Transformation of the food system in Nigeria and female participation in the Non-Farm Economy (NFE).

Transcription

1 Transformation of the food system in Nigeria and female participation in the Non-Farm Economy (NFE). Lenis Saweda O. Liverpool-Tasie, Serge G. Adjognon, Thomas A. Reardon, Agricultural, Food & Resource Economics Michigan State University DRAFT Selected Paper prepared for presentation at the 2016 Agricultural & Applied Economics Association Annual Meeting, Boston, Massachusetts, July 31-August 2 Copyright 2016 by Liverpool-Tasie, Adjognon and Reardon. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. 1

2 Abstract This paper uses a recently available panel dataset from Nigeria to explore some implications of the rapidly transforming food system in Sub Saharan Africa. We find that urban and rural households in Nigeria have rapidly transforming diets. Consumption has diversified greatly, shifting beyond self-sufficiency into heavy reliance on food purchases and with a heavy shift into consumption of processed foods. We find that the growing demand for processed foods has important implications for the midstream (processing and wholesale) and downstream (retail) sector of food systems. The rise of these two segments (on the supply side) paralleling the rise of processed and prepared foods (on the demand side) creates opportunities for employment and income generation. Furthermore the availability of processed foods (to serve as substitutes for home food processing and preparation, usually a heavy use of time for women in traditional settings) appears to have reduced women s time constraint and freed up time for them to engage more in non-farm activities in the local area just as it did a half century ago in the US. These findings demonstrate the potential benefits from the transforming foods systems that could increase employment and improve household welfare in developing countries. 2

3 Introduction The structural transformation of societies has historically been associated with changes in consumption patterns; diversification of diets (per Bennett s Law, Bennett (1954) and increased consumption of processed foods. With good economic growth figures for SSA over the last decade, processed food (to cook at home) as well as packaged meals (RTE or ready to eat meals) and meals purchased and consumed outside of the home (FAFH, or food away from home ) are constituting a higher share of households food budgets (Farfan et al., 2015, Tschirley et al., 2015a, Tschirley et al., 2015b)). The recent reality of the rural and urban food economy of Nigeria confirms this transformation process and belies the conventional wisdom in Nigeria that the food system has stayed traditional, based on subsistence in rural areas and purchase of raw grains and other foods to only process and cook at home in urban areas. We find that urban and rural households in Nigeria have rapidly transforming diets with a triangle of change among rural consumers including diversification beyond staples into horticulture, animal proteins (fish, meat, eggs, dairy), beyond consumption based on self-sufficiency into heavy reliance on food markets for purchases of food and thus what we call commercialization of consumption, and a heavy shift into consumption of processed foods, including in rural areas. Animal protein, nuts and oils as well and fruits and vegetables account for about 20%, 15% and 10% respectively in Nigeria based on our analysis of LSMS data. Furthermore we see that the majority of food consumed in rural areas of Nigeria (over 75%) is purchased. Third, we find that surprisingly (relative to the traditional view of the situation) more than 70% of the purchased food budget in Nigeria is spent on processed and semi processed foods. We also confirm that these shifts in Nigeria are not just in tiny enclaves of the middle class, but happening more broadly in both rural and urban areas, and over the income strata of households. This process of diet transformation also referred to as modernization or westernization of diets (Pingali, 2007) has several implications. One big issue is whether or not dietary changes will drive upstream and downstream food system changes in SSA. Do changing consumption patterns represent opportunities in the off farm segments, that is, for employment in food preparation, processing, wholesale and retail? If so, for whom are these opportunities likely to be created? The increasing consumption of processed foods can have particular implications for women. First as women are typically more involved in food production and marketing (particularly in rural areas), 3

4 does increased consumption of processed foods create opportunities for female engagement in non-farm business opportunities? Furthermore, does the increasing availability of processed, packaged and ready to eat food increase the options available to households to meet their household food consumption needs such that female time (previously allocated to that activity within the household) could be freed up to engage in non-farm activities? Claims, supported by empirical evidence from the US and Asia, show that as the opportunity cost of the time of women increases (due to wage employment participation), less time is devoted to cooking meals at home and the share of ready to eat foods in household food expenditure increases (Nayga, 1996; Byrne et al., 1996, Reardon, 2015, Stewart and Yen, 2004). The limited literature on SSA has also focused on how the convenience of processed foods and increased opportunity cost of time (particularly for women) explains the observed increased consumption of processed foods and FAFH. (Mason et al., 2015;; Reardon, 1997; Kennedy and Reardon, 1994; Senauer et al., 1986). However, there is no discussion about whether increased options for food (made possible by easy to cook semi processed or processed foods) actually encourage female participation in activities outside of the home. There are also no empirical studies found in SSA that have looked at the effect of the availability of alternative food options created by processed, packaged and FAFH options on household ability to engage in non-farm activities. In fact we have seen no analysis of processed/prepared food availability on women s employment in the US; the closest the literature has got was to analyze household purchases of time-saving durables (like vegetable processors and washing machines) and services on women s employment outside the home (Bryant, 1988). Consequently, this paper focuses on two potential implications of diversified diets on female employment opportunities. We explore if the increasing demand for processed food has increased the opportunity to invest in non -farm activities to meet the rising demand. We also explore if the increasing availability of alternative options to personal home cooked meals (or the increasing presence of foods easier to prepare) women s labor time is freed up, enabling and encouraging them to engage more in non-farm activities in Nigeria. The study is organized as follows: Section 2 describes food consumption patterns in Nigeria across various geographic and economic consideration with particular attention to consumption of processed foods. Section 3 presents the conceptual framework used to explore the effect of 4

5 increased opportunities for food processing, preparation and trading as well as the availability of alternative options to home prepared meals on female engagement in non-farm activities. Section 4 describes the empirical framework used while section 5 presents the study results and discussions. Section 6 concludes Food consumption patterns across Nigeria Recent evidence from nationally representative data indicates that over 80% of Nigerians consume processed foods. This cuts across spatial dimensions (rural and urban, north and south) and income levels. While about 94% of urban households purchase some high non-perishable processed foods such as fruit juices, oils, coffee and tea, the share is still over 80% for rural households. Apart from the share of the population that consumes high processed perishable food items (such as dairy products and confectionaries including jam ) which is identical between rural and urban areas, the share of the population consuming different forms of processed foods is generally slightly lower in rural areas (a maximum of 15 percentage points difference) at the bottom of the income distribution to being basically identical to urban households at the top end of the income distribution (see figure 1). Only about 20% of the value of food consumed in Nigerian households on average comes from own production. Not surprising, this is much lower in urban areas (about 5%) compared to about 30% in rural areas. The average budget share spent on processed foods is comparable at about 40% in rural areas compared to 50% in urban areas. Of the purchased food budget share, low processed foods (such as poultry, fish, meat, milled grains and flours) account for the highest share at 38% and 36% for urban and rural areas respectively. This partly reflects Bennett s law and the diversification of diets that comes with increases in income. The consumption of high processed foods such as dairy, juices, tea and coffee is increasing, currently accounting for about 20% of the food budget share in both sectors. There is also a significant share of the purchased food budget (13% and 18%) spent on foods consumed away from home in rural and urban areas respectively. These all indicate a very vibrant food system, quite contrary to the often perceived notion that the food economy of West Africa is poor and under-developed. Rather than finding a large share of households with traditional food habits narrowly limited to grain and root staples and sauces, with 5

6 little processed food or rural households relying mainly on home-consumption from own farming not market purchases we find both rural and urban households, rich and poor very engaged in the purchase of food items, of which a large fraction is processed. Conceptual framework We model the household s decision to allocate members time to a non-farm activity following Abdulai and CroleRees (2001) which is adapted from Bardhan and Udry (1999). The model assumes that households allocate their land and labor resources across various activities including farm and non-farm activities. Assuming a static model, households choose consumption in time t (C t ) in order to maximize their expected utility subject to various constraints as expressed in equations Max U t = u(c t ) subject to the following constraints: K Budget constraint: C t = k=1 g k (l kt, ε kt ; X) (1) Time endowment constraint: K k=1 l kt L (2) Non-negativity constraint: l kt 0, k=1 K (3) where l kt is the amount of labor (male and female) allocated to activity k at time t. g k (l kt, ε kt ; X) is the technology constraint that characterizes the returns from investing l kt units of labor in alternative activity k. X captures household s specific characteristics as well as geographical factors that influence the returns to labor use in each of the K options.solving the constrained utility maximization problem above implies that households allocate labor between different activities (k) to equate the marginal utility of allocating one unit of labor to each of them. 1 While we recognize that a dynamic model is likely more realistic we have resorted to a static model in order to be consistent with our empirical analysis 6

7 E t [U (C t ). g k (l kt, ε kt ; X)] = E t [U (C t ). g k (l kt, ε kt ; X)] (4) where k refers to activities other than k. In the case of Non-Farm activities (particularly non-farm enterprises and non-farm wage employment), the household s decision to allocate female labor to any or both non-farm activities depends on the expected returns from each activity and the maximum expected returns from all the other possible activities that labor could have engaged in including household chores and farming. At the extreme, if the expected returns from engaging in a non-farm activity such as a non-farm enterprise, conditional on the household s physical and human asset endowments, are very low compared to farming and other activities, no labor would be allocated to such an enterprise. While this indicates that there might be barriers to entry given household s resource endowments, the returns and consequent decision to engage in such also depends on the viability of those businesses in the household s community. It also depends on the presence of options to substitute for labor time allocated to other necessary activities within the household such as food production. A similar argument can be made for the household s decision to allocate labor between non-farm wage employment and non-farm self-employment. While the financial resources necessary to invest in a non-farm enterprise might pose a barrier to entry (as such activities can be in or out of the home), other resources such as education, or labor to fulfill other household chores (or the availability of substitutes to home produced food) might also be key for wage employment outside of the home for women. Increased expected returns from RNFE due to expanded opportunities (spurred by increased demand for processed foods) is expected to have a stronger direct effect on activities related to meeting that demand, such as the establishment of food related enterprises. However, it is less likely that employment in wage labor out of the home would be a direct response to these incentives 2. It is more likely that any correlation between the increased presence of processed foods in the community and a woman s decision to engage in wage employment (particularly jobs not in 2 We recognize that it is still possible for women to engage in wage employment in other food related enterprises so attempt to explore the effects on wage employment in non food related activities. 7

8 the food sector) is directly driven by the relaxed time constraint due to alternative affordable options to meet consumption needs or the increased expected returns from wage employment over labor costs to meet household food preparation and other household needs 3. Consequently we empirically investigate the role that increased consumption of processed foods in both rural and urban areas plays in female participation in various non-farm activities. We specifically test the following hypotheses. First, with increased consumption of processed foods, the return to non-farm activities, particularly those related to food production, processing and marketing will increase and more people will engage in non-farm activities. Because of the primary role of women within the food industry, such opportunities will have a higher expected return for female labor within the household. Second we hypothesize that the increasing availability of processed foods will relax the labor time constraint for food preparation (largely for women) and thus positively affect their decision to engage in non-farm activities in Nigeria. While such effects could encourage female engagement in any non-farm activity, we expect such effects of relaxing the labor constraint for food preparation on non-farm wage employment to be more arguably directly attributed to the increasing availability of convenience packaged and processed foods than engagement in self enterprises (conditional on particular community characteristics that are likely to affect both wage opportunities and processed food consumption) which could be also explained by hypothesis 1. Empirical methods: Our empirical analysis follows directly from the conceptual framework above. The first outcome variable considered in our analysis is whether or not at least one female adult of the household participates in the non-farm economy (NFE). We consider three types of non-farm activities: nonfarm wage employment, non-farm self-employment, and a combination of both. In each case, participation is a binary variable (Y1). So for example, Y1 NonFarmWageEmp =1 if a female adult (15 years and above) in the household was involved in nonfarm wage employment during the 7 days 3 Meeting this cost could also be through other available labor in the household that could prepare meals, typically other females. 8

9 prior to the interview, and 0 otherwise. Y1 NonFarmSelfEmp and Y1 NonFarmEmp are defined similarly for non-farm self-employment participation and overall non-farm employment participation respectively, by female adults in the household. Assuming a normal distribution, we adopt the following unobserved effect Probit model for the household s decision to supply some female labor to each non-farm activity: Prob(Y 1_itk = 1 X it, w tk, c i ) = Φ(X itk β + γ w tk + v it + c i ) (5) where Y1_ itk is the dependent variable defined as above for household i, in enumeration area k, at time t. Φ is the standard normal distribution function. The main explanatory variable used in the model is w tk, the average food budget share in processed foods in the household s enumeration area. As mentioned in the conceptual framework, our goal is to explore whether the increased consumption of processed foods serves as a pull factor for participation in non-farm employment. X itk is a set of controls likely to confound the effects of the main treatment variable on the outcome. For example, wealthier and better connected households might choose to live in more dynamic areas with more access to and/or need for processed foods and also be more able to find and take up a job as a professional wage earner. β is the vector of parameters associated with our control variables while γ is our key parameter of interest. Our model explicitly allows for time invariant unobservable heterogeneity thereby reducing bias due to such an omitted variable. c i captures the time invariant unobservable characteristics of the households that can affect their employment choice and may also be correlated with some explanatory variables such as education. While the Fixed Effects (FE) approach is useful for linear models, it is less desirable for non-linear models since it leads to the incidental parameter problem. Furthermore, the FE model does not allow the estimation of coefficients of time invariant control variables. Consequently, we follow Mundlak (1978) and (Chamberlain, 1982) and adopt the correlated random effect (CRE) approach as our primary estimation method (Green William, 2000, Wooldridge, 2010). We do however still consider a linear probability framework with fixed effects (FE) estimation as alternative estimation method for robustness purposes. One key assumption for consistency with both the FE approach and the CRE approach (also known as the Mundlak-Chamberlain device) is the strict exogeneity of the explanatory variables conditional on the time invariant heterogeneity. 9

10 The CRE approach requires an additional assumption that the time invariant unobserved heterogeneity is a function of the time-averages of the time-varying explanatory variables in the model. See Wooldridge (2010) and Green (2000) for further details on both approaches. The second main outcome we explore is the average number of hours that a female in the household spends in each non-farm activity. So for example, Y2 NonFarmWageEmp is average hours spent by household s female adults on non-farm wage employment. Y2 NonFarmSelfEmp and Y2 NonFarmEmp are defined similarly for non-farm self-employment participation and overall nonfarm employment respectively. Since this variable is characterized by a corner solution at 0 where some households do not supply any women hours to non-farm employment, the Tobit approach is suitable for estimation. We use the following Tobit model representation for variable Y2: Y 2_itk = max (0, Z it δ + θ w tk + c i + u it ) (6) D(u it Z it, c i ) = Normal (0, σ u 2 ) (7) where w tk remains, as defined above the main treatment variable of interest. Zit is the vector of other controls included in the model. u it is the error term assumed to follow a normal distribution with mean 0 and standard deviation σ u. For the same reason as in the Probit model, we use mainly the Mundlak and Chamberlain CRE approach for estimation. But a linear model is also estimated using FE for robustness check. One weakness of the CRE and FE approaches used with both the Probit and Tobit models described above is that they only deal with time invariant unobservable factors. Any remaining time-varying heterogeneity might still lead to inconsistent results unless they are properly captured using Instrumental Variables (IV) methods. However, appropriate IV s are difficult to find and bad instruments often lead to even worse bias. Second, IVs results are local in the sense that they depend on the choice of instruments and different instruments typically lead to different results, with limited guidelines to indicate which ones are the best In this particular study our key variable is at the community not household level. Thus it is less likely to be subject to the same sort of endogeneity concerns that the household s own budget share in processed foods might have on their decision to allocate labor to the NFE. To minimize the potential for endogeneity, demand for processed foods is estimated as the average share of other households budget in the community (EA) allocated to processed foods, excluding each household 10

11 in turn. To account for the fact that there might be other EA specific characteristics such as agricultural production or proximity to markets that are likely to affect the availability and consumption of processed foods as well as the opportunities for non-farm activities, we include EA characteristics and EA dummies in the CRE estimations. We also control for time effects. Preliminary results: The data come from four seasons over two years of The Nigerian Living StandardMeasurement Study (LSMS) surveys. This is a multi-dimensional nationally representative survey with detailed information about households assets, demographic characteristics, consumption and various household practices including agricultural production, business and other non-farm activities. It also includes some geographical and community level information. The first round of data includes households, from 204 enumeration areas (EAs), and interviewed over 4 periods in 2 years, 2010/2011 and 2012/2013 In addition to the average share of budget that goes to purchase processed foods in a farmer s community, additional explanatory variables used as control in the various model specifications are chosen to capture the human, physical, financial, and other factors that influence the relative shadow prices or marginal values of investing labor in various activities. These variables summarized in table 4 include household and household head socio economic characteristics, wealth and asset ownership, rainfall and rainfall seasonality, distance to markets geographical locations. The estimation models also control for time dummies for each of the 4 time periods. Figure 2 presents non parametric estimates of the relationship between the average budget share spent on processed foods in the local economy and the number of hours women spend on nonfarm activities in Nigeria. It reveals a clearly nonlinear relationship with decreasing returns to participation in non-farm activities as the average budget share in processed foods increases in the local economy. This could occur if the higher return from enterprises (geared to meet the increased demand for processed foods) encourages significant entry into the industry by many enterprises which induces competition and thus an eventual decline in profits and the returns from investing in such activities. 11

12 The results from the econometric analysis support both of the study hypotheses. With increasing opportunities for meeting the increased demand for processed foods, women in Nigeria tend to be more likely to participate in non-farm self-employment; largely engaging in a non-farm enterprise (table 5). They also tend to supply more hours to these non-farm enterprise activities (table 6). However, the effect is non-linear with evidence of an inverse u relationship between labor supply and increased processed food consumption in the local economy as suggested by the non-parametric analysis. These results are consistent across both the CRE probit and linear FE models for participation as well as for the CRE Tobit and Linear FE model for number of hours supplied by women to the various activities. The results are slightly different for wage employment. At lower levels of processed food budget shares, the participation rate of women in wage employment is actually negative and it is only at higher levels of the community food budget share allocated to processed foods that females participation rate in (and hours allocated to) non-farm activities rises. With low demand for processed foods in local economy, there are likely just a few micro enterprises with little demand for wage workers (typically women in this industry) in them. It is only when the demand for processed food becomes larger and the demand for wage workers in them increases that we see women participating in wage employment. The negative effect in table 5 and 6 most likely reflect the conventional idea that females have less access to the more traditional wage employment than men. The declining participation in self-enterprises but increased participation in wage employment (as consumption of processed foods increases in the local economy) might also indicate a switch in employment opportunities due to competition among firms. At higher levels of processed food consumption in the local economy, many of the micro-enterprises might no longer be competitive and thus women might shift from managing their own micro enterprise to offering their services to a more successful enterprises. Conclusions: This paper presents recent evidence (from a nationally representative data for Nigeria) that the rural and urban food economy of Nigeria are transforming significantly. Urban and rural households both have rapidly transforming diets with a triangle of change among rural consumers including diversification beyond staples into horticulture, animal proteins, and dairy, 12

13 beyond consumption based on self-sufficiency into heavy reliance on food markets for purchases of food and thus commercialization of consumption. We see a heavy shift to consumption of processed foods across all geographical locations and across all income levels. We find that the increased consumption of processed foods in the local economy has translated to increased employment opportunities in Nigeria, particularly for women. The growing demand for processed foods creates non-farm opportunities for women and the availability of processed foods (to serve as substitutes for own home production) has likely reduced women s time constraint and freed up time for them to actually engage more in non-farm activities. These results reveal some of the potential benefits in the mid and downstream sectors that are occurring as the diet transformation occurs in developing countries societies. The important role of the non-farm economy for welfare directly as well as for securing resources for investments in farming have clearly been demonstrated (Ackah, 2013; Owusu et al., 2011;Oseni and Winters, 2009; Smale et al., 2016). This study contributes to this discussion by showing how the food system transformation in Sub Saharan Africa can benefit women, households and communities through increased opportunities for women. Being able to earn income from nonfarm enterprises or wage employment to earn their own income is likely to have a strong and positive effect on the household in general through increased ability to invest in household health care, education and feeding. Further analysis on this and other similar issues is necessary to understand how the food system transformation in SSA can be leveraged to increase income earning opportunities for youth and other unemployed in the community. References ABDULAI, A. & CROLEREES, A Determinants of income diversification amongst rural households in Southern Mali. Food policy, 26, ACKAH, C Nonfarm employment and incomes in rural Ghana. Journal of International Development, 25, BARDHAN, P. & UDRY, C Development microeconomics, Oxford University Press. Bryant, W.K Durables and Wives' Employment Yet Again, Journal of Consumer Research, 15(1), June:

14 BYRNE, P. J., CAPPS, O. & SAHA, A Analysis of food-away-from-home expenditure patterns for US households, American Journal of Agricultural Economics, 78, CHAMBERLAIN, G Panel data. National Bureau of Economic Research Cambridge, Mass., USA. FARFAN, G., GENONI, M. E. & VAKIS, R You Are What (and Where) You Eat. GREEN WILLIAM, H Econometric analysis. Forth Edition, Prentice Hall International, Inc, New York University. KENNEDY, E. & REARDON, T Shift to non-traditional grains in the diets of East and West Africa: role of women's opportunity cost of time. Food Policy, 19, MASON, N. M., JAYNE, T. & SHIFERAW, B Africa's Rising Demand for Wheat: Trends, Drivers, and Policy Implications. Development Policy Review, 33, OWUSU, V., ABDULAI, A. & ABDUL-RAHMAN, S Non-farm work and food security among farm households in Northern Ghana. Food policy, 36, PINGALI, P Westernization of Asian diets and the transformation of food systems: implications for research and policy. Food policy, 32, REARDON, T Using evidence of household income diversification to inform study of the rural nonfarm labor market in Africa. World development, 25, REARDON, T The hidden middle: the quiet revolution in the midstream of agrifood value chains in developing countries. Oxford Review of Economic Policy, 31, SENAUER, B., SAHN, D. & ALDERMAN, H The effect of the value of time on food consumption patterns in developing countries: evidence from Sri Lanka. American Journal of Agricultural Economics, 68, STEWART, H. & YEN, S. T Changing household characteristics and the away-from-home food market: a censored equation system approach. Food Policy, 29, TSCHIRLEY, D., REARDON, T., DOLISLAGER, M. & SNYDER, J. 2015a. The rise of a middle class in East and Southern Africa: Implications for food system transformation. Journal of International Development, 27, TSCHIRLEY, D., SNYDER, J., DOLISLAGER, M., REARDON, T., HAGGBLADE, S., GOEB, J., TRAUB, L., EJOBI, F. & MEYER, F. 2015b. Africa s Unfolding Diet Transformation: Implications for Agrifood System Employment. Journal of Agribusiness in Developing and Emerging Economies, 5. WOOLDRIDGE, J. M Econometric analysis of cross section and panel data, MIT press. 14

15 Table 1: Food budget shares allocated to various processing attribute and forms in Nigeria Overall RURAL URBAN Mean SD Mean SD Mean SD Total food budget share Own production Unprocessed Some Processed Low processed High processed Purchased food budget share Unprocessed Some Processed Low processed High processed Food Away From Home Source: LSMS data 2012 Note: Some processed is the sum of high and low processed food 15

16 Table 2: Participation rates in different non-farm activities by gender of participant NIGERIA Male Female Variables Overall Participation in non-farm employment Participation in non farm wage employment Participation in non farm self employment Participation in non farm employment in the food sector Participation in non farm self employment in the food sector Source: Nigeria LSMS data Table 3: A breakdown of hours spent on different non- farm activities by gender NIGERIA Male Female Variables Overall Number of hours spent weekly in non farm employment Number of hours spent weekly in non farm wage employment Number of hours spent weekly in non farm self employment Number of hours spent weekly in non farm employment in the food sector Number of hours spent weekly in non farm self employment in the food sector Source: Nigeria LSMS data 16

17 Table 4: Summary statistics of explanatory variables used in the empirical analysis YEAR 2010 YEAR 2012 OVERALL POST PLANTING POST HARVEST POST PLANTING POST HARVEST VARIABLES mean sd mean sd mean sd mean sd mean sd EA average food budget share in processed foods Male headed household Age of the head Percentage of girls (5-14 yo) in the household Percentage of female adults (+15yo) in the household Household dependency ratio Household head completed level P6 at school (0/1) Access to loan (0/1) Household total daily per capita expenditure (in $equivalent) Landholdings Agricultural asset index Total Livestock Unit (TLU) Crop sales value per ha of land cultivated HH Distance in (KMs) to Nearest Market Avg 12-month total rainfall (mm) for Jan-Dec in EA Coefficient of Variation of rainfall in the EA Proportion of households in the North Proportion of households in urban areas Source: Nigeria LSMS data 17

18 Table 5: The effect of increased processed food consumption on female decision to participate (1/0) in non-farm activities Variables Female participation in Non-Farm Employment (CRE probit) Female participation in Non-Farm Wage employment (CRE probit) Female participatio n in Self Employmen t (CRE probit) Female participation in Non-Farm Employment (FE) Female participation in Non-Farm Wage Employment (FE) Female participatio n in Non- Farm Employmen t (FE) Average community budget share on processed foods Squared average community budget share on processed foods ** 1.261*** 0.229* * 0.370*** [0.131] [0.026] [0.002] [0.056] [0.096] [0.002] * 1.459** *** * 0.156* *** [0.097] [0.021] [0.001] [0.054] [0.099] [0.002] Age of the household head [0.675] [0.192] [0.770] [0.613] [0.376] [0.928] Male household head (0/1) [0.400] [0.706] [0.385] [0.545] [0.662] [0.637] Dependency ratio [0.841] [0.938] [0.804] [0.959] [0.662] [0.908] Percentage of female adults years of age ** [0.044] [0.693] [0.249] [0.737] [0.220] [0.974] Percentage of girls 0-14 years of age [0.937] [0.541] [0.637] [0.674] [0.559] [0.528] Household daily per capita expenditure (in $ equivalent) 0.036*** ** 0.004*** ** [0.000] [0.735] [0.044] [0.006] [0.712] [0.031] Household Landholdings (hectares) *** *** *** ** [0.005] [0.846] [0.007] [0.009] [0.949] [0.016] Agricultural Assets index ** [0.833] [0.044] [0.455] [0.883] [0.163] [0.606] 18

19 Tropical livestock units (cattle, pigs, goats, sheep) Someone in the household took a loan (1/0) Household head completed primary school (1/0) Average 12-month total rainfall (mm) for January-December Coefficient of Variation of rainfall in the EA Crop sales value per ha of land cultivated (in Naira) HH Distance in (KMs) to Nearest Market [0.255] [0.532] [0.324] [0.170] [0.982] [0.123] 0.066* * 0.025** * [0.076] [0.307] [0.094] [0.025] [0.169] [0.052] [0.549] [0.126] [0.315] [0.456] [0.198] [0.317] *** ** *** *** * *** [0.000] [0.035] [0.001] [0.000] [0.086] [0.001] * [0.997] [0.051] [0.685] [0.307] [0.963] [0.278] ** *** ** ** [0.027] [0.606] [0.008] [0.045] [0.961] [0.015] * * [0.432] [0.320] [0.353] [0.067] [0.481] [0.097] Urban (0/1) 0.617*** 0.263* 0.433*** * * [0.000] [0.063] [0.000] [0.055] [0.367] [0.052] North (0/1) [0.779] [0.292] [0.541] EA Dummies included Y Y Y CRE controls included Y Y Y Constant [0.843] [0.270] [0.984] [0.215] [0.581] [0.202] Observations 15,244 11,808 15,254 15,762 15,762 15,762 19

20 Source: Nigeria LSMS data Note: *** p<0.01, ** p<0.05, * p<0.1, + p< ,290 4,290 4,290 Table 6: The effect of increased consumption of processed foods on female hours supplied to non-farm activities (1) (3) (5) (7) (8) (9) Non Farm Employment (CRE Tobit) Non Farm Wage Employment (CRE Tobit) Non Farm Self Employment (CRE Tobit) Non Farm Employment (FE) Non Farm Wage Employment (FE) Non Farm Self Employment (FE) Average community budget share on processed foods * ** *** 8.720* ** *** [0.086] [0.015] [0.001] [0.095] [0.049] [0.004] Squared average community budget share on processed foods * ** *** * *** [0.080] [0.015] [0.001] [0.112] [0.083] [0.006] Age of the household head [0.723] [0.433] [0.494] [0.505] [0.993] [0.463] Male household head *

21 (0/1) [0.353] [0.304] [0.611] [0.339] [0.086] [0.765] Dependency ratio [0.196] [0.872] [0.193] [0.238] [0.714] [0.166] Percentage of female adults years of age *** *** *** ** *** [0.000] [0.214] [0.000] [0.000] [0.014] [0.000] Percentage of girls 0-14 years of age [0.715] [0.838] [0.715] [0.908] [0.291] [0.509] Household daily per capita expenditure (in $ equivalent) [0.117] [0.992] [0.120] [0.152] [0.542] [0.143] Household Landholdings (hectares) ** *** * * [0.010] [0.812] [0.007] [0.078] [0.859] [0.053] Agricultural Assets index ** ** [0.456] [0.013] [0.680] [0.131] [0.012] [0.574] Tropical livestock units (cattle, pigs, goats,

22 sheep) [0.694] [0.799] [0.685] [0.701] [0.917] [0.668] Someone in the household took a loan (1/0) 1.675* ** 0.551** [0.064] [0.186] [0.187] [0.032] [0.032] [0.258] Household head completed primary school (1/0) [0.723] [0.160] [0.802] [0.429] [0.353] [0.724] Average 12-month total rainfall (mm) for January-December *** ** *** *** *** [0.000] [0.038] [0.001] [0.004] [0.169] [0.009] Coefficient of Variation of rainfall in the EA * [0.490] [0.084] [0.575] [0.334] [0.982] [0.292] Crop sales value per ha of land cultivated (in Naira) *** *** *** *** [0.001] [0.403] [0.000] [0.001] [0.844] [0.000] HH Distance in (KMs) to Nearest Market [0.270] [0.515] [0.334] [0.211] [0.450] [0.440] 22

23 Urban (0/1) *** *** * [0.000] [0.177] [0.001] [0.340] [0.304] [0.061] North (0/1) [0.777] [0.395] [0.236] Post- harvest 2010 (1/0) *** 4.966*** *** ** 0.585*** *** [0.001] [0.000] [0.000] [0.040] [0.000] [0.000] Post planting period 2012 (1/0) *** *** *** * *** [0.000] [0.172] [0.000] [0.000] [0.051] [0.000] Post harvest period 2012 (1/0) *** ** *** *** [0.003] [0.130] [0.011] [0.002] [0.205] [0.009] EA Dummies included Y Y Y CRE controls included Y Y Y Constant ** ** [0.043] [0.355] [0.041] [0.253] [0.690] [0.227] Number of observations 15,762 15,762 15,762 15,762 15,762 15,762 Source: Nigeria LSMS data. Note: *** p<0.01, ** p<0.05, * p<0.1, + p<

24 Figure 1: Processed food consumption by income level Consumption of low processed non perishable food items by income level Consumption of high processed non perishable food items by income level Y-1 Y-2 Y-3 Y Y-1 Y-2 Y-3 Y-4 Rural Urban Rural Urban Consumption of low processed perishable food items by income level (Y) Consumption of high processed perishable food items by income level Y-1 Y-2 Y-3 Y-4 0 Y-1 Y-2 Y-3 Y-4 Rural Urban Rural Urban Source: Nigeria LSMS data. Y1= total expenditure (E) less than $2, Y2= $2<E<$4, Y3=$4<E<$10 Y5=E>$10 24

25 Figure 2: The relationship between the number of female adult hours in non-farm activities and processed food consumption Source: Nigeria LSMS data 25