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Summary: We study identification and estimation in the regression discontinuity design with a multivalued treatment. We show that heterogeneity in the first stage discontinuities can be used for the identification of the marginal treatment effects under an alternative assumption, namely, the homogeneity of the LATEs along some covariates. This assumption can often be tested and relaxed. Our estimator can be programmed as a simple two‐stage least squares regression, and packaged standard errors and tests can also be used. We apply our method to estimate the effect of Medicare insurance coverage on health care utilization. [ABSTRACT FROM AUTHOR]

An ongoing tension exists in the relationship between performance regimes and equity. On the one hand, performance regimes could set goals to reduce disparate outcomes. However, performance regimes are associated with strategic behaviors, such as cream skimming, that could worsen outcomes for marginalized groups. This article contributes to this debate by examining the use of growth measures of performance on achievement gaps between advantaged and disadvantaged subgroups of students in New York City public schools. Using a regression discontinuity design, this study credibly identifies the causal effects of performance signals on equity outcomes. Results show weak evidence of negative effects on equity, and the achievement gaps did not increase in most of the cases. The article also discusses how the incentives provided by growth measures can curb strategic behaviors. The findings provide measured optimism that the current generation of performance regimes can be designed to account for issues of equity. [ABSTRACT FROM AUTHOR]

Occupational licensing has grown dramatically in recent years, with more than 25% of the US workforce having a license as of 2008, up from 5% in 1950. Has licensing improved quality or is it simply rent-seeking behavior by incumbent workers? To estimate the impact of increased licensure of social workers in skilled nursing facilities (SNFs) on service quality, the authors exploit a federal staffing provision that requires SNFs of a certain size to employ licensed social workers. Using a regression discontinuity design, the authors find that qualified social worker staffing increases by approximately 10%. However, the overall increase in social services staffing is negligible because SNFs primarily meet this requirement in the lowest cost way—substituting qualified social workers for unlicensed social services staff. The authors find no evidence that the increase in licensure improves patient care quality, patient quality of life, or quality of social services provided. [ABSTRACT FROM AUTHOR]

Abstract Background The current study examines the negative impact of the coronavirus disease 2019 (COVID-19) emergency declarations on physical activity among the community-dwelling older adults, the participants of a physical activity measurement program, in Japan. Methods This retrospective observational study included 1,773 community-dwelling older adults (aged 74.6 ± 6.3 years, 53.9% women) who had participated in the physical activity measurement project from February 2020 to July 2021. We measured physical activity using a tri-axial accelerometer during 547 consecutive days. Three emergency declarations, requesting people to avoid going outside, occurred during the observational period. We multiply-imputed missing values for daily physical activity, such as steps, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) for several patterns of datasets according to the maximum missing rates on a person level. We mainly report the results based on less than 50% of the maximum missing rate (n = 1,056). Other results are reported in the supplemental file. Changes in physical activity before and after the start of each emergency declaration were examined by the regression discontinuity design (RDD) within 14-, 28-, and 56-day bandwidths. Results For all the participants in the multiply-imputed data with the 14-day bandwidth, steps (coefficients [ $${\widehat{\gamma }}_{1}$$ γ ^ 1 ] $$,$$ , 964.3 steps), LPA ( $${\widehat{\gamma }}_{1},$$ γ ^ 1 , 5.5 min), and MVPA ( $${\widehat{\gamma }}_{1},$$ γ ^ 1 , 4.9 min) increased after the first emergency declaration. However, the effects were attenuated as the RDD bandwidths were widened. No consistent negative impact was observed after the second and third declarations. After the second declaration, steps ( $${\widehat{\gamma }}_{1},$$ γ ^ 1 , -609.7 steps), LPA ( $${\widehat{\gamma }}_{1},$$ γ ^ 1 , -4.6 min), and MVPA ( $${\widehat{\gamma }}_{1},$$ γ ^ 1 , -2.8 min) decreased with the 14-day bandwidth. On the other hand, steps ( $${\widehat{\gamma }}_{1},$$ γ ^ 1 , 143.8 steps) and MVPA ( $${\widehat{\gamma }}_{1},$$ γ ^ 1 , 1.3 min) increased with the 56-day bandwidth. For the third declaration, LPA consistently decreased with all the bandwidths ( $${\widehat{\gamma }}_{1},$$ γ ^ 1 , -2.1, -3.0, -0.8 min for the 14, 28, 56-day bandwidth), whereas steps ( $${\widehat{\gamma }}_{1},$$ γ ^ 1 , -529 steps) and MVPA ( $${\widehat{\gamma }}_{1},$$ γ ^ 1 , -2.6 min) decreased only with the 28-day bandwidth. Conclusions For the community-dwelling older adults who regularly self-monitor their physical activity, the current study concludes that there is no evidence of consistently negative impacts of the emergency declarations by the COVID-19 pandemic.

With the availability of large-scale scholarly datasets, scientists from various domains hope to understand the underlying mechanisms behind science, forming a vibrant area of inquiry in the emerging “science of science” field. As the results from the science of science often has strong policy implications, understanding the causal relationships between variables becomes prominent. However, the most credible quasi-experimental method among all causal inference methods, and a highly valuable tool in the empirical toolkit, Regression Discontinuity Design (RDD) has not been fully exploited in the field of science of science. In this paper, we provide a systematic survey of the RDD method, and its practical applications in the science of science.

The authors evaluate the exemption of long-term unemployed job seekers from Germany's national minimum wage. Using linked survey and administrative micro data, they rely on a regression discontinuity design to identify the effects of the policy by comparing hiring rates, employment stability, and entry wages around the administrative threshold between short-term and long-term unemployment. They find that the exemption is very rarely used and that the minimum wage binds irrespective of past unemployment duration. While the minimum wage led to a relative rise in entry wages for the long-term unemployed compared to the short-term unemployed, the authors do not detect a relative deterioration in their employment prospects. [ABSTRACT FROM AUTHOR]

The politically motivated replacement in local governments is a pervasive fact in our modern democracies. Whether it has causal effects on the quality of public services, such as education, is a critical question and yet understudied. This paper uses a regression discontinuity design (RDD) for close elections to replicate Akthari, Moreira and Trucco (2022) who find negative effects on the quality of public education in Brazil (.05-.08 standard deviations of lower test scores). I first reproduce these main results, finding minor computational differences that have no effect on the conclusions. I also show that the estimates for Brazil are in general robust to different specifications following Brodeur, Cook and Heyes (2020). Finally, I implement the same RDD framework now applied to Chilean administrative records to find null effects on test scores. Taken together, these results suggest that political turnover has weakly negative effects on service quality. [ABSTRACT FROM AUTHOR]

Background Parental leave policies have been hypothesized to benefit mothers' mental health. We assessed the impact of a 6-week extension of parental leave in Denmark on maternal mental health. Methods We linked individual-level data from Danish national registries on maternal sociodemographic characteristics and psychiatric diagnoses. A regression discontinuity design was applied to study the increase in parental leave duration after 26 March 1984. We included women who had given birth between 1 January 1981 and 31 December 1987. Our outcome was a first psychiatric diagnosis following the child's birth, ascertained as the first day of inpatient hospital admission for any psychiatric disorder. We presented cumulative incidences for the 30-year follow-up period and reported absolute risk differences between women eligible for the reform vs not, in 5-year intervals. Results In all, 291 152 women were followed up until 2017, death, emigration or date of first psychiatric diagnosis. The median follow-up time was 29.99 years, corresponding to 10 277 547 person-years at risk. The cumulative incidence of psychiatric diagnoses at 30 years of follow-up was 59.5 (95% CI: 57.4 to 61.6) per 1000 women in the ineligible group and 57.5 (95% CI: 55.6 to 59.4) in the eligible group. Eligible women took on average 32.85 additional days of parental leave (95% CI: 29.20 to 36.49) and had a lower probability of having a psychiatric diagnosis within 5 years [risk difference (RD): 2.4 fewer diagnoses per 1000 women, 95% CI: 1.5 to 3.2] and up to 20 years after the birth (RD: 2.3, 95% CI: 0.4 to 4.2). In subgroup analyses, the risk reduction was concentrated among low-educated, low-income and single women. Conclusions Longer parental leave may confer mental health benefits to women, in particular to those from disadvantaged backgrounds. [ABSTRACT FROM AUTHOR]

Reliable shear strength determination of large in situ discontinuities is still a challenge faced by the rock mechanics field. This is principally due to the limited availability of surface roughness and morphology information of in situ discontinuities and the unresolved management of the 'scale effect' phenomenon. Recently, a stochastic approach for predicting the shear strength of large-scale discontinuities was established, encompassing random field theory, a semi-analytical shear strength model, and a stochastic analysis framework. A key aspect of the new approach is the application at field scale, thereby minimising or bypassing the scale effect. The approach has been validated at laboratory scale and an initial large-scale deterministic-based validation showed promising results. However, to date, no large-scale experimental-based validation has been undertaken. This paper presents the first rigorous application of the employed semi-analytical shear strength model and the stochastic approach on a 2 m-by-2 m discontinuity surface, with comparison of prediction to experimental shear strength data. The shear strength model was found to generally produce peak and residual predictions within a ± 10% relative error range, with good agreement between predicted and observed damage areas. It was observed that, applying the stochastic approach to seed traces with gradient statistics equivalent to that of the surface, produced predictions that closely resemble the experimental results. Whereas, predicting shear strength from different seed traces results in more variability of predictions, with many falling within ± 20% of the experimental data. The predictions of residual shear strength tended to be more accurate than peak shear strength. Highlights: The stochastic approach for discontinuity shear strength was applied to a 2 m per 2 m rock surface replica made of mortar. A sensitivity analysis conducted on the analytical shear strength model highlights the parameter influencing the most the shear strength prediction. The predictions of peak and residual strengths fall within 20% of the experimental data. The accuracy of prediction depends on the trace used to define the input statistics. [ABSTRACT FROM AUTHOR]

Education spending mandates are popular around the world, especially among central governments in large, decentralized nations, who find that they may be useful in influencing varied local fiscal behavior. I evaluate Indonesia's education spending mandate policy, which insists that district governments allocate at least 20 percent of their budgets to education. I find that the mandate has indeed induced some districts to spend more on education than they otherwise might have, thereby meeting the official target. However, the evidence indicates that the local spending mandate has had no significant effect on improving school participation and learning outcomes. [ABSTRACT FROM AUTHOR]

Do ethnic majorities and minorities have diverging preferences for fiscal capacity? Do these preferences converge during national emergencies such as interstate war? In this paper, we provide evidence from a natural experiment to demonstrate that politically salient minority-majority divisions undermine the development of fiscal capacity. In addition, we show that the pressure of interstate war is insufficient to supersede differences in support for the expansion of state's capacity for taxation between majority and minority groups. More specifically, we employ a regression discontinuity design using a natural border that separates linguistic groups and municipality outcomes of a popular vote on the introduction of direct taxation at federal level in Switzerland during the First World War. The findings suggest that salient minority-majority divisions have a negative effect on the expansion of states' capacity for taxation even during periods of interstate war. [ABSTRACT FROM AUTHOR]

This article studies the party alignment bias in Poland. Relying on quasi-experimental regression discontinuity design and 2006–2019 data for over 2,400 municipalities, we aim to establish whether the impact of political alignment on the distribution of grants from the central to the local governments has increased under the rules of populist, authoritarian government. We report robust evidence that provides an affirmative answer to this question. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Economics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: In many applications of regression discontinuity designs, the running variable used to assign treatment is only observed with error. We show that, provided the observed running variable (i) correctly classifies treatment assignment and (ii) affects the conditional means of potential outcomes smoothly, ignoring the measurement error nonetheless yields an estimate with a causal interpretation: the average treatment effect for units whose observed running variable equals the cutoff. Possibly after doughnut trimming, these assumptions accommodate a variety of settings where support of the measurement error is not too wide. An empirical application illustrates the results for both sharp and fuzzy designs. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 (COVID-19) pandemic severely affected global food security, but analyses of its impact on the cost and affordability of a healthy diet are limited. This study examines the immediate effect of the COVID-19 pandemic on the cost and affordability of a healthy diet among urban households in Bangkok, Thailand and Manila, the Philippines. Methods: We used official food price and household income and food expenditure data from the national statistics offices. The cost of recommended diet (CoRD) method was employed to assess the minimum cost of a healthy diet, following the healthy diet recommendations provided in the national food-based dietary guidelines of the specific countries. Regression discontinuity design was estimated to determine the COVID-19 effect on food prices and scenario analysis done to determine the effect of reduced food budgets with and without government relief programs. Results: The results show that the average cost of the recommended diet was US$ 1.55 per person/day in Bangkok and US$ 3.76 in Manila (2019 prices in purchasing power parities) immediately before the pandemic. This diet is generally affordable for all households in Bangkok, but only for 37% of households (4.98 million people) in Manila, indicating much higher poverty in the latter. The pandemic and associated government measures decreased the cost of the recommended diet with 6.5% in Bangkok (p = 0.001) but not in Manila (p = 0.167). Assuming contractions in people's food budgets of 15–20%, the recommended diet became unaffordable for 0.08–0.12 million people in Bangkok and 6.32–7.73 million people in Manila during the pandemic. Government relief largely compensated for this loss in Bangkok, but relief payments in Manila were not enough to compensate the effect. Conclusion: These results show that the main effect of the COVID-19 pandemic on the affordability of healthy diets was through the effect on reduced incomes of the poor rather than through prices. Government relief measures should target low-income households to give them the means to purchase healthy food items. [ABSTRACT FROM AUTHOR]

Background: There is sparse evidence on the impact of health information on mental health as well as on the mechanisms governing this relationship. We estimate the causal impact of health information on mental health via the effect of a diabetes diagnosis on depression. Methods: We employ a fuzzy regression discontinuity design (RDD) exploiting the exogenous cut-off value of a biomarker used to diagnose type-2 diabetes (glycated haemoglobin, HbA1c) and information on psycometrically validated measures of diagnosed clinical depression drawn from rich administrative longitudinal individual-level data from a large municipality in Spain. This approach allows estimating the causal impact of a type-2 diabetes diagnosis on clinica ldepression. Results: We find that overall a type-2 diabetes diagnosis increases the probability of becoming depressed, however this effect appears to be driven mostly by women, and in particular those who are relatively younger and obese. Results also appear to differ by changes in lifestyle induced by the diabetes diagnosis: while women who did not lose weight are more likely to develop depression, men who did lose weight present a reduced probability of being depressed. Results are robust to alternative parametric and non-parametric specifications and placebo tests. Conclusions: The study provides novel empirical evidence on the causal impact of health information on mental health, shedding light on gender-based differences in such effects and potential mechanisms through changes in lifestyle behaviours. [ABSTRACT FROM AUTHOR]

The assumption of cause-effect relationships in ex post facto research is a widely known issue in the field of research methods in social sciences. To address this important limitation, the use of causal inference techniques has become widespread in recent years. Causal inference establishes a set of statistical procedures for drawing causal conclusions in non-experimental research. Despite its wide popularity and diffusion in the social and health sciences, its use in educational research is still marginal. Thus, this paper introduces the main causal inference techniques available to the educational researcher when observational panel data are available. After addressing the key features and potential of propensity score matching, instrumental variables, and regression discontinuity design, we present an example application of each of these techniques. We used the available databases from the PISA 2018 assessments. We included the mathematical competence as the dependent variable in all the three models implemented. Given the different characteristics of each of these techniques, the independent variable used is different in the three models applied: attendance to early childhood education in propensity score matching; student academic expectations in instrumental variables; and size of the community in which the school is located in regression discontinuity design. The article concludes by discussing the potential of this set of techniques, taking into account the needs and methodological procedures most commonly applied in educational research. [ABSTRACT FROM AUTHOR]

Copyright of Electronic Journal of Educational Research, Assessment & Evaluation / Revista Electrónica de Investigación y Evaluación Educativa is the property of RELIEVE - Revista Electonica de Investigacion y Evaluacion Educativa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The emergency response to the COVID-19 pandemic had an extreme exogenous impact on society and the economy. This paper aims to explore the impacts of the national emergency response and the subsequent emergency response termination on air quality and its policy implications through regression discontinuity design (RDD) estimation by employing panel data on daily air quality from January 1, 2019, to July 31, 2020, for 290 cities in China. The empirical results showed that the emergency response resulted in a significant decrease in most of the major pollutant concentrations within a short time frame, and the average air quality index (AQI) decreased by approximately 11.0%. The concentrations of PM 2.5 , PM 10 , SO 2 , NO 2 , and CO decreased by approximately 18.8%, 13.1%, 13.5%, 11.1% and 6.7%, respectively, while the O 3 concentration did not change significantly. Further causal analysis found that mandatory traffic restrictions and the shutdown of industries were two important factors that contributed greatly to air quality improvement. Moreover, since the process of returning to normal daily activities and promoting the economy were gradual, the results showed that air pollution did not rebound immediately after the government called for the "resumption of production and work" and announced the "termination of the emergency response". Our findings suggest that to achieve a substantial and sustainable improvement in air quality, it is necessary to continuously implement strict emission control routines and take co-control measures for various VOCs precursors of ozone. [Display omitted] • Emergency response to COVID-19 (ER) had spill-over effect on air quality. • Empirical analysis was made with RDD to evaluate the impact of ER on air quality. • AQI and PM 2.5 , PM 10 , SO 2 and NO 2 concentrations decreased by approximately 10%. • Traffic restrictions and shutdown of industries during ER were important factors. • Socioeconomic resumption and ER termination did not affect air quality. [ABSTRACT FROM AUTHOR]

I study the consequences of letter grades serving as coarse measures of academic achievement using university administrative data that record both the letter grade and the precise mark (0–100) received for each course that a given student takes. I exploit a regression discontinuity design with marks as the running variable. I find that receiving a better grade in a single class results in USD 32 greater monthly earnings after graduation, a 1.4% increase. I also find that marginal students who receive a worse grade take significantly easier courses and earn lower grades in future semesters. [ABSTRACT FROM AUTHOR]

This article examines the long‐term effects of the administrative system using the Great Wall as a geographical discontinuity. Using town‐level nighttime light luminosity per capita as a measure of economic development, we find that today, luminosity per capita is significantly and robustly higher in towns south of the Great Wall than in those north of it. The holding of resource allocation authority by the administrative hierarchy and the uniform enforcement of justice by hierarchy officials were advantageous for city building and judicial cross‐territory enforceability on the south side of the great wall, which had a long‐term impact on the imperial bureaucracy. [ABSTRACT FROM AUTHOR]

Many pension reforms in OECD countries included pension statements with the objective of improving individuals' financial security in retirement. Our objective is to assess the effectiveness of the pension information policy implemented in France and to investigate whether the pension statement results in better informed workers, who then increase their retirement savings. Using regression discontinuity designs combined with quantile regressions, we assess whether the changes in retirement savings and holding of assets are due to the pension information system and then quantify the impact. We conclude that a pension estimate sent to workers encourages the wealthiest to increase their retirement savings while it does not influence the savings of individuals with a low level of wealth. [ABSTRACT FROM AUTHOR]

We study the distributional effects of education on health. In 1972, England, Scotland, and Wales raised their minimum school leaving age from 15 to 16 for students born after September 1, 1957. Using a regression discontinuity design and objective health measures for 129,000 individuals, we find that education reduced body size and increased blood pressure in middle age. The reduction in body size was concentrated at the upper tail of the distribution with an eight percentage point reduction in obesity. The increase in blood pressure was concentrated at the lower tail of the distribution, with no effect on stage 2 hypertension. [ABSTRACT FROM AUTHOR]

Educators must balance the needs of students who start the school year behind grade level with their obligation to teach grade-appropriate content to all students. Educational software could help educators strike this balance by targeting content to students' differing levels of mastery. Using a regression discontinuity design and detailed software log and administrative data, we compare two versions of an online mathematics program used by students in three education agencies. We find that although students assigned the modified curriculum did progress through content objectives more quickly than students assigned the default curriculum, they did not perform better on pre- and post-objective quizzes embedded in the software, and most never progressed far enough to reach the grade-level content. Furthermore, there was no statistically significant effect of the modified curriculum on formative test scores. These findings suggest policymakers and practitioners should exercise caution when assigning exclusively remedial content to students who start the school year behind grade level, even though this is a common feature of many math educational software programs. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and its mobility restrictions have been an external shock, influencing mental wellbeing. However, does risk exposure to COVID-19 affect the mental wellbeing effect of lockdowns? This paper examines the 'welcomed lockdown' hypothesis, namely the extent to which there is a level of risk where mobility restrictions are not a hindrance to mental wellbeing. We exploit the differential timing of exposure the pandemic, and the different stringency of lockdown policies across European countries and we focus on the effects on two mental health conditions, namely anxiety and depression. We study whether differences in the individual symptoms of anxiety and depression are explained by the combination of pandemic mortality and stringency of lockdown. We draw on an event study approach, complemented with a Difference-in-Difference (DiD), and Regression Discontinuity Design (RDD). Our estimates suggest an average increase in depression (3.95%) and anxiety (10%) symptoms relative to the mean level on the day that lockdown took effect. However, such effects are wiped out when a country's exhibits high mortality ('pandemic category 5'). Hence, we conclude that in an environment of high mortality, lockdowns no longer give rise to a reduction in mental wellbeing consistent with the 'welcome lockdown' hypothesis. [ABSTRACT FROM AUTHOR]

The 'nervous nineties' is a well-known cricket colloquialism that implies that batting within reach of 100 runs is mentally demanding. Despite common acceptance of this phenomenon, no study has used a historical test cricket dataset to examine how batting behaviour and performance changes on approach to a century. Accordingly, we explored opensource ball-by-ball data from 712 test cricket matches played between 2004 and 2022 to model the regression discontinuity of batting performance metrics either side of 100 runs. Models were fit using multi-level regression, adjusted for the clustering of balls within players (and where possible, the clustering of matches and innings within players). The analysis revealed that runs per ball and the probability of scoring a boundary increased as batters approached 100 runs. This was followed by a decline of -0.18 runs per ball (95% CI -0.22 to -0.14) and a three-percentage point decline (95% CI 2.2 to 3.8) in the probability of a boundary once a batter reached 100. The modelling revealed no evidence of a change in the probability of a dismissal before and after 100. Our results suggest many batters cope effectively with the psychological demands of playing through the nineties, including by batting aggressively and/or opportunistically to swiftly reach the milestone. [ABSTRACT FROM AUTHOR]

Background: To address concerns over the financial stability of South Korea's National Health Insurance (NHI) programme, the government transitioned from an outpatient copayment system to a coinsurance system in 2007. This policy aimed to reduce healthcare overutilization by increasing patients' financial responsibility for outpatient services. Methods: Using comprehensive data on NHI beneficiaries, this study employs a regression discontinuity in time (RDiT) design to assess the policy's impact on outpatient healthcare utilization and expenditures. We focus on changes in overall outpatient visits, average healthcare cost per visit and total outpatient healthcare expenditures. Results: Our findings indicate that the transition from outpatient copayment to coinsurance led to a substantial increase in outpatient healthcare utilization (up to 90%) while decreasing medical expenditures per visit by 23%. The policy shift incentivized beneficiaries to seek more medical treatments during the grace period and enroll in supplemental private health insurance, which provided access to additional medical services at lower marginal costs. Conclusions: The policy change and the emergence of supplemental private insurance contributed to moral hazard and adverse selection issues, culminating in South Korea becoming the country with the highest per capita utilization of outpatient health services worldwide since 2012. This study underscores the importance of carefully considering the unintended consequences of policy interventions in the healthcare sector. [ABSTRACT FROM AUTHOR]

In regression discontinuity design (RDD), the continuity of the density of a running variable is required. Hence, a discontinuity test of density is used for RDD. In previous studies, tests using difference estimators between the left- and right-hand limits of a density at a (potential) discontinuity point were suggested. In the present paper, a new discontinuity test based on direct density ratio estimation using a beta kernel is proposed. By using the ratio estimator in the proposed test statistic, rather than a difference estimator, the characteristic form of the asymptotic variance of the test statistic is obtained. Consequently, the power of the proposed test is shown to increase when used as a one-tailed test. Simulation studies illustrate the larger power of the proposed test when used as a one-tailed test. [ABSTRACT FROM AUTHOR]

Objective: To evaluate how an abrupt drop‐off, or "cliff," in Medicaid dental coverage affects access to dental care among low‐income Medicare beneficiaries. Medicaid is an important source of dental insurance for low‐income Medicare beneficiaries, but beneficiaries whose incomes slightly exceed eligibility thresholds for Medicaid have fewer affordable options for dental coverage, resulting in a dental coverage cliff above these thresholds. Data Source: Medicare Current Beneficiary Surveys (MCBS) from 2016 to 2019. Study Design: We used a regression discontinuity design to evaluate effects of this dental coverage cliff. This study design exploited an abrupt difference in Medicaid coverage above income eligibility thresholds in the Medicaid program for elderly and disabled populations. Data Collection: The study included low‐income community‐dwelling Medicare beneficiaries surveyed in the MCBS whose incomes, measured in percentage points of the federal poverty level, were within ±75 percentage points of state‐specific Medicaid income eligibility thresholds (n = 7508 respondent‐years, which when weighted represented 26,776,719 beneficiary‐years). Principal Findings: Medicare beneficiaries whose income exceeded Medicaid eligibility thresholds were 5.0 percentage points more likely to report difficulty accessing dental care due to cost concerns or a lack of insurance than beneficiaries below the thresholds (95% CI: 0.2, 9.8; p = 0.04)—a one‐third increase over the proportion reporting difficulty below the thresholds (15.0%). Conclusions: A Medicaid dental coverage cliff exacerbates barriers to dental care access among low‐income Medicare beneficiaries. Expanding dental coverage for Medicare beneficiaries, particularly those who are ineligible for Medicaid, could alleviate barriers to dental care access that result from the lack of comprehensive dental coverage in Medicare. [ABSTRACT FROM AUTHOR]

Joint inversion of magnetotelluric (MT) and geomagnetic depth sounding (GDS) responses can constrain the crustal and mantle conductivity structures. Previous studies typically use either deterministic inversion algorithms that provide limited information on model uncertainties or using stochastic inversion algorithms with a predetermined number of layers that is generally not known a priori. Here, we present a new open-source Bayesian framework for the joint inversion of MT and GDS responses to probe 1D layered Earth's conductivity structures. Within this framework, model uncertainties can be accurately estimated by generating numerous models that fit the observed data. A trans-dimensional Markov Chain Monte Carlo (MCMC) method is employed to self-parametrize the model parameters, where the number of layers is treated as an inversion parameter that is determined automatically by the data. This adaptability can overcome the under or over-parametrization problem and may be able to automatically detect the conductivity discontinuities in the Earth's interior. To accelerate the computations, a large number of Markov chains with different initial states can be run simultaneously using the MPI parallel technique. Synthetic data sets are used to validate the feasibility of our method and illustrate how separate and joint inversions, as well as various priors affect the posterior model distributions. The trans-dimensional MCMC algorithm is then applied to jointly invert the MT and GDS responses estimated at the Tucson geomagnetic observatory, North America. Our results not only contain model uncertainty estimates but also indicate two distinct conductivity discontinuities at around 85 and 440 km, which are likely related to the lithosphere-asthenosphere boundary and the upper interface of the mantle transition zone, respectively. [ABSTRACT FROM AUTHOR]

This paper assessed the impacts of environmental smog early-warning signals on road traffic deaths. For an accurate assessment, we used the daily traffic death data from 2016 to 2020 in 295 Chinese cities and constructed a rigorous Regression Discontinuity Design (RDD) strategy to identify the causality and adopted the high-dimensional fixed-effect method to deal with the interference of meteorological factors. The results indicate that light smog and moderate smog early warnings decreased road fatalities by about 3.6% and 4.3%, respectively. Surprisingly, the heavy smog early-warning signal had no significant effect, possibly because of the self-consciousness mechanism instead of the early-warning signal mechanism. Further heterogeneity analysis showed that women drivers, highly-educated drivers, older drivers (over 60 years), two-wheeled vehicle drivers, and drivers on country roads and freeways are more sensitive to smog early-warning signals. [ABSTRACT FROM AUTHOR]

This study presents the results of a regression discontinuity empirical approach to investigate the effects of postseason bowl game participation on student-athlete academic outcomes and subsequent football team success. The practice expectations for student-athletes on football teams that participate in a bowl game increase by between two and four weeks relative to student-athletes on teams that do not participate in a bowl game. Prior research has been inconclusive on whether this increased practice intensity is associated with academic or athletic outcomes. The sample includes 130 NCAA football bowl subdivision (FBS) teams between the years 2003 through 2018. We apply a fuzzy regression discontinuity design by exploiting the fact that teams in the NCAA FBS become eligible to participate in a bowl game when their regular season winning percentage is greater than 0.50. The results suggest that bowl game participation increased the team's eligibility rate by 0.8 percentage points, the team's Academic Progress Rate by 4.6 points, but had no effect on the team's retention rate. Bowl game participation was not found to affect the subsequent year's winning percentage or likelihood of bowl game participation. Athletic programs that are undecided about whether the costs, in finances or time, of participating in a bowl game are worthwhile might benefit from these findings. In particular, the results reveal that bowl game participation does not come as a detriment to the academic outcomes of their student-athletes. [ABSTRACT FROM AUTHOR]

The paper presents a discontinuity‐based analysis of the settlement pattern changes in the Czechia–Poland cross‐border historical region of Těšín Silesia. An approach based on a well‐known and popular method (regression discontinuity design) was applied to measure spatial discontinuity. To describe the spatiotemporal changes, a combination of spatial, statistical and cartographic methods was used. The observed differences have been developing for more than 150 years; at the start, this area belonged to the territory of one state, and later it was divided by a national border. The division of the region resulted in areas following different development trajectories. [ABSTRACT FROM AUTHOR]

We propose a kernel-based partial permutation test for checking the equality of functional relationship between response and covariates among different groups. The main idea, which is intuitive and easy to implement, is to keep the projections of the response vector Y on leading principle components of a kernel matrix fixed and permute Y's projections on the remaining principle components. The proposed test allows for different choices of kernels, corresponding to different classes of functions under the null hypothesis. First, using linear or polynomial kernels, our partial permutation tests are exactly valid in finite samples for linear or polynomial regression models with Gaussian noise; similar results straightforwardly extend to kernels with finite feature spaces. Second, by allowing the kernel feature space to diverge with the sample size, the test can be large-sample valid for a wider class of functions. Third, for general kernels with possibly infinite-dimensional feature space, the partial permutation test is exactly valid when the covariates are exactly balanced across all groups, or asymptotically valid when the underlying function follows certain regularized Gaussian processes. We further suggest test statistics using likelihood ratio between two (nested) Gaussian process regression models, and propose computationally efficient algorithms utilizing the EM algorithm and Newton's method, where the latter also involves Fisher scoring and quadratic programming and is particularly useful when EM suffers from slow convergence. Extensions to correlated and non-Gaussian noises have also been investigated theoretically or numerically. Furthermore, the test can be extended to use multiple kernels together and can thus enjoy properties from each kernel. Both simulation study and application illustrate the properties of the proposed test. [ABSTRACT FROM AUTHOR]

To date there has been little agreement on the effect of retirement on tourism consumption, and "retirement-tourism consumption puzzle" has not been empirically tested. Taking advantage of the mandatory retirement policy in China, this study adopts the fuzzy regression discontinuity design (RDD) approach to examine whether there is a "retirement-tourism consumption puzzle." The findings indicate that the retirement of household heads significantly boosts household tourism consumption. The results also illustrate the considerable moderating effects of self-assessed health level and pension level on household tourism consumption. The research framework can be generalized to other countries to identify the effect of retirement on tourism consumption. [ABSTRACT FROM AUTHOR]

We exploit a country-wide child labor regulation that eliminated the difference in school/work alternatives for children born at the beginning and the end of the year to identify the causal effect of education on migration at low levels of schooling. By not relying on changes to the school system, we are more confident that our results are not driven by unobserved changes in school quality evolving differentially across regions. The results of a difference-in-differences methodology combined with an exploration of maternal characteristics and a regression discontinuity design suggest that internal migration hardly changed after the reform. A consideration of the external validity of this finding is also provided. [ABSTRACT FROM AUTHOR]

This paper estimates the difference in academic performance of the oldest (treated) and youngest (control) students in a given grade. We employ Queensland Department of Education school administration panel data for the population of 2008–2016 state school students. Academic performance is measured using standardised test scores (National Assessments Program in Numeracy and Literacy) and teacher‐assessed measures of performance and effort for individuals in grades 3, 5 and 7. The empirical analysis employs a regression discontinuity design based on birth date relative to administrative rules on age of school enrolment. The class‐assigning mechanism operates via a known cut‐off date and results in the oldest child in the grade being almost a year older than the youngest. However, lack of enrolment compliance implies the regression discontinuity design is fuzzy rather than sharp, identifying the local average treatment effect rather than the average treatment effect. In particular, given the one‐sided nature of non‐compliance, we employ the partially fuzzy estimator of Battistin and Rettore (Journal of Econometrics, 142, 715–30, 2008). Exploiting a 2008 enrolment cut‐off date reform, which delayed school starting age by 6 months, we find a large increase in enrolment compliance. This exogenous change in the enrolment age enables one to gauge the importance of enrolment non‐compliance in estimating the treatment effect of being older versus younger in cohort. We find that pre‐reform the treatment effect is small and generally statistically insignificant and post‐reform it is sizeable and statistically significant. [ABSTRACT FROM AUTHOR]

We develop a nonparametric Bayesian analysis of regression discontinuity (RD) designs, allowing for covariates, in which we model and estimate the unknown functions of the forcing variable by basis expansion methods. In a departure from current methods, we use the entire data on the forcing variable, but we emphasize the data near the threshold by placing some knots at and near the threshold, a technique we refer to as soft-windowing. To handle the nonequally spaced knots that emerge from soft-windowing, we construct a prior on the spline coefficients, from a second-order Ornstein–Uhlenbeck process, which is hyperparameter light, and satisfies the Kullback–Leibler support property. In the fuzzy RD design, we explain the divergence between the treatment implied by the forcing variable, and the actual intake, by a discrete confounder variable, taking three values, complier, never-taker, and always-taker, and a model with four potential outcomes. Choice of the soft-window, and the number of knots, is determined by marginal likelihoods, computed by the method of Chib [ Journal of the American Statistical Association , 1995, 90, 1313–1321] as a by-product of the Markov chain Monte Carlo (MCMC)-based estimation. Importantly, in each case, we allow for covariates, incorporated nonparametrically by additive natural cubic splines. The potential outcome error distributions are modeled as student- t , with an extension to Dirichlet process mixtures. We derive the large sample posterior consistency, and posterior contraction rate, of the RD average treatment effect (ATE) (in the sharp case) and RD ATE for compliers (in the fuzzy case), as the number of basis parameters increases with sample size. The excellent performance of the methods is documented in simulation experiments, and in an application to educational attainment of women from Meyersson [ Econometrica , 2014, 82, 229–269]. [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.

The proficiency in Mathematics skills is fundamental to success in the STEM disciplines. In the US, beginning college students who are placed in remedial/developmental Mathematics courses frequently struggle to achieve academic success (Fay,2020). Therefore, Mathematics remediation in college has become an important concern, and providing Mathematics remediation is a prevalent way to help the students who may not be fully prepared for college-level courses. Programs vary however, and the effectiveness of a particular remedial Mathematics program must be empirically demonstrated. The purpose of this study was to apply the sharp regression discontinuity (RD) technique to determine the effectiveness of the Jack Leaps Summer (JLS) Mathematic remediation program in supporting improved Mathematics learning outcomes among newly admitted Mathematics students in the South Dakota State University. The researchers studied the newly admitted Fall 2019 cohort of Mathematics majors (n=423). The results indicated that students whose pretest score was lower than the cut-off point and who were assigned to the JLS program, experienced significantly higher scores on the post-test (Math 101 final score). Based on these results, there is evidence that the JLS program is effective in meeting its primary objective.

Abstract Change point detection has many practical applications, from anomaly detection in data to scene changes in robotics; however, finding changes in high dimensional data is an ongoing challenge. We describe a self-training model-agnostic framework to detect changes in arbitrarily complex data. The method consists of two steps. First, it labels data as before or after a candidate change point and trains a classifier to predict these labels. The accuracy of this classifier varies for different candidate change points. By modeling the accuracy change we can infer the true change point and fraction of data affected by the change (a proxy for detection confidence). We demonstrate how our framework can achieve low bias over a wide range of conditions and detect changes in high dimensional, noisy data more accurately than alternative methods. We use the framework to identify changes in real-world data and measure their effects using regression discontinuity designs, thereby uncovering potential natural experiments, such as the effect of pandemic lockdowns on air pollution and the effect of policy changes on performance and persistence in a learning platform. Our method opens new avenues for data-driven discovery due to its flexibility, accuracy and robustness in identifying changes in data.

Forests serve a crucial role in our fight against climate change. Secondary forests provide important potential for conservation of biodiversity and climate change mitigation. In this paper, we explore whether collective property rights in the form of indigenous territories (ITs) lead to higher rates of secondary forest growth in previously deforested areas. We exploit the timing of granting of property rights, the geographic boundaries of ITs and two different methods, regression discontinuity design and differencein-difference, to recover causal estimates. We find strong evidence that indigenous territories with secure tenure not only reduce deforestation inside their lands but also lead to higher secondary forest growth on previously deforested areas. After receiving full property rights, land inside ITs displayed higher secondary forest growth than land outside ITs, with an estimated effect of 5% using our main RDD specification, and 2.21% using our difference-in-difference research design. Furthermore, we estimate that the average age of secondary forests was 2.2 y older inside ITs with secure tenure using our main RDD specification, and 2.8 y older when using our difference-indifference research design. Together, these findings provide evidence for the role that collective property rights can play in the push to restore forest ecosystems. [ABSTRACT FROM AUTHOR]