The Impact of Letter Grades on Student Effort, Course Selection, and Major Choice: A Regression-Discontinuity Analysis

2014 ◽  
Vol 45 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joyce B. Main ◽  
Ben Ost
Author(s):  
Sarah Dykstra ◽  
Amanda L. Glassman ◽  
Charles Kenny ◽  
Justin Sandefur

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2582 ◽  
Author(s):  
Samuel Lotsu ◽  
Yuichiro Yoshida ◽  
Katsufumi Fukuda ◽  
Bing He

Confronting an energy crisis, the government of Ghana enacted a power factor correction policy in 1995. The policy imposes a penalty on large-scale electricity users, namely, special load tariff (SLT) customers of the Electricity Company of Ghana (ECG), whose power factor is below 90%. This paper investigates the impact of this policy on these firms’ power factor improvement by using panel data from 183 SLT customers from 1994 to 1997 and from 2012. To avoid potential endogeneity, this paper adopts a regression discontinuity design (RDD) with the power factor of the firms in the previous year as a running variable, with its cutoff set at the penalty threshold. The result shows that these large-scale electricity users who face the penalty because their power factor falls just short of the threshold are more likely to improve their power factor in the subsequent year, implying that the power factor correction policy implemented by Ghana’s government is effective.


2017 ◽  
Vol 59 (3) ◽  
pp. 275-284 ◽  
Author(s):  
Min Gyung Kim ◽  
Hyunjoo Yang ◽  
Anna S. Mattila

New York City launched a restaurant sanitation letter grade system in 2010. We evaluate the impact of customer loyalty on restaurant revisit intentions after exposure to a sanitation grade alone, and after exposure to a sanitation grade plus narrative information about sanitation violations (e.g., presence of rats). We use a 2 (loyalty: high or low) × 4 (sanitation grade: A, B, C, or pending) between-subjects full factorial design to test the hypotheses using data from 547 participants recruited from Amazon MTurk who reside in the New York City area. Our study yields three findings. First, loyal customers exhibit higher intentions to revisit restaurants than non-loyal customers, regardless of sanitation letter grades. Second, the difference in revisit intentions between loyal and non-loyal customers is higher when sanitation grades are poorer. Finally, loyal customers are less sensitive to narrative information about sanitation violations.


2021 ◽  
Author(s):  
Carl Bonander ◽  
Debora Stranges ◽  
Johanna Gustavsson ◽  
Matilda Almgren ◽  
Malin Inghammar ◽  
...  

Objectives: To study the impact of non-mandatory, age-specific social distancing recommendations for older adults (70+ years) in Sweden on isolation behaviors and disease outcomes during the first wave of the COVID-19 pandemic. Methods: Our study relies on self-reported isolation data from COVID Symptom Study Sweden (n = 96,053) and national register data on COVID-19 hospitalizations, deaths, and confirmed cases. We use a regression discontinuity design to account for confounding factors, exploiting the fact that exposure to the recommendation was a discontinuous function of age. Results: By comparing individuals just above to those just below the age limit for the policy, our analyses revealed a sharp drop in the weekly number of visits to crowded places at the 70-year-threshold (-13%). Severe COVID-19 cases (hospitalizations or deaths) also dropped abruptly by 16% at the 70-year-threshold. Our data suggest that the age-specific recommendations prevented approximately 1,800 to 2,700 severe COVID-19 cases, depending on model specification. Conclusion: The non-mandatory, age-specific recommendations helped control the COVID-19 pandemic in Sweden.


2019 ◽  
Vol 11 (6) ◽  
pp. 1682 ◽  
Author(s):  
Daxin Dong ◽  
Xiaowei Xu ◽  
Yat Wong

Prior studies have suggested the existence of a reverse causality relationship between air quality and tourism development: while air quality influences tourism, dynamic segments of the tourism industry (e.g., cruising, airline, foodservice) have impacts on air quality. This reverse causality hinders a precise estimate on the effect of air pollution on tourism development within a conventional econometric framework, since the variable of air pollution is endogenous. This study estimates the impact of air pollution on the inbound tourism industry in China, by controlling for endogeneity based on a regression discontinuity design (RDD). The estimate is derived from a quasi-experiment generated by China’s Huai River Policy, which subsidizes coal for winter heating in northern Chinese cities. By analyzing data from 274 Chinese cities during the period 2009–2012, it is found that air pollution significantly reduces the international inbound tourism: an increase of PM 10 (particulate matter smaller than 10 μ m) by 0.1 mg/m 3 will cause a decline in the tourism receipts-to-local gross domestic product (GDP) ratio by 0.45 percentage points. This study also highlights the importance of controlling for endogeneity, since the detrimental impact of air pollution would otherwise be considerably underestimated. This study further demonstrates that, although air pollution is positively correlated with the average expenditure of each tourist, it substantially depresses the number of inbound tourists. The results imply that air quality could potentially influence inbound tourists’ city destination choices. However, it is interesting to note that travelers in air polluted cities in China tend to spend more money.


2017 ◽  
Vol 40 (3) ◽  
pp. 188-195 ◽  
Author(s):  
David Ackerman ◽  
Christina Chung

This article looks at how marketing student ratings of instructors and classes on online rating sites such as RateMyProfessor.com can be biased by prior student ratings of that class. Research has identified potential sources of bias of online student reviews administered by universities. Less has been done on the sources of bias inherent in a ratings site where those doing the rating can see prior ratings. To measure how student online ratings of a course can be influenced by existing online ratings, the study used five different prior ratings experiment conditions: mildly negative prior ratings, strongly negative prior ratings, mildly positive prior ratings, strongly positive prior ratings, and a control condition of no prior ratings. Results of this study suggest prior online ratings, both positive and negative, do affect subsequent online ratings and bias them. There are several implications. First, both negative and positive ratings can have an impact biasing subsequent ratings. Second, sometimes negative prior ratings must be strong in valence in order to bias subsequent ratings whereas even mildly positive ratings can have an impact. Last, this bias can potentially influence student course selection.


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