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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.

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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.

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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),

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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

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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

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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

(𝐶𝑡) in order to maximize their expected utility subject to various constraints as expressed in

equations 1-31.

Max 𝑈𝑡 = 𝑢(𝐶𝑡) subject to the following constraints:

Budget constraint: 𝐶𝑡 = ∑ 𝑔𝑘(𝑙𝑘𝑡, 휀𝑘𝑡; 𝑿)𝐾𝑘=1 (1)

Time endowment constraint: ∑ 𝑙𝑘𝑡𝐾𝑘=1 ≤ 𝐿 (2)

Non-negativity constraint: 𝑙𝑘𝑡 ≥ 0, k=1…K (3)

where 𝑙𝑘𝑡 is the amount of labor (male and female) allocated to activity k at time t. 𝑔𝑘(𝑙𝑘𝑡, 휀𝑘𝑡; 𝑿)

is the technology constraint that characterizes the returns from investing 𝑙𝑘𝑡 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

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𝐸𝑡[𝑈′(𝐶𝑡). 𝑔′𝑘(𝑙𝑘𝑡, 휀𝑘𝑡; 𝑿)] = 𝐸𝑡[𝑈

′(𝐶𝑡). 𝑔′−𝑘(𝑙−𝑘𝑡, 휀−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

incentives2. 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.

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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 needs3.

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: non-

farm wage employment, non-farm self-employment, and a combination of both. In each case,

participation is a binary variable (Y1). So for example, Y1NonFarmWageEmp=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.

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prior to the interview, and 0 otherwise. Y1NonFarmSelfEmp 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(𝑌1_𝑖𝑡𝑘 = 1|𝑋𝑖𝑡, 𝑤𝑡𝑘 , 𝑐𝑖) = Φ(𝑋𝑖𝑡𝑘′ 𝛽 + 𝛾 ∗ 𝑤𝑡𝑘 + 𝑣𝑖𝑡 + 𝑐𝑖) (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 𝒘𝒕𝒌, 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.

𝑿𝒊𝒕𝒌 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. 𝑐𝑖 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.

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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, Y2NonFarmWageEmp is average hours

spent by household’s female adults on non-farm wage employment. Y2NonFarmSelfEmp and

Y2NonFarmEmp are defined similarly for non-farm self-employment participation and overall non-

farm 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:

𝑌2_𝑖𝑡𝑘 = max(0, 𝑍𝑖𝑡′ 𝛿 + 𝜃 ∗ 𝑤𝑡𝑘 + 𝑐𝑖 + 𝑢𝑖𝑡) (6)

𝐷(𝑢𝑖𝑡|𝑍𝑖𝑡, 𝑐𝑖) = 𝑁𝑜𝑟𝑚𝑎𝑙(0, 𝜎𝑢2) (7)

where 𝑤𝑡𝑘 remains, as defined above the main treatment variable of interest. Zit is the vector of

other controls included in the model. 𝑢𝑖𝑡 is the error term assumed to follow a normal distribution

with mean 0 and standard deviation 𝜎𝑢. 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

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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 5.000 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 non-

farm 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.

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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,

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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 non-

farm 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.

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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 0.22 0.25 0.28 0.26 0.06 0.14

Unprocessed 0.25 0.17 0.23 0.18 0.29 0.15

Some Processed 0.42 0.18 0.39 0.18 0.48 0.16

Low processed 0.29 0.16 0.27 0.16 0.33 0.14

High processed 0.13 0.09 0.12 0.09 0.15 0.09

Purchased food budget share

Unprocessed 0.31 0.18 0.31 0.19 0.31 0.15

Some Processed 0.55 0.19 0.56 0.20 0.52 0.17

Low processed 0.38 0.19 0.39 0.20 0.36 0.15

High processed 0.17 0.12 0.17 0.13 0.16 0.10

Food Away From Home 0.15 0.18 0.13 0.17 0.18 0.19

Source: LSMS data 2012

Note: Some processed is the sum of high and low processed food

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Table 2: Participation rates in different non-farm activities by gender of participant

NIGERIA Male Female

Variables Overall 2010 2012 2010 2012 2010 2012

Participation in non-farm employment 24.0 26.6 21.7 25.0 20.4 28.3 23.0

Participation in non farm wage employment 6.3 7.4 5.3 9.5 6.9 5.5 3.8

Participation in non farm self employment 18.2 19.9 16.8 16.1 14.0 23.5 19.6

Participation in non farm employment in the food sector 2.0 1.9 2.0 0.5 0.6 3.3 3.5

Participation in non farm self employment in the food sector 1.9 1.8 1.9 0.4 0.5 3.1 3.3

Source: Nigeria LSMS data

Table 3: A breakdown of hours spent on different non- farm activities by gender

NIGERIA Male Female

Variables Overall 2010 2012 2010 2012 2010 2012

Number of hours spent weekly in non farm employment 10.2 11.5 9.1 11.1 8.9 11.9 9.3

Number of hours spent weekly in non farm wage employment 2.7 3.2 2.3 4.2 3.1 2.2 1.5

Number of hours spent weekly in non farm self employment 7.5 8.3 6.8 6.8 5.8 9.7 7.8

Number of hours spent weekly in non farm employment in the food sector 0.7 0.7 0.7 0.2 0.2 1.1 1.1

Number of hours spent weekly in non farm self employment in the food sector 0.6 0.6 0.6 0.2 0.2 1.0 1.1

Source: Nigeria LSMS data

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 0.4 0.1 0.4 0.1 0.4 0.1 0.4 0.1 0.4 0.1

Male headed household 0.8 0.4 0.8 0.4 0.9 0.4 0.8 0.4 0.8 0.4

Age of the head 50.6 15.3 49.6 15.5 49.6 15.5 51.8 15.1 51.8 15.1

Percentage of girls (5-14 yo) in the household 16.7 17.2 17.3 17.6 17.4 17.6 16.0 16.7 15.9 16.7

Percentage of female adults (+15yo) in the household 27.4 19.0 28.5 19.8 28.4 19.6 26.6 18.2 25.9 18.4

Household dependency ratio 1.0 0.9 1.0 0.9 1.0 0.9 0.9 0.8 0.9 0.8

Household head completed level P6 at school (0/1) 0.4 0.5 0.4 0.5 0.4 0.5 0.4 0.5 0.4 0.5

Access to loan (0/1) 0.4 0.5 0.4 0.5 0.4 0.5 0.4 0.5 0.4 0.5

Household total daily per capita expenditure (in $equivalent) 2.0 2.5 1.4 1.3 2.3 2.3 2.6 3.8 1.7 1.9

Landholdings 0.7 0.9 0.5 0.9 0.9 1.0 0.8 0.8 0.8 0.8

Agricultural asset index 0.0 3.2 0.0 3.5 0.0 3.5 0.0 2.9 0.0 2.9

Total Livestock Unit (TLU) 0.9 11.6 0.7 3.9 1.2 21.9 0.7 3.7 0.8 4.1

Crop sales value per ha of land cultivated 23272 49304 23169 49732 23169 49732 23378 48868 23378 48868

HH Distance in (KMs) to Nearest Market 66.8 43.7 66.7 43.8 66.7 43.8 67.0 43.5 67.0 43.5

Avg 12-month total rainfall (mm) for Jan-Dec in EA 1294.0 441.2 1299.0 447.8 1299.0 447.8 1289.0 434.3 1289.0 434.3

Coefficient of Variation of rainfall in the EA 89.5 27.3 89.2 27.2 89.2 27.2 89.7 27.3 89.7 27.3

Proportion of households in the North 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

Proportion of households in urban areas 0.3 0.5 0.3 0.5 0.3 0.5 0.3 0.5 0.3 0.5

Source: Nigeria LSMS data

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

0.618+ -1.142** 1.261*** 0.229* -0.125* 0.370***

[0.131] [0.026] [0.002] [0.056] [0.096] [0.002]

Squared average community budget

share on processed foods

-0.793* 1.459** -1.492*** -0.273* 0.156* -0.429***

[0.097] [0.021] [0.001] [0.054] [0.099] [0.002]

Age of the household head 0.001 0.007 -0.001 0.001 0.000 -0.000

[0.675] [0.192] [0.770] [0.613] [0.376] [0.928]

Male household head (0/1) -0.191 -0.101 -0.175 -0.041 -0.020 -0.029

[0.400] [0.706] [0.385] [0.545] [0.662] [0.637]

Dependency ratio 0.008 0.005 0.009 0.001 0.003 -0.001

[0.841] [0.938] [0.804] [0.959] [0.662] [0.908]

Percentage of female adults 15-64

years of age

-0.004** -0.001 -0.002 -0.000 0.001 0.000

[0.044] [0.693] [0.249] [0.737] [0.220] [0.974]

Percentage of girls 0-14 years of age 0.000 -0.002 0.001 0.000 0.000 0.001

[0.937] [0.541] [0.637] [0.674] [0.559] [0.528]

Household daily per capita

expenditure (in $ equivalent)

0.036*** 0.003 0.011** 0.004*** -0.000 0.003**

[0.000] [0.735] [0.044] [0.006] [0.712] [0.031]

Household Landholdings (hectares) -0.083*** 0.010 -0.081*** -0.024*** 0.000 -0.022**

[0.005] [0.846] [0.007] [0.009] [0.949] [0.016]

Agricultural Assets index -0.001 -0.012** 0.004 -0.000 -0.001 0.001

[0.833] [0.044] [0.455] [0.883] [0.163] [0.606]

19

Tropical livestock units (cattle, pigs,

goats, sheep)

0.001 0.000 0.001 0.000 -0.000 0.000+

[0.255] [0.532] [0.324] [0.170] [0.982] [0.123]

Someone in the household took a loan

(1/0)

0.066* 0.053 0.058* 0.025** 0.010 0.021*

[0.076] [0.307] [0.094] [0.025] [0.169] [0.052]

Household head completed primary

school (1/0)

0.033 -0.135+ 0.054 0.013 -0.013 0.017

[0.549] [0.126] [0.315] [0.456] [0.198] [0.317]

Average 12-month total rainfall (mm)

for January-December

-0.003*** -0.002** -0.003*** -0.001*** -0.000* -0.001***

[0.000] [0.035] [0.001] [0.000] [0.086] [0.001]

Coefficient of Variation of rainfall in

the EA

-0.000 -0.059* 0.008 -0.084 -0.000 -0.084

[0.997] [0.051] [0.685] [0.307] [0.963] [0.278]

Crop sales value per ha of land

cultivated (in Naira)

-0.000** -0.000 -0.000*** -0.000** -0.000 -0.000**

[0.027] [0.606] [0.008] [0.045] [0.961] [0.015]

HH Distance in (KMs) to Nearest

Market

0.005 0.009 0.005 0.004* 0.001 0.004*

[0.432] [0.320] [0.353] [0.067] [0.481] [0.097]

Urban (0/1) 0.617*** 0.263* 0.433*** -0.206* 0.006 -0.208*

[0.000] [0.063] [0.000] [0.055] [0.367] [0.052]

North (0/1) 0.043 0.200 -0.090

[0.779] [0.292] [0.541]

EA Dummies included

Y Y Y

CRE controls included Y Y Y

Constant

0.286

-2.082

-0.026

9.289

0.425

9.001

[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

20

0.010 0.003 0.012

4,290 4,290 4,290

Source: Nigeria LSMS data

Note: *** p<0.01, ** p<0.05, * p<0.1, + p<0.15

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

17.820* -46.133** 37.727*** 8.720* -4.849** 13.569***

[0.086] [0.015] [0.001] [0.095] [0.049] [0.004]

Squared average

community budget

share on processed

foods

-21.618* 58.479** -44.033*** -10.235+ 5.701* -15.936***

[0.080] [0.015] [0.001] [0.112] [0.083] [0.006]

Age of the household

head

-0.031 0.170 -0.063 -0.030 0.000 -0.031

[0.723] [0.433] [0.494] [0.505] [0.993] [0.463]

Male household head -5.134 -9.434 -3.218 -2.657 -3.385* 0.728

21

(0/1)

[0.353] [0.304] [0.611] [0.339] [0.086] [0.765]

Dependency ratio 1.284 0.393 1.426 0.694 -0.094 0.789

[0.196] [0.872] [0.193] [0.238] [0.714] [0.166]

Percentage of female

adults 15-64 years of

age

-0.315*** -0.129 -0.264*** -0.277*** -0.046** -0.231***

[0.000] [0.214] [0.000] [0.000] [0.014] [0.000]

Percentage of girls 0-14

years of age

0.024 0.028 0.027 -0.005 0.021 -0.026

[0.715] [0.838] [0.715] [0.908] [0.291] [0.509]

Household daily per

capita expenditure (in $

equivalent)

0.282+ 0.003 0.318+ 0.152 -0.020 0.172+

[0.117] [0.992] [0.120] [0.152] [0.542] [0.143]

Household

Landholdings (hectares)

-2.097** 0.474 -2.423*** -0.707* 0.025 -0.732*

[0.010] [0.812] [0.007] [0.078] [0.859] [0.053]

Agricultural Assets

index

-0.100 -0.654** 0.063 -0.112+ -0.073** -0.039

[0.456] [0.013] [0.680] [0.131] [0.012] [0.574]

Tropical livestock units

(cattle, pigs, goats,

0.006 0.002 0.009 0.001 -0.000 0.001

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* 2.736 1.286 1.087** 0.551** 0.536

[0.064] [0.186] [0.187] [0.032] [0.032] [0.258]

Household head

completed primary

school (1/0)

-0.502 -5.337 0.379 -0.629 -0.366 -0.263

[0.723] [0.160] [0.802] [0.429] [0.353] [0.724]

Average 12-month total

rainfall (mm) for

January-December

-0.081*** -0.089** -0.080*** -0.041*** -0.009 -0.032***

[0.000] [0.038] [0.001] [0.004] [0.169] [0.009]

Coefficient of Variation

of rainfall in the EA

-0.280 -1.828* -0.222 -4.337 -0.009 -4.328

[0.490] [0.084] [0.575] [0.334] [0.982] [0.292]

Crop sales value per ha

of land cultivated (in

Naira)

-0.000*** -0.000 -0.000*** -0.000*** 0.000 -0.000***

[0.001] [0.403] [0.000] [0.001] [0.844] [0.000]

HH Distance in (KMs)

to Nearest Market

0.176 0.306 0.170 0.167 0.082 0.086

[0.270] [0.515] [0.334] [0.211] [0.450] [0.440]

23

Urban (0/1) 12.820*** 7.385 11.186*** -3.661 1.862 -5.522*

[0.000] [0.177] [0.001] [0.340] [0.304] [0.061]

North (0/1) -1.163 6.491 -5.325

[0.777] [0.395] [0.236]

Post- harvest 2010 (1/0) -1.880*** 4.966*** -3.591*** -0.702** 0.585*** -1.287***

[0.001] [0.000] [0.000] [0.040] [0.000] [0.000]

Post planting period 2012

(1/0)

-5.899*** -2.427 -5.994*** -3.602*** -0.375* -3.226***

[0.000] [0.172] [0.000] [0.000] [0.051] [0.000]

Post harvest period 2012

(1/0)

-2.274*** -2.606+ -2.110** -1.313*** -0.244 -1.069***

[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 60.865** -66.496 67.083** 466.879 14.337 452.542

[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<0.15

24

Figure 1: Processed food consumption by income level

Source: Nigeria LSMS data. Y1= total expenditure (E) less than $2, Y2= $2<E<$4, Y3=$4<E<$10 Y5=E>$10

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Y-1 Y-2 Y-3 Y-4

Consumption of low processed non perishable food items by income level

Rural Urban

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Y-1 Y-2 Y-3 Y-4

Consumption of high processed non perishable food items by income level

Rural Urban

0.00

0.20

0.40

0.60

0.80

1.00

Y-1 Y-2 Y-3 Y-4

Consumption of low processed perishable food items by income level (Y)

Rural Urban

0

0.2

0.4

0.6

0.8

1

1.2

Y-1 Y-2 Y-3 Y-4

Consumption of high processed perishable food items by income level

Rural Urban

25

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

Source: Nigeria LSMS data