An assessment of household food consumption patterns during the COVID-19 pandemic in Bali Province

Kharisma, Bayu; Hasanah, Alfiah; Remi, Sutyastie Soemitro; Sanjaya, I. Gusti Gede Gusna Yoga Article An assessment of household food consumption patterns during the COVID-19 pandemic in Bali Province Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Kharisma, Bayu; Hasanah, Alfiah; Remi, Sutyastie Soemitro; Sanjaya, I. Gusti Gede Gusna Yoga (2024) : An assessment of household food consumption patterns during the COVID-19 pandemic in Bali Province, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-20, https://doi.org/10.1080/23322039.2024.2409419 This Version is available at: https://hdl.handle.net/10419/321617 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 An assessment of household food consumption patterns during the COVID-19 pandemic in Bali Province Bayu Kharisma, Alfiah Hasanah, Sutyastie Soemitro Remi & I. Gusti Gede Gusna Yoga Sanjaya To cite this article: Bayu Kharisma, Alfiah Hasanah, Sutyastie Soemitro Remi & I. Gusti Gede Gusna Yoga Sanjaya (2024) An assessment of household food consumption patterns during the COVID-19 pandemic in Bali Province, Cogent Economics & Finance, 12:1, 2409419, DOI: 10.1080/23322039.2024.2409419 To link to this article: https://doi.org/10.1080/23322039.2024.2409419 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 29 Sep 2024. Submit your article to this journal Article views: 680 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 DEVELOPMENT ECONOMICS | RESEARCH ARTICLE An assessment of household food consumption patterns during the COVID-19 pandemic in Bali Province Bayu Kharisma a , Alfiah Hasanah a , Sutyastie Soemitro Remi a and I. Gusti Gede Gusna Yoga Sanjaya b a Department of Economics, Universitas Padjadjaran, Bandung, Indonesia; b Central Bureau of Statistics, Jembrana Regency, Bali, Indonesia ABSTRACT This study analyzes changes in household food consumption patterns and socio-economic demographic characteristics during the COVID-19 pandemic in Bali Province. This study uses secondary data collected by Statistics Indonesia, namely the National Socio-economic Survey (Susenas) data with the Quadratic Almost Ideal Demand System (QUAIDS) demand model. The results showed changes in household food consumption patterns during the pandemic. The average food expenditure per capita of the population decreased, while the average non-food expenditure per capita increased slightly. The average per capita food expenditure of residents in urban areas experienced a more significant decline compared to rural areas. The highest average per capita food expenditure increase occurred in the consumption of tubers, vegetables, and beans. In contrast, the highest decline occurred in the average per capita food expenditure on fruit, processed food, and meat commodities. Socio-demographic characteristics that significantly influence the share of household food expenditure are education of the household head, occupation of the household head, and household perception of food access. The price of own goods has a positive influence on the share of food consumption expenditure but will have a negative impact on the quantity of food consumption. The government can implement policies to achieve food self-sufficiency, particularly for grains and meat, by overseeing the availability of staple foods and the distribution of fruits and meats. Policies that assist low-income household groups in rural areas should also be prioritized. IMPACT STATEMENT This article discusses the impact of changes in household food consumption patterns and socioeconomic demographic characteristics during the COVID-19 pandemic in Bali Province. Since the beginning of the COVID-19 pandemic, Bali Province has been considered one of the most affected areas by the COVID-19 pandemic in Indonesia as its economy relies heavily on tourism. The results showed a shift in household food consumption patterns during the pandemic in Bali Province. Average per capita food expenditure decreased, while average per capita non-food spending increased slightly. Furthermore, per capita food expenditure in urban areas decreased compared to rural areas. Meanwhile, the most significant increase in per capita food expenditure occurred in the consumption of tubers, vegetables, and nuts. In contrast, the most significant decrease occurred in fruits, processed foods, and meat commodities. Sociodemographic factors that influence the share of household food expenditure are the education level of the household head, the occupation of the household head, and the household's perception of food access. The price of goods positively affects the share of food consumption expenditure but has a negative effect on the quantity consumed. ARTICLE HISTORY Received 3 October 2023 Revised 12 August 2024 Accepted 20 September 2024 KEYWORDS Food consumption patterns; socio-economic demographic; COVID-19 pandemic; Bali Province; QUAIDS SUBJECTS Microeconomics; Development Economics; Econometrics; Hazards & Disasters; Cultural Studies CONTACT Bayu Kharisma [email protected] Department of Economics, Universitas Padjadjaran, Jl. Ir. Soekarno Km.21 Jatinangor, Kabupaten Sumedang 45363, Jawa Barat, Indonesia ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2409419 https://doi.org/10.1080/23322039.2024.2409419 Introduction COVID-19 is a worldwide epidemic that has caused significant disruptions in several sectors of life across many countries, including Indonesia (Sumarni, 2020). As of December 26, 2021, Indonesia reported 4,261,739 positive COVID-19 cases, making it the country with the highest number of cases in Southeast Asia. In overcoming and preventing COVID-19, the World Health Organization (WHO) provides protective measures for affected countries to consider (WHO Media Briefing, 2020). Some implemented measures nearly worldwide are lockdowns and quarantines (Eftimov et al., 2020). To minimize the COVID-19 spread, the Indonesian government has enforced travel restrictions, social distancing, self-isolation, and Large-Scale Social Restrictions (PSBB) (Putri, 2020). These policies have indirectly disrupted economic activity, resulting in a decline in economic growth. This is evidenced by BPS data, which shows that the Indonesian economy experienced contractions in the second, third, and fourth quarters of 2020 consecutively (compared to the same quarter in the previous year (year-over-year/y-o-y)), wherein the second quarter slowed down by 5.32 percent, the third quarter slowed down 3.49 percent and the fourth quarter slowed down 2.19 percent. On an annual basis, Indonesia experienced a 2.2 percent economic slowdown in 2020 (BPS, 2022c). Nasution et al. (2020) explained that the restrictions during COVID-19 negatively impacted the economy in various sectors, including export-import, trade, industry, tourism, hotels, and restaurants. Additionally, the global economic slowdown significantly affected Indonesia’s economic growth. Similarly, smaller areas have also felt the effects on their economy, albeit with varying impacts. Wu et al. (2021) examined COVID-19’s regional effect on China’s economy. The results indicate a distinct impact on each province in China, with Hubei being the most affected. In Indonesia, Bali is one of the provinces that has had a significant economic impact. Bali experienced an economic slowdown of -9.31 percent in 2020 and -2.47 percent in 2021, representing the highest slowdown rate compared to other provinces (Badan Pusat Statistik (BPS), 2022). The economic slowdown and increased layoffs during COVID-19 resulted in the household economy stagnating and even decreasing income (Sina, 2020). This is consistent with the study by Shahreza and Lindiawatie (2021) on the economic resilience of families in Depok amid the COVID-19 pandemic. The study results indicate that the economic resilience of families in Depok has declined, particularly in terms of income and fulfilling family needs such as food consumption during the pandemic. Data from BPS indicates that the proportion of food consumption for Balinese people decreased during the pandemic compared to before. During the pandemic, the average monthly per capita food consumption for Bali Province was 42.79%. This is lower than the average household consumption per capita before the COVID-19 pandemic, which was 43.92% in 2019 and 44.72% in early 2020, just before the pandemic hit Indonesia (Badan Pusat Statistik Provinsi Bali, 2022). Table 1 reveals that food consumption expenditure decreased among residents in both rural and urban areas of Bali Province. Urban residents saw an average per capita decrease of 7.91 percent in food consumption expenditure, while rural residents experienced a decline of 5.28 percent. Regarding nonfood expenditure, urban residents’average per capita expenditure also slightly declined by 1.00 percent. In contrast, rural residents exhibited a different trend, with their average per capita expenditure for nonfood consumption increasing by 3.43 percent (Badan Pusat Statistik Provinsi Bali, 2022). This indicates that household consumption patterns during the pandemic in urban areas were more affected than in rural areas. In their research, Kumar and Abdin (2021) explained that this was attributed to the closure Table 1. Average per capita monthly expenditure by residence and type of expenditure in Bali Province in 2020–2021. Area of Residence Type of expenditure Food Non-Food 2020 2021 2020 2021 Urban 724,978 667,606 973,021 963,314 Changes (%) 11.56 −7.91 9.62 −1.00 Rural 565,445 534,662 526,846.00 544,917.00 Changes (%) 8.67 −5.28 −1.69 3.43 Source: Badan Pusat Statistik Provinsi Bali (2022). 2 B. KHARISMA ET AL. of dining establishments, restaurants, shopping centers, cinemas, and others in urban areas, which forced changes in the consumption habits of urban consumers. Several other studies have also demonstrated a shift in consumption patterns during the pandemic. Eftimov et al. (2020) researched using artificial intelligence on people’s consumption patterns before and during the COVID-19 pandemic and concluded that there had been alterations in food consumption patterns before and during the COVID-19 pandemic. A significant increase in consumption occurred in foods such as beans, pancakes/tortillas/oatcakes, and soups/thick soups, which increased by 300%, 280%, and 100%, respectively. Interestingly, the most significant decrease occurred in the consumption of foods such as Order Perciformes (a type of fish), corn/cereals/seeds, and wine, with a decline of 50%, 40%, and 30%. With a different method, Scarmozzino and Visioli (2020) investigated the influence of COVID-19 on the food consumption habits of the Italian population using a survey and indicated a shift in the food consumption patterns among the Italian population. Over 50% of respondents admitted that fruits and vegetables were unattractive during the lockdown. Furthermore, there was a significant decrease in prepared food purchases, reaching 50%. Ammar et al. (2020) researched the impact of social distancing, isolation, and home confinement during COVID-19 on healthy living behaviors and habits through online surveys and presented consistent results. The results showed that food consumption patterns and consumed food were more unhealthy during isolation, with only a significant reduction in alcohol consumption. Kartari et al. (2021) explored the impact of the pandemic on potential shifts in food consumption and dietary habits in China, Portugal, and Turkey, demonstrating that the coronavirus pandemic led to an increase in the consumption of fresh fruits and vegetables and more home-cooked meals. Specifically, Portugal experienced higher consumption of seafood, bread, and butter; China saw an increased intake of rice and meat; and Turkey observed a rise in meat and egg consumption. Conversely, Profeta et al. (2021) found that the pandemic significantly altered eating behaviors in Denmark, with increased consumption of convenience foods, canned goods, alcohol, and sweets but decreased fresh produce intake. Hajipoor et al. (2023) noted varied impacts of COVID-19 on various food groups and macronutrient consumption, indicating shifts in dietary patterns. These diverse outcomes highlight inconsistent food consumption trends during the pandemic, with some studies showing an increase in food consumption while others show a decrease. Consequently, this research examines changes in household food consumption patterns and socio-economic demographic characteristics during the COVID-19 pandemic in Bali Province. This study aims to provide significant empirical insight into the food consumption patterns during the COVID-19 pandemic in Bali Province. Bali is considered one of Indonesia’s most adversely affected regions by the pandemic, primarily because its economy relies heavily on tourism. Preventive measures to curb the spread of COVID-19 significantly hindered economic activities in Bali, leading to a downturn that impacted household food consumption. Specifically, these policies caused a decrease in the average per capita expenditure for food consumption in Balinese households. Thus, this research intends to shed light on how these unique circumstances have influenced dietary behaviors in Bali during the pandemic. Literature review COVID-19, a worldwide pandemic that emerged in 2020, profoundly impacted global activities, reaching nearly every part of the world. Measures such as activity restrictions, isolation protocols, and lockdowns to curb the virus’s spread greatly influenced economic conditions at both macro and micro levels. Public consumption was one of the most notably affected areas. Various empirical studies have reported shifts in individuals’behavior and consumption patterns due to the limitations imposed during the COVID-19 crisis. These changes reflect the pandemic’s broad and profound effect on everyday life, altering how people shop, eat, and consume goods and services. Sidor and Rzymski (2020) conducted a study on the effects of a national quarantine policy in Poland on adults’nutrition and consumption behaviors, revealing that over 43% reported increased food and snack consumption, and nearly 52% indulged more frequently in these items. About 30% of the participants noticed weight gain, correlating with a reduced intake of vegetables, fruits, and nuts, alongside a heightened consumption of meat and dairy products during the quarantine period. Similarly, COGENT ECONOMICS & FINANCE 3 Scarmozzino and Visioli (2020) explored the dietary impacts of COVID-19 containment measures in Italy, finding that 46.1% of individuals ate more during isolation, with 19.5% reporting weight gain. Notably, 56.2% of the respondents found fruits and vegetables less appealing during isolations, indicating a common trend of altered dietary preferences and potential nutritional effects during pandemic-related restrictions in different countries. In Spain, research by Rodr ıguez-P erez et al. (2020) aimed to analyze shifts in adult consumption and dietary behaviors during the COVID-19 confinement and isolation policies. Their findings indicated a shift towards healthier eating patterns among the study population during the pandemic isolation compared to their previous habits, showcasing improved dietary choices under isolation. Conversely, Ammar et al. (2020) presented contrasting findings, observing that food consumption and eating habits—including the types of food consumed, uncontrolled eating, snacking between meals, and the frequency of main meals—tended to deteriorate and become less healthy during periods of social restrictions, isolation, or self-isolation. These studies highlight the varied impacts of COVID-19-related confinement on dietary behaviors across different populations and settings. Eftimov et al. (2020) utilized artificial intelligence to analyze changes in worldwide consumption patterns before and during the COVID-19 pandemic through food preparation recipes from the AllRecipes website, which aggregated data across 24 countries. Their findings confirmed significant shifts in dietary habits correlating with the pandemic’s onset. Specifically, there was a notable increase in the consumption of items like nuts, pancakes/tortillas/flatbreads, and soups/broths, while the most substantial declines were observed in the consumption of Order Perciformes (a category of fish), corn/cereals/grains, and wine. As noted in other studies, these results and the varied outcomes from different regions underscore the diverse effects of the COVID-19 pandemic on consumption behaviors globally. Beyond the unique context of COVID-19, various general factors drive changes in consumption patterns across populations, such as income levels, price fluctuations, and socio-demographic attributes (Jayati et al., 2014). This is supported by Sari (2016), which investigated the relationship between socioeconomic characteristics and food consumption trends in East Kalimantan Province. The findings from this study underscored that income, as gauged through household expenditure, plays a crucial role in dictating food consumption volumes within families. Similarly, Faharuddin et al. (2015), in their analysis of food consumption dynamics in South Sumatra, found that income or expenditure elasticity for various food categories is invariably positive, suggesting that higher income levels boost food consumption. These conclusions align with the findings of Girik Allo et al. (2019), who determined that income positively and significantly impacts purchasing and consuming food commodities across households at a broader national scale in Indonesia. Price fluctuations are a critical factor for households when deciding their consumption. Kharisma et al. (2020) found that price significantly affects the portion of expenditure allocated to the animal food commodity group, resulting in a positive value. Nonetheless, when examining price elasticity, they noted that a price increase of commodities typically reduces the household demand for these items, assuming ceteris paribus. This is supported by Faharuddin et al. (2015), who reported that price elasticity, whether compensated or uncompensated, has a negative value across 14 food commodity groups. This aligns with the general economic theory that a rise in the prices of goods adversely affects consumption levels as households adjust their spending in response to price changes. Several studies have highlighted that household consumption is not solely influenced by income and prices but also by a range of internal factors, particularly the socio-demographic characteristics of the household. Research by Bangun et al. (2013), Kahar (2010), and Sari (2016) consistently shows that social attributes significantly affect consumption patterns or demand for various goods. These attributes include the household size, the education and age of the household head, and the proportions of young and school-aged children within the family. Further supporting these findings, Abdulai (2002) identified that household size is crucial in determining food consumption patterns in Swiss households. Similarly, Nugroho and Suparyono (2015) noted a specific relationship between household size and meat expenditure in Indonesia, aligning with the Deaton-Paxson Paradox, although pork was an exception in their findings. Additionally, Mittal (2010) highlighted the positive impacts of urbanization and temporal trends on food expenditure, observing that urban households allocate larger budgets for food than rural 4 B. KHARISMA ET AL. households. In a related study, Virgantari et al. (2017) discovered that urban households preferred consuming fresh shrimp and fish more than those in rural settings, emphasizing the influence of the living environment on consumption preferences. These studies illustrate how income, prices, and diverse socio-demographic factors shape household consumption behaviors and patterns. The exploration of commodity consumption models incorporating socio-economic demographic characteristics has gained considerable attention in scholarly research over the years. The seminal work in this field began with Ernst Engel in 1857. Engel (1821-1896) pioneered the analysis of the relationship between income levels and consumption patterns. He discovered a key economic principle: as income increases, the percentage of income spent on food decreases, with consumers allocating more to non-food items. This observation laid the foundation for what is now known as Engel’sLaw(Walter&Snyder,2007). Building on Engel’s foundational work, Deaton and Muellbauer (1980) further advanced the discourse by integrating the impact of prices into the consumption analysis, leading to the development of the Almost Ideal Demand System (AIDS). This model is predicated on a cost function delineating how consumers strive to optimize their utility, achieving maximum satisfaction at minimum costs for given price levels. The AIDS model is a cornerstone analytical framework in examining consumption patterns, and its adaptability has allowed for various modifications to suit specific research needs. Kahar (2010) applied the AIDS model, tailoring it to differentiate between household consumption patterns based on education levels and whether the households were in rural or urban settings. This study revealed notable disparities in expenditure levels between rural and urban areas and affirmed the significant impact of commodity prices and household income or expenditure on food and non-food commodities consumption. Building on this framework, Faharuddin et al. (2015) modified the Quadratic Almost Ideal Demand System (QUAIDS) framework to examine consumption across 14 food commodity groups. Their findings suggested that the quadratic model provided a better fit than the linear alternative, revealing positive income and negative price elasticity across all food groups, aligning with economic theory. Furthermore, they observed that income elasticity was more pronounced than price elasticity, indicating a more vigorous response in consumption patterns to income changes than price elasticity. Similarly, Girik Allo et al. (2019) utilized QUAIDS to explore how price increases affect consumption behaviors and the welfare levels of farmers in Indonesia. This research uses the QUAIDS model, integrating the Inverse Mills Ratio (IMR) as an independent variable to address a common challenge in household survey data: the occurrence of zero consumption expenditure on certain commodities. The study’s findings endorse the QUAIDS model as an effective tool for forecasting food demand functions in Indonesia. Expanding on this framework at a more localized level, Kharisma et al. (2020) applied a similar methodology in West Java Province, focusing their analysis on animal food commodities. Their research validates the utility of the QUAIDS model, augmented by variables such as price, income (via the expenditure approach), demographic and socio-economic factors, and the IMR in accurately estimating household expenditure shares on animal foods. Parallel conclusions were drawn by Bopape and Myers (2007), who examined household food demand in South Africa employing three distinct demand system models: the Linear Approximate Almost Ideal Demand System (LA-AIDS), QUAIDS, and a log-linear model. Their comparative analysis identified the QUAIDS model as the most suitable for capturing consumption and expenditure patterns, particularly its capacity to incorporate demographic details, account for structural shifts, and recognize seasonal variations. Drawing from various research findings, it is deduced that the QUAIDS model, when augmented with socio-demographic attributes and the IMR variable, is better used to describe consumption patterns than other models. This research intends to apply the QUAIDS model to changes in household consumption patterns before and during the COVID-19 pandemic and determine the relationship between income, prices, and socio-demographic characteristics. Data and method Data The object of this research is the share of expenditure of 14 household food commodity groups, household income/expenditure. Commodity grouping refers to the COICOP used by BPS (2022b). The unit of analysis is households spread across nine regencies and one city in the Province of Bali. The data utilized COGENT ECONOMICS & FINANCE 5 comes from Susenas in March 2019, representing the period before the COVID-19 pandemic, and March 2021, illustrating the situation during the pandemic. The research did not include Susenas data from 2020 because the COVID-19 outbreak in Bali Province had not yet spread significantly, affecting only a limited number of people then. This research uses secondary data from the Central Statistics Agency, namely The National Socio-economic Survey data (Susenas) with household sample units. The data comes from VSEN-KOR data (Information on Basic Household Members) and VSEN-KP Data (Household Expenditure Consumption). KOR data is used to collect explanatory variables in the form of household social information, including the age and gender of the household head, the number of household members, the education level of the household head, the region of the household’s residence, the occupation of the household head, and the household’s perceptions of food access. Then, the KP data is used to obtain information on household food expenditure’s quantity and consumption value, both from purchases and gifts during the past week. Method This study employs a quantitative approach, utilizing the QUAIDS model for analysis. This model is chosen because it retains consistency with the Engel curve and accounts for the effect of relative prices in the utility maximization process. Additionally, it allows for estimating more parameters than earlier models, such as LA/AIDS (Aepli, 2014). The QUAIDS model necessitates that all sample households consume all the commodities under study. To anticipate households with zero commodity consumption, it is necessary to merge food commodity groups into larger groups so that more sample households meet the requirements. Grouping commodities assumes that food commodity prices in one commodity group have the same movement. According to Zheng and Henneberry (2010), if the research is conducted over large areas with diverse demographic factors, the unit value approach may introduce errors, such as biased results when making measurements. These problems can be overcome using a unit value, which is corrected by a price differential approach. Price justification in this study uses the method proposed by Cox and Wohlgenant (1986), modified by Hoang. Furthermore, to tackle the issue of price variability, this research adopts the methodology used by Majumder et al. (2012), which employs unit values corrected through the price differential method. This approach adjusts the unit value by incorporating the median value of each district or city and the estimated residual from the regression that accounts for differences across districts/cities influenced by socio-demographic factors. This technique helps to standardize the price variable, minimizing biases that may arise from using unadjusted unit values and providing a more accurate representation of price effects on consumption patterns across different regions. This approach presumes that households in the same district or city experience identical commodity prices. The equation used to overcome the problems that arise due to the use of the value per unit is as follows: vi¼aiþuixþhDþei(1) pi¼viþ^ ei(2) Here, viindicates the value per unit of food commodity group i (i¼1,2,3, ….,14), viistheaggregateprice of the ith food commodity group, xis the total household expenditure for food consumption, Dis the vector of demographic characteristics (regional classification, average length of schooling of the head of the family, household size, sex of the head of the household and income group) and ekis the error term. Apart from pricing problems, household expenditure survey data has other common issues in the form of empty data (households that do not consume a particular commodity at all) or zero consumption (Girik Allo et al., 2019). According to Newman in Nugroho and Wardhani (2016), zero consumption can be attributed to economic and non-economic factors, such as household income levels, vegetarian households or individuals, and survey periods shorter than the demand cycle for goods. Omitting households with zero consumption from the survey data would reduce the dataset size and lead to biased estimation results, a phenomenon known as selection bias. To address this issue, commodity grouping was performed, and the Inverse Mills Ratio (IMR) variable was included and treated as an independent variable, as done by Girik Allo et al. (2019) in their research. 6 B. KHARISMA ET AL. The IMR is added to anticipate zero consumption after commodity grouping. The Heckman test derives the IMR variable through a two-step estimation process. Based on this, the model used in this study refers to the model developed by Hamzah and Huang (2023), Poi (2012), Girik Allo et al. (2019), and Kharisma et al. (2020). The QUAIDS model accommodates regional classification variables, household size, gender of the head of the household, income group, education of the head of household, and the use of the Inverse Mills Ratio (IMR) to address the zero consumption problem. Thus, the QUAIDS equation used in this study is as follows: wi¼aiþX n j¼1 yijln pjþbiln x aðpÞ  þki bðpÞln x aðpÞ  2 þai1dlociþai2hsizeiþai3genderiþai4educi þai5inciþai6IMRiþai7jobiþai8foodaccessiþei(3) where wirepresents the share of expenditure from the ith food commodity group (i ¼1,2,3, ….,14), lnpj is the aggregate price of the jth food commodity group, xis household expenditure for food and nonfood consumption, ln ða=pÞis price index, bðpÞis price aggregator, dloc is location (urban ¼1), hsizeiis household size, genderiis sex of head of household (male ¼1), educ is level of education (years of school), inc is income group (lower income ¼1 as reference, middle income ¼2, upper income ¼3), IMR is Inverse Mills Ratio, job is business field of head of household (agricultural sector ¼1), foodaccess indicates people’s perception of access to food (easy ¼1) and eiis the error term. The QUAIDS model above is then derived to obtain income/expenditure elasticity and price elasticity as follows: Income Elasticity li¼1þ1 wi biþ2ki bðpÞln x aðpÞ  "# (4) Uncompensated price elasticity (Marshallian) eNC ij ¼1 wi cij −liajþX n k¼1 kjkln pk ! − kibj bðpÞln x aðpÞ  2 () 2 43 5−dij (5) Compensated price elasticity (Hicksian) eC ij ¼eij þliwj(6) where eij is price elasticity, cij is the food price parameter, bi, and kiare linear and quadratic income parameters, and wiis the average share of food expenditure. Results The average monthly per capita consumption expenditure in Bali during the pandemic (2021) was IDR 1,468,624. This is a decrease from the previous year’s average expenditure of IDR 1,509,666. During the pandemic, households maintained consumption despite income and economic instability. Table 2 shows Table 2. Average per capita monthly expenditure by residence and type of expenditure in Bali Province in 2019–2021. Area of residence Type of expenditure Food Non Food 2019 2020 2021 2019 2020 2021 Urban 649,854 724,978 667,606 887,616 973,021 963,314 Changes (%) 11.56 −7.91 9.62 −1.00 Rural 519,389 564,445 534,662 535,918 526,846 544,917 Changes (%) 8.67 −5.28 −1.69 3.43 Bali Province 609,181 675,146 628,472 777,972 834,520 840,152 Changes (%) 10.83 −6.91 7.27 0.67 Source: The National Socio-economic Survey (Susenas), 2019-2021 (Processed Data). COGENT ECONOMICS & FINANCE 7 commodity group indicates that price increases have little impact on household consumption, especially those living in urban areas and belonging to the upper middle class. Before the pandemic, the own-price elasticity for fourteen food commodity groups was negative. During the pandemic, these groups’own-price elasticity remained negative except for grains, tobacco, and betel nuts. This finding supports previous research indicating that when the price of a food commodity rises, its consumption decreases (Green et al., 2013). Interestingly, the own-price elasticities for grains, tobacco, and betel nuts are positive. This means that even as household prices for these commodities rise, their consumption continues to increase. The grain commodity group has an elasticity value of 0.244, meaning that a 1 percent price increase will boost the quantity consumed by 0.244 percent. The increase likely reflects a shift in household behaviors due to movement restrictions and a preference for home cooking, particularly among urban and uppermiddle-income groups. In urban areas and among the middle and upper-income classes, the positive price elasticity for grain suggests that as prices increase, so does the expenditure share for grains, possibly due to lower price sensitivity or a lack of suitable substitutes. Conversely, the negative price elasticity observed in rural areas and lower-income groups indicates a more typical economic response where an increase in price leads to a decrease in consumption, reflecting greater price sensitivity or financial constraints. Then, the own price elasticity of the tobacco and betel nut commodity group is also positive. This indicates that an increase in the price of tobacco and betel nut commodities has little effect on cigarette consumption during the pandemic. This result is in line with previous findings that an increase in nicotine consumption during a pandemic can be caused by boredom, stress, and anxiety (Almeda & G omezG omez, 2022). In addition, the intensity of spending on cigarettes remains high due to easy access and the large selection of cigarettes sold on the market. Table 9. Income elasticity 2019. Food commodity group Total Area Education Level Income Group Urban Rural <¼9 Years >9 Years Lower Middle Upper Grains 1.890 1.787 1.534 1.565 1.876 1.508 1.890 2.380 Tubers −0.597 −0.491 −0.071 −0.123 −0.601 −0.009 −0.597 −0.795 Fish 1.020 1.013 0.992 1.002 1.008 1.006 1.020 1.002 Meat −0.162 −0.181 0.036 −0.062 −0.132 −0.141 −0.162 −0.247 Eggs and Milk 1.548 1.410 1.455 1.464 1.392 1.451 1.548 1.359 Vegetables 0.935 0.926 0.932 0.936 0.920 0.974 0.935 0.916 Nuts 0.491 0.534 0.619 0.622 0.490 0.715 0.491 0.313 Fruits −0.065 0.001 0.171 0.119 0.002 0.128 −0.065 −0.082 Oil and Fat 1.408 1.332 1.239 1.241 1.392 1.232 1.408 1.614 Beverage Ingredients 1.910 1.815 1.567 1.590 1.912 1.523 1.910 2.296 Spices 1.190 1.148 1.118 1.125 1.159 1.137 1.190 1.231 Other Consumption 0.826 0.849 0.854 0.857 0.840 0.871 0.826 0.799 Prepared Food and beverage 0.857 0.880 0.876 0.877 0.877 0.864 0.857 0.854 Tobacco and Betel nut 1.509 1.615 1.546 1.489 1.727 1.404 1.509 1.848 Source: The National Socio-economic Survey (Susenas), 2019 (Processed Data). Table 10. Income elasticity 2021. Food commodity group Total Area Education Level Income Group Urban Rural <¼9 Years >9 Years Lower Middle Upper Grains 1.147 1.165 1.127 1.133 1.177 1.139 1.233 1.332 Tubers 0.752 0.740 0.764 0.767 0.728 0.783 0.708 0.681 Fish 0.330 0.309 0.358 0.352 0.298 0.388 0.238 0.162 Meat 0.937 0.935 0.940 0.935 0.936 0.924 0.913 0.913 Eggs and Milk 1.854 1.816 1.926 1.970 1.773 1.874 1.969 1.895 Vegetables 1.096 1.115 1.074 1.083 1.117 1.085 1.132 1.173 Nuts 1.033 1.036 1.029 1.032 1.037 1.048 1.057 1.070 Fruits 0.928 0.926 0.930 0.925 0.929 0.919 0.910 0.907 Oil and Fat 1.242 1.270 1.207 1.208 1.296 1.192 1.336 1.482 Beverage Ingredients 0.922 0.922 0.922 0.928 0.916 0.947 0.922 0.899 Spices 1.086 1.090 1.081 1.088 1.088 1.103 1.114 1.127 Other Consumption 1.002 0.997 1.010 1.006 0.998 1.005 1.009 1.013 Prepared Food and beverage 1.056 1.039 1.095 1.076 1.040 1.071 1.058 1.032 Tobacco and Betel nut 0.205 0.184 0.230 0.188 0.197 −0.127 0.130 0.079 Source: The National Socio-economic Survey (Susenas), 2021 (Processed Data). 14 B. KHARISMA ET AL. Discussion The measures implemented during the COVID-19 pandemic have influenced shifts in household food consumption patterns in Bali Province. Before and during the pandemic, most household expenditures were allocated to processed foods, grains, tobacco, and betel nuts. During the pandemic, the focus shifted to processed foods, rice, grains, and vegetables. Significant variations in average consumption expenditure across different commodity groups are evident when examining the details. The expenditures for tubers, vegetables, and nuts were the most substantial increases. Conversely, the highest Table 11. Estimated uncompensated own price elasticities 2019. Food commodity group Uncompensated Area Education Level Income Group Urban Rural <¼9 Years >9 Years Lower Middle Upper Grains −0.238 −0.119 −0.371 −0.342 −0.040 −0.426 −0.100 0.868 Tubers −0.597 −0.535 −0.666 −0.639 −0.525 −0.636 −0.578 −0.143 Fish −0.652 −0.633 −0.677 −0.659 −0.642 −0.669 −0.625 −0.681 Meat −0.263 −0.215 −0.327 −0.242 −0.293 −0.107 −0.361 −0.310 Eggs and Milk −0.681 −0.698 −0.650 −0.646 −0.718 −0.631 −0.667 −0.914 Vegetables −0.838 −0.821 −0.859 −0.856 −0.805 −0.869 −0.807 −0.721 Nuts −1.047 −1.050 −1.042 −1.042 −1.054 −1.036 −1.052 −0.977 Fruits −0.489 −0.457 −0.532 −0.490 −0.489 −0.446 −0.516 −0.453 Oil and Fat −0.742 −0.712 −0.777 −0.776 −0.678 −0.801 −0.702 −0.881 Beverage Ingredients −0.930 −0.922 −0.940 −0.938 −0.917 −0.942 −0.927 −0.760 Spices −0.893 −0.887 −0.901 −0.900 −0.882 −0.909 −0.882 −0.897 Other Consumption −0.569 −0.578 −0.555 −0.575 −0.562 −0.603 −0.577 −0.715 Prepared Food and beverage −0.609 −0.644 −0.535 −0.567 −0.649 −0.547 −0.604 −0.778 Tobacco and Betel nut −0.367 −0.373 −0.353 −0.377 −0.332 −0.120 −0.412 0.418 Source: The National Socio-economic Survey (Susenas), 2019 (Processed Data). Table 12. Estimated compensated own price elasticity 2019. Food commodity group Compensated Area Education Level Income Group Urban Rural <¼9 Years >9 Years Lower Middle Upper Grains 0.014 0.117 −0.093 −0.072 0.190 −0.133 0.157 0.591 Tubers −0.600 −0.539 −0.667 −0.641 −0.529 −0.636 −0.583 −0.576 Fish −0.596 −0.580 −0.618 −0.602 −0.588 −0.610 −0.572 −0.608 Meat −0.269 −0.225 −0.324 −0.246 −0.302 −0.115 −0.372 −0.414 Eggs and Milk −0.617 −0.631 −0.590 −0.586 −0.648 −0.574 −0.602 −0.692 Vegetables −0.765 −0.754 −0.775 −0.774 −0.744 −0.775 −0.741 −0.711 Nuts −1.033 −1.039 −1.026 −1.026 −1.043 −1.016 −1.041 −1.063 Fruits −0.485 −0.456 −0.522 −0.484 −0.489 −0.440 −0.520 −0.573 Oil and Fat −0.710 −0.683 −0.742 −0.740 −0.650 −0.762 −0.670 −0.559 Beverage Ingredients −0.884 −0.879 −0.890 −0.889 −0.875 −0.892 −0.880 −0.865 Spices −0.872 −0.867 −0.879 −0.878 −0.863 −0.884 −0.861 −0.846 Other Consumption −0.557 −0.566 −0.543 −0.562 −0.550 −0.589 −0.565 −0.447 Prepared Food and beverage −0.287 −0.283 −0.273 −0.283 −0.278 −0.280 −0.274 −0.260 Tobacco and Betel nut −0.254 −0.258 −0.244 −0.266 −0.220 −0.036 −0.285 −0.159 Source: The National Socio-economic Survey (Susenas), 2019 (Processed Data). Table 13. Estimated uncompensated own price elasticities 2021. Food commodity group Uncompensated Area Education Level Income Group Urban Rural <¼9 Years >9 Years Lower Middle Upper Grains 0.042 0.153 −0.094 −0.098 0.246 −0.206 0.208 0.868 Tubers −0.207 −0.105 −0.326 −0.292 −0.093 −0.283 −0.147 −0.143 Fish −0.632 −0.622 −0.648 −0.631 −0.635 −0.617 −0.644 −0.681 Meat −0.249 −0.242 −0.260 −0.209 −0.286 −0.129 −0.304 −0.310 Eggs and Milk −0.809 −0.824 −0.784 −0.766 −0.845 −0.753 −0.845 −0.914 Vegetables −0.773 −0.751 −0.800 −0.792 −0.749 −0.801 −0.760 −0.721 Nuts −0.985 −0.984 −0.986 −0.986 −0.983 −0.987 −0.983 −0.977 Fruits −0.417 −0.391 −0.453 −0.418 −0.416 −0.374 −0.434 −0.453 Oil and Fat −0.921 −0.914 −0.930 −0.931 −0.908 −0.937 −0.911 −0.881 Beverage Ingredients −0.816 −0.805 −0.831 −0.836 −0.789 −0.840 −0.809 −0.760 Spices −0.907 −0.905 −0.909 −0.908 −0.904 −0.910 −0.906 −0.897 Other Consumption −0.736 −0.745 −0.720 −0.734 −0.737 −0.749 −0.731 −0.715 Prepared Food and beverage −0.722 −0.749 −0.662 −0.684 −0.752 −0.664 −0.733 −0.778 Tobacco and Betel nut 0.085 0.144 0.009 0.051 0.148 0.354 0.069 0.418 Source: The National Socio-economic Survey (Susenas), 2021 (Processed Data). COGENT ECONOMICS & FINANCE 15 declines were observed in the fruit, processed food, beverage, and meat commodity groups. The enforcement of the PSBB policy significantly altered consumption patterns due to its stringent limitations on public outings and communal activities. These restrictions, which curtailed access to restaurants, eateries, and shopping centers, naturally led households to minimize their consumption of outside-prepared food and beverages, pivoting instead towards in-home meal preparation. Moreover, the PSBB’s impact extended into the cultural and religious domains, particularly affecting the Balinese Hindu community, for whom fruits and meats are not merely dietary staples but also crucial components of religious observances. Therefore, the restrictions on religious gatherings directly diminished the communal consumption and demand for these products (Dewi et al., 2022; Spranz & Schl€ uter, 2023). The QUAIDS estimation results indicate that income, approximated by the average expenditure value, and the price of own goods significantly and positively impact the share of household food expenditure in Bali Province during the pandemic. Among socio-demographic characteristics, the occupation of the household head had the most significant influence. Households with heads working in the agricultural sector allocated a smaller share of their expenditure to food than those working in the non-agricultural sector. The findings align with observations from Czechia, where agrarian workers’incomes are lower than other sectors, an insight often mirrored in the equivalized household income data (Zden ek et al., 2022). Further reinforcing this pattern, the research by Hartoyo et al. (2021) in Boyolali Regency, Indonesia, delineates a similar scenario where households in rainfed areas—typically reliant on agriculture—derive a substantial portion of their income from non-agricultural activities. Income elasticity is positive for all 14 food commodity groups, indicating that these commodities are considered normal goods. This suggests that increased income will increase the quantity of food households consume (Girik Allo et al., 2019). Own-price elasticity is negative for the food commodity group except for the grain and tobacco/betel nut commodity groups. The own-price elasticity of the grain and tobacco/betel nut commodity group is positive, indicating that households consume more of these food commodities despite a price increase. The observed increase in grain consumption among urban households and those in the upper-middle-income bracket during the pandemic can be attributed to the constraints imposed by PSBB policy. This policy reduced the consumption of prepared foods and a corresponding increase in home cooking. Furthermore, the shift towards more time spent at home may have also influenced an uptick in the consumption of tobacco and betel nuts among active smokers. These trends align with observations from Iran, as reported by Hajipoor et al. (2023), where there was a notable increase in the consumption of grains and vegetables in urban areas during the COVID-19 quarantine. However, consuming fruits, dairy, meats, fats, and sweets decreased. Conclusion The results revealed a shift in household food consumption patterns during the pandemic in Bali Province. The average food expenditure per capita decreased, while the average non-food expenditure per capita Table 14. Estimated compensated own price elasticity 2021. Food commodity group Compensated Area Education Level Income Group Urban Rural <¼9 Years >9 Years Lower Middle Upper Grains 0.244 0.339 0.133 0.131 0.421 0.054 0.402 1.012 Tubers −0.198 −0.098 −0.316 −0.283 −0.086 −0.274 −0.140 −0.136 Fish −0.614 −0.605 −0.627 −0.611 −0.618 −0.595 −0.631 −0.672 Meat −0.183 −0.177 −0.193 −0.147 −0.217 −0.073 −0.235 −0.240 Eggs and Milk −0.717 −0.728 −0.697 −0.682 −0.744 −0.671 −0.746 −0.797 Vegetables −0.655 −0.641 −0.668 −0.664 −0.640 −0.668 −0.644 −0.616 Nuts −0.958 −0.959 −0.958 −0.958 −0.959 −0.957 −0.957 −0.955 Fruits −0.368 −0.344 −0.400 −0.369 −0.366 −0.328 −0.384 −0.401 Oil and Fat −0.885 −0.880 −0.890 −0.891 −0.876 −0.894 −0.876 −0.850 Beverage Ingredients −0.790 −0.780 −0.802 −0.806 −0.766 −0.809 −0.784 −0.740 Spices −0.882 −0.881 −0.884 −0.884 −0.881 −0.884 −0.881 −0.874 Other Consumption −0.718 −0.727 −0.703 −0.717 −0.720 −0.731 −0.714 −0.699 Prepared Food and beverage −0.421 −0.415 −0.413 −0.418 −0.414 −0.412 −0.421 −0.401 Tobacco and Betel nut 0.099 0.156 0.026 0.064 0.161 0.347 0.079 0.424 Source: The National Socio-economic Survey (Susenas), 2021 (Processed Data). 16 B. KHARISMA ET AL. slightly increased. Notably, the per capita food expenditure in urban areas decreased more significantly than in rural areas. The most considerable increase in per capita food expenditure was observed in the consumption of tubers, vegetables, and nuts, whereas the most significant decline occurred in fruit, processed food, and meat commodities. Socio-demographic factors that significantly influenced the share of household food expenditure included the education level of the household head, their occupation, and the household’s perception of food access. The price of one’s goods positively affected the share of food consumption expenditure but negatively impacted the quantity consumed. Future research should disaggregate data by region of residence, income group, and gender to identify how variables such as income, price, and socio-demographic characteristics influence household expenditure in the different areas and income groups. This study focused solely on household food consumption patterns during the abnormal conditions of the COVID-19 pandemic. Therefore, future research should compare estimation results from before, during, and after the pandemic. Additionally, this study is limited to food consumption patterns; future research should include non-food commodity variables to provide a more comprehensive view of household consumption patterns. This will allow for an analysis of the impact of COVID-19 on non-food commodities consumed by households during the pandemic. Policy implications During The Large-Scale Social Restrictions (PSBB) policy, households in Bali, like many others globally, experienced significant shifts in their consumption patterns, primarily due to increased home cooking activities. This behavioral shift underscores the government’s need to ensure price stability for essential commodities such as grains, vegetables, meat, beans, seasonings, and oils. Price stability is crucial for consumer protection and maintaining the economic viability of local producers and the broader agricultural sector. To effectively address these needs, the government can adopt several strategic measures, such as monitoring the supply of essential foods in the field and implementing distribution strategies for fruits and meats. This approach aims to prevent losses among producers, especially since these items were less favored by the Balinese population during the pandemic. Moreover, the government must prioritize policies that aid low-income village households, emphasizing community-based food initiatives that foster local empowerment. By implementing these strategies, the government can help ensure the population has stable access to affordable and nutritious food, supporting public health and economic stability during challenging times like a pandemic. Acknowledgments We are deeply grateful to our colleagues and friends from the Department of Economics, whose valuable insights and feedback greatly influenced the development of this research. We also extend our thanks to the numerous reviewers who generously shared their insights, making the completion of this paper possible. Author contributions Bayu Kharisma: conceptualization of the idea, methods, analysis, and writing; approved and revised the final manuscript and revisions from the review results. Alfiah Hasanah: Analyze and interpret results and writing. Sutyastie Soemitro Remi provided administrative and financial support and writing. I Gusti Gede Gusna Yoga Sanjaya: data collection, estimation, and interpretation of data and writing. Disclosure statement No potential conflict of interest was reported by the author(s). Funding This research is supported by DRPMI Universitas Padjadjaran (UNPAD) with the RKDU scheme. COGENT ECONOMICS & FINANCE 17 About the authors Bayu Kharisma is a lecturer at the Department of Economics, Universitas Padjadjaran. He obtained his bachelor's degree in economics from Universitas Padjadjaran, master's degree in Agribusiness Management from Bogor Agricultural University, and bachelor's degree in economics from Universitas Indonesia. He obtained his doctorate in Economics from Gadjah Mada University. His research interests focus on development and institutional economics. Alfiah Hasanah is a lecturer and researcher at the Department of Economics, Universitas Padjadjaran. She earned a bachelor's degree in economics from Universitas Padjadjaran, Indonesia (1998), a master's degree in international finance from the International Islamic University in Malaysia, Malaysia (2002), and a doctorate in economics from The University of Wollongong. Her research interests focus on Islamic finance, human resources, and health economics. Sutyastie Soemitro Remi is a lecturer and researcher at the Department of Economics, Faculty of Economics and Business, Universitas Padjadjaran, Indonesia. She received her doctorate degree from Universitas Padjadjaran, Indonesia. Her research interests focus on Development Economics and Population. I Gusti Gede Gusna Yoga Sanjaya graduated from Politeknik Statistika STIS and is currently working as a data analyst at the Central Bureau of Statistics of Jembrana Regency, Bali, Indonesia. His research interests focus on Statistics. ORCID Bayu Kharisma http://orcid.org/0000-0002-2625-5250 Alfiah Hasanah http://orcid.org/0000-0002-3927-8902 Sutyastie Soemitro Remi http://orcid.org/0000-0001-7771-0367 I. Gusti Gede Gusna Yoga Sanjaya http://orcid.org/0009-0008-1923-505X Data availability statement Data supporting this study’s findings can be accessed on the Central Statistics Agency (BPS) website at https://silastik.bps.go.id/v3/index.php/mikrodata/detail/U1BGcE5sYzFvamI2SGw0YmVUYUlDZz09. Data available upon reasonable request from the corresponding author, BK. References Abdulai, A. (2002). Household demand for food in switzerland. a quadratic almost ideal demand system. Swiss Society of Economics and Statistics,138(I), 1–18. Aepli, M. (2014). Consumer demand for alcoholic beverages in Switzerland: a two-stage quadratic almost ideal demand system for low, moderate, and heavy drinking households. Agricultural and Food Economics,2(1), 1–27. https://doi.org/10.1186/s40100-014-0015-0 Akbar, A., Darma, R., Fahmid, I. M., & Irawan, A. (2023). Determinants of household food security during the COVID19 pandemic in Indonesia. Sustainability,15(5), 4131. https://doi.org/10.3390/su15054131 Almeda, N., & G omez-G omez, I. (2022). The impact of the COVID-19 pandemic on smoking consumption: A systematic review of longitudinal studies. Frontiers in Psychiatry,13, 941575. https://doi.org/10.3389/fpsyt.2022.941575 Ammar, A., Brach, M., Trabelsi, K., Chtourou, H., Boukhris, O., Masmoudi, L., Bouaziz, B., Bentlage, E., How, D., Ahmed, M., M€ uller, P., M€ uller, N., Aloui, A., Hammouda, O., Paineiras-Domingos, L. L., Braakman-Jansen, A., Wrede, C., Bastoni, S., Pernambuco, C. S., …Hoekelmann, A. (2020). Effects of COVID-19 home confinement on eating behaviour and physical activity: Results of the ECLB-COVID19 international online survey. Nutrients,12(6), 1583. https://doi.org/10.3390/nu12061583 Antara, M., & Sri Sumarniasih, M. (2022). Impact of the COVID-19 pandemic on Bali’s and Indonesia’s economic growth. SOCA: Jurnal Sosial, Ekonomi Pertanian,16(2), 187. https://doi.org/10.24843/SOCA.2022.v16.i02.p06 Badan Pusat Statistik (BPS). (2022). Produk Domestik Regional Bruto Provinsi-Provinsi di Indonesia Menurut Pengeluaran, 2017-2021. Badan Pusat Statistik. Badan Pusat Statistik Provinsi Bali. (2022). Pola Konsumsi dan Distribusi pendapatan Provinsi Bali 2021. BPS Provinsi Bali. Bangun, H. P. P., Salmiah, & Hutajulu, A. T. (2013). Analisis Pola Konsumsi Pangan dan Tingkat Konsumsi Beras di Desa Sentra Produksi Padi (Studi Kasus: Desa Dua Ramunia, Kecamatan Beringin, Kabupaten Deli Serdang). Journal of Agriculture and Agribusiness Socioeconomics,2(7), 9–16. Retrieved from https://www.neliti.com/publications/15099/analisis-pola-konsumsi-pangan-dan-tingkat-konsumsi-beras-di-desa-sentra-produksi Baranov, V., Grosjean, P., Khan, F. J., & Walker, S. (2022). The impact of COVID-related economic shocks on household mental health in Pakistan. Health Economics,31(10), 2208–2228. https://doi.org/10.1002/hec.4571 18 B. KHARISMA ET AL. Bopape, L., & Myers, R. (2007). Analysis of household demand for food in South Africa: Model selection, expenditure endogeneity, and the influence of socio-demographic effects [Paper presentation]. African econometrics society annual conference (p. 22). Cox, T. L., & Wohlgenant, M. K. (1986). Prices and Quality Effects in Cross-Sectional Demand Analysis. American Journal of Agricultural Economics,68(4), 908–919. 10.2307/1242137 Deaton, A., & Muellbauer, J. (1980). An almost ideal demand system. The American Economic Review,70(3), 312–326. http://www.jstor.org/stable/1805222%5Cnhttp://www.jstor.org/stable/1805222?seq=1&cid=pdf-reference#references_ tab_contents%5Cnhttp://about.jstor.org/terms Dewi, N. L. M. S., Arisena, G. M. K., & Artini, N. W. P. (2022). Fruit consumption patterns in the community of Denpasar City. Agribusiness Journal,5(1), 1–8. https://doi.org/10.31327/aj.v5i1.1672 Eftimov, T., Popovski, G., Petkovi c, M., Seljak, B. K., & Kocev, D. (2020). COVID-19 pandemic changes the food consumption patterns. Trends in Food Science & Technology,104(August), 268–272. https://doi.org/10.1016/j.tifs.2020. 08.017 Faharuddin, N., Mulyana, A., & Yunita, N. (2015). Analisis Pola Konsumsi Pangan di Sumatera Selatan 2013: Pendekatan Quadratic Almost Ideal Demand System. Jurnal Agro Ekonomi,33(2), 121. https://doi.org/10.21082/jae. v33n2.2015.121-140 Girik Allo, A., Satiawan, E., & Arsyad, L. A. (2019). The impact of rising food prices on farmers’welfare in Indonesia. Journal of Indonesian Economy and Business,33(3), 193. https://doi.org/10.22146/jieb.17303 Green, R., Cornelsen, L., Dangour, A. D., Turner, R., Shankar, B., Mazzocchi, M., & Smith, R. D. (2013). The effect of rising food prices on food consumption:systematic review with meta-regression. BMJ (Clinical Research ed.), 346(7915), f3703. https://doi.org/10.1136/bmj.f3703 Hajipoor, M., Rahbarinejad, P., Irankhah, K., & Sobhani, S. R. (2023). Comparing food consumption during the COVID19 pandemic: analysis of household income and expenditure survey data in Iran. Journal of Health, Population and Nutrition,42(1), 1–13. https://doi.org/10.1186/s41043-023-00385-3 Hamzah, I. N., & Huang, W. (2023). The dynamics of strategically important food preference in Indonesia: An empirical evaluation of consumption pattern and welfare loss. Economic Analysis and Policy,79, 435–449. https://doi. org/10.1016/j.eap.2023.06.024 Hartoyo, B., Komalawati, & Sahara, D. (2021 Analysis of income and expenditure of farmers’household in the rain-fed area of Boyolali district [Paper presentation]. IOP Conference Series: Earth and Environmental Science (Vol. 653). https://doi.org/10.1088/1755-1315/653/1/012007 Jayati, L. D., Madanijah, S., & Khomsan, A. (2014). Pola Konsumsi Pangan, Kebiasaan Makan, Dan Densitas Gizi Pada Masyarakat Kasepuhan Ciptagelar Jawa Barat. Penelitian Gizi Makanan,37(1), 33–42. Kahar, M. (2010). Analisis Pola Konsumsi Daerah Perkotaan Dan Pedesaan Serta Keterkaitannya Dengan Karakteristik Sosial Ekonomi Di Propinsi Banten. Sekolah Pasca Sarjana. Institut Pertanian Bogor. Kartari, A., € Ozen, A. E., Correia, A., Wen, J., & Kozak, M. (2021). Impacts of COVID-19 on changing patterns of household food consumption: An intercultural study of three countries. International Journal of Gastronomy and Food Science,26, 100420. https://doi.org/10.1016/j.ijgfs.2021.100420 Kharisma, B., Alisjahbana, A. S., Remi, S. S., & Praditya, P. (2020). Application of the quadratic almost ideal demand system (QUAIDS) model in the demand of the household animal sourced food in West Java. Agris on-Line Papers in Economics and Informatics,12(01), 23–35. https://doi.org/10.7160/aol.2020.120103 Kumar, R., & Abdin, M. S. (2021). Impact of epidemics and pandemics on consumption pattern: evidence from Covid19 pandemic in rural-urban India. Asian Journal of Economics and Banking,5(1), 2–14. https://doi.org/10.1108/ AJEB-12-2020-0109 Majumder, A., Ray, R., & Sinha, K. (2012). Calculating rural-urban food price differentials from unit values in household expenditure surveys: A comparison with existing methods and a new procedure. American Journal of Agricultural Economics,94(5), 1218–1235. https://doi.org/10.1093/ajae/aas064 Mayasari, D., Satria, D., & Noor, I, Fakultas Ekonomi dan Bisnis, Universitas Brawijaya. (2018). Analisis Pola Konsumsi Pangan Berdasarkan Status IPM di Jawa Timur. Jurnal Ekonomi Dan Pembangunan Indonesia,18(2), 191–213. https://doi.org/10.21002/jepi.v18i2.801 Mittal, S. (2010). Application of the QUAIDS Model to the Food Sector in India. Journal of Quantitative Economics, 8(1), 42–54. http://www.jqe.co.in/journals/JQE_v8_n1_2010_p2.pdf Myers, B., Pickering, S., Tenrisanna, V., Wiendiyati, (2014). Food security of households with access to subsidized rice in west Timor where maize is the traditional staple. Food Security6(3, 385, 395). https://doi.org/10.1007/s12571014-0353-5 Nasution, D. A. D., Erlina, E., & Muda, I. (2020). Dampak Pandemi COVID-19 terhadap Perekonomian Indonesia. Jurnal Benefita,5(2), 212. https://doi.org/10.22216/jbe.v5i2.5313 Nugroho, S., & Suparyono, S. W, Magister Perencanaan dan Kebijakan Publik, Fakultas Ekonomi dan Bisnis Universitas Indonesia. (2015). Pola Permintaan Daging Tingkat Rumah Tangga di Indonesia: Analisa Data Mikro 2013. Jurnal Ekonomi Dan Pembangunan Indonesia,16(1), 47–58. https://doi.org/10.21002/jepi.v16i1.668 Poi, B. P. (2012). Easy demand-system estimation with quaids. The Stata Journal: Promoting Communications on Statistics and Stata,12(3), 433–446. https://doi.org/10.1177/1536867X1201200306 COGENT ECONOMICS & FINANCE 19 Profeta, A., Siddiqui, S. A., Smetana, S., Hossaini, S. M., Heinz, V., & Kircher, C. (2021). The impact of Corona pandemic on consumer’s food consumption: Vulnerability of households with children and income losses and change in sustainable consumption behavior. Journal Fur Verbraucherschutz Und Lebensmittelsicherheit ¼Journal of Consumer Protection and Food Safety,16(4), 305–314. https://doi.org/10.1007/s00003-021-01341-1 Putri, R. N. (2020). Indonesia dalam Menghadapi Pandemi Covid-19. Jurnal Ilmiah Universitas Batanghari Jambi,20(2), 705. https://doi.org/10.33087/jiubj.v20i2.1010 Rodr ıguez-P erez, C., Molina-Montes, E., Verardo, V., Artacho, R., Garc ıa-Villanova, B., Guerra-Hern andez, E. J., & Ru ızL opez, M. D. (2020). Changes in dietary behaviours during the COVID-19 outbreak confinement in the Spanish COVIDiet study. Nutrients,12(6), 1730. https://doi.org/10.3390/nu12061730 Sari, N. A. (2016). Analisis Pola Konsumsi Pangan Daerah Perkotaan Dan Pedesaan Serta Keterkaitannya Dengan Karakteristik Sosial Ekonomi Di Provinsi Kalimantan Timur. Jurnal Ekonomi & Manajemen Indonesia,16(2), 69–81. https://doi.org/10.53640/jemi.v16i2.743 Scarmozzino, F., & Visioli, F. (2020). Covid-19 and the subsequent lockdown modified dietary habits of almost half the population in an Italian sample. Foods (Basel, Switzerland),9(5), 675. https://doi.org/10.3390/foods9050675 Shahreza, D., & Lindiawatie, L. (2021). KETAHANAN EKONOMI KELUARGA DI DEPOK PADA MASA PANDEMI COVID-19. JABE (Journal of Applied Business and Economic),7(2), 148 10.30998/jabe.v7i2.7487 Sidor, A., & Rzymski, P. (2020). Dietary choices and habits during COVID-19 lockdown: Experience from Poland. Nutrients,12(6), 1657. https://doi.org/10.3390/nu12061657 Sina, P. G. (2020). Ekonomi Rumah Tangga Di Era Pandemi Covid-19. Journal of Management: Small and Medium Enterprises (SMEs),12(2), 239–254. https://doi.org/10.35508/jom.v12i2.2697 Spranz, R., & Schl€ uter, A. (2023). The plastic bag habit and the Ocean Bali. From banana leaf wrappings to reusable bags. In Ocean Governance (pp. 319–335). https://doi.org/10.1007/978-3-031-20740-2_14 Sumarni, Y. (2020). Pandemi COVID-19: Tantangan Ekonomi dan Bisnis. Al-Intaj: Jurnal Ekonomi Dan Perbankan Syariah,6(2), 46. https://doi.org/10.29300/aij.v6i2.3358 Virgantari, F., Daryanto, A., Harianto, H., & Kuntjoro, S. U. (2017). Analisis Permintaan Ikan Di Indonesia: Pendekatan Model Quadratic Almost Ideal Demand System (Quaids). Jurnal Sosial Ekonomi Kelautan Dan Perikanan,6(2), 191. https://doi.org/10.15578/jsekp.v6i2.5772 Walter, N., & Snyder, C. (2007). Demand relationships among goods. Microeconomic theory: Basic principles and extensions. WHO Media Briefing. (2020). WHO Director-General’s opening remarks at the media briefing on COVID-19 - April 22 2020. World Health Organization. https://www.who.int/director-general/speeches/detail/who-director-general-sopening-remarks-at-the-media-briefing-on-covid-19–13-april-2020%0A https://www.who.int/director-general/speeches/ detail/who-director-general-s-opening-remarks-at-the-media-briefin Wu, F., Liu, G., Guo, N., Li, Z., & Deng, X. (2021). The impact of COVID-19 on China’s regional economies and industries. Journal of Geographical Sciences,31(4), 565–583. https://doi.org/10.1007/s11442-021-1859-3 Zden ek, R., Lososov a, J., & Svoboda, J. (2022). Are the incomes of agricultural households lagging behind? Evidence from Czechia. Economic Research-Ekonomska Istra zivanja,35(1), 7066–7083. https://doi.org/10.1080/1331677X.2022. 2058977 Zheng, Z., & Henneberry, S. R. (2010). The impact of changes in income distribution on current and future food demand in Urban China. Journal of Agricultural and Resource Economics,35(1), 51–71. 20 B. KHARISMA ET AL.