2008.

Pradhan, Rajendra and Shrestha Ava. “Ethnic and Caste Diversity: Implications for Development, Working Paper Series No. 4, Asian Development Bank, June 2005.

18 However, positive discrimination in employment and other economic activities is required for groups which are endangered and marginalized (they constitute less than 1% of the total population). This issue is not dealt in the paper.

42

Sen, Amartya. “The Argumentative Indian: Writing on Indian History, Culture and Identity”, Picador: New York, 2005.

____________. Development as Freedom, Anchor Books: New York, 1999. ____________. India: Economic Development and Social Opportunity, Oxford University Press,

New Delhi, 1995. ____________. “Real National Income”, Review of Economic Studies, 43, 1976a, pp. 19-39. ___________. “Poverty an Ordinal Approach to Measurement", Econometrica, 46, 1976b, pp.

437-46. Shorrocks, A. and Wan, G. "Spatial Decomposition of Inequality" Journal of Economic Geography, 2004, 5(1) pg 59-81. The World Bank. “Unequal Citizens: Gender, Caste and Ethnic Exclusion in Nepal”, 2006a. ___________. “Nepal Resilience Amidst Conflict: An Assessment of Poverty in Nepal, 1995-

1996 and 2003-04”, June 2006b. ___________. World Development Index database, 2009. Thiel, H. Economics and Information Theory, North Holland, Amsterdam: 1967. Wikipedia, website. http://en.wikipedia.org/wiki/List_of_countries_by_income_equality.

43

Table A1. Summary by regions and provinces

Regions Focus group Number of

existing districts*

Population (%)

Region’s share in focus group’s population (%)

Focus group’s share in region’s population (%)

BC’s share in region’s population (%)

Kanchenjunga region Limbu 3 2.0 49.5 39.5 26.6 Sagarmatha region Rai 8 7.0 63.2 25.2 29.7 Eastern terai region - 3 9.2 8.3 - 27.7 Gaurishanker region Tamang 9 10.6 59.7 31.8 33.1 Kathmandu region Newar 3 7.2 46.8 35.4 37.8 Mithila region Maithali 5 13.2 82.8 77.2 5.4 Annapurna region Gurung 5 3.8 37.4 23.4 37.9 Ridi region Magar 13 14.0 51.1 26.0 41.2 Center terai region Bhojpuri 4 9.1 62.7 51.8 15.3 Khaptad region Dalit 11 8.0 17.8 18.0 68.0 Lumbini region Awadhi 3 6.9 91.7 32.6 19.6 Western terai region Tharu 4 8.1 46.0 38.4 34.7 Rara territory - 4 0.7 1.6 - 70.7

NATIONAL TOTAL 75 100 61.4 30.9

* The district names for different regions are as follows. Kanchenjunga includes, districts of Taplejung, Terhathum and Panchthar. Sagarmatha region includes eight districts: Ilam, Sankhuwasabha, Solukhumbu, Bhojpur, Khotang, Okhaldhunga, Dharan, Udayapur. The region of eastern terai includes Jhapa, Morang and Sunsari. Gaurishanker region has nine districts: Dolakha, Sindhuliplachok, Rasuwa, Ramechap, Kaverpalanchok, Nuwakot, Dhading, Sindhuli and Makawanpur. Kathmandu region includes, Kathmandu, Patan and Bhaktapur districts. Mithila region includes Saptari, Siraha, Dhanusa, Mahottari and Sarlahi. Further to the west is Annapurna region with five districts, Gorkha, Lamjung, Manang, Mustang and Kaski. Ridi region has 13 districts: Tanahu, Syanja, Parbat, Myagdi, Palpa, Gulmi, Baglung, Arghakhanchi, Pyuthan, Rolpa, Rukum Salyan and Nawalparasi. Central terai has districts of Rautahat Bara, Parsa and Chitwan. Khaptad region has 11 districts: Surkhet, Jajarkot, Dailekh, Kalikot, Achham, Bajura, Bajhang, Doti, Darchula, Baitadi and Dadeldhyra. Lumbini region includes Rupandehi, Kapilbastu and Banke. The four districts Dang, Bardia, Kailali, and Kanchanpur are in western terai region. Finally, Rara territory includes Dolpa, Jumla, Mugu and Humla.

44

Figure A1. Map of Nepal showing 13 regions used in the study

45

Table A2. Average annual per capita expenditure in Nepalese Rupee Poverty measure Cost for bringing to

poverty line amount Regions

Number of sample household

Mean expenditure per capita

(Rs)

Head count ratio

Poverty gap ratio

Mean of the lowest 30%

Total cost (in million

Rs)

Per head cost (Rs)

Kanchenjunga 120 11,225 0.383 0.075 6,109 482 1,514 Sagarmatha 300 11,698 0.473 0.151 5,247 2,060 2,449 Eastern Terai 372 17,728 0.144 0.028 6,210 487 1,486 Gaurishanker 432 10,094 0.465 0.142 5,340 2,754 2,356 Kathmandu 504 37,031 0.036 0.008 5,559 119 1,765 Mithila 420 12,940 0.265 0.056 6,036 1,421 1,622 Annapurna 228 29,359 0.170 0.044 5,627 282 1,987 Ridi 503 13,774 0.281 0.063 5,933 1,520 1,740 Center Terai 300 15,984 0.267 0.051 6,130 898 1,465 Khaptad 264 9,058 0.484 0.118 5,700 1,290 1,870 Lumbini 156 15,707 0.413 0.104 5,629 924 1,929 Western Terai 265 13,131 0.405 0.085 6,019 1,280 1,616 Rara Territory 48 9,972 0.410 0.103 5,764 177 1,932 National 3,912 15,707 0.309 0.076 5,760 13,694 1,881 Source: Authors’ calculation based on Nepal Living Standard Survey II, 2003/2004

Table A3. Average annual per capita expenditure in Nepalese Rupee Poverty measure Cost of eliminating

poverty Ethnic, linguistic and caste groups

Number of sample household

Mean expenditu

re per capita (R)

Head count ratio

Poverty gap ratio

Mean of the lowest 30%

Total cost (in million

Rs)

Per head cost (Rs)

Limbu 64 12,766 0.405 0.075 6,114 202 1,420 Rai 139 11,753 0.485 0.146 5,375 996 2,320 Tamang 225 8,828 0.612 0.221 4,871 2,372 2,776 Newar 411 32,696 0.140 0.035 5,664 479 1,936 Maithili speaking 12,668 0.275 0.057 6,055 1,225 1,598 Gurung 124 22,182 0.186 0.047 5,622 183 1,950 Magar 248 13,192 0.344 0.069 6,142 780 1,549 Bhojpuri speaking 11,480 0.376 0.042 6,112 486 854 Dalit 188 10,472 0.455 0.111 5,769 1,489 1,883 Awadhi speaking 7,754 0.703 0.192 5,460 743 2,098 Tharu 168 11,827 0.354 0.075 6,016 1,099 1,636 BC 1247 19,370 0.182 0.040 5,935 2,040 1,714 National 12,094.5 Source: Authors’ calculation based on Nepal Living Standard Survey II, 2003/2004 The total cost at the national level in this table is less than in the previous table by about Rs 1,600 million as "other" group is excluded in this table. Calculation shows that they are about 666,645 in numbers, and they need average of Rs 2400 per capita to be at the poverty line amount.

46

Table A4. Inequality measures by regions Regions Gini E(0) A(1) Kanchenjunga 0.283 0.125 0.118 Sagarmatha 0.421 0.295 0.256 Eastern Terai 0.374 0.232 0.207 Gaurishanker 0.347 0.204 0.185 Kathmandu 0.439 0.324 0.277 Mithila 0.315 0.164 0.152 Annapurna 0.540 0.510 0.399 Ridi 0.363 0.216 0.195 Center Terai 0.406 0.271 0.238 Khaptad 0.264 0.116 0.109 Lumbini 0.459 0.348 0.294 Western Terai 0.399 0.266 0.233 Rara Territory 0.255 0.105 0.100 National 0.437 Source: Authors’ calculation based on Nepal Living Standard Survey II, 2003/2004 Table A5. Inequality measures by ELC groups Gini E(0) A(1) Limbu 0.358 0.212 0.191 Rai 0.420 0.303 0.262 Tamang 0.376 0.226 0.202 Newar 0.528 0.485 0.384 Madhesi 0.318 0.165 0.152 Gurung 0.475 0.376 0.314 Magar 0.366 0.217 0.195 Dalit 0.203 0.161 0.151 BC 0.435 0.311 0.267 Tharu 0.321 0.167 0.154 Others 0.357 0.217 0.195 Others rich 0.448 0.365 0.306 National 0.437 Source: Authors’ calculation based on Nepal Living Standard Survey II, 2003/2004 Appendix B: Decomposing total inequality into within and between Let us suppose that the complete set of individuals in Nepal, n, is partitioned into 13 proper subgroups N (r = 1, 2, ..., 13), with respective income vectors , mean incomes ry rμ , population sizes , and population shares rn

r rn nυ = . Note that the sub-script r can indicate both region and ELC groups that we have considered, but for exposition purpose we will indicate it for regions. Let us start with ( )0EI , with the above notations and 13 regions to consider, (7a) can be written as:

(B1) ( )13

1

10 lnr

Eii n

In y

μ

∈

⎛ ⎞= ⎜ ⎟

⎝ ⎠∑ ∑

In other words, we can compute the log deviation of each person’s expenditure from national mean and sum it over all individuals in that region to have regional ( )0EI index and then sum the regional index for all regions. By adding and subtracting natural log of regional mean, rμ , for all the 13 regions in the above equation, we will have

(B2) ( )13 13

1 1

1 10 ln lnr r

rE

i rr i n r i nI

n y nμ μ

μ= ∈ = ∈

⎛ ⎞ ⎛ ⎞= +⎜ ⎟ ⎜ ⎟

⎝ ⎠⎝ ⎠∑ ∑ ∑ ∑

47

After dividing and multiplying the first component of the right-hand side above by regional population number, , we will have

rn

(B3) ( )13 13

1 1

1 10 lnr r

r rE

r ir i n r i n

nI

n n y nμ

lnr

μμ= ∈ = ∈

⎛ ⎞ ⎛ ⎞= +⎜ ⎟ ⎜ ⎟

⎝ ⎠⎝ ⎠∑ ∑ ∑ ∑

In the second component on the right-hand side, since the value of ( )ln rμ μ is the same for each person in region

r, (lnr

ri n

)μ μ∈∑ can be written as (lnrn )rμ μ . And using the share of regional population concept for both terms

on the right hand sides, the measure can be written as:

(B4) ( )13 13

01 1

0 lE r rrr r

I E n μυ υμ= =

⎛ ⎞= + ⎜ ⎟

⎝ ⎠∑ ∑

This is the equation (12) in the text.