Research shows that participation in rural non-farm activities exerts a pronounced impact on agriculture, household farm decisions, rural development, income and welfare as well as household food security. This paper investigates the impact of participation in non-farm activities on household income and food security among farm households in the Upper East and Upper West Regions of Ghana. Using the Recommended Daily Calorie Required (RDCR) approach, the study finds that 45 percent of households in the two regions are food insecure. Propensity score matching (PSM) results indicate that participation in non-farm work has significant positive effect on household income and food security status. The policy implications of the findings are discussed.
The problem of food insecurity is rife in many developing countries of the world. Although emerging economies are experiencing rapid economic growth, resulting in increased food security scores, the gap between developed and developing countries is still wide. The situation is more acute for Sub-Saharan Africa (SSA), with the region taking bottom spot in the 2015 Global Food Security Index (GFSI) rankings [
Literature buttresses the fact that participation in rural non-farm activities exerts a pronounced impact on rural agriculture [
Farming is the main economic activity in northern2 Ghana, where poverty rates are much higher than the rest of the country. While Ghana ranks3 favorably among Sub-Saharan African countries in terms of the food security situation, it cannot be said to be out of the woods yet, given the level of poverty in the northern parts of the country. [
The relevant questions this paper seeks to address are: 1) What are the food security status of farm households in northern Ghana? 2) What factors influence farmers’ involvement in non-farm activities in northern Ghana? 3) What impact do non-farm activities have on farm household income and food security status in northern Ghana? The main objective of the paper is to assess the impact of non-farm activities on household income and food security status of rural farm households in the Upper East and Upper West Regions of Ghana. Specifically, the paper seeks: 1) to determine the food security status of farm households in the two regions; 2) to identify factors that influence farm households’ involvement in non-farm activities in two the regions; and 3) to estimate the impact of non-farm activities on household income and food security status of rural farm households in the two regions.
The choice of these two regions is informed by the fact that they rank among the three poorest regions of Ghana.4 Moreover, these regions are often plagued by conflicts resulting in the loss of assets and increased vulnerability. In addition, these regions suffer adverse weather conditions and frequent fluctuations in crop prices as well as reduction in levels of food stock due to seasonality [
Earlier work to analyze the impact of non-farm activities on household food security status in Ghana have been done by [
While the work of [
The rest of the paper is structured as follows. The next section discusses the methods and the data collection process. Section three presents and discusses the findings, while section four concludes with policy recommendations.
To ensure reliability and a statistically representative sample, the survey took place in three districts in each of the two regions, namely, Bolgatanga Municipal, Bawku West and Kasena Nankana East districts in the Upper-East Region, and Wa Municipal, Nandom and Lambussie Karni districts in the Upper-West Region, which are all predominantly farming districts. Using [
In order to assess the impact of non-farm activities on household food security, we constructed a food security index. A Food Security Index (Z) is constructed to determine the food security status of each household based on a food security demarcation using the Recommended Daily Calorie Required (RDCR) approach employed by [
where Zi denotes Food Security Index for the ith household; Ai is Actual Daily Calorie Intake of ith household and Ri is the Recommended Daily Calorie Required (RDCR) of the ith household. The study employed [
The adult equivalent of the total energy requirement for children is obtained by multiplying the total number of children below age six in each household by a conversion factor of 0.4 and RDCR of 2900 kcal. The adult equivalent of the total food requirement for children between six and 18 years is obtained by multiplying the total number of household members within this age bracket by a conversion factor of 0.7 and RDCR of 2900 kcal. In the case of persons aged over 18 years, the total number in each household is multiplied by the recommended calorie requirement of 2900 kcal. The total Daily Calorie Requirement (DCR) for each household was obtained by summing the requirements for the three age groups.
Households’ daily food consumption or Daily Calorie Intake (DCI) was obtained from households’ own food production and purchases to supplement farm production. The data on actual food quantity of maize, millet, rice, beans groundnut, and yam consumed by each household per week or month was converted into kilogram. The energy content of 1kg of each foodstuff (maize, millet, rice, beans, groundnut, and yam) is presented in
The total quantity (in kilogram) of each food consumed was then multiplied by the energy content (e.g. total kilogram of maize consumed per week *3950 kcal = total kcal of maize consumed). This was done for all the other foodstuff. The total kilocalories of maize, millet, rice, beans groundnut, and yam consumed by each household were added, converted to yearly averages and divided by 365 to obtain Actual Daily Calorie Intake. Households whose DCI was greater than or equal to their RDCR were deemed food secure households while households whose DCI was less than their RDCR were considered food insecure households.
Age Category (Years) | Average Daily Energy Required | Equivalent Scale |
---|---|---|
Children (<6 years) | 1150 | 0.4 |
Children (6 - 18 years) | 2250 | 0.7 |
Adults (>18 years) | 2900 | 1.0 |
Source: [
Food Crop | Calorie/Kilogram |
---|---|
Maize | 3590 |
Millet | 3410 |
Rice | 3640 |
Beans | 3380 |
Groundnut | 3320 |
Yam | 297.5 |
Source: [
A farm household’s decision to either participate in non-farm work or not is assumed to be the outcome of a vector of factors related to the farmers’ resources and constraints. As noted by [
Based on theoretical and empirical considerations (see [
The dependent variable is non-farm work participation (Nfarm) and takes the value 1 if at least one member of the household participates in non-farm work, and zero otherwise. Participation in non-farm activity by a household is specified as a function of household demographic, socio-economic and community variables. The definition and measurement of the explanatory variables are presented in
The outcome variables are food security (FSS) as measured by the RDCR index and household income (HHINC) measured in local currency units (Ghana cedis, GH¢). According to [
[
However, having assumed confoundedness, one acceptable approach to assessing relevant outcomes in a counterfactual framework is to examine not only the ATT but also the ATU which captures the effect of treatment on individuals who do not participate in non-farm work. Several matching algorithms can be used including the nearest neighbor matching, caliper matching, and the kernel matching. This paper employs the nearest neighbor matching technique, which matches either with or without replacement each participant with its closest neighbour with similar observed characteristics. The advantage of this technique is that it reduces biasedness given that each treatment group can be matched to the nearest comparison group as a result of a reduction in the propensity score distance.
The propensity score
where
Variable | Definition and Measurement | |
---|---|---|
Household Characteristics | ||
SEX | Sex of HH Head (1 = Male; 0 = Female) | |
AGE | Farmers’ Age (In Years) | |
AGESQ | Square of farmers’ Age (Years * Years) | |
REG | Region of HH Head (1 = Upper East; 2 = Upper West) | |
BASICEDU | HH Head Attained Basic Education (1 = Yes; 0 = No) | |
SECEDU | HH Head Attained Secondary Education (1 = Yes; 0 = No) | |
TEREDU | HH Head Attained Tertiary Education (1 = Yes; 0 = No) | |
MARRIED | Marital Status of HH Head (1 = Married; 0 = Single) | |
HSIZE | Household Size (Continuous) | |
NUMBER5 | Number of Children below 5yrs (Continuous) | |
VGM | Village Group Member (1 = Yes; 0 = No) | |
ACCLOAN | Access to Credit (1 = Access; 0 = No Access) | |
RNET | Road Condition Index (1 = Bad; 2 = Fairly Good; 3 = Good) | |
HSEFAC | Distance from House to Nearest Health Facility (Km) | |
Farm Characteristics | ||
NUMFARMS | Number of Farms Owned (Continuous) | |
FARMMKT | Distance from Farm to Nearest Market (Km) | |
FARMMRD | Distance from Farm to Major Road (Km) | |
Livestock and Assets | ||
CATTLE | 1 = At Least a HH Member Raises Cattle; 0 otherwise | |
SHEEP | 1 = At Least a HH Member Raises Sheep; 0 otherwise | |
GOAT | 1 = At Least a HH Member Raises Goat; 0 otherwise | |
POULTRY | 1 = At Least a HH Member Raises Poultry; 0 otherwise | |
GNFOWL | 1 = At Least a HH Member Raises Fowls; 0 otherwise | |
BICYCLE | 1 = At Least a HH Member Has a Bicycle; 0 otherwise | |
MOTORBIKE | 1 = At Least a HH Member Has a Motor Bike; 0 otherwise | |
TRUCK | 1 = At Least a HH Member Has a Truck; 0 otherwise | |
TV | 1 = At Least a HH Member Has a Television; 0 otherwise | |
MPHONE | 1 = At Least a HH Member Has a Mobile Phone; 0 otherwise |
Source: Authors.
Given the propensity score
where
Equation (2) is estimated by logistic regression for the determinants of participation in non-farm employment. The results from the logit estimation are shown in
The propensity scores are computed based on the logistic model and they serve as a tool to balance the observed distribution of covariates across the treated and the untreated group [
The results of the treatment effects (ATE, ATT and ATU) for non-farm employment participation was computed by the near neighbor matching technique and is presented in
The results from this matching technique were very robust, signifying the appropriateness of the matching algorithm employed. The ATT for household income indicates that participating in non-farm work increases household income by some Ghȼ5528. The ATE is Ghȼ5210, indicating the magnitude by which non-farm work participation increases household income for the total population. Similarly, the ATU of Ghȼ 4695 on household income shows that if non-participants of non-farm work were to participate, their household incomes would increase by about Ghȼ4695. The ATT on food security from non-farm work participation is 0.42, which is statistically significant and indicates that participating in non-farm work increases the food security index by 0.42
Description | Food Secure | Food Insecure |
---|---|---|
Percentage of Households | 54.75 | 45.25 |
Number of Households | 219 | 181 |
Mean Food Security Index | 1.49 | 0.59 |
Standard Deviation | 0.37 | 0.24 |
Source: Authors’ computation using STATA 13.
Variable Name | Variable Definition | Participants N = (247) 61.75% | Non-participants N = (153) 38.25% | Difference in mean | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Mean | S.D. | Mean | S.D. | |||||||
Treatment Variable | ||||||||||
PARTICIPATE | 1 = Yes; 0 = No | |||||||||
Outcome Variables | ||||||||||
FSS | Food Security | 1.25 | 0.49 | 0.82 | 0.55 | 0.43*** | ||||
(Food secure if >1; Food insecure if <1) | ||||||||||
HHINC | Household Income (GH₵) | 8741.59 | 10,137.31 | 2355.36 | 2447.18 | 6386.23*** | ||||
Independent Variables | ||||||||||
Household Characteristics | ||||||||||
SEX | 1 = Male; 0 = Female | 0.76 | 0.43 | 0.80 | 0.40 | −0.04 | ||||
AGE | Farmers’ Age | 49.50 | 10.79 | 52.82 | 11.80 | −3.32*** | ||||
AGESQ | Age*Age | 2566.01 | 66.61 | 2928.02 | 99.13 | 362.01 | ||||
REG | 0 = Upper East; 1 = Upper West | 1.52 | 0.50 | 1.46 | 0.50 | 0.06 | ||||
BASICEDU | 1 = Yes; 0 = No | 0.74 | 0.44 | 0.61 | 0.49 | 0.12** | ||||
SECEDU | 1 = Yes; 0 = No | 0.33 | 0.47 | 0.25 | 0.44 | 0.07 | ||||
TEREDU | 1 = Yes; 0 = No | 0.19 | 0.39 | 0.11 | 0.32 | 0.08** | ||||
MARRIED | 1 = Married;0 = Single | 0.83 | 0.37 | 0.82 | 0.38 | 0.01 | ||||
HSIZE | Household Size | 11.04 | 9.14 | 13.42 | 12.73 | −2.38** | ||||
NUMBER5 | Children below 5yrs | 1.58 | 1.93 | 1.82 | 2.34 | −0.24 | ||||
VGM | Village Group Member | 0.49 | 0.50 | 0.24 | 0.44 | 0.26*** | ||||
ACCLOAN | 1 = Access; 0 = No Access | 0.30 | 0.44 | 0.21 | 0.41 | 0.09* | ||||
RNET | An index where 1 = Bad; 2 = Fairly Good; 3 = Good | 2.17 | 0.62 | 1.96 | 0.50 | 0.21*** | ||||
HSEFAC | House-Health Facility (Km) | 4.06 | 4.61 | 6.13 | 5.32 | −2.06*** | ||||
Farm Characteristics | ||||||||||
NUMFARMS | Number of Farms Owned | 2.85 | 1.72 | 2.27 | 1.50 | 0.58*** | ||||
FARMMKT | Farm-Market (Km) | 5.96 | 6.05 | 8.20 | 11.61 | −2.24** | ||||
FARMMRD | Farm-Major Road (Km) | 4.44 | 4.13 | 7.33 | 11.16 | −2.89*** | ||||
Livestock and Assets | ||||||||||
CATTLE | 1 = Raises Cattle; 0 otherwise | 0.63 | 0.48 | 0.60 | 0.49 | 0.03 | ||||
SHEEP | 1 = Raises Sheep; 0 otherwise | 0.65 | 0.48 | 0.70 | 0.46 | −0.05 | ||||
GOAT | 1 = Raises Goat; 0 otherwise | 0.88 | 0.32 | 0.91 | 0.29 | −0.03 | ||||
POULTRY | 1 = Raises Poultry; 0 otherwise | 0.86 | 0.35 | 0.86 | 0.35 | 0.00 | ||||
GNFOWL | 1 = Raises Fowls; 0 otherwise | 0.70 | 0.46 | 0.75 | 0.44 | −0.05 | ||||
BICYCLE | 1 = Has a Bicycle; 0 otherwise | 0.94 | 0.23 | 0.92 | 0.27 | 0.02 | ||||
MOTORBIKE | 1 = Has a Motor Bike; 0 otherwise | 0.75 | 0.43 | 0.56 | 0.50 | 0.19*** | ||||
TRUCK | 1 = Has a Truck; 0 otherwise | 0.32 | 0.47 | 0.24 | 0.43 | 0.08* | ||||
TV | 1 = Has a Television; 0 otherwise | 0.77 | 0.42 | 0.69 | 0.46 | 0.07 | ||||
MPHONE | 1 = Has a Mobile Phone; 0 otherwise | 0.97 | 0.17 | 0.92 | 0.27 | 0.05** | ||||
*Significant at 10%, **Significant at 5% and ***Significant at 1%; Source: Authors’ computation using STATA 13.
Variable | Coefficient | Std. Error | Z-Value | Marginal Effect | P > Z |
---|---|---|---|---|---|
Household characteristics | |||||
SEX | −0.1939 | 0.3699 | −0.52 | −0.0428 | 0.501 |
AGE | −0.0778 | 0.0811 | −0.96 | −0.0175 | 0.338 |
AGESQ | 0.0012 | 0.0008 | 1.47 | 0.0003 | 0.143 |
REG | −0.7204 | 0.3348 | −2.15 | −0.1606** | 0.029 |
BASICEDU | 0.5369 | 0.3059 | 1.76 | 0.1237* | 0.084 |
SECEDU | −0.4902 | 0.4238 | −1.16 | −0.1129 | 0.256 |
TERTIARY | 0.4537 | 0.5037 | 0.9 | 0.0963 | 0.336 |
MARRIED | −0.2169 | 0.4023 | −0.54 | −0.0475 | 0.58 |
HSIZE | −0.0331 | 0.0203 | −1.63 | −0.0074 | 0.102 |
NUMBER5 | 0.0456 | 0.0828 | 0.55 | 0.0102 | 0.582 |
VGM | 0.9426 | 0.2874 | 3.27 | 0.2117*** | 0.001 |
ACCLOAN | 0.1815 | 0.3469 | 0.52 | 0.0412 | 0.595 |
RNET | 0.7813 | 0.2613 | 2.99 | 0.1755*** | 0.003 |
HSEFAC | −0.1018 | 0.0411 | −2.48 | −0.0229*** | 0.009 |
Farm Characteristics | |||||
NUMFARMS | 0.2556 | 0.1013 | 2.52 | 0.0574*** | 0.011 |
FARMMKT | −0.0145 | 0.0167 | −0.87 | −0.0033 | 0.384 |
FARMMRD | −0.043 | 0.0403 | −1.07 | −0.0097 | 0.288 |
Livestock and Assets | |||||
CATTLE | −0.0597 | 0.3061 | −0.19 | −0.0134 | 0.845 |
SHEEP | −0.0392 | 0.3504 | −0.11 | −0.0088 | 0.911 |
GOAT | −0.4608 | 0.4754 | −0.97 | −0.0968 | 0.294 |
POULTRY | −0.2736 | 0.4006 | −0.68 | −0.0594 | 0.478 |
GNFOWL | −0.2387 | 0.3386 | −0.7 | −0.0526 | 0.473 |
BICYCLE | 0.3355 | 0.567 | 0.59 | 0.0784 | 0.567 |
MOTORBIKE | 1.0674 | 0.3306 | 3.23 | 0.2478*** | 0.001 |
TRUCK | 1.0875 | 0.3834 | 2.84 | 0.2218*** | 0.001 |
TV | −0.1032 | 0.3491 | −0.3 | −0.023 | 0.766 |
MPHONE | 1.0695 | 0.609 | 1.76 | 0.2593* | 0.077 |
CONSTANT | −2.4542 | 2.2282 | −1.1 | ||
LR χ2 (27) | 205.73 | ||||
Prob > chi2 | 0.01 |
*Significant at 10%, **Significant at 5% and ***Significant at 1%. Source: Authors’ computation using STATA 13.
Outcome Variable | PSM | Critical Value | Treated | Control | |||||
---|---|---|---|---|---|---|---|---|---|
ATT | ATU | ATE | Γ | On-support | Off-support | On-support | Off-support | ||
Food Security | FS | 0.42*** | 0.33*** | 0.37*** | 3.6 | 173 | 74 | 108 | 45 |
(6.27) | (3.23) | (4.40) | |||||||
Household Income | HHINC | 5527.7*** | 4695.2*** | 5209.6*** | 5.8 | 186 | 61 | 115 | 38 |
(8.39) | (5.71) | (7.96) |
*Significant at 10%, **Significant at 5% and ***Significant at 1%; Note: t-values in parentheses. Source: Authors’ computation using STATA 13.
points. An ATE of 0.37 on food security shows that non-farm work participation increases food security index by 0.37 for the total population. An ATU of 0.32 on food security indicates that if non-participants of non-farm work were to participate, their food security index points would increase by about 0.32.
The value of the coefficient of the treatment effects indicate that the Average Treatment Effects for the treated (ATT) are higher than the Average Treatment Effects for the whole sample (ATE) and the Average Treatment Effects for the Untreated (ATU) for both household income and food security status. This finding indicates that households that have a higher probability of participation in non-farm employment receive higher incomes and enjoy improved food security status compared to households that do not participate in non-farm work. Our findings corroborate [
Results from the sensitivity analysis on hidden bias, which shows the critical levels of gamma (Γ) at which the causal inference of participation impact may be questioned are also presented in
The indices of matching quality presented in
This indicates that the PSM technique significantly balanced the covariates. The pseudo-R2 for both outcome indicators after matching are generally low and all of the diagnostic statistics are significantly equal to zero, showing that the overall outcome from the matching are sufficient in balancing the covariates among non-farm work participants and non-participants [
This paper sought to investigate the impact of non-farm employment on household income and food security among farm households in the Upper East and Upper West regions of Ghana. A food security index, using the Recommended Daily Calorie Required (RDCR) approach, was constructed to ascertain the food security status of households. We employed the propensity score matching technique to avoid biases arising from unobservable factors that might influence participation in non-farm employment and the outcome variables, namely, household income and food security status.
We found 45 percent of households in the sample to be food insecure. The result suggests that in spite Ghana’s remarkable success in halving consumption poverty over the past decades, there exists a relatively high level of food insecurity in the northern Regions as reflected by high poverty incidence indices. The propensity score matching results show that non-farm employment has a statistically significant positive effect on the income of households as well as their food security status. Our findings confirm existing literature on the effects of non-farm activity participation on the welfare of rural farm households. The paper also identifies factors such as basic education attainment, village group membership, road network, distance to the nearest health facility, number of farms owned, and ownership of assets such as motorbike, truck or mobile phone as significant determinants of non-farm work participation.
The results imply that policy must focus on promoting non-farm employment opportunities in rural farming communities in Ghana, especially in the Upper East and Upper West regions, given its impact on food security and incomes. Any policies targeted at promoting food security should go beyond just food production measures; they should include both food production measures and measures that help generate additional incomes for rural farm households through the development of alternative livelihood opportunities. Not only is diversification into
Outcome Indicator | Pseudo-R2 Unmatched | Pseudo-R2 Matched | µ Bias Unmatched | µ Bias Matched | Bias Reduced |
---|---|---|---|---|---|
FS | 0.223 | 0.063 | 20.7 | 6.9 | 66.67 |
(0.000) | (0.362) | ||||
HHINC | 0.223 | 0.055 | 20.7 | 6.2 | 70.05 |
(0.000) | (0.444) |
Note: P-Values in parentheses. Source: Authors’ computation using STATA 13.
non-farm work a dependable supplementary source of income for rural households, it also helps in smoothing income and consumption. Thus, while this paper is not advocating for non-farm income activities as a substitute to farming, non-farm work could be a reliable complement to farming activities. Policy should therefore focus on making non-farm work opportunities available to rural households and help them overcome entry barriers. Such measures may include increasing the access of rural households to physical, financial, social and human capital. Physical capital measures such as good roads, dependable electricity supply and general infrastructural development will help to reduce production and transportation costs. Improving access to education in rural communities would also enhance non-farm employment opportunities, particularly, non-farm wage employment. The study shows that membership of a village group has a positive association with non-farm work participation. Promotion of the formation and participation in village groups and associations, including farmer-based organizations, will go a long way in enhancing non-farm employment opportunities for rural farm households.
Daniel Osarfo,Bernardin Senadza,Edward Nketiah-Amponsah, (2016) The Impact of Nonfarm Activities on Rural Farm Household Income and Food Security in the Upper East and Upper West Regions of Ghana. Theoretical Economics Letters,06,388-400. doi: 10.4236/tel.2016.63043