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Home sharing platforms have experienced a rapid growth over the last decade. Following negative publicity, many cities have started regulating the short-term rental market. Regulations often involve a cap on the number of days a property can be rented out on a short-term basis. We draw on rich data for short-term rentals on Airbnb and for the long-term rental market to examine the impact of short-term rental regulations with a day cap on various stakeholders: hosts, guests, the platform provider, and residents. Based on a difference-in-differences design, we document a sizable drop in Airbnb activity. Interestingly, not only targeted hosts (i.e., hosts with reservation days larger than the day cap), but also non-targeted hosts reduce their Airbnb activity. The reservation days of non-targeted hosts decrease between 26.27% and 51.89% depending on the treatment. Targeted hosts experience a similar decline. There is, nevertheless, significant non-compliance: more than one third of hosts do not comply with enacted short-term rental regulations. Additional analyses show that few properties are redirected from short-term rental to long-term rental use and that there is no significant drop in long-term rents. Drawing on a theoretical model, we tie the estimated effects to changes in stakeholders' welfare: Regulations significantly reduce the welfare of hosts, and the loss ranges between 46.30% and 9.02%. The welfare loss of the platform provider is proportional to the loss of the hosts. Welfare of guests decreases moderately ranging between 4.5% to 4.1%. The welfare of residents increases minimal. These results question the effectiveness and desirability of the studied short-term rental regulations. [ABSTRACT FROM AUTHOR]

Cancer is a highly prevalent noncommunicable disease with low levels of awareness among the general public, as reported in numerous studies worldwide. Given the daunting nature of cancer, poor knowledge and awareness levels of this disease with regard to its warning signs, risk factors, and the importance of cancer screening among the public may lead to unfavorable health-related outcomes, such as delayed diagnosis and treatment initiation. However, there is potential to tackle this issue with properly designed health educational interventions. Quasi-experimental studies can be implemented to raise awareness in this subject matter via a myriad of traditional and digitalized educational intervention methods. Relevant studies identified across electronic databases were categorized based on the various educational interventions employed in promoting cancer knowledge and awareness. The effectiveness of these interventional approaches was assessed based on the pre-test and post-test scores, which captured the changes in knowledge and awareness levels before and after the educational intervention. Varying outcomes were observed across the diverse methods in individuals of different age groups. Nevertheless, evidence-based cancer educational interventions often yield multifaceted positive outcomes. The aim of this article is to review the various approaches of educational interventions utilized to positively alter the knowledge and awareness on cancer among the general public.

Cancer is a highly prevalent noncommunicable disease with low levels of awareness among the general public, as reported in numerous studies worldwide. Given the daunting nature of cancer, poor knowledge and awareness levels of this disease with regard to its warning signs, risk factors, and the importance of cancer screening among the public may lead to unfavorable health-related outcomes, such as delayed diagnosis and treatment initiation. However, there is potential to tackle this issue with properly designed health educational interventions. Quasi-experimental studies can be implemented to raise awareness in this subject matter via a myriad of traditional and digitalized educational intervention methods. Relevant studies identified across electronic databases were categorized based on the various educational interventions employed in promoting cancer knowledge and awareness. The effectiveness of these interventional approaches was assessed based on the pre-test and post-test scores, which captured the changes in knowledge and awareness levels before and after the educational intervention. Varying outcomes were observed across the diverse methods in individuals of different age groups. Nevertheless, evidence-based cancer educational interventions often yield multifaceted positive outcomes. The aim of this article is to review the various approaches of educational interventions utilized to positively alter the knowledge and awareness on cancer among the general public. [ABSTRACT FROM AUTHOR]

Difference-in-differences and synthetic control methods have become common study designs for evaluating the effects of changes in policies, including health policies. They also have potential for providing real-world effectiveness and safety evidence in pharmacoepidemiology. To effectively add to the toolkit of the field, however, designs—including both their benefits and drawbacks—must be well understood. Quasi-experimental designs provide an opportunity to estimate the average treatment effect on the treated without requiring the measurement of all possible confounding factors, and to assess population-level effects. This requires, however, other key assumptions, including the parallel trends or stable weighting assumptions, a lack of other concurrent events that could alter time trends, and an absence of contamination between exposed and unexposed units. The targeted estimands are also highly specific to the settings of the study, and combining across units or time periods can be challenging. Case studies are presented for 3 vaccine evaluation studies, showcasing some of these challenges and opportunities in a specific field of pharmacoepidemiology. These methods provide feasible and valuable sources of evidence in various pharmacoepidemiologic settings and can be improved through research to identify and weigh the advantages and disadvantages in those settings. This article is part of a Special Collection on Pharmacoepidemiology. [ABSTRACT FROM AUTHOR]

This study aims to test the influence of the inductive learning model on the critical thinking ability of grade IX students in science subjects in junior high school. A quantitative approach with a quasi-experimental method is used in this study. The research subjects consisted of students of class IX A as the experimental group and class IX B as the control group. The research instruments included questionnaires and tests to measure critical thinking skills before and after treatment. The results of data analysis showed that the inductive learning model had a positive and significant effect on students' critical thinking skills. In conclusion, the application of the inductive learning model can improve critical thinking skills in science learning.

This article aims to broaden the understanding of quasi-experimental methods among marketing scholars and those who read their work by describing the underlying logic and set of actions that make their work convincing. The purpose of quasi-experimental methods is, in the absence of experimental variation, to determine the presence of a causal relationship. First, the authors explore how to identify settings and data where it is interesting to understand whether an action causally affects a marketing outcome. Second, they outline how to structure an empirical strategy to identify a causal empirical relationship. The article details the application of various methods to identify how an action affects an outcome in marketing, including difference-in-differences, regression discontinuity, instrumental variables, propensity score matching, synthetic control, and selection bias correction. The authors emphasize the importance of clearly communicating the identifying assumptions underlying the assertion of causality. Last, they explain how exploring the behavioral mechanism—whether individual, organizational, or market level—can actually reinforce arguments of causality. [ABSTRACT FROM AUTHOR]

Governments worldwide have spent billions of dollars on monetary incentives for consumers to encourage the adoption of eco-friendly ("green") products. However, there is little consensus on the effectiveness of delayed monetary incentives with complex structures, such as tax credits in increasing green product adoption and reducing carbon emissions. The literature is also limited on the mechanisms through which monetary incentives work in general. We address these issues by studying the impact of tax credit incentives on green and nongreen vehicle sales in the U.S. auto industry. A tax credit incentive could boost green vehicle sales through cost savings on the vehicle's price. However, the incentive may prove ineffective because of important barriers to adoption (e.g., long charging times for electric cars). To measure the sales and emissions impacts of tax credits, we study incentive changes in South Carolina and Oregon via various quasi-experimental approaches and assess the generalizability of our key findings to Colorado. Unlike recent studies showing an insignificant or negative correlation between tax credits and electric vehicle adoption, our analyses show that unit sales of incentivized plug-in hybrid electric vehicles (PHEVs) increase by an average of 3.7% (up to 52.7% in some counties) following a $2,000 incentive. In contrast, PHEV sales remain unchanged after the incentive's termination, implying a positive net sales effect. We also explore the underlying mechanisms for the incentive's impact by examining various purchase funnel stages. In the awareness stage, the incentive's positive effect on PHEV demand peaks around the consumers' tax-filing period. As for the consideration stage, our analyses of online consumer search indicate that the incentive does not expand the consumer pool considering PHEVs. In the conversion stage, the incentive generates more sales for PHEVs in counties where (i) consumers are more likely to have PHEVs in their consideration sets regardless of the incentive (i.e., Democratic counties) and (ii) consumers value cost-saving more (i.e., counties with lower middle income). Also, the heightened demand for PHEVs following the incentive stems from the substitution from gasoline vehicles with high fuel efficiency. We estimate the average cost of reducing carbon emissions through tax credits to be $109 per ton, which is less expensive than tax rebates for conventional hybrids and subsidies for residential solar panels. This paper was accepted by David Simchi-Levi, Special Section of Management Science on Business and Climate Change. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4781. [ABSTRACT FROM AUTHOR]

Research Summary: We examine the effect of organizational status on employment‐related corporate social responsibility (CSR). As employees derive nonpecuniary benefits from both organizational status and employment‐related CSR, lower status firms may invest in nonpecuniary employment‐related CSR to compete in a status‐segmented labor market. We identify the effect using a regression discontinuity design (RDD) in the context of the Fortune 1000 rankings, as we contend that the 500th rank position marks an artificial breakpoint in status where quality follows a smooth distribution. We find that firms just failing to make the Fortune 500 perform significantly better in nonpecuniary employment‐related CSR. Our findings provide causal evidence for the labor market advantage of organizational status and a richer window into the strategic motivations behind CSR investments. Managerial Summary: We examine one strategic investment that lower status firms make to compete in a status‐segmented labor market: employment‐based corporate social responsibility (CSR). We identify the effect using a regression discontinuity design (RDD) in the context of the Fortune 1000 rankings, as we argue that the 500th rank position creates a discontinuity in status at a precise location where quality differences can be assumed to follow a smooth distribution. We find that firms just failing to make it into the Fortune 500 perform significantly better in nonpecuniary employment‐related CSR as compared to firms just in the Fortune 500. The findings demonstrate that building a reputation for being socially responsible may offset differences in status and make a lower status organization more appealing to employees. [ABSTRACT FROM AUTHOR]

Background As the interest in and use of quasi-experimental methods to evaluate impacts of health policies have dramatically increased in the epidemiological literature, we set out this study to (i) systematically compare several quasi-experimental methods that use data before and after an intervention and contrast their performance within a simulation framework while providing a brief overview of the methods; and (ii) discuss challenges that could arise from using these methods as well as directions for future research in the context of epidemiological applications. Methods We considered single-group designs [pre-post and interrupted time series (ITS)] and multiple-group designs [controlled interrupted time series/difference-in-differences, synthetic control methods (SCMs): traditional SCMs and generalized SCMs]. We assessed performance based on bias and root mean squared error. Results We identified settings in which each method failed to provide unbiased estimates. We found that, among the methods investigated, when data for multiple time points and for multiple control groups are available (multiple-group designs), data-adaptive methods such as the generalized SCM were generally less biased than other methods evaluated in our study. In addition, when all of the included units have been exposed to treatment (single-group designs) and data for a sufficiently long pre-intervention period are available, then the ITS performs very well, provided the underlying model is correctly specified. Conclusions When using a quasi-experimental method using data before and after an intervention, epidemiologists should strive to use, whenever feasible, data-adaptive methods that nest alternative identifying assumptions including relaxing the parallel trend assumption (e.g. generalized SCMs). [ABSTRACT FROM AUTHOR]

Randomized Controlled Trials (RCTs) are considered the gold standard for measuring the efficacy of medical interventions. However, RCTs are expensive, and use a limited population. Techniques to estimate the effects of stroke interventions from observational data that minimize confounding would be useful. We used regression discontinuity design (RDD), a technique well-established in economics, on the Get With The Guidelines-Stroke (GWTG-Stroke) data set. RDD, based on regression, measures the occurrence of a discontinuity in an outcome (e.g., odds of home discharge) as a function of an intervention (e.g., alteplase) that becomes significantly more likely when crossing the threshold of a continuous variable that determines that intervention (e.g., time from symptom onset, since alteplase is only given if symptom onset is less than e.g., 3 h). The technique assumes that patients near either side of a threshold (e.g., 2.99 and 3.01 h from symptom onset) are indistinguishable other than the use of the treatment. We compared outcomes of patients whose estimated onset to treatment time fell on either side of the treatment threshold for three cohorts of patients in the GWTG-Stroke data set. This data set spanned three different treatment thresholds for alteplase (3 h, 2003-2007, N = 1,869; 3 h, 2009-2016, N = 13,086, and 4.5 h, 2009-2016, N = 6,550). Patient demographic characteristics were overall similar across the treatment thresholds. We did not find evidence of a discontinuity in clinical outcome at any treatment threshold attributable to alteplase. Potential reasons for failing to find an effect include violation of some RDD assumptions in clinical care, large sample sizes required, or already-well-chosen treatment threshold.

ObjectiveTo test the hypothesis that the earned income tax credit (EITC)-the largest US poverty alleviation program-affects short-term health care expenditures among US adults.Data sourcesAdult participants in the 1997-2012 waves of the US Medical Expenditure Panel Survey (MEPS) (N = 1 282 080).Study designWe conducted difference-in-differences analyses, comparing health care expenditures among EITC-eligible adults in February (immediately following EITC refund receipt) with expenditures during other months, using non-EITC-eligible individuals to difference out seasonal variation in health care expenditures. Outcomes included total out-of-pocket expenditures as well as spending on specific categories such as outpatient visits and inpatient hospitalizations. We conducted subgroup analyses to examine heterogeneity by insurance status.Principal findingsEITC refund receipt was not associated with short-term changes in total expenditures, nor any expenditure subcategories. Results were similar by insurance status and robust to numerous alternative specifications.ConclusionsEITC refunds are not associated with short-term changes in health care expenditures among US adults. This may be because the refund is spent on other expenses, because of income smoothing, or because of similar refund-related variation in health care expenditures among noneligible adults. Future studies should examine whether other types of income supplementation affect health care expenditures, particularly among individuals in poverty.

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Abstract Prime numbers have been attracting the interest of scientists since the first formulation of Euclid’s theorem in 300 B.C. Nowadays, physicists and mathematicians continue to formulate new theorems about prime numbers, trying to comprehensively explain their articulated properties. However, evidence from biology and experimental psychology suggest that prime numbers possess distinctive natural properties that pre-exist human grasping. The present work aims at reviewing the existing literature on prime numbers in the life sciences, including some recent experimental contributions employing newly hatched domestic chicks as animal model to test for spontaneous mechanisms allowing discrimination of primes from non-primes. Our overarching goal is that of discussing some instances of prime numbers in nature, with particular reference to their peculiar, non-mathematical, perceptual properties.

A growing body of research suggests that adult child educational attainment benefits older parents' cognitive outcomes via financial (e.g. direct monetary transfers) and nonfinancial (e.g. psychosocial) mechanisms. Quasi-experimental studies are needed to circumvent confounding bias. No such quasi-experimental studies have been completed in higher-income countries, where financial transfers from adult children to aging parents are rare. Using data on 8,159 adults aged ≥50 years in the Survey for Health, Aging and Retirement in Europe (2004/2005), we leveraged changes in compulsory schooling laws as quasi-experiments. Each year of increased schooling among respondents' oldest children was associated with better verbal fluency (β = 0.07, 95% CI: 0.02, 0.12) scores; overall associations with verbal memory scores were null, with mixed and imprecise evidence of association in models stratified by parent gender. We also evaluated associations with psychosocial outcomes as potentially important mechanisms. Increased schooling among respondents' oldest children was associated with higher quality-of-life scores and fewer depressive symptoms. Our findings present modest albeit inconsistent evidence that increases in schooling may have an "upward" influence on older parents' cognitive performance even in settings where financial transfers from adult children to their parents are uncommon. Associations with parents' psychosocial outcomes were more robust. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID‐19) pandemic is an unprecedented global public health challenge. In the United States (US), state governments have implemented various non‐pharmaceutical interventions (NPIs), such as physical distance closure (lockdown), stay‐at‐home order, mandatory facial mask in public in response to the rapid spread of COVID‐19. To evaluate the effectiveness of these NPIs, we propose a nested case‐control design with propensity score weighting under the quasi‐experiment framework to estimate the average intervention effect on disease transmission across states. We further develop a method to test for factors that moderate intervention effect to assist precision public health intervention. Our method takes account of the underlying dynamics of disease transmission and balance state‐level pre‐intervention characteristics. We prove that our estimator provides causal intervention effect under assumptions. We apply this method to analyze US COVID‐19 incidence cases to estimate the effects of six interventions. We show that lockdown has the largest effect on reducing transmission and reopening bars significantly increase transmission. States with a higher percentage of non‐White population are at greater risk of increased Rt$$ {R}_t $$ associated with reopening bars. [ABSTRACT FROM AUTHOR]

By design, randomized experiments (XPs) rule out bias from confounded selection of participants into conditions. Quasi-experiments (QEs) are often considered second-best because they do not share this benefit. However, when results from XPs are used to generalize causal impacts, the benefit from unconfounded selection into conditions may be offset by confounded selection into locations. This work shows that this tradeoff can lead to situations where estimates from QEs are less-biased from selection than are estimates from uncompromised XPs when drawing causal generalizations. This work establishes the conditions theoretically, demonstrates the idea empirically, and discusses the implications of the results. [ABSTRACT FROM AUTHOR]

Prime numbers have been attracting the interest of scientists since the first formulation of Euclid's theorem in 300 B.C. Nowadays, physicists and mathematicians continue to formulate new theorems about prime numbers, trying to comprehensively explain their articulated properties. However, evidence from biology and experimental psychology suggest that prime numbers possess distinctive natural properties that pre-exist human grasping. The present work aims at reviewing the existing literature on prime numbers in the life sciences, including some recent experimental contributions employing newly hatched domestic chicks as animal model to test for spontaneous mechanisms allowing discrimination of primes from non-primes. Our overarching goal is that of discussing some instances of prime numbers in nature, with particular reference to their peculiar, non-mathematical, perceptual properties. [ABSTRACT FROM AUTHOR]

Evaluating the impacts of population-level interventions (e.g. changes to state legislation) can be challenging as conducting randomized experiments is often impractical and inappropriate, especially in settings where the intervention is implemented in a single, aggregate unit (e.g. a country or state). A common nonrandomized alternative is to compare outcomes in the treated unit(s) with unexposed controls both before and after the intervention. However, the validity of these designs depends on the use of controls that closely resemble the treated unit on before-intervention characteristics and trends on the outcome, and suitable controls may be difficult to find because the number of potential control regions is typically limited. The synthetic control method provides a potential solution to these problems by using a data-driven algorithm to identify an optimal weighted control unit—a "synthetic control"—based on data from before the intervention from available control units. While popular in the social sciences, the method has not garnered as much attention in health research, perhaps due to a lack of accessible texts aimed at health researchers. We address this gap by providing a comprehensive, nontechnical tutorial on the synthetic control method, using a worked example evaluating Florida's "stand your ground" law to illustrate methodological and practical considerations. [ABSTRACT FROM AUTHOR]

The synthetic control method (SCM) is widely used to evaluate causal effects under quasi-experimental designs. However, SCM suffers from weaknesses that compromise its accuracy, stability and meaningfulness, due to the nested optimization problem of covariate relevance and counterfactual weights. We propose a decoupling of both problems. We evaluate the economic effect of government formation deadlock in Spain-2016 and find that SCM method overestimates the effect by 0.23 pp. Furthermore, we replicate two studies and compare results from standard and decoupled SCM. Decoupled SCM offers higher accuracy and stability, while ensuring the economic meaningfulness of covariates used in building the counterfactual. [ABSTRACT FROM AUTHOR]

To determine whether the introduction of prescription drug coverage under Medicare Part D increased opioid prescriptions, patient care-seeking for pain, and pain diagnoses among elderly Medicare-eligible adults. Study Setting: Office visits by adults aged 18 years or older from the 2000-2016 National Ambulatory Medical Care Survey (12 375 207 253 office visits), and respondents from the 2000-2017 Medical Expenditure Panel Survey (4 023 418 681 individuals). Study Design: We compared care-seeking for pain, provider-assigned pain diagnoses, and opioid prescriptions before and after the Medicare eligibility age of 65, and before and after Part D's implementation using a regression discontinuity, difference-in-differences design. Analyses were adjusted for age, sex, race, and year. Principal Findings: Patient care-seeking for pain increased by 11.4 office visits per 100 people (95% confidence interval 2.0-20.8), or 29%, in response to the implementation of Part D. Opioid prescriptions and diagnoses of pain-related conditions did not change significantly, but the financing of opioid prescriptions shifted from private to public payers at age 65. Conclusions: The introduction of Medicare Part D was not associated with increased opioid use among older adults. Rather, opioid use among the elderly has been driven by high levels of opioid use among commercially insured adults who subsequently age into Medicare. Our findings raise the question of whether more judicious prescribing to younger adults coupled with concerted efforts to deprescribe opioids when appropriate may prevent problematic opioid use among the elderly. [ABSTRACT FROM AUTHOR]

Most online content publishers have moved to subscription-based business models regulated by digital paywalls. But the managerial implications of such freemium content offerings are not well understood. We, therefore, utilized microlevel user activity data from the New York Times to conduct a large-scale study of the implications of digital paywall design for publishers. Specifically, we use a quasi-experiment that varied the (1) quantity (the number of free articles) and (2) exclusivity (the number of available sections) of free content available through the paywall to investigate the effects of paywall design on content demand, subscriptions, and total revenue. The paywall policy changes we studied suppressed total content demand by about 9.9%, reducing total advertising revenue. However, this decrease was more than offset by increased subscription revenue as the policy change led to a 31% increase in total subscriptions during our seven-month study, yielding net positive revenues of over $230,000. The results confirm an economically significant impact of the newspaper's paywall design on content demand, subscriptions, and net revenue. Our findings can help structure the scientific discussion about digital paywall design and help managers optimize digital paywalls to maximize readership, revenue, and profit. This paper was accepted by Chris Forman, information systems. [ABSTRACT FROM AUTHOR]

This study finds empirical macro-economic evidence for the effect of high-speed rails (HSRs) on national innovation through the analyses of 59 high- and upper-middle-income countries. Knowledge productivity (KP) is assumed to be represented by annual patent applications per million capita. The associations between HSR development and KP are analyzed with three methods: (1) estimation of KP function using 10-year panel data, covering 14 countries with HSRs; (2) estimation of average treatment effect using difference-in-differences analysis for a treatment group of 15 and a control group of 44 countries; and (3) estimation of average treatment effect using matching techniques for a dataset covering 29 countries, of which 10 received treatment. The results of all three analyses covering about 40 years of HSRs' history, robustly suggest that HSR development can have positive externalities on KP both in the short and long runs. In addition, it is found that the HSRs' impacts on countries with upper-middle income and countries with high urban population growth are larger than other countries. They suggest that HSR development can be a part of national innovation policy and that upper-middle-income countries, highly urbanizing countries, and countries with insufficient inter-regional transportation systems can have advantages in HSR introduction for promoting knowledge productivity. • Impact of high-speed rails (HSRs) on knowledge productivity (KP) was analyzed. • Panel data for 59 countries covering about 40 years of HSRs' history were used. • Estimated knowledge productivity function showed positive HSR's effect on KP. • Difference-in-differences analysis unveiled positive HSR's treatment effect on KP. • Quasi-experiment with matching methods reinforced HSR's positive treatment effect. [ABSTRACT FROM AUTHOR]

We empirically study the impact of the HOV-incentive launch and termination on green and non‐green product demand in the U.S. automobile industry. In response to growing environmental concerns, governments have promoted products that are less harmful to the environment—green products—through various incentives. We empirically study the impact of a commonly used nonmonetary incentive—namely, the single-occupancy permission to high-occupancy vehicle (HOV) lanes—on green and non‐green product demand in the U.S. automobile industry. The HOV incentive could increase unit sales of green vehicles by enhancing their functional value through time saving. On the other hand, the incentive may prove counterproductive if it reduces the symbolic value (i.e., signaling a proenvironmental image) consumers derive from green vehicles. Assessing the effectiveness of green-product incentives is challenging, given the endogenous nature of governments' incentive provisions. To identify the effect of the HOV incentive on unit sales of green and non‐green vehicles, we take advantage of HOV-incentive changes in two states, and we employ a multitude of quasi‐experimental methods using a data set at the county–model–month level. Unlike previous studies that only examine the launch of the HOV incentive and find an insignificant association between incentive launch and green-vehicle demand, we concentrate on its termination. We find that the termination of the HOV incentive decreases unit sales of vehicles covered by the incentive by 14.4%. We provide suggestive evidence that this significant negative effect of HOV-incentive termination is due to the elimination of the functional value the incentive provides: time saving. Specifically, we find that the negative effect is more pronounced in counties where consumers value time saving more (i.e., counties with a longer commute to work and higher income). Additionally, in line with prior literature, the launch of the HOV incentive is not found to have a significant effect on green-vehicle sales. Combined, our findings reveal that the effect of termination is not simply the opposite of that of launch, implying that governments' green-product incentives could backfire. [ABSTRACT FROM AUTHOR]

Randomized Controlled Trials (RCTs) are considered the gold standard for measuring the efficacy of medical interventions. However, RCTs are expensive, and use a limited population. Techniques to estimate the effects of stroke interventions from observational data that minimize confounding would be useful. We used regression discontinuity design (RDD), a technique well-established in economics, on the Get With The Guidelines-Stroke (GWTG-Stroke) data set. RDD, based on regression, measures the occurrence of a discontinuity in an outcome (e.g., odds of home discharge) as a function of an intervention (e.g., alteplase) that becomes significantly more likely when crossing the threshold of a continuous variable that determines that intervention (e.g., time from symptom onset, since alteplase is only given if symptom onset is less than e.g., 3 h). The technique assumes that patients near either side of a threshold (e.g., 2.99 and 3.01 h from symptom onset) are indistinguishable other than the use of the treatment. We compared outcomes of patients whose estimated onset to treatment time fell on either side of the treatment threshold for three cohorts of patients in the GWTG-Stroke data set. This data set spanned three different treatment thresholds for alteplase (3 h, 2003–2007, N = 1,869; 3 h, 2009–2016, N = 13,086, and 4.5 h, 2009–2016, N = 6,550). Patient demographic characteristics were overall similar across the treatment thresholds. We did not find evidence of a discontinuity in clinical outcome at any treatment threshold attributable to alteplase. Potential reasons for failing to find an effect include violation of some RDD assumptions in clinical care, large sample sizes required, or already-well-chosen treatment threshold.

Randomized Controlled Trials (RCTs) are considered the gold standard for measuring the efficacy of medical interventions. However, RCTs are expensive, and use a limited population. Techniques to estimate the effects of stroke interventions from observational data that minimize confounding would be useful. We used regression discontinuity design (RDD), a technique well-established in economics, on the Get With The Guidelines-Stroke (GWTG-Stroke) data set. RDD, based on regression, measures the occurrence of a discontinuity in an outcome (e.g., odds of home discharge) as a function of an intervention (e.g., alteplase) that becomes significantly more likely when crossing the threshold of a continuous variable that determines that intervention (e.g., time from symptom onset, since alteplase is only given if symptom onset is less than e.g., 3 h). The technique assumes that patients near either side of a threshold (e.g., 2.99 and 3.01 h from symptom onset) are indistinguishable other than the use of the treatment. We compared outcomes of patients whose estimated onset to treatment time fell on either side of the treatment threshold for three cohorts of patients in the GWTG-Stroke data set. This data set spanned three different treatment thresholds for alteplase (3 h, 2003–2007, N = 1,869; 3 h, 2009–2016, N = 13,086, and 4.5 h, 2009–2016, N = 6,550). Patient demographic characteristics were overall similar across the treatment thresholds. We did not find evidence of a discontinuity in clinical outcome at any treatment threshold attributable to alteplase. Potential reasons for failing to find an effect include violation of some RDD assumptions in clinical care, large sample sizes required, or already-well-chosen treatment threshold. [ABSTRACT FROM AUTHOR]

In the Dutch Postcode Lottery a postal code (19 households on average) is randomly selected weekly, and prizes--consisting of cash and a new BMW--are awarded to lottery participants living in that postal code. On average, this generates a temporary, unexpected income shock equal to about eight months of income for about one third of the households in a typical winning code, while leaving the incomes of nonwinning, neighboring households unaffected. We study the responses of consumption and reported happiness of both winners and nonwinners to these shocks. Consistent with simple models of in-kind transfers, the overwhelming majority of households who won a BMW convert it into cash. With the exception of food away from home, the only 'own' effects of cash winnings we detect are on durables expenditures and car consumption; these results support a version of the permanent income hypothesis in which durable spending is used to smooth consumption. We detect social effects of neighbors' winnings on two types of consumption: cars and exterior home renovations. Six months after the fact, winning the lottery does not make households happier, nor do neighbors' winnings reduce happiness.

This paper investigates the causal effect of COVID-19 quarantine measures on the electricity consumption of different types of regulated customers. In particular, the average energy consumption of a group of customers in Chile is examined, exploiting the weekly variations in the application of the stages of the Step by Step Plan as a natural experiment. Step by Step Plan was the Chile COVID containment strategy, which involved implementing partial lockdowns based on the COVID incidence in the municipalities, and adjusted on a weekly basis. An econometric model is specified, and estimated using the technique Difference-in-Difference-in-Difference (DDD) to quantify the causal impact of these measures. After applying the model, it is found that the quarantine measures have had a negative effect on the trend weekly consumption of customers located in municipalities where some of the phases of the Plan have been applied. This effect is significantly different depending on each stage of the plan analysis, and statistically significant. It is also observed that, on average, the savings for a representative customer in the sample range from $ 8 to $ 6. 15 for some municipalities, and the sample costs range from $ 0. 02 to $ 0. 26 for others, depending on the municipality analyzed. The results can be expressed in a total of 2.95 tons less CO 2 emitted on a monthly basis. This shows a great impact in terms of sustainability and planning of energy demand and supply. • Quarantines by Covid 19 affected energy consumption, like a "Natural Experiment". • Using Panel Data of 400 thousand observations with geographic, temporal and Daylight Saving Time controls. • Difference-in-Difference-in-Difference method to quantify causal impact of quarantines on electric consumption. • Applying quarantines generated Energetic Savings from 8 cents to 6.15 dollars depending on client type. • An Event Study was designed to check the estimation results. [ABSTRACT FROM AUTHOR]

The prevalence of inflammatory child health conditions—such as asthma, eczema, and food allergy—and their associated costs have increased rapidly over the last 30 years. While environmental factors likely underpin these increases, recent studies explain only a fraction of the trend and rely on associational methods. Caesarean (or C-) section rates increased dramatically in the period of interest, and this method of delivery is an understudied environmental factor linked to child health outcomes via the gut microbiome. We fuse 22 years of birth cohort data from the United States National Surveys of Children's Health with C-section rates from the National Vital Statistics System generated for subgroups based on state, sex, race, Hispanic origin, and birth year. Then, we model the effects of C-section on rates of asthma, eczema, and food allergy using a quasi-experimental fixed effects design. We find that C-section significantly predicts food allergy, with qualitatively significant implications. [ABSTRACT FROM AUTHOR]

Empirical support for the purported benefits of retail electric deregulation is mixed at best. Prior studies that identify states as simply "retail deregulated" overlook complex policy environments in which deregulation is implemented by regulators with a high degree of discretion. Prior studies also rely on Energy Information Administration data that do not account for core regulatory interventions that can take place during the process of implementing deregulation. Using robust time series household final bill survey data from the Public Utilities Commission of Ohio, this article provides a quasi-experimental analysis of the price impacts of retail electric restructuring in Ohio. The results suggest that residential electricity prices have increased following retail restructuring in all service territories in Ohio, with significant favourable welfare effects observed only in the Cincinnati area, where key policy implementation stages were not circumvented. [ABSTRACT FROM AUTHOR]

Several variability representations have been proposed over the years. Software maintenance in the presence of variability is known to be hard. One of the reasons is that maintenance tasks require a large amount of cognitive effort for program comprehension. In fact, the different ways of representing variability in source code might influence the comprehension process in different ways. Despite the differences, there is little evidence about how these variability representations – such as conditional-compilation directives or feature-oriented programming – influence program comprehension. Existing research has focused primarily on either understanding how code using modern paradigms evolves compared to the traditional way of realizing variability, namely conditional compilation, or on the aspects influencing the comprehension of conditional compilation only. We used two different programs implemented in Java and each of these variability representations. As Java does not support conditional compilation natively, we relied on the mimicking (i.e., preprocessing annotations in comments) that has been used in the literature. Our results show no significant statistical differences regarding the evaluated measures (correctness, understanding, or response time) in the tasks. Our heterogeneous sample allowed us to produce evidence about the influence of using CC and FOP variability representations on the aspects involved in the comprehension of feature-oriented software, while addressing bug-finding tasks. [ABSTRACT FROM AUTHOR]

Observational studies are ambiguous, difficult, and necessary for epidemiology. Presently, there are concerns that the evidence produced by most observational studies in epidemiology is not credible and contributes to research waste. I argue that observational epidemiology could be improved by focusing greater attention on 1) defining questions that make clear whether the inferential goal is descriptive or causal; 2) greater utilization of quantitative bias analysis and alternative research designs that aim to decrease the strength of assumptions needed to estimate causal effects; and 3) promoting, experimenting with, and perhaps institutionalizing both reproducible research standards and replication studies to evaluate the fragility of study findings in epidemiology. Greater clarity, credibility, and transparency in observational epidemiology will help to provide reliable evidence that can serve as a basis for making decisions about clinical or population-health interventions. [ABSTRACT FROM AUTHOR]

The use of field experiments in managerial accounting research has increased substantially in the last couple of years. One reason for this upsurge is the call in the literature to address causality more confidently, which can be best accomplished with field experiments. This research method provides a clear mechanism for identifying causal effects in the field because the researcher introduces exogenous manipulations in different experimental conditions to which observational units (e.g., individuals, groups, or companies) are randomly assigned. Hence, field experiments are well suited to address managerial accounting phenomena that are plagued with endogeneity concerns when analyzed by using the results of retrospective (observational) studies. This manuscript provides an introduction to field experiments and is especially directed toward managerial accounting researchers who wish to consider adding this research method to their toolbox. [ABSTRACT FROM AUTHOR]

The article defines, describes, and discusses the seven threats to the internal validity of experiments discussed by Donald T. Campbell in his classic 1957 article: history, maturation, testing, instrument decay, statistical regression, selection, and mortality. These concepts are said to be threats to the internal validity of experiments because they pose alternate explanations for the apparent causal relationship between the independent variable and dependent variable of an experiment if they are not adequately controlled. A series of simple diagrams illustrate three pre-experimental designs and three true experimental designs discussed by Campbell in 1957 and several quasi-experimental designs described in his book written with Julian C. Stanley in 1966. The current article explains why each design controls for or fails to control for these seven threats to internal validity. [ABSTRACT FROM AUTHOR]

In within-study comparison (WSC) designs, treatment effects from a nonexperimental design, such as an observational study or a regression-discontinuity design, are compared to results obtained from a well-designed randomized control trial with the same target population. The goal of the WSC is to assess whether nonexperimental and experimental designs yield the same results in field settings. A common analytic challenge with WSCs, however, is the choice of appropriate criteria for determining whether nonexperimental and experimental results replicate. This article examines different distance-based correspondence measures for assessing correspondence in experimental and nonexperimental estimates. Distance-based measures investigate whether the difference in estimates is small enough to claim equivalence of methods. We use a simulation study to examine the statistical properties of common correspondence measures and recommend a new and straightforward approach that combines traditional significance testing and equivalence testing in the same framework. The article concludes with practical advice on assessing and interpreting results in WSC contexts. [ABSTRACT FROM AUTHOR]

The difference in difference (DID) design is a quasi-experimental research design that researchers often use to study causal relationships in public health settings where randomized controlled trials (RCTs) are infeasible or unethical. However, causal inference poses many challenges in DID designs. In this article, we review key features of DID designs with an emphasis on public health policy research. Contemporary researchers should take an active approach to the design of DID studies, seeking to construct comparison groups, sensitivity analyses, and robustness checks that help validate the method's assumptions. We explain the key assumptions of the design and discuss analytic tactics, supplementary analysis, and approaches to statistical inference that are often important in applied research. The DID design is not a perfect substitute for randomized experiments, but it often represents a feasible way to learn about casual relationships. We conclude by noting that combining elements from multiple quasi-experimental techniques may be important in the next wave of innovations to the DID approach. [ABSTRACT FROM AUTHOR]

Economists often treat pesticide resistance as a common-pool resource problem. While pecuniary economic incentives are the standard prescription for open-access market failures arising from such resources, non-pecuniary behavioral approaches (e.g., "nudges") are also effective in some cases. Yet non-pecuniary instruments have not previously been evaluated for managing pesticide resistance. I empirically evaluate the performance of such an intervention to manage pest resistance to genetically engineered Bacillus thuringiensis (Bt) corn. The U.S. Environmental Protection Agency permits sale of Bt seed conditional on seed producers compelling customers to plant mandated levels of non-Bt refuge to delay the evolution of Bt resistance. Because of compliance challenges, the Bt seed producer Monsanto piloted a social marketing program to promote refuge in 17 North Carolina counties in 2013-2014. Using 2013-2016 sales data, I use difference-in-differences, fractional regression, discrete changes-in-changes, and matched differences econometric models to identify the average treatment effect of the program on refuge planting. Results suggest that if it had covered all corn growers in North Carolina, the intervention would have led the average grower to plant between 2.6% (preferred estimate) and 5.8% more refuge in 2014 compared to the counterfactual. The program increased by at least 12% the average probability of planting any refuge in 2014. I find little evidence that effects of the program persisted in subsequent years after cessation, nor that the program increased compliance with mandated refuge thresholds. Informed by behavioral economics research on other environmental and resource policies, I discuss the implications of these findings for pesticide resistance management. [ABSTRACT FROM AUTHOR]

Background: Assessments of vaccine efficacy and safety capture only the minimum information needed for regulatory approval, rather than the full public health value of vaccines. Vaccine efficacy provides a measure of proportionate disease reduction, is usually limited to etiologically confirmed disease, and focuses on the direct protection of the vaccinated individual. Herein, we propose a broader scope of methods, measures and outcomes to evaluate the effectiveness and public health impact to be considered for evidence-informed policymaking in both pre- and post-licensure stages.Discussion: Pre-licensure: Regulatory concerns dictate an individually randomised clinical trial. However, some circumstances (such as the West African Ebola epidemic) may require novel designs that could be considered valid for licensure by regulatory agencies. In addition, protocol-defined analytic plans for these studies should include clinical as well as etiologically confirmed endpoints (e.g. all cause hospitalisations, pneumonias, acute gastroenteritis and others as appropriate to the vaccine target), and should include vaccine-preventable disease incidence and 'number needed to vaccinate' as outcomes. Post-licensure: There is a central role for phase IV cluster randomised clinical trials that allows for estimation of population-level vaccine impact, including indirect, total and overall effects. Dynamic models should be prioritised over static models as the constant force of infection assumed in static models will usually underestimate the effectiveness and cost-effectiveness of the immunisation programme by underestimating indirect effects. The economic impact of vaccinations should incorporate health and non-health benefits of vaccination in both the vaccinated and unvaccinated populations, thus allowing for estimation of the net social value of vaccination.Conclusions: The full benefits of vaccination reach beyond direct prevention of etiologically confirmed disease and often extend across the life course of a vaccinated person, prevent outcomes in the wider community, stabilise health systems, promote health equity, and benefit local and national economies. The degree to which vaccinations provide broad public health benefits is stronger than for other preventive and curative interventions. [ABSTRACT FROM AUTHOR]

Empirical information available for causal judgment in everyday life tends to take the form of quasi-experimental designs, lacking control groups, more than the form of contingency information that is usually presented in experiments. Stimuli were presented in which values of an outcome variable for a single individual were recorded over six time periods, and an intervention was introduced between the fifth and sixth time periods. Participants judged whether and how much the intervention affected the outcome. With numerical stimulus information, judgments were higher for a pre-intervention profile in which all values were the same than for pre-intervention profiles with any other kind of trend. With graphical stimulus information, judgments were more sensitive to trends, tending to be higher when an increase after the intervention was preceded by a decreasing series than when it was preceded by an increasing series ending on the same value at the fifth time period. It is suggested that a feature-analytic model, in which the salience of different features of information varies between presentation formats, may provide the best prospect of explaining the results. [ABSTRACT FROM AUTHOR]