Food Security of Malang City: Before and During the Pandemic of COVID-19
Farida Rahmawati1, Adelia Meydina Maharani2, Hafidh Irfansyah3, Triafinna Khoyyun Istiqomah4
1,2,3,4 Faculty of Economics and Business, Universitas Negeri Malang, Malang, Indonesia
Corresponding Author: Farida Rahmawati, farida.rahmawati.fe@um.ac.id Abstract
The COVID-19 pandemic affects all sectors, one of which is food security. As one of the vital needs, food security is an important matter that needs to be prioritized in economic development. This study aims to evaluate the condition of food security before and after the pandemic in Malang City. The variables used are the number of family members, income before the pandemic, age of the respondent, expenditure for food needs, income during the pandemic, and income during the new normal. This study used a quantitative approach with data analysis techniques in the form of multiple regression tests, the accuracy test of BLUE (Best Linear Unbiased Estimate) estimation, and classical assumption test. The results showed that in the conditions before the pandemic, the variable age of the respondents had a significant effect on the level of food security, while the variables of the number of family members, income before the pandemic, and expenditure on food needs had no significant effect on the level of food security. During the pandemic, the income variable has a significant effect on the level of food security, while the variables for the number of family members and income during the pandemic have no significant effect on the level of food security in Malang City. The results of this study require further recommendations from relevant stakeholders.
Keywords: COVID-19 pandemic, new normal, food security
1. Introduction
Food is the most important basic human needs, and its fulfillment is a part of the basic human rights guaranteed by the 1945 Constitution (Kurniawan and Wibowo, 2017). Meanwhile, food security itself is the availability of food and a person's ability to access it. Food security is very important for a region. An area is claimed to have a good level of food security if its people are not in a state of hunger or are not haunted by the threat of hunger. So far, research has shown that food awards provide a lot of information on imports which in turn can reduce farmers to increase food production due to low product prices (Sunarminto, 2014). The level of food in an area can be seen with various existing indicators, such as the number of members, level of income, and family expenditure.
However, since the pandemic hit Indonesia in early March 2020, the level of food security has stabilized. The public has many complaints related to the increase of food price, the decreasing level of income, and the amount of food needed which has actually increased during the pandemic. Even though it is an issue during a pandemic, food security is also something that must be considered in addition to the health sector. For example, Malang City, this city is the second city after Surabaya, which has the highest number of positive patients in East Java Province. This is a challenge for the Malang City government to be able to continue to stabilize the level of food security in the area, amid the current COVID-19 pandemic. This study is aimed at seeing factors influencing the level of food security in Malang City as an evaluation material
to compare the level of food security between conditions before and during a pandemic; the variables are the number of family members, the age of the respondent, the income before the pandemic, the income during a pandemic, income when entering the new normal era, the number of COVID-19 patients, and the number of new COVID -19 cases. Based on Malang City APBD in 2019 and 2020, the budget in the agricultural sector and food security increased in the early 2020, but in fact it did not increase during the pandemic. It is recorded that the budget for the agricultural sector and food security before the 2019 pandemic was IDR 15,286,313743 (Malang City Government, 2019). Meanwhile, at the beginning of 2020, it increased to IDR 16,247,083,449 and when the pandemic hit, the budget was the same as at the beginning of 2020 (Malang City Government, 2020).
This, in turn, causes the availability of food needs to decrease during the pandemic when compared to the conditions before the pandemic. Several studies related to the condition of food security, especially those related to the factors that affect the level of food security in an area, have been carried out by Susanti (2019) using regression. The results show that household income variables have a significant effect on the level of food security. Subsequent research conducted by Erokhim and Gao (2020) using the relationship between parameters found that the number of sufferers of COVID-19 affects the status of community food security and the stability of food supply chains in developing countries.
In brief, the current threat to the food security of the millions of people affected by the COVID-19 crisis is not the result of the virus itself (infection, illness, or death), but by the consequence of the loss of income and purchasing power induced by the lockdown and shutting down of enterprises imposed by national/local governments (Béné, 2020). By (Kansiime, 2020) food security outcomes were found to be worse among the income-poor and those dependent on labor income, as they are less likely to have adequate savings for food purchase amidst the increasing food prices.
Based on the description of the background, this research will analyze a topic related to the evaluation of conditions before and during the pandemic (new normal era) on the food security level of Malang city community. This study used pre-pandemic and during pandemic conditions. The pre-pandemic conditions employed variables of the number of family members, age of respondents, income before the pandemic, and expenditure for food needs; while the conditions during a pandemic (new normal era) using variables of the number of family members, income before the pandemic, income during the pandemic, and income during the new normal. The results of this study are expected to be able to provide empirical references and to find out the factors affecting the level of food security in Malang City which later can be used as input for the Malang City government in continuing to stabilize the level of food security in the region.
2. Method
This study uses quantitative research methods where the data used in this study are numbers. The purpose of this study was to determine which variables have a significant effect on the level of food security in Malang. The data used are data on the number of family members, income before the pandemic, expenditure before the pandemic, household expenses, age of the respondent, income during the pandemic and income during the new normal. The population in this study was all residents in Malang City and the samples taken in this study were 6-7 people from each district in Malang City using purposive sampling techniques. In collecting data in this
study, it was carried out by distributing questionnaires to the respondents. The place of this research is Malang City, East Java and the questionnaires distributed to respondents were within the research timeline in 2019-2020. In processing secondary data using, Stata software was used with a significance level of 5%. The analysis techniques include the following tests.
Multiple Regression Test
The data analysis technique used is multiple linear regression analysis with the formula:
Y = β0 + β1X1 + β2X2 + e
This is to determine which variables have a significant effect on the level of food security in Malang City.
Y = predicted value of Y α = constant number
β1, β2, ..., βk = variable coefficient free X1, X2, X3, X4 = independent variables
X1 = number of family members (before and during the pandemic) X2 = income before a pandemic (before and during the pandemic)
X3 = age of the respondent (before pandemic) and current income pandemic (during the pandemic
X4 = household expenses (before the pandemic) and income when new normal (during the pandemic)
Test Accuracy of BLUE Estimation (Best Linear Estimate)
Three tests were utilized in this type of test, they are: Partial Significance Test (t Test), Simultaneous Significance Test (F Test), and Determination Coefficient Test (R2 Test). Partial significance test (t test) is used to determine how much influence the independent variable (X) has on the dependent variable (Y) partially. Hypothesis testing will be carried out using a significance level of 0.05 (α = 5%). The simultaneous significance test (Test F) shows whether all the independent or independent variables included in the model have a joint influence on the dependent variable. This test also uses a significance level of 5% or 0.05. The coefficient of determination test (R2 test) aims to measure how much the ability of the independent variable is significant to the dependent variable in the model. The coefficient of determination is between zero and one. With a value close to one, the independent variable provides almost all the information needed to predict the variation in the dependent variable.
Classic Assumption Test
In carrying out multiple linear regression analysis tests, several assumptions need to be fulfilled, for example the classical assumptions which consist of Heteroscedasticity Test and Multicollinearity Test. The former one is to determine whether in the regression model there is an inequality of variance from the residuals of one observation to another or not. Or it could be to find out the variance of the error term is constant or not. Meanwhile, the latter one aims to determine whether in the regression model there is a correlation between the independent variables or not. To test for Multicollinearity by looking at the Mean VIF value of each independent variable, where if the Mean VIF value is <10, then the data is free from Multicollinearity symptoms or there is no correlation between independent variables, and vice versa.
3. Results and Discussions Multiple Regression Test
This test is used to estimate the relationship pattern of one dependent variable with one or more independent variables. In addition, this test can also be used to see which variables have a significant effect on the level of food security, especially in Malang City. The results of multiple regression tests for the variables before the pandemic can be seen in the following table:
Table 1 Multiple Regression Test Results Before Pandemic
No. Variable Coef. Std. Err. T P > l t l 95% Conf Interval 1 Number of family
members
-32.30556 61.26749 -0.53 0.602 -157.6117 93.00053 2 Respondent age -20.90168 7.810362 -2.68 0.012 -36.87567 -4.9277 3 Income before the
pandemic
-.0000306 .0000344 -0.89 0.381 -.0001009 0.0000398 4 Expenditure for food
needs
.0000791 .0000775 1.02 0.316 -.0000794 .0002375 Cons 1053.6 368.2967 2.86 0.008 300.3484 1806.851
Table 1 shows the results of multiple regression tests. It shows that Pvalue which is the respondent's age variable or P>ltl is less than α=0.05. Hence, the age of the respondents has a significant effect on the level of food security in Malang City. Then, the variables during a pandemic can be seen in the following table.
Table 2 Multiple Regression Test Results During Pandemic
No. Variable Coef. Std. Err. T P > l t l 95% Conf Interval 1 Number of family
members
-64.22301 62.53097 -1.03 0.313 -192.1132 63.66717 2 Income before the
pandemic
.0002145 .0001041 2.06 0.048 1.54e-06 .0004275 3 Income during a
pandemic
.0002624 .0001395 1.88 0.070 -.000023 .0005478 4 New normal income -.0004686 .0002027 -2.31 0.028 -.0008832 -.000054
Cons 302.946 227.0603 1.33 0.193 -161.4444 767.3364
Table 2 shows the results of multiple regression tests. It shows that the Pvalue which is the income variable before the pandemic and new normal or P> ltl is less than α=0.05. So that income before the pandemic and income during the new normal have a significant effect on the level of food security in Malang City. Low household income can affect the level of household food consumption. However, high-income households also do not guarantee the nutrition of each household (Arida, Sofyan, dan Fadhiela, 2015). Meanwhile, according to Hernanda, Indriani, and Kalsum (2017), if income is higher, household food security will increase.
Test Accuracy of BLUE Estimation (Best Linear Unbiased Estimate) Partial Significance Test (t Test)
This test is used to see how much influence each independent variable has on the dependent variables in the model. In the following are the results of the t-test for the variables before the pandemic:
Y = 1053,6 – 32,30556 X1 – 20,90168 X2 – 0,0000306 X3 + 0,0000791 X4 + e.
Then, the interpretation is carried out only for variables that have a significant effect. So that when the respondent's age increases by an average of 1 year, the level of food security will decrease by 20.90168 hectares on average, assuming other variables remain. While the results of the t-test for variables during a pandemic are as follows:
Y = 302,946 - 64.22301 X1 +0.0002145 X2 + 0.0002624 X3 - 0.0004686 X4 + e.
Then, the next step is to interpret only the variables that have a significant effect. So that, when income before the pandemic increases by an average of one thousand rupiah, the level of food security will increase by 0.0002151 hectares on average with the assumption that other variables are constant. And when the income during new normal increases by an average of one thousand rupiah, then the level of resilience food will decrease by 0.0004699 hectares on average with the assumption that other variables are constant.
Simultaneous Significance Test (F Test)
This test is used to determine the effect of the independent variable on the dependent variable in the model simultaneously or as a whole. The results can be seen in the multiple regression calculation table below with the notation Prob>F. Below are the results of the F-test for the variables before and during a pandemic.
Table 3 F-Test Results for Variables Before and During a Pandemic No. Condition Number of obs F (4, 29) Prob > F
1 Before pandemic 34 2.05 0.1133
2 During a pandemic 34 1.65 0.1885
Based on the table, the value before pandemic of Prob>F 0.1133>0.05 means that simultaneously the average number of family members, age of respondents, income before the pandemic, and expenditure on food needs have no significant effect on the level of food security in Malang City. The value during a pandemic of Prob> F 0.1885> 0.05 means that simultaneously the average number of family members, income before the pandemic, income during the pandemic, and income during new normal do not have a significant effect on the level of food security in Malang City.
Determination Coefficient Test (R2 Test)
This test is used to explain how significant the ability of the independent variable toward the dependent variable in the model is. The results of this test are denoted in the R-squared
notation and the results are converted into the percentage. In the following are the results of the R2-test for variables before and during the pandemic.
Table 4 R2 Test Results for Variables Before and During a Pandemic No. Condition Number of obs R-squared Adj R-squared
1 Before pandemic 34 0.2205 0.1130
2. During a pandemic 34 0.1854 0.0731
Based on the results of the table, the value of R-squared before pandemic is 0.2205 or 22.05%, so the ability of the number of family members, age of the respondent, income before the pandemic, and expenditure for food needs in explaining the level of food security is 22.05%
by other variables outside the model. Meanwhile, the value of the R-squared during a pandemic is 0.1854 or 18.54%, so the ability of the number of family members, income before the pandemic, income during the pandemic, and income during new normal in explaining the level of food security is 18.54% described by other variables outside the model.
Classic Assumption Test Heteroscedasticity Test
This test is used to determine whether there is a difference between the residue of one observation and another or it can be used to determine whether the variance of the error term is constant or not. The results of this test are denoted by the symbol Prob> chi2. In the following are the results of the heteroscedasticity test for variables before and during a pandemic.
Table 5 Heteroscedasticity Test Results for Variables Before and During a Pandemic No. Condition chi2 (1) Prob > chi2
1 Before pandemic 88.65 0.0000
2 During a pandemic
79,90 0.0000
Based on the table, for variables before and during a pandemic, the value of Prob> chi2 is 0.0000 <alpha 0.05, this means that there is no heteroscedasticity of variance from the constant error term.
Multicollinearity Test
This test is used to see if there is a high correlation between the independent variables in the model. The results of this test can be seen based on the Mean of VIF results. In the following are the results of the multicollinearity test for variables before and during a pandemic.
Table 6 Multicollinearity Test Results for Variables Before and During a Pandemic
No. Condition Mean VIF
1 Before pandemic 1.59
2 During a pandemic 29.13
Based on these results, the value of the Mean VIF is 1.59<10 which means that there is no multicollinearity or high correlation between the independent variables in the model; while the multicollinearity test results for variables during a pandemic with the value of the Mean VIF is 29.13>10 means that there is multicollinearity or there is a high correlation between the independent variables in the model.
4. Conclusions
Food security is something vital that must be taken into account, especially during a pandemic as now. The income that did not increase during the pandemic has made the availability of foodstuffs decrease when compared to the availability of food before the pandemic. Besides, the price has also increased. There are various variables used in this study to determine the factors affecting the level of food security in Malang. The variables used for pre- pandemic conditions include number of family members, age of respondents, income before the pandemic, and expenditure for food needs. Meanwhile, the variables used for conditions during a pandemic include the number of family members, income before the pandemic, income during the pandemic, and income during the new normal.
Based on the results of multiple regression tests before the pandemic, the respondent's age variable has a significant effect on the level of food security in Malang City; while the results of multiple regression tests during the pandemic shows that the income variable before the pandemic and the income during the new normal have a significant effect on the level of food security in Malang City. However, simultaneously, all variables had no significant effect on the level of food security in Malang City. In addition, the pre-pandemic variable only has an R2 value of 22.05% and the variable during the pandemic only has an R2 value of 18.54% to explain the level of food security in Malang City. Meanwhile, for the heteroscedasticity test results, there is no heteroscedasticity for both the variables before and during the pandemic. Then, for the multicollinearity test, only the variables during the pandemic have a high correlation.
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