International Journal of Social Science Research (IJSSR) eISSN: 2710-6276 | Vol. 4 No. 3 [September 2022]
Journal website: http://myjms.mohe.gov.my/index.php/ijssr
AGRICULTURAL TECHNOLOGY, CREDIT, AND MENTAL HEALTH: THEIR EFFECTS ON FOOD SECURITY OF
WOMEN FARM WORKERS OF CAMARINES SUR, PHILIPPINES
Delia P. Casasis1* and Jeanette Isabelle V. Loanzon2
1 2 University of Santo Tomas, Metro Manila, PHILIPPINES
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 18 September 2022 Revised date : 24 September 2022 Accepted date : 29 September 2022 Published date : 30 September 2022
To cite this document:
Casasis, D. P., & Loanzon, J. I. V.
(2022).AGRICULTURAL TECHNOLOGY, CREDIT, AND MENTAL HEALTH: THEIR
EFFECTS ON FOOD SECURITY OF WOMEN FARM WORKERS OF CAMARINES SUR, PHILIPPINES.
International Journal of Social Science Research, 4(3), 281-293.
Abstract: This study investigated how agricultural technology, credit, and mental health affect the food security of women farmers in the province of Camarines Sur, Philippines, during the COVID-19 pandemic. The study adopted the Household Food Insecurity Access Scale (HFIAS) to assess household food access. It applied logistic regression modeling to estimate the effects of demographics, access to technology, access to extension services, credit, and their state of mental health towards food security. The Hopkins Symptoms Checklist-21 (HSCL-24) was utilized to measure symptoms of anxiety and depression and were analyzed using linear regression estimation. Descriptive statistics and the survey method were employed to attain the study’s objectives. The result of the logistic regression model indicated that food security is significantly influenced by household income, farm occupation, and access to agricultural technology. Farm size and access to extension services showed a significant relationship with women’s access to agricultural technology.
Analysis also showed that the state of mental health has a significant and negative effect on household size, household income, farm size, land ownership and access to technology. In addition, women are unlikely to have anxiety and depression if the land is owned and purchased by them. Results of the study suggest the importance of conducting training for women farmers on relevant farming technologies to increase food productivity in the household. Land ownership is strongly associated with mental well-being among women farmers. Hence, government programs and policies especially for women in the agriculture sector should be in line with the Department of Health.
1. Introduction
Ending world hunger and malnutrition in all its forms by 2030 is one of the major goals of Sustainable Development Goals. Challenges of food security at all levels have grown with the enduring effect of the COVID-19. In fact, the pandemic has increased food security risks in Asia and the Pacific because of strict lockdowns and quarantine measures (ADB, 2020).
Food security is the ability to access adequate quality food and can be achieved from individual to global levels. It exists when people at all times have physical, social, and economic access to sufficient, safe, and nutritious food that meets their dietary needs and food preferences for an active and healthy life (FAO, 2014). However, global hunger, climate change, and conflict are just some of the global problems that undermine food security in the world’s most vulnerable countries (Robinson, 2019) with women in the rural areas being greatly affected (Oxfam, 2019). Central to these issues are women, whose role in food security has been neglected from a different perspective (Moyo, 2013). Women in general, in the degree in which the elements of discrimination are well-pronounced in rural areas where people are more conservative and traditional and where lower economic opportunities exist, are disadvantaged (Dacuycuy, 2018).
2. Literature Review
Historically, the concept of food security first merged in the 1970s following the acknowledgment of food as a vital determinant of people’s well-being by the Universal Declaration of Human Rights in 1948 and by the global crisis of 1972-1974. This has led to an increase in rice production eventually resulting in the adoption of agricultural technology that has a significant impact in rice production, and income, in improving the livelihood of rural farmers in Nigeria (Ojo, 2018). The same is true to other African countries like Ghana, which also encourages technology adoption in rice production. Small farmers and the continuous growing proportion of women farmers face serious resource restraints and poverty in developing countries (Agarwal, 2014). Nevertheless, in spite of women’s productive contribution to agriculture they have faced constraints and are less likely than men to use modern inputs (Kumari, 2015).
Accordingly, the Philippine Development Plan integrates gender issues aside from acknowledging that women’s labor force participation has barely improved through the years (Dacuycuy, 2018). According to Dacuycuy (2018), women take disadvantaged positions in rural areas where elements of discrimination are well-pronounced in that people are more conservative and traditional and lower economic opportunities exist for them.
A replete review of literature of selected scholars reveals that access to land, modern agricultural technologies, agricultural extension services, and credit facilities are vital so women’s farmers capability in boosting food production and thereby ensuring food security can be increased (Njoh & Akiwumi, 2012). The Fundamental SDG (n.d.) explains that mental health is now considered as one of the most pressing problems across the world today. The inclusion of mental health in the development international agenda is a big historical step for Keywords: food security, agricultural technology, credit, mental health, COVID-19, logistic regression
humankind. In fact, several studies conducted reveal a positive association of food security and mental health in developing countries (Weaver, 2009).
2.1 Problem Statement
A considerable amount of research in the area of food security among women farmers has given much importance to the world today. However, empirical studies are still limited to the certain aspects of the literature. (1) Women’s access to agricultural technologies: From available studies on women's access to agricultural technologies, credit facility, extension and marketing services have been conducted outside the Philippines particularly in sub-Saharan-Africa.
Creating an urgency for policy recommendations as well as public finance in the creation of women-friendly technology in the Philippine context. (2) Food security and agricultural technologies: There are some indications that gender-sensitive technologies are being adopted among many women farmers (Zunguze, 2007). However, there are limited studies conducted on the availability and adoption of farm technologies by women farmers in the Bicol region as well as in the Philippines. (3) Credit: Some studies reveal that women are gradually experiencing financial independence through credit (Tarozzi, 2015). However, there are limited studies on the impact of access to credit on food security, especially in Philippine setting. (4).
Food insecurity and mental health: Mental effects of food insecurity remain largely understudied due to the fact that most measures of food insecurity are focused on nutritional aspects (Weaver, 2009).
The current study is distinguishable from the studies reviewed as it focuses on quantifying the hypothesized relationship between food security and its predictor variables such as demographic backgrounds, access to agricultural technologies, access to credit, access to marketing and extension services, and the effect of food security to the state of mental health of women farmers in Camarines Sur. The study is not a duplicate of any other study that was conducted or is being conducted.
3. Method
The study was framed using the exploratory research design. Primary data was used via structured questionnaire, which consisted merely of closed-end questions that had pre- specifying possible answers. Semi-structured questionnaires were also utilized to measure the association between food security and mental health. Moreover, the study adopted two-part questionnaires: the first part contained questions on food security using Household Food Insecurity Access Scale (HFIAS), while the second part was the Hopkins Symptoms Checklist- 24 (Karl Rickels,1954). The household score on food security (FS) was determined and responses of the participants were coded: 1 if the answer is affirmative, and 0, if negative. The dependent variable was presented in a four-point scale based on the HFIAS guide with responses such as food secure, food insecure without hunger, food insecure with moderate hunger, and food insecure with severe hunger (Coates et al., 2007).
The number of independent variables as categorical and quantitative were considered. These variables included age, marital status, household size, occupation, income, elementary education, high school education, college education, farm size, land ownership, land owned by women, access to technology, access to credit, access to extension service, and mental health.
3.1 Materials
Structured questionnaires were administered to respondents in order to collect primary data.
Interviews and focus group discussions (FGDs) were also conducted to gather information from the randomly selected respondents. Open-ended questions were also asked to allow respondents to answer in their own words and encourage them to give more information. To avoid ambiguity in the collection process of data, questionnaires were piloted then formally administered to the target population. In this study, questionnaires consisted mainly of closed- ended questions that had pre-specifying possible answers.
3.1.1 Samples
The study focused on 200 women rice farmers from the 10 municipalities in Camarines Sur.
Only active women rice farmers were the respondents. Active women farmers is defined as a female from age 20 to 59 years (WHO, 2013). To set thing clear, there arose the need to adjust the boundary age for active women farmers as several studies have shown that women still engage in agricultural activities even at their age of 70 (Hoppe & Korb, 2013). for this research, 65 years is considered as the maximum age of the respondents. Moreover, women’s participation in agriculture was the basis of the selection, including financing, planning, land clearing, planting, fertilizer and pesticide applications, weeding, harvesting, storage, processing, and marketing.
Another important criterion of the respondent was access to land; acquisition was through inheritance, hire, a personal purchase or through their husbands. After the conduct of the survey on and FGD with the respondents, no followup visit was made because of the COVID-19 rising cases in Camarines Sur, Philippines.
3.1.2 Site
The research setting was the 10 municipalities of Camarines Sur in Bicol region, situated in the southeastern section of Luzon, the country’s biggest island. The study focused on rice production having the most organized production system and source of food and cash crop in the province. Camarines Sur’s population, as reported by the Philippine Statistics Authority (PSA) as of 2020, was 2,068,244 with a population density of 375 per square kilometer or 972 people per square mile. The 2020 figures from PSA also show that Camarines Sur had an average household size of 5.38 persons.
3.1.3 Procedures
Variables utilized in the modelling in this study are described in Table 1. These variables included name, definition, and measurement.
Empirical Models
The study consisted of four (4) analyses so the posited hypotheses could be captured. Food security, access to technology, and access to credit were examined and analyzed using the logistic regression models. Linear regression model was used to measure symptoms of anxiety and depression among the participants.
Logit model of food security
ᴢ(FS) = ᵦ0 + ᵦ1age + ᵦ2mstatus + ᵦ3HHS + ᵦ4 occfarm + ᵦ5income +ᵦ6edelem +ᵦ7edhigh + ᵦ8edcol + ᵦ9farmsize + ᵦ10landowner + ᵦ11landwomen + ᵦ12accredit + ᵦ13actech
+ ᵦ14acext + ᵦ15mentalh + ( 1 )
Logit model of access to agricultural technology
ᴢ(actech) = ᵦ0 + ᵦ1age + ᵦ2mstatus + ᵦ3HHS + ᵦ4 occfarm + ᵦ5income +ᵦ6edelem +ᵦ7edhigh + ᵦ8edcol + ᵦ9farmsize + ᵦ10landowner + ᵦ11landwomen + ᵦ12accredit + ᵦ13actech
+ ᵦ14acext + ( 2 )
Logit model of access to credit
ᴢ(accredit) = ᵦ0 + ᵦ1age + ᵦ2mstatus + ᵦ3HHS + ᵦ4 occfarm + ᵦ5income +ᵦ6edelem +ᵦ7edhigh
+ ᵦ8edcol + ᵦ9farmsize + ᵦ10landowner + ᵦ11landwomen + ᵦ12actech + ᵦ13acext + (3 ) Linear regression of mental health
Y (mentalh) = ᵦ0 + ᵦ1age + ᵦ2mstatus + ᵦ3HHS + ᵦ4 occfarm + ᵦ5income +ᵦ6edelem +ᵦ7edhigh
+ ᵦ8edcol + ᵦ9farmsize + ᵦ10landowner + ᵦ11landwomen + ᵦ12actech + ᵦ13acext + ᵦ14accrdit + ᵦ15fsscor + (4)
The dependent variables are Z(FS) for food security (eq.1), Z(actech) for access to technology (eq. 2) , Z( accredit) access to credit (eq. 3), and Y(mentalh) for mental health (eq. 4). The
ᵦ
0sare the constant terms, while the other
ᵦ
s in the corresponding variables are the parameters to be estimated, and ’s is the error term.3.2 Measurement
To measure the link between food insecurity and mental health, the study adopted the two-part questionnaire. The first part had questions on food security using the Household Food Insecurity Access Scale (Appendix I) The tool has 18 items, 8 of which are specific to households with children and the recall period is 30 days. The adapted tool has been tested in several developing countries like Bolivia, the Philippines and Burkina Faso. The second part contained the Hopkins Symptom Checklist (HSCL-24) (Appendix II). Originally, the HSCL has 25 questions but question number 14 (loss of sexual interest) was dropped for modesty reasons. Four categories were defined to discuss the percentage of the household scores:
1. Food secure means the household show minimal or no evidence of food insecurity and coded as (0).
2. Food insecure without hunger is evident when household members worry about the adequacy of household food supply and adjustments to household food management, reduced quality of food and increased unusual coping patterns. There is no evidence of little or reduction in members’ food intake. This was coded as number one (1).
3. Food insecure with hunger (moderate) is when food intake for adults in the household is reduced to the extent of repeatedly experiencing the physical sensation of hunger. In most (but not all) food-insecure households with children, such reductions are not observed at this stage for children. This category was coded two (2).
4. Food insecure with hunger (severe) is when all households with children have reduced children’s food intake to an extent that the children experienced hunger. Children (in this category) have already suffered hunger severity, while adults in households repeatedly experienced more extensive reductions in food intake. This category was coded three (3).
Questions that have two response categories, ‘often times’, ‘sometimes true’ are considered as affirmation because they indicate that the condition occurs at some time during the 30-day period.
To determine the scale value and household is classified as household with children and household without children. Say, a household with children gave 6 out of 18 affirmative answers, that household was assigned a scale value of 3.9 and classified as food insecure without hunger. However, if a household without children gave 6 out of 10 affirmative answers, it was assigned a scale value of 5.0 and classified as food insecure with hunger (moderate).
3.3 Data Analysis
Demographic Characteristics
The mean age of women farmers in Camarines Sur is 44 years old, with the oldest farmer being 65 while the youngest being 24. In 2002, the National Statistics Office reported that the average age of female agricultural operators was 56 years old therefore, women farmers in Camarines Sur are relatively younger. The majority of the respondents were married (88%), widowed (7%), and separated (4%). The average household size was around 3.67 which is quite lower than the household size for the whole country in the 2015 census of 4.4 as reported by the PSA.
Around 66% of the respondents were engaged in farming as their occupation. The average monthly income of the respondents was Php 13,477.00 which is lower as compared with the national average monthly income of Php 22,000 as reported by the Family Income and Expenditure Survey in 2015. The majority of the respondents were high school level (54%), elementary level (17%), and college level (29%). The average farm size was 1.33 hectares which is quite higher compared with the national figures. About 76% of the total farms were below one hectare. Of these, 16% were between 1 to 2.99 hectares and 8% were above 3 hectares. Most of the farms cultivated were acquired through their husband (63%), through inheritance (9%), personal purchase by the respondent (13%), and hired or rented (15%).
3.3.1 Validity and Reliability
Several tests were conducted and examined to examine the quality of econometric models. For example, the coefficient of determination or R2 is used to determine the goodness-of-fit of the models. The Multicollinearity test is conducted to examine the presence of severe correlations among regressors in the model, the Variance Inflation factor (VIF) and tolerance.
The error terms are said to be heteroskedastic if the variance of error term (µ) is not constant.
Hetersokedasticity, the Breusch-Pagan/Cook-Weisberg test, is conducted to determine heteroskedasticity in the model. The specification test is conducted to confirm that the probability function is correctly specified. The model is correctly specified whether the estimated value is statistically significant and or not.
The R2, sensitivity test, specificity test, McFadden R squared, adjusted McFadden R squared, LR statistics, Variance Inflation Factor (VIF), and the mean VIF. McFadden’s Pseudo R squared must range from 0.2 to 0.4, indicating a very good model fit. The model shown are likely not terrible models but are not particularly strong. Gujarati and Porter (2009), Kennedy (2003), and Wooldridge (2009) advise not to put too much emphasis on goodness-of-fit.
There is a problem of heteroskedasticity of non-constant variance, that is why the model was adjusted using heteroskedasticity-robust standard error. There is also problem of specification bias in the model and to solve this, an interaction term for income and credit was added to the model. Ramsey RESET was used to test if there were omitted variables in the model. Table 4 shows the summary of the estimated marginal effects (the difference in the regressand change in the regressor).
Farm occupation and household income have been empirically proven to increase the likelihood of increasing food security in the households. In addition, access to agricultural technology has a significant and negative effect on food security because of high costs of accessing these technologies causing families to have additional expenditure in the family.
Farm size and access to extension services statistically and positively affecting the likelihood of accessing the agricultural farm technologies and improved inputs. Furthermore, the extension service has a significant and negative effect on borrowing. The more informed the respondents are, the less likely they will resort to credit.
Stata version 15 was extensively utilized in this as regards econometric analysis. Data preparation and figure presentation were made using Microsoft Excel.
4. Results and Discussion
The result of the logistic regression indicated that household income and farm occupation are statistically significant on food security.
Access to agricultural technology had a significant and negative effect because, based on the interviews, accessing modern technology and improved inputs was costly, hence was an additional expenditure among the farmers.
Farm size and access to extension services are statistically significant to access to technology.
The larger the tract of land cultivated by the household the higher the probability of accessing modern technologies in rice farming.
The more informed the women farmers are, the higher the probability of accessing modern equipment and improved inputs in farming. Agricultural cooperatives emerge as tools through which individual farmers meet their economic and social needs.
Results showed that household size, income, farm size, land ownership, and access to extension services have a significant and negative effect on the respondents’ mental health. In contrast, age and women’s land ownership has a positive and significant effect on mental well-being.
As women farmers get older, the lower are their chances of having anxiety and depression. In addition, women are unlikely to have a mental problem if the land is owned and purchased by them.
5. Conclusion
Women farmers in Camarines Sur have limited access to modern equipment used from farm preparation to post-harvest activities. The majority of the respondents interviewed wanted to attend the training program to improve their farming skills but very few of them know the existence of extension agents in their municipalities. Results also revealed that respondents tend to borrow from their respective cooperatives and CARD banks. Farm size and access to extension services were found to increase the likelihood of access to modern equipment and improved inputs in rice farming. Credit was observed to not help the farming households;
instead, they rely on borrowing from friends and relatives especially when they face income shocks.
The regression results showed that as women get older the lower the chances of having anxiety and depression. In addition, it was found that women are unlikely to have mental health problems if they own the land and or purchased by them.
Considering the mental well-being of women farmers, government programs and policies especially for women in the agriculture sector should be in line with the programs of the Department of Health. The researcher also recommends that the government provide credit facility, subsidies, incentives, specialized training, and capacity building training for women to further development country’s farming sector.
6. Acknowledgement
The researcher wishes to extend her heartfelt message of thanks to all the people who have been part in the realization of this noble study. Special recognition to the panelists: Manuel Morga, Ph.D., EJ Lopez, Ph.D., Nancy Eleria, Ph. D; Ma. Socorro Calara, Ph.D., and Virgilio M. Tatlonghari, Ph. D, for their support, consideration, comments, suggestions, and recommendations contributory to the completion and success of this study.
To Jeanette V. Loanzon, Ph.D. and Roperto Deluna, Ph.D. for their unwavering support, guidance, and extra effort to keep me on track.
To my husband Jhun, and to our children Kristine Ann, Gian Kenneth, Gian Karlo, Gian Kirby, and Theresa Ann for the love, understanding, and inspiration to keep me going.
To the respondents, for the cooperation, patience, and participation in answering the questionnaires.
To Mr. Jerry M. Noveno, for his expertise as English critic, and to everyone whom I failed to mention, the researcher is very grateful for your magnanimous contributions. Blessed be God forever.
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Appendix I
Household Food Insecurity Access Scale (HFIAS) To be administered to all households
Household Stage 1: Question HH2-HH4
HH1: Now, I am going to read you several statements that people have made about their food situation. For these statements, please tell me whether the statement was OFTEN true, SOMETIMES true, or NEVER true for (you/your household) in the last 30 days.
The first statement is “(I/We) worried whether (my/our) food run out before (I/We) got money to buy more”. Was that often true, sometimes true, or never true for (you/your) household in the last 30 days?
HH2: The food that (I/We) bought just didn’t last, and (I/We) didn’t have money to get more”.
Was that often, true, sometimes true, or never true for (you/your household) in the last 30 days?
HH3: “(I/We) couldn’t afford to eat balanced meals”. Was that often true, sometimes true, or never true for (you/your household) in the last 30 days?
Adult Stage 2: HH2 passing the screener for stage 2 adult-referneced questions)
AD1: In the last 30 days, since last (name of current month), did (youy/your or other adults in the household) ever cut the size of your meals or skip meals because there wasn’t enough money for food?
AD1a. [ If YES Above, Ask] How often did this happen-almost every day, some days but not every day, or only 1 or 2 days.
AD2: In the last 30 days, did you ever eat less than you felt you should because there wasn’t enough money for food?
AD3: In the last 30 days, were you very hungry but didn’t eat because there wasn’t enough money for food?
AD4: In the last 30 days, did you lose weight because there wasn’t enough money for food?
ADULT STAGE 3; AD5-AD5a
AD5: In the last 30 days, did (you/you or other adults in your household ever not to eat for a whole day because there wasn’t enough money for food?
AD5a. [If YES above, Ask] How often did this happen- almost every day, some days but not every day, or in only 1 o2 days
TRANSITION INTO CHILD-REFERENCED QUESTIONS
Now, I am going to read you several statements that people have made about the food situation of their children. For these statements, please tell me whether the statement was OFTEN true, SOMETIMES true, or NEVER true in the last 30 days for (your child/your children) living in the household who are under 18 years old.
CH1: “(I/We) relied on only a few kinds of low-cost food to feed (my/our child/children) because (I was/ we were) running out of money to buy food”. Was that often true, sometimes true, or never true for (you/your household) in
the last 30 days?
CH2: “(I/ We) couldn’t feed (my/our) child/ the children) a balanced meal because (I/We) couldn’t afford that.” Was that often true, sometimes true, or never true for (you/your household) in the last 30 days?
CH3: “(My/Our child was /the children were) not eating enough because (I/We) just couldn’t afford enough food”. was that often, sometimes, or never true for (you/your household) in the last 30 days.
CHILD STAGE 2: QUESTIONS CH4-CH7
CH4: In the last 30 days since (current day) of last month, did you ever cut the size of (your child’s/ any of the children’s) meals because there wasn’t enough money for food?
CH5: In the last 30 days, did (child’s name/any of the children) ever skip meals because there wasn’t enough money for food?
CH5a: [If YES above, Ask] How often did this happen-almost every day, some days but not very day, or in only 1 or 2 days?
CH6: In the last 30 days, (was your child/ were the children) ever hungry but you just couldn’t afford more food?
CH7: In the last 30 days, did (your child/any of the children) ever not eat for a whole day because there wasn’t enough for food?
Appendix II
Hopkins Symptom Checklist -24
Instructions: I will read to you some symptoms of strain that people sometimes have. Tell me how much that symptom has bothered you during the past month.
(1) Not at all (2) A little (3) Quite a bit (4) Extremely
1. Suddenly scared for no reason K1.
2. Feeling fearful K2.
3. Faintness, dizziness or weakness K3
4 Nervousness or shakiness inside K4.
5 Heart pounding or racing K5.
6 Trembling K6
7 Feeling tense or keyed up K7
8 Headaches K8
9 Spells of terror or panic K9.
10 Feeling restless or can’t sit still K10
11 Feeling low in energy-slowed down K11
12 Blaming yourself for things K12.
13 Crying easily K13
14 Poor appetite K14
15 Difficulty falling asleep, staying asleep K15
16 Feeling hopelessness about the future K16
17 Feeling blue K17
18 Feeling lonely K18
19 Feeling trapped or caught K19
20 Worrying too much about things K20
21 Feeling no interest in things K21
22 Thoughts of ending your life K22
23 Feeling everything is an effort K23
24 Feeling of worthlessness K24