The Effects of Price, Income, and Household Characteristics on Ultra-Processed Food Consumption In Jakarta, Indonesia
DOI:
https://doi.org/10.34123/jurnalasks.v14i1.386Abstract
During the 2010s, ultra-processed food consumption in Indonesia increases and leads to high rates of obesity and chronic non-communicable diseases. DKI Jakarta has the highest ultra-processed food consumption and obesity prevalence in Indonesia. Therefore, raw data from “Core” and “Consumption and Expenditure” modules of March 2019 Susenas (Indonesia National Socioeconomic Survey) are analysed to examine ultra-processed food consumption and how economic factors and household characteristics affect it in Jakarta. The analysis is conducted using M-estimation robust regression due to a large number of influential outliers in the data. The research sample is divided into three classes based on daily per capita expenditure. The results show that ultra-processed food consumption increases with income class. Higher ultra-processed food consumption occurs in households that pay higher price for ultra-processed food, have higher per capita income, have more children or adolescents, and have working female household head or wife. For Class 3 households, formal sector households consume more ultra-processed food than informal sector households. While for Class 1 households, households with female household head or wife with senior high school degree or above consume more ultra-processed food than households with female household head or wife with junior high school degree or below.
Downloads
References
[2] BPS-Statistics Indonesia 2013, 2014, 2015, 2016, 2017, 2018, 2019 Consumption of calorie and protein of Indonesia population and province Jakarta Indonesia BPS-Statistics Indonesia ISSN: 1979-6250.
[3] BPS-Statistics Indonesia (2020) Gross regional domestic product of provinces in Indonesia by industry Jakarta Indonesia BPS-Statistics Indonesia ISSN: 2622-805X.
[4] Ministry of Health, Indonesia 2018 Baseline health research report Jakarta Indonesia Ministry of Health http://labdata.litbang.kemkes.go.id/
[5] Wulansari A, Martianto D, and Baliwati Y F 2016 Estimation of economic loss due to obesity in Indonesian adults Jurnal Gizi Pangan 11(2) 159-168 https://doi.org/10.25182/jgp.2016 -.11.2.%25p
[6] Witkowski T H 2007 Food marketing and obesity in developing countries: analysis, ethics, and public policy Journal of Macromarketing 27(2) 126-137 https://doi.org/10.1177/ 0276146707300076
[7] Ponce J A M, Pimienta T G S, Louzada M L C, and Batis C 2017 Energy contribution of NOVA food groups and sociodemographic determinants of ultra-processed food consumption in the Mexican population Public Health Nutrition 21(1) 87-93 doi: 10.1017/S1368980017 002129. PMID: 28937354.
[8] Boysen O, Urban K B, Bradford H, and Balié J 2019 Taxing highly processed foods: what could be the impacts on obesity and underweight in sub-Saharan Africa? World Development 119 55-67 doi: 10.1016/j.worlddev.2019.03.006. PMID: 31274950; PMCID: PMC6559279.
[9] Passos C M, Maia E G, Levy R B, Martins A P B, and Claro R M 2019 Association between the price of ultra-processed foods and obesity in Brazil Nutrition, Metabolism & Cardiovascular Diseases 30 589-598 https://doi.org/10.1016/j.numecd.2019.12.011
[10] Monteiro C A, Levy R B, Claro R M, Castro I R R, and Cannon G 2010 A new classification of foods based on the extent and purpose of their processing Reports in Public Health 26(11) 2039-2049 doi: 10.1590/s0102-311x2010001100005. PMID: 21180977.
[11] Sim?es B D, Cardoso L O, Bensenor I J M, Schmidt M I, Duncan B B, Luft V C, Molina M C B, Levy R B, and Giatti L 2018 Consumption of ultra-processed foods and socioeconomic position: a cross-sectional analysis of the Brazilian Longitudinal Study of Adult Health Reports in Public Health 34(3) doi: 10.1590/0102-311X00019717. PMID: 29513858.
[12] Sparrenberger K, Friedrich R R, Schiffner M D, Schuch I, and Wagner M B 2015 Ultra- processed food consumption in children from a Basic Health Unit Jornal de Pediatria 91(6) 535-542 doi: 10.1016/j.jped.2015.01.007. PMID: 26092227.
[13] Monteiro et al 2016 NOVA. The star shines bright [Food classification, Public health] World Nutrition 7(1-3) 28-38 ISBN: 2041-9775.
[14] Pindyck R S and Rubinfeld D L 2013 Microeconomics (8th ed) Upper Saddle River New Jersey Pearson Education, Inc
[15] Clements K W and Si J 2017 Engel’s law, diet diversity, and the quality of food consumption American Journal of Agricultural Economics 100(1) 1-22 https://doi.org/10.1093/ajae/ aax053
[16] Asian Development Bank 2010 Key Indicators for Asia and the Pacific 2010 Mandaluyong City, Philippines: Asian Development Bank ISSN: 0116-3000.
[17] Andrianjaka R R 2017 Middle-class composition and growth in middle-income countries. Asian Development Bank Institute Working Paper Series 753 Asian Development Bank Institute, Tokyo, Japan https://www.adb.org/publications/middle-class-composition-and -growth-middle-income-countries
[18] Ferreira F and Páramo C S 2017, 13th October A richer array of international poverty lines. https://blogs.worldbank.org/developmenttalk/richer-array-international-poverty-lines
[19] Yuliana R 2011 Evaluation of changes in household welfare levels due to increasing fuel price in Indonesia during February 2005 to March 2006 Jurnal Aplikasi Statistika dan Komputasi Statistik 2(1) 1-39 ISSN: 2086-4132.
