Analyzing Socioeconomic Status through Culinary Ingredients: A Large-Scale Study of Pita and Pizza Dishes

This study investigates whether the ingredients listed on restaurant menus can provide insights into a city’s socioeconomic status. Using data from an online food delivery system, the study compares menu items with local education rates and rental prices. A machine learning model is developed to predict menu prices based on ingredients and socioeconomic factors. An efficiency metric is proposed to cluster restaurants to address autocorrelation, comparing ingredient averages to socioeconomic indicators. The analysis focuses on hundreds of menus, specifically examining pizza and Turkish pita in Ankara, Türkiye. The results indicate that including nearby rental prices significantly improves the accuracy of predicting menu prices, especially for pizza. The study also notes that wealthier areas tend to feature menus with more unique or expensive ingredients, particularly in the case of pizza, aligning with previous research on eating habits and income levels. Key contributions of this research include a comprehensive examination of restaurant menus, insights into how menus vary based on location and cuisine, and the development of Turkish-English word lists for pita and pizza menu items. Our datasets are also shared. This methodology aids in understanding local taste preferences and provides valuable information for strategic decisions regarding restaurant location and menu planning.

[1]  Vedat Ekergi̇l,et al.  A New Menu Analysis Approach: Time-Driven Menu Engineering (TDME) , 2023, Journal of Quality Assurance in Hospitality & Tourism.

[2]  C. Whitton,et al.  Development of the Menu Assessment Scoring Tool (MAST) to Assess the Nutritional Quality of Food Service Menus , 2023, International journal of environmental research and public health.

[3]  Murat Doğan,et al.  Menu Engineering in the Restaurant Business: A Study on Kitchen Chefs , 2022, Journal of Tourism and Gastronomy Studies.

[4]  M. L’Abbé,et al.  Cross-Sectional Nutritional Information and Quality of Canadian Chain Restaurant Menu Items in 2020. , 2022, American journal of preventive medicine.

[5]  C. Spence,et al.  Contextual acceptance of novel and unfamiliar foods: Insects, cultured meat, plant-based meat alternatives, and 3D printed foods , 2021, Food Quality and Preference.

[6]  A. Kimura,et al.  Effect of co-eating on unfamiliar food intake among Japanese young adults , 2021, Food Quality and Preference.

[7]  Kimberley Peters,et al.  Factors influencing consumer menu-item selection in a restaurant context , 2020, Food Quality and Preference.

[8]  Makoto Nakayama,et al.  Same sushi, different impressions: a cross-cultural analysis of Yelp reviews , 2019, J. Inf. Technol. Tour..

[9]  Y. Wei,et al.  Analyzing the private rental housing market in Shanghai with open data , 2019, Land Use Policy.

[10]  J. Nezlek,et al.  Food neophobia and the Five Factor Model of personality , 2019, Food Quality and Preference.

[11]  Sylvie Chollet,et al.  Consumers’ expectation and liking for cheese: Can familiarity effects resulting from regional differences be highlighted within a country? , 2019, Food Quality and Preference.

[12]  Hannes Werthner,et al.  What is the “Personality” of a tourism destination? , 2018, Information Technology & Tourism.

[13]  Egbert van der Zee,et al.  Finding patterns in urban tourist behaviour: a social network analysis approach based on TripAdvisor reviews , 2018, Information Technology & Tourism.

[14]  B. Demirtaş The Effect of Price Increases on Fresh Meat Consumption in Turkey , 2018, Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis.

[15]  E. Pagliarini,et al.  Associations between food neophobia and responsiveness to “warning” chemosensory sensations in food products in a large population sample , 2018, Food Quality and Preference.

[16]  Yong Rui,et al.  You Are What You Eat: Exploring Rich Recipe Information for Cross-Region Food Analysis , 2018, IEEE Transactions on Multimedia.

[17]  A. Assaf,et al.  Does restaurant menu information affect customer attitudes and behavioral intentions? A cross-segment empirical analysis using PLS-SEM , 2016 .

[18]  R. Olmos,et al.  Inconsistencies in Reported p-Values in Spanish Journals of Psychology , 2016 .

[19]  A. Michalsen,et al.  Personality Profiles, Values and Empathy: Differences between Lacto-Ovo-Vegetarians and Vegans , 2016, Complementary Medicine Research.

[20]  Sofiane Abbar,et al.  You Tweet What You Eat: Studying Food Consumption Through Twitter , 2014, CHI.

[21]  Silke Anger,et al.  The Impact of Education on Personality: Evidence from a German High School Reform , 2014, SSRN Electronic Journal.

[22]  Evrim Çeltek,et al.  Turizm İşletmelerinde E-Ticaret: yemeksepeti.com’da Satış Yapan Yiyecek-İçecek İşletmelerinin İncelenmesi , 2013 .

[23]  Helen W. Wu,et al.  What's on the menu? A review of the energy and nutritional content of US chain restaurant menus , 2012, Public Health Nutrition.

[24]  I. Deary,et al.  Personality traits and eating habits in a large sample of Estonians. , 2012, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[25]  Lada A. Adamic,et al.  Recipe recommendation using ingredient networks , 2011, WebSci '12.

[26]  Ümit Çelen,et al.  Temsili Bir Örneklemde Sosyo-Ekonomik Statü (SES) Ölçüm Aracı Geliştirilmesi: Ankara Kent Merkezi Örneği , 2010 .

[27]  E. Serrano,et al.  Comparison of fast-food and non-fast-food children's menu items. , 2009, Journal of nutrition education and behavior.

[28]  Michael McCall,et al.  The Effects of Restaurant Menu Item Descriptions on Perceptions of Quality, Price, and Purchase Intention , 2008 .

[29]  C. Akbay,et al.  Consumer characteristics influencing fast food consumption in Turkey , 2007 .

[30]  J. Gil,et al.  Spanish Demand for Food Away from Home: Analysis of Panel Data , 2007 .

[31]  Katerina Annaraud Restaurant Menu Analysis , 2007 .

[32]  A Furnham,et al.  Aesthetic activities and aesthetic attitudes: influences of education, background and personality on interest and involvement in the arts. , 2006, British journal of psychology.

[33]  R. Scribner,et al.  Fast food, race/ethnicity, and income: a geographic analysis. , 2004, American journal of preventive medicine.

[34]  A. Astrup,et al.  Sociodemographic differences in dietary habits described by food frequency questions — results from Denmark , 2003, European Journal of Clinical Nutrition.

[35]  L. R. Goldberg,et al.  Personality traits and eating habits: The assessment of food preferences in a large community sample. , 2002 .

[36]  R. Nayga,et al.  Impact of socio-economic and demographic factors on food away from home consumption: number of meals and type of facility , 1994 .

[37]  A. Cardello,et al.  Relationships Between Food Preferences and Food Acceptance Ratings , 1982 .

[38]  F. Chapin A quantitative scale for rating the home and social environment of middle class families in an urban community: a first approximation to the measurement of socio-economic status. , 1928 .

[39]  Tugba Taskaya-Temizel,et al.  The Use of Big Mobile Data to Gain Multilayered Insights for Syrian Refugee Crisis , 2019, Data for Refugees Challenge.

[40]  M. Peparaiof,et al.  Associations between food neophobia and responsiveness to “ warning ” chemosensory sensations in food products in a large population sample , 2018 .

[41]  J. Gil,et al.  Spanish demand for food away from home: a panel data approach /доклад на 10 конгрессе ЕААЕ, Exploring Diversity in the European Agri-Food System, Zaragoza, Spain, 28-31 August 2002 , 2002 .