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Beyond local climate: rainfall variability as adeterminant of household nonfarm activities incontemporary rural Burkina FasoSarah Ann Lise D'haenab, Jonas Østergaard Nielsencd & Eric F. Lambinb

a Institute of Geosciences and Natural Resource Management, University of Copenhagen,Øster Voldgade 10,1350 Copenhagen K, Denmarkb Georges Lemaître Center for Earth and Climate Research, Earth and Life Institute,Université catholique de Louvain, Place Louis Pasteur 3, 1348 Louvain-la-Neuve, Belgiumc IRI-THESys, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germanyd Waterworlds Research Centre, University of Copenhagen, Øster Farimagsgade 5, 1353Copenhagen K, DenmarkPublished online: 09 Jan 2014.

To cite this article: Sarah Ann Lise D'haen, Jonas Østergaard Nielsen & Eric F. Lambin , Climate and Development (2014):Beyond local climate: rainfall variability as a determinant of household nonfarm activities in contemporary rural BurkinaFaso, Climate and Development

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Beyond local climate: rainfall variability as a determinant of household nonfarm activities incontemporary rural Burkina Faso

Sarah Ann Lise D’haena,b*, Jonas Østergaard Nielsenc,d and Eric F. Lambinb

aInstitute of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10,1350 Copenhagen K,Denmark; bGeorges Lemaître Center for Earth and Climate Research, Earth and Life Institute, Université catholique de Louvain, PlaceLouis Pasteur 3, 1348 Louvain-la-Neuve, Belgium; cIRI-THESys, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin,Germany; dWaterworlds Research Centre, University of Copenhagen, Øster Farimagsgade 5, 1353 Copenhagen K, Denmark

(Received 12 July 2012; final version received 27 August 2013)

At the household level, nonfarm activities are thought to help rural poor households buffer against agricultural risks related tolocal climate variability by providing them with cash to buy food in the case of harvest shortfalls. Over the recent decades,households in rural Sub-Sahara have been found less dependent on land and subsistence agriculture and an increasing numberof households here derive their income from nonfarm activities. This study tests the hypothesis that rural households inBurkina Faso have diversified to the extent that they no longer rely on nonfarm activities as a safety net against adverselocal rainfall events. Results show that household decisions to participate in the nonfarm economy could not be directlylinked with local rainfall events during the study period in the mid-2000s. However, household participation wasdetermined by adverse rainfall conditions in the major staple food production zone of the country, presumably becausethese caused a rise in food prices. Results also suggested that Burkinabe households adopted a flexible approach tononfarm participation in terms of locality and plurality, depending on short-term rainfall conditions.

Keywords: income diversification; nonfarm activities; rainfall variability; panel analysis; multilevel analysis; West AfricanSahel; Burkina Faso

1. Introduction

1.1. Problem statement and objectives

Rural households in developing countries are generallythought to engage in nonfarm activities for two distinctreasons: because they need cash to buy food and becausethey want to invest some of their assets and maximizetheir income. The predominance of one of these motivesis mostly determined by the biophysical potential toproduce food and by the household endowments such aseducational level and economic wealth. With respect tothe former, there are many examples of permanent higherhousehold engagement in the nonfarm economy in agricul-turally marginal regions of Sub-Sahara Africa (Barrett,Bezuneh, Clay, & Reardon, 2004; Nielsen & Reenberg,2010a, 2010b; Rasquez & Lambin, 2006). The nonfarmeconomy is often also regarded as fulfilling a ‘safety net’function to overcome temporary income and harvest short-falls (Lanjouw, 2007), with previous studies findingexamples of nonfarm activities taken up temporarilyduring agricultural off seasons or after drought shocks(Bahrwani et al., 2005; Reardon & Taylor, 1996;

Thomas, Twyman, Osbahr, & Hewitson, 2007; Ziervogel,Bharwani, & Downing, 2006). However, reoccurrence of‘temporary’ adverse conditions or a long-term degradationof rainfall or of environmental conditions can trigger amore permanent change in livelihood strategies (Reid &Vogel, 2006; Roncoli, Ingram, & Kirschen, 2001).

Most of Sub-Sahara Africa’s rural population are stillassumed to be subsistence farmers that rely on the use of(local) natural resources (Denton, Sokona, & Thomas,2000; Jones & Thornton, 2009) and rainfall is here regardedas the most significant climate variable affecting people’slivelihoods (Brooks, 2004; Hulme, 2001). In the AfricanSahel, a diversification of livelihood strategies amongstrural households is therefore often viewed as both an adap-tation and a coping strategy in response to (a combinationof) local climate variability, recurrent shocks and climatechange (Mortimore & Adams, 2001; Nielsen & Reenberg,2010b; Reenberg & Paarup-Laursen, 1997). It has beenargued that the (recurrent) droughts have caused a closer inte-gration into the market and a greater focus on nonfarmincome opportunities and circular labour migration

© 2014 Taylor & Francis

*Corresponding author. Email: sdh@geo.ku.dk

Climate and Development, 2014http://dx.doi.org/10.1080/17565529.2013.867246

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(de Bruijn & van Dijk, 1994; Cordell, Joel, & Piché, 1996;Davies, 1996; Findley, 1994; McCann, 1999; Mortimore,1989;Mortimore&Adams, 2001; Nielsen, D’haen, &Reen-berg, 2012; Nielsen & Reenberg, 2010a; Rain, 1999;Raynaut, Gregoire, Janin, Koechlin, & Lavigne Delville,1997; Reardon & Taylor, 1996; Reenberg, Nielsen, & Ras-mussen, 1998; Reenberg & Paarup-Larsen, 1997; Roncoliet al., 2001). Similar trends have been observed in otherparts of dry-land sub-Saharan Africa (Thomas et al., 2007;Ziervogel et al., 2006). In linewith observed increased house-hold nonfarm participation everywhere in rural Sub-SaharaAfrica (Barrett, Bezuneh, & Aboud, 2001a; Barrett,Reardon, & Webb, 2001b; Bryceson, 2002; Davis et al.,2010; Wouterse & Taylor, 2008), Nielsen and Reenberg(2010b) recently stated that households in a small village innorthern Burkina Faso were ‘beyond climate’, suggestingthat they had diversified out of agriculture to the extent thatthey were ‘less dependent’ on rainfall for their livelihoods.

If this is the case, then what are the implications for the‘safety net function’ of the nonfarm economy after localharvest shortfalls caused by low rainfall? Do rural house-holds still temporarily increase or decrease engagement innonfarm activities (including temporary migration) withlocal seasonal rainfall or are they indeed ‘beyond climate’?

The objective of this study is to test the hypotheses that:(1) short-term lower local rainfall no longer triggers tem-porary household engagement in the nonfarm economy incontemporary Burkina Faso and (2) short-term local rain-fall does not influence a household’s choice of migrationas a nonfarm activity. The study as such investigates theemergency ‘safety net’ function of the nonfarm economyin contemporary Burkina Faso.

1.2. Study design: national survey data

Understanding the impact and importance of climate changeand variability on rural livelihoods in Sub-Saharan Africa isdifficult due to the complexity and intertwinement of manydifferent drivers of change (Mertz et al., 2011, 2012; Mertz,Mbow, Reenberg, & Diouf, 2009; Mortimore & Turner,2005; Nielsen & Reenberg, 2010a; Reid & Vogel, 2006;Reynolds et al., 2007; Thomas & Twyman, 2005; Thomaset al., 2007; Tschakert, 2007; Wardell, Reenberg, &Tøttrup, 2003; Ziervogel et al., 2006). Contemporary Sub-Saharan African households are subjected to a number ofsimultaneously occurring turbulent processes each withtheir own particular effect on livelihood decisions such as:climate change, land degradation, development aid, anincreasingly globalized food trade, social change, urbaniz-ation, changes in (tele)communications and rising popu-lation pressure (Haggblade, Hazell, & Reardon, 2007a;Leichenko & O’Brien, 2008; Nielsen & Vigh, 2012;Nielsen et al., 2012; O’Brien & Leichenko, 2000).

To control for these multiple drivers, most studies on cli-mate’s impact on livelihood diversification are performed

on relatively small data-sets of a limited geographicalreach. It is argued that ‘adaptation [to climate impacts] isexceedingly variable and linked to local contexts andplaces’ (Leichenko & O’Brien, 2002; O’Brien et al.,2004). Cross-sectional national data-sets are often foundunfit for exploring the mechanisms behind livelihooddecisions. However, despite the conceptual and practicalobstacles to their use for livelihood studies, recent nationalsurveys of developing countries form a pool of readily col-lected detailed household-level data opening possibilitiesfor comparative research across countries and over time(Davis et al., 2010; Winters et al., 2009).

For this study, three recent national Burkinabe surveyswere used for the analysis of household activity diversifica-tion. The socio-economic data were linked to a longitudinalseries of rainfall data. The relationship between rainfallanomalies and household nonfarm activities was analysedcontrolling for additional socio-economic determinantsboth at village and household levels. Intra-householddynamics in terms of the locality and plurality ofnonfarm activities were identified.

After a short literature review on the expected relation-ships between drivers of livelihood strategies, the data-setand methods are described. The following section reportson the observed trends as captured by the combination ofdata-sets and the intra-household labour dynamics underdifferent rainfall anomalies. Finally, the major findingsare discussed.

2. Conceptual framework and expectedrelationships

2.1. Rainfall and nonfarm activities

Concern about climate and its impact on human popu-lations and the natural resource base in the Sahel was animmediate response to the (most recent of) recurrentdrought periods commencing in the early 1970s (Brooks,1993; Nicholson, 1978). Following very wet years in the1950s and 1960s, annual rainfall in the Sahel fell by 20–30% in the following three decades (Hulme, 2001). Sincethe late 1990s, a recovery from dry conditions has beenobserved as annual rainfall totals have been increasedagain (Lebel & Ali, 2009; Nicholson, 2005). However,there are indications that the West-African climate mightbe changing slowly (Lebel & Ali, 2009).

Burkina Faso’s average annual rainfall varies roughlylinearly along a north–south gradient with vegetation andagricultural practices following this trend. In terms ofstaple food production potential, the country is subdividedinto a southern surplus zone, a north-eastern deficit zoneand an intermediate zone (FEWS NET, 2009). In terms oflivelihoods, the southern zone has good potential for agricul-tural (including cash crops such as cotton) and forestry-based activities. Livestock herding is more important in

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the north of the country, as are nonfarm income activities andmigration. Due to historical reasons, the intermediate zone isdensely populated in spite of a mediocre agricultural pro-duction potential due to poor soils, causing households tostruggle to produce enough food all year round.

A first expectation for this study was that, during thestudy period, households in the north-eastern and centralpart of Burkina Faso participated more in nonfarm activi-ties than those in the southern part of the country, due tolower (biophysical) production potential. Following thesafety net paradigm, a second expectation was that house-holds here participated more in nonfarm activities immedi-ately after dry rainfall conditions or interruptions in therainy season.

Expectations for the southern part of the country weredifferent in light of the abundant cross-section evidence ofthe co-evolution of the nonfarm and farm sectors in agricul-turally favourable areas in rural Africa (Haggblade, Hazell,& Brown, 1989; Haggblade et al., 2007a). In these areas,consumption and production linkages are thought to gener-ate amutually enforcing upward spiral in terms ofwages andemployment (Evans & Ngau, 1991; Haggblade, Hazell, &Reardon, 2007b; Lanjouw, 2007; Reardon, 1997). A thirdexpectation for this study was, therefore, that in the southernsurplus zone, the uptake of nonfarm activities declined afterlow rainfall due to sectorial linkages.

Many Burkinabe households traditionally engage incircular labour migration as their nonfarm activity(Nielsen & Reenberg, 2010a; Rain, 1999; Raynaut et al.,1997). Migrants typically leave just after the agriculturalseason in November or December and return at the startof the next rainfall season in May or June (Nielsen &

Reenberg, 2010a). Correlations between both immediateand historical rainfall and labour migration in BurkinaFaso have been identified (Henry, Schoumaker, & Beau-chemin, 2004; Nielsen & Reenberg, 2010b; Reardon,Delgado, & Matlon, 1992; Reardon, Matlon, & Delgado,1988; Reardon & Taylor, 1996). A number of thesestudies found that households in an agro-climatologicallyrisky environment have a higher engagement in migrationthan households in more favourable areas (Hampshire &Randall, 1999; Henry et al., 2004; Reardon et al., 1988).However, Henry et al. (2004) have also found that, inBurkina Faso, short-term international migration did notincrease following a recent and severe rainfall deficit. Afourth expectation for this study was, therefore, that house-holds engaged more in migration as their nonfarm activityin areas with less favourable rain and that short-termunfavourable rain could, but not necessarily did causehigher migration.

2.2. Village development and opportunities

Participation in the nonfarm economy is also influenced by anumber of non-climatic factors.Uptake of nonfarm activitiesin rural Sub-SaharanAfrica is thought to have accelerated bythe implementation of the 1980s structural adjustment pro-grammes (Bah et al., 2003; Bryceson, 1996) and by sub-sequent market failures during the economic liberalizationprocess (Ellis, 2006) as the effect of both was a deteriorationof the (support for the) farming sector.

More locally, household participation in nonfarmactivities is influenced by two other important factorssuch as: village economic opportunities and development

Figure 1. Conceptual framework of factors influencing the engagement in nonfarm activities for rural household in Sub-Saharan Africa.

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characteristics and household characteristics (Figure 1).First, for household to be able to take up nonfarm activities,nonfarm opportunities (sectors) have to be available. Vil-lages with markets are likely to have nonfarm opportunitiesthat come with market days, such as small-scale commerce,porter jobs, bicycle repairs, etc. Second, households need tobe able to engage in these activities. Typical entry barriersfor Sub-Saharan household engagement in the nonfarmeconomy include education, available manpower, financialcapital and health (Barrett et al., 2004; Barrett et al.,2001b). A number of public services, therefore, have tobe present in the village, such as educational institutionsand health centres.

In rural Sub-Saharan Africa, better road access, betterroad quality, telecommunications and electrification havealso been positively associated with market access, mobi-lity and consequently nonfarm opportunities (Abdulai &CroleRees, 2001; Barrett et al., 2001b; Lanjouw, Quizon,& Sparrow, 2001; Muto & Yamano, 2009; Nielsen & Reen-berg, 2010b; Reid & Vogel, 2006; Smith, Gordon,Meadows, & Zwick, 2001; Thomas et al., 2007; Winters,Davis, & Corral, 2002; Winters et al., 2009; Ziervogelet al., 2006). The development of a local nonfarmeconomy and the facilitating factors for household involve-ment together with road access also influence the house-hold choice between migration and local nonfarmengagement (Beauchemin & Schoumaker, 2005; Nielsen& Reenberg, 2010b).

Based on the above, the expectations for this study werethat household engagement in nonfarm activities washigher in villages with more nonfarm opportunities and invillages with markets, educational institutions, healthcentres, telecommunication infrastructure, electricity andgood road access.

2.3. Household structure, educational level, wealthand social position

In general, household characteristics are regarded as themaindeterminants of household participation in the nonfarmactivities in rural areas (Berdegue, Ramírez, Reardon, &Escobar, 2001).Household structure, gender and age compo-sition, educational and wealth levels are important factors inhousehold livelihood strategy decisions (Bryceson, 2002;Ellis, 2000; Evans & Ngau, 1991; Lanjouw, 1999;Lanjouw & Shariff, 2002; Marenya, Oluoch-Kosura, Place,& Barrett, 2003; Moser, 1996; Niehof, 2004; Taylor &Yunez-Naude, 2000; Yunez-Naude & Taylor, 2001). Theage of the household head and household size can, forexample, be very significant for the participation in multiplenonfarm activities (nonfarm pluriactivity) (Ellis, 2006;Lanjouw, 1999; Lanjouw & Shariff, 2002; Niehof, 2004;Reardon et al., 1992).

Participation in nonfarm activities is not uniform acrossindividuals within a household. Personal skills, gender and

status within the household often determine whether andhow an individual is to participate in the nonfarmeconomy (Ellis, 1998; Fiske, 1991; Skinner, 1988;Werthmann, 2009). Many women in Sub-Saharan Africaengage, for example, in small commercial activities(Canagarajah, Newman, & Bhattamishra, 2001; David &Niang, 1995; Okoth-Ogendo, 1989) and earn with theseand other activities (such as working for the developmentprojects and gardening) a substantial amount of theannual household income (Nielsen, 2010; Thorsen,2002). Education is found to positively determine an indi-vidual’s capacity to engage in nonfarm activities in generaland in high-return activities in particular (Berdegue et al.,2001; Lanjouw et al., 2001; Taylor & Yunez-Naude,2000; Winters et al., 2009).

Household capacity to engage in the nonfarm economyis further determined by access to financial capital. In fact,most literature on household diversification deals with itsrelationship with household wealth. Many studies find aU-shaped relationship between the share of incomederived from nonfarm activities and wealth for householdsin rural Africa (Barrett et al., 2001b; Bryceson, 2002; Ellis,2000; Niehof, 2004) meaning that both very poor and rela-tively rich rural households are more engaged in nonfarmactivities than those in the middle of the wealth scale (Bry-ceson & Jamal, 1997; Ellis, 1998, 2000; Niehof, 2004). TheU-shape is believed to emerge out of different motivationsand possibilities at either end of the scale (Abdulai & Cro-leRees, 2001; Lanjouw, 2001; Lay, Mahmoud, & M’Mu-karia, 2008; Smith et al., 2001; Tacoli, 2003). Otherstudies in West Africa found a more unidirectional positiveassociation between wealth and income or livelihood diver-sification (Burkina Faso: Reardon et al., 1992; Coted’Ivoire: Barrett et al., 2001a; Mali: Abdulai & CroleRees,2001).

Finally, social factors such as norms, values and rulesstrongly influence household strategies. Engaging in a par-ticular strategy is often a result of household ‘social-embeddedness’ (Rogaly, 1997) or ‘social exclusion’(Coulthard, 2008; Davies & Hossain, 1997; Djoudi, Broc-khaus, & Locatelli, 2013; de Haan & Zoomers, 2005;Harts-Broekhuis & de Jong, 1993; Nielsen & Reenberg,2010a; Reenberg & Paarup-Laursen, 1997). A household’s(social) safety nets and networks can reduce its need forcash (Cox & Fafchamps, 2007; Dercon, 2002; Fafchamps& Lund, 2003; Reardon & Taylor, 1996; Roncoli et al.,2001; Rosenzweig, 1988; Rosenzweig & Stark, 1989) orhelp securing a nonfarm job (Agergaard & Vu, 2011;Werthmann, 2009). Official social transfers and (inter)national food aid can additionally reduce the need forcash (Dercon & Krishnan, 2003; Nielsen & Vigh, 2012;Reardon & Taylor, 1996).

In summary, the Burkinabe households in this studywere expected to have higher nonfarm engagement with ahigher dependency ratio (older, larger and more established

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households), with higher female labour shares and with ahigher educational level. The dependency ratio and theshare of female labour, moreover, are expected to influencenonfarm pluriactivity. The expected association betweennonfarm engagement and wealth is less obvious, but the lit-erature suggests that it will be either positive or U-shaped.The data available did not allow control for all socialfactors.

3. Data and methods

The socio-economic data for this study come from threenational cross-sectional household surveys, the EnquêtesBurkinabe des Conditions de Vie des Ménages, executedin 2003, 2005 and 2007, respectively (Institut National dela Statistique et de la Démographie [INSD], 2003, 2005,2007). The multi-topic surveys contained detailed ques-tions on household composition, housing quality, assetownership, education, employment, migration and econ-omic activities. The subsequent surveys used an almostunchanged questionnaire in the same 277 rural villages.Within each village, 20 households were randomlysampled for each survey. The surveys hence track villagesover time rather than individual households. Aggregated atvillage level, the data form a panel set that can be analysedboth across space and over time. However, this aggregationimplies a loss of information at the household level. Tomake optimal use of the information available, two comp-lementary sets of statistical analyses were, therefore, per-formed in this study, one at the village level and the otherone at the household level.

3.1. Dependent variables

Six dependent variables were defined in total, three at eachlevel (village and household). They focus, respectively, onengagement in nonfarm activities, their locality andplurality.

3.1.1. General definitions

Households were identified as engaging in nonfarm activi-ties if one of their active members either held a job outsidethe village or engaged in one of the following nonfarmactivities inside the village:1 mining, manufacturing, con-struction, (small) commercial activities, transportation,small services (such as financial services or domesticservices) or working for international organizations (non-governmental organisations [NGOs]). The above sevennonfarm activities were chosen as they were specificallytargeted for support under the 2004 rural poverty reductionstrategy of the Burkinabe government (Government ofBurkina Faso and the EU, 2007, p. 65). Other nonfarmactivities such as working in public administration, edu-cation or health services were not taken into account for

this study as they often have a more permanent natureand are therefore less likely to fulfil a safety net function.

Active members held a job and were of age 10 or older.Around a third of active people in the data-set had a second-ary job (extra job or by job). All jobs performed outside thevillage were defined as migration activities. For eachhousehold, the overall number of jobs rather than thenumber of members with a job was taken into account aslimiting analysis to primary employment underestimatesthe scale and importance of nonfarm activities (Haggbladeet al., 2007b; Winters et al., 2009). An equal weight wasattributed to all activities.

3.1.2. Village-level dependent variables

A first dependent variable at village level was identified ashousehold participation in the nonfarm economy. Itmeasured the share of households in a village that partici-pated in either one (or several) of the seven specific localnonfarm sectors or in migration. Households participatingin these nonfarm sectors will be called ‘nonfarm house-holds’ for the remainder of the study. The locality depen-dent variable identified how many nonfarm households(out of the total number of nonfarm households in avillage) predominantly performed their nonfarm activitieswithin the village (local). The plurality dependent variableidentified how many of the nonfarm households wereactive in more than one nonfarm sector.

3.1.3. Household-level dependent variables

To refine the analyses, a set of parallel binary dependentvariables was defined at the household level. These regis-tered, respectively, household participation in the specificnonfarm sectors, whether a nonfarm household was predo-minantly engaged in local activities and whether a nonfarmhousehold was involved in more than one nonfarm activity(pluriactivity).

3.2. Explanatory variables

Three groups of potential determinants of nonfarm engage-ment were analysed, namely: rainfall, village developmentand household characteristics (Tables 1 and 2).

The rainfall data were extracted from Lebel and Ali’s(2009) database of 0.5° latitude–longitude gridded 10-day(hereafter called decades) rainfall amounts for the Sahel(Figure 2). Three seasonal rainfall variables were sub-sequently calculated, which includes: the number of verywet spells and the number of very dry spells during the pre-ceding rainy season and the anomaly of the total rainfallamount of the rainy season prior to each survey. To charac-terize the wetness or dryness of a particular decade (e.g.10–20 July), the deviation of the rainfall in a particularyear from the long-term mean rainfall for this decade

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Table 1. Summary statistics of the explanatory variables for panel analysis at village level.

All rural villages All rural villages with nonfarm households

Overall BetweenWithin

Overall BetweenWithin

Frequency Percent Frequency Percent Percent Frequency Percent Frequency Percent Percent

Rainfall variablesOver two very wet decades 245 29.48 205 74.01 39.84 226 30.25 192 69.31 43.58Over two very dry decades 206 24.79 145 52.35 47.36 187 25.03 141 50.9 52.72Dry or very dry year 329 39.59 257 92.78 42.67 293 39.22 239 86.28 45.4Normal year 409 49.22 246 88.81 55.42 371 49.67 237 85.56 57.59Wet or very wet year 93 11.19 93 33.57 33.33 83 11.11 83 29.96 38.55

Spatial variablesSurplus zone 285 34.3 95 34.3 100 245 32.8 95 34.3 100Minor deficit zone 234 28.16 78 28.16 100 207 27.71 78 28.16 100Major deficit zone 312 37.55 104 37.55 100 295 39.49 104 37.55 100

Local development variablesSecondary school < 1 h 46 5.54 31 11.19 49.46 47 6.29 31 11.19 53.23Health centre < 1 h 136 16.37 84 30.32 53.97 137 18.34 87 31.41 55.56Food market < 1 h 232 27.92 136 49.1 56.86 213 28.51 129 46.57 60.72Telecommunications 296 35.62 203 73.29 48.6 265 35.48 185 66.79 52.52Electricity 86 10.35 67 24.19 42.79 78 10.44 62 22.38 46.24No road < 10 km 381 46.18 127 46.18 100 339 45.38 129 46.57 100Laterite road < 10 km 106 12.85 39 14.18 90.6 93 12.45 39 14.08 90.6Laterite road < 5 km 153 18.55 55 20.00 92.73 140 18.74 55 19.86 93.64Paved road < 10 km 67 8.12 30 10.91 74.44 63 8.43 30 10.83 74.44Paved road < 5 km 118 14.3 48 17.45 81.94 112 14.99 46 16.61 84.42

Survey year2003 277 33.33 277 100 33.33 254 34 254 91.7 37.662005 277 33.33 277 100 33.33 238 31.86 238 85.92 35.782007 277 33.33 277 100 33.33 255 34.14 255 92.06 37.71

Total observations 831 747

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(anomaly) was calculated using z-scores (Lebel & Ali,2009):

Anomaly

= Decadal rainfall− long-termmean decadal rainfall

Standard deviation of long-term mean decadal rainfall.

Values between −0.49 and +0.49 indicate normal con-ditions and values between |0.5| and |0.99| indicate wet/dry conditions. Decades were categorized as very wet orvery dry if their anomaly in a particular year was, respect-ively, larger than 1 or smaller than −1. A binary variableidentified the occurrence of two or more very wet or verydry decades during the season. Similarly, magnitudes of

annual rainfall anomalies were characterized by the devi-ation of a particular year from the long-term annualmean. A year was categorized as ‘dry or very dry’ if theanomaly was equal to or lower than −0.5 and as ‘wet’ or‘very wet’ if the anomaly was equal to or higher than 0.5.As surveys were conducted at the beginning or in themiddle of the rainy season and survey questions wereapplied to the 12 months prior to the survey, it wasassumed that the recorded situation at the household levelreflected a response to the rainfall of the previous ratherthan of the current year.

At the village level, the distance to secondary schools,health centres and markets was introduced to control forthe development of local facilities. Variables on telecom-munication infrastructure, electricity and the type of and

Table 2. Summary statistics of the explanatory variables for multilevel analysis at the household level.

All householdsHouseholds involved in

nonfarm

Frequency Percent Frequency Percent

Rainfall variablesOver two very wet decades 4690 29.27 1023 30.7Over two very dry decades 3984 24.86 770 23.11Dry or very dry year 6420 40.06 1270 38.12Normal year 7841 48.93 1649 49.49Wet or very wet year 1763 11 413 12.39

Spatial variablesSurplus zone 5512 34.4 987 29.62Minor deficit zone 4493 28.04 811 24.34Major deficit zone 6019 37.56 1534 46.04

Local development variablesSecondary school < 1 h 947 5.91 227 6.81Health centre < 1 h 2745 17.13 703 21.1Food market < 1 h 4525 28.24 1001 30.04Telecommunications 5588 34.87 1308 39.26Electricity 1604 10.01 416 12.48No road < 10 km 7508 46.85 1530 45.92Laterite road < 10 km 2049 12.79 403 12.09Laterite road < 5 km 2938 18.33 634 19.03Paved road < 10 km 1285 8.02 232 6.96Paved road < 5 km 2244 14 533 16

Household-level variablesHighest diploma in the household = no diploma 12,214 76.22 2289 68.7Primary school 2994 18.68 793 23.8Professional secondary school lower 13 0.08 3 0.09Professional secondary school higher 20 0.12 4 0.12Secondary school (gymnasium) 682 4.26 211 6.33Final year of secondary school 67 0.42 23 0.69University or higher education 34 0.21 9 0.27Under 30th percentile poverty line 4448 27.76 725 21.76Under 85th percentile poverty line 12,993 81.08 2535 76.08

Total observations 16,024 3332

Continuous variables at household level Mean Std. dev. Mean Std. dev.

Proportion of members that is dependent 0.41 0.23 0.41 0.22Active women out of total active 0.49 0.25 0.49 0.23

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distance to the nearest road as a proxy for access tomarkets were also included. Most of these data wereextracted from the household questionnaires. Data on theproximity and quality of roads were extracted from thenational database on places and territories of the Institutde Géographie Burkinabe (IGB, 2002). At village level,the panel analysis spans several surveys and could hencecontrol for survey- and year-specific effects. A dummyvariable identifying the survey year was, therefore,included at this level.

At the household level, the following variables wereincluded: the highest diploma obtained in the household;the proportion of dependent members, representing thehouseholds’ age and structure; the share of female labourin the household, to capture gender-differentiated engage-ment; and household economic wealth status relative totwo poverty lines. The two poverty lines, at the 30th and85th percentiles of the wealth distribution in 2003, wereused to control for a possible U-shape relationship withdiversification. The upper percentile line was chosen suffi-ciently high to maximize the possibility of capturing apotential U-shape. Weights for an asset indicator, includinghousing materials and consumer assets,2 were calculatedwith polychoric Principal Component Analysis for 2003(Kolenikov & Angeles, 2004). The weights were thenapplied to the same variables in the other surveys. Thevalues of the 30th and 85th percentiles were calculated in2003 and set as poverty lines for all survey years.

The data-set did not contain information on (intra)household status, household ethnicity, language or socialembeddedness or on (local or household) social safety

nets. These factors were consequently not controlled forin the analyses. Their potential effect is discussed in the dis-cussion section.

3.3. Statistical analyses

3.3.1. Binomial panel regression models at thevillage level

Panel models were developed to analyse the associationbetween rainfall and each of the three dependent variablesat village level. To account for potentially different relation-ships in the different production zones of the country,models were estimated for each production zone. Thepanel models considered both the spatial – i.e. between vil-lages within each survey – and temporal – i.e. betweensurveys within each village – dimensions of the data. Asthe dependent variables expressed a proportion (makingthem limited dependent variables), models were estimatedassuming the binomial distribution for n trials. Binomialmodels are similar to models for binary dependent variables(logistic or probit models), but expand these to n trials(Rabe-Hesketh & Skrondal, 2008). Applied to this study,this meant looking at the dependent variables as thenumber of successes (e.g. number of households engagedin the specific nonfarm sectors in a village), out of ntrials (total number of households sampled in eachvillage), each with a potential outcome of either 0 (noengagement) or 1 (engagement).

One way of formulating the model is via a so-calledlatent response variable. Underlying the dependent

Figure 2. Map of the sampling areas for the socio-economic surveys. Overlay of a 0.5° rainfall grid (INSD, 2004 and own adaptations).

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(response) variable yi is an unobserved or latent continuousresponse variable y*i.

yi = 1 if y∗i . 0,yi = 0 if otherwise.

A linear regression model can then be specified for thelatent response variable:

y∗i = bxi + 1i, (1)

where 1i is the residual error term with E(1i | xi) = 0 andthe error terms of the different villages i are independentand follow a binomial distribution.

At village level, observations within the same villagecould be correlated over time. Adding a village-specificrandom effect αi to (1) relaxes the assumption that theresponses for the same village are independent overtime. The final models estimated at the village levelwere binomial random intercept panel models:

y∗it = ai + bxit + 1it , (2)

with αj following a normal distribution and εit following abinomial distribution.

3.3.2. Three-level logistic regression models at thehousehold level

Panel models are specific cases of so called mixed models.Whereas the village panel models apply to clustering overtime within observational units (villages), a mixed modelapplies, for example, to data such as individuals clusteredwithin villages, which are in turn clustered within districtsof a country. Such a mixed model can be seen as structuredin hierarchical levels and is also called a hierarchical model.

Given that explanatory variables for the household-level analysis in this study were collected at three hierarch-ical levels (household, village and rainfall grid cell levels),three-level hierarchical regression models were estimated.These models took into account the fact that householdsin the same village all had the same value for village-level variables in a particular survey year. For example,they all lived in a village with electricity and they alllived within 10 km of a major road. Similarly, the models

took into account the fact that all households within a par-ticular rainfall cell had the same rainfall values.

As the dependent variables at the household level werebinary variables, the final models were termed as three-level logistic random intercept models:

y∗ijk = a jk + ak + bxijk + 1ijk , (3)

where i identifies the household level, j the village level andk the rainfall cell level. αjk represents the village-specificeffects and αk the rainfall cell-specific effects. Household-level models were estimated for each survey separatelyand their results were then compared over time.

4. Descriptive results: trends in nonfarmengagement and local rainfall

Between 2003 and 2007, on average 60% of rural Burki-nabe households were involved in nonfarm activities(Table 3). Most nonfarm households were involved inlocal ‘other’ activities (mainly public services). Morehouseholds were engaged in the nonfarm economy ingeneral in 2007 compared to 2003 or 2005, a trend alsoobserved for the focus nonfarm sectors of this study.

Around 63% of this study’s focus nonfarm householdswas engaged in migration activities. Manufacturing (21%),commerce (10%) and international NGOs (also 10%) werethe next largest sectors. Engagement in migration declinedover the surveys, and the average share of nonfarm jobs thatwere local at the cost of migration went up steadily between2003 and 2007. The average number of different sectors perworking household member was significantly higher in2007 than in 2003 or 2005 as was the percentage of house-holds with more than one specific nonfarm activity.

The rainfall season preceding the 2003 survey was dryfor many Burkinabe households. Wet spells were rare(Figure 3) and a large part of the central latitudes and thesouthwest of the country experienced a dry or very dryrainy season (Figure 4). In 2004, the western half and thenorth of the country were affected by negative rainfallanomalies and dry spells (Figure 5). The rainfall preceding2007 was compared with that preceding 2003 and 2005 wetbecause of a near absence of dry spells, a presence of wetspells in large parts of the country and a positive rainfallanomaly in both the north and southwest.

Table 3. Descriptive statistics of household engagement in the nonfarm sector in Burkina Faso between 2003 and 2007.

2003 2005 2007 All years

Rural households engaged in nonfarm (%) 55.9 55.6 74.0* 61.6Rural households engaged in specific nonfarm (%) 19.2 18.6 24.7* 20.8Specific nonfarm hh that have migration (%) 69.4* 66.5* 54.5* 62.8Specific nonfarm hh predominantly active locally (%) 36.4* 42.7* 58.5* 46.8Specific nonfarm hh with more than one nonfarm activity (%) 6.3* 8.6* 11.3* 8.9

*p < .05.

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Figure 3. Areas with at least two very wet spells during the rainy season preceding the respective survey years. The figures depict thedifferent production zones of the country, the sampling villages and the 0.5 × 0.5 grid cells where at least two very wet spells occurred(own figures, original rainfall data from Lebel & Ali, 2009).

Figure 4. Areas with at least two very dry spells during the rainy season preceding the respective survey years. The figures depict thedifferent production zones of the country, the sampling villages and the 0.5 × 0.5 grid cells where at least two very dry spells occurred(own figures, original rainfall data from Lebel & Ali, 2009).

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The combination of the above nonfarm participationtrends and rainfall trends do not suggest that householdswere more engaged in nonfarm activities after dryanomalies, but rather that temporary wet rainfall conditionsfavoured nonfarm engagement, plurality of nonfarm activi-ties and a concentration on local nonfarm activities.However, rainfall anomalies, dry and wet spells displayedconsiderable spatial and temporal variability over the yearsand affected different regions in different years. Results,moreover, suggest a continuous trend over time towardsmore local and plural nonfarm engagements (Table 3).

5. Explanatory results

5.1. Effect of rainfall

At the national scale, a low rainfall was uncorrelated withhousehold nonfarm participation, a concentration of house-hold nonfarm activities in migration and a more plural char-acter of activities. However, significant correlations werefound for specific zones. In the surplus zone, forexample, significantly fewer households participated inthe nonfarm economy during a dry year (Table 4). Thispoints towards sectorial linkages between the agriculturaland the nonfarm economy here. In the major deficit zone,nonfarm households did favour migration over local

nonfarm activities after a dry year, as indicated by the nega-tive correlation coefficient (Table 5).

Finally, interruptions in the rainy season were importantin the minor deficit zone. Dry breaks in the rainy seasonwere here associated with higher nonfarm engagementand a more local and plural character of nonfarm activities(Table 6).

5.2. Year and zone-specific effects

The results show strong year- and zone-specific effect interms of nonfarm participation. Model V1 indicates thatthe proportion of households engaged in the nonfarmeconomy was lower in 2005 than in 2003 for villages inthe surplus zone and higher for villages in the majordeficit zone. By 2007, nonfarm participation at villagelevel had moved back to 2003 levels in the surplus zone,but not in the major deficit zone, where it continued torise. Similar trends were observed in the household-levelmodels (H1). In 2003, no difference in household propen-sity to participate in the nonfarm economy was foundbetween the different zones. In 2005, however, householdsin the two deficit zones were more likely to participate thanthose in the surplus zone. This could be explained by acombined temporary lower participation in the surplus

Figure 5. Areas with an anomaly in total annual rainfall amounts during the rainy season preceding the respective survey years. Thefigures depict the different production zones of the country, the sampling villages and the 0.5 × 0.5 grid cells where a rainfall anomalyoccurred (own figures, original rainfall data from Lebel & Ali, 2009).

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zone and a higher participation in the deficit zones in thatyear. In 2007, no difference in household participationwas observed between the surplus and major deficitzones. Households in the minor deficit zone were,however, less likely to participate than those in thesurplus zone in that year.

With respect to the locality of the nonfarm activities,households in the major deficit zone seemed more locallyengaged in nonfarm activities compared with householdsin the surplus zone, except for the year 2005 (models

H2). According to the village-level models (V2), nonfarmhouseholds in the two deficit zones were considerablymore locally engaged in 2007 than in 2003. In the minordeficit zone, this was already the case in 2005.

The village-level models (V3) furthermore indicatedthat, nationally, the proportion of households with morethan one nonfarm activity was increased. The householdmodels (H3) additionally reveal that by 2007, householdsin the deficit zones were more pluriactive than those inthe surplus zone.

Table 4. Determinants of household nonfarm engagement at the village level and at the household level.

Village Household

Binomial panel model Three-level logistic model

V1 H1

Proportion of households engaged in the specificnonfarm sectors

Household participation in the specificnonfarm sectors

Surplus zone Minor deficit zone Major deficit zone 2003 2005 2007

RainfallOver two very wet decades −0.145 −0.053 0.033 −0.425* 0.129 −0.047Over two very dry decades −0.393*** 0.221** 0.097 0.131 −0.208 −0.274Dry or very dry year −0.716*** 0.081 0.151 −0.182 −0.115 −0.194Normal year R R R R R RWet or very wet year −0.332** (−0.912**) −0.056 (−1.022) −0.091

Spatial variableSurplus zone R R RMinor deficit zone −0.005 0.554** −0.442**Major deficit zone 0.16 1.169*** 0.238

Survey year2003 R R R2005 −0.990*** −0.072 0.249**2007 −0.093 0.04 0.412***

Local developmentSecondary school < 1 h 0.164 −0.018 −0.582** −0.626** 0.199 −0.105Health centre < 1 h 0.277** 0.04 0.394** 0.501** 0.292 0.392**Food market < 1 h −0.114 0.028 0.06 −0.233 −0.181 0.198Telecommunications 0.133 0.087 0.194** 0.129 −0.075 0.115Electricity 0.362** 0.178 −0.041 0.299 −0.099 0.152No road < 10 km R R R R R RLaterite road < 10 km 0.254 −0.363* −0.212 −0.152 0.138 0.124Laterite road < 5 km 0.246* −0.18 0.099 0.174 0.044 −0.02Paved road < 10 km 0.086 −0.22 −0.283 0.081 −0.590** −0.298Paved road < 5 km 0.013 0.292* −0.286 0.223 0.233 0.099

Household characteristicsHighest diploma in the household 0.146*** 0.173*** 0.177***Share of dependent members 0.23 0.18 −0.1Active women out of total active 0.368** −0.018 −0.059Under 30th percentile poverty line −0.398*** −0.421*** −0.171*Under 85th percentile poverty line 0.007 −0.455*** −0.299***

Constant −0.924*** −1.723*** −1.568*** −1.829*** −1.977*** −1.040***

Note: Italic values represent result based on villages in only one or two rainfall cells.*p < .1.**p < .05.***p < .001.

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5.3. Effect of local development

Village development, as represented by the presence oflocal facilities, influenced household engagement in thespecific nonfarm sectors. The coefficients for a secondaryschool in the vicinity of the village were not consistentover the different zones and years, but the negative coeffi-cient for the major deficit zone in the village-level model(V1) suggested that secondary schools here were notassociated with higher nonfarm participation. Both village(V1) and household-(H1) level models associated the

presence of a health centre with higher participation innonfarm activities. The presence of a food market was posi-tively associated with local engagement in the surplus zone,and nationally, in the year 2007.

Telecommunications were associated with significantlyhigher participation in the nonfarm economy in the majordeficit zone (V1). In the surplus and minor deficit zones,they were associated with a lower local character of theengagement. Electricity was positively associated withparticipation in the surplus zone and was positively

Table 5. Determinants for the locality of household nonfarm activities at the village level and at the household level.

Village Household

Binomial panel model Three-level logistic model

V2 H2

Proportion of nonfarm households that have theiractivities predominantly local

Chance that a nonfarm household has itsactivities predominantly local

Surplus zone Minor deficit zone Major deficit zone 2003 2005 2007

RainfallOver two very wet decades 0.365 −0.166 0.247 0.77 −0.297 0.168Over two very dry decades −0.263 0.346* −0.166 0.043 −0.146 0.113Dry or very dry year −0.397 0.292 −0.433** −0.078 −0.455 −0.112Normal year R R R R R RWet or very wet year −0.161 (−0.866) −1.194** (−0.601) −0.432

Spatial variableSurplus zone R R RMinor deficit zone −0.021 0.560 0.056Major deficit zone 1.220** 0.349 1.431***

Survey year2003 R R R2005 0.387 1.309*** −0.0762007 0.611 1.603*** 1.394***

Local developmentSecondary school < 1 h −0.415 0.394 −0.005 −0.342 0.455 −0.329Health centre < 1 h 0.182 −0.251 0.306 0.431 0.248 0.209Food market < 1 h 0.376* 0.365 0.239 0.33 0.16 0.747**Telecommunications −0.489** −0.384* 0.024 −0.274 −0.045 −0.653**Electricity −0.11 0.584** −0.371 0.292 0.088 0.377No road < 10 km R R R R R RLaterite road < 10 km 0.232 −0.308 0.005 0.033 0.366 −0.319Laterite road < 5 km 0.497 0.193 0.066 -0.038 0.188 0.404Paved road < 10 km −1.098** −0.204 0.126 −0.52 0.135 −0.019Paved road < 5 km 0.423 0.135 0.776** 0.333 0.298 0.544*

Household characteristicsHighest diploma in the household 0.225** 0.190** 0.045Share of dependent members 0.046 −0.141 0.01Active women out of total active 0.344 0.293 0.032Under 30th percentile poverty line 0.245 −0.007 0.123Under 85th percentile poverty line 0.885*** 0.637*** 0.439**

Constant −0.915** −1.560*** −0.304 −2.494*** −1.319** −0.678

Note: Italic values represent result based on villages in only one or two rainfall cells.*p < .1.**p < .05.***p < .001.

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associated with a more local character of nonfarmactivities in the minor deficit zone. The vicinity of aroad stimulated participation in the surplus and minordeficit zones and influenced a more local engagement inthe major deficit zone.

In summary, the models at village level suggested thatmore households participated in the nonfarm economy iflocal development was higher. Good market possibilities,proxied by the presence of a food market and the near-ness of a paved road, were associated with more house-holds engaging locally rather than concentrating on

migration activities. Telecommunications seemed tofacilitate migration.

5.4. Effect of household characteristics

For the majority of the household characteristics, coeffi-cients were consistent in terms of sign and significanceacross survey years. Education positively affects partici-pation and is associated with a more local and plural char-acter of activities. Household composition, represented bythe share of dependent members and the share of women

Table 6. Determinants for household nonfarm pluriactivity at the village level and at the household level.

Village Household

Binomial panel model Three-level logistic model

V3 H3

Proportion of nonfarm households that are pluriactiveChance that a nonfarm household is

pluriactive

Surplus zone Minor deficit zone Major deficit zone 2003 2005 2007

RainfallOver two very wet decades −0.333 0.015 −0.067 −1.245 −0.149 0.157Over two very dry decades 0.051 0.730** −0.499 0.629 0.119 −0.104Dry or very dry year −0.097 0.743 0.327 −0.598 0.757 0.567Normal year R R R R R RWet or very wet year −1.029** (−22.038***) 0.493 −0.164

Spatial variableSurplus zone R R RMinor deficit zone −0.229 0.431 0.737**Major deficit zone 0.567 0.479 0.694**

Survey year2003 R R R2005 0.163 1.054** 0.1532007 1.230* 2.237*** 0.493*

Local developmentSecondary school < 1 h 0.017 −0.115 −0.11 −0.5 0.868 −0.435Health centre < 1 h 0.698* 0.074 0.760** 0.548 0.564 0.127Food market < 1 h 0.268 0.507 −0.153 0.342 −0.375 0.299Telecommunications −0.115 −0.388 0.156 −0.091 0.113 −0.332Electricity 0.201 0.371 −0.162 −0.574 −0.86 0.313No road < 10 km R R R R R RLaterite road < 10 km −2.021* −0.716 −0.138 −1.057 −0.3 −0.661*Laterite road < 5 km 0.384 −0.097 0.037 −0.202 0.48 0.275Paved road < 10 km −0.047 0.058 −0.142 0.638 0.337 −0.888*Paved road < 5 km 0.642* −0.155 −0.224 −0.198 0.39 −0.339

Household characteristicsHighest diploma in the household 0.443*** 0.255** 0.193**Share of dependent members −0.393 −0.91 −0.437Active women out of total active 0.582 −0.726 0.464Under 30th percentile poverty line −0.495 0.217 −0.252Under 85th percentile poverty line −0.519 −0.603** −0.549**

Constant −3.293*** −4.078*** −2.714*** −2.991*** −2.918*** −2.222***

Note: Italic values represent result based on villages in only two rainfall cells.*p < .1.**p < .05.***p < .001.

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under the active members, showed no (consistent)relationship to any of the dependent variables. Beingpoor meant a lower propensity to participate and thisrelationship was unidirectional, rather than U-shaped.The richest 15% households were more likely to engagein migration and to have more than one nonfarm activity,but in general, poorer households showed no higher orlower propensity in terms of a local and plural characterof their nonfarm activities than non-poor (not rich)households.

6. Discussion and conclusionThis study tested the hypothesis that the safety net function ofthe rural nonfarm economy loses in importance in contem-porary Burkina Faso as households already participate inthis economy to the extent that they are less dependent onrainfall. It found no national level evidence that short-termlocal rainfall deficits caused temporary higher nonfarmengagement in Burkina Faso between 2003 and 2007.During the study period, the associations between livelihoodsand short-term rainfall depended, however, on the region ofthe country, with a clear contrast between the semi-arid andwetter regions. Some evidence pointed to a safety net func-tion in the minor deficit zone, where interruptions in therainy season were associated with a higher participation inthe specific nonfarm sectors and higher pluriactivity, and inthe major deficit zone, where after a dry or very dry rainyseason nonfarm households chose to migrate temporarily atthe expense of local activities.

There were indications that harvest shortfalls after lowrains in the southern production zones of the country had amore significant influence on national household behaviour.A delay in the start of the season and dry spells during Julycaused cereal production in 2005 to be lower than average inthe production zone (FEWS NET, 2004d). As Burkina Fasois mainly self-reliant in terms of staple foods, this translatedinto a steep rise in food prices on the national market. Such asudden rise is problematic in a country where between 40%and 70% of household expenditure is devoted to food (Ellis,2006; FEWS NET, 2010). The major deficit region was hardhit as households there rely more on purchased food than inother parts of the country due to a low local biophysicalpotential to produce food. Moreover, the need for purchasedfood was higher here in 2005 than in other years as localadverse rainfall conditions and a locust invasion had led tovery low local harvests (FEWS NET, 2004a, 2004b,2004d). The low rainfall conditions also affected counterseason fruits and vegetables production and livestock fatten-ing, one of the main economic activities in this area (FEWSNET, 2004c). A continued closure of the trade with theprime outlet market Cote d’Ivoire resulted further in lowcattle prices. The combination of these factors led to verylimited household purchasing power all over the country,but especially in the northern region (FEWS NET, 2004e).

This contrasted sharply with the higher purchasing powerin 2003 and 2007; after very successful harvests, favourablelivestock raising conditions, high cattle prices and a goodcounter season production (FEWS NET, 2002, 2003,2006, 2007). The village-level models registered significanthigher nonfarm engagement in the major deficit zone in2005, compared with 2003 (Table 4, model V1), suggestingthat households in this region used the nonfarm economy tocope with the financial stress they experienced that year.Similarly, nonfarm households in the minor deficit zoneshowed higher pluriactivity in 2005, pointing to their pre-ferred strategy to deal with financial stress.

Associating and/or dissociating livelihood decisionsfrom climate perturbations is difficult and has been thetopic of recent literature on adaptation, vulnerability andresilience (Adger, 1999; Adger et al., 2009; Belliveau,Smit, & Bradshaw, 2006; Eakin, 2000, 2005; Leichenko& O’Brien, 2008; Mertz et al., 2009, 2011; Nielsen &Reenberg, 2012; O’Brien & Leichenko, 2000; Reid &Vogel, 2006; Roncoli et al., 2001; Thomas et al., 2007;Tschakert, 2007). Several authors emphasize that ifclimate does influence diversification decisions, it is but asmall player in household decision-making. There is evi-dence from the Sahel that, although households are awareof climate variability and change, they often identify econ-omic, political and social rather than climate factors as themajor reasons for change (Mertz et al., 2009, 2011; Nielsen& Reenberg, 2010b; Tschakert, 2007). The different house-hold strategies between the three production zonesobserved in this paper might similarly be associated withnon-climatic factors as the southern zone of the countryin general is much better developed and served by infra-structure and industry than the northern zone.

Household nonfarm engagement decisions in BurkinaFaso seemed also influenced by wider socio-economictrends. Increased engagement in the nonfarm economybetween 2003 and 2007, amore local focus, and an increasedplurality were national trends (Table 3) as confirmed in thevillage-level statistical analyses. Even though rainfall con-ditions and purchasing power recovered in 2007 (FEWSNET, 2007), the village-level models showed a continuedtrend of more households entering the nonfarm economyin the major deficit zone and, in all zones, an increasinglocal and plural character of nonfarm activities in nonfarmhouseholds after 2005. This could indicate the re-enforcingnature of nonfarm engagement once it has been triggered byother factors such as high food prices in this case. This slowupward trend of increased participation, local focus andpluriactivity is probably also associated with a combinationof push – e.g. population growth, structural adjustment pro-grammes and land degradation – and pull factors – e.g. newemployment opportunities, better transport networks,improved telecommunication and growth in urban demand(Ellis, 2000; Rasquez & Lambin, 2006). The results of thisstudy suggest that households in more developed villages

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have a better access to labour markets and therefore moreopportunities to engage in nonfarm activities.When villageshave markets and are located near asphalt roads, the localdevelopment of the nonfarm economy is stimulated at theexpense of migratory activities. Nonfarm income is notonly an insurance against rainfall deficits for agriculturallivelihoods, but it also delivers higher incomes, more resili-ent livelihoods and a higher standard of living as seen else-where in the developing world (Blaikie, Cameron, &Seddon, 2002). Households and their members pursue themix of activities that enables them to both keep their farms(even if only on a nominal basis) and to increase theirwealth and the opportunities that come with it (e.g. edu-cation and healthcare) (Rigg, 2006). Nonfarm engagementcan be linked to a rainfall-related (deficit or surplus) stimu-lus, but rainfall anomalies are more often associated withtemporary changes in the composition and character of theactivities. Indeed, the surveyed households did not consist-ently engage or disengage in nonfarm activities because ofrainfall anomalies. Instead, they maintained their livelihoodand shifted labour around between migration and multiplelocal nonfarm activities.

Are households in Burkina Faso ‘beyond climate’? Ourresults suggest a nuanced answer. Households in contem-porary Burkina Faso are indeed beyond local climate. Inthe agriculturally unfavourable north of the country house-holds do not seem to be influenced by local rainfall factors.Households in that region are diversified to the extent thatrainfall only influences how they engage in the nonfarmeconomy in a particular year. However, the greater relianceof households in this part of the country on purchased foodmakes them vulnerable to adverse climatic conditions in thestaple food production areas through (national) food prices.The policy question is therefore, how the nonfarm economycan make these households more resilient to spill-overeffects of adverse climate impacts elsewhere affecting theprice of their staple foods?

The relations between rainfall and livelihood nonfarmengagement are complex and other factors affectdecision-making at the household level. In this study,household- and village-level factors were controlled forwhen testing the influence of rainfall variables, thereby cap-turing the internal variation in general developmentbetween the three agricultural zones in Burkina Faso.However, the association between a higher diploma andall three dependent variables in the household-levelmodels indicated that the entry barriers to high-earning,nonfarm activities were very likely present. In thesemodels, a non-negligible part of the variance in nonfarm-related decisions remained unexplained (Appendix TablesA1–A3). Additional household-level variables couldinclude information on household capacity, ethnicity andsocial networks. Policies to facilitate the entry of house-holds in the nonfarm economy could include governmentsupport for access to education, credit schemes, (global)

markets and communication infrastructure. More generally,and in the light of increased regional and global climatevariability, diversification outside agriculture should notbe solely viewed as a coping mechanism in the face oflocal harvest shortfalls but rather as a core strategy forrural development.

AcknowledgementsThis study was part of the African Monsoon MultidisciplinaryAnalysis (AMMA) project. Based on a French initiative,AMMAwas created by an international scientific group and wassubsequently funded by a large number of agencies, especiallyfrom France, the UK, the USA and Africa. It was the beneficiaryof a major financial contribution from the European Community’sSixth Framework Research Programme. Detailed information onscientific coordination and funding is available on the AMMAInternational website: http://www.amma-international.org.We are grateful to Thierry Lebel of the Laboratoire d’étude des

Transferts en Hydrologie et Environnement in Grenoble for pro-viding the longitudinal decadal rainfall data and to Michel Koneof the Institut National de la Statistique et de la Demographie inBurkina Faso for facilitating access to and analysis of the socio-economic survey data.

Notes1. In this article, ‘nonfarm activities’ refers to all activities that

are not agricultural or not local. Activities performed duringmigration can be agricultural in nature, but figure, for thepurpose of this article, in the category ‘nonfarm activities’.

2. The poverty indicator was calculated based on the followingassets: building material of the walls (earthen or solid), floors(sand/earth, concrete and tiles), source of drinking water(surface water, pit or tap water), number of rooms (<2, 3, 4and >4), possession (yes/no) of a radio, bicycle or motorbikeand the number of cattle. The final index aggregation func-tion is determined through polychoric Principal ComponentAnalysis (pPCA), which unlike ordinary Principal Com-ponent Analysis (PCA), developed for continuous variables,is especially designed to deal with continuous, binary andcategorical variables at the same time. Measuring povertyin an African context is challenging due a general lack ofdata and due the intertwinement of the different causalfactors and effects of poverty in general. One of the mainadditional challenges of deriving poverty indices from exist-ing survey data is that the choice of components is limited.This is predominantly because the surveys from which dataare obtained are originally not designed for the measurementof poverty. Another reason is that the index is used for com-paring poverty over time. To make comparison more straight-forward, only assets common to all years are included,thereby further reducing the choice of variables. Of themany dimensions of poverty suggested in the literature, theindex in this paper only explores consumption poverty(asset poverty). The index, therefore, can only be seen asan approximation of wealth/poverty. Moreover, differentareas and groups within Burkina Faso may value differentkinds of assets; in the north, well-off households may havefew consumer durables, as they tend to invest in livestockmore than assets, while the opposite may be true in thesouth. The paper tries to accommodate for this by includingboth consumer items and livestock possession in the index.

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Appendix 1.

Table A1. Model specifications of the models estimate in Table 4.

Village Household

Binomial panel model Three-level logistic model

Proportion of households engaged in the specific nonfarmsectors

Household participation in thespecific nonfarm sectors

Surplus zone Minor deficit zone Major deficit zone 2003 2005 2007

Wald χ (23) 153.85 31.41 58.96 65.62 130.95 96.13Prob > χ2 0.00 0.01 0.00 0.00 0.00 0.00Log likelihood −613.88 −522.71 −830.45 −2523.62 −2376.69 −2702.28Total number of observations 285 234 312 5396 5436 5192Number of groups (raincell level) 87 87 87Minimum observations per group 13 17 14Mean observations per group 62 62.5 59.7Maximum observations per group 179 177 156Number of groups (village level) 95 78 104 277 277 277Minimum observations per group 3 3 3 13 14 6Mean observations per group 3 3 3 19.5 19.6 18.7Maximum observations per group 3 3 3 20 20 20Var. red. raincell level (prop) 0.27 0.40 0.44Var. red. village level (prop) 0.22 0.36 0.06 0.06 0.08 0.18Residual IC (raincell) 0.05 0.07 0.03Residual IC (village) 0.03 0.03 0.07 0.13 0.18 0.15

Note: IC, intraclass coefficient.

Appendix 2.

Table A2. Model specifications for the models in Table 5.

Village Household

Binomial panel model Three-level logistic model

Proportion of nonfarm households that have their activitiespredominantly local

Chance that a nonfarm householdhas its activities predominantly

local

Surplus zone Minor deficit zone Major deficit zone 2003 2005 2007

Wald χ (23) 40.97 49.41 151.05 52.82 30.16 69.87Prob > χ2 0.00 0.00 0.00 0.00 0.07 0.00Log likelihood −332.02 −282.56 −452.83 −606.43 −639.17 −739.34Total number of observations 245 207 295 1037 1011 1283Number of groups (raincell level) 83 81 84Minimum observations per group 1 1 2Mean observations per group 12.5 12.5 15.3Maximum observations per group 40 76 69Number of groups (village level) 95 78 104 254 238 255Minimum observations per group 1 1 1 1 1 1Mean observations per group 2.6 2.7 2.8 4.1 4.3 5Maximum observations per group 3 3 3 16 18 18Var. red. raincell level (prop) 0.15 0.27 0.55Var. red. village level (prop) −0.15 0.54 0.07 0.54 −0.02 0.52Residual IC (raincell) 0.13 0.03 0.10Residual IC (village) 0.22 0.06 0.20 0.22 0.27 0.25

Note: IC, intraclass coefficient.

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Appendix 3.

Table A3. Model specifications for the models in Table 6.

Village Household

Binomial panel model Three-level logistic model

Proportion of nonfarm households that are pluriactiveChance that a nonfarm household

is pluriactive

Surplus zone Minor deficit zone Major deficit zone 2003 2005 2007

Wald χ (23) 32.57 54.34 18.98 38.23 28.50 40.59Prob > χ2 0.01 0.00 0.21 0.01 0.07 0.00Log likelihood −125.23 −117.01 −250.72 −215.99 −272.50 −428.88Total number of observations 245 207 295 1037 1011 1283Number of groups (raincell level) 83 81 84Minimum observations per group 1 1 2Mean observations per group 12.5 12.5 15.3Maximum observations per group 40 76 69Number of groups (village level) 95 78 104 254 238 255Minimum observations per group 1 1 1 1 1 1Mean observations per group 2.6 2.7 2.8 4.1 4.3 5Maximum observations per group 3 3 3 16 18 18Var. red. raincell level (prop) −0.13 0.39 1.00Var. red. village level (prop) 1.00 0.72 0.75 −0.02 0.34Residual IC (raincell) 0.14 0.08 0.00Residual IC (village) 0.00 0.02 0.15 0.19 0.04

Note: IC, intraclass coefficient.

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