WORK IN PROGRESS
Relative Deprivation and Civil Conflict in Nepal
Karen Macours*
SAIS – Johns Hopkins University
Version: March 15, 2006
Abstract
This paper investigates the relationship between relative deprivation and civil conflict in
Nepal between 1995 and 2003. Poverty in Nepal has decreased substantially in this
period, which seems puzzling given the political instability and the raise and
strengthening of the insurgency. We hypothesize that increasing differences in welfare
among different groups - i.e., relative deprivation as opposed to absolute deprivation can help explain this puzzle. The hypothesis is tested with data from 2 nationalrepresentative household surveys, matched with information regarding mass abductions
by the Maoists, obtained from an extensive search of newspaper articles. The
identification strategy relies on the fact that the months following finalization of the
second round of data collection were characterized by a strengthening of the insurgency.
The paper shows that returns to land have increased quite drastically between 1995 and
2003, and disproportionally so for households with relatively large land holdings,
resulting in relative deprivation of the (near) landless. Recruiting by Maoists through
abduction of young people is found to be more important in districts where inequality
between the landed and the landless has increased.
This paper has greatly benefited from many discussions with Isabel Lavadenz. Many thanks go also to Berk Ozler,
Smriti Lakhey and participants at presentations in Leuven, Namur, SAIS, and NEUDC for comments on an earlier
version; to Elena Glinskaya, Mona Sur, and Michael Bontch for useful input and for discussions on the NLSS data;
and to Bobo Nge who provided invaluable research assistance related to the conflict data. Access to the NLSS data
provided by the World Bank is gratefully acknowledged. All errors are mine.
* Contact: [email protected]
1
Relative Deprivation and Civil Conflict in Nepal
1. Introduction
Over the last decade, the Kingdom of Nepal has seen the birth and expansion of an increasingly violent
civil war. While started in 1996 as a relatively small insurgency that only affected a few isolated districts,
the Maoist movement today has grown into a dominant force that controls most of the Nepali territory.
Interestingly, despite almost a decade of civil war, economic growth has been steady, and poverty has
decreased substantially (World Bank, 2005). As noted by Devarajan (2005), Nepal’s recent growth and
poverty reduction seems to contradict the common wisdom that civil conflict is an impediment to
economic growth. Looking at Nepal’s growth surprise from the other side, the perseverance and
strengthening of the Maoist insurgency, and the deepening of the conflict, at the very same time of robust
growth and poverty reduction seems also completely puzzling. Indeed, the literature on the economics of
conflict has emphasized the role of economic underdevelopment and poverty as key factors to explain
civil conflict (e.g. Collier and Hoeffler, 1998; Fearon and Laitin, 2003). Collier et al. (2003) has provided
an overview of the different related arguments and the cross-country empirical evidence. Miguel et al.
(2004) convincingly established an inverse relationship between economic growth and conflict using an
instrumental variable approach for a panel of African countries. Finally, empirical evidence for Nepal
itself indicates that there is a relationship between chronic poverty and the intensity of the conflict at the
district level (Do and Iyer, 2005; Murshed and Gates, 2004).
Clearly, economic factors might affect conflict with a lag, and factors explaining the origin of civil
conflict are not necessarily the same as factors explaining the continuation and expansion of conflict.
Zartman (2005) has argued that, while the initiation of conflict requires the presence of a political
entrepreneur, the continuation of the conflict requires the successful mobilization of a population
subgroup, which depends on the ability to seize on their sense of discrimination. In this light, the increase
in (consumption) inequality that has gone hand in hand with the recent gains in poverty reduction in
Nepal is worth considering. The hypothesis this paper investigates is that increasing differences in welfare
among different socio-economic groups, i.e. relative deprivation instead of absolute deprivation, can help
explain the seemingly puzzling coinciding trends of poverty reduction and conflict perseverance.
We define socio-economic groups in terms of their land ownership, to reflect the importance that is
attributed to land in defining status in Nepali society (e.g. CSRC 2003). The socio-economic importance
of land ownership is not too surprising in a country where 85% of the population lives in rural areas and
81% of the population is employed in the agricultural sector. It is probably best illustrated by the fact that
2
land titles are (perceived to be) a prerequisite to obtain a citizenship certificate or to get access to a
number of government services (Goyal et al, 2005).1 Limited access to land is a characteristic that typifies
women, Dalits, and Janajatis (indigenous ethnic groups), groups that are generally identified to suffer
from social exclusion in Nepal (e.g. UNDP and World Bank, 2005). Moreover, the fight against the
marginalization of the landless is an important part of the Maoist discourse, not only in Nepal but also
among other leftist insurgencies across South Asia. In fact, it has been argued, e.g. for West Bengal
(Bandhopadhyay, 2000), that successful land reform measures were key to contain such insurgencies in
the past, because they addressed the origins of the discontent. In Nepal, the Maoist party’s chief ideologue
(Baburam Bhattarai) issued a booklet in 1998 on the “Political-Economic Rationale of People’s War in
Nepal”, that highlights landlessness and poverty, and advocates economic development through a radical
land reform program based on the policy of ‘land to the tiller’ (Bray et al., 2003).
To investigate the relationship between land access, poverty reduction, and marginalization, this paper
first establishes that household’s land access is largely exogenous. Land sales markets are thin, and
changes in land ownership are mostly related to life-cycle events (inheritance and other intra-family
transfers). The paper then analyzes the relationship between consumption and land ownership. It shows
that returns to land have increased quite drastically between 1995 and 2003, and disproportionally so for
households with relatively large land holdings, resulting in relative deprivation of the (near) landless. We
then analyze the relationship between the relative returns to land and local Maoist activity. In particular,
we show that recruiting by Maoists through mass abduction of young people is more important in districts
where inequality between the landed and the landless has increased. We find that the expansion of Maoist
recruitment activities beyond their initial heartlands occurred in districts where the relative returns to
land, and therefore the relative deprivation of the (near) landless, had increased significantly in the
preceding period.
Hence, while the existing empirical studies on conflict have mainly focused on the relationship between
the conflict and levels of underdevelopment and inequality, we show that changes in inequality over time
can play an important role, in particular when trying to understand the geographic expansion and
escalation of the conflict. Given the geographic characteristics of Nepal, and the related remoteness of
many districts, we focus on changes in inequality between households within a district (as opposed to
inequality across districts), to test whether recruitment by the Maoist might be linked to perceptions of
1
The importance of land ownership in the Nepali society is further illustrated by a recent study that showed that of
30.000 court cases investigated, 70% were related to land (Basnet, 2004). Furthermore, the poor themselves have
identified land access as a key determinant of poverty (World Bank, 1999).
3
discrimination.2 We hypothesize that discontent by traditionally marginalized households who notice that
other households within the same district are benefiting more from economic growth, while they are
lagging further behind, fuels salient support for the Maoist insurgency. This study further differs from
previous empirical work on the economics of conflict, by analyzing the relationship between economic
factors and recruitment by the insurgency in a more direct way. While the theory of the economics of
conflict in part focuses on factors that might explain successful recruitment, the empirical studies mostly
focus on the relationship between these factors and conflict outcomes (e.g. the number of conflict-related
deaths). The Maoist strategy of using mass abductions for recruitment purposes allows us to define a
dependent variable that more closely matches the theory.
The possible relationship between the increase in inequality and insurgency recruitment resonates with
some of the findings in the economics of crime literature. Demombynes and Ozler (2005) find a
relationship between local inequality and crime in South Africa. Fajnzylber et al. (2002), drawing on
Becker’s (1968) paradigm to model crime as a rational decision based on a cost-benefit analysis, present
and test a reduced-form model in which increases in inequality motivate individuals to take up criminal
behavior. This paper discusses how increases in inequality might affect an individual’s decision to join
the insurgency, and then discusses the impact recruitment efforts might have on this individual decisionmaking. Large increases in inequality might not only increase the expected benefits but also lower the
moral thresholds to join the insurgency, in particular for the disenfranchised population.3
Mass
abductions and subsequent indoctrination are likely to reinforce those effects. This can help explain the
geographic targeting of these recruitment efforts and the widening of the conflict.
The argument in this paper is related - but distinct - from the hypothesis that inequality between groups,
i.e. horizontal inequality, can help explain violent conflict (e.g. Stewart, 2000; Murshed and Gates, 2004).
In particular, the hypothesis in this paper is that increases in inequality between socio-economic groups,
leading to further exclusion of already marginalized groups, help explain expansion of civil conflict in
Nepal. As such it relates to Gurr (1970)’s emphasis on relative deprivation in explaining collective
violence and to Hirschman and Rothschild (1973) model of changing tolerance for inequality. In their
model, increases in inequality related to growth are initially tolerated because it increases the anticipation
of gains for those left behind. When such gains however don’t materialize over time, this tunnel effect
2
Fafchamps and Shilpi (2003a) have shown that individual’s subjective well-being in Nepal depends in part in how
their income compares to others’ in their village. This indicates that relative incomes indeed matter. In another
context, Stark and Taylor (1991) show that relative deprivation might affect household’s decision making.
3
Increases in inequality might hence increase both “greed” and “grievance”, two potential rationales for conflict that
have received a lot of attention in the literature (see Collier and Hoeffler, 2004).
4
decays and social injustice no longer goes unresisted.4 Our finding on the importance of increasing
inequality to explain the spread of violent conflict, also relates to Andre and Platteau (1998)’s analysis of
the role of increases in land ownership inequality in the Rwandan genocide. This paper focuses, however,
on the role of (unfair) changes in the returns to land.5 The paper also fits into the broader literature on the
role of perceived (or subjective) well-being in economic development, as recently reviewed by Graham
(2005).
The empirical analysis is based on a repeated cross section of two national-representative household
surveys, NLSS1, collected in 1995-1996, and NLSS2, collected in 2003-2004.6 In 03/04 data was also
collected on subset of the 95/96 households, providing a panel of 978 households.7 Because returns to
urban and rural land are likely to be different, and the Nepali population is predominantly rural, we focus
only on rural households (2657 in NLSS1, 2748 in NLSS 2, and 796 in the panel). This survey data is
matched with information regarding Maoist related incidents obtained from an extensive search of
newspaper articles. The identification strategy relies on the fact that the months after the finalizing of the
NLSS2 data collection were characterized by a strengthening of the insurgency and consequently, a surge
in reports on Maoist related incidents.
The structure of the paper is as follows: Before analyzing changes in the returns to land ownership, we
first analyze the determinants of changes in land ownership itself to shed light on alternative land access
mechanisms. We show that changes in land ownership are limited (section 2) and mainly related to lifecycle events. Section 3 uses both parametric and non-parametric regressions to establish that large
changes in returns to land ownership have occurred. In section 4, we analyze the relationship between the
relative deprivation of the landless and Maoist activities at the district level, and section 5 concludes.
4
Hirschman uses the analogy of a traffic jam in a two-lane tunnel. Drivers in a non-moving lane will initially
tolerate that the other lane starts moving, and in fact become hopeful because it does. If their lane however does not
start moving at some point too, they will eventually “become quite furious and ready to correct manifest injustice by
taking direct action.” In a similar fashion, people who initially might not be inclined to take up violence can be
pushed over their moral threshold by continued and deepening injustice or inequality.
5
In fact, in the period under study, land ownership inequality, as measure by the land Gini coefficient, slightly
decreased in Nepal.
6
Survey years are henceforth referred to in short as 1995 and 2003.
7
1135 households were sampled for the second round, indicating an attrition of about 14% after 8 years.
5
2. Determinants of access to land in ownership
2.1. Land sales markets
The descriptive statistics in table 1 suggest that land sales activity in rural Nepal is rather limited and has
not changed much between the survey years. If anything, it seems slightly less in 2003 than in 1995. In
both years, 50% of transactions involve only 0.1 ha or less, and only 0.1% of all households bought more
than 1 ha. Differences between the two years are insignificant except for the distribution of the share of
total land sold among those selling. Village level statistics confirm that land sales activity was similar in
both survey years. In analyzing the characteristics of households that participate in transactions, we find
they differ from the rest of the population in a number of aspects. Not surprisingly, the size of land
holdings is correlated with being active on the land market (this might partly be pure mechanically). In
addition however, a number of indicators suggest that households with relatively higher living standards
are both more likely to sell and more likely to buy, indicating a segmented market.
2.2. Government land access policies
In addition to land acquisition through the land sales market, households might have acquired land
through government policies. Nepal’s Land Reform Act of 1962 in fact attempted to address land access
by drawing on land ownership ceilings and securing tenancy rights. Nevertheless, only 1.5% of total
agricultural land was distributed because of widespread evasion of land ceilings and this redistribution
took place long before the period of relevance for this paper. One particular related policy initiative that
occurred in the period between the surveys deserves however some attention. A 1995 amendment of the
Land act provided for abolition of dual ownership of land under tenancy by physically splitting up the
land between tenants and landowners. The survey data suggest however that this legislative change did
not result in a large shift from tenancy to ownership, which is consistent with anecdotal evidence related
to lack of implementation of the Amendment. In fact, the share of households that lease land has slightly
increased (from 25% in 1995 to 27% in 2003). Also at the level of individual households, we don’t
observe a significant correlation between 1995 tenancy status and 2003 ownership (see further).
2.3. Intra-family transfers and inheritance
A third type of channel for changes in land ownership are intra-family transfers as part of inheritance
practices. In fact, data for 1995 show that 85% of all rural land plots were obtained through inheritance.
While for 2003, this information is unfortunately not available, the panel data allow us to analyze changes
in land ownership that can shed further light on this issue. Dividing the changes in land ownership by
quartile and analyzing differences in household characteristics across quartiles shows that significant
6
differences can mainly be traced back to life cycle events, suggesting the importance of inheritance. The
transfer of land from parents to children in Nepal can occur both during the lifetime of the parents, and
upon their death. The death of a parent of the household head is hence likely to increase landownership,
except in the cases where the parent lived with the household. In those cases (which we’ll refer to as death
of a resident parent), parts of the parent’s land is likely to be transferred to siblings that don’t live in the
same household. The data show patterns that are consistent with these inheritance practices (table 2).
In particular, we see that for households where a non-resident parent died between the two survey years,
an increase or stabilization of land ownership was much more likely than a decrease. This contrasts with
land ownership changes coinciding with the death of a resident parent. Furthermore, decreases in land
ownership are also correlated with children (in particular sons) moving out of the household. And
households without non-resident parents in 1995 are less likely to have a land ownership increase. The
significant differences between the variables capturing life cycle events is all the more striking when
comparing them with variables indicating household assets or income strategies (including migration), for
which we find no significant difference.8 It seems at least reasonable to assume that the death of a parent
who does not live in the household anymore, is rather exogenous to the household. The possibility of
using this exogenous shock for identification purposes will be further discussed in section 3.
In order to consider the three different channels of land access together, we regress the change of land
ownership on the set of exogenous household characteristics. The results confirm the relationship between
parent’s deaths and increases in land ownership (table 3). None of the variables capturing other household
assets is significant. Adding (admittingly possibly endogenous) variables indicating whether male and
female dependents (children or grandchildren of the household head) left the household to start an own
family also shows an expected negative correlation between land ownership and male dependents leaving
(as opposed to female).9 Finally, a variable indicating the amount of land rented in (to detect possible
effects of the dual ownership legislation) shows a positive but insignificant effect.
8
While differences in variables indicating caste or ethnicity are insignificant it should be noted that lack of
observations prevents analyzing the possible effects of a government policy targeting land ownership to former
bonded laborers (Ex-Kamaiya), which are predominantly Tharu. In the panel dataset, only 18 observations are Tharu
in the districts where this policy was targeted. The limited number makes identification of specific effects for this
subgroup very difficult. Moreover, because a number of complementary programs was targeted to this subgroup
after 2001, we would expect that belonging to this group has affected changes in consumption also directly, and not
just through land ownership changes.
9
Indeed, female inheritance of land was long outlawed in Nepal. Due to a change in legislation in 2001, land
inheritance to daughters is now allowed, but girls have to return the land to their parents or male siblings upon
marriage.
7
3. Returns to land ownership.
We now turn to the possible heterogeneous impacts of growth in Nepal, by analyzing returns to land
ownership, and changes between 1995 and 2003, relying both on the repeated cross-section and on the
panel datasets.
3.1.Returns to land ownership in repeated cross-section
Theory, as well as evidence from other countries (e.g. Finan et al., 2005) suggests that the relationship
between holding size and welfare indicators might be non-linear. We therefore first estimate a set of
locally weighted regressions using Cleveland’s (1979) LOWESS estimator with bandwidth of 0.2.10
Confidence intervals were obtained by bootstrapping the locally weighted regressions 100 times,
accounting for stratification and clustering. While holding size varies between 0 and 32 hectares in
NLSS1 and between 0 and 20 hectares in NLSS2, both survey rounds have only few and dispersed
observations beyond 8 hectares (1% in NLSS1 and 0.25% in NLSS2). Therefore figures were trimmed at
8 hectares. 11
Figure 1A shows that returns to land ownership have increased quite dramatically between the two survey
years. While the relationship between landownership and household per capita expenditures suggests
rather limited returns to land ownership in 95/96, a very strong positive relationship emerges in 03/04.
The confidence intervals indicate that while for holding sizes close to zero, differences between the two
years are statistically insignificant, the difference becomes significant and is increasing beyond
approximately 1 hectare. These patterns are even stronger for the relationship between land ownership
and per capita food consumption (figure 1B). Consistent with these patterns, a strong negative
relationship between land ownership and rural poverty appears for 2003, but not for 1995 (figure 1C).
Differences between years are significant beyond 1.6 hectares.
The non-parametric regressions hence establish that the unconditional relationships between
landownership and welfare is strikingly different between the 2 years. To further analyze these
differences, and control for differences in household and regional-specific variables, we turn to
10
In the choice of bandwidth, the trade-off between the smoothness of the estimated function and bias was
considered. The emerging patterns are robust to the choice of alternative bandwidths.
11
Observations beyond 8 hectares were used for calculation of the local slope upto 8 ha. For the LOWESS
estimator, the observations included in the local regression and the weights are determined based on closeness
defined in terms of closest neighbor. (Given the dispersion of the data at large holding sizes, the LOWESS technique
is more suitable than Fan’s (1992) locally weighted regression technique.)
8
parametric regressions. The figures show that at least within the range of holdings that contains most of
the variation in the data, a linear regression can be used as a first approximation for the relationships in
1995 and 2003. In particular, we specify a parametric specification that allows for differences in returns to
land between the two years. Let
Yi 03 = X i 03 β 03 + Ziγ 03 + φi +εi 03
(1)
Yi 95 = X i 95 β 95 + Ziγ 95 + φi +εi 95
(2)
where Yi95 and Yi03 are the dependent variable in 1995 and 2003. Xi95 and Xi03 are vectors of household
characteristics that might change exogenously, Zi is a vector of observed fixed household and regional
characteristics, φi a household specific unobserved variable, and εi95and εi03 are the error terms. The β95
and β03 are the vectors of marginal returns to assets X in 1995 and 2003; γ 95 and γ 03 are the slope
coefficient for the other household characteristics, which can also differ between survey years.
We can rewrite (1) and (2) into
Yit = X it ( β 95 + Tt * δ ) + Ziγt + φi +εit
(3)
with Tt a time dummy equal to 0 for 1995, and 1 for 2003, δ = β 03 − β 95 , and t standing for time (1995
or 2003). Using the repeated cross-section dataset to estimate (3) will give unbiased estimates of the
coefficients if the error term, φi +εit , is orthogonal to the variables included in Xit and Zi.
Table 4A shows the results for a specification where X only includes land ownership. All regressions
account for sampling weights, and standard errors account for clustering and stratification. The strongly
significant interaction effect between the time dummy and land ownership confirms the large increase in
returns to land between the two survey years. Returns to land holdings are 5 to 6 times larger in 2003 than
in 1995. Also, while consumption in 2003 is in general higher than in 1995, the increase more than
doubles for each additional hectare in ownership. Considering the specifications in columns 1 to 4 shows
that these results are robust to inclusion of different sets of control variables, including interaction effects
of other household characteristics with the 2003 dummy (column 3 and 4). Similar results are found for
per capita food consumption (table 4B) and poverty (table 4C).
9
In column 5 and 6 we test for robustness of the results to exclusion of outliers. In column 5, two
observations that were more than 2 standard deviations of the next biggest value for land ownership were
excluded. In column 6, all observations with more than 8 hectares (1% largest for 1995) were excluded.
The qualitative results are robust to exclusion of these outliers. The slope coefficient for 1995 does
increase however in the last column.
In column 7 and 8, we allow for non-linearity at zero, by including a dummy indicating the landless.
These variables are not significant for any of the dependent variables. Interestingly, without including the
interaction effect, a marginally significant positive effect of being landless on poverty emerges,
suggesting a possible extra penalty for complete landlessness.
In column 9, we introduce a quadratic term for landownership, and the interaction effect with the time
dummy. The returns to land increase at a decreasing rate for 1995, but not for 2003 for food and total
expenditures. This confirms the finding of the non-parametric regression that the relative gains for the
(near) landless have been much smaller than for the landed. For the poverty regression, the quadratic
terms is significant for both years, suggesting decreasing marginal returns in both years, but the negative
effect remains larger for 2003.12
3.2. Returns to land ownership in the panel
Based on the discussion in section 2, exogeneity of land ownership seems a reasonable assumption in the
Nepal context. We further analyze this issue using the panel data, which allow testing whether earlier
results might have been driven by omitted variables. In particular, if there exists a correlation between an
unobserved household specific variable, and one of the variables in X or Z, estimating model (1), (2) or
(3) with cross section data will result in biased estimations of the slope coefficients. To eliminate this
potential bias we estimate the following first-difference equation, eliminating all fixed household effects:
∆Yi = ( Xi 03 β 03 − Xi 95 β 95) + Z i (γ 03 − γ 95 ) + νi
(4)
with ∆Yi the change in expenditures between 1995 and 2003 and νi = εi03-εi95 the error term. The
estimates of the coefficients of β03 and β95 when estimating (4) will be consistent estimates of β03 and β95
12
The turning points (where returns to land turn negative) are between 10 and 12 hectares, i.e. outside of the range
of 99.5 % of the data.
10
in (1) and (2), as long as X only contains household assets for which changes can be assumed to be
exogenous, after controlling for household fixed effects. Zi contains household characteristics that are
constant over time. Hence this regression allows controlling for unobserved household characteristics, in
as far as they are constant between years, when analyzing the changes in returns to land ownership.
First we tested for endogeneity, using the findings on determinants of exogenous land ownership changes
we discussed before. Specifically we use the death of a non-resident father, death of a non-resident
mother, and the area rented in 1995 as instruments for landownership in 2003. The three instruments are
jointly significant. Using these instruments we tested for endogeneity of land ownership in 2003, and
could not reject exogeneity for total expenditures nor for food expenditures. This is indeed consistent with
our earlier discussion (in section 2). Over-identification tests confirm the exogeneity of the instruments.
Table 5 presents the results of the OLS panel estimations for total per capita consumption, and per capita
food consumption. 13 In the first column, we first show a specification that only includes land ownership in
X. We find a positive significant coefficient for Xi03 and an insignificant coefficient for Xi95. More
importantly the difference β 03 − β 95 is estimated to be significant at the 5% level. This is consistent with
our findings using the repeated cross-section, and in fact the orders of magnitude are remarkable similar.
The regression also indicates changes in the return to other characteristics (such as region and caste). In
column 2 we also allow for differences in the coefficients for distance to markets between both years, and
find no significant differences in this regard. The robustness of the land ownership results in this
specification is important, as Fafchamps and Shilpi (2003b, 2005) have shown that distance to markets
(cities) has strong effects on wide set of household outcomes. 14 The next 2 columns show similar results
for food expenditures. Also here the returns to land ownership are significantly larger in 2003 than in
1995, and the order of magnitudes is similar to the repeated cross-section. These results suggests that
observed changes in returns to land are not due to possible differences in unobserved household effects
between survey years.15
13
Given the discrete nature of the poverty variable, a panel regression that would be comparable to the regressions
in table 4C cannot be estimated.
14
Households that had changed location between survey years are not included in the panel. Changes in distance to
markets therefore are coming from possible changes in road conditions that can be argued to be exogenous to
individual household members.
15
Non-parametric regressions for the panel data (not reported) further confirm the consistency of the results between
the repeated cross-section and the panel data.
11
4. Relative Deprivation and Maoist Recruiting
The changes in returns to landownership in Nepal, and the relative deprivation of the near landless that
results from that, indicate there might be equity concerns related to the economic development of the last
decade. Such equity concerns are particularly relevant given the political instability that is continuing till
today. In this light, an important hypothesis to be tested is whether increasing differences in welfare
among different groups can help explain the strengthening of the Maoist insurgency during a period of
economic growth.
4.1. Background on the conflict
While the insurgency has been active since 1996, initially activities were mainly limited to isolated
districts and direct confrontations with the Nepali police (Gautam, 2001). The conflict reached a higher
level of intensity after a break-down of the ceasefire in 2001, when the government decided to make the
army play a more direct role (Murshed and Gates, 2004). The insurgency slowly expanded and started to
affect civilian life more directly (through violence, abductions and blockades), in particular after the
break-down of a second cease-fire in August 2003. In the spring of 2004 the number and intensity of
Maoist actions, and their territorial coverage, jumped to a new level (INSEC, 2005). This culminated in
the declaration of the state of emergency by King Gyanendra in February 2005. In order to analyze the
possible relationship between returns to landholdings and the conflict, we take “advantage” of this surge
in Maoist activities that occurred around the time that the NLSS data collection was finished. Data
collection for the second round took place between March 2003 and May 2004. Hence it so happened the
end of the data collection coincided with the surge of Maoist activities.
In order to shed more light on the relationship between relative deprivation and the Maoist activities, we
analyze whether differences in Maoist activities across different districts are related to differences in the
returns to landownership. In particular, we hypothesize that in districts where the relative situation of the
(near) landless has gotten worse over the last decade, salient support for Maoist activities is stronger. In
such districts, we expect activity of the Maoists to be more frequent and possibly of a different nature. In
particular, one would expect that support for recruiting tactics of the Maoists and possibly support for
attempts by the Maoists to control socio-economic and political life (and/or resistance towards such
tactics and activities), might be stronger. Hence, even if inequalities related to land ownership may or
may not have been one of the causes of the civil conflict, independently they might be related to the
continuation and strengthening of the insurgency.
12
To analyze these issues empirically, district-level time-series information regarding the Maoist related
incidents affecting civilians was collected based on newspaper reports through a search on the Nepal
press selection list compiled by the BBC. These reports include not only the BBC’s own daily broadcast,
but also BBC Monitoring, that picks up reports from all major national newspapers and other news
sources. Reports were gathered for all days from April 2002 to December 2004, resulting in a total of
1055 incidents. To avoid problems of reverse causality, we will focus on reports of incidents after the end
of the data collection (May 2004-Dec 2004). In analyzing the press reports, the broad variety of Maoistrelated incidents was first classified in 7 types of activities: murders, abductions, blockades,
explosions/destruction, threats/decrees/bans, expropriations/extortions, and personal attacks resulting in
injuries. Apart from the type and the location of the incidents, information on the number of people
affected is often also included in the articles. While the newspaper reports are likely to be incomplete, we
will use them as a proxy for the actual occurrence and scale of incidents.
Considering the relative frequency of the different activities, it is revealing that abductions are the most
common incident (21% of all cases), and about 65% of all abductions are mass abductions.16 This is
particularly interesting as mass abductions, often of teachers and students in schools, are an important
recruitment mechanism of the Maoists. In most cases, Maoists abduct teachers and/or students from
schools that they take control of, to bring them to “re-education camps”. The number of people abducted
in this fashion can go as high as 1500 in a single action. In May 2004 alone, at least 7787 people were
abducted. Abductees are typically set free after a few days, and interviews with returned students indicate
that the rebels treated them nicely, while trying to convince them of their cause through indoctrination
sessions (Agence Français Press, July 23, 2004). Independent accounts of human rights activists confirm
that the Maoists used mass abductions of children as recruitment strategies in 2004 (e.g. Human Rights
Watch, 2004; Asian Human Rights Commission, 2004).
In this paper, we focus on mass abductions because it is the only observable and therefore measurable
recruitment mechanisms. Clearly, the Maoists must have also recruited many people through other means.
The number of mass abductions should hence be seen as proxy for overall recruitment. Even if that is a
very noisy measure of recruitment, it arguably is a better measurement of recruitment than the number of
civilian death, which is the outcome variable that is typically used in the literature.
4.2. Individual decision-making and recruitment through mass abductions
16
Mass abductions are defined as incidents involving more than 10 abductees at once. The other abductions are
mainly targeted abductions of individual opponents, such as local politicians, policy or army personnel, etc.
13
The use of the type of mass abductions that was widely reported in Nepal, suggests that indoctrination
plays an important role in the recruitment by the Maoists. The rebel leaders, in deciding on those
abductions, face a trade-off between the costs related to this type of indoctrination, and the likely success
of recruitment. The success in turn will depend on the decision-making process of the individual
abductees, who consider the potential costs and benefits from joining the insurgency. Following the
reasoning of Fajnzylber, et al. (2002), (unfair) increases in inequality is likely to augment the number of
recruits, because it both increases the expected gains from redistribution (in case the insurgency would
succeed), and because it might lower the moral threshold of the marginalized population to take up
violence. In particular, perceptions of unfairness related to unequal distributions of the gains from growth
might decrease the moral threshold for those that feel left behind. This effect might be particularly strong
if the losers are those groups that have traditionally suffered from social exclusion. Indoctrination
targeted at those groups can then serve to further decrease their moral threshold. It might also increase the
individuals expected benefits by providing information on the potential future gains from redistribution.
As argued by Collier and Hoeffler (2004), the individual’s decision to join the insurgency might also be
affected by his reservation utility.
In deciding whether and where to conduct mass abductions for recruitment purposes, rebel leaders are
hence likely to account both for local changes in inequality and for the level of underdevelopment on the
one hand, and for the costs related to abductions on the other. By targeting the abductions to schools,
rebels probably aim to take advantage of economies of scales (many young and possibly impressionable
people located together), even although they are aware that a substantial share of the abductees might not
decide to join. Targeting such recruitment efforts to areas where the number of people just below the
threshold is likely to be large can then help to increase the “returns” to abductions. Hence, one would
expect that the Maoists target these actions towards schools and localities with a large disenfranchised
population. Indeed a number of the newspapers reports specifically mention that marginalized groups
were targeted for abductions. Such groups are more likely to support, or at least less likely to resist, the
Maoist movement.
4.3. Empirical analysis of mass abductions
To take a first look at the data, we investigate the time trend in the monthly number of incidents. Figure
2A clearly shows that while overall incidents jumped upwards after the collapse of cease-fire in August
2003, the surge in the number of mass abductions only occurs in the spring of 2004. Furthermore, where
mass abductions were limited to a relatively small set of districts in 2003, they spread rapidly to over
14
more than 35 districts around April/May 2004 (figure 2B). This relatively sudden shift happened to
coincide with the end of the data collection of the second round of the survey. The large majority of data
was collected by March 2004, with only 18 households collected after Mid-April (i.e. in the year 61 of the
Nepali calendar) of which 5 in early May 2004.
We use the repeated cross-section to further analyze whether districts where relative deprivation of the
landless is higher also tend to be the districts were abductions took place. In particular, we rewrite
equation (3) to allow the differences between returns to land in 1995 and 2003 to vary across districts. Let
Yit = X it * Ld * ( β 95 + Tt * δ d ) + Ld ξ d + Ziγt + φi +εit
(8)
with Ld containing dummy variables for each district, ξ d the fixed effects related to each district, and δ d a
vector of slope coefficients that capture the differences in returns to land between 1995 and 2003 for each
district. A positive slope coefficient for a given district indicates that returns to land ownership have
increased, leading to a relative deprivation of the near landless. Hence, once we have estimated (8) we can
analyze whether there exists a relationship between the district-specific slope coefficients and the Maoist
related incidents in that district.
In particular, we generate a district-level variable indicating whether the slope in 2003 was significantly
higher than in 1995 (which is the case in 17 out of 68 districts). We then analyze whether this variable, a
proxy for the relative deprivation of the landless, together with a set of control variables, help to explain
the variation in mass abductions across districts. Specifically, we estimate a district-level Poisson model
to analyze the correlates of the number of mass abductions between May and December 2004. During this
period, the average number of mass abductions per district was 1.5, with numbers ranging between 0 and
8. We control for factors that are likely to affect the costs related to abductions, i.e. geography (% areas
with 30 degree slopes and per capita forest area) and road density. A set of additional independent
variables is added to capture the opportunity costs, i.e. the level of under-development (GDP per capita,
life expectancy and adult literacy) and the level of inequality (measured by land Gini coefficient, the % of
landless households, or the % land owned by advantaged castes). These variables will also allow
comparing our findings with earlier studies on the Nepal conflict (Do and Iyer, 2005; Murshed and
Gates, 2004). All control variables capture the initial situation in 1996 (or before), with the exception of
the variables related to the level of land inequality, which is based on data from 2001 (see footnotes of
table 6 and 7 for description and sources of the variables). Estimates with different sets of control
15
variables are shown in column 2 to 7 in table 6. To further distinguish between the level and the change in
inequality we add a specification in column 8, with a dummy variable indicating whether the slope in
2003 is significant (as opposed to the difference in slope between 1995 and 2003). Finally, we test
whether our results are robust to exclusion of the Rolpa and Rukum district (column 9 and 10), which
constitute the heartland of the Maoist insurgency, and therefore might have different dynamics. The
repeated cross-section allows us to obtain estimates of the district slope coefficients for 71 districts (out of
a total of 75, 4 districts were not included in one or both of the survey rounds). Because our estimates of
returns to land relate to rural areas, we exclude the 3 largely urban districts in the Kathmandu Valley
(Kathmandu, Bhaktapur, and Lalitpur), resulting in 68 observations.
The results in table 6 (column 1 to 7) show that there is indeed a positive relationship between a districtlevel increase in the returns to land, and subsequent number of mass abductions. While we cannot
completely rule out possible omitted variable bias, the robustness of the coefficient estimate across the
specifications with different sets of control variables is encouraging. The results suggest that districts with
relative deprivation of the landless and land-poor had approximately 50% more mass abductions than
other districts. The control variables on geography and the level of underdevelopment largely confirm the
findings of earlier studies of the conflict: a quadratic relationship between the % of mountainous area and
conflict, and a significantly negative relationship between GDP per capita and conflict. Interestingly, we
don’t find strong significance for variables measuring levels of inequality. Finally, results are robust to
exclusion of Rukum and Rolpa.
As illustrated by figure 2A, mass abductions only surged in the spring of 2004, and were relatively
isolated phenomena before April/May 2004. To understand the expansion of Maoist activities into new
areas (districts), we next control for the earlier abductions. Column 1 to 5 in table 7 therefore presents the
results of OLS regressions with as dependent variable the difference between the number of mass
abductions in the 8 months between May 2004 and December 2004, and the 8 months prior to that (Sept
2003-April 2004, i.e. from the collapse of the cease fire to the beginning of the surge in mass abductions).
Because the coefficients of OLS and Poisson regressions are not directly comparable, column 6 and 7
present results of OLS regressions on the level of mass abductions. We find a strong and significant
positive effect of relative deprivation on the change in the number of mass abductions. A significant
increase in the returns to land is associated with an increase of about 1.3 mass abductions in the 8-month
period. Comparing with the results in column 6 and 7, we see that the coefficient is larger in the
regression on changes. These results are robust to corrections for spatial correlation as suggested by
Conley (1999). Overall, the results suggest that relative deprivation of the landless and land-poor might
16
have provided fruitful grounds for recruitment by the Maoists in new areas and as such facilitated their
geographic expansion.
5. Conclusions
Average rural per capita consumption in Nepal has increased drastically between 1995 and 2003. This
happened disproportionally so for households with relatively large land holdings, resulting in relative
deprivation of the (near) landless. This paper further shows that there exists a strong and robust
relationship between this relative deprivation and the expansion of Maoist activity into new districts. In
particular, recent recruiting by Maoists through abduction of young people is found to be more important
in districts where inequality between the landed and the landless had increased.
The empirical results of this paper are consistent with the hypothesis that relative deprivation of the (near)
landless has contributed to salient support for - or at least lack of resistance against - the insurgency.
Further analysis is needed to understand the sources of the changes in returns to landownership.
Nevertheless, the findings suggest that policies targeted at the marginalized landless and the land-poor
households, might be important to address lingering discontent and reduce related conflict in Nepal. More
generally, the paper provides empirical evidence of the possible role of relative deprivation as a breeding
ground for civil conflict.
17
References
Andre, Catherine, and Jean-Philippe Platteau, 1998, “Land Relations under Unbearable Stress: Rwanda
caught in the Malthusian trap”, Journal of Economic Behavior and Organization, 34: 1-47.
Asian Human Rights Watch, 2004, “Nepal: Nepal Rebels Plan to Train 50,000 Child Soldiers”, AHR
Weekly Newsletter, 3(10).
Bandyopadhyay, D., 2000, “Land Reform in West Bengal: Remembering Hare Krishna Konar and Benoy
Chaudhury”, Economic and Political Weekly, May 27, 2000.
Basnet, 2004,“Poverty of the Landless”, http://action.web.ca/home/sap/nepal_resources.shtml?
x=69983&AA_EX_Session=4d6388380663d562885c5b66ea952678
Becker, Gary S., “Crime and Punishment: An Economic Approach”, The Journal of Political Economy,
66(2): 169-217.
Bray, John, Leiv Lunde, and S. Mansoob Murshed, 2003, “Nepal: Economic Drivers of the Maoist
Insurgency”, in Ballentine, Karen and Jake Sherman (eds.), The Political Economy of Armed
Conflict, Lynne Riener Publishers, Boulder&London.
Cleveland, William S., 1979, “Robust locally weighted regression and smoothing scatter plots”, Journal
of the American Statistical Association, 74: 829-36.
CSRC (Community Self Reliance Center), 2003, Reflections 2003, CSRC, Kathmandu, Nepal.
Collier, Paul, and Anke Hoeffler, 1998, “On Economic Causes of Civil War”, Oxford Economic Papers,
50: 563-573.
Collier, Paul, and Anke Hoeffler, 2004, “Greed and Grievance in Civil War”, Oxford Economic Papers,
56: 563-595.
Collier, Paul, V.L. Elliott, Havard Hegre, Anke Hoeffler, Marta Reynal-Querol, and Nicholas Sambanis,
2003, Breaking the Conflict Trap: Civil War and Development Policy, World Bank Policy
Research Report, Oxford University Press: Oxford.
Central Bureau for Statistics (CBS), 2001, Population Census Nepal, Kathmandu, Nepal.
Conley, Timothy, 1999, “GMM Estimation with Cross-Sectional Dependence”, Journal of Econometrics,
92: 1-45.
Demombynes, Gabriel, and Berk Őzler, 2005, “Crime and Local Inequality in South Africa”, Journal of
Development Economics, 76: 265-292.
Devarajan, Shantayanan, 2005, “South Asian Surprises”, keynote speech at the World Bank/IMF/DFID
conference on “Macroeconomic Policy Challenges in Low Income Countries” Washington DC,
Feb 15-16, 2005.
DFID and World Bank, 2005, Citizens with (out) rights: Nepal Gender and Social Exclusion Assessment.
Do, Quy-Toan, and Lakshmi Iyer, 2005, “Civil Conflict in Nepal: An empirical analysis’, mimeo, World
Bank and Harvard Business School.
Fafchamps, Marcel and Forhad Shilpi, 2003a, “Subjective Well-being, Isolation, and Rivalry”, mimeo,
Oxford University and World Bank.
Fafchamps, Marcel and Forhad Shilpi, 2003b, “The Spatial Division of Labor in Nepal”, Journal of
Development Studies, 39(6): 23-66, 2003.
Fafchamps, Marcel and Forhad Shilpi, 2005, “Cities and Specialization: Evidence from South Asia”, The
Economic Journal, 115(503): 477-504.
18
Fajnzylber, Pablo, Daniel Lederman, and Norman Loayza, 2002, “What Causes Violent Crime?”,
European Economic Review, 46: 1323-1357.
Fan, Jianqing, 1992, “Design-adaptive nonparametric regression”, Journal of the American Statistical
Association, 87: 998-1004.
Fearon, James D., and David D. Laitin, 2003, “Ethnicity, Insurgency, and Civil War”, American Political
Science Review, 97: 75-90.
Finan, Frederico, Elisabeth Sadoulet, and Alain de Janvry, “Measuring the Poverty Reduction Potential of
Land in Rural Mexico, Journal of Development Economics, 77(1): 27-51.
Gautam, Shobha, 2001, Women and Children in the Periphery of People’s War. Kathmandu: Institute of
Human Rights Communication Nepal (IHRICON).
Graham, Carol, 2005, “Insights on Development from the Economics of Happiness”, World Bank
Research Observer, 20(20): 201-231.
Goyal, Rajeev, Puja Dhawan, and Smita Narula, 2005, “The Missing Piece of the Puzzle: Caste
Discrimination and the Conflict in Nepal”, Center for Human Rights and Global Justice, NYU
Law School, New York.
Gurr, Ted Robert, 1970, Why Men Rebel, Princeton University Press, Princeton.
Hirschman, Albert O., and Michael Rothschild, 1973, “The Changing Tolerance for Income Inequality in
the Course of Economic Development”, Quarterly Journal of Economics, 87(4): 544-566.
Human Rights Watch, 2004, “Between a Rock and a Hard Place: Civilians Struggle to Survive in Nepal’s
Civil War”, Human Rights Watch report 16(12).
Informal Sector Service Centre (INSEC), 2005, Human Rights Yearbook.
ICIMOD (International Centre for Integrated Mountain Development), 1997, Districts of Nepal:
Indicators of Development, ICIMOD, Kathmandu, Nepal
Miguel, Edward, Shaker Satyanath, and Ernest Sergenti, 2004, “Economic shocks and civil conflict: an
instrumental variables approach”, Journal of Political Economy, 112(4): 725-753.
Murshed S. Mansoob, and Scott Gates, 2004, “Spatial Horizontal Inequality and the Maoist Insurgency in
Nepal”, WIDER research Paper No. 2004/43.
Shneiderman, Sara, and Mark Turin, 2004, “The Path to Jan Sarkar in Dolakha District: Towards an
Ethnography of the Maoist Movement” in Michael Hutt (ed.), Himalayan ‘People’s War’:
Nepal’s Maoist Rebellion, Hurst and Company, London.
Stark, Oded, and Edward Taylor, 1991, “Migration Incentives, Migration Types: The Role of Relative
Deprivation”, The Economic Journal, 101: 1163-1178.
Stewart, Frances, 2000, “Crisis Prevention: Tackling Horizontal Inequalities”, Oxford Development
Studies, 28: 245-62.
UNDP, 1998 and 2001, Nepal Human Development Reports, Kathmandu: UNDP.
World Bank, 1999 and 2005, Nepal Poverty Assessment, World Bank, Washington DC.
Zartman, William I., 2005, “Need, Creed, and Greed in Intrastate Conflict”, in Arnson, Cynthia J., and
William A. Zartman (eds.), Rethinking the Economics of War: The Intersection of Need, Creed,
and Greed (2005), Johns Hopkins University Press.
19
Figure 1A: Relationship between land holdings and total real per capita expenditures
Real per capita expenditures
5000 10000150002000025000
Returns to land ownership
0
.5
1
1.5
2
2.5
3 3.5 4 4.5 5
land ownership(ha)
5.5
6
6.5
7
7.5
8
2003
95% upper band 2003
95% lower band 2003
1996
95% upper band 1996
95% lower band 1996
Real per capita food expenditures
2000 4000 6000 8000 10000
Figure 1B: Relationship between land holdings and real per capita food expenditures
Returns to land ownership
0
.5
1
1.5
2
2.5
3 3.5 4 4.5 5
land ownership(ha)
5.5
6
6.5
7
7.5
8
2003
95% upper band 2003
95% lower band 2003
1996
95% upper band 1996
95% lower band 1996
20
Figure 1C: Relationship between land holdings and probability of poverty
-.2
Probability(poverty)
0
.2
.4
.6
Returns to land ownership
0
.5
1
1.5
2
2.5
3 3.5 4 4.5 5
land ownership(ha)
5.5
6
6.5
7
7.5
8
2003
95% upper band 2003
95% lower band 2003
1996
95% upper band 1996
95% lower band 1996
21
4
D
ec
O
ct
-0
4
-0
4
g04
-0
r-0
4
Ju
n
Ap
Au
-0
3
-0
3
b04
D
ec
O
ct
mass abductions
Fe
3
g03
-0
r-0
3
Ju
n
Ap
Au
-0
2
-0
2
b03
D
ec
Fe
2
g02
O
ct
Au
-0
r-0
2
Ju
n
Ap
4
4
-0
4
ct
-0
g04
D
ec
O
Au
r-0
-0
4
n04
Ap
Fe
b
3
-0
3
ct
-0
Ju
3
g03
D
ec
O
Au
r-0
-0
3
-0
2
n03
Ap
Fe
b
2
g02
ct
-0
Ju
2
n02
r-0
D
ec
O
Au
Ju
Ap
Figure 2A: Monthly number of Maoist related violent incidents
120
100
80
60
40
20
0
all incidents
Figure 2B: Cumulative number of districts with mass abductions
50
45
40
35
30
25
20
15
10
5
0
22
a
total
2657
44.67
0.13
1.89
0.32
2.98
0.12
0.24
4.83
0.25
0.88
bought
131
44.15
0.11
2.27
0.37
3.48***
0.15
0.23
4.18
0.26
1.56***
b
29
14
0.21
0.10
7.00
2.14
0.01
0
0.010
0
0.33
0.10
4.72
land bought
bought
100
43.92
0.23
3.59***
0.33
4.26***
0.42
0.38
3.86
0.30
1.04***
b
sold
156
47.04*
0.08*
2.27
0.41***
3.50***
0.10
0.22
3.19**
0.17***
1.17**
b
0.01
0
0.007
0
0.25
0.08
3.67
land bought
33
14
0.18
0.10
6.93
2.12
total
2748
45.62
0.20
2.46
0.29
3.49
0.39
0.33
5.01
0.27
0.66
2003
20
11
0.31
0.10
6.02
1.97
0.01
0
0.017
0
0.34
0.11
5.65
land sold
(difference between buying and selling households is only significant for renting land in 1995 and for distances, age and gender hh head in 2003
a: distributions in 1995 and 2003 are not significantly different at the 5% level, except for the share of total land sold among households that sold. Differences in mean between years are not significant at 5%.
b: *, **, *** indicate 10, 5, and 1 % significance of difference with population means
All numbers refer to transactions over the last 12 months
Household characteristics
Age household head
Female household head
Education hh head
Upper caste (Brahmin or Chhettri)
Number of rooms in dwelling
Household head ever migrated
Remittances reveived (rs)
Hours to paved road
Renting land from others
Land owned
Only households involved in transaction
mean share of total land transacted
median share of total land transacted
mean size land transacted (ha)
median size land transacted (ha)
% hh transacted >0.5 ha
% hh transacted >1 ha
mean share of total land transacted
median share of total land transacted
mean size land transacted (ha)
median size land transacted (ha)
% hh transacted >0.5 ha
% hh transacted >1 ha
Rural households
% of hh involved in transaction
1995
Table 1: Land sales activities in 1995 and 2003: descriptive statistics
b
sold
87
47.28
0.14
3.76***
0.30
4.05**
0.33
0.31
2.00***
0.23
0.92**
28
22
0.19
0.13
4.71
1.16
0.01
0
0.006
0
0.12
0.04
3.29
land sold
23
Table 2: Descriptive statistics land ownership change categories (1995-2003)
Decrease
Q1
195
Q2
176
Household life cycle event
non-resident father diedº
non-resident mother diedº
resident father diedº
resident mother diedº
0.04
0.05
0.03
0.07
0.10
0.06
0.02
0.07
0.12
0.13
0.01
0.05
0.09
0.09
0.02
0.05
***
***
**
**
**
0.51
0.81
0.12
0.17
0.08
0.57
0.16
0.22
0.19
0.61
0.23
0.32
0.18
0.63
0.26
0.28
***
*
***
***
***
45.80
0.11
0.42
2.16
3325.14
0.23
0.11
0.34
0.05
0.75
0.32
0.08
43.42
0.15
0.37
1.85
4971.92
0.25
0.06
0.31
0.05
0.76
0.32
0.10
42.73
0.12
0.37
1.72
2719.26
0.25
0.08
0.37
0.04
0.80
0.26
0.10
44.20
0.14
0.43
1.98
3227.20
0.24
0.10
0.37
0.03
0.75
0.42
0.09
0.42
1.74
0.46
0.72
0.47
1.00
0.64
1.59
number of male dependents left
number of female dependents left
non-resident fatherº
non-resident motherº
Assets - hh income strategy
age hh head
female hh head
literate hh head
years of education head
remittances received (rs)
receives remittances
head ever migrated
number of depdendents left for work
head left for work
father born in same district
upper caste
dalit
Context
hours to market
hours to paved road
No changes or Increase
Q3
Q4
212
198
Significance
pairwise difference
Q1-Q3
Q1-Q4
Changes in land ownership
By Quartile
Nr. Of observations
***
**
*
º resident father/mother refers to parent that lived in the same household
*, **, *** indicate 10, 5, and 1 % significance
24
Table 3: Correlates of land ownership changes
non-resident father died
non-resident mother died
(1)
(2)
(3)
(4)
(5)
(6)
0.245***
(2.84)
0.314***
(2.99)
0.256**
(2.32)
0.324**
(2.57)
0.220***
(2.76)
0.288***
(2.67)
-0.241**
(2.22)
-0.017
(0.19)
0.241**
(2.33)
0.298**
(2.36)
-0.238**
(2.09)
-0.017
(0.20)
0.225***
(2.83)
0.291***
(2.71)
-0.246**
(2.27)
-0.015
(0.17)
0.035
(0.91)
-0.252***
(3.56)
784
0.01
0.001
(0.20)
-0.030
(1.26)
0.104
(0.84)
-0.012
(0.63)
-0.183
(0.54)
-0.132
(0.63)
0.128
(0.86)
-0.266
(1.09)
-0.198
(0.64)
-0.318
(0.80)
-0.163
(0.59)
-0.254
(0.67)
742
0.02
-0.178**
(2.18)
784
0.02
0.003
(0.55)
-0.031
(1.31)
0.104
(0.83)
-0.014
(0.74)
-0.178
(0.51)
-0.102
(0.52)
0.071
(0.47)
-0.254
(1.02)
-0.166
(0.54)
-0.271
(0.67)
-0.163
(0.61)
-0.262
(0.68)
742
0.04
0.243**
(2.37)
0.303**
(2.41)
-0.243**
(2.15)
-0.017
(0.19)
0.039
(0.95)
0.003
(0.60)
-0.031
(1.30)
0.111
(0.90)
-0.014
(0.73)
-0.153
(0.44)
-0.090
(0.46)
0.073
(0.48)
-0.239
(0.92)
-0.170
(0.55)
-0.260
(0.64)
-0.154
(0.58)
-0.329
(0.72)
742
0.04
Number of male dependents left
Number of female dependents left
land(ha) rented in 1995
age hh head
nr years of education
female headed hh
distance to markets
middle caste
dalit
newar
hilljan
tharu
muslim
other caste or ethnic group
Constant
Observations
R-squared
-0.192**
(2.50)
784
0.02
Absolute value of t statistics in parentheses - standard errors are adjusted survey design (weights, stratification and clustering)
* significant at 10%; ** significant at 5%; *** significant at 1%
With regional fixed effect
25
Table 4A: Least squares regressions of the determinants of real per capita total expenditures
(1)
(2)
(3)
(4)
(5)
land owned(hectares)
180.904**
190.430***
190.609***
201.771***
230.393***
(2.30)
(2.67)
(2.65)
(2.83)
(2.72)
land owned * 03 dummy
1,205.943*** 1,085.120*** 1,073.483*** 1,063.853*** 1,045.695***
(4.12)
(3.76)
(3.74)
(3.66)
(3.58)
03 year dummy
730.262**
569.526*
303.725
323.428
601.618*
(2.09)
(1.85)
(0.40)
(0.72)
(1.94)
No landownership
(6)
441.989***
(3.27)
914.648**
(2.08)
710.117**
(1.98)
No landownership* 03 dummy
(7)
196.296***
(2.60)
1,129.534***
(3.74)
483.707
(1.42)
142.945
(0.37)
355.785
(0.61)
(8)
204.790***
(2.74)
1,103.285***
(3.78)
557.949*
(1.82)
332.333
(0.91)
land owned ^ 2
33.974***
(5.29)
1,516.193***
(5.18)
738.451***
(2.70)
220.472***
(4.72)
-86.909***
(3.24)
999.083**
(1.98)
135.899
(0.24)
523.723
(0.87)
2,405.364***
(2.77)
922.285
(1.56)
-233.638
(0.44)
-1,159.951***
(3.21)
33.572***
(5.21)
1,524.042***
(5.17)
746.889***
(2.75)
221.320***
(4.72)
-88.638***
(3.29)
1,433.911***
(3.07)
-133.781
(0.33)
559.614
(1.22)
1,954.563***
(2.96)
871.088*
(1.95)
52.839
(0.10)
-722.822*
(1.93)
32.866***
(5.10)
1,543.336***
(5.25)
746.540***
(2.77)
215.853***
(4.62)
-88.038***
(3.25)
1,418.596***
(3.02)
-102.030
(0.25)
598.610
(1.30)
1,949.893***
(2.93)
850.706*
(1.90)
58.599
(0.11)
-744.377**
(1.99)
33.830***
(5.25)
1,525.522***
(5.18)
751.391***
(2.76)
221.737***
(4.73)
-87.496***
(3.26)
1,466.991***
(3.17)
-89.618
(0.21)
574.786
(1.27)
1,974.132***
(3.02)
905.849**
(2.05)
65.527
(0.12)
-746.324**
(2.01)
33.913***
(5.29)
1,520.818***
(5.16)
752.702***
(2.77)
221.596***
(4.74)
-87.519***
(3.26)
1,467.801***
(3.18)
-88.659
(0.21)
573.360
(1.26)
1,973.855***
(3.02)
906.426**
(2.05)
67.220
(0.12)
-738.195**
(1.98)
-18.720***
(2.85)
8.243
(0.37)
32.966***
(5.12)
1,534.688***
(5.22)
735.326***
(2.71)
221.175***
(4.72)
-89.014***
(3.29)
1,414.714***
(3.03)
-156.760
(0.39)
573.426
(1.25)
1,947.304***
(2.93)
855.821*
(1.91)
29.028
(0.05)
-725.709*
(1.94)
858.208
(1.41)
-683.718
(0.82)
25.048
(0.04)
-806.417
(0.70)
-82.626
(0.13)
551.495
(0.53)
754.616
(1.15)
-1,173.339**
(2.39)
-703.710
(1.25)
617.700
(1.23)
4,107.718***
(6.58)
5237
0.09
-1,180.439**
(2.40)
-677.620
(1.20)
634.692
(1.28)
3,947.569***
(6.61)
5235
0.09
-1,092.956**
(2.20)
-600.330
(1.05)
711.248
(1.41)
3,768.953***
(6.20)
5204
0.08
-1,183.711**
(2.41)
-685.111
(1.22)
571.845
(1.12)
3,936.964***
(6.43)
5237
0.09
-1,182.425**
(2.41)
-686.108
(1.22)
571.580
(1.12)
3,897.530***
(6.52)
5237
0.09
-1,149.545**
(2.32)
-655.494
(1.16)
659.757
(1.31)
3,827.148***
(6.37)
5237
0.09
land owned^2 * 03 dummy
age hh head
female hh head
literate hh head
Nr of years education hh had
distance to market (hours)
upper caste
middle caste
dalit
newar
hilljan
tharu
muslim
33.554***
(5.21)
1,521.979***
(5.16)
751.046***
(2.77)
221.458***
(4.73)
-88.551***
(3.29)
1,434.441***
(3.07)
-130.720
(0.32)
559.070
(1.22)
1,955.552***
(2.97)
874.499*
(1.96)
60.808
(0.11)
-720.217*
(1.93)
age hh head * 03 dummy
female hh head * 03 dummy
literate hh head * 03 dummy
Education hh head * 03 dummy
distance to market * 03 dummy
34.909***
(4.69)
1,164.790***
(3.02)
326.871
(1.18)
205.668***
(4.21)
-75.457***
(4.00)
1,407.847***
(3.05)
-146.351
(0.35)
544.401
(1.20)
1,950.073***
(2.97)
882.532**
(1.99)
36.457
(0.07)
-704.416*
(1.88)
-2.645
(0.20)
604.799
(1.05)
835.483
(1.57)
21.166
(0.24)
-36.026
(0.54)
upper caste * 03 dummy
middle caste * 03 dummy
dalit * 03 dummy
newar * 03 dummy
hilljan * 03 dummy
tharu * 03 dummy
muslim * 03 dummy
West Hill&Mountain
-762.998
(1.47)
East Hill&Mountain
-46.390
(0.08)
East Terai
288.846
(0.55)
Constant
6,809.343***
(15.07)
Observations
5405
R-squared
0.04
Absolute value of t statistics in parentheses
-1,188.093**
(2.41)
-685.858
(1.22)
629.984
(1.27)
3,982.678***
(6.69)
5237
0.09
-1,123.293**
(2.32)
-635.273
(1.14)
661.809
(1.34)
4,091.033***
(6.58)
5237
0.09
(9)
459.957***
(3.46)
898.071**
(2.17)
703.895**
(2.08)
26
Table 4B: Least squares regressions of the determinants of real per capita food expenditures
(1)
(2)
(3)
(4)
(5)
land owned(hectares)
70.334**
70.217**
69.141**
74.198***
85.910***
(2.39)
(2.52)
(2.49)
(2.72)
(2.60)
land owned * 03 year dummy
381.416***
341.523***
341.056***
337.940***
326.043***
(5.83)
(5.30)
(5.25)
(5.36)
(4.89)
03 year dummy
461.193***
382.701***
196.724
253.574*
395.304***
(3.61)
(3.09)
(0.64)
(1.67)
(3.18)
No landownership
(6)
176.785***
(3.96)
263.211***
(3.14)
444.987***
(3.45)
No landownership* 03 year dummy
(7)
73.781**
(2.50)
342.711***
(5.08)
387.105***
(2.75)
81.833
(0.50)
-31.932
(0.14)
(8)
73.019**
(2.54)
345.067***
(5.32)
380.442***
(3.08)
64.835
(0.55)
land owned ^ 2
15.524***
(5.41)
529.437***
(4.89)
218.376**
(2.08)
69.681***
(4.90)
-17.603
(1.36)
82.445
(0.46)
477.553**
(2.24)
3.492
(0.01)
1,124.506***
(2.85)
550.530**
(2.06)
-53.083
(0.25)
-517.187***
(2.93)
15.273***
(5.30)
538.006***
(4.90)
220.522**
(2.11)
70.443***
(4.92)
-19.108
(1.46)
396.473***
(2.95)
267.690
(1.64)
141.309
(0.85)
691.655***
(3.00)
481.585***
(2.86)
-184.082
(1.37)
-384.994***
(2.95)
15.072***
(5.22)
541.503***
(4.95)
219.707**
(2.11)
69.261***
(4.83)
-18.476
(1.41)
389.314***
(2.87)
280.476*
(1.76)
160.166
(0.96)
689.748***
(2.97)
473.316***
(2.78)
-180.764
(1.37)
-393.207***
(3.01)
15.343***
(5.29)
536.555***
(4.90)
222.595**
(2.13)
70.511***
(4.93)
-18.872
(1.44)
403.265***
(3.00)
277.183*
(1.67)
143.761
(0.87)
695.575***
(3.04)
489.192***
(2.87)
-179.549
(1.34)
-386.749***
(2.98)
15.336***
(5.30)
536.977***
(4.90)
222.478**
(2.13)
70.524***
(4.93)
-18.870
(1.44)
403.193***
(3.00)
277.097*
(1.67)
143.889
(0.87)
695.600***
(3.04)
489.140***
(2.87)
-179.701
(1.34)
-387.478***
(2.98)
-8.422***
(3.00)
-3.075
(0.39)
14.983***
(5.19)
544.223***
(4.97)
214.252**
(2.05)
70.429***
(4.91)
-19.237
(1.46)
386.040***
(2.87)
250.887
(1.55)
149.053
(0.89)
689.163***
(2.97)
472.910***
(2.79)
-192.884
(1.44)
-386.382***
(2.95)
631.914***
(2.88)
-519.753
(1.62)
250.681
(0.79)
-774.091*
(1.71)
-116.301
(0.35)
-244.212
(0.95)
235.350
(0.92)
-425.300**
(2.21)
-222.373
(1.17)
10.454
(0.07)
2,898.565***
(13.16)
5237
0.09
-438.836**
(2.26)
-211.398
(1.09)
8.731
(0.06)
2,830.938***
(13.29)
5235
0.08
-395.720**
(2.03)
-176.136
(0.90)
48.355
(0.32)
2,743.203***
(12.85)
5204
0.08
-440.655**
(2.27)
-214.742
(1.11)
-4.535
(0.03)
2,824.563***
(12.59)
5237
0.08
-440.770**
(2.28)
-214.652
(1.11)
-4.512
(0.03)
2,828.103***
(12.93)
5237
0.08
-425.514**
(2.18)
-202.716
(1.04)
22.981
(0.15)
2,776.084***
(12.97)
5237
0.09
land owned^2 * 03 year dummy
age hh head
female hh head
literate hh head
Nr of years education hh had
distance to market (hours)
upper caste
middle caste
dalit
newar
hilljan
tharu
muslim
15.266***
(5.30)
537.203***
(4.89)
222.155**
(2.13)
70.497***
(4.92)
-19.071
(1.46)
396.685***
(2.95)
268.891
(1.65)
141.101
(0.85)
692.029***
(3.01)
482.912***
(2.87)
-180.952
(1.35)
-383.971***
(2.94)
age hh head * 03 dummy
female hh head * 03 dummy
literate hh head * 03 dummy
Education hh head * 03 dummy
distance to market * 03 dummy
17.176***
(4.58)
317.031*
(1.93)
30.140
(0.21)
68.082***
(3.56)
-34.311***
(3.65)
402.411***
(2.99)
253.904
(1.58)
142.732
(0.86)
705.187***
(3.07)
464.484***
(2.77)
-186.717
(1.38)
-371.614***
(2.81)
-3.247
(0.55)
376.004*
(1.70)
374.465*
(1.87)
2.166
(0.08)
38.421
(1.42)
upper caste * 03 dummy
middle caste * 03 dummy
dalit * 03 dummy
newar * 03 dummy
hilljan * 03 dummy
tharu * 03 dummy
muslim * 03 dummy
West Hill&Mountain
-183.205
(0.94)
East Hill&Mountain
120.045
(0.63)
East Terai
-6.476
(0.04)
Constant
3,862.200***
(25.08)
Observations
5405
R-squared
0.05
Absolute value of t statistics in parentheses
-441.876**
(2.28)
-214.604
(1.11)
6.883
(0.05)
2,844.714***
(13.38)
5237
0.08
-445.346**
(2.29)
-207.991
(1.07)
19.063
(0.13)
2,912.643***
(12.20)
5237
0.09
(9)
191.521***
(3.80)
308.455***
(3.05)
418.984***
(3.23)
27
Table 4C: Least squares regressions of the determinants of poverty
(1)
(2)
(3)
(4)
land owned(hectares)
-0.012*
-0.012*
-0.012*
-0.012**
(1.90)
(1.88)
(1.88)
(1.97)
land owned * 03 year dummy
-0.050*** -0.042*** -0.043*** -0.041***
(4.27)
(3.66)
(3.77)
(3.65)
03 year dummy
-0.047*
-0.036
-0.137**
-0.077**
(1.88)
(1.54)
(2.25)
(1.97)
No landownership
(5)
-0.014*
(1.87)
-0.040***
(3.33)
-0.037
(1.59)
(6)
-0.035***
(3.35)
-0.042***
(2.94)
-0.039
(1.55)
No landownership* 03 year dummy
(7)
-0.011*
(1.69)
-0.036***
(3.21)
-0.045*
(1.75)
0.027
(0.75)
0.035
(0.74)
(8)
-0.010
(1.57)
-0.039***
(3.53)
-0.037
(1.60)
0.046*
(1.89)
land owned ^ 2
-0.003***
(5.69)
-0.099***
(4.63)
-0.102***
(5.62)
-0.009***
(3.93)
0.008***
(3.03)
-0.060
(1.55)
-0.142***
(2.80)
-0.011
(0.23)
-0.225***
(3.29)
-0.046
(0.89)
-0.025
(0.45)
0.006
(0.13)
-0.003***
(5.53)
-0.099***
(4.65)
-0.102***
(5.58)
-0.009***
(3.95)
0.008***
(3.12)
-0.065**
(2.55)
-0.075**
(2.02)
0.017
(0.53)
-0.125***
(2.97)
0.008
(0.25)
0.011
(0.30)
0.056
(1.56)
-0.003***
(5.43)
-0.100***
(4.70)
-0.099***
(5.44)
-0.009***
(3.96)
0.008***
(3.04)
-0.064**
(2.49)
-0.076**
(2.12)
0.013
(0.40)
-0.126***
(2.91)
0.010
(0.31)
0.009
(0.25)
0.058
(1.59)
-0.003***
(5.41)
-0.099***
(4.64)
-0.102***
(5.61)
-0.009***
(3.92)
0.008***
(3.17)
-0.060**
(2.38)
-0.069*
(1.88)
0.019
(0.60)
-0.123***
(2.89)
0.012
(0.38)
0.011
(0.31)
0.053
(1.47)
-0.003***
(5.40)
-0.099***
(4.66)
-0.102***
(5.60)
-0.009***
(3.93)
0.008***
(3.16)
-0.060**
(2.37)
-0.069*
(1.88)
0.019
(0.59)
-0.123***
(2.89)
0.012
(0.38)
0.011
(0.32)
0.054
(1.49)
0.001*
(1.91)
0.004**
(2.57)
-0.003***
(5.43)
-0.101***
(4.74)
-0.100***
(5.50)
-0.009***
(3.94)
0.008***
(3.09)
-0.062**
(2.43)
-0.069*
(1.86)
0.015
(0.46)
-0.126***
(2.93)
0.010
(0.32)
0.011
(0.31)
0.056
(1.56)
-0.016
(0.33)
0.147**
(2.04)
0.052
(0.81)
0.180**
(2.32)
0.096
(1.59)
0.063
(0.89)
0.087
(1.23)
0.038
(1.10)
0.001
(0.04)
-0.110***
(3.64)
0.639***
(14.13)
5237
0.08
0.039
(1.14)
0.001
(0.02)
-0.107***
(3.61)
0.614***
(15.09)
5235
0.08
0.033
(0.94)
-0.003
(0.08)
-0.115***
(3.80)
0.630***
(15.18)
5204
0.08
0.040
(1.16)
0.001
(0.04)
-0.115***
(3.85)
0.604***
(14.46)
5237
0.08
0.040
(1.16)
0.001
(0.03)
-0.115***
(3.86)
0.600***
(14.46)
5237
0.08
0.037
(1.07)
0.000
(0.01)
-0.111***
(3.69)
0.622***
(14.98)
5237
0.08
land owned^2 * 03 year dummy
age hh head
female hh head
literate hh head
Nr of years education hh had
distance to market (hours)
upper caste
middle caste
dalit
newar
hilljan
tharu
muslim
-0.003***
(5.53)
-0.099***
(4.65)
-0.102***
(5.59)
-0.009***
(3.95)
0.008***
(3.12)
-0.065**
(2.55)
-0.075**
(2.03)
0.017
(0.53)
-0.126***
(2.97)
0.008
(0.24)
0.010
(0.29)
0.056
(1.56)
age hh head * 03 dummy
female hh head * 03 dummy
literate hh head * 03 dummy
Education hh head * 03 dummy
distance to market * 03 dummy
-0.004***
(5.52)
-0.105***
(3.07)
-0.102***
(3.84)
-0.010**
(2.58)
0.008***
(3.06)
-0.065***
(2.59)
-0.076**
(2.07)
0.015
(0.47)
-0.129***
(3.06)
0.006
(0.18)
0.010
(0.28)
0.056
(1.56)
0.002**
(2.08)
0.015
(0.35)
0.001
(0.02)
0.002
(0.32)
0.001
(0.13)
upper caste * 03 dummy
middle caste * 03 dummy
dalit * 03 dummy
newar * 03 dummy
hilljan * 03 dummy
tharu * 03 dummy
muslim * 03 dummy
W est Hill&Mountain
0.037
(1.03)
East Hill&Mountain
-0.015
(0.43)
East Terai
-0.097***
(3.08)
Constant
0.411***
(13.94)
Observations
5405
R-squared
0.03
Absolute value of t statistics in parentheses
0.039
(1.14)
0.001
(0.04)
-0.107***
(3.60)
0.612***
(15.09)
5237
0.08
0.038
(1.10)
0.001
(0.02)
-0.108***
(3.59)
0.663***
(13.92)
5237
0.08
(9)
-0.031***
(2.77)
-0.065***
(3.62)
-0.028
(1.11)
28
Table 5: Panel regressions on determinants of consumption changes
land owned in 1995 (hectares)
land owned in 2003 (hectares)
Distance to markets in 1995
Distance to markets in 2003
age hh head
female hh head
literate hh head
Nr of years education hh had
upper caste
middle caste
dalit
newar
hilljan
tharu
muslim
West Hill&Mountain
East Hill&Mountain
East Terai
Constant
Observations
R-squared
P-value: β 03=β 95 (land ownership)
P-value: β 03=β 95 (distance markets)
total expenditure change
(1)
(2)
94.441
95.443
(0.41)
(0.42)
850.901*
853.149*
(1.84)
(1.85)
-62.544
-37.751
(0.88)
(0.34)
-61.147
(0.30)
-7.283
-7.032
(0.64)
(0.62)
1,082.982
1,060.496
(1.38)
(1.38)
-262.016
-271.708
(0.45)
(0.46)
234.397**
231.734**
(2.30)
(2.28)
2,668.631*** 2,688.715***
(3.42)
(3.48)
-807.508
-804.282
(0.90)
(0.90)
1,198.906
1,247.490
(1.46)
(1.51)
1,392.910
1,361.457
(1.20)
(1.17)
1,575.443
1,584.020*
(1.65)
(1.67)
473.120
477.143
(0.47)
(0.47)
1,694.739
1,686.425
(1.54)
(1.53)
-1,273.777*
-1,199.616*
(1.98)
(1.72)
-2,135.598*** -2,038.774**
(2.85)
(2.41)
1,819.191**
1,832.383**
(2.56)
(2.60)
171.225
182.108
(0.19)
(0.20)
753
753
0.10
0.10
0.0297
0.0293
0.4651
Absolute value of t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
food expenditure change
(3)
(4)
110.580
109.651
(1.20)
(1.21)
238.122*
236.037*
(1.70)
(1.68)
11.967
-11.029
(0.36)
(0.19)
56.717
(0.60)
0.040
-0.192
(0.01)
(0.03)
784.200*
805.057*
(1.78)
(1.82)
-112.999
-104.010
(0.37)
(0.34)
106.714**
109.184**
(2.35)
(2.39)
676.071*
657.442*
(1.76)
(1.71)
-334.923
-337.915
(0.84)
(0.85)
-237.346
-282.410
(0.55)
(0.65)
46.238
75.412
(0.08)
(0.12)
105.430
97.474
(0.29)
(0.27)
186.279
182.548
(0.50)
(0.49)
808.242**
815.954**
(2.04)
(2.07)
443.235
374.447
(0.90)
(0.76)
-406.716
-496.525
(0.83)
(0.97)
722.561*
710.325*
(1.96)
(1.91)
-179.501
-189.596
(0.34)
(0.36)
753
753
0.08
0.08
0.0006
0.0006
0.4074
29
0.249**
(2.06)
68
0.477**
(2.20)
-3.335***
(3.49)
68
0.120***
(3.48)
-0.001***
(2.74)
-0.014
(0.05)
0.461**
(1.98)
(2)
-0.327
(0.20)
68
0.101***
(2.94)
-0.001**
(2.06)
-0.500
(1.42)
0.047
(0.81)
-1.141**
(2.15)
-0.029
(1.24)
0.001
(0.08)
0.494**
(2.00)
(3)
-2.086**
(1.96)
68
0.111***
(3.20)
-0.001**
(2.15)
-0.403
(1.20)
0.076
(1.54)
-1.503***
(3.30)
0.568**
(2.38)
(4)
-2.228
(1.10)
68
0.118***
(3.24)
-0.001**
(2.39)
-0.429
(1.21)
0.049
(0.83)
-1.128**
(2.09)
-0.029
(1.22)
0.001
(0.06)
2.887*
(1.72)
0.504**
(2.05)
(5)
-0.468
(0.27)
68
0.698
(0.37)
0.107***
(2.79)
-0.001**
(2.04)
-0.522
(1.46)
0.042
(0.70)
-1.198**
(2.16)
-0.029
(1.24)
0.001
(0.04)
0.482*
(1.94)
(6)
GDP per Capita, Life expectancy, adult literacy: 1996 data: source: UNDP (1998)
Land gini coefficient: source: UNDP (2001)
% households without land and % land owned by advantaged castes: (upper caste or Newar): source: CBS (2001)
Number of mass abductions: source: BBC monitoring
(7)
0.529
(0.75)
-1.165
(0.58)
68
0.098***
(2.87)
-0.001**
(2.07)
-0.435
(1.21)
0.050
(0.85)
-1.132**
(2.12)
-0.016
(0.54)
-0.000
(0.02)
0.468*
(1.88)
Percentage of Area with Slopes above 30 Degrees: Land with slopes above 30 degrees as a per cent of the total surface area: 1986
Per Capita Forest Area: Per capita forest area (ha) with more than 10 per cent crown density (1978/79)
Road density: Weighted sum of different categories of road in km as a per cent of 100 sq km of total surface area (1994)
Source: International Centre for Integreated Mountain Development, 1997, Districts of Nepal - Indicators of Development, Kathmandu, Nepal
Absolute value of z statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Observations
Constant
% land owned by advantaged castes
% landless households
Land gini coefficient
Adult literacy
Life expectancy
GDP per capita ('000 US$ PPP)
Road Density
Per Capita Forest Area
(% Area with Slopes above 30 Degrees)^2
% Area with Slopes above 30 Degrees
Significant returns to land
Significant increase returns to land
(1)
Table 6: District-level poisson regression for the number of mass abductions from May 2004-December 2004
-0.421
(0.25)
68
0.327
(1.32)
0.106***
(3.08)
-0.001**
(2.25)
-0.388
(1.12)
0.036
(0.63)
-1.042**
(1.99)
-0.032
(1.33)
0.004
(0.26)
(8)
-0.317
(0.19)
66
0.107***
(3.04)
-0.001**
(2.21)
-0.454
(1.27)
0.051
(0.87)
-1.243**
(2.31)
-0.029
(1.23)
0.002
(0.09)
0.472*
(1.87)
(9)
-2.390
(1.15)
66
30
0.125***
(3.35)
-0.001**
(2.54)
-0.381
(1.06)
0.055
(0.93)
-1.232**
(2.24)
-0.028
(1.17)
0.001
(0.05)
3.021*
(1.77)
0.500**
(2.00)
(10)
Number of mass abductions: source: BBC monitoring
% households without land and % land owned by advantaged castes: (upper caste or Newar): source: CBS (2001)
Land gini coefficient: source: UNDP (2001)
GDP per Capita, Life expectancy, adult literacy: 1996 data: source: UNDP (1998)
Road density: Weighted sum of different categories of road in km as a per cent of 100 sq km of total surface area (1994)
Source: International Centre for Integreated Mountain Development, 1997, Districts of Nepal - Indicators of Development, Kathmandu, Nepal
Per Capita Forest Area: Per capita forest area (ha) with more than 10 per cent crown density (1978/79)
Percentage of Area with Slopes above 30 Degrees: Land with slopes above 30 degrees as a per cent of the total surface area: 1986
Absolute value of z statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 7: District-level OLS regressions for changes and total number of mass abductions
Change in the number of mass abductions
(1)
(2)
(3)
(4)
(5)
mean
Significant increase returns to land
0.22
1.035**
1.211**
1.378*** 1.308*** 1.383***
(2.13)
(2.47)
(2.87)
(2.66)
(2.86)
% Area with Slopes above 30 Degrees
51.38
0.046
0.031
0.032
0.038
(1.40)
(0.95)
(0.93)
(1.01)
(% Area with Slopes above 30 Degrees)^2 3268
-0.000
-0.000
-0.000
-0.000
(0.52)
(0.08)
(0.06)
(0.19)
Per Capita Forest Area
0.51
-0.605
-1.408** -0.987
-1.393**
(1.00)
(2.17)
(1.53)
(2.13)
Road Density
4.31
0.013
0.009
0.011
(0.16)
(0.10)
(0.13)
GDP per capita ('000 US$ PPP)
1.08
-0.601
-1.140
-0.547
(0.74)
(1.43)
(0.66)
Life expectancy
55.84
-0.056
-0.058
(1.37)
(1.40)
Adult literacy
34.33
-0.041
-0.039
(1.65)
(1.57)
Land gini coefficient
0.48
1.361
(0.38)
Constant
0.698*** -0.733
4.964**
0.792
4.175
(3.06)
(1.08)
(2.02)
(0.68)
(1.30)
Observations
68
68
68
68
68
R-squared
0.06
0.18
0.29
0.21
0.29
1.588
(1.31)
68
0.28
1.065**
(2.09)
0.048
(1.35)
-0.000
(0.28)
-0.992
(1.48)
0.034
(0.40)
-1.906**
(2.31)
1.016**
(2.02)
0.062
(1.60)
-0.000
(0.64)
-1.314*
(1.93)
-0.011
(0.12)
-1.124
(1.29)
-0.096**
(2.22)
0.007
(0.27)
4.334
(1.18)
3.966
(1.18)
68
0.34
number of mass abductions
(6)
(7)
31