Are high value agri-food supply chains participants better insulated from shocks? Evidence from Senegal

Transcription

1 WORKING PAPER NUMBER 2017/3 Are ig value agri-food supply cains participants better insulated from socks? Evidence from Senegal Sènakpon F. A. DEDEHOUANOU University of Abomey Calavi (UAC)/FASEG, Benin & Institut d Etudes Economiques et Sociales (IEES), Dakar, Senegal dfidelus@yaoo.fr/f.dedeouanou@iees.sn November 2017 ABSTRACT: Empirical studies in general sow tat participation in ig value agri-food supply cains as a positive impact on ouseold wealt, income and poverty reduction. Oter more qualitative studies, owever, point to potential adverse effects, suc as increased vulnerability for participants. In tis paper, we use an endogenous switcing regression model to re-examine te extent to wic ouseolds involved in ig value agri-food supply cains insure teir against income fluctuations. Using ouseold data from Senegal, we find tat contract farming ouseolds are not able to better insure teir total and food against canges in (farm) income. In contrast, ouseolds tat are involved in te ig value supply cain troug te labor market are effectively able to better insure temselves. Tese results suggest not focusing solely on poverty reduction issues wen it comes to te welfare implications of participation in te ig value agrifood supply cains from te rural ouseolds perspective. JEL Codes: D1, R2, O16. Acknowledgement: Te autor gratefully acknowledges funding from te African Economic Researc Consortium (AERC) under te tematic researc program. Te autor wises to tank te resources persons and participants to Biannual Researc worksops for useful comments on earlier versions of te paper. IEES Working Papers aim to contribute to te scientific debate over various issues. Teir circulation is also meant to gater feedback tat would contribute to improving teir quality. A peer-review process guarantees some quality of te publication, wic in end does not reflect te views of te Institute. Te autor is solely responsible for te content by Sènakpon F.A. DEDEHOUANOU. All rigts reserved. Sort sections not exceeding one paragrap may be quoted witout te autor s permission. 1

2 I. Introduction As in many sub-saaran African countries, agriculture is te main activity in rural Senegal. About alf of te active population is involved in tis activity wic as long been te engine of economic and social development of te country. In most rural agricultural areas in developing countries, low incomes or te lack of an income source is seen as te cause of poverty precariousness. In tis regard, Senegal as undertaken a strategy of diversification toward ig value commodities and te promotion of agricultural export products to integrate international agricultural markets. Tis strategy is concomitant wit te canges observed in international food markets during te last two decades. Mainly in te fres fruit and vegetable (FFV) sectors, te transformation includes a sift from traditional tropical exports to non-traditional ig value exports from developing countries. It also includes te increased importance of food quality and safety standards; te increasing consolidation of te agri-food cain wit large retail cains and food multinationals gaining importance; and te increased industrialization of te agriculture sector (see Swinnen, 2007). Te resulting modern agri-food supply cains ave canged te agri-food systems in developing countries like Senegal (e.g., Swinnen, 2007; Henson and Jaffee, 2008). Two important aspects of te development of modern agri-food supply cains in Senegal are te increasing of non-traditional ig value exports to developed countries and agro-industry employment opportunities for small-scale farmers (Maertens and Swinnen, 2009). Some farmers, troug contracts wit exporting agro-industry companies, cultivate green beans or mangoes some ig value products mainly intended for te international export market. Several farmers are also involved in te non-skilled wage employment from tese agro-industry companies 1. An important and ongoing question raised in te literature concerns te welfare implications of te integration of developing countries in te global market, particularly ow te structural canges in international food markets affect rural ouseolds participating in te ig value agri-food supply cains tat evolved. Optimistic arguments advance tat contract farming wit large agro-industries alleviate te constraints faced by small-scale farmers (Warning and Key, 2002). Tey also stress te new employment opportunities for rural ouseolds (Key and Runsten, 1999). Studies provide empirical evidence tat most of te FFV exports from Madagascar (Minten et al., 2009) and Kenya (Minot and Ngigi, 2004) stem from contract farming wit small-scale farmers. Oter studies sow tat despite increasing standards, te vegetable export cain in Senegal can benefit small-scale farmers and rural ouseolds eiter troug contract farming from large-scale estate production or troug employment from agro-industrial processing (Maertens, 2009; Maertens and Swinnen, 2009). Neverteless, many oter studies remain sceptical wile considering as new trade barriers te stringent food standards and vertical coordination wic as resulted (Unnever, 2000; Brenton and Mancin, 2002; Augier et al., 2005). Some indicate tat small-scale farmers are more and more excluded from te ig value commodities cain (Keralla, 2000; Reardon and Barrett, 2000; Gibbon, 2003). In addition, oters argue tat even if tey participate, te revenue gain resulting from tis trade is likely to be small for resource-poor farmers (Reardon et al., 1999; Dolan and Humprey, 2000). Oters point to potential adverse effects suc as increased vulnerability of participants (e.g., Porter and Pillips-Howard, 1997; Key and Runsten, 1999). In tis paper we propose an alternative and complementary framework to study te welfare implication of rural ouseolds participating in te ig value agri-food supply cains in Senegal. Previous quantitative analyses focusing on income report positive impact from te participation in te supply cains. Qualitative analyses igligt negative impacts as well yet bot types of results are wort considering witin te same framework. Houseold welfare depends on income as well as on oter factors. Moreover, agriculture per se is itself considered as a risky activity rendering te income 2

3 unstable. Te welfare of rural ouseolds does not depend on te income level, but is also likely to be subject to income fluctuations. We build on te literature on insurance 2 and go beyond te measure of income and poverty used by previous studies to analyse te welfare implication of rural ouseolds participating in te ig value agri-food supply cains. To te best of our knowledge we are not aware of a study in te literature of supply cains tat considers te framework on insurance to analyse tis issue. Te development of agri-food supply cains tat was emerging in Senegal as resaped smallolder liveliood strategies. We ask weter ouseolds involved in te ig value supply cains are able to better insure teir against socks defined as income fluctuations. More specifically, we analyse weter te of te supply-cain participants is better insured against income fluctuations tan te of teir non-participating counterparts. We Also analyse wat segment of te market (production market or labour market) makes ouseolds less vulnerable. Te literature on vulnerability assessment underlines te ouseold sense of well-being witin te framework of poverty eradication and risky environment. Houseolds face uncertainty about te future wic stems from various sources of risks or socks. Tis plays a central role in te dynamic and scale of poverty (Cauduri, 2003). Moreover, Murata and Miyazaki (2014) found tat diversification is a way to reduce risk exposure. It is tus desirable to analyse te welfare implications of te integration of rural ouseolds in te global market troug ig value supply cains by considering a measure of ouseold welfare wic takes into account bot average outcomes and te risk ouseolds bear (Ligon and Scecter, 2003). Te cross section data (one year) used by most of te previous literature tat focused mainly on te income-based measure of welfare is tus limited to dealing wit te issue of ouseold s vulnerability and te dynamic of te agri-food supply cain sector. Te remainder of te paper is structured as follows. Te next section discusses a conceptual framework on insurance. In section tree, we describe our case study and te data. Section four presents some descriptive statistics. In section five we propose an empirical metodology to test te insurance ypotesis. Results and discussions are presented in Section six. Section seven concludes. II. Conceptual framework We rely on te framework of insurance widely used in te literature (Altonji and Siow, 1987; Mace, 1991; Townsend, 1994). Te teoretical model is based on te consumer s optimization problem tat supposes a specific preference for a given ouseold (see e.g., Cocrane, 1991). Irrespective of te smooting mecanisms used, ouseolds are supposed to maximize teir expected utility function subject to te expected value budget constraint. Te complete insurance equation tat is derived after te optimization problem is summarized in a logaritmic form as: ln c t 1 f ( z t 1 ) ln t (1) were c t is ouseold in time t; is te coefficient of constant absolute risk aversion; f ( z t ) is a function denoting te influence of time-varying taste factors ln ct ln ct ln ct 1 t is te time-specific component of te marginal utility of in time t. Troug Equation 1, te complete insurance ypotesis states tat te growt rate in ouseold ; and 3

4 is a function only of te growt rate in te aggregate or covariate risk ( 1 ln ), after controlling for te influence of te time-varying taste factors. Different testable empirical forms of te insurance model ave been used for many purposes in developing countries. Wile naturally is used as te dependent variable, te empirical specifications derived from Equation 1 depend on te variables used in te rigt and side. In any case, te specification takes te general form (see e.g., Skoufias and Quisumbing, 2005): t ln c Covrisk Idiorisk X (2) tg tg tg tg tg were ln c tg refers to te cange in log (or growt rate) per ouseold member, in period t, in a given insurance community group g; common to all ouseolds witin te community group g; and risks affecting te ouseold. Te vector in) and preferences. Covrisk tg indicates te aggregate or covariate risks Idiorisk tg represents te idiosyncratic X tg controls for some ouseold caracteristics (or canges tg for unobservable canges in ouseold specific caracteristic suc as ouseold Te test of full insurance derived from te empirical form implies tat te coefficient tends to zero, tat is, te idiosyncratic risks sould not play any significant role in explaining canges in ouseold. In te literature, te difference in te specifications often stems from te variables used for te representative idiosyncratic socks against wic ouseold sould be insured and also from te identified insurance group. Some empirical forms (e.g., Skoufias and Quisumbing, 2005) include directly, wen available, variables representing te socks (S i ) tat can affect income so tat Idiorisk S( i). Oters (e.g., Morduc, 2002) include idiosyncratic income canges so tat tg Idiorisk tg i ln i y tg tg, were y represents ouseold income per capita. Wit regard to te covariate risk, most studies usually use village or community as te insurance group (e.g., Jalan and Ravallion, 1999). In tat case te term Covrisk in Equation 2 is replaced by a set of binary interaction terms identifying communities or villages by survey rounds. However, some studies focus on an insurance network limited to member of family, friends or etnicity (e.g., Fafcamps and Lund, 2003; De Weerdt and Dercon, 2006). Tis paper does not limit te insurance network to family member, friends or etnicity. Wile te insurance framework primary seeks weter or not full insurance takes place as revealed in te data used, we aim to analyse ow different is insurance between participants in agri-food supply cains and non-participants. Tis type of comparative analysis as been used in oters studies. For example, Skoufias (2007) examines ow te replacement of pre-existing subsidy programmes by a conditional cas transfer programme affects te insurance of ouseolds in rural Mexico. Working on two villages in nortern Banglades, Amin et al. (2003) tested if members of microcredit programmes are poorer and more vulnerable tan non-members. Tey define as vulnerable ouseolds tose wic are unable to smoot in te face of idiosyncratic income fluctuations. In tis paper we use te village as an insurance community were formal or informal insurance arrangements are potentially available for ouseolds. Te extent to wic ouseolds insure tg 4

5 from socks or income fluctuations depends on teir ability to cope wit te risks. Given tat ouseold decisions on income generating activities are related to risk considerations (Dercon, 1996; Reardon et al., 2007; Lanjouw, 2007), it is likely differences be observed in te coefficient between groups of ouseolds witin te insurance village. Particularly in te case of agri-food supply cains, we expect tat differences between ouseolds participating in te supply cains and tose wic are not participants. Te involvement in te ig value supply cains, eiter troug te export production by contract farming or troug te labour market by working as an agro-industry employee, is a way to reduce poverty, as found in previous studies. Tree important aspects are noted from Maertens (2009) and Maertens and Swinnen (2009) wo focused on te same area as our researc). First, bot types of ouseolds are different in terms of some demograpic caracteristics, wealt and income. Second, income from te supply cains is a non-negligible component of total ouseold income for participants. Tird, poverty is muc iger among non-participants tan among participants. III. Data We use two rounds of ouseold surveys conducted in te region of les Niayes in Senegal, in July August 2007 and in July August Te surveys took place in a orticulture zone around te regions of Dakar and Tiès. Most (80%) of te orticultural production of te country stems from tis zone (RFAU/AOC, 2007); tis is were most of te export companies source produce from and recruit labourers. Te surveys collected data on 40 villages randomly selected in 4 rural communities. A total of 455 ouseolds were initially randomly selected in te first round of te survey, but some observations were dropped due to attrition between te two rounds and missing values in te variables of interest. Te sample we use consists of a balanced panel of 439 ouseolds in te 2 years 2007 and Data on ouseold demograpic caracteristics and on different types of ouseold activities tat allow calculating ouseold income were collected. Most ouseolds in tis area practise smallolder orticulture farming and ence derive teir income from agricultural activities. However, ouseold income is also supplemented wit non-negligible revenues from off-farm activities. We use tis detailed information on farm and off-farm activities to calculate total ouseold income and its components, namely farm and off-farm income. Total ouseold income is income calculated over te 12 monts prior to te survey from farming, from employment (agro-industrial or oters), from oter non-farm business activities (self-employment), and from remittances (non-labour). Income from farming is calculated as revenue from sale of produce, plus te imputed value of unsold produce, less te costs of production. Te surveys also collected information on ouseold expenditure. Different modules were included in te questionnaires to collect data on te expenses of ouseolds on food and non-food items. Te recall periods for items are set to be more disaggregated to get as muc as possible reliable information on te expenses. Daily or weekly information was requested for some basic food suc as bread, vegetables and oter foods. Te recall periods were more extended to (te last) one, two or tree monts prior to te surveys for non-food items suc as water, electricity, combustibles, cloting expenses and te like and to te 12 last monts prior to before te surveys for expenses related to eat, scooling, etc. Nonfood items do not include expenses in durable goods. All expenses were converted to ave a yearly basis. An additional module was added in te 2010 survey round tat asked questions related to te socks experienced by te ouseolds during te past tree years, tat is, from te first 2007 round to te last 2010 round (see Figure A1 in Appendix). Tese data were used to describe te vulnerability context of te study area. 5

6 IV. Houseold livelioods and socks Tis section provides descriptive statistics on ouseold assets and te vulnerability context tat migt influence ouseold liveliood strategies and liveliood outcomes. IV.1. Defining ig value agri-food supply cains participants We define two types of participants. Te first are contracting ouseolds: ouseolds tat exclusively differentiated towards ig value produce mainly for te export market troug contract wit agroindustry companies at least once in te two rounds of surveys. About a tird (33.72%) of ouseolds of tis type in our sample grew green bean and mangoes eiter in 2007, in 2010 or in bot years mainly for te export market troug contract wit agro-industry companies. Te second category of ouseolds is defined as agro-industry employee ouseolds. Tese are ouseolds tat ad one or more members working as employees in an agro-industrial export firm eiter in 2007, in 2010 or in bot years. Of te ouseolds in te sample, 20.41% were in tis category 4. Houseolds tat are not included in any type of participants as defined above were considered as not participating in te agri-food supply cains in te two rounds of surveys. IV.2. Size and importance of ouseold liveliood assets Participants, namely contracting and agro-employees, and non-participants ouseolds differ according to te type of assets. We calculate te average amount of uman, pysical and social capital of ouseolds over te period and compare tese values between different types of ouseolds as defined above (Table 1). Contracting and agro-industrial employees ave relatively iger liveliood assets. Significant differences are observed in some cases. Agro-industrial employees ave at teir disposal a significantly iger number of workers, fewer dependents and more women as ead of ouseold. Contracting ouseolds are sligtly older wit significantly iger education. Tey also ave significantly iger access to larger land (5.10 a and 0.69 a per capita) and to more livestock (3.36 units). Agro-industrial employees ave access to less pysical capital of tis kind compared to nonparticipants ouseolds. Bot contracting and agro-industrial employees live in sligtly smaller ousing area tan non-participants ouseolds and could rely on a significantly iger social capital suc as teir etnicity or te farmer organization tey belong to. Our conclusion on te comparison of pysical and social capital between participants and nonparticipants is close to tat of Maertens and Swinnen (2009) tat used information limited to te 2005 sample. Our sligtly different results on uman capital may be due to te inclusion of additional ouseolds in te 2007 and 2010 samples. Neverteless, te general trend observed concerning te liveliood assets of ouseolds was tat wile agro-industrial employees are relatively better off in terms of uman and social capital, contracting ouseolds ave more access to pysical and social capital. Compared to non-participants ouseolds, tis is in line wit te general view tat communities or ouseolds wit a relatively ig level of financial capital are also likely to ave control over more liveliood assets, notably uman and pysical capital. Bot contracting ouseolds and agro-industrial employees ave average total incomes over 2007 and 2010 tat are iger tan tose of nonparticipants ouseolds (Figure 1). Incomes from off-farming activities tat necessitate more active labour are relatively ig for participants in agro-industrial employment wo ave more access to uman capital. Likewise, as depicted in Figure 1, incomes derived from farming activities tat require 6

7 more farm input are relatively ig for contracting ouseolds wic ave more access to pysical capital. Table 1: Houseold caracteristics (means, participants versus non-participants) Sample Nonparticipants Contracting ouseolds Agro-industrial employees Number of ouseolds Human capital Age of ouseold ead Number of labourers *** Dependency ratio *** Female-eaded ouseold (%) Years of education of ouseold ead * 1.49 Pysical capital Farm size (a) *** 2.45 Per capita landoldings (a) *** 0.30** Units of livestock Housing surface (m 2 ) Social capital Etnicity (Wolof ouseold) (%) *** Membersip of a farmer organization (%) *** Socks experienced between 2007 and 2010 Severe drop in agricultural production due to rain, flood, pets or disease affecting crops (%) Destruction of ousing or loss of items due to flood or fire (%) *** Divorce or separation (%) Deat of a ouseold member (%) ** Drastic increase in product prices bougt by te ouseold (%) * Disease or deat affecting livestock (%) Illness or injury of a ouseold member more tan two weeks (%) Loss of a paid employment (%) 2 4 1* 1* Loss or teft items valued more tan 5,000 FCFA (%) Source: Autor, calculated from ouseold survey data. 7

8 Figure 1: Average ouseold income over 2007 and 2010 (in 1000 FCFA) Total ouseold income Income from farming Income from off-farming Non-participant ouseolds Contracting ouseolds Agro-employees ouseolds Source: Autor, calculated from ouseold survey data. Taking into account tese conclusions, at first sigt, insurance is expected to be relatively iger for participants as tey are more likely to ave access to te means of or mecanisms for dealing wit socks. Indeed, some studies ave found tat insurance depends on occupation and tat ricer residents are better insured in teir (see, e.g., Townsend, 1995; Jalan and Ravallion, 1999). IV.3. Socks and ouseold expenditures Tis section portrays te vulnerability context tat caracterizes te area and te period of study. During te survey conducted in July August 2010, we asked ouseolds some information about te last year between 2007 and 2010 tey experienced any specific socks and weter tese socks ad led to a drop of income and. Some descriptive evidence on te extent and te severity of socks is presented in Table 2. First, ouseolds in te study area ave faced different types of socks, some more tan oters. According to te number of ouseolds reporting tese socks, te most important ones are related to te increasing of product prices, illness, te drop in agricultural production, livestock disease and affective socks. Socks related to agricultural productionit significantly more contracting ouseolds. Affective socks it significantly fewer agro-industrial employees.. Most of te socks tend relatively to it all types of ouseolds, participants as well as non-participants (see Table 1). Tis is particularly true for te drastic increase in product prices bougt by ouseolds: at least 60% of ouseolds of any type reported being affected by tat sock wic was probably induced by te world food prices crisis tat occurred during te period of our study. 8

9 Type of socks Table 2: Extent and severity of socks experienced by ouseolds between 2007 and 2010 Houseolds reporting tis sock Only tis ouseold Some ouseolds in tis village Sock affecting All ouseolds in tis village All ouseolds in tis community Sock leading to Drop in income Reduction in food Severe drop in agricultural production due to rain, flood, pets or disease affecting crops Destruction of ousing or loss of items due to flood or fire Divorce or separation Deat of a ouseold member Drastic increase in product prices bougt by te ouseold Disease or deat affecting livestock Illness or injury of a ouseold member more tan two weeks Loss of a paid employment Loss or teft items valued more tan FCFA5, Source: Autor, calculated from ouseold survey data. 9

10 Second, te socks experienced by te ouseolds are relatively idiosyncratic. Neiter all te villages in te rural community nor all ouseolds in all te villages included in our sample were affected by tese socks. Agricultural production sock, food prices sock and illness socks take a form of aggregate socks in some villages as 12%, 33% and 4% respectively of all villages included in our sample ad all ouseolds tat were it by tese socks (Table 2). Tird, ouseold incomes are more subject to specific socks and tis is for any type of socks. More tan 50% of te ouseolds reported a fall in income resulting from socks, a percentage far iger tan tat reported for a drop in food. Figure 2: Percentage of ouseolds in 2007 remaining or falling into poverty in Variation of incidence of poverty at rural poverty line Variation of incidence of poverty at rural (food) poverty line 0 Non-participant ouseolds Contracting ouseolds Agro-employees ouseolds Source: Autor, calculated from ouseold survey data. Te statistics in Table 2 suggest tat some ouseolds were more resilient to socks tan oters. Oters were unable to cope wit socks and were even more subject to vulnerability to tese socks. Using ouseold () expenditures data for 2007 and 2010 as te welfare measure, we calculated te incidence of poverty in te study area for eac year, tat is, te proportion of ouseolds wit food and total per capita expenditures tat fall below te national rural poverty lines for tese years. We ten calculated rougly a measure of vulnerability, tat is, te proportion of ouseolds tat were poor or not in 2007 but remaining or falling into poverty in Figure 2 sows tat at least 8% of ouseolds remain vulnerable in regard to total expenditures and at least 4% of ouseolds in regard to food expenditures. Some differences exist between participants and non-participants: vulnerability is muc lower among contracting ouseolds and particularly among agro-industrial employees wen considered food expenditures. 10

11 V. Econometric analysis We used a modified version of te equation (2) to test te insurance for ouseolds participating in agri-food supply cains. We account for potential endogeneity problems tat may lead to biased regression coefficients. For example, abstracting from te literature on risk management strategy (Rosenzweig and Binswanger, 1993; Jacoby and Skoufias, 1998; Morduc, 1995), difference in smooting between ouseolds may be due to unobserved managerial abilities to cope wit risk. Tese abilities migt also be related to teir adoption of management strategies suc as te likeliood to become involved or not in agri-food supply cains. Furtermore, some sources of selection bias could come from ouseolds, from exporting companies and from geograpical conditions (see Maertens and Swinnen, 2009) 5. We use an endogenous switcing regression (ES) model tat deals wit te selectivity and te endogeneity issue: cv v1 ln y v 1 y v 1 ln ln X v 1 v if PART = 1 (3) cv 0 v0 ln y v 0 ln y v 0 ln X if PART = 0 (4) v v PART = Z v α + u v and PART = { 1 if PART > 0 0 oterwise (5) were ln c v 1 and ln c v 0 is te cange in log (or growt rate) per ouseold member, in te village v; ln y v is te growt rate in average village income and represents te aggregate or covariate risks common to all ouseolds witin te village 6 ; ln y is te growt rate of ouseold income per capita; and X v are exogenous control for ouseold (ead) caracteristics suc as te age of te ead, weter te ead is a female, te education of te ead, te etnicity of te ouseold and te cange in family size in te two rounds of surveys. Te variable PART is a dummy variable indicating participation of te ouseold in te agri-food supply cains. Z are te same set of variables included in X, wit additional exclusion restrictions to improve identification. Te ES model estimates separately te equations for participant ouseolds (3) and for non-participants ouseolds (4), conditional on te participation decision modeled in (5) 7. Equation 5 assumes tat ouseolds become involved in te ig value supply cains wen te difference te latent variable PART between te marginal net benefits of participating and not participating in te supply cains is positive 8. Te ES model is estimated simultaneously by full maximum likeliood using te Stata command etregress (see STATACorp, 2013). We use as exclusion restrictions eiter te canges in female labour, or te canges in male labour, or weter te ouseold as an irrigated system or te distance of te exporter firms from te villages. Results of te selection Equation 5 sow tat tese instruments jointly and significantly determine te participation process as most of te export companies source produce from contracting farming and recruit labourers. Te instruments are assumed not directly related to te canges in log per capita oterwise tan troug te participation process. In addition, selection bias and endogeneity are an issue as indicated by te Wald test of independence equations (see Table A1 and Table A2 in Annex) 9. Te extent to wic v 11

12 is insured from idiosyncratic income canges for participants and for non-participants is tested troug te coefficient vector. VI. Results and discussion Table 3 sows tat tere is a difference in te extent to wic participants and non-participants insulate from canges in income (column 1). ouseold income per capita ave no significant effect on canges in total and food for non-participants. In general nonparticipants seem to be fully insulated from income canges contrary to participants ouseolds. However, column (2) and column (3) sow tat bot types of ouseolds are able to insure total and food from canges in farm income wile tey experience at least a drop of 0.29% of total and 0.22% of food following a 10% drop in off-farm income. How different are tese results wen we distinguis between contract farming ouseolds and agro-industrial employee ouseolds? We report in Table 4 and Table 5 te estimates of te degree of insurance for te two different types of participants and for non-participants. Two observations are noted. First, column 1 of Table 5 sows tat tere is not significant difference in te extent to wic agro-industry employee ouseolds and non-participants in te supply cains insulate from socks. Te results suggest tat non-participants are fully insulated from income canges as equal as agro-industry employee ouseolds. Rater, canges in ouseold income per capita ave a significant effect on canges in total and food for contracting ouseolds. For tat type of participant, a decrease of 10% in income would result in a drop of 0.64% of total and a drop of 0.57% of food (column 1 of Table 4). Second, participants of any kind and non-participants are able to insure total and food from farm income canges (column 2 of Table 4 and Table 5). However, total and food are still subject to ouseold off-farm income canges for bot non-participants and ouseolds involved in green bean or mango contracts wit an export agro-industry (column 3 of Table 4 and Table 5). 12

13 Table 3: Estimated impact of canges in ouseold income on canges in (participants vs. nonparticipants) (1) total income (2) farm income (3) off-farmincome total food total food total food β non-participants ** 0.034*** (0.023) (0.024) (0.009) (0.009) (0.012) (0.012) β participants 0.052** 0.043** *** 0.022** (0.020) (0.018) (0.009) (0.009) (0.011) (0.010) Note: Additional controls are included but not reported ere. See Table A1 in Annex for more detail. Significant effects are indicated wit *** p<0.01, ** p<0.05, * p<0.10. Robust standard errors in parenteses. 13

14 Table 4: Estimated impact of canges in ouseold income on canges in (contracting vs. nonparticipants ouseolds) (1) total income (2) farm income (3) off-farm income total food total food total food β non-participants * *** 0.037*** (0.023) (0.025) (0.010) (0.010) (0.013) (0.012) β contracting ouseold 0.064** 0.057** ** 0.034** (0.027) (0.027) (0.013) (0.013) (0.016) (0.017) Note: Additional controls are included but not reported. See Table A2 in Annex. Significant effects are indicated wit *** p<0.01, ** p<0.05, * p<0.10. Robust standard errors in parenteses. 14

15 Table 5: Estimates of te impact of canges in ouseold income on canges in (agro-industry employees ouseolds and non-participants ouseolds) (1) total income (2) farm income (3) off-farm income total food total food total food β non-participants ** 0.031*** (0.022) (0.022) (0.008) (0.009) (0.012) (0.012) β agro-industry employees ouseold ** (0.028) (0.024) (0.014) (0.015) (0.022) (0.023) Note: Additional controls are included but not reported. See Table A2 in Annex. Significant effects are indicated wit *** p<0.01, ** p<0.05, * p<0.10. Robust standard errors in parenteses. 15

16 We recall tat te measure of socks used so far is based on te assumption tat all specific socks tat occurred between te two survey rounds ad an impact on te growt rate of ouseold only troug ouseold income canges. As suc, ouseold income canges migt be te result of te combined effect of suc socks and te reaction of tese socks. As regards to te above, it could be argued tat ouseolds face different types of socks and tat socks experienced by agro-industry employee ouseolds are not persistent so tat tey are able to cope wit, contrarily to te oters type of ouseolds. However, te descriptive statistics in Table 1 sow tat in many cases tere is no difference in terms of te nature of socks experienced by te different types of ouseolds. Tis suggests tat tere is a difference in actual combination of strategies to deal wit socks between contracting ouseolds and ouseolds involved in agro-industrial employment. Te liveliood strategies used by agro-industry employee ouseolds to deal wit te socks are more likely to be efficient tan are tose used by contracting ouseolds. We explain our findings in two perspectives. Te first plausible explanation is tat agro-industry employee ouseolds are better endowed in uman capital - wic is less subject to socks - wic terefore is more likely to be used easily as coping mecanisms. On te contrary, contracting ouseolds ave more access to pysical capital but wicis more subject to socks. Tis pysical capital is ten less susceptible to be used efficiently by contracting ouseolds wen socks occur. Te second explanation is tat agriculture is intrinsically a risky activity due to variability in agro-climatic conditions and volatility in commodity markets wic may lead to increased income uncertainty. Contract farming may cange production and marketing risk for farmers and tereby affect teir welfare. Contract farming may reduce or increase te risk tat farmers face (see Dedeouanou et al., 2013). In our case, ouseolds wic ad diversified towards export production troug contracting farming, migt bot limit teir portfolio of occupations and devoted mainly teir livelioods assets to tis activity. Tis migt explain weter contracting ouseolds seem still more vulnerable from socks stemming from te labour market as tey are unable to insure against off-farm income fluctuation even wen fully insured from farm income canges. VII. Conclusion and policy implications We used te insurance framework to study weter ouseolds involved in ig value supply cains are able to better insulate teir against socks or income fluctuations. We found tat tere is a difference in te extent to wic bot types of participants in te supply cains insulate from socks. Houseolds wic are involved in te ig value supply cain as producers are not able to better insure teir total and food against income socks tan teir non-participating counterparts. In contrast, ouseolds involved in te ig value supply cains as agro-industry employees are effectively able to better insure temselves. In fact all te ouseolds are able to cope wit socks from te product market wile only ouseolds involved in agro-industry employment are more likely to insure teir from socks from te labour market. Te conclusion from tis study is tat wile integration in international markets as a positive impact on economic development and poverty reduction it may not be sustainable enoug to protect ouseolds, specifically contracting ouseolds, from vulnerability. Our results suggest tat policy makers sould not focus only on poverty reduction issues wen studying te welfare implication of rural ouseolds participating in ig value agri-food supply cains. Complementary safety net programmes sould be combined wit programmes intending to increase te participation of ouseolds in contract farming. In addition, initiatives creating conditions conducive to dealing wit risks for contracting ouseolds sould also direct effort on off-farm activities given tat 16

17 labour market is found to be more vulnerable for contracting ouseolds. Our analysis on insurance of ouseolds is based on two data points. We used te endogenous switcing regression model to deal wit te endogeneity and te selectivity issue entailed by te participation process. Yet te availability of panel data of more tan two years could give more definitive conclusions about te vulnerability of ouseolds participating in ig value agri-food supply cains. 17

18 Notes 1. Maertens and Swinnen (2009) provide details of te ig value agri-food supply cain process in Senegal. 2. In tis study, insurance is viewed intercangeably as smooting. 3. Note tat wile our data consists of panel data for 2007 and 2010, our empirical analysis was crosssectional based on te difference between te two years. 4. Note tat ouseolds tat participated bot as contracting and as agro-industry employees were excluded from te initial sample. 5. Houseolds may self-select into contract farming or into agro-industrial employment based on teir access to resources and teir preferences. Exclusion or selection of potential contractors may be at te discretion of exporting companies because of te ig transaction costs in sourcing from isolated farmers or teir skills and teir access to resources. Exclusion or selection of potential employees may be due to te long distance from employment location. 6. Tis form of covariate risk is used in oter studies for te assessment of risk saring in a given community (see, for example, Skoufias, 2007). It is probable tat ouseolds use some specific insurance networks. We do not explicitly focus on tis issue in our study because of data availability. We assumed tat te witin-village risk saring mecanism is likely and used te village as insurance group. 7. Usual tecniques suc as te Heckit Model or te Instrumental Variables approac do not deal wit full interaction. 8. In our case te benefit is, for example, a welfare improvement suc as te insurance. 9. Te first difference-alike specification used in te ES model addresses furter te issue of endogeneity as long as ouseold beaviour in insurance and te participation decision are bot related to a ouseold s unobserved time-invariant variables. 18

19 References Altonji, J. and A. Siow Testing te response of to income canges wit (noisy) panel data. Quarterly Journal of Economics, 102: Amin, S., A. Rai, G. Topa Does microcredit reac te poor and vulnerable? Evidence from nortern Banglades. Journal of Development Economics, 70(1): Augier, P., M. Gasiorek and C. Lai Tong Te Impact of rules of origin on trade Policy, 20(43): flows. Economic Brenton, P. and M. Mancin Making te EU trade agreements work. Te role of rules of origin. CEPS Working Document 183. Centre for European Policy Studies, Brussels. Cauduri, S Assessing vulnerability to poverty: concepts, empirical metods and illustrative examples. New York, Columbia University.. Cocrane, J.H A simple test of insurance. Journal of Political Economy, 99: Dedeouanou, S.F.A., M. Maertens and J.F.M. Swinnen Does contracting make farmers appy? Evidence from Senegal. Review of Income and Wealt, 59: S De Weerdt, J. S. Dercon Risk-saring networks and insurance against illness. Journal of Development Economics, 81: Dercon, S Risk, crop coice, and savings: Evidence from Tanzania". Economic Development & Cultural Cange, 44(3): 485. Dolan, C. and J. Humprey. 2000, Governance and trade in fres vegetables: Te impact of UK supermarkets on te African orticulture industry. Journal of Development Studies, 37(2): Fafcamps, M. and S. Lund Risk-saring networks in rural Pilippines. Journal of Development Economics, 71: Gibbon, P Value-cain governance, public regulation and entry barriers in te global fres fruit and vegetable cain into te EU. Development Policy Review, 21(5 6): Henson, S. and S. Jaffee Understanding developing country strategic responses to enancement of food safety standards. World Economy, 31(4): Jacoby Hanan G. and Emmanuel Skoufias, "Testing Teories of Consumption Beavior Using Information on Aggregate Socks: IncomeSeasonality and Rainfall in Rural India". American Journal of Agricultural Economics, Vol. 80, No. 1 (Feb., 1998), pp Jalan, J. and M. Ravallion Are te poor less well insured? Evidence on vulnerability to income risk in rural Cina. Journal of Development Economics, 58(1): Key, N. and D. Runsten Contract farming, smallolders, and rural development in Latin America: te organization of agroprocessing firms and te scale of outgrower production. World Development, 27: Kerrala, M Access of smallolder farmers to te fruits and vegetables market in Kenya. IFPRI Mimeo. International Food Policy Researc Institute, Wasington, D.C. te 19

20 Lanjouw, P Does te rural nonfarm economy contribute to poverty reduction?" In S. Haggblade, P. Hazell, and T. Reardon, eds., Transforming te Rural Nonfarm Economy: Opportunities and Treats in te Developing World.. Baltimore: Jons Hopkins Press. Ligon, E. and L. Scecter Measuring vulnerability. Economic Journal, 113(486): C95 C102. Mace, B.J Full insurance in te presence of aggregate uncertainty. Journal of Political Economy, 99: Maertens, M. and J.F.M. Swinnen Trade, standards, and poverty: Evidence from Senegal. World Development, 37(1): Maertens, M Horticulture exports, agro industrialization, and farm nonfarm linkages wit te smallolder farm sector: Evidence from Senegal. Agricultural Economics, 40(2): Minot, N. and M. Ngigi Are orticultural exports a replicable success story? Evidence from Kenya and Côte d Ivoire. EPTD/MTID Discussion Paper. International Food Policy Researc Institute, Wasington, D.C. Minten, B., L. Randrianarison and J. Swinnen Global retail cains and poor farmers: Evidence from Madagascar. World Development, 37(11): Morduc, J Consumption smooting across space testing teories of risk-saring in te ICRISAT study region of Sout India. Discussion Paper No. 2002/55. UNU World Institute for Development Economics Researc (UNU/WIDER). Morduc, J "Income Smooting and Consumption Smooting". Te Journal of Economic Perspectives, Vol. 9, No. 3 (Summer, 1995), pp Murata Akira and Suguru Miyazaki Ex-post risk management among rural Filipino farm ouseolds. Working paper n 67. Japan International Cooperation Agency Researc Institute, Tokyo, Marc. Porter, G. and K. Pillips-Howard Comparing contracts: An evaluation of contract farming scemes in Africa. World Development, 25(2): Reardon, T. and C.B. Barrett Agroindustrialisation, globalization and international development: an overview of issues, patterns and determinants. Agricultural Economics, 23: Reardon, T., J. Berdegue, C.B. Barrett and K. Stamoulis Houseold income diversification into rural non-farm activities. In S. Haggblade, P. Hazell and T. Reardon (eds.), Transforming te Rural Nonfarm Economy: Opportunities and Treats in te Developing World. Baltimore: Jons Hopkins Press. Reardon, T., J.-M. Codron, L. Busc, J. Bingen and C. Harris, 1999, Global cange in agrifood grades and standards: Agribusiness strategic responses in developing countries. International Food & Agribusiness Management Review, 2(3): RFAU/AOC Etude de cas sur le financement des agriculteurs et agricultrices urbains de la zone des Niayes (Sénégal). Report. Réseau Francopone pour l Agriculture Urbaine en Afrique de l ouest eu centre (RFAU/AOC). Rosenzweig Mark R. and Hans P. Binswanger "Wealt, Weater Risk and te Composition and Profitability of Agricultural Investments". Te Economic Journal, 103(416): Skoufias, E Poverty alleviation and insurance: Evidence from PROGRESA in Mexico. Te Journal of Socio-Economics 36:

21 Skoufias, M. and A. Quisumbing Consumption insurance and vulnerability to poverty: A syntesis of te evidence from Banglades, Etiopia, Mali, Mexico and Russia. European Journal of Development Researc,17(1): STATACorp STATA Treatment-Effects Reference Manual: Potential outcomes/ counterfactual outcomes, Release 13. Texas: StataCorp LP. Swinnen, J.F.M. (ed.) Global supply cains. Standards and te poor. Oxford: CABI Publising. Townsend, R Consumption insurance: an evaluation of risk-bearing systems in low income economies. Journal of Economic Perspectives, 2: Townsend, R Risk and insurance in Village India. Econometrica, 62(3): Unnever, L.J Food safety issues and fres food product exports from LDCs. Agricultural Economics, 23: Warning, M. and N. Key Te social performance and distributional impact of contract farming: An equilibrium analysis of te Aracide de Bouce Program in Senegal. World Development, 30:

22 Percentage Annex Figure A1: Reported socks experienced by te ouseolds between te first 2007 round and te last 2010 round Severe drop in agricultural production due to rain, flood, pets or disease affecting crops 2.28 Destruction of ousing or loss of items due to flood or fire 6.61 Divorce or separation Deat of a ouseold member Drastic increase in product prices bougt by te ouseold Disease or deat affecting livestock Illness or injury of a ouseold member more tan two weeks Source: Autor, calculated from ouseold surveys data Loss of a paid employment Loss or teft items valued more tan 5000 FCFA 22

23 Table A1: Results from te ES model: Impact of canges in income on canges in Selection Non-participants Participants Variables equation Female ead Age ead Education ead Cange in ouseold size *** ** Etnicity (Wolof) 0.446*** 0.172* 0.455*** Dlog (income per capita) ** Dlog (average village income per capita) ** Dlog (female labour) Dlog(male labour) 0.139*** Distance of te exporter firms ** Constant ** Wald test of joint significance of instruments: ci *** Wald test of independence of equations: ci *** Log pseudo-likeliood Wald ci *** Observations Significant effects are indicated wit *** p<0.01, ** p<0.05, * p<

24 Table A2: Results from te ES model: impact of canges in income on canges in by types of participants Agroindustry Selection Non Nonparticipa Contracting Selection participa ouseolds equation employees equation nts nts Variables ouseolds Female ead * Age ead Education ead ** ** Cange in ouseold size 0.047*** Etnicity (Wolof) 0.368*** 0.284* *** *** Dlog (income per capita) ** 0.045* ** Dlog (average village income per capita) ** Dlog (female labour) Dlog(male labour) 0.150*** 0.117* Dummy if ouseold as an irrigated system 0.260* Distance of te exporting firms *** Constant ** Wald test of joint significance of instruments: 18.19*** 10.18** ci 2 Wald test of independence 2 of equations: ci 10.06*** 4.39** Log pseudolikeliood Wald ci *** 69.45*** Observations Significant effects are indicated wit *** p<0.01, ** p<0.05, * p<0.10