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UNIVERSAL TRUTHS OR HIDDEN REALITIESCHRONIC POVERTY IN RURAL ETHIOPIA KEETIE ROELEN 1 * and LAURA CAMFIELD 2 1 Institute of Development Studies (IDS), University of Sussex, Brighton, UK 2 School of International Development, University of East Anglia, Norwich, UK Abstract: Combining qualitative and quantitative longitudinal data to study chronic poverty is now recognised to provide deep and reliable insights. This paper uses quantitative and qualitative data collected by Young Lives, a longitudinal study of childhood poverty, to identify factors that contribute to households becoming or remaining poor in rural Ethiopia and the effects of movements in and out of poverty on children within those households. Findings highlight the cumulative nature of shocks and their intersection with pre-existing vulnerabilities and suggest that improvements in household welfare may go at the expense of child well-being. Copyright © 2013 John Wiley & Sons, Ltd. Keywords: chronic poverty; poverty dynamics; mixed methods; Ethiopia; children; indicator development 1 INTRODUCTION Ethiopia is known for being one of the most poverty-stricken countries in Africa with a history of centralised and authoritarian rule. It ranked 174 out of 187 countries on the basis of the 2011 Human Development Index (HDR, 2011). Nevertheless, poverty in Ethiopia has fallen considerably in recent years, reecting improved living conditions across the country. The Ethiopian report on the progress on the Millennium Development Goals presents a drop in poverty rates from 49.5 per cent in 1994/1994 to 38.7 per cent in 2004/2005 and an estimated decline to 29.2 per cent in 2009/2010 (MoFED, 2010). Other indicators of well-being also point towards improvements; average life expectancy has risen from 43.9 in 1980 to 51.7 in 2000 and 59.3 in 2011, whereas the expected years of schooling increased from 2.6 in 1995 to 8.5 in 2011 (HDR, 2011). Notwithstanding these *Correspondence to: Keetie Roelen, Institute of Development Studies (IDS), University of Sussex, Brighton BN1 9RE, UK. E-mail: [email protected] Copyright © 2013 John Wiley & Sons, Ltd. Journal of International Development J. Int. Dev. 26, 10241038 (2014) Published online 8 July 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/jid.2931

UNIVERSAL TRUTHS OR HIDDEN REALITIES-CHRONIC POVERTY IN RURAL ETHIOPIA

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UNIVERSAL TRUTHS OR HIDDENREALITIES—CHRONIC POVERTY IN

RURAL ETHIOPIA

KEETIE ROELEN1* and LAURA CAMFIELD2

1Institute of Development Studies (IDS), University of Sussex, Brighton, UK2School of International Development, University of East Anglia, Norwich, UK

Abstract: Combining qualitative and quantitative longitudinal data to study chronic poverty isnow recognised to provide deep and reliable insights. This paper uses quantitative and qualitativedata collected by Young Lives, a longitudinal study of childhood poverty, to identify factors thatcontribute to households becoming or remaining poor in rural Ethiopia and the effects ofmovements in and out of poverty on children within those households. Findings highlight thecumulative nature of shocks and their intersection with pre-existing vulnerabilities and suggestthat improvements in household welfare may go at the expense of child well-being. Copyright© 2013 John Wiley & Sons, Ltd.

Keywords: chronic poverty; poverty dynamics; mixedmethods; Ethiopia; children; indicator development

1 INTRODUCTION

Ethiopia is known for being one of the most poverty-stricken countries in Africa with ahistory of centralised and authoritarian rule. It ranked 174 out of 187 countries on the basisof the 2011 Human Development Index (HDR, 2011). Nevertheless, poverty in Ethiopiahas fallen considerably in recent years, reflecting improved living conditions across thecountry. The Ethiopian report on the progress on the Millennium Development Goalspresents a drop in poverty rates from 49.5 per cent in 1994/1994 to 38.7 per cent in2004/2005 and an estimated decline to 29.2 per cent in 2009/2010 (MoFED, 2010). Otherindicators of well-being also point towards improvements; average life expectancy hasrisen from 43.9 in 1980 to 51.7 in 2000 and 59.3 in 2011, whereas the expected years ofschooling increased from 2.6 in 1995 to 8.5 in 2011 (HDR, 2011). Notwithstanding these

*Correspondence to: Keetie Roelen, Institute of Development Studies (IDS), University of Sussex, BrightonBN1 9RE, UK.E-mail: [email protected]

Copyright © 2013 John Wiley & Sons, Ltd.

Journal of International DevelopmentJ. Int. Dev. 26, 1024–1038 (2014)Published online 8 July 2013 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/jid.2931

positive trends, large differences persist between geographical areas and demographicgroups, as well as between seasons (Dercon and Krishnan, 2000; Devereux and Sharp,2006).Unpacking universal truths about poverty requires in-depth and detailed investigation.

This holds especially true for children given their more disadvantaged position and thefar-reaching consequences of deprivation during childhood (Kurukulasuriya andEngilbertsdottir, 2012). Children can be said to have a ‘differential experience’ of poverty(Jones and Sumner, 2011) following different requirements in terms of basic needs andtheir initial dependence on others for having those basic needs fulfilled. The interest inresearching the particular situation of children has led to a wide expansion of the evidencebase on child poverty, including global and comparative studies, national assessments aswell as more focused analyses (see Ortiz et al., 2012, and Minujin and Nandy, 2012, forrecent overviews). Nevertheless, the majority of studies employ either quantitative orqualitative methods and focus on the analysis of cross-sectional data. The analysis of childpoverty from a longitudinal perspective using mixed-method approaches remainsrelatively unexplored.In this paper, we use data from the Young Lives study on childhood to reveal hidden

realities about chronic poverty for children and their families in rural Ethiopia. We utilisequantitative and qualitative panel data to gain insight into the lives of one cohort ofchildren and their households and their poverty trajectories. The remainder of the paperis structured as follows: Firstly, we provide a selective overview of available researchand evidence on poverty dynamics in Ethiopia and the use of mixed methods to analysethose dynamics. Secondly, we describe the data and methods used. We then presentanalysis of data from several methods and discuss the findings. Finally, we conclude bydiscussing the paper's main substantive findings and reflections on the use of mixedmethods.

2 LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK

2.1 Poverty in Ethiopia

As discussed earlier, not only are many Ethiopians living in vulnerable circumstances withadverse developmental outcomes, but there are also large differences across seasons,locations and social groups. Various studies have pointed towards the importance ofseasonality with rainfall and crop failure, causing temporary drops in consumption andfood security (Dercon and Krishnan, 2000). Well-being outcomes and the likelihood ofmoving in and out of poverty may differ considerably by region or community. Devereuxand Sharp (2006) find that levels of destitution and vulnerability increased in Wollowhereas national figures (e.g. World Bank, 1999) reported improvements. Whereas infantmortality and under-five mortality rates have dropped considerably in the 2000s, thedistribution of these rates along spatial lines reveals large regional discrepancies (Abebaw,2011). Finally, differential experiences with respect to poverty also persist acrossdemographic groups and even within households. Whereas Woldehanna et al. (2008)found that the percentage of younger children living in materially poor householdsdecreased across most of Ethiopia, the percentage of underweight children in the olderage cohort increased.

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Baulch (2011) distinguished between factors that drive people and households intopoverty (drivers) and factors that lock them into poverty (maintainers). A review ofliterature on poverty dynamics in Ethiopia points towards a number of recurrent factorsthat can be classified as poverty drivers and maintainers. In terms of poverty drivers,illness, adverse coping strategies such as selling land, livestock and other assets, risingfood prices and falling crop prices can be identified as the main factors driving people intopoverty (summarised in Dercon and Porter, 2011). Poverty maintainers include lack ofassets such as land and livestock, lack of education, illness, crop failure, ethnicity andregion of residence (e.g. Bigsten et al., 2003; Devereux and Sharp, 2006; Dercon andPorter, 2011). The review of literature further suggests that drivers of poverty are rarelysingle events; the cumulative nature of shocks appear to be crucial for causing householdsto fall into or remain locked in poverty.

2.2 Mixed-Methods Research

Since the beginning of the 2000s, the use of mixed methods to assess and understandpoverty has gained considerable traction within debates in poverty measurement andpolicy analysis (Shaffer, 2012). It is now widely acknowledged that the combined ratherthan exclusive use of quantitative and qualitative data can deepen our understanding ofissues pertaining to poverty and deprivation, in part due to the different reflections ofpoverty offered by assessments based on a single type of data (Appleton and Booth,2001; Kanbur and Shaffer, 2005; Davis and Baulch, 2011).Generally, one can identify three broad approaches in mixing methods. Firstly,

‘triangulation’ or ‘putting together’ (Shaffer et al., 2008) serves to challenge or enrichfindings based on data from a single method by using a second method or multiplemethods. Many studies employing this approach combine quantitative household surveyswith ethnographic data or participatory methods to verify quantitative findings. Secondly,sequential integration uses outputs from one method as input into the design of a secondmethod. Davis and Baulch (2009), for example, used focus group discussions to informthe household survey design, which consequently informs life history interviews. Thirdly,holistic integration seeks to use multiple methods to produce a contextualised ‘casearchive’. The latter approach is the one adopted in this study. Quantitative and qualitativemethods are employed in an iterative process with equal priority.

3 DATA AND METHODOLOGY

3.1 Young Lives Data

Young Lives is a study of childhood poverty in four countries (Ethiopia, India, Peru andVietnam) collecting both quantitative and qualitative panel data across a period of 15 years.Three rounds of quantitative data and two rounds of qualitative data have been used for thepurposes of this study and enable us to follow the same cohort of children over a period of7 years. The first round of quantitative data collection took place in 2002 when children were7–8 years of age. The second round of quantitative data collection took place in 2006 whenthe children were 11–12 years old and the third in 2009 when they were 14–15 years old.The Young Lives Ethiopian sample covers 20 sites in Amhara, Oromia, SNNP (Southern

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Nations, Nationalities and Peoples) and Tigray, as well as in the capital Addis Ababa.Together, these five regions cover different geographical characteristics, levels ofdevelopment, urban/rural locations and population characteristics (Outes-Leon and Dercon,2008). Thirteen of these sites are classified as rural, and it is these that form the basis ofour analysis. The panel is composed of 552 children, reflecting a reasonable level of attritionof less than 10 per cent (Table 1).Qualitative data were collected from children and adults in four of the 13 rural sites from

which data were collected in 2007, 2008 and 2009 (the latter was for a sub-study on socialprotection, vulnerability and social mobility). Three sites were selected for longitudinalqualitative work in 2007, following analysis of preliminary data from round 2, to explorehow variations in location, ethnicity and socio-economic status affect access to educationand formal healthcare, government support and child labour participation. For example,TachMeret had high scores on indicators of poverty, child labour and receipt of governmentbenefits, but low educational participation and access to healthcare. The fourth site wasadded so that the sample contained equal numbers of near and remote rural sites.The interviews and group activities were conducted separately with children and adults

during the three fieldwork periods (2007–2009), and informed consent was obtained fromparents and children on all occasions. The interviews were semi-structured or entirely openended because information from closed-ended questions was collected during the surveyrounds. Full details of the qualitative data can be found in Camfield and Roelen (2013)and Roelen and Camfield (2012).Different sets of variables and factors available in the quantitative and qualitative data

are considered in terms of their role in causing movements in and out of poverty. Theconceptual notion of poverty drivers and maintainers as developed by Baulch (2011) isused in the remainder of this paper to denote factors that cause children and householdsto fall or stay in poverty.

3.2 Krishna's Stages of Progress and the Mixed-Method Taxonomy of ChildPoverty

A first step in this mixed-method investigation consists of the development of a taxonomythat allows for classifying children and their households as ultra-poor, poor, near-poor andnon-poor by using the quantitative survey data. We adopt the ‘Stages of Progress’ methodas developed by Krishna (2007, 2009) to translate the qualitative information on whatchildren and adults associate with poverty or a good standard of living into a taxonomyof child poverty. As such, we aim to integrate qualitative understandings of the routes thathouseholds take out of poverty and the thresholds that demarcate poor and non-poor with

Table 1. Sample size and composition in each survey round (%)

Panel R1 R2 R3

Boys 52.4 52.1 51.9 52.5Girls 47.6 47.9 48.1 47.5Amhara 24.5 25.0 24.5 24.7Oromia 24.6 24.9 24.7 24.6SNNP 25 25.0 25.2 24.9Tigray 25.9 25.0 25.7 25.8Total (n) 552 599 584 570

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quantitative measures of poverty. The individual indicators and category thresholds foreach poverty group derive from qualitative work in eight rural sites undertaken in 2008and 2009 (the 2009 data collection was undertaken during round 3 of the quantitative datacollection). As the collection of Young Lives qualitative data was not designed to classifyhouseholds as poor or non-poor, the information gathered does not allow for a povertyanalysis on purely qualitative terms. The data do, however, contain valuable informationabout what children and adults think constitutes poverty and what is required to moveout of this situation or prevent a fall into vulnerable conditions. Using all three roundsof data allows for verification of such indicators over time, which increases the robustnessof the approach in analysing children and households' life trajectories. The definition ofindicators and thresholds as well as the methodology for aggregating those into overallpoverty headcount rates is discussed in detail in Camfield and Roelen (2013) and in Roelenand Camfield (2012).

3.3 Life Histories, Qualitative Comparative Analysis and Case Studies

The content of the life histories and interviews is analysed using a simplified form ofRitchie and Spencer's (1994) framework analysis, which involves (i) reading and re-reading transcripts and noting key ideas and recurrent themes; (ii) focusing on themes thatrelate to becoming and remaining in poverty; (iii) identifying and ‘charting’ portions of thedata that correspond to a particular theme; and (iv) looking at the nature/frequency of theseacross the sample and within the narratives of individual respondents and their households.Qualitative Comparative Analysis (QCA) was developed by Ragin (1987) as a case-

centred alternative to variable-based analyses such as regression. We use QCA to verifydrivers and maintainers of poverty and understand both their relative prevalence and thecharacteristics of the households that are most likely to experience and be affected bythem. QCA requires qualitative knowledge of cases and respects both their diversity andtheir heterogeneity in relation to their causal conditions (for example, the way differentcombinations of causal conditions can generate the same outcome). The fsQCA (fuzzysets/QCA) software uses fuzzy set theory, which indicates degrees of membership of adefined category (e.g. declining into poverty). It also uses combinatorial logic to identifycharacteristics that are not necessary to produce an outcome and Boolean minimisationto identify those that are not sufficient to produce an outcome. It operates by comparingcases that differ by only one variable to work out what combinations of characteristicsare necessary or sufficient to produce an outcome. It then selects the smallest number ofcausal combinations that will cover all the positive instances of the outcome. The mainoutput is a ‘truth table’, which shows different combinations of causal factors that wouldbe sufficient for the outcome to occur. The reliability of these configurations is assessedusing in-depth knowledge of the cases and by generating a consistency score, which isequivalent to a measure of statistical significance, and a score for coverage, which indicateshow many cases conform to this pattern.Finally, we use two different sets of case studies. Eight studies across three different

sites (Leki, Tach Meret and Semhal) are based on interviews with children across allpoverty trajectories to analyse the effects on children of movements in and out of poverty.Four studies are based on interviews with adults from two different communities (Leki andBuna) that look at processes, causing households to become and remain poor (seeCamfield and Roelen, 2013; Roelen and Camfield, 2012) for a more extensive discussion).

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The case studies provide an understanding of the effects of transitions on children inhouseholds that varied according to their starting point (for example, ultra-poor) and thedirection of travel over the three rounds of data.

4 FINDINGS

Table 2 presents the proportions of children in the different poverty groups across the threerounds of data collection. Findings support the picture of an overall drop in poverty in ruralEthiopia. Estimates indicate that many children experienced an improvement in their livingconditions. Although half of all children were identified as being poor in round 1, thisproportion reduced to less than 20 per cent in round 3. Similarly, the proportion of non-poor children increased from 8 per cent in round 1 to 33 per cent in round 3.Considering the set of indicators underlying the taxonomy, these drops in poverty can

largely be attributed to the growing availability of draught animals in the household andimprovements in dwelling conditions, particularly the roof. These results are consistentwith findings in other studies. Dercon et al. (2012) find that the percentages of ruralhouseholds owning oxen and other assets increased significantly in the period 1994 to2009. Rates of ultra-poverty, however, have been largely stable and have even seen a slightincrease from rounds 2 to 3.The notion of a persistent and hard-to-reach group of chronically and severely poor

people is widely acknowledged (Shepherd, 2011) and has been identified in Ethiopia aswell (Sharp, 2007). The existence of a chronically poor group is confirmed in consideringmovements in and out of poverty. Transition matrices show the percentages of childrenhaving moved from one poverty group to the next or remained stable. Estimates in Tables 3and 4 indicate that it is not the case that people move in and out of ultra-poverty,suggesting that particular groups find themselves locked into poverty.

Table 2. Per cent of children in different poverty groups

Category R1 R2 R3

Sample size 599 584 570Ultra-poor 7 8.6 8.4Poor 49.8 24 17.2Nearly poor 35.6 55.5 41.8Not poor 7.7 12 32.6Total 100 100 100

Note: Calculations are based on cross-sectional samples in individual rounds.

Table 3. Transition matrix rounds 1 to 2

R1povertystatus

R2 poverty status

Ultra-poor Poor Near poor Non-poor Total

Ultra-poor 1.81 2.54 1.99 0.36 6.7Poor 5.62 17.93 23.19 3.08 49.82Near poor 0.91 2.9 26.81 5.62 36.23Non-poor 0 0.54 3.44 3.26 7.25Total 8.33 23.91 55.43 12.32 100

Note: Calculations are based on full sample in panel data set (n= 552).

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The transition matrices also show that children having escaped poverty have largelyfollowed the stages of progress as identified in the taxonomy. The proportions representingmoves in poverty classifications are highest for the groups having shifted from poverty tonear poverty from rounds 1 to 2 and from near poverty out of poverty from rounds 2 to 3.Quantitative analysis suggests that characteristics at the household level matter for being

or becoming non-poor, falling into poverty or remaining in poverty. Table 5 presents thedistribution of children in households having been identified as chronic poor (i.e. pooror ultra-poor across all three rounds), ‘new’ poor (having become poor or ultra-poor fromrounds 1 to 3) and non-poor/near-poor (having moved or remained out of poverty fromrounds 1 to 3) on the basis of the mixed-method taxonomy. An assessment of the sharesof each of these poverty groups provides insight into the extent to which particularsocio-demographic characteristics and life events are biased to such groups.Children in households where the head is female, young, divorced, separated or

widowed, or disabled, or there are more women than men in the household are more likelyto be chronically or newly poor. The discrepancies between children from male-headed andfemale-headed households and their shares in the different poverty groups are especiallynotable; 24 per cent of all children in female-headed households have become poor orultra-poor from rounds 1 to 3, which compares with 6 per cent of children in male-headedhouseholds. Similarly, although approximately one out of 10 children in male-headedhouseholds is chronically poor, this holds for more than one out of five children infemale-headed households. The reason for this relates mainly to lack of adult labour, whichmeans that female household heads share crop out their land on unfavourable terms (forexample, Minya, a girl from a remote rural site in Tigray, describes how her household onlyreceives a quarter of the produce from their land). The female household heads whorecounted their life histories also described experiencing discrimination in accessingProductive Safety Net Programme (PSNP) and in defending their rights. Wukro Tagesudescribed how she

‘constantly face[s] conflict with my neighbour due to land border. He usuallydestroys what I sow in the farmland, and then he insults me […] There is no maleor any one that can defend me for that matter’.

Despite indications in existing research that lack of education is a maintainer of poverty (seeDercon et al., 2012, for example), education of the household head does not point tosignificant differences in this analysis. This can be explained by the structure of the rurallabour market, which provides few ‘white collar’ or non-manual opportunities and thehistorically low uptake and quality of Ethiopian education. This means that even those who

Table 4. Transition matrix rounds 2 to 3

R2 poverty status

R3 poverty status

Ultra-poor Poor Near poor Non-poor Total

Ultra-poor 3.08 3.08 1.45 0.72 8.33Poor 1.09 11.05 7.25 4.53 23.91Near poor 0.91 5.43 29.53 19.57 55.43Non-poor 0.36 0.18 3.62 8.15 12.32Total 5.43 19.75 41.85 32.97 100

Note: Calculations are based on full sample in panel data set (n= 552).

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Table 5. Socio-demographic characteristics

Non-poor/nearly poor Chronic poor ‘New’ poor

%/(n) %/(n) %/(n)

Total 77.5 12.9 9.6(427) (72) (53)

Sex of household head (R3) *** *** ***Male 83 10.7 6.3

(371) (48) (28)Female 53.8 22.1 24

(56) (23) (25)Marital status caregiver (R1) *** *** ***Permanent partner 82.9 10.3 6.8

(379) (47) (31)Divorced or separated 46.2 25 28.8

(24) (13) (15)Single 55.6 22.2 22.2

(5) (2) (2)Widowed 55.9 29.4 14.7

(19) (10) (5)Gender structure household (R1) *** *** ***More men than women 85 7 7.9

(180) (20) (21)More women than men 70.2 18.9 11

(158) (45) (7)Equal numbers 77.1 12.5 10.4

(89) (21) (11)Age of household head (years) *** *** ***≤30 55.6 22.2 22.2

(5) (2) (2)31–35 60.6 18.2 21.2

(20) (6) (7)36–40 60.9 29.7 9.4

(39) (19) (6)41–45 81.4 8.8 9.8

(83) (9) (10)46–50 84.4 8.3 7.3

(81) (8) (7)51–55 81.8 13.6 4.5

(72) (12) (4)56–60 85.5 5.5 9.1

(47) (3) (5)61–70 75.6 12.8 11.5

(59) (10) (9)>+71 77.8 11.1 11.1

(21) (3) (3)Education level of household head (R1)Completed primary 71.6 18.9 9.5

(53) (14) (7)Did not complete primary 76.5 13.6 9.9

(286) (51) (37)Missing 84.6 6.7 8.7

(88) (7) (9)Disability household head * * *

(Continues)

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have completed the first cycle of primary school are unlikely to be literate. For example, in thelife history interviews, Ato Tufa described how 60years ago ‘there was no education. It wasonly farming’.Analysis of the eight case studies representing different poverty trajectories confirms the

notion that characteristics of chronically poor households are different from those havingmoved out of poverty and provides a meaningful illustration of this. It is often the multitudeof vulnerabilities that entrenches a household in poverty and is both a driver and maintainerof poverty. For example, Gabra, whose household was ultra-poor across all three rounds,lives in a female-headed household with land that is share-cropped-out (lent to others inexchange for a small percentage of the harvest) and subsistent on piece-work (cleaningharicot beans) carried out by the whole family. By contrast, Degife, whose household hasmoved from being in poor in round 1 to being non-poor in round 3, lives in a household thatonly has a small landholding but also has four siblings at secondary school in a nearby townwho may provide support in the future, assuming that they are able to complete theireducation. The importance of a combination of shocks, the experience of multiple shocksin rapid succession and the lack of resilience to resist such shocks has been found to bean important driver of poverty in other contexts as well (Baulch, 2011; Davis, 2011;Shepherd, 2011).Further quantitative analysis allows for investigating the extent to which life events relate

to driving people into or moving people out of poverty. Findings in Table 6 suggest that thereceipt of transfers such as pensions does not play an important role in driving poverty status;the shares of the non-poor, chronic poor and ‘new’ poor are not statistically different betweenthose receiving or not receiving transfers. The insignificance of findings may be attributed tothe broadness of the category of transfers, which could include divorce payments, childsupport or food aid, or more positively pensions, cash grants and transfers from wealthyrelatives (Camfield and Roelen, 2013; Roelen and Camfield, 2012). In the case of Naomi, agirl from Leki, transfers encompassed money from a European investor to enable her toobtain medical treatment:

‘it was the ferenji [foreigner] who gave […] us 140 birr and we went to Kuyera. […The doctor] took blood and gave me many medicines […] we had to call a relativewho could give us some money [as] my parents had 50 birr only’.

The shares of chronic and ‘new’ poor amongst PSNP beneficiaries are significantly higherthan amongst non-beneficiaries, although not to the extent that one might expect given theprogramme's objective to support poor and vulnerable labour-constrained households and

Table 5. (Continued)

Non-poor/nearly poor Chronic poor ‘New’ poor

%/(n) %/(n) %/(n)

Disabled 58.3 25 16.7(14) (6) (4)

Not disabled 78.4 12.3 9.3(413) (65) (49)

Note:*p< 0.1;**p< 0.05;***p< 0.01 based on chi-squared group equality of means.

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to graduate households with able-bodied members out of poverty. This may point to failuresin targeting at the community level (for example, pressures to include as many households aspossible, regardless of individual needs) as well as under coverage due to shortage of funds.Wukro Tagesse, for example, commented on how unfair she felt their exclusion from PSNPwas, given that ‘there are other, wealthier participants’ and attributed it to not having anyoneto argue her case with the Kebele (local authority). The large majority of households that havegraduated from PSNP are non-poor or nearly poor, suggesting that graduation from PSNPreflects moves out of poverty.The quantitative analysis does not suggest that multiple shocks are a poverty driver or

maintainer, although relative to their numbers the chronic and new poor experience ahigher percentage of shocks than the non-poor or nearly poor. These inconclusive resultsmay be due to quantitative surveys' inability to pick up on subtle events that may havefar-reaching consequences—consequences that cannot necessarily be predicted at the time.Some of the shocks in the questionnaire also require a status or level of resources, forexample, ‘Place of employment shut down/destroyed’ or ‘Contract disputes’ that thepoorest households may never attain.Life history analysis allows for a qualitative investigation of the types of shocks that

drive or maintain poverty and allows for the identification of a list of factors that causehouseholds to remain or become poor, as presented in Table 7. The first column reportson factors identified by all households, whereas the second and third columns reportsfactors that are mentioned by particularly vulnerable groups such as households headedby women and elderly.Climate is the main reason for households becoming or remaining poor, a finding that

holds across all study sites as well as household types. Timing and quantity of rainfall havebeen found to be a driver of poverty in Ethiopia in other studies as well (Devereux andSharp, 2006; Dercon and Porter, 2011; Dercon et al., 2012). Family illness is identifiedas the second most important poverty driver and maintainer, confirming findings byBaulch (2011), Dercon and Porter (2011) and Ellis and Woldehanna (2005). Other factorsinclude high food prices, death of animals and disputes (see also Ellis and Woldehanna,2005; Dercon and Porter, 2011). Lack of labour is mentioned by six out of eight female-headed households but does not seem an important factor when considering all householdsor those headed by elderly. The cost of fertiliser and seeds is mentioned by half of allhouseholds and five out of six households headed by elderly but is only identified as acause by one female-headed household, possibly because most female-headed householdshave had to share-crop out their land so that they no longer purchase their own inputs.Analysis of the life histories does support the notion that it is often the cumulative nature

of shocks that causes downwards movements or poverty traps. For example, poor climatelowers everyone's productivity, thereby increasing the demand for and price of food;family illness decreases available labour and increases expenditures on treatment; and lackof labour—often as a result of illness—means that families need to share-crop out theirland, which further reduces the produce that they receive from their plots and increasesthe need to purchase additional food. Lack of land was found to be an important factorin locking households into poverty, particularly in Leki where households were awaitinga re-allocation of land by the government. Households headed by women were share-cropping out their land, and other households in our sample experienced reduced plot sizefollowing an increase in the number of adult household members and sharing of landacross generations, as illustrated by one case of a father giving three-quarters of his landto his sons.

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Table 6. Life events

Non-poor/nearly poor Chronic poor ‘New’ poor

%/(n) %/(n) %/(n)

Total 77.5 12.9 9.6(427) (72) (53)

Receipt of transfersTransfers (R1)No transfers 79.2 13.5 7.3

(240) (41) (22)Received transfers 75.1 12.4 12.4

(187) (31) (31)Transfers (R2)No transfers 79.4 11.8 8.7

(309) (46) (34)Received transfers 72.4 16 11.7

(118) (26) (19)Transfers (R3) * * *No transfers 80.6 10.6 8.8

(274) (36) (30)Received transfers 72.2 17 10.8

(153) (36) (23)Beneficiary of PSNP (R3) * * *No 80.8 11.9 7.4

(252) (37) (23)Yes 72.9 14.6 12.5

(175) (35) (30)Graduated from PSNP (R3) * * *No 76.5 13.6 9.8

(404) (72) (52)Yes 95.8 0 4.2

(23) (0) (1)Negative events affecting family# events R1No event 78.9 11.3 9.9

(56) (8) (7)1 event 59.4 20.3 20.3

(38) (13) (13)2 or more events 79.9 12.2 7.9

(333) (51) (33)# events R2No event 75.5 9.4 15.1

(40) (5) (8)1 event 71.9 18.8 9.4

(46) (12) (6)2 or more events 78.4 12.6 9

(341) (55) (39)# events R3No event 71.4 14.3 14.3

(5) (1) (1)1 event 63.9 19.4 16.7

(23) (7) (6)2 or more events 78.4 12.6 9

(399) (64) (46)Note:*p< 0.1;**p< 0.05;***p< 0.01 based on chi-squared group equality of means.

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Qualitative Comparative Analysis identifies the most common combination of factorsthat lead to drops into poverty or chronic poverty. The most common combination ofpoverty drivers—evident in 41 per cent of cases—were (i) climate, typically lack ofrainfall; (ii) family illness; (iii) lack of labour; and (iv) high food prices. Although thesewere the main configurations that emerged, there were many possible combinations, andfew households reported fewer than five or six causes of becoming or remaining poor.Despite coming from different sites and spanning a range of household types, the four

case study households representing chronic poor show a number of similarities that pointtowards common maintainers of poverty. They are located in cash-cropping areas but areunable to benefit because of lack of land or oxen to plough it. Where a valuable asset suchas irrigated land is available, inputs to make best use of it proves unaffordable. Also, it isnot affordable to send children to school with the appropriate materials. This means thateven when children are enrolled, they drop out periodically because, as pointed out byone mother: ‘[…] my children do not like to learn without getting basic things [needs]’.Although they may have benefitted from PSNP in the past, households now receive lessincome because of a reduction in the number of household members covered or deductionsfor credit, or have been excluded altogether. This makes them more vulnerable to healthshocks that have already caused asset sales in all the households.None of the four case study households describe themselves as well connected (in the context

of decision making around the PSNP quotas). When asked about participation in Kebeledecision making, one male respondent said: ‘[…] our membership is just at home. It is uselesssince we can't see and read. That is why we don't go.’ This comment highlights the role ofsocial, authority and governance structures at the local level in maintaining poverty. Socialnorms and prohibitions, such as cultural restrictions on women ploughing and administrativerestrictions on travel to the local town, act as a further constraint on social mobility.Finally, analysis of the eight case studies based on interviews with children across all

trajectories suggests that there is often a trade-off between the prosperity of the householdand the well-being of children within that household. Three children were excluded from

Table 7. Causes of becoming or remaining poor

Factor

Total no. ofhouseholds reporting

factor (n= 32)

No. of female-headedhouseholds reporting

factor (n= 8)

No. of older householdheads reporting factor

(n= 6)

Climate (e.g. drought,timing of rains andstorms)

30 7 6

Family illness 24 6 5High food prices 17 4 5Own illness 17 2 4Death of animals 17 2 4Exclusion from PSNP 16 3 3Disputes (e.g.neighbours andcriminal authorities)

16 5 3

Cost of fertiliser andseeds

15 1 5

Lack of labour 13 6 3Bad debt 9 1 2Low prices forproduce

7 1 0

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full-time education as they were required to support their household's economic activities. Inone case, the household's move from poverty in round 1 to non-poverty in round 3 seems tohave come at the expense of the child's health as he is performing hard physical labour.

5 DISCUSSION AND CONCLUSION

This paper points towards a number of substantive findings with respect to theinvestigation of poverty dynamics in rural Ethiopia on the basis of a mixed-methodanalysis of quantitative and qualitative panel data from the Young Lives study. It alsoprovides a number of methodological reflections.In substantive terms, this research confirms and enriches findings in existing studies on

poverty dynamics in Ethiopia. Ethiopia has experienced large drops in poverty in recentyears but is struggling with persistent chronic poverty. Factors preventing children andhouseholds from moving out of poverty or causing them to fall into poverty—povertymaintainers and drivers—include shocks such as drought, ill health, crop failure and lossof livestock. Lack of labour and high food prices have also been identified as part of themost common combinations of shocks affecting poverty. Children living in householdsheaded by female, single or young household heads are more likely to be chronically pooror fall into poverty. However, it is especially the cumulative nature of shocks and theirintersection with pre-existing vulnerabilities that impede upwards mobility.The unique combination of methods and different forms of analysis enriches existing

evidence by showing that, despite the identification of general trends and patterns, thereare no universal truths when it comes to movements in and out of poverty for childrenand their families. Combined analysis of quantitative data, life histories and case studiesshows that trends and underlying factors differ by region, community and household.Vulnerable household heads are also impacted differently by different shocks; lack oflabour disproportionately affects female-headed households, whereas older householdheads more frequently point towards the high costs of fertiliser and seeds as a cause forbecoming or remaining poor. The analysis also suggests trade-offs between householdprosperity and children's well-being. Although quantitative indicators may point to animprovement in observable living conditions, such as acquisition of draught animals andirrigated land, qualitative data nuance these improvements by pointing towards serioussacrifices in terms of children's education and health. This creates the counterintuitivebut probably not uncommon situation of a poor child in a non-poor household.Methodologically, this paper shows the value of the use of an emic taxonomy and the

combination of methods in analysing poverty dynamics. The use of qualitative informationin the development of the taxonomy of child poverty allows for capturing issues that aredeemed relevant by those categorised using the taxonomy but might be omitted by aconventional asset index. The mix of qualitative and quantitative methods in the analysisof poverty transitions over time allows for verification and triangulation as well as gainingmore nuanced understandings of underlying dynamics and processes. Although an analysisbased on quantitative data and methods only would conclude that multiple adverse lifeevents cannot be considered drivers or maintainers of poverty, the qualitative analysispoint towards the fact that the cumulative nature of shocks is indeed an important factor.Following Du Toit (2009), the distinction between chronic, transitory and non-poor onthe basis of the quantitative classification is only part of the story—it is the underlyingdynamics and processes that lock people into poverty in the long run, and only a truly

1036 K. Roelen and L. Camfield

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integrated mixed-method approach will provide insight into what it is that prevents peoplefrom moving out of poverty.

ACKNOWLEDGEMENTS

Young Lives (www.younglives.org.uk) is a long-term international research projectinvestigating the changing nature of childhood poverty. Young Lives is core-funded byUK aid from the Department for International Development (DFID) for the benefit ofdeveloping countries. Sub-studies are funded by the Bernard van Leer Foundation, theInter-American Development Bank (in Peru), the International Development ResearchCentre (in Ethiopia) and the Oak Foundation. The views expressed are those of theauthor(s). They are not necessarily those of, or endorsed by, Young Lives, the Universityof Oxford, DFID or other funders.

REFERENCES

Abebaw D. 2011. Infant and child health in Ethiopia: Reflections on regional patterns and changes.Journal of International Development. DOI: 10.1002/jid.1842.

Appleton S, Booth D. 2001. Combining participatory and survey-based approaches to povertymonitoring and analysis, background paper prepared for Uganda workshop, 30 May–1 June.

Baulch B. (ed.) 2011. Why Poverty Persists: Poverty Dynamics in Asia and Africa. Edward Elgar:Gloucester.

Bigsten A, Kebede B, Shimeles A, Taddesse M. 2003. Growth and poverty reduction in Ethiopia:evidence from household panel surveys. World Development 31(1): 87–106.

Camfield C, Roelen K. 2013. Household Trajectories in Rural Ethiopia: What Can a Mixed MethodApproach Tell Us About the Impact of Poverty on Children? Social Indicators Research. DOI:10.1007/s11205-013-0298-7.

Davis P. 2011. Passing on poverty: the intergenerational transmission of wellbeing and ill-being inrural Bangladesh. Chronic Poverty Research Centre Working Paper series, 192, Manchester:University of Manchester.

Davis P, Baulch B. 2009. Parallel realities: exploring poverty dynamics using mixed methods in ruralBangladesh. Paper presented at Escaping Poverty Traps: Connecting the Chronically Poor toEconomic Growth conference, Washington DC, February 26–27, 2009.

Dercon S, Krishnan P. 2000. Vulnerability, seasonality and poverty in Ethiopia. Journal ofDevelopment Studies 36(6): 25–53.

Dercon S, Porter C. 2011. A poor life? Chronic poverty and downward mobility in rural Ethiopia,1994 to 2004. In Why Poverty Persists: Poverty Dynamics in Asia and Africa, Bob B (ed).Edward Elgar: Gloucester.

Dercon S, Hoddinott J, Woldehanna T. 2012. Growth and chronic poverty: evidence from ruralcommunities in Ethiopia. Journal of Development Studies 48(2): 238–253.

Devereux S, Sharp K. 2006. Trends in poverty and destitution in Wollo, Ethiopia. Journal ofDevelopment Studies 42(4): 592–610.

Du Toit A. 2009. Poverty measurement blues. Beyond ‘Q-squared’ approaches to understandingchronic poverty in South Africa. In Poverty Dynamics—Interdisciplinary Perspectives, Tony A,Ravi K (eds). Oxford University Press: New York.

Universal Truths or Hidden Realities 1037

Copyright © 2013 John Wiley & Sons, Ltd. J. Int. Dev. 26, 1024–1038 (2014)DOI: 10.1002/jid

Ellis F, Woldehanna T. 2005. Ethiopia participatory poverty assessment 2004–05. Ministry ofFinance and Economic Development: Addis Ababa.

HDR. 2011. Sustainability and Equity: A Better Future for All. Explanatory Note on 2011 HDRComposite Indices. UNDP: Ethiopia.

Jones N, Sumner A. 2011. Child Poverty, Evidence and Policy: Mainstreaming Children inInternational Development. The Policy Press: Bristol.

Kanbur R, Shaffer P. 2005. Epistemology, normative theory and poverty analysis: implications forQ-squared in practice, Q-squared working paper.

Krishna A. 2007. Subjective assessments, participatory methods and poverty dynamics: the stages-of-progress method, CPRC Working Paper 93, Manchester: Chronic Poverty Research Centre.

Krishna A. 2009. Subjective assessments, participatory methods and poverty dynamics: the stages ofprogress method. In Poverty Dynamics: Interdisciplinary Perspectives, Addison, David H, Ravi K(eds). Oxford University Press: New York.

Kurukulasuriya S, Engilbertsdottir S. 2012. A multidimensional approach to measuring childpoverty. In Child Poverty and Inequality. New Perspectives, Ortiz, Moreira D, Engilbertsdottir(eds). UNICEF: New York; 23–34.

Minujin A, Nandy S. 2012. Global Child Poverty and Well-Being. The Policy Press: Bristol.MoFED. 2010. Ethiopia: 2010 MDGs Report. Trends and Prospects for Meeting MDGs by 2015.

MoFED: Addis Ababa, Ethiopia.Ortiz I, Morreira Daniels L, Engilbertsdottir S. (eds.) 2012. Child Poverty and Inequality: New

Perspectives. UNICEF: New York.Outes-Leon I, Dercon S. 2008. Survey attrition and attrition bias in YL. YL Technical Note 5. Young

Lives: Oxford.Ragin CC. 1987. The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies.

University of California Press: Berkeley/Los Angeles/London.Ritchie J, Spencer L. 1994. Qualitative data analysis for applied policy research. In Analysing

Qualitative Data, Alan B, Bob B (eds). Routledge: London, 173–194.Roelen K, Camfield C. 2012. A Mixed-method Taxonomy of Child Poverty – the case of Ethiopia.

Applied Research in Quality of Life. DOI: 10.1007/s11482-012-9195-5.Shaffer P. 2012. Beneath the ‘methods debate’ in impact assessment: baring assumptions of a mixed

methods impact assessment in Vietnam. Journal of Development Effectiveness 4(1): 134–150.Shaffer P, Kanbur R, Hang NT, Aryeetey E. 2008. Introduction to Q-squared in policy. International

Journal of Multiple Research Approaches 2(2): 134–144.Sharp K. 2007. Squaring the “Q”s? Methodological Reflections on a Study of Destitution in Ethiopia.

World Development 35(2): 264–280.Shepherd A. 2011. Tackling chronic poverty: the policy implications of research on chronic poverty

and poverty dynamics. Chronic Poverty Research Centre Working Paper series, 192, Manchester:University of Manchester.

Woldehanna T, Mekonnen A, Alemu T. 2008. Young Lives: Ethiopia Round 2 Survey Report.Young Lives: Oxford.

World Bank. 1999. Ethiopia: Poverty and Policies for the New Millennium. World Bank:Washington, DC.

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