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The continuous expansion of China’s cities has led to a divergence in economics, population, and public service levels among different districts within the city. This has led to different housing prices, due to the resulting impact on housing supply and demand. Previous studies, although taking into account the possible differences in housing prices among different districts, have not focused on the extent to which districts affect housing prices. This study analyzes the housing price boundary effects among different districts in Hangzhou, China, using spatial discontinuity regression methods and data on newly built housing transactions from 2010 to 2021. This study also examines the impact of the integration policy, which acts to integrate suburban counties with the main urban area of Hangzhou, and whether that policy decreases the district boundary effect. The results show that the administrative boundary effect of housing prices in Hangzhou is significant, with most districts experiencing a house price boundary effect exceeding 10%. Encouraging regional integration policies effectively reduces the housing price gap that results from internal administrative divisions within the city.

We aimed to compare the findings of three research designs to bracket effect estimates of a strongly worded warning letter delivered by certified mail to students on academic probation. We embedded an experiment within a regression discontinuity design and calculated two achievement estimates, average GPA and percentage of students remaining on probation. Study participants attended a large Midwestern college. Cohen's d experimental effect size was .45. Regression discontinuity design results were validated by our experimental evidence, and outcome measures were generally statistically significant. We provided additional supportive evidence using comparative RD control group design logic. Regression point displacement design results were successfully replicated using a within-study comparison inside the experiment. In the context of probation, a diverse design, replicative approach provided considerable promise for more precise estimation of intervention effectiveness. We found no deleterious impact on reenrollment and concluded that the certified letter represents an inexpensive probation policy.

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

Even though there are many quasi-experimental research in recent literature, there is still no consensus on whether an increase in school funding improves student achievement. Leveraging a natural experiment in South Korea, this study exploits the discontinuity in school funding rules to identify the impact of increased funding on the test scores of high-school students in a national assessment exam. The setting provides a useful context to study the effect of school funding because students typically attend largely similar schools that follow a standardized curriculum, thus eliminating the possibility of the results being contaminated by idiosyncratic variation in school-level characteristics. This study reports mean regression discontinuity estimates as well as quantile regression discontinuity estimates using a procedure suggested by Frandsen et al. (J Econom 168:382–395, 2012). The findings reveal that an increase in school funding, which is equal to approximately 300,000 won per student, results in improved exam performance, particularly in mathematics. Contrary to the stated purpose of the program, however, the evidence suggests that students in the middle and top of the ability distribution gained the most from the intervention, rather than students who are at the highest risk of failing.

In this paper, we determine treatment effects when the treatment assignment is based on two or more cut-off points of covariates rather than on one cut-off point of one assignment variable. using methods that are referred to as multivariate regression discontinuity designs (MRDD). One major finding of this paper is the discovery of new evidence that both matric points and household income have a huge impact on the probability of eligibility for funding from the National Student Financial Aid Scheme (NSFAS) to study for a bachelor’s degree program at universities in South Africa. This evidence will inform policymakers and educational practitioners on the effects of matric points and household income on the eligibility for NSFAS funding. The availability of the NSFAS grant impacts greatly students’ decisions to attend university or seek other opportunities elsewhere. Using the frontier MRDD analytical results, barely scoring matric points greater than or equal to 25 points compared to scoring matric points less than 25 for students whose household income is less than R350,000 (≈US$2500) increases the probability of eligibility for NSFAS funding by a significant 3.75 ( p-value = 0.0001 < 0.05) percentage points. Therefore, we have shown that the frontier MRDD can be employed to determine the causal effects of barely meeting the requirements of one assignment variable, among the subjects that either meet or fail to meet the requirements of the other assignment variable.

It is common in regression discontinuity analysis to control for third, fourth, or higher-degree polynomials of the forcing variable. There appears to be a perception that such methods are theoretically justified, even though they can lead to evidently nonsensical results. We argue that controlling for global high-order polynomials in regression discontinuity analysis is a flawed approach with three major problems: it leads to noisy estimates, sensitivity to the degree of the polynomial, and poor coverage of confidence intervals. We recommend researchers instead use estimators based on local linear or quadratic polynomials or other smooth functions.

The majority of Swiss children attend mandatory and cost-free kindergarten at age four. We examine the effect of this policy on maternal labour market outcomes. Using administrative data from Switzerland, we exploit the birthday cut-off for kindergarten entry in the same or in the following year and apply a non-parametric regression discontinuity design (RDD). We find that mandatory kindergarten has a statistically significant positive effect on the labour market attachment of previously non-employed mothers, increasing their employment probability by 4 percentage points. In contrast, there are no significant effects on other groups or in the total sample of mothers.

In this paper we explore the effect of the long-gone Habsburg Military Frontier on modern institutions in Croatia. We use the Life in Transition Survey and geographic regression discontinuity design to identify the causal mechanism between historical institutions and attitudes towards trust and corruption. We find that the areas of the former Military Frontier are underdeveloped and poorer with worse economic performance indicators. Our results suggest that respondents living in the former Military Frontier territory have lower levels of interpersonal trust, a higher level of trust in public authorities, but also tend to bribe those institutions more often when they interact with them. We claim that the war in Yugoslavia in the 1990s is not just a confounding factor in the analysis but also a potential channel and find evidence that attitudes towards bribery can survive even harsh wars, while trust in public institutions collapses during extreme events of violence.

In the regression-discontinuity (RD) design, units are assigned to treatment based on whether their value of an observed covariate exceeds a known cutoff. In this design, local polynomial estimators are now routinely employed to construct confidence intervals for treatment effects. The performance of these confidence intervals in applications, however, may be seriously hampered by their sensitivity to the specific bandwidth employed. Available bandwidth selectors typically yield a \"large\" bandwidth, leading to data-driven confidence intervals that may be biased, with empirical coverage well below their nominal target. We propose new theory-based, more robust confidence interval estimators for average treatment effects at the cutoff in sharp RD, sharp kink RD, fuzzy RD, and fuzzy kink RD designs. Our proposed confidence intervals are constructed using a bias-corrected RD estimator together with a novel standard error estimator. For practical implementation, we discuss mean squared error optimal bandwidths, which are by construction not valid for conventional confidence intervals but are valid with our robust approach, and consistent standard error estimators based on our new variance formulas. In a special case of practical interest, our procedure amounts to running a quadratic instead of a linear local regression. More generally, our results give a formal justification to simple inference procedures based on increasing the order of the local polynomial estimator employed. We find in a simulation study that our confidence intervals exhibit close-to-correct empirical coverage and good empirical interval length on average, remarkably improving upon the alternatives available in the literature. All results are readily available in R and STATA using our companion software packages described in Calonico, Cattaneo, and Titiunik (2014d, 2014b).

A classic thesis is that scientific achievement exhibits a “Matthew effect”: Scientists who have previously been successful are more likely to succeed again, producing increasing distinction. We investigate to what extent the Matthew effect drives the allocation of research funds. To this end, we assembled a dataset containing all review scores and funding decisions of grant proposals submitted by recent PhDs in a €2 billion granting program. Analyses of review scores reveal that early funding success introduces a growing rift, with winners just above the funding threshold accumulating more than twice as much research funding (€180,000) during the following eight years as nonwinners just below it. We find no evidence that winners’ improved funding chances in subsequent competitions are due to achievements enabled by the preceding grant, which suggests that early funding itself is an asset for acquiring later funding. Surprisingly, however, the emergent funding gap is partly created by applicants, who, after failing to win one grant, apply for another grant less often.