4,167 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
The Gray Zone
Full text linkThe Gray Zone ∗
Federico Crudu†
University of Siena and CRENoS
Roberta Di Stefano ‡
Sapienza University of Rome
Giovanni Mellace§
University of Southern Denmark
Silvia Tiezzi¶
University of Siena
March 2022
Abstract
On March 23, 2020, in response to the COVID-19 pandemic, Italy declared a nation-
wide lockdown. A month earlier, on February 23, the Italian government ordered its
military police to seal the borders and declared a Red Zone around 10 municipalities
of the province of Lodi and in Vo’ Euganeo, a small town in Padua province. On
the same day, Confindustria Bergamo, the province’s industrial association, posted
a video on social media against having a lockdown in the area of Bergamo and was
supported by key business leaders and local administrators. Despite having a similar
infection rate to the Red Zone municipalities, the government decided not to extend
the Red Zone to the municipalities of Bergamo province with high infection rates.
Bergamo later became one of the deadliest outbreaks of the first wave of the virus
in the Western world. What would have happened had the Red Zone been extended
to that area? We use the Synthetic Control Method to estimate the causal effect of
(not) declaring a Red Zone in the Bergamo area on daily excess mortality. We find
that about two-thirds of the reported deaths could have been avoided had the Italian
government declared the area a Red Zone
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
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
Mediation Analysis Synthetic Control
Full text linkThe synthetic control method (SCM) allows estimating the causal effect of an
intervention in settings where panel data on a small number of treated and
control units are available. We show that the existing SCM, as well as its
extensions, can be easily modified to estimate how much of the ``total'' effect
goes through observed causal channels. Our new mediation analysis synthetic
control (MASC) method requires additional assumptions that are arguably mild in
many settings. We illustrate the implementation of MASC in an empirical
application estimating the direct and indirect effects of an anti-smoking
intervention (California's Proposition 99).Comment: We have benefited from comments by Simone De Angelis and participants
at several seminars, workshops, and conferences. The views expressed in this
paper are those of the authors and do not necessarily reflect those of the
Bank of Italy. Addresses for correspondence: Giovanni Mellace
([email protected]) and Alessandra Pasquini
([email protected]
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
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