if this message does not display correctly, click here | Table of Contents Leonardo Becchetti, University of Rome Tor Vergata - Faculty of Economics
Sara Mancini, University of Rome Tor Vergata - Department of Economics and Finance
Sara Savastano, University of Rome Tor Vergata - Faculty of Economics, International Fund for Agricultural Development (IFAD), World Bank Riccardo Di Francesco, University of Rome Tor Vergata, Department of Economics and Finance, Students Aleksey Kolokolov, University of Manchester - Manchester Business School
Giulia Livieri, Scuola Normale Superiore
Davide Pirino, Department of Economics and Finance, University of Rome "Tor Vergata" | |
CEIS: CENTRE FOR ECONOMIC & INTERNATIONAL STUDIES
Furio Camillo Rosati - Director "Food Security during the COVID-19 Pandemic: the Impact of a Rural Development Program and Neighbourhood Spillover Effect in the Solomon Islands" 
CEIS Working Paper No. 545 LEONARDO BECCHETTI, University of Rome Tor Vergata - Faculty of Economics
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SARA MANCINI, University of Rome Tor Vergata - Department of Economics and Finance
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SARA SAVASTANO, University of Rome Tor Vergata - Faculty of Economics, International Fund for Agricultural Development (IFAD), World Bank
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We examine differences in food security indicators between (rural development program) treated and untreated farmers in the Solomon Islands in the COVID-19 post-treatment period. Our findings show that treated farmers report significantly lower nutrition problems in the pandemic period. We as well consider that the project in its components (building local infrastructure, transmitting knowledge and competences and providing links and easier access to business partners) can produce positive spillovers in terms of externalities to control farmers in proportion to their geographical proximity to treatment farmers. Our findings are consistent with this hypothesis since the majority of treatment nutrition score outcomes are enhanced when controlling for spillover effects. "Aggregation Trees"
CEIS Working Paper No. 546 RICCARDO DI FRANCESCO, University of Rome Tor Vergata, Department of Economics and Finance, Students In this paper, I propose a data-driven approach to discover heterogeneous subpopulations in a selection-on-observables framework that avoids the risk of data snooping and the drawbacks of pre-analysis plans. The approach constructs partitions of the population in a completely nonparametric fashion and can handle covariate spaces of arbitrary dimensions and arbitrary patterns of interaction among covariates. I exploit estimated unit-level treatment effects to grow and prune an 'aggregation tree' that aggregates observations into groups. This approach formalizes the trade-off between parsimony and granularity implicit in the aggregation process. By varying the key parameter of the assumed cost-complexity criterion, a sequence of 'optimal' partitions is generated, one for each level of granularity. The resulting sequence is nested, as previous groupings are never undone when moving to coarser levels. I illustrate the use of the proposed methodology through an empirical exercise that revisits the effects of maternal smoking on infants’ weight. "Testing for Endogeneity of Irregular Sampling Schemes"
CEIS Working Paper No. 547 ALEKSEY KOLOKOLOV, University of Manchester - Manchester Business School
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GIULIA LIVIERI, Scuola Normale Superiore
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DAVIDE PIRINO, Department of Economics and Finance, University of Rome "Tor Vergata"
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In the context of high-frequency financial data it is often assumed that sampling times are exogenous. This entails that financial asset prices, sampled on a grid of trade instants, are independent from the sampling times. We derive statistical tests capable of determining whether or not, and to what extent, this hypothesis is rejected by the data. We test for sampling time endogeneity in relation to both the efficient and the noise components of the observed price. Using a vast dataset of financial asset prices we give empirical evidence that the efficient component of the observed price process does not show a dependence with trade arrival times of the kind that may jeopardize well-known results on convergence of power variations. In addition, we provide empirical evidence that the assumption of independence between market microstructure noise and trading instants is not supported by the data. | | ^top
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