1,721,013 research outputs found
Replication Data for: "Why Do Tougher Caseworkers Increase Employment? The Role of Programme Assignment as a Causal Mechanism"
No full textHuber, Martin, Lechner, Michael, and Mellace, Giovanni, (2017) "Why Do Tougher Caseworkers Increase Employment? The Role of Programme Assignment as a Causal Mechanism." Review of Economics and Statistics 99:1, 180-183
Replication Data for: "Why Do Tougher Caseworkers Increase Employment? The Role of Programme Assignment as a Causal Mechanism"
No full textHuber, Martin, Lechner, Michael, and Mellace, Giovanni, (2017) "Why Do Tougher Caseworkers Increase Employment? The Role of Programme Assignment as a Causal Mechanism." Review of Economics and Statistics 99:1, 180-183
Replication data for: Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints
No full textHuber, Martin, and Mellace, Giovanni, (2015) "Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints." Review of Economics and Statistics 97:2, 398-411
Replication data for: Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints
No full textHuber, Martin, and Mellace, Giovanni, (2015) "Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints." Review of Economics and Statistics 97:2, 398-411
Principal Stratification in Sample Selection Problems with Non Normal Error Terms
Full text linkThe aim of the paper is to relax distributional assumptions on the error terms, often imposed in parametric sample selection models to estimate causal effects, when plausible exclusion restrictions are not available. Within the principal stratification framework, we approximate the true distribution of the error terms with a mixture of Gaussian. We propose an EM type algorithm for ML estimation. In a simulation study we show that our estimator has lower MSE than the ML and two-step Heckman estimators with any non normal distribution considered for the error terms. Finally we provide an application to the Job Corps training program
Inference in instrumental variable models with heteroskedasticity and many instruments
Full text linkThis paper proposes novel inference procedures for instrumental variable models in the presence of many, potentially weak instruments that are robust to the presence of heteroskedasticity. First, we provide an Anderson–Rubin-type test for the entire parameter vector that is valid under assumptions weaker than previously proposed Anderson–Rubin-type tests. Second, we consider the case of testing a subset of parameters under the assumption that a consistent estimator for the parameters not under test exists. We show that under the null, the proposed statistics have Gaussian limiting distributions and derive alternative chi-square approximations. An extensive simulation study shows the competitive finite sample properties in terms of size and power of our procedures. Finally, we provide an empirical application using college proximity instruments to estimate the returns to education
The short-run effects of public incentives for innovation in Italy
No full textInvesting in innovation is considered as a crucial step for economic growth of a country; yet there is little
consensus in the literature on the short-run effectiveness of tax benefits for innovative firms. For this reason, this
study evaluates an Italian public program introduced in 2012 aimed at fostering young innovative firms. A
discontinuity in the eligibility rules generates a quasi-experimental design, which allows us to estimate the causal
effects of the policy. The results show an increase in the number of partners, and therefore generation of new
investors, but no significant effects on firms’ share of intangible assets, turnover, or number of employees. These
developments are driven by a generous tax benefit offered by the policy to investors with no strict requirements.
We conclude that a policy that links tax cuts to actual investments in innovation is necessary to achieve the policy
maker’s target
The gray zone: How not imposing a strict lockdown at the beginning of a pandemic can cost many lives
Full text linkThe public debate on the effectiveness of lockdown measures is far from being settled. We estimate the impact of not having implemented a strict lockdown in the Bergamo province, during the first wave of the COVID-19 pandemic, despite observing an infection rate in this area similar to the one observed in nearby municipalities where a strict lockdown was instead promptly implemented. We estimate the causal effect of this policy decision on daily excess mortality using the synthetic control method (SCM). We find that about two-thirds of the reported deaths could have been avoided had the Italian government declared a Red Zone in the Bergamo province. We also clarify that, in this context, SCM and difference-in-differences implicitly restrict effect heterogeneity. We provide a way to empirically assess the credibility of this assumption in our setting
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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