DP2015-01

Does Non-Farm Sector Employm ent
Reduce Rural Poverty and
Vulnerability? Evidence from
Vietnam and India*
Katsushi S. IMAI
Raghav GA IHA
Ganesh THAPA
January 5, 2015

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Does non-farm sector employment reduce rural poverty and vulnerability?
Evidence from Vietnam and India
Katsushi S. Imai *School of Social Sciences, University of Manchester, UK & RIEB, Kobe University, Japan
Raghav Gaiha
Faculty of Management Studies, University of Delhi, India
Ganesh Thapa
International Fund for Agricultural Development, Rome, Italy
This Draft: 2nd January 2015

Abstract
The present study examines whether rural non-farm employment has any poverty and/or
vulnerability-reducing effect in Vietnam and India. To take account of sample selection
bias associated with it, we have applied treatment-effects model. It is found that log per
capita consumption or log mean per capita expenditure significantly increased as a resultof access to the rural non-farm employment in both Vietnam and India - which isconsistent with its poverty reducing role of accessing - with the aggregate effect larger in
Vietnam than in India. Access to the rural non-farm employment significantly reduces
vulnerability too in both countries, implying that diversification of household activities
into non-farm sector would reduce such risks. When we disaggregate non-farm sector
employment by its type, we find that poverty and vulnerability reducing effects are muchlarger for sales, professionals, and clerks than for unskilled or manual employment inboth countries. However, because even unskilled or manual non-farm employment
significantly reduces poverty and vulnerability in India and poverty in some years in
Vietnam, this has considerable policy significance as the rural poor do not have easy
access to skilled non-farm employment.
Key Words: Poverty, Vulnerability, Non-farm sector, Treatment Effects Model, Vietnam, India
JEL Codes: C21, C31, I32, O15

Contact Address
Katsushi S. Imai (Dr) Economics, School of Social Sciences, University of Manchester,
Arthur Lewis Building, Oxford Road, Manchester M13 9PL, UK; Telephone:+44-(0)161-275-4827, Fax: +44-(0)161-275-4812 Email: Katsushi.Imai@manchester.ac.uk.
Acknowledgements
This project was funded by IFAD. The authors are grateful to (then) Director of Asia and thePacific Division, Thomas Elhaut, for his guidance and support. The authors acknowledge
valuable comments and advice from Samuel Annim, Md. Azam, Md. Faruq Hassan, WoojingKang, Armando Barrientos, Takahiro Sato and two anonymous referees. They thank Woojin
Kang for preparing VHLSS Data sets at the early stage of the project. The second author
would like to acknowledge the support and guidance of David Bloom at Harvard School of
Public Health during the completion of this study. The views expressed are, however, thoseof the authors’ and do not necessarily represent those of the organisations to which they areaffiliated.

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Does non-farm sector employment reduce rural poverty and vulnerability? Evidence
from Vietnam and India
1.

Introduction

Across the developing world, it is well recognized that rural economies are not purely
agricultural and farm households earn an increasing share of their income from non-farmactivities. Traditionally, rural non-farm economy (RNFE hereafter) was considered to be alow-productivity sector diminishing over time where agricultural households simply
supplemented their income. But, since the late 1990s, its role in economic growth andpoverty reduction began to be increasingly recognised given the growing share of RNFE
across developing countries (e.g. Reardon et al., 1998, 2000, Lanjouw and Lanjouw, 2001,
van de Walle and Cratty, 2004, Haggblade, et al., 2010, Himanshu et al., 2013) The shareof income from RNFE in total rural income varies - from 34% in Africa, to 47% in Latin
America and 51% in Asia (Thapa et al., 2013), but it is recognised that RNFE is becoming
increasingly important in terms of its share and growth as well as potential roles in poverty
reduction in Asia, particularly in emerging countries, such as China and India. Althoughmost of the low and middle-income Asian countries traditionally relied on agriculture, they
have undergone structural changes in recent years, due to industrialisation and globalisation
as well as commercialisation of agriculture.
Within Asia, the share of income from RNFE varies from over 70% for the Philippines
and Sri Lanka to below 40% for China, India and Nepal (Thapa et al., 2013). With
constraints on farm expansion and continuing growth of rural population, greater attention isthus being given to non-farm activities. Policy interest in RNFE arises not just because of its
significance in generating incomes, but also because of its increasing importance in creating
employment, especially for rural women and the poor.

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Among Asian countries, the present study focuses on Vietnam and India, both ofwhich experienced impressive economic growth but varying poverty reduction in recent
years. These two countries are characterised by high average GDP per capita growth rate in
1990-2010 (Vietnam 5.8%; India 4.9%) and a decreasing share of agricultural value added in
GDP in the same period (Vietnam 39% to 20%; India 29% to 16%) (World Bank, 2014).
Poverty indices have declined during this period, but there is a variation in the speed ofpoverty reduction. While Vietnam experienced a faster poverty reduction in terms ofheadcount ratio based on US$1.25 (64% in 1993 to 21% in 2006, further down to 13% in2008), the speed of poverty reduction has been relatively slow in India (45% in 1994, 37% in
2005, 32% in 2009) (Himanshu and Sen, 2014). As shown by Imai et al. (2012, 2014) andGaiha et al. (2012, 2014), the speed of improvement in nutritional indicators has been slow inIndia in recent years despite the country's economic growth. There is a need for investigating
the reasons for diverse progress in income and non-income poverty focusing on household's
livelihood strategies, including the choice of farm and non-farm employment. The present
study aims to provide insights into varying pace of poverty reduction and vulnerability inthese two countries.
The main hypothesis we examine is whether access to rural non-farm employment
reduces poverty and vulnerability - defined as a probability of falling into poverty in the next
period - in rural areas in Vietnam and India. We focus only on rural areas because rural
economy is distinct from urban economy in its structure and rural poverty is still
predominant in these countries. We will use Vietnam Household Living Standards Survey(VHLSS) in 2002, 2004 and 2006 for Vietnam and National Sample Survey (NSS) Data in
1993-4 and 2004-5 for India. Given the sample selection bias associated with access to
RNFE or non-farm sector employment and the data structure where only large

3cross-sectional data are available and the panel data are not available 1 , we will apply
treatment effects model, a variant of Heckman two-step sample selection model (Heckman,
1979). We also examine whether the effect of reducing poverty and vulnerability is different
among different types of non-farm sector employment, namely, “unskilled manual work”,“production”, “sales”, and “professionals/ clerk”.
While the farm or agricultural sector has played a central role in these countries, theshare of non-farm activities has increased significantly in recent years. However, detailed
empirical studies estimating the direct and/or indirect effects of rural non-farm income or
employment on poverty remain limited and the present study seeks to fill this gap.
Our empirical analysis of the role of rural non-farm employment in reducing
household poverty can be placed in a large literature of growth and development theories.
Our basic assumption is that overall economic growth is beneficial for the poor and theirescape from poverty can further promote overall economic growth (e.g. Dollar and Kraay,2002). This basic assumption can be theoretically justified as follows.2 First, the poor typically the unskilled labour – can benefit from the increase in wage rate, which is enabledby physical capital accumulation by the rich during the growth process. The poor can theninvest in assets and education, which further reinforces development (Galor and Moav,
2004)). Furthermore, an increase in the amount of skilled labour increases the wage rate of
unskilled labour, strengthening the positive correlation between ability and intergenerational
mobility (Maoz and Moav, 1999). Non-farm employment - particularly skilled - can thus
have a substantial poverty-reducing effect. The second justification can be made in terms ofthe connection between the division of labour and the economic growth, which originated in
Adam Smith’s (1776) emphasis on the role of the division of labour in the increase in labour
1

It is possible to construct a small panel based on the intersections of different rounds of householdcross-sectional data of VHLSS in Vietnam, but attrition bias is serious as only a small subset of thetotal households were revisited.
2
We are grateful to one of the reviewers for this valuable suggestion.

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productivity or its further extensions. For instance, Becker and Murphy (1992) examined theprocess of specialization and the division of labour and showed that a more extensive
division of labour raises productivity. Kim (1989) in a similar vein analysed the impact ofhuman capital investment decisions on the depth and breadth of skills and showed thatworkers invest more in skill depth than skill breadth as the size of the labour market
increases. Our empirical results on the role of skilled employment in RNFE are in line withthese studies.
The rest of the paper is organised as follows. The next section reviews extant studies ofthe effects of non-farm sector on poverty in Vietnam and India. Section 3 briefly summarises
the data sets we will use. Sections 4 and 5 discuss the specification of econometric models
and results, respectively. Concluding observations are offered in the final section.

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2.

Literature Review

While the farm or agricultural sector has played a central role in Vietnam and India, the shareof non-farm has increased significantly in recent years. However, formal empirical studies toestimate the direct and/or indirect effects of income or employment in non-farm sector
employment on poverty are still few. On the direct effects, van de Walle and Cratty (2004)
using Vietnam Living Standard Survey (VLSS) data in 1993 and 1998 found significant
effects of non-farm employment in reducing poverty. While van de Walle and Cratty (2004)
claim that they consider the endogeneity of non-farm sector in reducing poverty, they simply
estimated the share of hours worked in non-farm sector in total (or the probability ofparticipating in non-farm sector) and poverty separately and compared the signs andstatistical significance of coefficient estimates of explanatory variables without taking
account of simultaneity. Thus their results are only suggestive of different covariates of
non-farm employment and poverty.
More recently, using both long-term survey data in Palanpur, a village in western Uttar
Pradesh, and the NSS data, Himanshu et al. (2013) have shown that the diversification into
RNFE not only increased household income but also reduced poverty. They have also
provided the evidence from Palanpur which suggests that the income inequality has increased
with this diversification process. However, no discussions or formal analyses have been
carried out by Himanshu et al. (2013) of the endogneity associated with household access to
RNFE.
RNFE would be potentially important for breaking the poverty traps caused by, forinstance, lack of education or nutrition. For example, people who are educated at secondary
school or higher levels are likely to have a higher probability of finding a job in rural
non-farm sector (e.g. in trading, manufacturing office works) and their children tend to be
more educated, which creates a 'virtuous' circle (e.g. Knight et al., 2009, 10). However, those6

who are not educated tend to be trapped in a 'vicious' circle. Likewise, undernourished people
tend to be trapped in poverty as low nutritional levels imply low efficiency and highprobability of being unemployed as predicted by the efficiency-wage hypothesis (e.g. Bliss
and Stern, 1978, Dasgupta and Ray, 1986, 87). The poverty-nutrition hypotheses have been
recently examined by Jha et al. (2009) and Imai et al. (2012) in the context of rural India.
Reardon et al. (2000) also emphasise the barriers faced by poor households that prevent themfrom investing in non-farm assets, suggesting the existence of the poverty trap. That is, it is
not an automatic process for poor agricultural households to enter the non-farm sector.
Unlike agricultural jobs, rural non-farm employment tends to be less physically intensive andrequires lower calories, as the activity intensity determines the nutritional status in rural India(Imai et al., 2012). Since RNFE tends to better promote food security to the poor than farm
employment (Owsu et al., 2011), the former has the potential to break the poverty trap.
While building upon van de Walle and Cratty (2004) and Himanshu et al. (2013), ourstudy takes account of the endogeneity issues based on national data in Vietnam and India. In
our estimations, we have also estimated wage equations for male and female workers toconsider the effect of male or female wage rates on non-farm labour market participation.
The novelty of our study relative to the existing empirical literature includes (i) addressing
the endogeneity issue formally using national data in India and Vietnam; (ii) considering theeffects of male and female wage rates on the non-farm labour market participation; and (iii)
estimating the effects of non-farm labour market participation on vulnerability of households
after taking into account its endogeneity.

3.

Data

Vietnamese Data

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We will use Vietnam Household Living Standards Surveys (VHLSS) 2002, 2004, and 2006.
The VHLSSs were initially implemented in 2002 to collect detailed household and commune
level data. These are multi-topic household surveys with nationally representative household
samples. They commonly cover a wide range of issues, including household composition andcharacteristics (e.g. education and health); detailed record on expenditure for both food andnon-food items, health and education; employment and labour force participation (e.g.
duration of employment and the precise categories of occupations); income by sources (e.g.
salary/wage, payment in cash and in kind, farm and non-farm production); housing,ownership of other assets and durable goods; and participation of households in anti-poverty
programs. Commune level surveys collect data on demography, economic conditions,agricultural production, and non-farm employment, local infrastructure, public services suchas education and health facilities. Occupational code of employment provided by VHLSS
would enable us to classify non-farm sector employment in several sub-components broadly
defined (i.e., manual/unskilled; production; sales; professionals/clerk).
Indian Data
The NSS, set up by the Government of India in 1950, is a multi-subject integrated sample
survey conducted all over India in the form of successive rounds relating to various aspectsof social, economic, demographic, industrial and agricultural statistics. We use the data in the‘Household Consumer Expenditure’ schedule, quinquennial surveys in the 50th round,
1993–94, and in the 61st round, 2004-05.3 These form repeated cross-sectional data sets,
each of which contains a large number of households across India. The consumption
schedule contains a variety of information related to mean per capita expenditure (MPCE),
disaggregated expenditure over many items together with basic socio economic3

We are not using 55th round in 1999-2000 as the consumption data in 55th round are not comparablewith those in 50th or 61st round because of the change in recall periods. The consumption data arecomparable between 50th round and 61st round.

8

characteristics of the household (e.g., sex, age, religion, caste, and land-holding). To derive
wages at the level of NSS agro-climatic region, we supplement the consumption schedule byEmployment and Unemployment schedule because the consumption survey and theemployment survey collect data on different households and can be linked only at theaggregate level (e.g. NSS region level).4 Non-farm sector employment can be classified into
sub-categories by using National Classification of Occupations (NCO).

4. Methodologies(1) Treatment Effects ModelTo estimate the effect of non-farm sector employment on poverty and vulnerability, we
employ a version of treatment effects model. The main idea of treatment effects model is toestimate poverty defined by household consumption per capita for two different regimes (deJanvry et al., 2005) - households participating only in the farm labour market and thoseparticipating in both farm and non-farm labour markets. It is a version of the Heckman
sample selection model (Heckman, 1979), which estimates the effect of an endogenous
binary treatment. This would enable us to take account of the sample selection bias
associated with access to non-farm sector. In the first stage, access to non-farm sector isestimated by the probit model.5 In the second, we estimate log of household consumption orvulnerability measure after controlling for the inverse Mills ratio which reflects the degree ofsample selection bias.
The merit of treatment effects model is that sample selection bias is explicitly
estimated by using the results of probit model. However, the weak aspects include: (i) strong

4

Definitions of the variables of VHLSS and NSS data are given in Appendix Table.
More specifically, we run the probit model at the household level for whether any household
members have access to non-farm sector and then estimate the consumption or vulnerability equationin the second stage.
59

assumptions are imposed on the distributions of the error terms in the first and second stages;(ii) the coefficient estimates may be sensitive to choice of the explanatory variables andinstruments; and (iii) valid instruments are rarely found in non-experimental data and if the
instruments are invalid, the results will depend on the distributional assumptions.
The selection mechanism by the probit model for accessing rural non-farm economy(RNFE) can be more explicitly specified as (e.g., Greene, 2003):
D*   X  ui

and

(1)

D* 1 if D*   X  0

D*  0 otherwise

where PrD  1 X    ( X )PrD  0 X  1   ( X )D * is a latent variable. In our case, D takes the value 1 if an ith household has at least one
household member who has access to non-farm employment and 0 otherwise. X is a vectorof individual, household and regional characteristics and other determinants at commune orcommunity levels.  denotes the standard normal cumulative distribution function.
Since available variables are different for Vietnam and India, we assume different
specifications (or the choice of explanatory variables) for individual access to RNFE for X .
Vietnam:mf
D*  D( Wˆi , Wˆi , M i , Ei , H i , Li , Ri )(2)mŴi : a household average of predicted wages of male members. Daily wage rate is estimatedby individual characteristics, such as, age, its square, dummy variables of educational
categories, whether he is working for the household’s own farm (or non-farm) sector as awage worker, whether the household belongs to ethnic majorities, size of land and its square,and regional and locational dummy variables.

10

f

m

f

Ŵi : a household average of predicted wages of female members. Ŵi and Ŵi serve as
instruments for the household’s non-farm participation equation.6
If non-farm jobs emerge as a result of the division of labour and the non-farm wages
exceed the reservation wage implied by the agricultural sector, people would work as
nonfarm workers. If this is the case, there should be a positive link between the average
wages and non-farm labour market participation. Alternatively, we can assume that thelabour productivity proxied by wage rate is an important determinant of participation in
non-farm sector employment. That is, only high productivity worker with higher agricultural
wages rate can participate in RNFE as an analogy of theory of workfare where only highproductivity workers can participate in workfare scheme or higher waged workers can afford
exercising the ‘real option’ of switching from the agriculture labour marker to workfare or
the non-farm labour market given the switching costs (Scandizzo et al., 2009).

M i : whether the household head is male.
Ei : a set of dummy variables of educational attainment of the household head (whether he or
she has no education; whether completed primary education; whether completed lower
secondary education; whether completed upper secondary education; whether completed
technical education; whether completed higher education).

H i : household composition/ characteristics (household size; the share of female members;
dependency burden (the share of household members below 15 years or above 65 years;
whether a household belongs to ethnic majority) of the ith household).

Li : size of land (in hectare) owned by the household and its square for the ith household.
6

We estimate the wage equations for male and female workers separately given the segmentation oflabour markets by gender in developing countries. If we use the household averages of individual
variables (e.g. education or age) as well as household-level variables in estimating the householdprobability of accessing the non-farm sector employment in one equation, some of the individual
variables (e.g. education) will be dropped automatically due to multicollinearity. Hence, to utilise thedata structure of our survey data, we estimate the wage equations first using the individual data andthen estimating the rural non-farm employment equation by using predicted wages for Vietnam.

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Ri : a set of regional dummy variables (whether a household is located in red river deltaregion; northeast region; northwest region; north central coast region; south central coast
region; central highlands region; north east south region; Mekong river delta region; central
coast region; low mountains; and high mountains).

India:
Because of data limitations, a different set of explanatory variables is chosen as determinantsof accessing rural non-farm employment.
D*  D(Wi , Ei , H i , Li , Bi , Ri )(3)

W i : wage rate estimated using employment data and aggregated for NSS region. 7 8
Explanatory variables in the wage rate equations include age and its square, a number ofdummy variables on literacy and educational attainments, land, Scheduled Tribe (ST),
Scheduled Caste (SC), non-agricultural or agricultural self- employment, religion. State fixed
effects are considered by inserting state dummy variables.

Ei : a set of variables on the highest level of educational attainment of household members(e.g. whether completed primary school, secondary school, or higher education).

H i : a set of variables indicating household composition, such as whether a household isheaded by a female member, number of adult male or female members, dependency burden:
the share of household members under 15 years old or over 60 years old.

Li : owned land as a measure of household wealth.

7

Unlike VHLSS data for Vietnam, matching of individual wage data and household data in NSS for
India is only possible at the level of NSS agro-climatic region, because, as we have already noted,
different sets of households or individuals are surveyed for the Employment and Unemployment
Schedule which contains the individual wage data, and for the Expenditure Schedule covering
household consumption or other household variables.
8
The results for wage equations for Vietnam and India will be provided on request.

12Bi : social backwardness of the household in terms of (i) whether a household belongs to
Scheduled Castes (SCs) and (ii) whether it belongs to Scheduled Tribes (STs).

Ri : a vector of state dummy variables.
The linear outcome regression model in the second stage is specified below to examine
the determinants of poverty - as proxied by household consumption (log of MPCE for the
Indian data and log of per capita real household consumption for the Vietnamese data) or
vulnerability derived by Chaudhuri’s (2003) method which captures the probability of ahousehold falling into poverty in the next period. It is noted here that non-farm labour market
participation is estimated in the first stage of the treatment effects model, while poverty is
estimated (proxied by log per capita household consumption or household vulnerability) inthe second stage. We use log household consumption and vulnerability as a measure ofpoverty because treatment effects model requires that the dependent variable in the second
stage is continuous and the standard binary measure of poverty (0 or 1) cannot be used.
Moreover, as suggested by previous literature, households in India and Vietnam tend to bevulnerable to shocks (e.g. Imai et al, 2011; Gaiha and Imai, 2009). We denote household
poverty - either log per capita household consumption or vulnerability – as Y .
Y  Z  D   iu,   ~ bivariate normal 0, 0,1,   ,  .

(4)

where  is the average net effect (ANE) of access to rural non-farm sector employment. In
case log per capita household consumption is estimated, the positive estimate of  implies
that accessing RNFE increases consumption and thus decreases poverty unless income
distribution changes. In the case of vulnerability, the negative estimate of  implies thataccess to rural non-farm sector employment decreases vulnerability.
Here Z is a vector of determinants of Y . For Vietnam this is estimated by:
Z  Z (M i , Ei , Hi , Li , Ri )

(5)

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and for India
Z  Z ( Ei , Hi , Li , Bi , Ri )(6)

That is, we include all the variables used for the non-farm sector participation equation ((2)
or (3)) except the instruments, predicted wages.
Using a formula for the joint density of bivariate normally distributed variables, theexpected poverty for those with access to rural non-farm sector employment is written as:
E Y D  1   Z    E  i D  1   Z      X  X (7)

where  is the standard normal density function. The ratio of  and  is called the inverse
Mills ratio.
Expected poverty (or undernutrition or vulnerability) for non-participants is:
E Y D  0   Z  E  i D  0   Z    X 
1   X 

(8)The expected effect of poverty reduction associated with RNFE is computed as(Greene, 2003, 787-789):

E Y D  1  E Y D  0       X  X 1   X (9)If  is positive (negative), the coefficient estimate of  using OLS is biased upward(downward) and the sample selection term will correct this. Since   is positive, the signand significance of the estimate of  (usually denoted as  ) will show whether there
exists any selection bias. To estimate the parameters of this model, the likelihood function
given by Maddala (1983, p.122) is used where the bivariate normal function is reduced to theunivariate function and the correlation coefficient  . The predicted values of (7) and (8) are14

derived and compared by the standard t test to examine whether the average treatment effector poverty reducing effect is significant.
The results of treatment effects model will have to be interpreted with caution
because the results are sensitive to the specification of the model or the selection of
explanatory variables and/or the instrument. Also important are the distributional
assumptions of the model. Despite these limitations, the model is one of the few available
methods to control for sample selection bias and capable of yielding insights into whether
access to rural non-farm sector employment leads to poverty reduction.
(2) Vulnerability Measure
It would be ideal to use panel data to derive household’s vulnerability measures, but, in its
absence, we can derive a measure of ‘Vulnerability as Expected Poverty’ (VEP), an ex ante
measure, based on Chaudhuri (2003) and Chaudhuri, Jalan and Suryahadi (2002) who
applied it to a large cross-section of households in Indonesia9 and defined vulnerability as
the probability that a household will fall into poverty in the future after controlling for theobservable household characteristics. Accordingly, it takes the value from 0 to 1, and thehigher the value of vulnerability measure, the higher is the probability of a household falling
into poverty in the next period. Imai et al. (2011) derived and analysed Chaudhuri’s
vulnerability measure using the VHLSS data for Vietnam, and Imai (2011) did so using theIndian NSS data. We will use these cross-sectional vulnerability measures subject to the
caveat of estimating vulnerability from a single cross-section that cannot capture the effect ofaggregate shocks affecting all the households in the sample area. The details of derivation of
Chaudhuri’s vulnerability measure is found in Hoddinott and Quisumbing (2003b). Imai et al.
(2011) and Imai (2011) provide a full set of results of vulnerability for Vietnam and India.

9

See a summary by Hoddinott and Quisumbing (2003a, b) of methodological issues in measuring
vulnerability.

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4.

Econometric Results

This section summarises the results of treatment effects model which is applied to estimate
the effects of accessing rural non-farm sector employment. Vulnerability estimates based on
VHLSS and NSS data are reported in Imai et al. (2011) and Imai (2011) and we highlight
only the results of treatment effects model.
Table 1 gives the results of treatment effects model applied to VHLSS data in 2002,
2004 and 2006. For each year, two different proxies for poverty have been tried as adependent variable - log of per capita consumption and vulnerability. The first panel reports
the results of the first stage probit model for whether a household member participates in the
non-farm sector labour market and the second panel gives the results for OLS whereby log
per capita consumption or vulnerability is estimated.
The first panel of Table 1 suggests that predicted wage rates as well as household
characteristics (e.g. educational attainment, household composition) affect the probability ofa household member participating in the non-farm sector. In 2002 and 2006, both predictedmale wage rate and female wage rate positively and significantly increased the probability of
the household having a member participating in the non-farm sector employment, while in2004 only male wage rate was positive and significant. A household owing more land tends
to have a smaller possibility of participating in the non-farm labour market with an indicationof non-linearity, suggested by a positive and significant coefficient estimate of the size ofland squared (with its coefficient estimate in absolute term larger than the level of size ofland). This indicates that, other things being equal, the households with some amount of land
tend to concentrate on only farming, while the landless or those with a large area of land tend
to participate in non-farm employment, either because their earnings from farming are smallor uncertain, or they can invest in some skills due to the large income from farming. Other

16

variables show more or less expected results (e.g. higher educational attainment tends toincrease the probability of participating in non-farm employment; belonging to ethnic
majority increases the probability; a younger household head is more likely to participate in
non-farm employment; location affects the probability).  or  in equation (7) is
statistically significant (except in case of consumption in 2004 and in 2006), implying that
there exists sample selection bias that should be corrected for in deriving the averagetreatment effects. Use of treatment effects model is justified in most cases.
The second panel of Table 1 shows the results of determinants of per capita household
consumption and household vulnerability for 2002, 2004 and 2006. For example, size ofhousehold significantly decreases consumption in all the years and significantly decreases
vulnerability in 2002, 2004 and 2006. An older household head tends to have higher per
capita consumption and lower vulnerability with non-linear effects. Higher dependency
burden is associated with lower per capita consumption and higher vulnerability. Educationand location are important determinants of both consumption and vulnerability. ˆ , anestimate of  in equations (7) and (9) shows the Average Net Effects (ANE) and it ispositive and significant except in the case of consumption for 2004. However, ANE should
not be treated as a treatment effect if the sample selection term,   , is statistically significant.
At the bottom of the table, we report the Average Treatment Effect (ATE), the difference ofthe expected outcome for participants in non-farm employment and for non-participants after
controlling for sample selection (as in equation (9), the sum of ANE and the sample selectionterm). In order to evaluate the effect of access to non-farm employment on poverty after
taking account of sample selection, we need to base our discussion on ATE, rather than ANE(Imai, 2011).
In 2002, per capita consumption was significantly higher by 19.2% for participants inthe non-farm labour market than for non-participants after taking account of sample selection,

17

which is consistent with the poverty reducing role of rural non-farm employment. In thesame year, vulnerability as a probability of falling into poverty is reduced by 14.9% as aresult of participating in non-farm employment. In 2004, per capita consumption issignificantly higher by 12.9% for non-farm labour market participants than for
non-participants after controlling for sample selection, while the vulnerability is lower for
non-farm labour market participants by 7.3%. In 2006, per capita consumption is higher by
13.1% and vulnerability is lower by 5.9% if the household has access to non-farm
employment. In sum, we confirm that rural non-farm employment substantially reduced
consumption poverty and vulnerability throughout the period 2002 to 2006. The results may
suggest that RNFE opens up a new set of consumption bundles which others could not availof.
(Table 1 to be inserted)
To see how non-farm sector employment in different categories affects poverty andvulnerability, we have repeated the same model by changing only the definition ofbinary-classification of non-farm employment. Only the final results of ATE are summarisedin Table 2. Sub-categories are broadly defined by occupational code of individual members
participating in the non-farm employment - “Unskilled manual employment” (mechanicaland physically demanding jobs e.g. unskilled construction works), “Production” (jobsclassified in manufacturing sector or associated with production, e.g. employment in plantand machine operators/ assemblers or craftsman), “Sales” (jobs associated with sales andtrade) and “Professionals/ clerks” (managers, professionals, technicians, clerks). It should be
noted that these categories are broadly defined by the occupational categories within which
ranks or skill requirements are diverse. Hence, the results should be interpreted with caution.
Also, different occupational coding systems are used for Vietnam and India and the results
are not necessarily comparable.

18

(Table 2 to be inserted)
Given the above caveats, ATE on consumption and vulnerability is different acrossdifferent categories of non-farm employment. For example, “Unskilled and Manual”
non-farm employment increased consumption by 5.1% in 2002 and by 11.0% in 2004, but inthe meantime increased vulnerability (by 5.2% in 2004 and 5.8% in 2006). This implies that
non-farm manual employment may increase household consumption only at the cost of
greater vulnerability in Vietnam. Non-farm sector in “Production” increased consumption
significantly over the years with some variation (by 15.7% in 2002, 3.2% in 2004 and 13.8%
in 2006) and decreased vulnerability by 15.6% in 2002 and by 2.1% in 2004. The poverty
and vulnerability reducing effects of non-farm employment on “Sales” and “Professionals/
Clerks” are more clearly observed. On average, access to employment in “Sales” increased
per capita consumption by 21.0% to 29.6% and reduced vulnerability by 6.0% to 26.7%. The
effect of non-farm employment in “Professionals/ Clerks” on per capita consumption was
also substantial over time (ranging from 15.4% to 22.0%), while vulnerability was
substantially reduced at the same time. That is, with some variation, poverty andvulnerability reducing effects of skilled non-farm employment are much stronger than thoseof unskilled or manual employment after controlling for sample selection biases associatedwith participation in such non-farm employment.
Table 3 gives the results of treatment effects model for the Indian NSS data. As before,
the first panel presents the results of participation equation (probit model). Female
headedness negatively affected participation in NSS61 (in 2004-2005). 10 Dependency
burden is negative and significant, that is, the household with higher dependency burden isless likely to participate in the rural non-farm sector employment. A younger household headis more likely to participate in non-farm employment but with non-linear effects. A
10Because female headedness is measured with error in NSS50/year, it was not used in the
regression.

19

household with more educated members tends to participate in non-farm employment. If thehousehold has more land, the probability of participating in non-farm employment is larger,
which is suggested by a positive and significant coefficient for the dummy variable onwhether a household owns land greater than 2.5 hectares (the first, second and the lastcolumns). This is because in India where farmers may face a greater degree of uncertainty,
owning a larger land is necessary for acquiring some skills to be engaged in non-farm
employment. But this is only possible if the farms own a certain amount of land (i.e. greaterthan 2.5 hectares) as we also observe a negative and significant coefficient estimate for adummy on owning a small area of land (between 0.1and 2.5 hectares) (the third and fourthcolumns, 2004-5). Belonging to the SCs and STs is also associated with lower probability ofparticipating in non-farm employment. For NSS50, i.e., in 1993-1994, higher predictedwages significantly lead to higher probability of participating in non-farm employment. The
coefficient estimate for regional price is positive, but not statistically significant for NSS61.
The coefficient estimate of  or  is significant except the cases for “vulnerability” in
1993-1994 and “log MPCE” in 2004-5.
(Table 3 to be inserted)
The second panel of Table 3 reports the regression results of the second-stage equation
for log MPCE or vulnerability. We report the regression results only selectively. For instance,in contrast to Vietnam, somewhat surprising is the finding that dependency burden
significantly increased log MPCE, but decreased vulnerability. In 1993-1994, a householdwith an older head was more vulnerable with a strong non-linear effect, while age of the head
had no significant effect on per capita consumption. On the contrary, a household with anolder head consumed more with a strong non-linear effect in 2004-2005. In general, ahousehold with a more educated household consumed more and was less vulnerable. As
expected, the larger the size of the land a household owned, it consumed more and was less20

vulnerable. Belonging to the SCs or STs was associated with a lower level of consumption as
well as a higher level of vulnerability.
We have summarised the results of ATE at the bottom of Table 3. It is confirmed thataccess to non-farm employment increased per capita consumption on average by 10.2% in
1993-4 and 10.4% in 2004-5. That is, the consumption increasing effect (or the effect of
reducing consumption poverty) continued to be substantial. Vulnerability was significantly
reduced by participation in non-farm employment - by 3.8% in 1993-4 and by 7.1% in
2004-2005 (in terms of the probability of falling into poverty in the next period). It can beconcluded that in India participation in RNFE is likely to reduce household vulnerability
significantly.
Table 4 reports a summary of the results for India where non-farm employment isdisaggregated by occupational categories. “Professionals/ Clerks” has the largest poverty andvulnerability reducing effects in 1993-1994 and 2004-2005, followed by “Production” and“Sales” which have similar magnitudes of poverty and vulnerability reducing effects.
“Unskilled/ Manual” employment involves the smallest poverty and vulnerability reduction
effects among the four categories, but the role of these effects should not be neglected giventhat the poor do not have easy access to skilled employment in non-farm sector. Access tounskilled/ manual employment in non-farm sector increased per capita consumption by 6.0%
(8.4%) in 1993-1994 (2004-2005) on average, while it reduced the probability of falling intopoverty by 4.0% (7.6%) in 1993-1994 (2004-2005).
(Table 4 to be inserted)

5.

Concluding Observations

The present study has contributed to the existing empirical literature, which has alreadyestablished that rural non-farm income is an important resource for rural agricultural

21

households, in the following three ways. First, this is the first study to show that rural
non-farm sector employment has significantly reduced poverty and vulnerability in Vietnam
and India while taking account of the endogeneity problem. Second, and related to the firstpoint, we have identified a larger poverty reducing effect of skilled employment in rural
non-farm sector. Finally, by focusing on both Vietnam and India, we have developed acomparative perspective in terms of the role of rural non-farm employment.
More specifically, the present study has examined whether participation in the rural
non-farm sector employment or involvement in activity in rural non-farm economy (RNFE)
has any poverty-reducing or vulnerability-reducing effect in Vietnam and India drawing uponnation-wide cross-sectional household data sets. To take account of sample selection bias
associated with RNFE, we applied treatment-effects model, a variant of Heckman sample
selection model. We find that participation in non-farm sector employment significantly
increased per capita consumption or expenditure – as a proxy for poverty reduction - in 2002,
2004, and 2006 in rural Vietnam and in 1993-1994 and 2004-2005 in rural India. The results
are consistent with poverty and vulnerability reducing roles of accessing RNFE. This isimportant as a significant number of households were found to be not only poor but also
vulnerable to shocks in the future (e.g. weather shocks, illness of household members,
macro-economic slowdown) in Vietnam as well as India (Gaiha and Imai, 2009; Imai et al.,2011). Diversification of household activities into non-farm sector would reduce such risks.
Disaggregation of non-farm sector employment by occupational categories shows thataccess to more skilled employment is likely to have larger poverty and vulnerability reducing
effects than unskilled or manual employment. Non-farm employment in “Sales” and“Professionals/ Clerks” categories has stronger effects in reducing poverty and vulnerability
in both Vietnam and India. “Unskilled/ Manual” employment significantly reduces poverty
and vulnerability in India over the years and access of the rural poor to unskilled or manual22employment is likely to be important given that the poor do not have easy access to skilledemployment in non-farm sector. On the contrary, the poverty reducing effect of unskilled/manual non-farm employment is observed in 2002 and 2004, but not in 2006 in Vietnam butwith greater household vulnerability in 2004 and 2006. Non-farm employment associatedwith “Production” significantly reduced poverty and vulnerability over time in both India andVietnam, except in 2006 when vulnerability rose in Vietnam. That is, we observe moreconsistent poverty and vulnerability effects of relatively unskilled/ physical demanding jobs
in non-farm sector for India than for Vietnam. This finding has considerable policy
significance as the rural poor do not have easy access to skilled non-farm employment. The
issue is whether there is enough incentive for the non-farm or business sector to create
preferred job types in the rural areas. Given the potential role of non-farm sector in reducing
vulnerability, central or local governments, or international donors or NGOs might want tosupport this process from both demand and supply sides. For instance, it will be important forgovernments to relocate some business activities from urban to rural areas, support thebusiness for processing and retailing agricultural food products in rural areas, and/or develop
the infrastructure necessary for promoting non-farm economy. In the meantime, it will benecessary for governments or donors to help poor farmers to take up skills necessary for rural
non-farm employment, e.g. through microfinance programmes or newly created training
centre.
Our results are consistent with recent views that non-farm sector plays a key role inhelping poor households escape poverty. Policy interventions designed to help agricultural
households diversify into non-farm sector activities (e.g. skill training; microfinance) would
potentially reduce not only poverty but also vulnerability.
Our results have indicated that there are more similarities than differences in theimpact of rural non-farm employment between Vietnam - an economy in transition - and23

India - an emerging economy. Some clues may, however, emerge from a deeper
understanding of differences in constraints to expansion of land, variation in populationpressure, and productivity, access to credit, decentralised structures of governance, and weakrural infrastructure, which is left for future research. That Vietnam has adapted rapidly to amarket-oriented policy regime may in fact be key to why similarities in the impact of rural
non-farm employment are so much more striking in these two countries.
If this observation is combined with our main result that rural non-farm employment
reduces poverty and vulnerability, it can be inferred that more market-oriented policy regime
in Vietnam has enabled the country to reduce poverty more rapidly than India where the shift
to market-oriented policy regime is slower and the average aggregate benefit from access torural non-farm employment is smaller. This is another interesting topic for future research.

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28

Table 1
The Results of Treatment Effects Model on the Effects of Individual Participation in Rural Non-Farm Employment on Poverty and Vulnerability
for Vietnam
1st Stage:

2002
Participation in Non-farm sector employment
Coef.
Z value *1
Coef.
Z value

2004
Participation in Non-farm sector employment
Coef.
Z value
Coef.
Z value

2006
Participation in Non-farm sector employment
Coef.
Z value
Coef.
Z value

0.205
0.180
-0.1700.051(20.57)*(13.74)*(-6.12)*(1.47)

0.139
0.076-0.1280.004(15.78)**(6.53)**(-4.05)**(0.11)

0.017-0.006-0.064-0.323(4.56)**(-1.56)(-0.82)(-1.08)0.012-0.004-0.077-0.652(4.78)**(-1.57)(-1.05)(-2.42)*

0.007
0.010
0.190
0.139

(4.22)**(3.94)**(2.30)*(0.44)

0.003
0.006
0.112
0.090

(3.49)**(3.56)**(1.45)(0.29)

0.260(7.32)**

0.181(4.98)**-0.083(-0.28)-0.361(-1.34)0.290(0.92)

0.284(0.92)

0.259
0.347

(6.51)**(7.04)**

0.296
0.478

(7.25)**(9.30)**

0.115
0.276

(0.38)(0.91)-0.1400.032(-0.51)(0.12)

0.424
0.619

(1.34)(1.94)†

0.425
0.595

(1.36)(1.91)†-0.0090.033
0.023(-0.15)(5.41)**(0.47)

0.277
0.029-0.066(4.35)**(4.56)**(-1.32)

0.330
0.031
-0.068(1.04)(2.17)*(-0.54)

0.035
0.014
-0.075(0.12)(1.08)(-0.65)

0.740
0.049
-0.099(2.20)*(3.30)**(-0.79)

0.673
0.048
-0.114(2.08)*(3.52)**(-0.99)0.171-24.483
30.071-0.1200.001-0.1220.389
0.049(3.73)**(-22.71)**(16.90)**(-23.71)**(25.47)**(-3.81)**(10.53)**(0.35)-0.079-16.29642.264-0.1110.001-0.1000.383
0.355(-1.66)†(-14.29)**(9.77)**(-20.72)**(23.28)**(-2.86)**(9.34)**(2.39)*

0.020-20.50156.908-0.1320.001-0.1810.317
2.161(0.22)(-7.63)**(5.56)**(-10.51)**(10.88)**(-2.01)*(3.55)**(4.68)**-0.052-13.88542.433-0.0980.001-0.0320.807
1.082(-0.61)(-6.18)**(4.90)**(-8.44)**(9.26)**(-0.38)(9.62)**(2.55)

0.171-10.523
21.561-0.1230.001-0.2720.187
1.136(1.54)(-4.29)**(2.59)*(-8.98)**(9.04)**(-3.00)*(2.24)*(2.25)-0.200-7.27017.278-0.0970.001-0.1780.554
0.387(-1.92)†(-3.34)**(2.50)*(-7.59)**(7.97)**(-2.09)*(7.38)**(0.81)

-0.217(-21.12)**-0.207(-57.62)**0.041(0.47)-0.157-0.056(-0.80)-0.151(-49.12)**̂-0.473(-23.89)**log per capita
consumption
Coef.
Z value *1-0.035(-3.75)**0.120(10.93)**-0.795(-95.06)**-0.865(-122.40)**

Vulnerability
Coef.
Z value
0.022(1.99)**-0.076(-1.90)†-0.142(-0.81)log per capita
consumption
Coef.
Z value-0.005(-0.22)0.175(1.94)†-0.879Vulnerability
Coef.
Z value
0.064(9.95)**-0.085(-12.99)**0.103(0.47)
log per capita
consumption
Coef.
Z value-0.044(-1.85)†0.112(1.26)(-45.61)**(-106.47)*
*

0.222

(19.48)**-0.225(-33.20)**0.260(2.97)**-0.192(-4.77)**0.270(2.97)**-0.257(-6.61)**

0.397(30.68)**-0.338(-43.49)**

0.439(4.97)**-0.272(-6.71)**

0.442(4.75)**-0.309(-7.87)**

Dependent Variable

Explanatory Variables *2
Predicted Daily Male Wage Rate
Predicted Daily Female Wage Rate
Whether a head is male
Whether completed primary school
Whether completed lower secondary
schoolWhether completed upper secondary
schoolWhether completed technical school
Whether completed higher school
education
Size of household
Share of female members
Dependency Burden (share of household
members under 15 or above 60)
Size of land (hectare)
Size of land squared
Age of a household head
Age squared
Whether a household head is married
Whether belonging to ethnic majorities
Constantˆ

2nd Stage:

Dependent Variable

Whether a head is male
Whether completed primary school
Whether completed lower secondary
schoolWhether completed upper secondary
school29

Vulnerability
Coef.
Z value-0.001(-0.11)-0.144(-3.70)**Whether completed technical school
Whether completed higher school
education
Size of household
Share of female members
Dependency Burden (share of household
members under 15 or above 60)
Size of land (hectare)
Size of land squared
Age of a household head
Age squared
Whether a household head is married
Whether belonging to ethnic majoritiesˆConstant
No. of Observations
Wald Chi2(27)0.501(31.54)**-0.430(-43.33)**

0.580(6.47)**-0.327(-8.03)**0.561(5.85)**-0.350(-8.89)**0.802-0.091-0.050(46.73)**(-48.98)**(-3.21)**-0.383-0.0030.048(-33.38)**(-2.63)*(4.78)**

0.803-0.086-0.075(8.55)**(-20.23)**(-2.02)*-0.3080.000
0.039(-7.28)**(-0.12)(2.29)*

0.753-0.093-0.008(7.27)**(-19.71)**(-0.22)-0.343-0.0070.002(-8.39)**(-4.00)**(0.11)-0.276
6.474-8.582
0.026
0.000
0.123
0.188

(-19.39)**(21.36)**(-12.87)**(16.02)**(-15.18)**(11.79)**(15.96)**0.401-0.767
1.611
0.017
0.000
-0.011-0.463(44.52)**(-3.64)**(1.71)†(17.53)**(-20.14)**(-1.57)(-64.07)**-0.121
5.500-15.160
0.018
0.000
0.099
0.273

(-4.49)**(5.17)**(-4.27)**(2.59)**(-2.26)*(3.52)**(9.68)**

0.071-0.2361.675-0.0030.000-0.008-0.424(5.69)**(-0.75)(1.35)(-1.66)*(0.19)(-0.64)(-37.32)**-0.2577.029-15.385
0.014
0.000
0.109
0.276

(-7.72)**(9.43)**(-6.97)**(2.12)*(-1.93)†(3.78)**(11.49)**0.202-0.306
1.585
0.011
0.000
0.010
-0.305(14.29)**(-1.09)(1.77)†(6.18)**(-6.55)**(0.85)(-30.36)**0.574(33.94)**7.019(153.30)25136
20778**
log per capita
consumption
8.015
7.823

0.208
0.231
20205
1010**(32.31)**(8.29)

0.060
7.415
4032
2698**(0.42)(32.41)

0.196
0.748
4030
7227**(31.62)**(12.43)0.226(1.93)†
6.982(37.16)4091
3050**
log per capita
consumption
7.650
7.519

0.197
0.304
4091
6039**(35.30)**(4.90)

Variable
Vulnerability
log per capita consumption
Vulnerability
Vulnerability
Treat With RNFE
0.115
8.040
0.088
Control Without RNFE
0.265
7.912
0.162
Average Treatment Effect (ATE)  X i (     ) X i 1   X i t statistics in brackets+19.2%(55.34)**-14.9%(-63.84)**+12.9%(18.40)**-7.3%(16.42)**+13.1%(17.73)**-5.9%(-16.46)**
Does RNFE Reduce Poverty(or Vulnerability) Significantly?
(based on ATE)
YES
YES
YES
YES
YES
YES
Notes: *1. z or t statistics in brackets; + p<.10, * p<.05, ** p<.01. *2. Regional and locational dummy variables are included in both stages, but are not shown to save the space. Dummy variables are“whether in river delta region; North East region; North West region; North Central Coast region; South Central Coast region; Central Highlands region; North East South region; Central Coast region;
Inland Delta; Hills; Low Mountains.
Source: Authors Calculation based on VHLSS 2002, 2004 and 2006.

30

Table 2
The Results of Averaged Treatment Effect (ATE) of Rural Non-Farm Employment by Occupational Categories in Vietnam
2002
2004
2006
Dependent Variable
log per capita consumption
Vulnerability
log per capita consumption
Vulnerability
log per capita consumption
vulnerability
Explanatory Variables *2ATEt value *1ATEt valueATEt valueATEt valueATEt valueATEt value
Aggregate Effect+19.2%(55.34)**-14.9%(-63.84)**+12.9%(18.40)**-7.3%(16.42)**+13.1%(17.73)**-5.9%(-16.46)**
Does RNFE Reduce Poverty (orVulnerability) Significantly?*3.
YES
YES
YES
YES
YES
YES
Unskilled/ Manual+5.1%(12.78)**+0.5%(1.30)+11.0%(12.84)**+5.2%(7.35)**+1.3%(1.46)+5.8%(10.07)**
Does RNFE Reduce Poverty (orVulnerability) Significantly?
YES
NO
YES
NO
NO
NO
Production+15.7%(41.31)**-15.6%(-45.70)**+3.2%(3.91)**-2.1%(-3.20)**+13.8%(16.23)**+1.2%(8.15)**
Does RNFE Reduce Poverty (orVulnerability) Significantly?
YES
YES
YES
YES
YES
NO
Sales
+29.6%(78.37)**-26.7%(-100.00)**+21.0%(24.80)**-8.7%(-13.60)**+22.2%(25.48)**-6.0%(-11.62)**
Does RNFE Reduce Poverty (orVulnerability) Significantly?
YES
YES
YES
YES
YES
YES
Professionals/ Clerks+20.0%(5.64)**-24.9%(-84.47)**+15.4%(17.02)**-5.5%(-7.88)**+22.0%(23.72)**-7.3%(-13.71)**
Does RNFE Reduce Poverty (orVulnerability) Significantly?
YES
YES
YES
YES
YES
YES
Notes: *1. t statistics in brackets; + p<.10, * p<.05, ** p<.01. *2. The same specification we used for the aggregate case (Table 1) is used for sub-categories of non-farm sector employment,
determined by the occupational code (available in questionnaires for VHLSS data). *3. The case with an answer “YES” is shown in bold.
Source: Authors Calculation based on VHLSS 2002, 2004 and 2006.

31

Table 3
The Results of Treatment Effects Model on the Effects of Participation in Rural Non-Farm Employment for Vietnam on Poverty and
Vulnerability for India
1st Stage:

Dependent Variable

1993-94 (NSS 50)
Participation in Non-farm sector employment
Coef.
Z value*1
Coef.
Z value

2004-2005 (NSS 61)
Participation in Non-farm sector employment
Coef.
Z value
Coef.
Z value

Explanatory Variables*2
Whether a household is headed by a female member

-

-

-

-

-0.039(-1.18)-0.064(-2.02)*

Number of adult female members

0.030(2.03)*

0.034(2.31)*

0.002(0.12)-0.034(-2.12)*

Number of adult male members
Dependency Burden (share of household members under 15 orabove 60)

0.066

(4.92)**

0.059(4.38)**

0.046(2.88)**

0.012(0.77)-0.150(-3.33)**-0.186(-4.05)**-0.099(-2.23)**0.143(3.39)**

Age of household head

-1.237(-2.93)**-1.386(-3.28)**-2.658(-5.74)**-2.308(-5.24)**Age squared

1.073(2.29)*

1.262(2.70)**

2.237(4.58)**

2.072(4.48)**The max. education of adult (Primary)

0.301(8.94)**

0.305(9.02)**

0.195(7.73)**

0.232(9.48)**The max. education of adult (Middle)

0.481(11.95)**0.389(14.89)**0.429(17.01)**

0.481(12.10)**The max. education of adult (>=Matriculates)

0.527(9.12)**

0.528(9.01)**

0.541(13.02)**0.561(14.27)**

Land (0.1<=2.5 ha) (default: the landless)-0.033(-0.66)-0.040(-0.80)-0.091(-4.31)**-0.053(-2.60)**Land (>2.5 ha) (default: the landless)

0.164(1.99)*

0.242(2.90)**

0.147(1.31)

0.171(1.68)†

Whether a household belongs to SC (Scheduled Caste)-0.021(-0.68)-0.028(-0.89)-0.119(-3.82)**-0.153(-5.02)**

Whether a household belongs to ST (Scheduled Tribe)-0.170(-7.29)**-0.176(-7.46)**-0.166(-7.51)**-0.195(-9.14)**

Predicted male wages (at NSS region)

0.012(14.71)**

0.008(9.40)**

-

-

-

-

Aggregate Price (at NSS region)
Constant”
ˆ-0.470(-2.63)-0.235(-1.32)

0.026
0.848

(1.19)(2.70)

0.013
0.730

(1.17)(3.87)-0.196(-14.38)**

0.012(1.27)-0.061(-1.42)-0.212(-49.99)**̂
2nd Stage:

Dependent Variable-0.452(-15.93)**log per capita MPCE

0.049
(1.27)
Vulnerability-0.163(-1.44)log per capita MPCE

-0.800(-85.56)**
Vulnerability

Whether a household is headed by a female member

-

-

-

-

-0.036(-3.90)**0.051(7.69)**

Number of adult female members-0.402(-92.40)**0.153(64.48)**-0.149(-32.14)**0.101(30.57)**

Number of adult male members-0.339(-86.29)**0.152(70.72)**-0.093(-20.08)**

0.094(29.32)**2.343(176.86)**-1.543(-213.27)**0.662(52.52)**-0.527(-59.97)**

0.051(0.41)

0.989(14.38)**0.596(4.09)**

0.084(0.90)

Dependency Burden (share of household members under 15 orabove 60)
Age of household head32

(-0.96)-0.997(-13.13)**-0.291(-1.97)*-0.331(-3.40)**0.052(4.78)**-0.055(-9.18)**0.048(5.71)**-0.143(-28.39)**

The max. education of adult (Middle)

0.096(7.05)**-0.116(-15.43)**

0.121(10.02)**-0.269(-50.48)**

The max. education of adult (>=Matriculates)

0.182(9.35)**-0.228(-21.24)**0.259(14.48)**-0.342(-40.25)**

Land (0.1<=2.5 ha) (default: the landless)

0.048(3.38)**-0.078(-10.11)**

0.026(4.10)**-0.047(-11.21)**Land (>2.5 ha) (default: the landless)

0.040(1.47)-0.093(-6.18)**0.093(2.98)**-0.188(-8.51)**

Whether a household belongs to SC (Scheduled Caste)-0.140(-15.01)**

0.090(17.79)**-0.147(-16.15)**0.222(36.19)**Whether a household belongs to ST (Scheduled Tribe)-0.070(-10.17)**0.057(15.20)**-0.067(-9.07)**0.121(27.63)**(18.65)**(143.21)-0.0591.180(-3.61)**(38.56)

0.205
9.330

(2.90)**(123.29)0.284-0.024(37.95)**(-0.87)ˆ-0.134The max. education of adult (Primary)

Age squared

ˆ

0.456
7.927

Constant
No. of Observations

21883

21883

Wald Chi2(37) [Wald Chi2(95) for NSS61]

52256**

62554**

Variable

Log MPCE

Treat With RNFE
Control Without RNFE
ATE (       X i  X i 1   X i 

Vulnerability

Log MPCE

Vulnerability

8.693

0.60369.58870.17058.591

0.64159.48480.2412);t value in brackets.

+10.2%(15.99)**-3.79%(-9.94)**+10.4%(38.47)**-7.08%(-24.50)**
Does RNFE Reduce Poverty (or Vulnerability) Significantly?
(based on ATE)
YES
YES
YES
YES
Notes: *1. z or t statistics in brackets; + p<.10, * p<.05, ** p<.01. *2. State dummies and included, but are not shown to save the space.
Source: Authors Calculation based on NSS50 and NSS61.

33

Table 4
The Results of Averaged Treatment Effect (ATE) of Rural Non-Farm Employment by Occupational Categories in India
1993-94 (NSS 50)
2004-2005 (NSS 61)
log per capita
log per capita
Dependent Variableconsumption
vulnerability
consumption
Vulnerability
Explanatory Variables *2ATEt value *1ATEt valueATEt valueATEt value
Aggregate Effect+10.2%(15.99)**-3.8%(-9.94)**+10.4%(38.47)**-7.1%(-24.50)**
Does RNFE Reduce Poverty (or Vulnerability) Significantly?*3
YES
YES
YES
YES
Unskilled/ Manual+6.0%(9.06)**-4.0%(-10.08)**+8.4%(30.06)**-7.6%(-24.05)**
Does RNFE Reduce Poverty (or Vulnerability) Significantly?
YES
YES
YES
YES
Production+14.3%(20.54)**-2.8%(-6.69)**+15.3%(47.38)**-9.5%(-26.49)**
Does RNFE Reduce Poverty (or Vulnerability) Significantly?
YES
YES
YES
YES
Sales
+14.7%(20.37)**-2.6%(-6.04)**+13.3%(42.55)**-9.7%(28.95)**
Does RNFE Reduce Poverty (or Vulnerability) Significantly?
YES
YES
YES
YES
Professionals/ Clerk+24.1%(33.98)**-4.6%(-10.74)**+24.1%(72.89)**-15.2%(45.99)**Does RNFE Reduce Poverty (or Vulnerability) Significantly?
YES
YES
YES
YES
Notes: *1. t statistics in brackets; + p<.10, * p<.05, ** p<.01. *2. The same specification we used for the aggregate case (Table 1) is used for
sub-categories of non-farm sector employment, determined by the occupational code (available in questionnaires for VHLSS data).
*3. The case with an answer “YES” is shown in bold.
Source: Authors Calculation based on NSS50 and NSS61.

34

Appendix Table Definitions of the Variables of VHLSS and NSS data
Variable

Definition

VHLSS Data
Rlconpc

log real per capita consumption expenditure in 2002 value

HeadageAge of household head

Headage2(Age of household head)2MarriedWhether a household head has a spouseFemaleshare

Share of female members

Femaleshare2(Share of female members)2

HhsizeSize of Household

Depburden

Ratio of dependency burden

Majorities

Whether a household belongs to ethnic majority

Noschooling_Head

Whether a household had no education

Primary_Head

Whether a household finished with primary school education

Lowersecon_Head

Whether a household finished with lower secondary school education

Uppersecon_Head

Whether a household finished with upper secondary school education

Technical_Head

Whether a household finished with technical school education

Higher_Head

Whether a household finished with higher school education

RedRiverDelta

Whether a household is located in red river delta region

NorthEast

Whether a household is located in northeast region

NorthWest

Whether a household is located in northwest region

NorthCentralCoast

Whether a household is located in north central coast region

SouthCentralCoast

Whether a household is located in south central coast region

CentralHighlands

Whether a household is located in central highlands region

NorthEastSouth

Whether a household is located in north east south region

MekongRiverDelta

Whether a household is located in mekong river delta region

CentralCoast

Whether a household is located in central coast region

Land

Size of Land (million hactare)

Land2(Size of Land)2

NSS Data (India)
Whether a household is headed by a female
memberNumber of adult female members

Whether a household is headed by a female member,(=1 if yes, =0 if no).
Number of adult female members (15 years old or above)in a household

Number of adult male members

Number of adult male members (15 years old or above) in a household

Dependency Burden

The share of children under 15 years old or adults over 60 years old in the total number of household members.

Age of household headAge of household head (years)

Age squared

Square of age of household head
The maximum level of educational attainment of adult member in the household is the completion of primaryschool.
The maximum level of educational attainment of adult member in the household is the completion of middle
school.

The max. education of adult (Primary)
The max. education of adult (Middle)
The max. education of adult(>=Matriculates)
Land (0.1<=2.5 ha) (default: the landless)

The maximum level of educational attainment of adult member in the household is matriculates or higher.
The area of owned land of the household is from 0,1 hectare to 2.5 hectare.

Land (>2.5 ha) (default: the landless)

The area of owned land of the household is larger than 2.5 hectare.

Land pc

The area of owned land per capita
Whether the occupation type of the household head is self-employed in non-agriculture (=1 if yes, =0 if no).default of the four choices is ‘others’.
Whether the occupation type of the household head is agricultural labour

Whether self-employed in non-agriculture
Whether agricultural labour(=1 if yes, =0 if no).

Whether non-agricultural labour

Whether the occupation type of the household head is labour in non-agriculture (=1 if yes, =0 if no).

Whether self-employed in agriculture

Whether the occupation type of the household head is self-employed in agriculture (=1 if yes, =0 if no).

35

Whether a household belongs to SCs(Scheduled Castes)
Whether a household belongs to STs(Scheduled Tribes)
RPW

Whether a household belongs to SC (Scheduled Caste) (=1 if yes, =0 if no).
Whether a household belongs to ST (Scheduled Tribe) (=1 if yes, =0 if no).
Whether a household has access to Rural Public Works.

FFW

Whether a household has access to Food for Work Programme.

Predicted agricultural wage rate for males

Agricultural Wage Rate for male workers averaged at NSS region.

Poor

Whether the household per capita expenditure is under the national poverty line for rural areas.

poor (calorie based)

Whether the household is undernourished in terms of calorie intakes.

poor (protein based)

Whether the household is undernourished in terms of protein intakes.

Vulnerability Measure (based on 100%income poverty line)

Whether the household is vulnerable

Vulnerability Measure (based on 80%
income poverty line)
Vulnerability Measure (based on 120%income poverty line)(based on 100% of the national poverty line).
Whether the household is vulnerable (based on 80% of the national poverty line).
Whether the household is vulnerable (based on 120% of the national poverty line).

36