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https://doi.org/10.56556/gssr.v2i1.440 

                                                                  

 

Global Scientific Research     22 
 

Productive Safety Net Program Determinants and their Impact on Rural Household 
Food Security in Somali Regional State: The Case of Kebri Dehar District) Ethiopia  

Abdi Hassen Habib1*, Mohamed Abdirizak Haji1, Abdukerim Ahmed Mumed2 

 

1Department of Accounting and Finance, College of Business and Economics, Kebri Dehar University, Kebri Dehar, 
Ethiopia  
2Rural development and Agricultural Extension, Kebri Dehar University, Kebri Dehar, Ethiopia 

Corresponding Author: Abdi Hassen Habib; email: abdihassen1170@gmail.com 

Received: 06 February, 2023, Accepted: 18 March, 2023, Published: 20 March, 2023 

 

Abstract: The study looked specifically at household food security, factors influencing people's decisions to participate 

in productive safety net programs, and the impact of production safety net programs on rural household food security, as 

measured by calorie intake. Primary data were collected from 334 households in four kebeles samples in Kebri Dehar, 

the district, using a multistage sampling technique. A binary logit model and a propensity score matching model were 

used to investigate the factors influencing the decision to participate and their impact on food security, respectively. The 

age of the household head and the size of the family have a positive effect on the decision to join the household. 

However, extended contact and distance from the property market had a negative impact on the decision to join. In this 

study, the nearest neighbor match method (5) was used to estimate the mean treatment effect for those treated. The 

propensity score matching results also show that the production safety net program has a positive and significant impact 

on household food security. Households participating in the production safety net program have 214.5 kcal/adult/day 

more than households not participating. The study concluded that there was a significant difference in household calorie 

intake between participants and nonparticipants. 

 

Key words: Food security; Impact; Productive safety net program; Propensity score matching 

 

Introduction  

Background of Study 

Food security emerged as a concept in the mid-1970s 

after a number of implications sparked debates about the 

global food supply and its responsiveness at both the 

global and global levels (Ingela and Nagothu, 2017). 

However, problems such as family or man-woman 

supply, dietary quality, and environmental sustainability 

have not been noted (Ingela and Nagothu, 2017).  

In terms of food security, Ethiopia is one of the poorest 

international locations in Sub-Saharan Africa. A large 

proportion of the country's population suffers from 

chronic and power meal insecurity (Anderson et al., 

2015). Many elements contribute to rural poverty and 

persistent food insecurity. The important causes of food 

insecurity in Ethiopia are drought and animal diseases, 

restrained rural infrastructure, a very susceptible 

agricultural technical base, constrained access to 

fundamental services, and fluctuating file costs   (FISN, 

2017).   

The population's meal insecurity is anticipated to 

increase from 5.6 million in 2016 to 8.5 million in 2017 

(WFP, 2017). Food insecurity is often understood in 

Ethiopia in the context of repeated meal crises and 

famines, and the response to meal insecurity is regularly 

dominated by the meal emergency response. Between 

1994 and 2005, nearly 5 million Ethiopians were 

declared "inclined" and "wanting" assistance. However, 

large proportions of households receive emergency food 

assistance, participate in community service projects, 

and no longer go hungry every year; they are, however, 

frequently food insecure. Because of constrained 

agricultural manufacturing and poverty, they face 

predictable annual meal shortages. 

As a result, despite a long history of providing large 

amounts of food, the safety of their meals has 

deteriorated over time. Instead, reliance on meals as a 

useful resource has progressively extended over time, as 

has the wide variety of Ethiopians experiencing 

continual meal insecurity (Devereux et al. 2006). The 

purpose of the Safety Net Program is to reallocate assets 

to chronically food-insecure households and enhance 

long-term options for food-insecure households (USAID, 

2012). 

 Statement of Problem  

The Food Security Program used to be created to tackle 

the difficulty of meal manufacturing while additionally 

enhancing people's lives and alleviating poverty. 

Recognizing its significance, the authorities have taken a 

mailto:abdihassen1170@gmail.com


Global Sustainability Research 

 
Global Scientific Research   23 

 

number of steps. Beginning in 2005, the Ethiopian 

authorities and donor companies carried out a new kind 

of protection, the Productive Safety Net Program 

(PSNP), with ambitions to minimize persistent meal 

insecurity, asset depletion, and productive funding to 

overcome long-term meal insecurity (Gilligan et al., 

2008).  

The application is aimed at her 8.5 million inhabitants, 

who make up 10% of Ethiopia's population. These 

human beings are chronically food insecure; 60% of 

them stay in pastoral areas, and their buying power has 

decreased due to farm animals' losses. The final 40% are 

affected by erratic rainfall, which reduces crop 

manufacturing (WFP, 2017)  

In the Somali region, the Ethiopian authorities have 

prepared to assist negative rural households registered in 

chronically food-insecure districts. Learn about the 

effect of productive protection internet packages (PSNP) 

on family livelihoods with the help of (Mohamed, 2017): 

Babile Case discovered that effective internet security 

programs had a significant impact on the food safety of 

families. Various studies have been performed on the 

outcomes of PSNP in special fields and at exclusive 

times. 

The findings of these investigations varied. Food safety 

research, on the other hand, necessitates a 

multidimensional shift supported by well-informed 

lookup disciplines. By assessing the influence of PSNPs 

on meal safety in rural households, this study contributes 

to the current body of knowledge. As a result, the goal of 

this study is to fill knowledge gaps about the factors that 

influence productive safety net programs determinants 

and their impact on rural household food security.  

 

Objective of the study  

The primary aim of this study was to assess the 

Productive Safety Net Program Determinants and Their 

Impact on Rural Household Food Security in Somali 

Regional State: The Case of Kebri Dehar District) 

Ethiopia 

Specific objectives are as follows: 

1. To investigate the current food security 

situation of rural households in the study area. 

2. Determining the Impact of Productive Safety 

Net Programs on Food Security in Rural Households 

3. Assessing the Impact of Productive Safety Net 

Programs on Food Security in Rural Households 

Significance of the Study  

It was thought that the study's findings will benefit the 

ongoing RPSNP efforts in rural Kebri Dahar households 

to enhance the current state of food security. By adding 

fresh information to the research of food security in 

relation to PSNP in Rural households of the Korahey 

zone, the study thereby fills a gap in the existing 

understanding regarding PSNP. Additionally, it might 

assist other academics who want to look into the PSNP 

issue more deeply. Additionally, the study's 

methodological importance helped other researchers by 

imparting knowledge and learning lessons to better 

understand the difficulties, achievements, and improved 

living standards of RPSNP. The study served as a 

starting point for future investigations into the PSNP 

problem. 

Empirical Literature 

Results of a find out about carried out via of Abdusalem 

(2017) we learned that household size, schooling level, 

increased seed use, and distance to the nearest market 

have been drastically positively associated with a rural 

household's likelihood of participating in a productive 

safety net program. It shows that you made an impact. 

There were significant negative effects on participation 

in productive safety net programs 

According to Abdukarim (2015), family size, active 

workforce, access to credit, off/off farm income, farm 

income, and household education level significantly 

influence participation in productive food security 

programs.  In the same study by  Tadelee (2011) Family 

safety nets in productive programs have been linked to 

the gender of the family head, the educational level of 

family members, meal protection issues, and the 

frequency of contact with improved employees. 

Additionally, a high-quality affiliation was confirmed 

between big household measurement and participation in 

the PSNP, suggesting that small family measurement 

was once related to larger meal demand compared with 

smaller households excessive and may additionally be 

related to a greater possibility of meal insecurity. 

According to Ayalneh and Wubshet (2012) livestock 

ownership correlates positively with well-being but 

negatively with program participation, whereas having 

the ability to use credit services correlates positively 

with program participation. The study undertaken by 

Yibrah (2010) discovered that as a household's age 

increased, so did the likelihood of participating in a 

productive safety net program, which had a negative 

effect on participation.  

The find out by Aman (2014) holdings, cultivated land, 

and distance from markets significantly affect 

participation in productive safety net programs. 

Participation in the PSNP was positively and 

significantly affected by variable distances from the 

market, whereas participation was negatively and 

significantly affected by access to credit services, 

extension contacts, the number of oxen, livestock 

holdings, and cultivated land. Similarly, the study by 

Anwar (2015) indicated that family head educational 

level, household size, livestock holding, extension 

remoteness, and market distance were significantly 

associated with participation in productive safety net 

programs. Also, the study undertaken by Mesfin (2018) 

indicated that the educational level of households  



Global Sustainability Research 

Global Scientific Research     24 
 

Conceptual Framework 

 It is clear that several factors may help to explain the 

determinants of household food security and the Rural 

Productive Safety Net Program (RPSNP). Based on the 

objective of the study, the independent variables selected 

to achieve the ultimate objective of the study are broadly 

categorized into socioeconomic, institutional, and 

demographic factors the relationship between two 

variables in this study. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1: Conceptual Frame work of food security and UPSNP 

Source:  Own design based on literature review   

Method of Research. 

In this chapter, the research methodology for the study is 

described, along with a description of the research field, 

data sources and types, target population, sampling 

techniques, sample size, data collection methods, data 

analysis techniques, and justifications for their use. 

 

Study Area 

The Korahey Zone contains the study area. The distances 

between the city and Addis Ababa, the capital of 

Ethiopia, and Jigjiga, the regional capital of Somalia, 

respectively, are approximately 405 km and 1015 km. 

The Korahey Zone had a total population of 312,713 

people as of the 2007 Central Statistics Agency (CSA) 

census, 177,919 of whom were men and 134,794 of 

whom were women. The majority of these people 

belonged to pastoral societies. I'm right here. This 

region's latitude and longitude are 6° 44' N, 44° 16' E / 

6.733° N, 44.267° E, and its elevation is 493 meters 

above sea level. Kabri Dahar Governorate has a total 

population of 136,142, of which 77,685 are men and 

58,457 are women, according to the Central Bureau of 

Statistics for 2007. The remaining 50,361 people (6 .99 

percent) are pastoralists, while 29,241 people (21. 48 

percent) live in cities. 

Research Design  

 

For this study, the researchers used descriptive and 

explanatory research designs. The research design refers 

to the processes and methods used to gather and analyze 

the necessary data. The goals a researcher wants to 

accomplish or the research questions they want to 

address will determine the study strategy they use 

(Croswell, 2007). According to (Kothar, 2004), claims 

that surveys and diverse types of fact-finding enquiries 

are included in descriptive research.   

Socio economic factors 

✓ Livestock household 

ownership, 

✓ off/farm income 

 

 

Scio demographic  

✓ Gender of head of 

household, 

✓ Number of family  

✓ Age of head of household, 

✓ Educational background of 

head of household, 

 

 

 

 Institutional factor 

✓   Credit service 

✓ Access to market information 

✓  Distance from Market,  

✓ Extension contact 

 

 

Dependent variable  

Household’s 

Participation in 

PSNP  

 

Outcome variable 

 

Food security Status 

 



Global Sustainability Research 

 
Global Scientific Research   25 

 

Sampling Procedure and Determine the sample size. 

Several steps of a sampling process were used to create a 

sample of respondents in the first phase. The Kebri-

Dehar region was deliberately chosen because of its 

widespread application of productive social protection 

programs. With 11 in the second-stage rural kebeles, five 

kebeles were randomly chosen as participants and non-

participants in a productive social protection program. In 

the third step, sample 1 obtained from Kebele's office is 

divided into two groups, and proportions are used. 

 

                                                    n =
Z2pqN

e2(N−1)+Z2pq
   

                                 n =
(1.96)20.5 ∗0.5 ∗4577

(0.05)2(4577 −1)+(1.96)2∗0.5∗0.5 
=354 

The survey was carried out with the help of development 

agents (DAs) in each of the target. Where: n = sample 

size; N = total population (1,925); Z = 95% confidence 

interval under the normal curve (1.96); e = acceptable 

error term (0.05); and P and q are estimates of the 

proportion of the population to be sampled (P = 0.5 and 

p + q = 1). 

Sources and Types of Data 

 

To obtain the necessary information for this study, both 

quantitative and qualitative data were collected from 

primary and secondary data sources. Demographic, 

socio-economic, market and institutional-related 

variables relevant to the study were collected based on 

the nature of the information needed on various aspects 

of this study, employing a mixed method of data 

collection methods to generate adequate and reliable data 

from the respondents. In addition, data on the type of 

food items consumed by households in the last seven 

days were also collected. 

 

Data analysis techniques 

 

The methods of data analysis used in this study were 

both quantitative and qualitative. To analyze the data, 

descriptive and econometric methods were employed. 

Using frequencies, percentages, means, and standard 

deviations, descriptive statistical analysis techniques 

were used to discuss the results. To confirm the 

existence of statistically significant differences and 

systematic associations between the program and 

program participants on the hypothesized variables, chi-

square tests and t-tests were used. Frequency, percent, 

and chi-square tests were used to analyze different types 

of quantitative categorical data. The Social Science 

Statistics Package (SPSS) version 25, STATA 13, and 

Excel were used to analyze the data for this study. 

Food Security Measurement Models  

The household caloric acquisition approach: this model 

was used to measure household food security, which is 

measured by daily calorie intake, which is a continuous 

variable measured by Kcal/AE/day at the household 

level. To measure the food security of households in the 

study area, information concerning the types and 

amounts of food items prepared for consumption by each 

household in the last seven days preceding the survey 

was collected.      

Econometrics model specification  

Binary logistic regression  

 

The binary logit model is defined as follows, according 

to Gujarati, (1995): 

𝑃𝑖=∈ (𝑌=1/X𝑖) 

=
1

 1+e−(β0+β1Xi) ………………………….…………………

…………………. (2) 

Equation (1) can be expressed by: 

P𝑖=
1

1+e−Zi…... (3) 

Where; Zi=β0+β1X¡ 

If Pi is, the probability of being participated and the 

probability of not participated in productive safet net 

program 1- Pi, which is expressed, follows in equation 3. 

1−P𝑖= 
1

1+eZi ………………………………….…………………

…………………...…… (4) 

Equation 4 is obtained by dividing the participator to 

non-participator 
Pi

1−Pi 
=

1+eZi

1+e−Zi =eZi .…………………..…….………………

…………………………….. (5) 

Therefore; 

 Is the odds-ratio (the ratio of the probability that an 

individual would choose an alternative). 

 Pi is the probability of household participating ranging 

from 0 to 1. 

Taking natural logarithms of
pi

(1+pi)
 =ezi 

Li = Ln 
pi

(1+pi)
 = β0+β1X1+……………+ βkXk+µi………

…………………………… ... (6) 

Where; Zi = β0+β1X1+ 

β2X2................................................βkXk 

Zi = is a function of k-independent variables β0 = is the 

intercept or constant term  

Xi = ith independent variable K = represents number of 

independent variables  

Xk= Total number of independent variables. 

 

Propensity Score Matching (PSM) 

 

The propensity score matching method was used in this 

study to analyze the impact of a productive safety net 

program on rural household food security.  

According to Caliendo and Kopeinig (2008), some steps 

apply in PSM. These steps are predicting propensity 



Global Sustainability Research 

Global Scientific Research     26 
 

scores, choosing matching algorithms, restricting 

common support areas, testing the matching quality or 

balancing tests, and performing sensitivity analysis. 

These are described as follows: 

Step 1: Propensity scores: A logistic model is used to 

estimate propensity scores for each observation. The 

advantage of this model is that the probabilities are 

bounded between zero and one. The dependent variable 

is dichotomous, taking two values: 1 if an individual 

participated in a productive safety net, and 0 otherwise. 

The covariates used to predict treatment assignment 

using logistic regression are specified as follows: 

Li = ln (
pi

1 − pi

) = ln (eβ° + ∑ βjXJi

n

j=1

) = Zi

= β° + ∑ βjXJi

n

j=1

 

Where  Li  is a log of the odds ratio in favor of 

participating in productive safety net program? 

Zi = participant 

β° = intercept 

βj  = regression coefficient to be estimated 

 

Discussion and conclusions 

 

Descriptive statistics study findings 

 

Age of Household Heads: The mean age of the entire 

sample of all interviewed household heads was 48.03 

years, with a standard deviation of 12.08. As a result, she 

had an average age difference of 1.48 years between 

households that were enrolled in the program, which had 

an average age of 48.80 years, and households that were 

not enrolled, which had an average age of 47.32 years. 

The average age of the sample household is 86, while the 

median age is 21, and vice versa. 

 

Education: The highest grade was ninth grade, with the 

lowest grade being the 0th. The average educational 

background of all household heads in the survey area 

was 1.74. Thus, with a mean difference of 0.32 years, the 

average number of school years for households in the 

program versus those who did not participate was 1.58 

for the former and 1.885 for the latter. Between 

households with and without program participants, they 

discovered that there was no statistically significant 

difference in the level of education. 

 

Family Size: There were 5.1706 people living in each 

household on average in our sample of respondents. 

When respondents were split into households with and 

without program participants, the average family size 

was 5.575 and 4.798, respectively. According to the 

statistical analysis, there was a statistically significant 

difference at the level of 5% (Chi2 = -0.426 and p = 

0.0061).Livestock: The average number of livestock 

owned by participating and non-participating households 

in the sample surveyed was 2.979 at TLU. According to 

the survey's findings, livestock had a mean difference in 

TLU of 0.062 and 3.01 TLU for program participants' 

households and 2.948 for non-participating households, 

respectively. The results of a t-test (t = -0.426 and p = 

0.000) also indicated that this difference was not 

statistically significant.    

 Extension Contacts: Across all households in the study 

area, there were, on average, 2,455 extension 

contacts. There were, on average, 2.13 and 2.74 contacts 

between households participating in the program and 

households not participating, with a mean difference of 

0.04. The difference was also statistically significant 

with a probability of 5%, according to a t-test (t=4.7603 

and p=0.0000). According to statistical findings of the 

typical distance across the sample of respondents from 

the market center, the market distance (km) between 

participating and non-participating households for a 

sample of respondents is 15.82 km. The findings 

revealed that program participants were spaced apart on 

non-participants was 10.51 km and 20.71 km, with a 

mean difference of 10.02 km 

 

 

Table 1: Descriptive statistics for continuous variables 

 

Variables 

Total sample 

Households (334) 

Participant (160) Non-participant (174)  

T-test 

 

P-value 

Mean Std. Mean Std. Mean Std. 

Age (years) 48.03 12.08 48.80 11.60 47.32 12.51 -1.121 0.2629 

Education level (years) 1.74 2.61 1.58 2.498 1.885 2.709 1.040 0.149 
Family size 5.1706 2.59 5.575 3.119 4.798 1.920 -2.762 0.0061 

Livestock holding (TLU) 2.979 1.37 3.01 1.37 2.948 1.373 -0.426 0.6703 

Extension contacts            2.455 1.206 2.13 1.162 2.74 1.175 4.7603 0.0000 

Market distance(km) 15.82 15.47 10.51 12.18 20.71 16.55 6.365 0.0000 

. 



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Global Scientific Research   27 

 

Determinant of Rural participant households 

programs Food Security 

According to Table 2 of the PSNP participant 

households program, the binary logit model was 

estimated to determine the primary factors influencing 

household decision-making. The dependent variable in 

the PSNP is a dummy that represents the household 

program and has a value of 1 for participant household 

programs and 0 for non-participant household programs. 

The specified binary logit model's explanatory power is 

adequate because the model's overall validity has been 

established and it is statistically significant at a P-value 

of 000. The pseudo-R-square was found to be around 

0.1898, indicating that all explanatory significant 

variables included in the model explained 18.98 percent 

of the probability of household program participant 

households. The logit model's overall significance can 

also be inferred from the LR chi2 (10) = 87.76 and p-

value (Prob > chi2) = 0.000.  

Table 2. Marginal effect from logit estimation for 

determinants of participation in PSNP 

PSNP Coef. Std. Err. Z P>z 

SEXHH -.7766406 .2727943 -2.85 0.004 

AHH .0273654 .011947 2.29 0.022 

EDL -.0416035 .0540904 -0.77 0.442 

FMS .1186982 .0526031 2.26 0.024 

OFFACTV .0395364 .2829799 0.14 0.889 

LOWSHIP -.1033271 .0959166 -1.08 0.281 

MKINFRMN .3018203 .261217 1.16 0.248 

EXTCONT -.4575165 .1117969 -4.09 0.000 

CRDTSERV .5167317 .2851846 1.81 0.070 

MRKTDST -.0564234 .011167 -5.05 0.000 

_cons .4784761 .7796265 0.61 0.539 

 

 

334 obs are present. 

LR chi2(10) 87.76 

Prob > chi2 0.0000. 

-187.33573 log likelihood. 

Pseudo-R2 0.1898 

 

The binary logit model's results demonstrate that, of the 

10 explanatory variables used for analysis, five are 

significantly related to households participating in the 

program, while the remaining five have a minimal 

impact and are more useful in describing the variation of 

households participating in the dependency program in 

the study area. At a 5 percent significance level, these 

are the respondent's age, family size, household age, 

gender, and distance to the market. Other factors like 

household education, market information access, credit 

service availability, and off-farm/non-farm activities did 

not differ significantly between program participants and 

non-participants (above table). 

Age of the household head: The results of the binary 

logit model indicate that, at a 5% level of significance, 

the household head's age positively and significantly 

affects the likelihood of households participating in the 

PSNP program. When compared to younger people, 

household heads are more likely to participate in the 

program as they get older. The outcomes agree with 

Mohammed (2017). 

Family size: The results of the binary logit model 

indicate that, at the 5% level of significance in the study 

area, the age of the household head has a positive and 

significant impact on the household probability. 

Statistical analysis reveals that, in terms of household 

size, there is a statistically significant difference between 

participants and non-participants. Similar to this, focus 

group discussions reveal that households participating in 

the PSNP have more family members than non-

participating households According to Mohammed 

(2017) and Mesfin (2018). 

Extension contact: The results of the binary logit model 

indicate that, at the 5% level of regional significance, the 

age of the household head significantly and positively 

influences the likelihood of households participating in 

the PSNP program. When compared to younger people, 

household heads are more likely to participate in the 

program as they get older According to ((Mohamed, 

2017). 

 

Propensity scores matching model on PSNP's effect 

on rural household food security 

Calculating the propensity score 

Using the propensity score matching technique, the 

impact of a production safety net program on rural 

households' diets was evaluated. PSM deployment 

consists of five steps. These include calculating p-scores, 

selecting a matching method, ascertaining overall 

support, calculating fit quality and effect, and carrying 

out a sensitivity analysis. Using the logit model, 

propensity scores (ps scores) for participating and non-

participating households were calculated. This stage 

gathers all the information on the independent variables 

that were generated using propensity score matching, 

which was used to perform the match on a single 

variable. 



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Global Scientific Research     28 
 

A very low R2 value of 0.189, as seen in the table below, 

signifies that there aren't many differences between the 

typical characteristics of the sample's households. As a 

result, it might not be difficult to find a good match 

between effective safety net participants and non-

participants. The results of the point estimates show that 

the household head's gender, household age, family size, 

extension contacts, service credit, and marketing strategy 

have a significant impact on the production safety net 

program. Negative and insignificant (-187.33573) is the 

predicted logit intercept According to (Caliendo and 

Kopeinig, 2005).  

Imposing Common Support Region between 

Participant and Non-Participant                                               

Based on the likelihood of participation, estimates of 

PSNP participation and propensity scores for all 

participating and nonparticipating households are 

created. The general support condition is the next stage 

in the propensity score matching technique after 

generating propensity score values for participants and 

nonparticipants using logit models. As the primary 

criterion for determining the area of common support 

between the two groups, eliminate any observations with 

a propensity score that is higher than the non-maximum 

participant's and lower than the participant's minimum 

propensity score (Calindo and Kopeinig, 2008). In order 

to determine the general area of support where the 

distribution of propensity scores for the treatment and 

comparison groups overlap, this is necessary (Shahidur 

et al., 2010). 

 Potential scores are estimated for participating or treated 

households and the corresponding non-participating 

(control) household. The estimated propensity scores 

range from 0.0087 to 0.9118, with a mean of 0.6040 for 

participants and 0.3691 for those who have not been 

treated. 

Table 3. Shows the estimated propensity score 

distribution 

Group Observatio

n 

Mea

n 

STD Min Max 

All rural 

househol

ds 

334 .481

6 

.236

6 

.0087 .9669 

Participan

t 

160 .604

0 

.205

5    

.0586   .9669

8 

Non-

participan

t 

174  

.369

1 

.205

8 

.0087

6 

.9118 

In order to ensure the greatest possible comparability 

between the treatment groups (PSNP) and the 

comparison households (no PSNP), local households 

were used as the samples for matching, as previously 

mentioned in the table above. Both PSNP and non-PSNP 

households' propensity score values fall within common 

support categories. This method's fundamental criterion 

is the elimination of all observations with trend values in 

the opposite group that are below the minimum and 

above the maximum.(Kopeinig, 2005)  

 

Figure 2. Density distributions of propensity scores using NNM n (4) 

0 .2 .4 .6 .8 1
Propensity Score

Untreated: Off support Untreated: On support

Treated: On support Treated: Off support



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Global Scientific Research   29 

 

Looking for Common Support 

It was discovered that 316 observations (147 from 

untreated participants and 158 from treated participants) 

were within common support, while 18 observations (13 

from untreated participants and 5 from treated 

participants) were outside the purview of common 

support and regional analysis. But 316 observations—

161 from untreated (non-participants) and 155 from 

treated (participants)—were within common support and 

were included in the analysis. These few observations 

served as the foundation for an analysis of the PSNP 

program's effects on household food security in the 

district. 

Table 4. Support for Psmatch2 

 

Psmatch2 

Treatment 

assignment 

Common support 

Off 

support  

On 

support 

Total 

Untreated (non- 

participant) 

13 161 174 

Treated(participant) 5 155 160  

Total 18 316 334 

  

Choosing of Matching Algorithm. 

The general livelihood realm, additional comparable 

estimators were used to match participating and non-

participating family units. The best outcomes for a good 

estimator depend on a number of factors, including 

testing for equality of means (also known as pseudo-R2) 

and examining the size of paired samples. The ideal 

estimator is one that is fitted, controls for all explanatory 

variables, has a small mean difference between groups, a 

low pseudo R2 value, and a large fitted sample size. The 

conformance quality test estimates are based on the 

following performance standards: The results show that 

5-neighbor agreement with 0.1 bandwidth is the best 

estimator of the available data. 

PSM and covariance balance before and after 

matching are statistically tested     .  

The average standardized bias before and after matching, 

or the overall bias reduction made possible by the 

matching procedure, are shown in the table below. For 

prematched covariates, standardized differences had an 

absolute value ranging from -0.8% to 115.8%. Following 

matching, the residual standardized differences of 

covariates for all covariates fell below the 20 percent 

critical value suggested by Rosenbaum and Rubin (1985) 

and ranged from 1 point 2 to 16 point 7 percent.    

Table 5. Balancing tests of the 

covariates (Pseudo R2, Rubin’s 

B and Rubin’s R) 

Sample Ps R2 LR chi2 p>chi2 Mean Bias Med Bias B R %Var 

         

Unmatched 0.202 93.39 0.000 35.3 30.0 117.7* 1.15 29 

Matched 0.031 13.37 0.270 10.6 11.3 42.1* 1.20 29 

 

As a result, the matching procedure generates a highly 

covariate balance between the treated and control 

samples that is prepared for use in the estimation 

procedure. Similar to this, the t-test showed that all 

covariates were non-significant after adjustment, 

whereas 8 of them were significant before adjustment. 

This demonstrates that the standardized mean difference 

of all covariates used to calculate propensity scores, 

which was 35 point 3 percent before adjustment, is now 

only about 10 point 6 percent. Furthermore, the 

likelihood ratio tests' p-values show that the joint 

significance of the covariates was always rejected after 

adjustment but not before. Low pseudo R2, low 

standardized bias, significantly reduced overall bias, and 

no significant p-values in the adjusted likelihood ratio 

test suggest that trends between the treatment and control 

groups have been successfully identified. 

 

 

4.3.6. The average treatment effects (ATT) are 

estimated. 

The impact of production safety net programs on rural 

households' access to food is demonstrated in this 

section. As a result, the mean therapeutic effect (ATT) of 

PSM was calculated with a neighbor of 0.5. The 

corresponding results only provide proof that production 

safety net programs have a statistically significant effect 

on rural households' access to food. Thus, the program 

participant's 214.5 kcal/EA/household-day increases her 

PSM model results in Table 6 below, showing that 

households taking part in the production safety net 

program have a true average wage guarantee means that 

having a household's food security affected in any way 

by participation in production safety net programs. This 

suggests that under the same covariates, the production 

safety net program has an impact on food security of 

214.5 kcal/AU. The fact that households choose to take 



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Global Scientific Research     30 
 

part in the program seems to make them relatively safer 

and less prone to food insecurity than households who 

do not is encouraging for ATT. 

Table 6. Impact of program participant households 

PSNP on household resilience to food security 

 

Sensitivity Analysis 

It is becoming more and more important for researchers 

to test how robust their findings are to changes in certain 

presumptions. Sensitivity analysis can be used to address 

this problem because non-experimental data cannot be 

used to estimate the level of selection bias. To test the 

putative ATT's sensitivity to departures from the CIA, 

Rosenbaum (2002) suggests employing the Rosenbaum 

boundary approach. 

 

 

 

Table 7. Rosenbaum 

Sensitivity Analysis for 

Hidden Bias 

 

Gamma 

(Γ) 

sig+ Sig

- 

t-hat+ t-hat-   CI+      CI- 

1 0 0 2484.9 2484.9 2350.44 2628.7 

1.25 0 0 2364.47 2614.34 2233 2767.48 

1.5 0 0 2267.76 2724.09 2144.79 2878.3 

1.75 0 0 2193.02 2816.38 2074.93 2966.75 

2 0 0 2132.69 2894.05 2018.08 3041.87 

 

 

 

Recommendations and Conclusion 

Conclusion 

In order to find out how the production safety net 

program affects rural households' access to food, four 

rural areas in the Kabri Dahar district of the Somali 

Regional State of Korahey were randomly selected out 

of a total of 11 rural areas. After thorough research, 

design was used. A multi-stage sampling method was 

used to collect data from the 334 household heads that 

were sampled. 

Results from descriptive statistics reveal a statistically 

significant difference in household characteristics, such 

as age, gender, family size, distance from the closest 

market, and extended contact, between program 

participants and non-participants. However, there were 

no appreciable differences between participants and non-

participants in terms of other factors like household 

education, market information access, financial services 

access, and off-farm activity. Aside from age, family 

size, and distance to the closest market, the logit model's 

findings also revealed contact information for Extension. 

was significantly and negatively affected by PSNP 

participation, whereas household education status, access 

to market information, access to credit services, and off-

farm activity were not significantly different between 

program participants and non-participants. 

The findings show that a total of 194 (58.80%) of the 

sampled households were found to be food secure, 

providing the minimum daily calorie recommendation, 

while 140 (41.92%) were food insecure, not meeting the 

minimum daily calorie requirement. This was 

established by using a cut point of a minimum of 2200 

kcal/AE/day. 

Variable  Sample Treated Controls Difference S. E T-stat 

  Kcal Unmatched  2726.523 2451.010 275.512838 37.5 2.26 

  ATT 2730.041 2515.652 214.388826 165.5 1.30 



Global Sustainability Research 

 
Global Scientific Research   31 

 

The outcome of the impact estimation indicates that the 

study area's household food security was improved by 

the productive safety net program. Compared to non-

participating households, rural households that took part 

in the program consumed 214.5 kcal/AE/day more food 

on average. In comparison to non-participants, program 

participants were older, had larger households, and lived 

farther away from local shops and extended networks. 

Participating families were more likely to match their 

age, gender, family size, and distance from the closest 

marketplace and extension contact person. In order to 

address the issue of food scarcity, the study site's PSNP 

program is essential. The production safety net program 

has had a significant positive impact on participants' 

food security in the study area. These studies support the 

idea that PSNPs significantly improve household food 

security. 

 

Recommendations 

 

The following recommendations are provided in 

consideration of the study's findings mentioned above: 

➢ Household length becoming substantially 

longer has an effect on family participation in PSNP. A 

family with a large age range, a large circle of relatives, 

a long distance from the nearest marketplace, and 

extended touch may be eligible to participate in an 

application. As a result, it is far from optional to take 

into account personal family planning in any 

improvement interventions carried out by authorities and 

non-governmental organizations that may manipulate 

rapid population growth to provide solutions to the 

family meals security problem.  

➢  Finally, given the sure diploma of versions of 

application effect studies, if similarly conducted studies 

with extra scope and in a one-of-a-kind region examined 

the effect of PSNP and different meal protection 

applications on meal protection, it would be better. 

 

Future researches  

The study included a limited number of households and 

did not include all important factors and other aspects of 

food security. It focuses on how the producer safety net 

program affects household food consumption and 

examines the variables that affect PSNP program 

participation. Accurate data on food consumption in the 

research area can be difficult to collect because rural 

households frequently struggle to recall all of their 

consumption details. It's possible that some participants 

and non-participants will be unwilling to provide an 

honest response to inquiries about their animals. This 

limitation may arise due to a lack of information and 

awareness in the rural household study areas. Regardless 

of these limitations, it is anticipated that the study will 

create valuable information that may not be 

advantageous to various stakeholders interested in the 

field. 

 

 

Acknowledgments: The Kebri Dehar University 

provided financial and logistical support, for which the 

authors are grateful. The interviewers, local managers, 

and interviewees were all thanked for their contributions 

Contributions of the author: This research paper was 

written by all authors 

Funding: The Research-Publishing Department at 

KEBRI DEHAR UNIVERSITY and the Technology 

Transfer department provided funding for this study as 

part of their underfunded projects for 2022 

Interests conflict: Regarding the publication of this 

article, the authors affirm that there are no conflicts of 

interest. 

Ethical Consideration: One of the most significant 

aspects of research is ethical considerations. The 

researchers was approach the research participants first 

by seeking their willingness to engage in the study, and 

then identify themselves, and all research participants 

involved in this study was properly informed about the 

study's goal and their willingness to participate in it. 

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