A for Effort? Using the Crowd to Identify Moral Hazard in New York City Restaurant Hygiene Inspections

Published Online:https://doi.org/10.1287/isre.2019.0866

References

  • Abbasi A, Chen H (2008) CyberGate: a design framework and system for text analysis of computer-mediated communication. MIS Quart. 32(4):811–837.CrossrefGoogle Scholar
  • Akerlof GA (1970) The market for “lemons”: Quality uncertainty and the market mechanism. Quart. J. Econom. 84(3):488–500.CrossrefGoogle Scholar
  • Angrist JD, Pischke JS (2009) Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton University Press, Princeton, NJ).Google Scholar
  • Angulo FJ, Jones TF (2006) Eating in restaurants: A risk factor for foodborne disease? Clinical Infectious Diseases 43(10):1324–1328.CrossrefGoogle Scholar
  • Aral S, Dellarocas C, Godes D (2013) Introduction to the special issue—Social media and business transformation: A framework for research. Inform. Systems Res. 24(1):3–13.LinkGoogle Scholar
  • Archak N, Ghose A, Ipeirotis PG (2011) Deriving the pricing power of product features by mining consumer reviews. Management Sci. 57(8):1485–1509.LinkGoogle Scholar
  • Athey S (2015) Machine learning and causal inference for policy evaluation. Proc. 21st ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 5–6.CrossrefGoogle Scholar
  • Athey S (2017) Beyond prediction: Using big data for policy problems. Science 355(6324):483–485.CrossrefGoogle Scholar
  • Athey S, Imbens GW (2006) Identification and inference in nonlinear difference‐in‐differences models. Econometrica 74(2):431–497.CrossrefGoogle Scholar
  • Bajari P, Nekipelov D, Ryan SP, Yang M (2015) Machine learning methods for demand estimation. Amer. Econom. Rev. 105(5):481–485.CrossrefGoogle Scholar
  • Balsmeier B, Assaf M, Chesebro T, Fierro G, Johnson K, Johnson S, Li GC, et al.. (2018) Machine learning and natural language processing on the patent corpus: Data, tools, and new measures. J. Econom. Management Strategy 27(3):535–553.CrossrefGoogle Scholar
  • Barron J (2015) Restaurants follow consultants’ advice to the letter for an A grade. New York Times (October 4), https://www.nytimes.com/2015/10/05/nyregion/health-exam-help-for-restaurants-to-avoid-rodents-or-worse-a-c.html.Google Scholar
  • Berger J, Sorensen AT, Rasmussen SJ (2010) Positive effects of negative publicity: When negative reviews increase sales. Marketing Sci. 29(5):815–827.LinkGoogle Scholar
  • Bertrand M, Duflo E, Mullainathan S (2002) How much should we trust differences-in-differences estimates? Working paper, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Bichler M, Gupta A, Ketter W (2010) Research commentary—Designing smart markets. Inform. Systems Res. 21(4):688–699.LinkGoogle Scholar
  • Brownstein JS, Freifeld CC, Madoff LC (2009) Digital disease detection—Harnessing the web for public health surveillance. New England J. Medicine 360(21):2153–2157.CrossrefGoogle Scholar
  • Brynjolfsson E, Mitchell T (2017) What can machine learning do? Workforce implications. Science 358(6370):1530–1534.CrossrefGoogle Scholar
  • Buchholz U, Run G, Kool J, Fielding J, Mascola L (2002) A risk-based restaurant inspection system in Los Angeles County. J. Food Protection 65(2):367–372.CrossrefGoogle Scholar
  • Buhrmester M, Kwang T, Gosling SD (2011) Amazon’s Mechanical Turk a new source of inexpensive, yet high-quality, data? Perspect. Psych. Sci. 6(1):3–5.CrossrefGoogle Scholar
  • Buiso G (2014) City restaurant health inspection grades a sham: Expert. New York Post (April 13), http://nypost.com/2014/04/13/city-restaurant-health-inspection-grades-a-shame-expert/.Google Scholar
  • Cambria E, Schuller B, Xia Y, Havasi C (2013) New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems 28(2):15–21.CrossrefGoogle Scholar
  • Cao Q, Duan W, Gan Q (2011) Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems 50(2):511–521.CrossrefGoogle Scholar
  • Centers for Disease Control and Prevention (2016a) Foodborne germs and illnesses. Retrieved April 10, 2016, http://www.cdc.gov/foodsafety/foodborne-germs.html.Google Scholar
  • Centers for Disease Control and Prevention (2016b) Trends in foodborne illness. Retrieved April 10, 2016, http://www.cdc.gov/foodborneburden/trends-in-foodborne-illness.html.Google Scholar
  • Chan EH, Sahai V, Conrad C, Brownstein JS (2011) Using web search query data to monitor dengue epidemics: A new model for neglected tropical disease surveillance. PLoS Neglected Tropical Diseases 5(5):e1206.CrossrefGoogle Scholar
  • Cheung CMK, Thadani DR (2012) The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision Support Systems 54(1):461–470.CrossrefGoogle Scholar
  • City of New York (2016) NYC Open Data: Restaurant inspections. Retrieved April 10, 2016, https://nycopendata.socrata.com/.Google Scholar
  • Crespi JM, Marette S (2001) How should food safety certification be financed? Amer. J. Agricultural Econom. 83(4):852–861.CrossrefGoogle Scholar
  • Decker R, Trusov M (2010) Estimating aggregate consumer preferences from online product reviews. Internat. J. Res. Marketing 27(4):293–307.CrossrefGoogle Scholar
  • Dellarocas C (2003) The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Sci. 49(10):1407–1424.LinkGoogle Scholar
  • Dellarocas C, Narayan R (2006) A statistical measure of a population’s propensity to engage in post-purchase online word-of-mouth. Statist. Sci. 21(2):277–285.CrossrefGoogle Scholar
  • Dellarocas C, Zhang X, Awad NF, (2007) Exploring the value of online product reviews in forecasting sales: The case of motion pictures. J. Interactive Marketing 21(4):23–45.CrossrefGoogle Scholar
  • DrivenData (2016) Keeping it fresh: Predict restaurant inspections. Retrieved April 10, 2016, https://www.drivendata.org/competitions/5/.Google Scholar
  • Duan W, Gu B, Whinston AB (2008) Do online reviews matter?—An empirical investigation of panel data. Decision Support Systems 45(4):1007–1016.CrossrefGoogle Scholar
  • Egan M, Raats M, Grubb S, Eves A, Lumbers M, Dean M, Adams M (2007) A review of food safety and food hygiene training studies in the commercial sector. Food Control 18(10):1180–1190.CrossrefGoogle Scholar
  • Einav L, Levin J (2014) Economics in the age of big data. Science 346(6210):1243089.CrossrefGoogle Scholar
  • Farley T (2011) Restaurant letter grading: The first year. Report, New York City Department of Health and Mental Hygiene, New York.Google Scholar
  • Farley T (2012) Restaurant grading in New York City at 18 months. Report, New York City Department of Health and Mental Hygiene, New York.Google Scholar
  • Farley T (2016) Enforcement guidelines for common sanitary violations. Report, New York City Department of Health and Mental Hygiene, New York.Google Scholar
  • Feldman R (2013) Techniques and applications for sentiment analysis. Comm. ACM 56(4):82–89.CrossrefGoogle Scholar
  • Filoso V (2013) Regression anatomy, revealed. Stata J. 13(1):92–106.CrossrefGoogle Scholar
  • Frisch R, Waugh FV (1933) Partial time regressions as compared with individual trends. Econometrica 1(4):387–401.CrossrefGoogle Scholar
  • Gao GG, McCullough JS, Agarwal R, Jha AK (2012) A changing landscape of physician quality reporting: Analysis of patients’ online ratings of their physicians over a 5-year period. J. Medical Internet Res. 14(1):e38.CrossrefGoogle Scholar
  • Gaynor M, Haas‐Wilson D, Vogt WB (2000) Are invisible hands good hands? Moral hazard, competition, and the second‐best in healthcare markets. J. Political Econom. 108(5):992–1005.CrossrefGoogle Scholar
  • Gentzkow M, Kelly BT, Taddy M (2017) Text as data. Working paper, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Ghose A, Ipeirotis PG (2011) Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Trans. Knowledge Data Engrg. 23(10):1498–1512.CrossrefGoogle Scholar
  • Ghose A, Ipeirotis PG, Li B (2012) Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Sci. 31(3):493–520.LinkGoogle Scholar
  • Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford University, Stanford, CA.Google Scholar
  • Godes D, Mayzlin D (2004) Using online conversations to study word-of-mouth communication. Marketing Sci. 23(4):545–560.LinkGoogle Scholar
  • Goodman JK, Cryder CE, Cheema A (2013) Data collection in a flat world: The strengths and weaknesses of Mechanical Turk samples. J. Behav. Decision Making 26(3):213–224.CrossrefGoogle Scholar
  • Gould LH, Rosenblum I, Nicholas D, Phan Q, Jones TF (2013) Contributing factors in restaurant-associated foodborne disease outbreaks, FoodNet sites, 2006 and 2007. J. Food Protection 76(11):1824–1828.CrossrefGoogle Scholar
  • Hand DJ, Mannila H, Smyth P (2001) Principles of Data Mining (MIT Press, Cambridge, MA).Google Scholar
  • Hansen BE (2016) The risk of James–Stein and Lasso Shrinkage. Econometric Rev. 35(8–10):1456–1470.CrossrefGoogle Scholar
  • Harrison C, Jorder M, Stern H, Stavinsky F, Reddy V, Hanson H, Waechter H, Lowe L, Gravano L, Balter S (2014) Using online reviews by restaurant patrons to identify unreported cases of foodborne illness—New York City, 2012–2013. Morbidity Mortality Weekly Report 63(20):441–445.Google Scholar
  • HealthMap (2017) About. Retrieved March 24, 2017, http://www.healthmap.org.Google Scholar
  • Hedberg CW, Smith SJ, Kirkland E, Radke V, Jones TF, Selman CA, Group ENW (2006) Systematic environmental evaluations to identify food safety differences between outbreak and nonoutbreak restaurants. J. Food Protection 69(11):2697–2702.CrossrefGoogle Scholar
  • Ho DE (2012) Fudging the nudge: Information disclosure and restaurant grading. Yale Law J. 122(3):574–688.Google Scholar
  • Hölmstrom B (1979) Moral hazard and observability. Bell J. Econom. 10(1):74–91.CrossrefGoogle Scholar
  • Hornik K, Rauch J, Buchta C, Feinerer I, Hornik MK (2016) Package “textcat.” Retrieved April 10, 2016, https://cran.r-project.org/web/packages/textcat/index.html.Google Scholar
  • Hu M, Liu B (2004) Mining and summarizing customer reviews. Proc. 10th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 168–177.Google Scholar
  • Irwin K, Ballard J, Grendon J, Kobayashi J (1989) Results of routine restaurant inspections can predict outbreaks of foodborne illness: The Seattle-King County experience. Amer. J. Public Health 79(5):586–590.CrossrefGoogle Scholar
  • Jean N, Burke M, Xie M, Davis WM, Lobell DB, Ermon S (2016) Combining satellite imagery and machine learning to predict poverty. Science 353(6301):790–794.CrossrefGoogle Scholar
  • Jin GZ, Lee J (2014a) Inspection technology, detection, and compliance: Evidence from Florida restaurant inspections. RAND J. Econom. 45(4):885–917.CrossrefGoogle Scholar
  • Jin GZ, Lee J (2014b) A tale of repetition: Lessons from Florida restaurant inspections. Working paper, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Jin GZ, Leslie P (2003) The effect of information on product quality: Evidence from restaurant hygiene grade cards. Quart. J. Econom. 118(2):409–451.CrossrefGoogle Scholar
  • Jin GZ, Leslie P (2009) Reputational incentives for restaurant hygiene. Amer. Econom. J. Microeconom. 1(1):237–267.CrossrefGoogle Scholar
  • Kang JS, Kuznetsova P, Luca M, Choi Y (2013) Where not to eat? Improving public policy by predicting hygiene inspections using online reviews. Ng HT, ed. Proc. 2013 Conf. Empirical Methods Natl. Language Processing (Association for Computational Linguistics, Stroudsburg, PA), 1443–1448.Google Scholar
  • Kankanhalli A, Zuiderwijk A, Tayi GK (2017) Open innovation in the public sector: A research agenda. Government Inform. Quart. 34(1):84–89.CrossrefGoogle Scholar
  • Klein TJ, Lambertz C, Stahl KO (2016) Market transparency, adverse selection, and moral hazard. J. Political. Econom. 124(6):1677–1713.CrossrefGoogle Scholar
  • Kleinberg J, Ludwig J, Mullainathan S, Obermeyer Z (2015) Prediction policy problems. Amer. Econom. Rev. 105(5):491–495.CrossrefGoogle Scholar
  • Kleinberg J, Lakkaraju H, Leskovec J, Ludwig J, Mullainathan S (2017) Human decisions and machine predictions. Quart. J. Econom. 133(1):237–293.CrossrefGoogle Scholar
  • Lafontaine F (1992) Agency theory and franchising: Some empirical results. RAND J. Econom. 23(2):263–283.CrossrefGoogle Scholar
  • Lee TY, Bradlow ET (2011) Automated marketing research using online customer reviews. J. Marketing Res. 48(5):881–894.CrossrefGoogle Scholar
  • Lee YJ, Hosanagar K, Tan Y (2015) Do I follow my friends or the crowd? Information cascades in online movie ratings. Management Sci. 61(9):2241–2258.LinkGoogle Scholar
  • Li F (2010) The information content of forward-looking statements in corporate filings—A naïve Bayesian machine learning approach. J. Accounting Res. 48(5):1049–1102.CrossrefGoogle Scholar
  • Li X, Hitt LM (2008) Self-selection and information role of online product reviews. Inform. Systems Res. 19(4):456–474.LinkGoogle Scholar
  • Liang KY, Zeger SL (1986) Longitudinal data analysis using generalized linear models. Biometrika 73(1):13–22.CrossrefGoogle Scholar
  • Liu B (2015) Sentiment Analysis: Mining Opinions, Sentiments, and Emotions (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Lodhi H, Saunders C, Shawe-Taylor J, Cristianini N, Watkins C (2002) Text classification using string kernels. J. Machine Learn. Res. 2(February):419–444.Google Scholar
  • Loughran T, Mcdonald B (2011) When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Finance 66(1):35–65.CrossrefGoogle Scholar
  • Lovell MC (1963) Seasonal adjustment of economic time series and multiple regression analysis. J. Amer. Statist. Assoc. 58(304):993–1010.CrossrefGoogle Scholar
  • Lu Y, Jerath K, Singh PV (2013a) The emergence of opinion leaders in a networked online community: A dyadic model with time dynamics and a heuristic for fast estimation. Management Sci. 59(8):1783–1799.LinkGoogle Scholar
  • Lu X, Ba S, Huang L, Feng Y (2013b) Promotional marketing or word-of-mouth? Evidence from online restaurant reviews. Inform. Systems Res. 24(3):596–612.LinkGoogle Scholar
  • Luca M, Zervas G (2016) Fake it till you make it: Reputation, competition, and Yelp review fraud. Management Sci. 62(12): 3412–3427.LinkGoogle Scholar
  • Ludwig S, de Ruyter K, Friedman M, Brüggen EC, Wetzels M, Pfann G (2013) More than words: The influence of affective content and linguistic style matches in online reviews on conversion rates. J. Marketing 77(1):87–103.CrossrefGoogle Scholar
  • McCallum A, Nigam K, (1998) A comparison of event models for naive Bayes text classification. Cohn A, ed. Learning for Text Categorization: Papers from the 1998 AAAI Workshop (Association for the Advancement of Artificial Intelligence, Menlo Park, CA), 41–48.Google Scholar
  • Mejia J, Mankad S, Gopal A (2015) More than just words: Latent semantic analysis, online reviews and restaurant closure. Acad. Management Proc. 2015(1), https://doi.org/10.5465/ambpp.2015.13912abstract.Google Scholar
  • Miller GA (1995) WordNet: a lexical database for English. Comm. ACM 38(11):39–41.CrossrefGoogle Scholar
  • Moreno A, Terwiesch C (2014) Doing business with strangers: Reputation in online service marketplaces. Inform. Systems Res. 25(4):865–886.LinkGoogle Scholar
  • Mullainathan S, Spiess J (2017) Machine learning: an applied econometric approach. J. Econom. Perspect. 31(2):87–106.CrossrefGoogle Scholar
  • Netzer O, Feldman R, Goldenberg J, Fresko M (2012) Mine your own business: Market-structure surveillance through text mining. Marketing Sci. 31(3):521–543.LinkGoogle Scholar
  • New York City Department of Health and Mental Hygiene (2016a) How we score and grade. Retrieved April 1, 2016, https://www1.nyc.gov/assets/doh/downloads/pdf/rii/how-we-score-grade.pdf.Google Scholar
  • New York City Department of Health and Mental Hygiene (2016b) Letter grading workshop materials. Retrieved April 1, 2016, https://www1.nyc.gov/assets/doh/downloads/ppt/rii/letter_grading_workshop.pps.Google Scholar
  • New York City Department of Health and Mental Hygiene (2016c) What to expect when you’re inspected: A guide for food service operators. Retrieved April 1, 2016, https://www1.nyc.gov/assets/doh/downloads/pdf/rii/blue-book.pdf.Google Scholar
  • Oh O, Kwon K, Rao H (2010) An exploration of social media in extreme events: Rumor theory and Twitter during the Haiti earthquake 2010. Proc. 31st Internat. Conf. Inform. Systems (Association for Information Systems, Atlanta), 7332–7336.Google Scholar
  • Pan Y, Huang P, Gopal A (2018) Board independence and firm performance in the IT industry: The moderating role of new entry threats. MIS Quart. 42(3):979–1000.CrossrefGoogle Scholar
  • Park A (2015) New York City restaurants are cleaner than ever. Time (June 29), http://time.com/3940482/new-york-city-restaurants-clean/.Google Scholar
  • Park SY, Allen JP (2013) Responding to online reviews problem solving and engagement in hotels. Cornell Hospitality Quart. 54(1):64–73.CrossrefGoogle Scholar
  • Ross SM (1996) Stochastic Processes (John Wiley & Sons, New York).Google Scholar
  • Rubinstein A, Yaari ME (1983) Repeated insurance contracts and moral hazard. J. Econom. Theory 30(1):74–97.CrossrefGoogle Scholar
  • Schomberg JP, Haimson OL, Hayes GR, Anton-Culver H (2016) Supplementing public health inspection via social media. PLoS One 11(3):e0152117.CrossrefGoogle Scholar
  • Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput. Surveys 34(1):1–47.CrossrefGoogle Scholar
  • Shapiro C (1986) Investment, moral hazard, and occupational licensing. Rev. Econom. Stud. 53(5):843–862.CrossrefGoogle Scholar
  • Shavell S (1979) On moral hazard and insurance. Quart. J. Econom. 93(4):541–562.CrossrefGoogle Scholar
  • Starbird SA (2005) Moral hazard, inspection policy, and food safety. Amer. J. Agricultural Econom. 87(1):15–27.CrossrefGoogle Scholar
  • Starbird SA, Amanor-Boadu V (2007) Contract selectivity, food safety, and traceability. J. Agricultural Food Indust. Organ. 5(1):Article 2.Google Scholar
  • Stiglitz J (2002) Information and the change in the paradigm in economics. Amer. Econom. Rev. 92(3):460–501.CrossrefGoogle Scholar
  • Stiglitz J (2010) Regulation and failure. Rev. Econom. Inst. 12(23):13–28.Google Scholar
  • Tang H, Tan S, Cheng X (2009) A survey on sentiment detection of reviews. Expert Systems Appl. 36(7):10760–10773.CrossrefGoogle Scholar
  • Tetlock PC, Saar-Tsechansky M, Macskassy S (2008) More than words: Quantifying language to measure firms’ fundamentals. J. Finance 63(3):1437–1467.CrossrefGoogle Scholar
  • Tsai ACR, Wu CE, Tsai RTH, Hsu JY (2013) Building a concept-level sentiment dictionary based on commonsense knowledge. IEEE Intelligent Systems 28(2):22–30.CrossrefGoogle Scholar
  • Valitutti A, Strapparava C, Stock O (2004) Developing affective lexical resources. PsychNology J. 2(1):61–83.Google Scholar
  • Varian HR (2014) Big data: New tricks for econometrics. J. Econom. Perspect. 28(2):3–27.CrossrefGoogle Scholar
  • Wallach HM, Mimno DM, McCallum A (2009) Rethinking LDA: Why priors matter. Bengio Y, Schuurmans D, Lafferty JD, Williams CKI, Culotta A, eds. Advances in Neural Information Processing Systems, vol. 22 (Curran Associates, Inc., Red Hook, NY), 1973–1981.Google Scholar
  • Wang R, Wang W, daSilva A, Huckins JF, Kelley WM, Heatherton TF, Campbell AT (2018) Tracking depression dynamics in college students using mobile phone and wearable sensing. Proc. ACM Interactive Mobile Wearable Ubiquitous Tech. 2(1):Article 43.Google Scholar
  • Wattal S, Schuff D, Mandviwalla M, Williams CB (2010) Web 2.0 and politics: The 2008 U.S. presidential election and an e-politics research agenda. MIS Quart. 34(4):669–688.CrossrefGoogle Scholar
  • Young L, Soroka S (2012) Affective news: The automated coding of sentiment in political texts. Political Comm. 29(2):205–231.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.