DP2013-16

Pov erty Dyna mics of Households
in Rural China*
Katsushi S. IMAI
Jing YOU
Revised June 1, 2013

* 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.

Poverty Dynamics of Households in Rural China

KATSUSHI S. IMAI† and JING YOU*‡† Economics, School of Social Sciences, University of Manchester & Research Institute for
Economics and Business Administration (RIEB), Kobe University:
Email: Katsushi.Imai@manchester.ac.uk‡ School of Agricultural Economics and Rural Development, Renmin University of China
Email: jing.you@ruc.edu.cn
First Draft: 17th October 2011
(An Old Title: “Poverty Dynamics of Households in Rural China: Identifying Multiple
Pathways for Poverty Transition)
This Draft: Revised on 1st June 2013

Abstract
The objective of our study is to identify patterns and causes of households’ transitions
into and out of poverty using the long household panel data on rural China in the period
1989-2009. We propose a discrete-time multi-spell duration model that not only corrects
for correlated unobserved heterogeneity across transitions and various destinations within
the transition, but also addresses the endogeneity due to dynamic selection associatedwith household’s livelihood strategies. Duration dependence is generally found to be
negative for both poverty exit and entry. The household choosing either farming orout-migration as a main livelihood strategy was more likely to escape from the persistentpoverty than those taking local non-agricultural employment. Overall, the present study
emphasises the central role of agriculture in helping the chronically poor escape frompoverty.
JEL codes: C33, C41, I32, O15

Keywords: poverty transition, discrete-time duration model, correlated unobserved
heterogeneity, dynamic selection, rural China*Corresponding Author:
Dr. Jing You
School of Agricultural Economics and Rural Development,
Renmin University of China
No. 59 Zhongguancun Street, Beijing 100872, China
Email: jing.you@ruc.edu.cn

Acknowledgements
The authors acknowledge useful comments and advice from Armando Barrientos, Obbey Elamin,
Raghav Gaiha, Masashi Hoshino, Kunal Sen, Xiaobing Wang, and participants in Workshop on
Poverty and Inequality in China and India, the University of Manchester in March 2012, RIEB
Seminar at Kobe University in April 2012, Econometrics Seminar in Manchester in May 2012, andGRIPS/TWID Conference on "Risks, Social Networks, and Development" in Tokyo in December
2012. The second author would like to express the deepest thanks to Kristian Bernt Karlson andFrancesco Devicienti for their help in programming. The research is supported by the Fundamental
Research Funds for the Central Universities, and the Research Funds of Renmin University of China(No. 13XNK014). The authors greatly appreciate useful comments from three anonymous referees
and Prof. John Knight, an editor of the journal. Only the authors are responsible for any errors.
1

Poverty Dynamics of Households in Rural China
I.

Introduction

While rural poverty has continued to decline in China due to spectacular economic growthover the last three to four decades, much of the reduction is concentrated in two relatively
brief periods: 1979-1984 and 1995-1997 (Ghosh, 2010). Substantial reduction in rural
poverty was achieved in 1979-1984 as a result of de-collectivisation of agricultural
production and the introduction of the Household Responsibility System which dramaticallyraised agricultural productivity (Lin, 1992). Reduction of rural poverty was accelerated again
in 1995-1997 by significant increases in procurement prices of agricultural products which
pushed up income growth of rural households (Benjamin et al., 2005). Since then, although
the speed of poverty reduction has slowed down (Chen and Ravallion, 2010), it has been
increasingly difficult for the government or international agencies to direct their poverty
alleviation policies or aid programmes to those who remain poor in rural China as they tend
to live in remote areas (World Bank, 2009). While some rural households seem to be
chronically poor, considerable mobility in and out of poverty has been reported in rural China(e.g. Gustafsson and Sai, 2009; McCulloch and Calandrino, 2003) and thus ‘transientpoverty’ is a non-negligible part of total poverty (Jalan and Ravallion, 1998; Duclos et al.,2010). An effort to help the poor more efficiently thus calls for understanding of the patternand causes of households’ poverty transitions as households manage their livelihoods in
response to the changing environment. Incorporating a time dimension into the analysis ofhousehold poverty is crucial not only for understanding the evolution of households’ poverty
status and underlying causes, but also for designing and implementing effective anti-poverty
programmes.
The empirical literature of ‘poverty dynamics’ has addressed transitions of poverty
status for a household or an individual over long periods in both developed and developing2

countries, usually using household panel data. A typical approach to analysing poverty
dynamics is to include lagged poverty status as an additional independent variable to capture
the dynamics of poverty (Cappellari and Jenkins, 2002; Antman and McKenzie, 2007).
However, considering poverty status only in the previous year may oversimplify the
dynamics across many years and fail to recognize the cumulative nature of household
poverty.
To address this concern, the present study analyses poverty dynamics by using theduration model which takes account of how long the household has been or has not been inpoverty as well as when it moved in or out of poverty. One of the significant advantages ofthe duration model is to track an individual’s unique history and experience. That is, ourmodel captures the story in which, e.g., a farming household experienced poverty for four
years due to the low productivity, but escaped from poverty as one of the household members
had access to non-farm employment and stayed ‘non-poor’ for four years and then slipped
into poverty and remained poor for the next six years due to the illness of a household head.
While some studies have analysed poverty in developed countries using the duration model(Canto, 2002; Devicienti, 2002, 2011; Maes, 2011), there have been few works on developing
countries.1 More specifically, to explore the pattern of poverty dynamics, we incorporate
unobserved heterogeneity in a discrete-time duration model and apply a fully non-parametricapproach to the long household panel data on rural China. This methodology aims tominimize possible misspecifications to offer better estimates. Our framework controls for (i)unobserved heterogeneity that can be correlated across multiple poverty transitions of eachhousehold and (ii) the dynamic selection underlying multi-path transitions, neither of which
has been done in the context of developing countries. This enables us not only to understandtrajectories of household poverty status, but also to identify the underlying socio-economic
1

Exceptions include Baulch and McCulloch (2002) for Pakistan, Bigsten and Shimeles (2008) for
Ethiopia, and Glauben et al. (2012) and You (2011) for China.
3

factors which influence the changes in poverty status.
It is noted that the above empirical literature on “poverty dynamics” is closely
associated with the parallel empirical literature on “poverty traps”. For example, Carter andBarrett (2006) proposed an asset-based approach to distinguish a structural component ofpoverty that is systematically poor over the years from ‘poverty that passes naturally withtime due to systemic growth process’ (p.178). They also suggested the need for controllingfor unobserved household characteristics and the initial endogeneity (p.194). This is fully
taken into account by the present study. Considering the asset dynamics, Adato et al. (2006)
have applied both quantitative and qualitative approaches for South African panel data andhave shown that a large number of households are trapped into poverty without a pathway out.
Antman and McKenzie (2007) used the pseudo panel data of Mexico with nonlinear income
dynamics and allowed for measurement errors and heterogeneity. They showed that there isno evidence for poverty traps. These studies point to the need for identifying the structuralcomponent of poverty by eliminating stochastic components or measurement errors. While
our binary classification of poverty depends on the raw consumption data which are to someextent subject to measurement errors or stochastic fluctuations, the present study will provide
explanations on why poor households are trapped into poverty by tracking the individual’s
unique history and experience, which has been neglected in the literature on poverty traps.
The rest of the paper proceeds as follows. The next section presents our econometricmodels. Section III introduces the data and examines the trend of poverty in rural China.
Econometric results are then discussed and explained in Section IV. The final section offers
concluding remarks and policy implications.

II. Methodology
Modelling poverty exit and entry4

In the baseline model, households are indexed by i. In the time interval j, a standard
discrete-time hazard model is defined by:hi t j   PrTi  t j | Ti  t j (1)where Ti is the time a (non-)poverty spell ends. Empirically, we use a complementary log-log
specification to accommodate the underlying discrete time when a transition into or out ofpoverty occurs. As in Devicienti (2002) and You (2011), the probability that household iescapes from poverty at duration d at time tj, given it has stayed in poverty spells up to tj,
takes the following form:









ei d , X ij | iP  1  exp  exp f P d   X ij  P  uiP



(2)

where f P d  is the baseline hazard which is a function of duration that i has been stuck inpoverty spells; Xij includes household-specific characteristics and aggregate covariates thatare time-varying and supposed to affect poverty transition; uiP  log iP  denotes theunobserved household-specific heterogeneity which is time-invariant and shared by i’s all
poverty spells. By analogy, the probability that household i enters poverty at duration d attime tj, given that it has been non-poor up to tj, is written by:









ri d , X ij |iN  1  exp  exp f P d   X ij  N  uiN



(3)

where f P d  is a function of duration that i has successfully maintained non-poverty spells;
Xij is defined as before; uiN  log iN  is the unobserved heterogeneity accounting fornon-poverty spells.
It is useful to elaborate on two empirical issues which may bias the estimation of theequations (2) and (3). First, how to define two baseline hazards could potentially makesignificant differences in estimated duration dependence. We attempt a fully non-parametricform, that is, a set of dummy variables specifying duration as well as an interval at which

5households are at risk of shifting out of (non-)poverty spells. 2 Second, failure to consider theunobserved heterogeneity would seriously bias the estimated duration-dependence and the
proportionate responses of the hazards to estimated coefficients (Jenkins, 2005). In Section IV,
we will take into account unobserved heterogeneity in estimating the equations (2) and (3).
This second step further involves two problems that deserve attention. For one thing, theestimation of hazard models with unobserved heterogeneity requires knowledge of the
distribution of these unobservables in order to integrate them into the estimation. We use
Heckman and Singer’s (1984) non-parametric maximum likelihood (NPML) estimation
where the distribution of unobserved heterogeneity is approximated by a bivariate discrete
distribution with a number of latent classes - also termed mass points - which are left
determined by the data. This is a more general method than the parametric approach in which,for example, normal and gamma distributions are assumed for the unobserved heterogeneity.
Specifically, suppose there are w  1,2 , ,W  groups of households within the study
population who are endowed with different but unobserved characteristics that underlie
different hazards of poverty exit and entry. Falling into the group w is attached by aWprobability  w with   w  1 . For the type w, the hazard functions of poverty exit andw1

re-entry (equations (2)-(3)) can be re-written by:











(4)









(5)

ei d , X ij |  wP  1  exp  exp f P d   X ij  P   wPand

ri d , X ij |  wN  1  exp  exp f N d   X ij  N   wN

where wP and  wN with w  1,2 , ,W  are known as location parameters which are anumber of discrete values capturing the effects of the latent classes on the exit and entry rates,
2

We have also tried (i) a parametric specification making the baseline hazard dependent on the log
time spent in (non-)poverty spell and (ii) a piece-wise semi-parametric specification grouping
different durations into time periods. Both specifications yielded broadly consistent results.
6respectively.3
Another issue attached to heterogeneity is that we have so far implicitly assumed that
there is no correlation between uiP and uiN for parametric estimations and independent wPand  wN in the non-parametric case, i.e., they follow the non-parametric distributions
G1P , 2P , , WP  and G1N , 2N , , WN  with their own optimal numbers of latent classes

W and W’, respectively. 4 In other words, the unobservables pushing households up to apoverty line are irrelevant to those pulling them back again, which is an over-simplified andstrict assumption. It would be a matter of concern if the unobservables pertinent to poverty
and non-poverty spells were actually correlated. Devicienti (2011) and Maes (2011) introduce
a discrete-time hazard model that relaxes this assumption and allows for the initial poverty
status to be determined endogenously.
To minimise misspecifications, we rely on the non-parametric setup (equations (4)-(5))
and stick to NPML. Drawing upon Devicienti’s work, we assume that wP and  wN are
jointly distributed with the un-predetermined distribution function G1P , , WP , 1N , , WP  together with optimal numbers of mass points W for the exit regression and W’ for the entry
one. These adjusted models are estimated by ML.
The models presented in this subsection are aimed at identifying the determinants ofpoverty exit or entry. As the estimations are virtually based on pooled non-poverty spells
across households and over time, these models can also be understood as a static model of
3

The optimal number of the latent classes W is determined by the data itself using the Gâteauxderivative method (Lancaster, 1990). That is, we begin by assuming 2 mass points and estimate theequations (3)-(4). The maximum likelihood and estimators of the model are saved and passed to a newround of estimation with an additional mass point which is located by moving from the smaller masspoint to the bigger one with some number of steps (grids). We check the maximum increase inlikelihood by computing the Gâteaux derivatives which are directional derivatives along the location
we insert new mass points. We keep adding new mass points until the maximum increase inlikelihood is zero, i.e., the maximum Gâteaux derivative is not positive. It is noted that W is notnecessarily the same across exit and entry regressions.
4
Here we distinguish between W and W’ as distributions of heterogeneity can be different for exitand entry regressions.
7

poverty transition. In what follows, we proceed to investigate who moves in and out ofpoverty and why they do so by tracking individual household’s history of multiple transitions.
In this sense, we will provide a dynamic picture that will unveil time-varying and‘transition-destination’ specific impacts of the correlates on poverty transitions.
Modelling multi-path of multiple poverty transitions
We are interested not only in the actual transition outcome which is simply labelled exit orentry, but also in the specific destinations of such a transition. For example, suppose there are
two households A and B who have the same high probability (hazard rate) of shifting out of apoverty spell and they experience only this single spell of poverty. The household A has
realised this probability because it has members who have migrated and have regularly sent
remittances back, while B has escaped because it has succeeded in increasing the efficiency
and profitability of agricultural production. A similar argument can be applied to multiple
spells during which two households descend into poverty following the first exit and thenescape again. The causes of the first and second shifts out of poverty are not necessarily
identical for the same household, or across households. In cases of both single and multiple
transitions, latent heterogeneity also plays a role in households’ decision-making besides theirobserved characteristics. These complex and endogenous pathways underlying multiple
transitions cannot be captured by the baseline model unless we track individual household’s
spells and transitions of non-poverty as well as associated household choices.
We give particular attention to (i) multiple spells of poverty and non-poverty, (ii)endogenous ‘dynamic selection’, and (iii) unobserved heterogeneity correlated across spells
as well as various destinations within the spell. Figure 1 presents pathways of poverty
transition according to different household livelihood strategies. 5 We classify household
livelihood strategies into three categories: farming, local non-agricultural employment and5

We also examined pathways of poverty transition according to the availability of social protection.
The details will be provided on request.
8

out-migration. These categories are defined by the household’s labour allocation. A
household, for example, is regarded as a ‘farming’ household if the household's labour input
in agricultural production (in terms of the number of household members who reported themain employment as ‘farming’) is the largest of the three. Defined in this way, three
categories are made mutually exclusive and interdependent. That is, they are competing but
correlated destinations - also known as ‘dependent competing risks’ in duration analysis which are faced by the household when it shifts from the current spell.
[Figure 1 to be inserted around here]
As shown in Figure 1, we are supposed to have “a full sample” prior to the first survey
in 1989. In 1989 when we first observed households’ poverty status, some of them were poor
while others were not, which could be determined by observed as well as certain unobserved
characteristics, such as intrinsic capabilities, effort levels and cognitive abilities of household
members. Households endogenously “selected” themselves to be poor in 1989. A few of themexperienced chronic poverty until 2009. In this case, these households were considered tohave experienced only a single poverty spell. By contrast, some households were able toescape from poverty, that is, they faced the second transition and started the second spell of“non-poverty”. We stop tracking households at the third transition.
The transition (or the hazard rate) at the end of the first spell is associated with threecorrelated destinations associated with households’ different livelihood strategies. Latent
heterogeneity matters along the entire chain of shifts. The unobserved heterogeneity affecting
households’ initial poverty status in the first transition and the one forcing them to fulfil
different routes of poverty exit and re-entry in the following transitions might be correlated.
Moreover, there might be a correlation between unobservables (e.g. ability, skills or
entrepreneurship) and observed variables (e.g. educational attainment) which would bias theestimates of observed covariates. This sort of endogeneity along the household’s observed

9sequence of transitions over time is termed ‘dynamic selection’ in Cameron and Heckman(1998).
Having laid out Figure 1, we match multiple transitions into and out of poverty withinterdependent destinations at each of them. We therefore follow Jenkins’ (2005) multinomial
logit framework to estimate the dependent competing risks model, while extending it to theMultinomial Transition Model with Unobserved heterogeneity (MTMU) developed by
Karlson (2011) who applied it to individuals’ educational choices for various stages of
education. In the remainder of this section, we will first present standard multinomial models
but relax the well-known assumption of Independent from Irrelevant Alternatives (IIA) ateach transition to accommodate dependent competing risks, and then link each transition as inits observed sequence with the jointly distributed unobserved heterogeneity to phase out
endogeneity caused by the dynamic selection.
*
We assume that each household i embodies unobserved latent propensity yiak
towards

choosing the alternative path a at transition k  1,2 ,3. Within each transition, there are A*
different alternative pathways indexed by a and A could vary across transitions. yiak
canbe described by a number of factors xij as follows:
J

*
yiak
  bajk xij   iak

(6)

j 1

where bajk measures the influence of the covariate xij on i’s latent propensity for choosing
the alternative a at transition k;  iak denotes the transition-alternative-specific random

error terms that are distributed extreme value,  iak ~ EV 0,  k2 2 6 . 6 Let yik denotehousehold i’s observed status at the kth transition. The household i would choose a if itsuggests the largest propensity for a , that is,

A standard logit model is traditionally normalised to π2/6. See Train (2009) for detailed discussion
about the normalisation with i.i.d. errors and the scale parameter k.
6

10

*
yik  a if yiak
 yi*ak for all a  a(7)

In the standard multinomial logit framework,  iak ought to be uncorrelated across allalternative pathways within each transition, which is the IIA assumption. Let a  1 be thereference alternative against which other contrast choices (or “competing risks” in the
duration analysis) are defined. The probability of choosing a  1 in a standard multinomial
form is:Pryik  a | xij  exp Jj 1  akj xij 

1   sA 2 exp Jj 1  skj xij 

for a  1

(8)

where  akj  bakj  k is the logit coefficient (log odds-ratio) with the scale factor k; 1kj  0
for normalising the model so that the baseline alternative is recognised by a  1 .
We have so far presented standard multinomial logit models at each transition k with
the IIA assumption binding. Recall that we have argued that the unobservables could affectsimultaneously poverty spells and non-poverty spells. Here the same argument will hold.
Households’ choices may be correlated through  iak because if removing one alternative,
those who would have chosen this pathway are less likely to distribute their choices randomly
across the remaining alternatives (Karlson, 2011). The violation of IIA could therefore beunderstood as ‘correlated unobserved heterogeneity’ across alternative choices within thetransition. To see this, consider that  iak  iak  iak where iak denotes the household
unobserved heterogeneity influencing its choice over a at the kth transition; iak is arandom residual which is alternative-irrelevant and satisfies i.i.d. In this way, we can also
refer to Heckman and Singer (1984) to relax the IIA assumption on  iak and handle theproblem of omitted important unobservables. We assume that households fall into

uakw  uak1, uak 2 ,, uakW  latent classes with the probability  w being attached to each latentclass w to approximate the unobserved heterogeneity ( iak ) for household’s choosing
11alternative a at the kth transition. Thus, for those falling into the class w at the kth transition,
the standard multinomial logistic model (8) can be extended to the one which is conditionalon the unobserved heterogeneity as:Pr  yik  a | xij ,iak  

where

uakw



exp  j 1  akj xij  uakw
J





J



locationparameter.

1   s  2 exp  j 1  skj xij  uskw

is

A

the

for a  1

(9)

The

distributionfunctionGuak1,, uakW ,, uAk1,, uAkW  can be approximated non-parametrically by a number oflatent classes for each alternative. As such, the choice of each alternative destination within
the transition is made dependent on ‘jointly distributed’ and ‘alternative-specific’ unobserved
heterogeneity of the household.
Now we proceed to link transitions by households’ own unique routes. Suppose
household i opts for the alternatives, a , a  and a from the first to the third transition in
turn, as illustrated in Figure 1. Based on (9), the probability of making three consecutive
transitions is defined by:Pryik  a | xij ,ua1w   Pryik  a | xij ,ua 2 w   Pryik  a | xij ,ua 3w 

(10)Households fall into the latent class w in each transition (i.e., ua1w , ua 2w , ua3w  ) with theprobability  w making them choose the routea, a, a . The multivariate probability

unconditional on unobserved heterogeneity is therefore expressed by a finite mixture model:
Pr  yik  a, a, a | xij   Pr  yi1  a | xij , ua1  Pr  yi 2  a | xij , ua 2   Pr  yi 3  a | xij , ua 3   w
W

I

I

w 1



exp  j 1  a1 j xij  ua1w







J


exp  j 1  a 2 j xij  ua  2 w




A
J
A
J

exp

x

u
1

exp

x

u
w 1 1  
 j 1 s1 j ij s1w  s 2  j 1 s2 j ij s2 w 
s2
W



J







I

J


exp  j 1  a3 j xij  ua3w
 w
1   A exp  J  s3 j xij  us3w 
s   2
j 1







12





I

(11)

where I (I’) is an indicator variable taking the value one if the household who has ‘survived’
to face the second (third) transition and zero otherwise. As stated earlier, we have assumed ajoint unspecified distribution for the unobservables affecting households’ separate choices inthreetransitions.

The

distributionfunctionGua1, ua 2 , ua3 isapproximatednon-parametrically by a number of latent classes w as in Heckman and Singer (1984). Hereunobservables are allowed not only to affect alternatives within transitions, but also to becorrelated across transitions. This captures the ‘dynamic selection’ and hence, addresses theendogeneity associated with the initial poverty status. The finite mixture multinomial logit
model (11) is what we mean by MTMU and can be estimated by NPML.

III. Data
We employ a balanced panel tracking the same rural households over time. The panel is
extracted from China Health and Nutrition Surveys (CHNS) in 1989, 1991, 1993, 1997, 2000,
2004, 2006 and 2009 covering 7 provinces. The provinces included are Jiangsu andShandong, the coastal provinces with a higher level of economic and human development interms of the provincial Human Development Index (HDI) (UNDP, 2005), Henan, Hubei and
Hunan, the central provinces with their GDP per capita and HDI ranked in the middle andGuizhou and Guangxi, south-western provinces with the high population share of ethnic
minority groups and with the lowest levels of GDP per capita and human development. The
population in these provinces covers 35.57 percent of the total population and 37.84 percentof the rural population at the end of 2006. While no household in the north east or in themunicipalities are included, the data capture well the diversity in rural China. The survey
used a multistage, random cluster process to select individuals. Counties in every province
were stratified based on the gross value of agricultural and industrial output, and one county
is selected from each quintile. The same criteria were applied to select villages in each county,

13and finally households were randomly chosen in each village.
We begin by selecting ‘rural’ households as those with rural registration (Hukou) andliving in villages in 1989. From this pool, we picked up those who have been re-interviewed
in the seven follow-up rounds and kept living in villages full-time. There are 1304 rural
households in our balanced panel data.789 The sample households are equally spread acrossprovinces and income quintiles. 10
It is worth noting that the choice of poverty indicator could affect seriously the picture
7

Households were tracked using the ‘dwelling’ rule. Those who migrated entirely from one sample
community to a new one out of sample areas were not followed. However, when only some family
members migrated and the original family still lived in the sample areas, this family would still be
included and the family members not living in the family at the time of interview would be tracked aslong as they could be contacted.
8
An important question is whether a household is an appropriate unit of analysis and is comparable
over such a long period. First, it is difficult to estimate individual income or consumption not only
because a significant part of income or consumption is shared among household members but also
because reliable individual income or consumption data are unavailable in China. Second, while theproblem of attrition is inevitable for such a long panel in which “(o)ver time, households form, grow,shrink, and spilt apart” (Jenkins, 2011, p.36), it would not be totally unreasonable to assume that we
can track the same household and compare its consumptions consistently over time despite thelimitations. This is because (i) non-random attrition was observed, but it was not as serious as
expected for such a long panel in which the annual equivalent attrition rate of households was 3.5%
and much lower than 7% for Brazil, 6% for South Africa or 9.5%-12.5% for developing countries(Barrientos and Mase, 2012): on average only 24.5% of households in the panel reported ‘excluded’family members in one of the eight survey years due to events like marriage, migration and death, andin these households the average number of ‘excluded’ family members was only 1.5; from 1993, only
4 to 14 out of 1304 households reported new family members in various survey years; (ii) all thehousehold members who lived elsewhere (i.e., ‘excluded’) at the time of interview were included ifthey maintained economic connections with the household, e.g., sending remittances; and (iii) thesame questionnaire has been used over the years. However, our results will have to be interpreted withcaution because of these structural changes in household composition over time. It is also noted thatre-estimating the same models using an unbalanced panel has given broadly similar results (Table 1).
The initial sample size for the unbalanced panel was 2537.
9
We have tested whether attrition stems from some observed characteristics like demographic
changes or asset accumulation, and unobserved characteristics and whether it affects household
consumption by the added regressor test (Giesbert and Schindler, 2012) and a Heckman-type selection
method proposed by Wooldridge (2002). We found that attrition appears to be irrelevant tohouseholds’ both unobserved and most observed characteristics (except migration) and its effects onhousehold per capita consumption are not statistically significant. Migration increased the likelihoodof household attrition with marginal statistical significance at 10% level in 2000 and 2009. In order tocheck further the sensitivity of our results to this non-negligible attrition caused by migration, we
adopted Fitzgerald et al.’s (1998) procedure to weight observations by inverse probabilities, that is, bygiving excluded households relatively more weight. Re-estimating Columns (1) and (4) of Table 1
with the weighted balanced panel yielded similar results, which will be furnished on request. However,these are only statistical validations and do not imply that the structural changes in householdcomposition are unimportant.
10
See Appendix for the list of variables.
14

we can draw from the data about sample households’ welfare. Income has been widely used
to study poverty in China, while this indicator has been criticised as underestimating China’s
poverty headcounts by about 10% since average income is 10-20% higher than expenditure(Park and Wang, 2001). It may also overstate income mobility (Naschold and Barrett, 2011)
and inflate the dynamics of poverty (Baulch and Hoddinott, 2000) due to greater volatility
coming from measurement errors and/or households’ consumption-smoothing behaviour.11 In
the context of developing countries where individual or household income is measured withconsiderable errors due to over- or under-reporting problems or other factors, per capita
household consumption is the best available indicator for measuring poverty over time
(Deaton, 1997; World Bank, 2009).12 Jenkins (2011) rightly argues that individual income,despite its limitations, should be ideally used as an indicator to analyse poverty dynamics
because of the difficulty of tracking the household income or consumption consistently overtime, but this option is usually difficult in developing countries and the World Bank has thus
proposed a use of per capita household consumption as a benchmark of international poverty
using the US$1.25 or 2 a day cut-off (Ravallion et al. 2009). The present study will follow theWorld Bank approach while remaining aware of its limitations.
Taking these empirical issues into account, we use consumption as the welfare

11

Naschold and Barrett (2011) argued that estimated income mobility is greater as it is magnified by
the stochastic factors and that degree of income mobility is likely to be sensitive to the duration
chosen. First, the use of consumption, not income, would greatly mitigate these problems. Second, we
have used different poverty thresholds/durations as sensitivity tests for measurement errors withbroadly similar results.
12
Closely following the definition taken by Benjamin et al. (2005) who constructed consumption
data by RCRE data from the Ministry of Agriculture, we calculated our household consumption as thesum of household food expenditure (including the own consumption of self-produced crop) as well as
non-food expenditure on consumer durables, medical and health insurance, services, and other items
available in CHNS data. For food consumption, we used the imputed price based on the NBS datatimes the quantity recorded in the CHNS. For other kinds of expenditure, CHNS directly recorded themonetary values. All monetary variables have been translated into real terms by using the spatial priceindex for rural areas which was constructed by the CHNS team and comparable across sample
provinces and over time. We compared constructed average household per capita consumption in
CHNS with relevant indicators in Rural Household Survey which is conducted annually by the NBS
and is the most representative survey for rural China. They appear to be very close to each other.
15

indicator and study household poverty measured by per capita consumption against a set ofmonetary poverty lines. Specifically, we first recalculate the international poverty lines of
US$1.25/day and US$2/day to accommodate different cost-of-living for the poor in rural andurban areas (37% higher for the urban poor in 2005 as suggested by Chen and Ravallion,2010). Then, to better insulate consumption from the influence of measurement errors, wefollow Devicienti (2002) and define the poor as those whose per capita household
consumption falls below 90% (or 110%) of the poverty line of US$1.25/day or US$2/day.
This is what we call ‘adjusted’ poverty lines in contrast to the ‘unadjusted’ ones which aresimply the 1.25 dollar-a-day and 2 dollar-a-day lines. At the same time, we use a food poverty
line of 620 yuan in 2002 prices based on 2,100 calories intake per person per day as arobustness check.13
Figure 2 depicts the changes of poverty rates measured by household per capita
consumption against various poverty lines. Whichever poverty line is used, there is an overall
decreasing trend of poverty over time. On a slowdown of poverty reduction we discussed in
Section I, the poverty rate decreased by only 0.15 to 0.53 in percentage points under the three
World Bank poverty lines and increased by 4.2 in percentage points under the food poverty
line in 1997-2000. Poverty reduction has accelerated again since 2004. However, the lower
the poverty line applied, the slower the pace of reduction. This signals that the poorest
households tend to remain poor and that inequality has increased over time.
[Figure 2 to be inserted around here]

IV. Results and discussion
Correlates of transitions into or out of poverty
Following the discussions in Section II, we have estimated single competing risk models

13This is an average food poverty line for rural China (Ravallion and Chen, 2007).
16

without controlling for heterogeneity14 (for the equations (2)-(3)) and the model controllingfor heterogeneity and allowing for interdependent unobservables across spells of poverty andnon-poverty (for (4)-(5)) - the latter of which has been estimated with and without additional
covariates. To save space, we present only the latter with additional covariates in Table 1.
Columns (1)-(3) report the results for exit from poverty and Columns (4)-(6) show those forentry into poverty. Columns (1)’ and (4)’ present the results based on the unbalanced panel. 15[Table 1 to be inserted around here]
Coefficient estimates of dummy variables (D1-D6) indicate a first decreasing and thenincreasing duration dependence in Columns (1)-(3) of Table 1. If the household experiences
one or as much as three spells of poverty, the probability of escaping poverty decreases overtime with coefficient estimates that are statistically significant. The more time spent inpoverty, the less likely is the household to escape. However, the coefficient estimate turns toincrease in absolute value from D4 to D5, which implies that the probability of exit becomes
more or less stable for those who have been chronically poor for three to four consecutive
periods and the probability of escaping from poverty would increase if they experienced fiveconsecutive periods in poverty. On the other hand, consistently negative and increasing‘duration dependence’ appears in the entry regressions (Columns (4)-(6)). For both exit andentry, the magnitude of coefficient estimates for D1 to D5 reveals non-linearity of negative
duration dependence.
Among various demographic characteristics, a significantly negative coefficient ofhousehold size for exit indicates that a larger household is more likely to stay in chronic
poverty. However, the household with more adult members is more likely to escape from14

We have applied a fully parametric baseline hazard function, the piece-wise semi-parametricspecification and the fully non-parametric specification and obtained broadly consistent results.
15
It is noted that the use of the unbalanced panel may significantly bias the observed number andlength of poverty/non-poverty spells and the time when exit or entry happens. This is particularly
problematic in the MTMU model which relies on the sequence of transition. The results of unbalanced
panel should be thus interpreted with caution and our analysis primarily uses the balanced panel data.
17

poverty. Age of the household head is positive and significant for exit, implying that ahousehold with an older head is more likely to exit from poverty. 16 Education also plays animportant role. The households with more members having completed primary, secondary
and tertiary education are all more likely to escape from poverty. For the entry into poverty,
the coefficient estimates of primary and secondary education are statistically insignificant,but that of tertiary education is positive and significant. That is, households with moremembers who have completed tertiary education are more likely to exit from poverty but, inthe meantime, they are more likely to enter into poverty. The latter result sounds
counterintuitive, while we suspect that this is due to the soaring cost of higher education in
China (Démurger et al. 2010; Gustaffson and Li, 2004) and/or large opportunity costs (deBrauw and Giles, 2008a).17
On household wealth, more cultivated land helps the poor escape from poverty(Columns (1)-(3)). Land is collectively allocated to each rural resident within the village on
the basis of family size and the land rental market remains nascent. This induces land
fragmentation and a mismatch between land and labour, for example, potentially idle land forsome affluent families participating mainly in non-farm activities (Jin and Deininger, 2009).
Endowing poor rural households who lack access to non-farm opportunities with more
cultivated land can bring about substantial agricultural productivity gains (ibid., 2009) andthus facilitate their escape from poverty. On the other hand, agricultural asset accumulation
has a poverty-preventing effect (Columns (4)-(6)). The coefficient estimate for running asmall business is statistically insignificant.
Recently, there has been a resurgence of interest in the role of agriculture in reducing
poverty (Barrett et al., 2010; Christiaensen et al., 2011; de Janvry, 2010; de Janvry and16

The squared term of age cannot be included as this will make impossible convergence during our
maximising the likelihood functions.
17
Unfortunately, the expenditure on education cannot be verified by CHNS data.
18

Sadoulet, 2010). Drawing upon cross-country data, Christiaensen et al. (2011) find that thepoverty-reducing effect of agriculture is most prominent for the poor living under US$2/day.
Agricultural development can also be crucial for poverty reduction for economies where there
are extensive market failures in the factors market (Dercon, 2009), like China. Echoing thesestudies, our estimation documents the paramount role of agriculture in determining rural
households’ poverty status. This is also consistent with the finding that productivity gains inagriculture are key to increase in rural households’ income as well as poverty reduction in
China (Christiaensen et al., 2013; de Janvry and Sadoulet, 2010; Montalvo and Ravallion,
2010; Ravallion and Chen, 2007). Overall, agricultural development is essential for healthierstructural transformation, which in turn paves a sustainable pathway out of poverty (Barrett etal., 2010).
Our results show that having more members engaged in local non-agricultural
employment does not affect poverty exit or entry significantly. Limited local non-agricultural
participation reflected by our data may explain this. In our dataset, only 18-22% of the poor
had family members involved in local non-farm employment for various waves (that is,78-82% of them were pure agricultural households). Among those households, the average
number of family members in local non-farm employment was only 1.5-1.65 persons. Huang
et al. (2009) find that participation in off-farm employment is associated more with younger
and well-educated households, but less with the poor.
It should be noted, however, that Knight and Song (2005) and Knight et al. (2010)
found that the average and marginal returns to migrant and non-farm local employment are
far higher than the returns to agricultural employment in rural China. If we take our results at
face value, it is conjectured that the chronically poor did not have easy access to non-farm
sector employment even though it is potentially highly productive, and they could get out ofpoverty only by increasing agricultural assets or land. However, it should be noted that our19studies differ from Knight and Song and Knight et al. in a number of ways. First, wehighlight consumption poverty at household levels, while Knight and Song and Knight et al.
focus on returns to the factor of production at individual levels. Second, the data sources aredifferent. These studies are based on much larger cross-sectional national household survey
data sets in 1994 and 2002, while we use the long panel data for a smaller sample ofhouseholds. In other words, the possibility that our results will hold only in the context of ourdata cannot be completely ruled out.
Also, village out-migration networks increase considerably the chance of escape frompoverty (Columns (2)-(3)), which is consistent with Du et al. (2005) and de Brauw and Giles(2008b). However, these effects of out-migration are not statistically significant for
prevention of entry into poverty (Columns (5)-(6)).
As revealed by Table 1, another prominent attribute to poverty transitions is healthinsurance. We observe that health insurance facilitates poverty exit and prevents poverty entry
with large and statistically significant coefficient estimates. This has a significant policy
implication for central and local governments in China particularly because rural residents in
China have long been excluded from many social protection schemes that have been enjoyedmainly by urban residents. A typical example is health insurance. In 1993, only 12.8% of therural population was covered by health insurance, such as voluntary community-based
insurance, public medical care, social medical insurance, and full- or semi-labour-related
medical insurance. The share was smaller even after a decade of remarkable economic
development (11.2% in 2003). If only the voluntary community-based insurance is accounted
for, the corresponding share was only 6.6% in 1998 and 9.5% in 2003. 18 Since 2003, thegovernment has re-launched community-based cooperative health insurance, the New
Cooperative Medical Scheme (NCMS), aiming to expand social welfare for the rural
Authors’ calculations based on Liu and Rao (2006) and China Health Statistical Yearbook 2008
published by the Ministry of Health.
18

20

population. Given the ongoing debate on whether and how far the introduction of NCMS
effectively limits rural households’ financial risks (Wagstaff et al., 2009), we disaggregated
the results into different kinds of health insurance. Columns (2) and (5) show that the positive(negative) and significant effect of health insurance on exit from or entry into poverty mainly
works through the NCMS. Free insurance provided by the government, which was launchedin a small range of areas and population in the early 1990s, has no statistically significant
effect on exit or entry. The purchase of commercial health insurance tended to prevent a
household escaping from poverty given that it involved large opportunity costs. Given the
disaggregated results of health insurance, government might want to consider further
extending community-based cooperative health insurance schemes in rural areas.
Urbanisation helps rural households end poverty, but is not significant for preventing
entry. Here urbanisation is measured by “the urbanisation index”, a proxy for comprehensive
development changing the rural-urban environment gradually over time, such as population
structure, economic (typically non-agricultural) activities, marketisation, infrastructure,communication, and delivery of education, health and other social services. The urbanisationindex at the village level which was constructed by Jones-Smith and Popkin (2010) has been
incorporated into the CHNS by the survey team. Columns (1)-(3) point to a significant
poverty-reducing effect of urbanisation. However, it does not prevent households fromentering into poverty (Columns (4)-(6)).19
We have also disaggregated the urbanisation index into three sub-components:
economic activities, access to markets and social services. More economic activities in termsof higher wages for males and the percent of population in non-agricultural work are
19

Columns (1)’ and (4)’ report the results based on unbalanced panel data. The results are similar to
those in Columns (1) and (4), but urbanisation significantly reduces the probability of entering intopoverty only in the case of the unbalanced panel. This may imply the greater importance ofurbanisation in poverty prevention for households that cannot be tracked for all years, e.g. due to theirmigration to urban areas involving all family members in later rounds.
21

statistically insignificant in both exit and entry regressions. On the other hand, easier accessto markets and more social services (i.e., provision of preschool for children under three
years old) significantly facilitate exit from poverty, but neither of them prevents entry intopoverty. The exact mechanism whereby urbanisation helps poverty reduction is not madeclear by these results. However, it is conjectured that (i) urbanisation would guarantee easieraccess to markets and facilitate everyday sales and purchases of agricultural andnon-agricultural commodities - which would have positive effects on both household income
and consumption of the poor; (ii) provision of preschool for children under three years would
make easier women’s participation in the labour market; and (iii) insurance benefit would
help the poor cope with temporary shocks.

Multiple pathways underlying poverty transition
From the preceding analyses, household livelihood strategies stand out as important
determinants of poverty transitions over time. This subsection presents our findings on whichroute steadily lifts households out of poverty by the MTMU model outlined in Section II. In
Table 2, the baseline alternative at the first transition is ‘non-poor’. The first column reports
coefficient estimates and standard errors for the probability being under ‘initially poor’ after
taking account of the endogeneity of initial poverty status. The second transition corresponds
to (the transition from poverty to) ‘non-poverty’ for each livelihood strategy. The results for‘Agriculture’ and ‘Out-migration’ are presented in the second and the third columns. The lasttwo columns are the results for the third transition, ‘poverty’ (from ‘non-poverty) for‘Agriculture’ and ‘Out-migration’. 20 We have found that the initial poverty status isendogenous and dynamic selection exists21, which justifies our use of the MTMU model

The results for the case where the first transition is ‘initially non-poor’ and those with focus onsocial protection will be furnished on request.
21
Employing Gâteaux derivatives, we have detected two latent groups under each20

22

specification.
Taking non-poverty as the baseline alternative at the first transition, we find strongly
negative duration-dependence in Table 2 because the positive estimate of the logarithm ofyears in poverty (ln(d)) implies that the longer a household experiences poverty, the more
likely is it to be observed as being poor. That is, there appears to be strong persistence ofpoverty for some households. However, duration-dependence in poverty disappears during
the second transition for those choosing agriculture and out-migration, compared to those
who embark on local non-agricultural employment as a route to escape from poverty. It isstriking to find that the duration dependence becomes positive (or the coefficient of ln(d) is
negative) for both agricultural and out-migration pathways during the third transition,
indicating a good chance to escape at longer duration. That is, a household, while staying
longer in ‘poverty’ during the third transition, is more likely to escape from poverty should its
household members be engaged in agriculture or out-migration. Comparing these two routes,
the likelihood of escaping from poverty was higher for the households having chosen‘out-migration’, as reflected in the larger absolute value of coefficient estimate of ln(d).
[Table 2 to be inserted around here]
Among households’ demographic characteristics, a larger family size and the age ofhead are correlated with a lower likelihood of being ‘initially poor’. Particularly during thethird transition, both tend to reduce the possibility of re-entry into poverty. More members
having primary and secondary education can help households reduce the possibilities ofdestination-specific transition. The distinction between these two groups is determined by the
likelihood of a household following a specific transition by taking into account both householdobservable and unobservable characteristics. In our case, there is a probability of 44.8% forhouseholds to be endowed with one group that predisposes them toward poverty at the first transition,
while 55.2% of them fall in another group which makes them intrinsically less likely to be initiallypoor. On dynamic selection, for example, households under the initially ‘non-poor’ group consistently
have lower likelihood of choosing agriculture or out-migration as a means to escape than local
non-agricultural employment in subsequent transitions. Another clue is ‘correlated heterogeneity’ thatis indicated by non-zero elements in covariance matrices of latent heterogeneity acrossdestination-specific transitions.
23‘being poor’ at the first transition with 11% and 9.5% respectively - which are calculated as
the average partial effects (APE).22 However, these variables do not affect significantly either
exit or re-entry in the following transitions, that is, primary and secondary education reduces
initial poverty only. More members receiving tertiary education can increase the chance ofinitial poverty by 9.3% during the first transition and double the re-entry rate during the third
transition for farming households and APE for those following the route of out-migration is
24.6%. Higher education carries a threat of re-entry into poverty during the third transition.
We find a positive and selective role played by agricultural asset accumulation: it
reduces the probability of being initially poor during the first transition. However, itsselectivity dissipates in subsequent transitions. Surprisingly, more cultivated land appears to
be correlated with initial poverty, which might be ascribable to inefficient land allocation
policy in rural China (Brandt et al., 2002), and less likelihood of exit during the second
transition. This seems inconsistent with the results in Table 1 which shows that more
cultivated land is an impetus to exit.23 It is noted, however, that maintaining a larger area ofcultivated land reduces the chance of re-entry into poverty for those who were initially poorand chose the route of out-migration during the third transition. In sum, land holdings andagricultural production appear to serve as safety nets, especially for those who migrated into
cities. A larger share of household members in local non-agricultural employment seems to be
associated with a higher probability of initial poverty. Nevertheless, non-agricultural
employment serves as a valuable complement to the initially poor who select the agriculturalroute, as it reduces their likelihood of re-entry into poverty by 31.8% (APE) during the third
transition. Village out-migration networks suggest strong negative correlation with initialpoverty. However, this relationship disappears in the following transitions for the initially
22

Computational details of APE as well as the estimates of APEs for all the variables will befurnished on request.
23
In the MTMU model, we have controlled for households’ history of transitions, which might have
led to different results.
24

poor.

V. Conclusion
The objective of the present study is to identify the pattern and causes of households’
transitions into and out of poverty using the long panel household data on rural China in the
period 1989- 2009, which were constructed from China Health and Nutrition Survey. We
have proposed a discrete-time multi-spell duration model that not only corrects for correlated
unobserved heterogeneity across transitions and various destinations within the transition, butalso addresses the endogeneity due to ‘dynamic selection’ (Cameron and Heckman, 1998)
associated with household livelihood strategies. The model identifies multiple pathways ofpoverty transitions through the household’s endogenous choice on livelihood strategies. Our
main empirical findings are summarised below.
First, we have found (i) ‘first decreasing and then increasing hazard rates’ of exit ashouseholds spend more time in poverty and (ii) overall negative duration dependence
between the entry rates and households’ experience of non-poverty. Persistent poverty would
arise from negative duration dependence as well as some latent heterogeneity predisposing
households to poverty. However, households would still have a good chance to exit eventhough they have been subject to destitution for a long period, were they engaged more in
agricultural production or out-migration. Second, primary and secondary education appears togreatly facilitate poverty exit. Although higher education tends to increase the probability ofentry into poverty possibly due to the expensive tuition fees and/or large opportunity costs, itsignificantly increases the chance of exiting from poverty if household members chose to beengaged in agricultural employment or out-migration. Third, cultivated land is highly
selective for households’ initial poverty status, though it reduces the probability of falling intopoverty again as a safety net if the household opted for agricultural employment or25

out-migration. Agricultural asset accumulation emerges to be an effective means as it reduces
the probability of being poor at the initial transition. By contrast, out-migration is less likely
to assist the exit from poverty for those who are initially poor. Overall, our study finds theprimary role of agriculture in alleviating rural poverty given the limited roles of local
non-agricultural sector and recurrent hardship accompanied by out-migration rife withvarious uncertainties associated with unstable jobs in cities. However, these conclusions
should be interpreted cautiously as they are contradictory to Knight and Sing (2005) orKnight et al. (2010) who found that the returns to local non-farm employment are higher than
the returns to agricultural employment. We argued that many of the poor households may nothave easy access to local non-farm employment.
Deriving any policy implication from the present study needs considerable caution given
the rapid transformation rural areas of China are now experiencing. However, it would
probably be safe to derive the following implications for policy from our empirical findings.
First, poverty is a dynamic phenomenon as a majority of rural households have experienced
multiple transitions between poverty and non-poverty. Policies to target the poor based on the
single-year data would therefore be misleading. Public policies which would promoteurbanisation during rural transformations should be carefully phased and implemented, as
they can have a differential effect on poverty-reduction depending on the stage oftransformations. Second, although the total number of the poor has declined, there are a
substantial number of households which have been chronically poor and need to be supported
by public interventions. We have seen that poverty tends to be perpetuated particularly if we
adopt the lower poverty lines. Third, agriculture holds great potential to address rural poverty.
The policy of promoting the agricultural sector, in particular providing poor households withcultivated land and agricultural assets would be crucial to help them escape from the chronic
poverty in the middle or long run. However, our results are likely to be context-specific and26they will never undermine the importance of the non-farm sector in poverty alleviation inrural China. There is also room for agriculture to serve as a safety net in terms of preventing
recurrent poverty, especially for those relying on out-migration to escape from poverty,
because the migrants are exposed to many uncertainties without being covered by social
protections. Finally, while health insurance was not universally effective as an instrument foralleviating poverty, our disaggregated analysis has shown that only the New Cooperative
Medical Scheme (NCMS) - a community-based cooperative health insurance scheme - waseffective in helping the poor escape from poverty and prevent the non-poor from backsliding
again. This implies that the type of insurance is crucial and government might want toconsider further extending the NCMS in rural areas. In sum, supporting the agricultural sector
with a particular focus on the poorest households and providing appropriate measures for
insurance for them would be an optimal policy focus for the alleviation of poverty in rural
China.
References
Adato, M., Carter, M. and May, J. (2006). ‘Exploring Poverty Traps and Social Exclusion inSouth Africa using Quantitative and Qualitative Data,’ Journal of Development Studies,Vol. 42, pp.226-247.
Antman, F., and McKenzie, D. (2007). ‘Poverty traps and nonlinear income dynamics withmeasurement error and individual heterogeneity’, Journal of Development Studies, Vol. 43,
pp. 1057-1083.
Barrett, C. B., Carter, M. R., and Timmer, C. P. (2010). ‘A century-long perspective on
agricultural development’, American Journal of Agricultural Economics, Vol. 92, pp.
447-468.
Barrientos, A. and Mase, J. (2012) Poverty transitions among older households in Brazil andSouth Africa. European Journal of Development Research, Vol. 24, pp. 570-588.
Baulch, B., and Hoddinott, J. (2000). ‘Economic mobility and poverty dynamics indeveloping countries’, Journal of Development Studies, Vol. 36, pp.1-24.
Baulch, B., and McCulloch, N. (2002). ‘Being poor and becoming poor: Poverty status andpoverty transitions in rural Pakistan’, Journal of Asian and African Studies, Vol. 37, pp.
168-185.
Benjamin, D., Brandt, L., and Giles, J. (2005). ‘The evolution of income inequality in rural
China’, Economic Development and Cultural Change, Vol. 53, pp.769-824.
27

Bigsten, A., and Shimeles, A. (2008). ‘Poverty transition and persistence in Ethiopia:
1994-2004’, World Development, Vol. 36, pp.1559-1584.
Brandt, L., Huang, J., Li, G., and Rozelle, S. (2002). ‘Land rights in Rural China: Facts,
fictions and issues’, The China Journal, Vol. 47, pp.67-97.
Cameron, S. V., and Heckman, J. J. (1998). ‘Life Cycle Schooling and Dynamic Selection
Bias: Models and Evidence for Five Cohorts of American Males’, Journal of Political
Economy, Vol. 106, pp.262-333.
Canto, O. (2002). ‘Climbing out of poverty, falling back in: Low income stability in Spain’,
Applied Economics, Vol. 34, pp.1903-1916.
Carter, M., and Barrett, C. (2006). ‘The Economics of Poverty Traps and Persistent Poverty:
An Asset-based Approach’, Journal of Development Studies, Vol. 42(2), pp.178-199.
Cappellari, L., and Jenkins, S. P. (2002). ‘Who stays poor? Who becomes poor? Evidence
from British household panel survey’, The Economic Journal, Vol. 112, pp.C60-C67.
Chen, S., and Ravallion, M. (2010). ‘China is poorer than we thought, but no less successful
in the fight against poverty’, in: Anand, S., Segal, P., Stiglitz, J. E. (Eds.) Debates on the
Measurement of Global Poverty. Oxford University Press, Oxford.
Christiaensen, L., Demery, L., and Kuhl, J. (2011). ‘The (evolving) role of agriculture inpoverty – An empirical perspective’, Journal of Development Economics, Vol. 96,
pp.239-254.
Christiaensen, L., Pan, L., and Wang, S. (2013). ‘Drivers of poverty reduction in lagging
regions: Evidence from rural western China’, Agricultural Economics, Vol. 44, pp. 25-44.
de Brauw, A., and Giles, J. (2008a). ‘Migrant opportunity and the educational attainment ofyouth in rural China’, Policy Research Working Paper, No. 4526. World Bank, Washington
D.C.
de Brauw, A., and Giles, J. (2008b). ‘Migrant labor markets and the welfare of rural
households in the developing world’, Policy Research Working Paper, No. 4585. World
Bank, Washington D.C.
de Janvry, A. (2010). ‘Agriculture for development: New paradigm and options for success’,
Agricultural Economics, Vol. 41, pp.17-36.
de Janvry, A., and Sadoulet, E. (2010). ‘Agricultural growth and poverty reduction:
Additional evidence’, The World Bank Research Observer, Vol. 25, pp.1-20.
Démurger, S., Fournier, M., and Yang, W. (2010). ‘Rural households’ decisions towards
income diversification: Evidence from a township in northern China’, China Economic
Review, Vol. 21, pp.S32-S44.
Deaton, A. (1997). The Analysis of Household Surveys: A Microeconometric Approach to
Development Policy, Johns Hopkins University Press, Baltimore, Maryland.
Dercon, S. (2009). ‘Rural poverty: Old challenges in new contexts’, The World Bank
28

Research Observer, Vol. 24, pp.1-28.
Devicienti, F. (2002). ‘Poverty persistence in Britain: a multivariate analysis using BHPS,
1991-1997’, Journal of Economics, Supp 9, pp.307-340.
Devicienti, F. (2011). ‘Estimating poverty persistence in Britain’, Empirical Economics, Vol.
40, pp.657-686.
Du, Y., Park, A., and Wang, S. (2005). ‘Migration and rural poverty in China’, Journal ofComparative Economics, Vol. 33, pp.688-709.
Duclos, J., Araar, A., and Giles, J. (2010). ‘Chronic and transient poverty: Measurement andestimation, with evidence from China’, Journal of Development Economics, Vol. 91,
pp.266-277.
Fitzgerald, J., Gottschalk, P. and Moffitt, R. (1998). ‘An analysis of sample attrition in panel
data: The Michigan study of income dynamics’. Journal of Human Resources, Vol. 33, pp.
251-299.
Giesbert, L. and Schindler, K. (2012). ‘Assets, shocks, and poverty traps in rural
Mozambique’. World Development, Vol. 40, pp. 1594-1609.
Glauben, T., Herzfeld, T., and Wang, X. (2012). ‘Persistence of poverty in rural China: Where,
why, and how to escape?’ World Development, Vol. 40, pp.784-795.
Ghosh, J. (2010). ‘Poverty reduction in China and India: Policy implications of recent years’,
DESA Working Paper No.92, the United Nations, New York.
Gustafsson, B., and Sai, D. (2009). ‘Temporary and persistent poverty among ethnic
minorities and the majority in rural China’, Review of Income and Wealth, Vol. 55,
pp.588-606.
Heckman, J. J., and Singer B. (1984). ‘A method for minimizing the impact of distributional
assumptions in econometric models for duration data’, Econometrica, Vol. 52, 271-320.
Huang, J., Wu, Y., and Rozelle, S. (2009). ‘Moving off the farm and intensifying agricultural
production in Shandong: A case study of rural labor market linkages in China’,
Agricultural Economics, Vol. 40, pp.203-218.
Jalan, J., and Ravallion, M. (1998). ‘Transient poverty in postreform rural China’, Journal ofComparative Economics, Vol. 26, pp.338-357.
Jenkins, S. P. (2005). ‘Survival analysis’, Unpublished manuscript, Institute for Social andEconomic Research, University of Essex, Colchester, UK. http://www.iser.
essex.ac.uk/files/teaching/stephenj/ec968/pdfs/ec968lnotesv6.pdf (accessed on 29th
January 2012).
Jenkins, S. P. (2011). ‘Changing Fortunes. Income Mobility and Poverty Dynamics in Britain’,
Oxford University Press, Oxford.
Jin, S., and Deininger, K. (2009). ‘Land rental markets in the process of rural structuraltransformation: Productivity and equity impacts from China’, Journal of Comparative29

Economics, Vol. 37, pp.629-646.
Jones-Smith, J. C., and Popkin, B. M. (2010). ‘Understanding community context and adult
health changes in China: Development of an urbanicity scale’, Social Sciences & Medicine,Vol. 71, pp.1436-1446.
Karlson, K. B. (2011). ‘Multiple paths in educational transitions: A multinomial transition
model with unobserved heterogeneity’, Research in Social Stratification and Mobility, Vol.
29, pp.323-341.
Knight, J. and Song, L. (2005). Towards a Labour Market in China, Oxford University Press,
New York.
Knight, J. Li, S, and Deng, Q. (2010) ‘Education and the poverty trap in rural China: closing
the trap’, Oxford Development Studies, Vol. 38, pp.1-24.
Lancaster, T. (1990). ‘The econometric analysis of transition data’, Cambridge University
Press, Cambridge.
Lin, J. Y. (1992). ‘Rural reforms and agricultural growth in China’, American Economic
Review, Vol. 82, pp.34-51.
Liu, Y., and Rao, K. (2006). ‘Providing health insurance in rural China: From research topolicy’, Journal of Health Politics, Policy and Law, Vol. 31, pp.71-92.
Maes, M. (2011). ‘Poverty persistence among the elderly in the transition from work toretirement’, Journal of Economic Inequality. published on line on 26 August 2011 (DOI:
10.1007/s10888-011-9200-5).
Montalvo, J. G., and Ravallion, M. (2010). ‘The pattern of growth and poverty reduction in
China’, Journal of Comparative Economics, Vol. 38, pp.2-16.
McCulloch, N., and Calandrino, M. (2003). ‘Vulnerability and chronic poverty in rural
Sichuan’, World Development, Vol. 31, pp.611-628.
Naschold, F., and Barrett, C. B. (2011). ‘Do short-term observed income changes overstate
structural economic mobility?’, Oxford Bulletin of Economics and Statistics, Vol. 73,
pp.705-717.
Park, A., and Wang, S. (2001). ‘China’s poverty statistics’, China Economic Review, Vol. 12,
pp.384-398.
Ravallion, M., and Chen, S. (2007). ‘China’s (uneven) progress against poverty’, Journal of
Development Economics, Vol.82, pp.1-42.
Ravallion, M., Chen, S., Sangraula, P. (2009). ‘Dollar a Day Revisited’, World Bank
Economic Review, Vol. 23(2), pp. 163-184.
Train, K. (2009). ‘Discrete time methods with simulation’, Cambridge University Press,
Cambridge.
UNDP (2005) China Human Development Report 2005, UNDP, New York.
30

Wagstaff, D., Yip, W., Lindelow, M., and Hsiao, W. C. (2009). ‘China’s health system and its
reform: A review of recent studies’, Health Economics, Vol. 18, pp. S7-S23.
World Bank (2009). ‘From poor areas to poor people: China’s evolving poverty reduction
agenda’, Poverty Reduction and Economic Management Department, East Asia andPacific Region, the World Bank, Washington D.C.
Wooldridge, J. M. (2002). ‘Introductory Econometrics A Modern Approach’, South-WesternCollege Publishing, Mason.
You, J. (2011). ‘Evaluating poverty duration and persistence: A spell approach to rural China’,
Applied Economics Letters, Vol. 18, pp.1377-1382.
Figure 1 Pathways of poverty transition (by livelihood strategy)

Figure 2 Profile of poverty rates

Source: Authors’ calculation based on CHNS data.

31

TABLE 1
Correlates of poverty transition (by disaggregated measures)
Independent
variable

Exit(Balanced Panel)(1)
Duration dependence
D1-0.154**(0.073)D2-0.320***(0.090)D3-0.367***(0.110)D4-0.033(0.115)D5-0.108(0.160)
D6
0.839
***
(0.204)
Household characteristics
hh size-0.043*(0.024)age of hh head
0.021
***
(0.003)% primary edu.
0.442
***
(0.145)% secondary edu.
0.588
***
(0.146)% tertiary edu.
0.180
(0.183)
no. of adults
0.047(0.031)
Wealthln(cultivated land)
0.065**(0.026)index of agricultural
0.087
assets(0.086)hh small business
0.064(0.051)
Access to off-farm labour market% local-0.054non-agricultural(0.129)employment withinhh% village
2.453***
out-migration(0.305)Social protection% hh members
1.500
***
having health(0.075)insurance% hh members
having commercial
insur.
% hh members
having government
free insur.
% hh members
having newcooperative
medical scheme(NCMS)
Local development
urbanisation
0.800
***
(0.210)economic activities

(2)

(3)

Exit
(Unbalanced
Panel)(1)’

Entry(Balanced Panel)
(4)

(5)

(6)

Entry
(Unbalanced
Panel)(4)’-0.112(0.073)-0.357***(0.089)-0.358***(0.110)-0.038(0.114)-0.096(0.160)0.834***(0.203)-0.142*(0.074)-0.285***(0.091)-0.352***(0.110)-0.018(0.115)-0.093(0.160)0.848***(0.204)-0.113(0.080)-0.278***(0.094)-0.329***(0.114)-0.001(0.118)-0.069(0.162)0.864***(0.205)-0.391***(0.116)-0.945***(0.171)-2.831***(0.453)-3.568***(0.712)-2.581***(0.508)-3.418***(1.007)-0.404***(0.116)0.961***(0.171)-2.830***(0.454)-3.576***(0.717)-2.566***(0.508)-3.394***(1.008)-0.381***(0.115)-0.926***(0.172)-2.830***(0.454)-3.563***(0.712)-2.568***(0.508)-3.433***(1.008)-0.362***(0.119)-0.918***(0.174)-2.802***(0.454)-3.552***(0.712)-2.570***(0.509)-3.406***(1.008)-0.043*(0.024)0.020***(0.003)0.426***(0.143)0.631***(0.145)0.237(0.185)0.085***(0.031)-0.047*(0.024)0.021***(0.003)0.461***(0.145)0.618***(0.146)0.262(0.184)0.053*(0.031)-0.046*(0.025)0.021***(0.003)0.428***(0.146)0.578***(0.148)0.203(0.187)0.048(0.032)0.030(0.040)0.001(0.004)0.171(0.261)
0.256(0.272)1.996***(0.311)-0.018(0.049)0.041(0.039)0.0002(0.004)0.142(0.260)0.195(0.270)1.836***(0.302)-0.026(0.049)0.030(0.040)0.002(0.004)0.192(0.262)0.276(0.272)2.035***(0.314)-0.021(0.049)0.034(0.040)-0.018(0.029)0.204(0.266)0.221(0.280)2.037***(0.319)-0.036(0.050)0.022(0.026)0.043(0.086)-0.008(0.052)0.049*(0.026)0.074(0.087)0.063(0.052)0.072***(0.027)0.109(0.087)0.059(0.052)-0.013(0.042)-0.628***(0.187)0.003(0.081)0.001(0.042)-0.617***(0.187)0.014(0.081)-0.017(0.042)-0.640***(0.187)0.004(0.081)-0.007(0.043)0.659***(0.197)-0.012(0.084)0.111(0.127)-0.017(0.129)-0.060(0.130)-0.285(0.383)-0.368(0.386)-0.259(0.383)-0.169(0.384)1.994***(0.310)2.178***(0.315)2.598***(0.351)-0.091(0.566)0.145(0.576)-0.060(0.609)-0.030(0.647)1.572***(0.075)1.509***(0.079)-0.451***(0.168)-0.439***(0.168)-0.404*(0.212)-0.641***(0.241)-0.040(0.798)-0.313(0.302)0.119(0.639)1.515***(0.075)-0.542***(0.206)1.427***(0.203)0.008***(0.002)0.009(0.372)-0.002(0.367)-0.011(0.011)access to markets
0.026
***
(0.008)social services
0.054
***
(0.013)Log-likelihood-4413.743 -4435.291 -4405.084 -4419.693-4413.743-4435.291
Note: ***, ** and * denote 1%, 5% and 10% significance levels. Standard errors are in parentheses.
Source: Authors’ calculation based on CHNS data.

32-0.452***(0.174)-0.023(0.023)0.001(0.014)0.019(0.038)-4405.084-4419.693

TABLE 2
Multinomial transition model with unobserved heterogeneity (by livelihood strategies)st

Independent variablesndrd

1 transition

2 transition

3 transition

Initial State: Poverty

Non-poverty

PovertyLivelihood Strategy

Agriculture

Out-migration

Agriculture

Out-migrationst

Baseline alternative at the 1 transition is ‘non-poor’ln(d)0.399 (0.141)***-0.107 (0.259)

0.047 (0.283)-1.349 (0.325)***-2.378 (0.432)***

hh size-0.065 (0.030)**-0.133 (0.107)-0.152 (0.118)-0.189 (0.100)*-0.243 (0.128)*

age of hh head

-0.022 (0.003)***

0.026 (0.014)

0.019 (0.015)-0.006 (0.009)-0.025 (0.012)**% primary edu.

-0.570 (0.184)***

1.333 (1.292)

1.613 (1.331)-0.166 (0.618)-1.121 (0.838)-0.494 (0.199)**

0.711 (1.186)% tertiary edu.

0.481 (0.268)*ln(cultivated land)

0.179 (0.042)

index of agricultural assets% secondary edu.

*

1.033 (1.227)

3.804 (1.532)

**

***

-0.414 (0.188)**-0.397 (0.136)***

0.648 (0.447)

1.063 (0.334)***% village out-migration-2.157 (0.770)***% hh members having health insurance-0.122 (0.145)% local non-agricultural employment in hhurbanisation
Log-likelihood

0.724 (0.354)**

0.052 (0.575)

3.935 (1.596)**

6.216 (1.536)-0.438 (0.200)**-0.026 (0.140)

0.547 (0.487)-0.234 (0.688)

0.114 (0.467)

1.011 (0.774)-1.721 (0.714)

2.562 (1.913)

0.042 (2.079)-2.931 (2.052)-0.279 (0.442)-0.377 (0.482)-0.249 (0.359)

4.022 (1.255)-5285.704

Note: ***, ** and * denote 1%, 5% and 10% significance levels. Standard errors are in parentheses.
Source: Authors’ calculation based on CHNS data.

33

***

3.102 (1.331)-0.883 (0.782)*****-2.506 (1.329)

6.227 (1.582)***-0.764 (0.186)***-0.738 (0.696)**-0.616 (0.827)-0.521 (2.273)
0.008 (0.479)*-2.171 (1.405)

Appendix 1: List of Variables
Variable

Definition

Mean

S.D.

hh per capita consumption

Household total consumption including remittances in 2009 prices divided by household size

1839.50

1767.81

hh size

No. of household members interviewed, including those living in the household full-time and currently living elsewhere (due to

4.11

1.51

studying, migration, etc.) but still registering with the household.
age of hh head

Age (in years) of household head

49.45

12.54

% primary edu.

% of household members having primary education

0.33

0.27

% secondary edu.

% of household members having secondary education

0.33

0.27

% tertiary edu.

% of household members having tertiary education

0.16

0.22

no. of adults

No. of household members aging between 18 and 60

2.24

1.19

2

ln(cultivated land)

Log mu of cultivated land owned by the household (1 mu667m )

0.20

1.26

index of agricultural assets

The index of agricultural assets owned by the household, which is constructed by principle component analysis

0.17

0.33

small hh business

Categorical variables indicating the types of small business run by the household: 0 as no small business; 1 as commerce,

0.17

0.53

service and peddler; 2 as manufacturing and construction.
% local non-agricultural employment in hh% household members doing local non-agricultural jobs and currently living in the household

0.08

0.18

% village out-migration% of sample villagers currently working and living outside of the village but still registering with their families in the vi llage

0.08

0.10

% hh members having health insur.

% household members having any form of health insurance

0.26

0.37

% hh members having commercial insur.

% household members having commercial health insurance

0.01

0.09

% hh members having gov. free insur.

% household members having government free health insurance

0.02

0.09

% hh members having cooperative insur.

% household members participating in Newly Cooperative Medical Scheme

0.15

0.31

Index indicating the degree of urbanisation of the village where the household locates.

0.45

0.16

Index reflecting typical daily wage for ordinary male worker (reported by community official) and percent of the population

3.28

2.61

Index reflecting the distance to the market and number of days of operation for eight different types of market.

3.76

3.46

Index reflecting provision of preschool for children under 3 years old, availability of (offered in community) commercial medical

1.10

1.76

% change (at the provincial level) of price at which farm households selling their agricultural product

0.04

0.11

% cultivated land affected by natural disasters within the sample province

0.17

0.07

1

urbanisation1

economic activity

engaged in non-agricultural work.
access to markets

11

social service

insurance, free medical insurance, and/or insurance for women and children.
purchasing price change of farm product2

prov. % cultivated land in natural disasters

3

Note: 1. The index is constructed by Jones-Smith and Popkin (2010) and complied into the CHNS data by the CHNS team.
2. Authors’ calculations based on the data from China Data Centre at the University of Michigan.
3. Authors’ calculations based on the data of natural disasters from Sixty Years of New China Agricultural Statistics (published by the Ministry of Agriculture in 2009) and the data of provincial cultivated land fromvarious issues of China Statistical Yearbooks (published annually by the National Bureau of Statistics of China).

34