​Department of Economics Management and Statistics

Analysis Unit: organization Universe: Active joint-stock companies in Italy during the period 1883-1913 Sample Procedure: complete sample Weight: No weight used Collection Mode: content coding

We analyse the impact of a junior farmer field school project in Northern Uganda on students’ agricultural knowledge and practices. We also test for the presence of intergenerational learning spillover within households. We use differences-in-differences estimators with ex-ante matching and find evidence that the programme had positive effects on students’ agricultural knowledge and adoption of good practices. The project also produced spillover effects in terms of improvements of household agricultural knowledge and food security. Overall, our results point to the importance of adapting the basic principles of farmer field schools to children.

BayesANT is a package for the taxonomic classification of DNA sequences. It trains a taxonomic classifier on a dataset of DNA barcodes and returns probabilistic predictions for query DNA sequences. BayesANT explicitly accounts for potential taxonomic novelty of the query sequences by relying on Bayesian nonparametric species sampling priors to model the taxonomic tree.

This folder contains the data and the R code to reproduce the figures and tables in the paper Zito, Rigon and Dunson (2022) - "Inferring Taxonomic placement from DNA barcoding aiding in discovery of new taxa", accepted as open access publication in Methods in Ecology and Evolution. The file "main_FinBOL.R" reproduces the tables in the main document and in the Supporting information available online for the analysis of the FinBOL data, while "main_Simulation_Section4_SI.R" reproduces the simulation in Section 4 of the Supporting information. All data are saved in the folder "data". For replicability purposes, we added version 2.13 of the RDP classifier to the repository, in the folder "RDP/java". This has been downloaded from https://sourceforge.net/projects/rdp-classifier/. For questions, contact the author at alessandro.zito@duke.edu

Cultural amenities are the expression of a cultural environment, given by a combination of aesthetic factors, styles, rhythms and behaviours, which contribute to make a neighbourhood vibrant and more enjoyable. Following the hedonic approach, we propose an empirical strategy to capture the multiple effects of cultural amenities, as well as the effects produced by green areas, public transport and university proximity. The results are used to determine whether cultural amenities are optimally provided by the municipality of Milan, Italy. It emerged that investments in culture generate positive effects to society, and that governments should devote far more resources to culture.

This dataset contains the data from the a set of laboratory experiments with human subjects run at the University of Milano Bicocca. The experiment was programmed using zTree (Fischbacher, 2007). Our experimental design involved four sessions of each of our two treatments. As we have 27 subjects per session, we have collected data from a total of 216 subjects, from October 2014 to January 2015. Subjects were recruited from the undergraduate population of the University of Milano-Bicocca, via the ORSEE software (Greiner, 2004). No subject participated in more than one session of this experiment. In December 2015 we have run two additional sessions per treatment where the order of the games played by the subject was reversed.

Data for "Convergence of European natural gas prices"

Stata data set

We present a dataset for fuel sales analysis at the Italian provincial (NUTS3) level from January 2015 to October 2023 (release V3, January 2024). Fuel sales data are collected at monthly frequency, and are organized by fuel type, usage, and point of sale (highway, municipal road, extra-network road). Fuels data are augmented by a set of socio-economic and geographical variables, which help explain the impact of economic phenomena and topography on fuel sales. The data is collected from the Monthly oil Bullettin of Italian Ministry of Environment and Energy Security (MITE), ISTAT (Istituto Nazionale di Statistica), Bank of Italy and Eurostat, and has been collected through both automated web scraping and manual downloads, then cleaned and reshaped to be suitable for analysis. The produced dataset may be useful for spatiotemporal fuel sales forecasting, air quality analysis, urban mobility, econometric research, as well as machine learning applications. To further assist the user in finding valuable insight, an R Shiny app (freely available at the webpage https://ale-ch.shinyapps.io/it-fuel-dashboard/) was developed for data exploration. App code and the data have been made fully available on the following Github repository (https://github.com/ale-ch/it-fuel-dashboard). The app consists of interactive plots that allow the user to visualize every variable in the dataset at different time ranges and locations, allowing full flexibility in data exploration.

The AgrImOnIA dataset is a comprehensive dataset relating air quality and livestock (expressed as the density of bovines and swine bred) along with weather and other variables. The AgrImOnIA Dataset represents the first step of the AgrImOnIA project. The purpose of this data set is to give the opportunity to assess the impact of agriculture on air quality in Lombardy through statistical techniques capable of highlighting the relationship between the livestock sector and air pollutants concentrations. This dataset is a collection of estimated daily values for a range of measurements of different dimensions as: air quality, meteorology, emissions, livestock animals and land use. Data are related to Lombardy and the surrounding area for 2016-2021, inclusive. The surrounding area is obtained by applying a 0.3° buffer on Lombardy borders. The data uses several aggregation and interpolation methods to estimate the measurement for all days. For more details see the paper: A. Fassò, J. Rodeschini, A. Fusta Moro, Q. Shaboviq, P. Maranzano, M. Cameletti, F. Finazzi, N. Golini, R. Ignaccolo, and P. Otto (2022) Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy. Arxiv preprint, arxiv:2210.10604. (click here). The files in the folder are: Agrimonia_Dataset.csv(.Rdata,.mat) which is built by joining the daily time series related to the AQ, WE, EM, LI and LA variables. In order to simplify access to variables in the Agrimonia dataset, the variable name starts with the dimension of the variable, i.e., the name of the variables related to the AQ dimension start with 'AQ_'. This file is archived also in the and format for MATLAB and R software, respectively. Metadata_Agrimonia.csv which provides further information for the sources used, variables imported, transformations applied, and about the Agrimonia variables. Metadata_AQ_imputation_uncertainty.csv which contains the daily uncertainty estimate of the imputed observation for the AQ to mitigate missing data in the hourly time series. Metadata_LA_CORINE_labels.csv which contains the label and the description associated with the CLC class. Metadata_monitoring_network_registry.csv which contains all details about the AQ monitoring station used to build the dataset. Information about pollutant stations includes: station type, municipality code, environment type, altitude, pollutants sampled and other information. Each row represents a single sensor. Metadata_LA_SIARL_labels.csv which contains the label and the description associated with the SIARL class. The dataset can be reproduced using the code available at the GitHub page: https://github.com/AgrImOnIA-project/AgrImOnIA_Data