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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.

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.

We consider nonparametric identification and estimation in a nonseparable model where a continuous regressor of interest is a known, deterministic, but kinked function of an observed assignment variable. We characterize a broad class of models in which a sharp \"Regression Kink Design\" (RKD or RK Design) identifies a readily interpretable treatment-on-the-treated parameter (Florens, Heckman, Meghir, and Vytlaèil (2008)). We also introduce a \"fuzzy regression kink design\" generalization that allows for omitted variables in the assignment rule, noncompliance, and certain types of measurement errors in the observed values of the assignment variable and the policy variable. Our identifying assumptions give rise to testable restrictions on the distributions of the assignment variable and predetermined covariates around the kink point, similar to the restrictions delivered by Lee (2008) for the regression discontinuity design. Using a kink in the unemployment benefit formula, we apply a fuzzy RKD to empirically estimate the effect of benefit rates on unemployment durations in Austria.

We examine the impact of the cash transfer programme on food-seeking behaviour among ultra-poor segments of society. Food-seeking behaviour includes per adult’s daily calorie intakes, food diversity, stable availability of food, and a composite index of food security. The empirical analysis is based on three rounds of panel household surveys (2011, 2013, and 2016) using the regression discontinuity design (RDD). The results have shown that BISP beneficiaries, relative to non-beneficiaries, have a higher level of calorie intakes. The cash transfer helps them diversify their food basket with stable food availability and improved food security level in both short and long-run periods. Moreover, BISP cash transfer increases access to quality food groups such as meat, fish, and fruits in the long run. These beneficial influences of the cash transfer reveal much stronger long-run impacts as compared to short-run effects. The findings of this paper provide helpful policy insights related to the importance of the cash transfer programme. The BISP cash transfer appears to be an effective social assistance programme that holds sustainable long-run effects on ensuring household food and dietary requirements through incomeand substitution effects.

Recent empirical work in several economic fields, particularly environmental and energy economics, has adapted the regression discontinuity (RD) framework to applications where time is the running variable and treatment begins at a particular threshold in time. In this guide for practitioners, we discuss several features of this regression discontinuity in time framework that differ from the more standard cross-sectional RD framework. First, many applications (particularly in environmental economics) lack cross-sectional variation and are estimated using observations far from the temporal threshold. This common empirical practice is hard to square with the assumptions of a cross-sectional RD, which is conceptualized for an estimation bandwidth shrinking even as the sample size increases. Second, estimates may be biased if the time-series properties of the data are ignored (for instance, in the presence of an autoregressive process), or more generally if short-run and long-run effects differ. Finally, tests for sorting or bunching near the threshold are often irrelevant, making the framework closer to an event study than a regression discontinuity design. Based on these features and motivated by hypothetical examples using air quality data, we offer suggestions for the empirical researcher wishing to use the RD in time framework.

We investigate whether the event of women being competitively elected as state legislators encourages subsequent political participation among women. Using a regression discontinuity design on Indian constituency level data, we find that female incumbents are more likely than male incumbents to re-contest and that there is a decline in the entry of new women candidates. This decline is most pronounced in states with entrenched gender bias and in male-headed parties, suggesting an intensification of barriers against women in these areas. Similar results for (mostly male) Muslim candidates indicate the presence of institutionalised demand-side barriers rather than gender-specific preferences and constraints.

Corporate social responsibility (CSR) reports are important carriers of enterprises non-financial information disclosure, which are inextricably related to the production efficiency and performance of enterprises. The objective of this paper is discovering the causal effect of the CSR mandatory disclosure policy and the total factor productivity (TFP) of enterprises. This paper uses the sharp regression discontinuity design based on the micro data of the enterprises to study the impact by taking China's mandatory disclosure policy in 2008 as a quasi-natural experiment. This paper makes some contribution to the impact of mandatory CSR disclosure on enterprise TFP and the mechanism and heterogeneity of this impact. The research draws the following conclusions: First, the CSR mandatory disclosure can significantly improve the TFP of enterprises on the whole, and this effect has the characteristics of long-term and dynamic decline. Second, the mechanism of mandatory disclosure of CSR on TFP is through the mediating effect of R&D and innovation expenditures. Third, the heterogeneity of the impact of CSR mandatory disclosure on TFP is reflected in two aspects: industry and equity nature differences. These conclusions are strongly correlated with the contingent decision-making behaviour of enterprises and give some ideas to the policy makers.

We investigate the choice of the bandwidth for the regression discontinuity estimator. We focus on estimation by local linear regression, which was shown to have attractive properties (Porter, J. 2003, \"Estimation in the Regression Discontinuity Model\" (unpublished, Department of Economics, University of Wisconsin, Madison)). We derive the asymptotically optimal bandwidth under squared error loss. This optimal bandwidth depends on unknown functionals of the distribution of the data and we propose simple and consistent estimators for these functionals to obtain a fully data-driven bandwidth algorithm. We show that this bandwidth estimator is optimal according to the criterion of Li (1987, \"Asymptotic Optimality for C p , C L , Cross-validation and Generalized Cross-validation: Discrete Index Set\", Annals of Statistics, 15, 958–975), although it is not unique in the sense that alternative consistent estimators for the unknown functionals would lead to bandwidth estimators with the same optimality properties. We illustrate the proposed bandwidth, and the sensitivity to the choices made in our algorithm, by applying the methods to a data set previously analysed by Lee (2008, \"Randomized Experiments from Non-random Selection in U.S. House Elections\", Journal of Econometrics, 142, 675–697) as well as by conducting a small simulation study.

In the United States, 42% of public infrastructure projects report delays or cost overruns. To mitigate this problem, regulators scrutinize project operations. We study the effect of oversight on delays and overruns with 262,857 projects spanning 71 federal agencies and 54,739 contractors. We identify our results using a federal bylaw: if the project’s budget is above a cutoff, procurement officers actively oversee the contractor’s operations; otherwise, most operational checks are waived. We find that oversight increases delays by 6.1%–13.8% and overruns by 1.4%–1.6%. We also show that oversight is most obstructive when the contractor has no experience in public projects, is paid with a fixed-fee contract with performance-based incentives, or performs a labor-intensive task. Oversight is least obstructive—or even beneficial—when the contractor is experienced, paid with a time-and-materials contract, or conducts a machine-intensive task. This paper was accepted by Serguei Netessine, operations management.

In the regression discontinuity design (RDD), it is common practice to assess the credibility of the design by testing whether the means of baseline covariates do not change at the cut-off (or threshold) of the running variable. This practice is partly motivated by the stronger implication derived by Lee (2008), who showed that under certain conditions the distribution of baseline covariates in the RDD must be continuous at the cut-off. We propose a permutation test based on the so-called induced ordered statistics for the null hypothesis of continuity of the distribution of baseline covariates at the cut-off; and introduce a novel asymptotic framework to analyse its properties. The asymptotic framework is intended to approximate a small sample phenomenon: even though the total number n of observations may be large, the number of effective observations local to the cut-off is often small. Thus, while traditional asymptotics in RDD require a growing number of observations local to the cut-off as n → ∞, our framework keeps the number q of observations local to the cut-off fixed as n → ∞. The new test is easy to implement, asymptotically valid under weak conditions, exhibits finite sample validity under stronger conditions than those needed for its asymptotic validity, and has favourable power properties relative to tests based on means. In a simulation study, we find that the new test controls size remarkably well across designs. We then use our test to evaluate the plausibility of the design in Lee (2008), a well-known application of the RDD to study incumbency advantage.