DP2012-25

Does non-farm sector em ployment
red uce rural poverty and vulnerability?
Evidence from Vietnam and India *Katsushi S. IMA I
Raghav GAIH A
G ane sh THAPA

Revised November 30, 2012

* 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
First Draft: 3rd September 2012
This Draft: 29th November 2012

Abstract
The present study examines whether employment in the rural non-farm sector (RNFE)
has any poverty-reducing and/or vulnerability-reducing effect in Vietnam and India. To
take account of sample selection bias associated with RNFE, we have applied
treatment-effects model, a variant of Heckman sample selection model. It is found thatlog per capita consumption or log mean per capita expenditure (MPCE) significantly
increased as a result of access to RNFE in Vietnam and India - which is consistent withpoverty reducing role of accessing RNFE - with the aggregate effect larger in Vietnam
than in India. Access to RNFE significantly reduces vulnerability too in both countries,
implying that diversification of household activities into non-farm sector would reducesuch risks. When we disaggregate non-farm sector employment by its type, we find thatpoverty and vulnerability reducing effects are much larger for sales, professionals, andclerks than for unskilled or manual employment in the non-farm sector in both countries.
However, as 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 is funded by IFAD. We acknowledge valuable comments and advice from
Samuel Annim, Md. Azam, Md. Faruq Hassan, Woojing Kang, Armando Barrientos, andTakahiro Sato. We thank Woojin Kang for preparing VHLSS Data sets at the early stage ofthe project. The views expressed are, however, those of the authors’ and do not necessarily
represent those of the organisations to which they are affiliated.

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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) was considered to be alow-productivity sector diminishing over time where agricultural households simply
supplement their income. But, since the late 1990s, its role in economic growth and poverty
reduction began to be increasingly recognised given the increasing share of RNFE acrossdeveloping countries (e.g. Lanjouw and Lanjouw, 2001, Haggblade, et al., 2010). The shareof income from RNFE in total rural income varies - from 34% in Africa, to 47% in Latin
America and 51% in Asia, but it is recognised that RNFE is becoming increasingly importantin 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. Although most of the low andmiddle-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. With constraints on farm expansion
and continuing growth of rural population, greater attention is thus being given to non-farmactivities. 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 forrural women and the poor.
Among Asian countries, the present study focuses on two countries – Vietnam andIndia, both of which experienced impressive economic growth but varying poverty reduction2

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 agriculturalvalue added in GDP in the same period (Vietnam 39% to 20%; India 29% to 16%). Poverty
indices have declined during this period, but there is a variation in the speed of poverty
reduction. While Vietnam experienced a faster poverty reduction in terms of headcount ratio
based on US$1.25 (64% in 1993 to 21% in 2006, further down to 13% in 2008), the speed ofpoverty reduction has been relatively slow in India (49% in 1994 to 42% in 2005). As shownby Imai et al. (2012a, b) and Gaiha et al. (2012 a, b), the speed of improvement in nutritional
indicators has been slow in India 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 andnon-farm employment. The present study will aim to provide insights into varying pace ofpoverty reduction and vulnerability in these two countries.
The main hypothesis we examine is whether access to RNFE reduces poverty and a
related but distinct aspect, vulnerability, defined as a probability of falling into poverty in thenext 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 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 cross-sectional data are
available and the panel data are not available 1, we will apply treatment effects model, avariant of Heckman two-step sample selection model (Heckman, 1979). We also examine1

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.

3

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. The rest of
the paper is organised as follows. The next section reviews extant studies on the 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.

2.

The Literature

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 VLSS data on Vietnam 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 theshare of hours worked in non-farm sector in total (or the probability of participating in
non-farm sector) and poverty separately and compared the signs and statistical significanceof 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.
Informal evidence from India and Bangladesh suggests that indirect effects also matter, for4example, the labour market tightening, or expansion of casual non-farm employment isstrongly correlated with growth in agricultural wages. While building upon van de Walle andCratty (2004), our proposed research applies improved methodologies to take account of theendogeneity issues to more comprehensive and recent data sets.
RNFE would be potentially important for breaking the poverty traps through various
routes - such as lack of education and/or nutrition. For example, people who are educated atsecondary school or higher 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 causes a 'virtuous' circle (e.g. Knight et al., 2009, 10). However, those
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. (2012a) in the context of rural India.
Reardon et al. (2000) also emphasises the barriers faced by poor households that prevent
them from 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 into the non-farm
sector. Unlike agricultural jobs, rural non-farm employment tends to be less physicallyintensive and requires lower calories, as the activity intensity determines the nutritional status
in rural India (Imai et al. (2012b). Since RNFE tend to better promote food security to thepoor than farm employment (Owsu et al., 2011), the former has the potential to break thepoverty trap.

3.

Data

Vietnamese Data5

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 etc.); housing,ownership of 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, 2004-05.2 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 economic2

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.

6

characteristics of the household (e.g., sex, age, religion, caste, and land-holding). To derive
wages at the level of NSS region, we supplement the consumption schedule by Employment
and Unemployment schedule because the consumption survey and the employment survey
collect data on different households and can be linked only at the aggregate level (e.g. NSS
region level).3 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. 4 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
assumptions are imposed on distributions of the error terms in the first and second stages; (ii)3

Definitions of the variables of VHLSS and NSS data are provided in Table A1.
More specifically, we run the probit model at household levels for whether any household members
have access to non-farm sector and then estimate the poverty equation in the second stage.
47

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):

Di  X i   ui
*

and

(1)

Di 1 if Di  X i   0**

Di  0 otherwise*where Pr Di  1 X i   ( X i )PrDi  0 X i  1   ( X i )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 i .
Vietnam:*
Di  Di (Wˆi m ,Wˆi f , M i , Ei , H i , Li , R)

(1)’

Ŵi m : 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 (see Table A2).

8

Ŵi f : a household average of predicted wages of female members. Ŵi m and Ŵi f serve as
instruments for the household’s non-farm participation equation.
We assume here that the labour 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 theoryof workfare where only high productivity workers can participate in workfare scheme or
higher waged workers can afford exercising the ‘real option’ of switching from theagriculture labour marker to workfare or the non-farm labour market given the switchingcosts (Scandizzo et al., 2009).5

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.
R : 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 coast5

Two issues should be discussed regarding the effects of wage rates on participation in RNFE. First,
access to non-farm sector employment is likely to have an indirect effect of reducing consumption
poverty of an agricultural household through increased agricultural wage rates. This would requiredisaggregated non-farm and farm wages data (preferably over time), which neither VHLSS nor NSS
data have. Second, related to the first point, the wage rate is endogenous. While we estimate daily
wage rate for men and women separately for Vietnam, we use the estimated wage rate aggregated at
NSS region level for India due to data constraints.

9

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.

Di  Di (W , Ei , H i , Li , Bi , R)
*

(1)’’

W : wage rate estimated using employment data and aggregated for NSS region. The results
for wage equations (based on NSS50-10 and NSS61-10) are given in Table A3. Explanatory
variables in the wage rate equations include age and its square, a number of dummy variables
on literacy and educational attainments, land, Scheduled Tribe (ST), Scheduled Caste (SC),
non-agricultural or agricultural self- employment, religion. State fixed effects are consideredby inserting state dummy variables.6

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. 7

Li : owned land as a measure of household wealth. 8
Bh : Social backwardness of the household in terms of (i) whether a household belongs to
SCs and (ii) whether it belongs to STs.
R : a vector of state dummy variables.

6

However, for NSS61, we have used the regional price because aggregate wage rate is automatically
dropped due to the collinearity problem in the non-farm sector employment participation equation.
7
Female headedness was dropped in all the regressions based on NSS50, because it consistently
shows a counter-intuitive sign.
8
Square of land is dropped due to the collinearity problem.

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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 NSS data and log of per capita real household consumption for the Vietnamese
VHLSS data) or vulnerability derived by Chaudhuri’s (2003) method which captures theprobability of a household falling into poverty in the next period. 9 It is noted here that
non-farm labour marker participation is estimated in the first stage of the treatment effects
model, while poverty is estimated (proxied by log per capita household consumption orhousehold vulnerability) in the second stage. We use log household consumption andvulnerability as a measure of poverty because treatment effects model requires that thedependent variable in the second stage is continuous and the standard binary measure ofpoverty (0 or 1) cannot be used. Moreover, as suggested by previous literature, households in
India and Vietnam tend to be vulnerable to shocks (e.g. Imai et al, 2011; Gaiha and Imai,2009). We denote household poverty - either log per capita household consumption orvulnerability - as Wi .

Wi  Z i   Di   iu,   ~ bivariate normal 0, 0,1,  ,  .

(2)

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. In the case ofvulnerability, the negative estimate of  implies that access to rural non-farm sector
employment decreases vulnerability.
Here Z i is a vector of determinants of W . For Vietnam this is estimated by:

Z i  Z i (M i , Ei , H i , Li , R)

(2)’for Vietnam and
9

The methodology will be discussed in the next subsection.

11

Z i  Z i ( Ei , H i , Li , Bi , R)

(2)’’

for India. That is, we include all the variables used for the non-farm sector participation
equation ((1)’ or (1)’’) 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 Wi Di  1   Z i    E  i Di  1   Z i       X i   X i (3)where  is the standard normal density function. The ratio of  and  is called the inverse
Mill’s ratio.
Expected poverty (or undernutrition or vulnerability) for non-participants is:
E Wi Di  0   Z i  E  i Di  0   Z i     X i 
1    X i (4)The expected effect of poverty reduction associated with RNFE is computed as(Greene, 2003, 787-789):

EWi Di  1  EWi Di  0        X i  X i 1   X i 

(5)

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 (3) and (4) are

12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 Indonesia 10 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) derived it using
the Indian 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 Appendix. Imai et al. (2011) and Imai (2011)10

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

13

provide a full set of results of vulnerability for Vietnam and India.

4.

Econometric Results

This section summarises the results of treatment effects model which is applied to estimate
the effects of participation in RNFE (Rural Non-farm Economy) or non-farm sector
employment. Vulnerability estimates based on VHLSS and NSS data are reported in Imai etal. (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 labour market. In 2002 and 2006,
both predicted male wage rate and female wage rate positively and significantly increased theprobability of the household having a member participating in the non-farm sector
employment, while in 2004 only male wage rate was positive and significant. Other variables
show more or less expected results (e.g. higher educational attainment tends to increase theprobability of participating in non-farm employment; belonging to ethnic majority increases
the probability; a household head with a younger head is more likely to participate in
non-farm employment; locations affect the probability).  or  in equation (3) is
statistically significant (except the case of consumption in 2004 and in 2006), implying that14

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. A household headed by an older head tends to havehigher per capita consumption and lower vulnerability with non-linear effects. Higher
dependency burden is associated with lower per capita consumption and higher vulnerability.
Education and location are important determinants of both consumption and vulnerability.

ˆ , an estimate of  in equations (3) and (5) shows the average net effects (ANF) and it ispositive and significant except in the case of consumption for 2004. However, ANF 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 of the
expected outcome for participants in non-farm employment and for non-participants after
controlling for sample selection (as in equation (5), sum of ANF 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 ANF(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,
which is consistent with the poverty reducing role of RNFE. In the same year, vulnerability
as a probability of falling into poverty is reduced by 14.9% as a result of participating in
non-farm sector employment. In 2004, per capita consumption is significantly higher by
12.9% for non-farm labour market participants than for non-participants after controlling forsample selection, while the vulnerability is lower for non-farm labour market participants by15

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 RNFE
substantially reduced consumption poverty and consumption vulnerability (as a probabilityof falling into consumption poverty) 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 building construction works), “Production”(jobs classified in manufacturing sector or associated with production, e.g. employment inplant and machine operators/ assemblers or craftsman), “Sales” (jobs associated with sales
and trade) 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 interpretedwith caution. Also, different occupational coding systems are used for Vietnam and India andthe results are not necessarily comparable.
(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 mean time increased vulnerability (by 5.2% in 2004 and 5.8% in 2006). This implies that16

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.
(Table 3 to be inserted)
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). 11 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. Household headed by ayounger head is more likely to participate in non-farm employment but with non-linear
effects. A household with more educated members tends to participate in non-farm
employment. If the household has more land, the probability of participating in non-farm
employment is smaller. Belonging to Scheduled Caste and Scheduled Tribes is also
associated with lower probability of participating in non-farm employment. For NSS50,
higher predicted wages significantly lead to higher probability of participating in non-farm

11

Because female headedness is measured with error in NSS50, it was not used in the regression.

17

employment. The coefficient estimate for regional price is positive, but not statisticallysignificant for NSS61. The coefficient estimate of  or  is significant except the cases
for “vulnerability” in 1993-1994 and “log MPCE” in 2004-5.
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 are the findings that dependency burden
significantly increased log MPCE , but decreased vulnerability. In India, higher dependency
seems to imply that households need to earn more per person, and thus they tend to consume
more and to be less vulnerable.(This sentence could be deleted as the result is intriguing). In
1993-1994, a household with 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 thecontrary, a household with an older head consumed more with a strong non-linear effect in2004-2005. In general, a household with a more educated household consumed more and wasless vulnerable. As expected, the larger the size of the land a household owned, it consumed
more and was less vulnerable. Belonging to Schedule Castes or Scheduled Tribes 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.

18

Table 4 reports a summary of the results for India where non-farm employment isdisaggregated by occupational categories, as in Table 2. “Professionals/ Clerks” has thelargest poverty and vulnerability reducing effects in 1993-1994 and 2004-2005, to befollowed by “Production” and “Sales” which have similar magnitudes of poverty andvulnerability reducing effects. “Unskilled/ Manual” employment involves the smallest
poverty and vulnerability reduction effects among the four categories, but the role of theseeffects should not be neglected given that the poor do not have easy access to skilledemployment in non-farm sector. Access to unskilled/ 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 into poverty by 4.0% (7.6%) in 1993-1994(2004-2005).
(Table 4 to be inserted)

5.

Concluding Observations

The present study examines 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 upon nation-wide cross-sectional
household data sets. To take account of sample selection bias associated with RNFE, weapplied 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 in 2002, 2004, and 2006 for rural Vietnam and in
1993-1994 and 2004-2005 for rural India. The results are consistent with poverty andvulnerability reducing roles of accessing RNFE. This is important as a significant number ofhouseholds 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

19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/ Clerk” categories has stronger effects in reducing poverty and vulnerability inboth Vietnam and India. “Unskilled/ Manual” employment significantly reduces poverty andvulnerability in India over the years and access of the rural poor to unskilled or manual
employment is likely to be important in India given that the poor do not have easy access to
skilled employment 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 but with greater household vulnerability in 2004 and 2006. Non-farm employment
associated with “Production” significantly reduced poverty and vulnerability over time inboth India and Vietnam, except in 2006 when vulnerability rose in Vietnam. That is, weobserve more consistent poverty and vulnerability effects of relatively unskilled/ physicaldemanding jobs in non-farm sector for India than for Vietnam.
Our results are consistent with recent views that non-farm sector plays a key role inhelping poor agricultural households escape poverty, as emphasised by Knight et al. (2009,2010) in the context of rural China. 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.
That there are more similarities than differences in the impact of rural non-farm
employment between Vietnam -an economy in transition- and India -an emerging economyis somewhat intriguing. Some clues may, however, emerge from a deeper understanding ofdifferences in constraints to expansion of land, variation in population pressure, and20

productivity, access to credit, decentralised structures of governance, and weak rural
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.

Acknowledgements
This project is funded by IFAD. We acknowledge valuable comments and advice from
Samuel Annim, Md. Azam, Md. Faruq Hassan, Woojing Kang, Armando Barrientos, andTakahiro Sato. We thank Woojin Kang for preparing VHLSS Data sets at the early stage ofthe project. The views expressed are, however, those of the authors’ and do not necessarily
represent those of the organisations to which they are affiliated.
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24

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

Dependent Variable

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)†

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-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)**

̂
2nd Stage:

Dependent Variable

Whether a head is male
Whether completed primary school
Whether completed lower secondary
schoolWhether completed upper secondary
school25

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

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)

Vulnerability
0.115
0.265

log per capita consumption
8.040
7.912

Vulnerability
0.088
0.162

Vulnerabilityt 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.

26

Table 2
The Results of Averaged Treatment Effect (ATE) on the Effects 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/ Clerk+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 us ed 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.

27

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

0.066(4.92)**

0.059(4.38)**

0.046(2.88)**

0.012(0.77)

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

-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”-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)**

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

2.343

(176.86)**-1.543(-213.27)**0.662(52.52)**-0.527(-59.97)**

Age of household head

0.051

(0.41)

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

0.084(0.90)

28

Age squared-0.134(-0.96)-0.997The max. education of adult (Primary)

0.052(4.78)**The max. education of adult (Middle)

0.096(7.05)**The max. education of adult (>=Matriculates)

0.182

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

0.048(-13.13)**-0.291(-1.97)*-0.331(-3.40)**-0.055(-9.18)**-0.116(-15.43)**

0.048(5.71)**-0.143(-28.39)**0.121(10.02)**-0.269(-50.48)**(9.35)**-0.228(-21.24)**0.259(14.48)**-0.342(-40.25)**(3.38)**-0.078(-10.11)**

0.026(4.10)**-0.047(-11.21)**(1.47)-0.093(-6.18)**0.093(2.98)**-0.188(-8.51)**-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

0.040

Whether a household belongs to SC (Scheduled Caste)Land (>2.5 ha) (default: the landless)

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.

29

Table 4
The Results of Averaged Treatment Effect (ATE) on the Effects 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 (T able 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.

30

Table A1
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
Whether non-agricultural labour(=1 if yes, =0 if no).
Whether the occupation type of the household head is labour in non-agriculture (=1 if yes, =0 if no).

31

Whether self-employed in agriculture
Whether a household belongs to SC(Scheduled Caste)
Whether a household belongs to ST(Scheduled Tribe)
RPW

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

32

Table A2
Wage Equations for male and female workers in rural areas of Vietnam based on VHLSS
data in 2002, 2004 and 2006 (Tobit estimations)
2002
Dependent Variable

Explanatory Variables*2
Age
Age2

2004

2006

Male
wage
Coef.

Female
Wage
Coef.

Male
wage
Coef.

Female
Wage
Coef.

Male
Wage
Coef.

FemaleWage
Coef.

(t value)*1(t value)(t value)(t value)(t value)(t value)

0.358

0.108

1.612

1.612

0.628

0.257

(8.89)**(2.77)**(3.48)**(3.15)**(1.29)(0.72)-0.0004-0.0008-0.018-0.017-0.010-0.002(7.53)**(1.24)(2.77)**(2.19)**(1.40)(0.38)

Whether completed primary

0.561

0.506

2.453

8.180

6.628

-1.348education(3.26)**(4.07)**(0.55)(2.31)*(2.36)*(0.23)

Whether completed lower

0.479

0.682

1.191

10.46410.903

2.190

secondary education(2.04)*(4.41)**(0.29)(3.19)**(3.37)**(0.37)

Whether completed upper

1.150

2.023

3.725

11.84914.3497.789

secondary education(5.07)**(8.97)**(0.79)(3.46)**(4.54)**(1.28)

Whether completed

2.673

3.719

8.193

14.24539.283

9.872

technical education(8.43)**(15.34)**(1.79)†(4.34)**(5.20)**(1.65)

Whether completed

5.086

6.628

17.78425.776-4.50725.513higher education(13.77)**(12.16)**(3.57)**(6.30)**(3.01)**(3.89)**

Whether working for their own-2.960-2.016-11.609-3.782-4.507-5.709farm sector as a wage worker(16.84)**(13.75)**(7.80)**(1.78)†(3.01)**(4.57)**

Whether working for their own-1.878-1.453-6.787-0.0845-0.668-3.480nonfarm sector as a wage worker(9.53)**(5.65)**(2.50)*(0.03)(0.20)(1.14)

Whether belonging to

0.343-0.005-20.440-0.0842.461-0.798ethnic majorities(1.36)(0.02)(1.56)(0.03)(1.13)(0.56)-2.32-1.934-1.171

0.824

1.455

-3.00

(0.33)(0.40)(0.75)(0.61)(0.50)(0.95)

Size of land (hectare)
Size of land squared

Constant
Sigma

-9.51

21.6510.074-0.139-0.2200.534(0.19)(1.80)†(0.49)(1.46)(0.59)(1.32)

0.601

2.562

18.79

9.78

27.38828.306(0.90)(3.79)(1.77)(0.99)(2.73)(2.84)

8.240

5.884

29.91824.95538.47322.932(6.07)**(10.05)**(8.06)**(5.92)**(3.64)**(19.67)**

Observations

12280

7502

1243

795

1456

1293

Joint significant test

F(23,12257)=68.06**F(23,7479)=53.25**F(23,1220)=7.85**

F(23,772)=7.11**

F(23,1433)=5.13**

F(23,1293)=7.30***Notes: *1. t statistics in brackets; + p<.10, * p<.05, ** p<.01. *2. Regional and locational dummy variables are included as explanatory variables, 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. Regional dummy
variables (used in Table 1) are included, but are omitted to save the space.
Source: Authors Calculation based on VHLSS 2002, 2004 and 2006.

33

Table A3
Wage Equations for male and female workers in rural areas of India based on NSS data
in 1993 and 2004 (Tobit estimations)
1993

Age
Age2
Whether is literate, but has not completed primary school
Whether completed primary school
Whether completed middle school
Whether completed secondary or higher secondary school
Whether completed higher education
Land Owned
Scheduled Tribe (ST) dummy (ST=1, otherwise=0)
Scheduled Caste (SC) dummy (SC=1, otherwise=0)
non-agricultural self employment dummy (non-agricultural
self employment=1 otherwise)
agricultural self employment dummy (agricultural self
employment=1 otherwise=0)

2004

Male
wage
Coef.

Female
Wage
Coef.

Male
Wage
Coef.

FemaleWage
Coef.

(t value)(t value)(t value)(t value)662.822204.695

139.62549.933(8.65)**(3.65)**(37.08)**(10.15)**-4.072-1.257-1.638-0.637(4.17)**(1.69)(39.07)**(10.24)**

3,542.99

2,126.39

92.081-205.98(12.71)**(7.36)**(5.10)**(8.72)**

7,518.66

3,208.70

175.043-227.04(23.01)**(7.49)**(9.45)**(9.53)**

14,163.75

10,200.92

360.514-192.21(29.57)**(8.09)**(19.49)**(7.37)**

35,055.00

38,201.86

810.913201.04(56.87)**(26.88)**(33.86)**(5.63)**

57,151.06

53,253.26

1,473.09

1,004.51(47.65)**(17.32)**(64.15)**(20.43)**0.349-0.3240.00-0.082(0.98)(4.86)**(2.39)*(8.35)**-322.569-1,018.14-121.41-108.96(0.87)(4.08)**(9.13)**(7.53)**-2,177.57-381.166--(7.95)**(1.89)

7,216.57

2,324.92

1,859.26

566.23(10.27)**(5.49)**(68.44)**(21.97)**

7,899.48

5,204.41

2,196.08

880.79(15.13)**(14.37)**(69.07)**(22.83)**

Muslim dummy(Muslim=1, otherwise=0)746.744185.894113.494-330.9(1.61)(0.46)(5.59)**(10.79)**Constant-2,171.00

4,216.78-2,940.20-1,749.97(1.50)(4.18)**(34.97)**(16.65)**Observations

33720

15849

67168

59221

*Notes: *1. Robust z statistics in brackets; + p<.10, * p<.05, ** p<.01. *2. State dummy variables are included as explanatory variables, but are notshown to save the space.
Source: Authors Calculation based on NSS 50 and NSS61.

34

Appendix: Deriving Vulnerability Measure 12
Vulnerability measure as an expected poverty is specified as:

VEPit  Vit  Prc i,t 1  z

(A.1)

where vulnerability of household i at time t, Vit , is the probability that the i-th household’s
level of consumption at time t+1, ci,t 1 , will be below the poverty line, z.
Three limitations, amongst others, should be noted in our measure of vulnerability.
First, the present analysis is confined to a consumption (used synonymously with income)threshold of poverty. Second, our measure of vulnerability in terms of the probability of ahousehold’s consumption falling below the poverty threshold in the future is subject to the
choice of a threshold. Third, while income/consumption volatility underlies vulnerability, theresilience in mitigating welfare losses depends on assets defined broadly-including human,
physical and social capital. A household with inadequate physical or financial asset or
savings, for example, may find it hard to overcome loss of income. This may translate into
lower nutritional intake and rationing out of its members from the labour market (Dasgupta,
1997; Foster, 1995). Lack of physical assets may also impede accumulation of profitableportfolios under risk and generate poverty traps.
The consumption function is estimated by the equation (A.2).13

ln ci  X i   ei

(A.2)

where ci is log of real per capita household consumption (for Vietnam) and mean per capita
consumption (MPCE) (i.e. food and non-food consumption expenditure) (for India) for thehousehold and X is a vector of observable household characteristics and other determinantsof consumption. It is further assumed that the structure of the economy is relatively stable
over time and, hence, future consumption stems solely from the uncertainty about the12
13This Appendix is based on Imai (2011) and Imai et al. (2011).
We have used White-Huber sandwich estimator to overcome heteroscedasticity in the sample.

35

idiosyncratic shocks, ei . It is also assumed that the variance of the disturbance term depends
on:e2,i  X i (A.3)

The estimates of  and  are obtained using a three-step feasible generalized leastsquares (FGLS) 14 . Using the estimates ̂ and ˆ , we can compute the expected log
consumption and the variance of log consumption for each household as follows.
E[ln C i X i ]  X iˆ(A.4)

V[ln Ci Xi ]  Xi ˆ(A.5)

By assuming ln ci as normally distributed and letting   denote the cumulative density
function of the standard normal distribution, the estimated probability that a household will
be poor in the future (say, at time t+1) is given by: ln z  X ˆ i VÊP i  v̂ i  P̂r ln c i  ln z X i   
X i ˆ 

(A.6)

This is an ex ante vulnerability measure that can be estimated with cross-sectional
data. Note that this expression also yields the probability of a household at time t becoming
poor at t+1 given the distribution of consumption at t.
A merit of this vulnerability measure is that it can be estimated with cross-sectional data.
However, it correctly reflects a household’s vulnerability only if the distribution of
consumption across households, given the household characteristics at time t, represents
time-series variation of household consumption. Hence this measure requires a large sample
in which some households experience positive shocks while others suffer from negative
14

See Chaudhuri (2003), Chaudhuri et al. (2002), and Hoddinott and Quisumbing (2003b) for
technical details.

36

shocks. Also, the measure is unlikely to reflect unexpected large negative shocks (e.g., Asianfinancial crisis), if we use the cross-section data for a normal year.

37