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The COVID-19 pandemic has significantly affected access to adequate and nutritious food, resulting in hunger, malnutrition and increased food insecurity. The purpose of this study was to identify the households in Indonesia that were most likely to experience the pandemic's effects.

Using raw data from nationwide Indonesian household socioeconomic and expenditure surveys (SUSENAS 2020 and SUSENAS 2021), food insecurity was measured using the Food Insecurity Experience Scale (FIES). A multinomial logistic regression model was used to analyze the data.

The prevalence of mild, moderate and severe food insecurity increased from 20.61% to 21.67% and from 1.28% to 1.37%, respectively, between 2020 (the initial stage of the COVID-19 pandemic) and 2021 (the period during which the infection rapidly spread nationwide). The estimation results of the multinomial logistic regression analysis indicated that urban households, which may have had more difficulties with income generation and access to food due to behavioral constraints, were more likely to be affected by the pandemic. Additionally, economically vulnerable poor households, households with low levels of education or unemployed heads were more likely to be affected by the pandemic.

To the best of the authors knowledge, this is the first study to access the impact of economic downturn and social restrictions due to the COVID-19 pandemic on household food insecurity in Indonesia, using the nationwide household survey data.

The purpose of this paper is to clarify the socioeconomic determinants of household food insecurity in Indonesia using individual household data obtained from the 2015 nationwide household socioeconomic and expenditure survey called Survei Sosial Ekonomi Nasional.

A stereotype logistic regression model is applied to detect factors determining household food security.

The results from the cross-analysis between calorie consumption and share of food expenditure to total household expenditure (Engel coefficient) indicate that 20.8 percent of households were in the “food insecure” category, 21.5 percent in the “lack of food” category, 26.6 percent in the “vulnerable” category and the remaining 31.2 percent in the “food secure” category.

The main limitation relates to the data set because the collection was conducted in March 2015. Furthermore, the analysis is restricted because of the limited availability of information on socioeconomic factors of respondents. Further research based on the latest data set with more detailed information on respondents is necessary to deepen the discussion.

Researchers have not specifically discussed the factors determining household food security in Indonesia using reliable nationwide household survey data. The estimation results clearly indicate that a household fulfilling one or more of the following conditions is more likely to be in the “food insecure” category: many members, low education level of household head, divorced household head, household head is a smoker, household head engages in agriculture or construction work and residence is in rural or backward regions.

Using nationally representative data, this study estimated the probability of a household receiving RASKIN rice in general as well as the rate of excluding poor households and including nonpoor households in relation to the targeting accuracy of the RASKIN program.

The data came from the National Socioeconomic Survey conducted by the Central Bureau of Statistics in March 2015. Several independent variables were included, such as the head of household's age, gender and marital status, in addition to highest educational level among family members, household size, economic status, regional district and residential classification.

The results showed that a household's probability of receiving RASKIN rice increases if the head of household is older, female and a widower, and the household has more human capital accumulation, more family members, lower economic status and is in a rural area and/or Java and Nusa Tenggara. The estimated probabilities of poor households excluded from the RASKIN program and nonpoor households included are 44.8 and 35.1%, respectively, suggesting mistargeting occurred where eligible recipients were undercovered, and revealed the loss of funds to ineligible households.

The present study focused on the program's targeting accuracy while at the same time keeping in mind the social and geographical conditions in Indonesia. To the best of the authors’ knowledge, little to no such research has been conducted.

 Social assistance programs have been implemented in Madagascar to assist the most vulnerable and neediest poor in achieving a minimum standard of living during critical times. In the Grand South and Southeast Madagascar, where climate change, price fluctuations and sociopolitical instability threaten the population’s livelihood, the effectiveness of such aid-based programs is uncertain because of possible mistargeting. This study aims to assess the accuracy of the targeting methodology of four different types of social protection programs in Southern Madagascar.

The authors draw evidence from a national representative data set and use multiple techniques that integrate targeting performance and regression-based analyses.

Results show that cash transfers constitute the only program that effectively reaches the poorest 20% living in drought- and cyclone-affected regions. However, mistargeting is likely to occur in food, seed transfers, and the HIMO public works programs. Social inclusion information related to proximity to urban zones, religious affiliation, and number of long-lasting insecticide-treated nets are significantly associated to all program participation. Nevertheless, a serious under-coverage of the eligible poor is noted for Southern Madagascar.

This study is among the first to evaluate the targeting effectiveness of social programs in Madagascar. It introduces a novel approach to evaluating the poverty rate by utilizing a composite-based wealth score. The authors enhance the classic targeting assessment methodology by incorporating geospatial covariates, categorical, geographical and social network information into an econometric model. The study provides a comprehensive view of the main profile of beneficiaries reached by four social assistance programs in Southern Madagascar.