IDEAS home Printed from https://ideas.repec.org/f/pan560.html
   My authors  Follow this author

Monica Andini

Personal Details

First Name:Monica
Middle Name:
Last Name:Andini
Suffix:
RePEc Short-ID:pan560
[This author has chosen not to make the email address public]

Affiliation

Banca d'Italia

Roma, Italy
http://www.bancaditalia.it/
RePEc:edi:bdigvit (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Monica Andini & Michela Boldrini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Andrea Paladini, 2019. "Machine learning in the service of policy targeting: the case of public credit guarantees," Temi di discussione (Economic working papers) 1206, Bank of Italy, Economic Research and International Relations Area.
  2. Monica Andini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Viola Salvestrini, 2017. "Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy," Temi di discussione (Economic working papers) 1158, Bank of Italy, Economic Research and International Relations Area.
  3. Andini, Corrado & Andini, Monica, 2015. "A Note on Unemployment Persistence and Quantile Parameter Heterogeneity," IZA Discussion Papers 8819, Institute of Labor Economics (IZA).
  4. Guido de Blasio & Alberto Dalmazzo & Monica Andini, 2013. "The Size of Political Jurisdictions: Evidence from a Fascist Consolidation," ERSA conference papers ersa13p276, European Regional Science Association.
  5. Monica Andini & Guido de Blasio & Gilles Duranton & William C. Strange, 2013. "Marshallian labor market pooling: evidence from Italy," Temi di discussione (Economic working papers) 922, Bank of Italy, Economic Research and International Relations Area.
  6. Monica Andini & Guido de Blasio, 2013. "Local development that money can�t buy: Italy�s Contratti di Programma," Temi di discussione (Economic working papers) 915, Bank of Italy, Economic Research and International Relations Area.
  7. Monica Andini & Guido de Blasio, 2012. "Local Development that Money Can’t Buy: Italy’s Contratti di Programma," ERSA conference papers ersa12p464, European Regional Science Association.

Articles

  1. Corrado Andini & Monica Andini, 2023. "Mussolini meets Marshall in the city," Regional Studies, Regional Science, Taylor & Francis Journals, vol. 10(1), pages 184-193, December.
  2. Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022. "Machine learning in the service of policy targeting: The case of public credit guarantees," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
  3. Corrado Andini & Monica Andini, 2019. "Social capital and growth: causal evidence from Italian municipalities," Journal of Economic Geography, Oxford University Press, vol. 19(3), pages 619-653.
  4. Andini, Monica & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Salvestrini, Viola, 2018. "Targeting with machine learning: An application to a tax rebate program in Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 86-102.
  5. Andini, Corrado & Andini, Monica, 2018. "Unemployment Persistence And Quantile Parameter Heterogeneity," Macroeconomic Dynamics, Cambridge University Press, vol. 22(5), pages 1298-1320, July.
  6. Andini, Monica & Dalmazzo, Alberto & de Blasio, Guido, 2017. "The size of political jurisdictions: A model with some evidence from a fascist consolidation," Journal of Comparative Economics, Elsevier, vol. 45(4), pages 889-909.
  7. Monica Andini & Guido de Blasio, 2016. "Local development that money cannot buy: Italy’s Contratti di Programma," Journal of Economic Geography, Oxford University Press, vol. 16(2), pages 365-393.
  8. Andini, Monica & Andini, Corrado, 2014. "Finance, growth and quantile parameter heterogeneity," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 308-322.
  9. Andini, Monica & de Blasio, Guido & Duranton, Gilles & Strange, William C., 2013. "Marshallian labour market pooling: Evidence from Italy," Regional Science and Urban Economics, Elsevier, vol. 43(6), pages 1008-1022.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Monica Andini & Michela Boldrini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Andrea Paladini, 2019. "Machine learning in the service of policy targeting: the case of public credit guarantees," Temi di discussione (Economic working papers) 1206, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Emanuele Ciani & Marco Gallo & Zeno Rotondi, 2020. "Public credit guarantee and financial additionalities across SME risk classes," Temi di discussione (Economic working papers) 1265, Bank of Italy, Economic Research and International Relations Area.
    2. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    3. Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.
    4. Borrotti, Matteo & Rabasco, Michele & Santoro, Alessandro, 2023. "Using accounting information to predict aggressive tax location decisions by European groups," Economic Systems, Elsevier, vol. 47(3).
    5. Michele Rabasco & Pietro Battiston, 2023. "Predicting the deterrence effect of tax audits. A machine learning approach," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 531-556, July.

  2. Monica Andini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Viola Salvestrini, 2017. "Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy," Temi di discussione (Economic working papers) 1158, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Aiello, Francesco & Albanese, Giuseppe & Piselli, Paolo, 2019. "Good value for public money? The case of R&D policy," Journal of Policy Modeling, Elsevier, vol. 41(6), pages 1057-1076.
    2. Andreas Joseph, 2019. "Parametric inference with universal function approximators," Papers 1903.04209, arXiv.org, revised Oct 2020.
    3. Concetta Rondinelli & Roberta Zizza, 2020. "Spend today or spend tomorrow? The role of inflation expectations in consumer behaviour," Temi di discussione (Economic working papers) 1276, Bank of Italy, Economic Research and International Relations Area.
    4. Michele Rabasco & Pietro Battiston, 2023. "Predicting the deterrence effect of tax audits. A machine learning approach," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 531-556, July.

  3. Andini, Corrado & Andini, Monica, 2015. "A Note on Unemployment Persistence and Quantile Parameter Heterogeneity," IZA Discussion Papers 8819, Institute of Labor Economics (IZA).

    Cited by:

    1. Guglielmo Maria Caporale & Luis A. Gil-Alana & Pablo Vicente Trejo, 2021. "Unemployment Persistence in Europe: Evidence from the 27 EU Countries," CESifo Working Paper Series 9392, CESifo.

  4. Monica Andini & Guido de Blasio & Gilles Duranton & William C. Strange, 2013. "Marshallian labor market pooling: evidence from Italy," Temi di discussione (Economic working papers) 922, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Combes, Pierre-Philippe & Gobillon, Laurent, 2015. "The Empirics of Agglomeration Economies," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 247-348, Elsevier.
    2. Tomoya Mori, 2017. "Agglomeration," KIER Working Papers 960, Kyoto University, Institute of Economic Research.
    3. Dauth, Wolfgang & Findeisen, Sebastian & Moretti, Enrico & Suedekum, Jens, 2019. "Matching in Cities," IZA Discussion Papers 12278, Institute of Labor Economics (IZA).
    4. Zidong Yu & Jinyan Zu & Yang Xu & Yimin Chen & Xintao Liu, 2022. "Spatial and functional organizations of industrial agglomerations in China’s Greater Bay Area," Environment and Planning B, , vol. 49(7), pages 1995-2010, September.
    5. Yudai Higashi, 2020. "Urbanization Effects on Job Search Decision," Discussion Paper Series DP2020-26, Research Institute for Economics & Business Administration, Kobe University, revised Sep 2022.
    6. Lutgen, Vanessa & Van der Linden, Bruno, 2013. "Regional Equilibrium Unemployment Theory at the Age of the Internet," IZA Discussion Papers 7763, Institute of Labor Economics (IZA).
    7. Brunello, Giorgio & Langella, Monica, 2016. "Local agglomeration, entrepreneurship and the 2008 recession: Evidence from Italian industrial districts," Regional Science and Urban Economics, Elsevier, vol. 58(C), pages 104-114.
    8. Antonio Accetturo & Andrea Lamorgese & Sauro Mocetti & Paolo Sestito, 2019. "Local Development, Urban Economies and Aggregate Growth," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 5(2), pages 191-204, July.
    9. tobias König & thomas Brenner, 2022. "Skill shortages and industry clusters – Empirical evidence from German establishment data," IAW Discussion Papers 140, Institut für Angewandte Wirtschaftsforschung (IAW).
    10. Mäkinen, Taneli & Ohl, Björn, 2012. "Information Acquisition and Learning from Prices Over the Business Cycle," SSE/EFI Working Paper Series in Economics and Finance 740, Stockholm School of Economics, revised 19 Mar 2013.
    11. Michele Leonardo Bianchi & Svetlozar T. Rachev & Frank J. Fabozzi, 2018. "Calibrating the Italian Smile with Time-Varying Volatility and Heavy-Tailed Models," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 339-378, March.
    12. Yudai Higashi, 2021. "Agglomeration Effects on Job Matching Efficiency: Evidence from Japan," Discussion Paper Series DP2021-03, Research Institute for Economics & Business Administration, Kobe University.
    13. Berlingieri, Francesco, 2015. "Local labour market size and qualification mismatch," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113102, Verein für Socialpolitik / German Economic Association.
    14. David Morris & Enrico Vanino & Carlo Corradini, 2020. "Effect of regional skill gaps and skill shortages on firm productivity," Environment and Planning A, , vol. 52(5), pages 933-952, August.
    15. Stockinger, Bastian & Zwick, Thomas, 2017. "Apprentice poaching in regional labor markets," IAB-Discussion Paper 201708, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    16. Marte C.W. Solheim & Ron Boschma & Sverre Herstad, 2018. "Related variety, unrelated variety and the novelty content of firm innovation in urban and non-urban locations," Papers in Evolutionary Economic Geography (PEEG) 1836, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Oct 2018.
    17. Monica Andini & Guido de Blasio & Gilles Duranton & William Strange, 2012. "Marshallian Labor Market Pooling: Evidence from Italy," ERSA conference papers ersa12p467, European Regional Science Association.
    18. Basile, Roberto & Donati, Cristiana & Pittiglio, Rosanna, 2013. "Industry structure and employment growth: evidence from semiparametric geoadditive models," MPRA Paper 47621, University Library of Munich, Germany.
    19. Jianglin Lu & Keqiang Wang & Hongmei Liu, 2023. "Land Development Rights, Spatial Injustice, and the Economic Development in Net-Incremental Reduction Regions of Construction Land: Evidence from Shanghai, China," IJERPH, MDPI, vol. 20(3), pages 1-25, January.
    20. Berlingieri, Francesco, 2017. "Local labor market size and qualification mismatch," ZEW Discussion Papers 17-055, ZEW - Leibniz Centre for European Economic Research.
    21. Irene Brunetti & Valerio Intraligi & Andrea Ricci & Valeria Cirillo, 2020. "Low‐skill jobs and routine tasks specialization: New insights from Italian provinces," Papers in Regional Science, Wiley Blackwell, vol. 99(6), pages 1561-1581, December.
    22. Roberto Basile & Cristiana Donati & Rosanna Pittiglio & Maria Savarese, 2015. "Dinamiche dell?occupazione e struttura produttiva locale in Italia," SCIENZE REGIONALI, FrancoAngeli Editore, vol. 2015(2), pages 33-68.
    23. Wei Zhao & Chao Zhu & Yaoyao Zhu, 2022. "The Comparative Advantage of Cities and Innovation Value Chain: Evidence from China," Sustainability, MDPI, vol. 14(6), pages 1-14, March.
    24. Abel, Jaison R. & Deitz, Richard, 2015. "Agglomeration and job matching among college graduates," Regional Science and Urban Economics, Elsevier, vol. 51(C), pages 14-24.
    25. Yang, Guangliang & Li, Lixing & Fu, Shihe, 2017. "Do Rural Migrants Benefit from Labor Market Agglomeration Economies? Evidence from Chinese Cities," MPRA Paper 80713, University Library of Munich, Germany.
    26. Solheim, Marte C.W. & Boschma, Ron & Herstad, Sverre J., 2020. "Collected worker experiences and the novelty content of innovation," Research Policy, Elsevier, vol. 49(1).
    27. Taelim Choi, 2020. "Agglomeration Effect of Skill-Based Local Labor Pooling: Evidence of South Korea," Sustainability, MDPI, vol. 12(8), pages 1-15, April.
    28. Mathieu Sanch-Maritan & Lionel Vedrine, 2019. "Are larger labor market more resilient? Evidence of the French army restructuring on exit from unemployment," Working Papers hal-02332809, HAL.
    29. Ivaldi, Marc & Quinet, Emile & Ruiz Mejia, Celia, 2022. "Agglomeration Transport and Productivity: Evidence from Toulouse Metropolitan Area," TSE Working Papers 22-1385, Toulouse School of Economics (TSE).
    30. KIKUCHI Shinnosuke & Daniel G. O'CONNOR, 2024. "The Granular Origins of Agglomeration," Discussion papers 24005, Research Institute of Economy, Trade and Industry (RIETI).
    31. Eliasson, Kent & Westerlund, Olle, 2019. "Graduate migration, self-selection and urban wage premiums across the regional hierarchy," Umeå Economic Studies 962, Umeå University, Department of Economics.
    32. Burger, Peter & Baumohl, Eduard & Vyrostova, Eva, 2017. "Funding Structure of the European and North American Clusters: Results from an Independent Questionnaire," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 65(6), pages 485-504.
    33. Rudiger Ahrend & Alexander Lembcke & Abel Schumann, 2017. "The Role of Urban Agglomerations for Economic and Productivity Growth," International Productivity Monitor, Centre for the Study of Living Standards, vol. 32, pages 161-179, Spring.
    34. Gaigné, Carl & Sanch-Maritan, Mathieu, 2019. "City size and the risk of being unemployed. Job pooling vs. job competition," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 222-238.

  5. Monica Andini & Guido de Blasio, 2013. "Local development that money can�t buy: Italy�s Contratti di Programma," Temi di discussione (Economic working papers) 915, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Barone, Guglielmo & Narciso, Gaia, 2015. "Organized crime and business subsidies: Where does the money go?," Journal of Urban Economics, Elsevier, vol. 86(C), pages 98-110.

  6. Monica Andini & Guido de Blasio, 2012. "Local Development that Money Can’t Buy: Italy’s Contratti di Programma," ERSA conference papers ersa12p464, European Regional Science Association.

    Cited by:

    1. Mara Giua, 2014. "Spatial Discontinuity for the Impact Assessment of the EU Regional Policy. The Case of Italian Objective 1 Regions," Departmental Working Papers of Economics - University 'Roma Tre' 0197, Department of Economics - University Roma Tre.

Articles

  1. Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022. "Machine learning in the service of policy targeting: The case of public credit guarantees," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
    See citations under working paper version above.
  2. Corrado Andini & Monica Andini, 2019. "Social capital and growth: causal evidence from Italian municipalities," Journal of Economic Geography, Oxford University Press, vol. 19(3), pages 619-653.

    Cited by:

    1. Mauro Caselli & Paolo Falco, 2019. "Your vote is (no) secret! How low voter density harms voter anonymity and biases elections in Italy," DEM Working Papers 2019/8, Department of Economics and Management.
    2. Mauro, Luciano & Pigliaru, Francesco & Carmeci, Gaetano, 2023. "Decentralization, social capital, and regional growth: The case of the Italian North-South divide," European Journal of Political Economy, Elsevier, vol. 78(C).
    3. Roberto Ganau & Andrés Rodríguez‐Pose, 2023. "Firm‐level productivity growth returns of social capital: Evidence from Western Europe," Journal of Regional Science, Wiley Blackwell, vol. 63(3), pages 529-551, June.
    4. Diemer, Andreas, 2023. "Divided we fall? The effect of manufacturing decline on the social capital of US communities," LSE Research Online Documents on Economics 120355, London School of Economics and Political Science, LSE Library.
    5. Xue, Xindong & Reed, W. Robert & Menclova, Andrea, 2020. "Social capital and health: a meta-analysis," Journal of Health Economics, Elsevier, vol. 72(C).
    6. Xindong Xue & W. Robert Reed & Robbie C.M. van Aert, 2022. "Social Capital and Economic Growth: A Meta-Analysis," Working Papers in Economics 22/20, University of Canterbury, Department of Economics and Finance.
    7. Pilatin Abdulmuttalip & Hacıimamoğlu Tunahan, 2023. "The relationship between social capital and economic growth on a provincial and regional basis," Economics and Business Review, Sciendo, vol. 9(3), pages 153-180, October.
    8. Roberto Ganau & Andres Rodriguez-Pose, 2023. "Firm-level productivity growth returns of social capital: Evidence from Western Europe," Papers in Evolutionary Economic Geography (PEEG) 2305, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Feb 2023.

  3. Andini, Monica & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Salvestrini, Viola, 2018. "Targeting with machine learning: An application to a tax rebate program in Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 86-102.

    Cited by:

    1. Luca Villamaina & Paolo Acciari, 2023. "Taxation and labour supply decisions: an evaluation of the earned income tax credit in Italy," Working Papers wp2023-20, Ministry of Economy and Finance, Department of Finance.
    2. Michael Allan Ribers & Hannes Ullrich, 2020. "Machine Predictions and Human Decisions with Variation in Payoffs and Skills," Discussion Papers of DIW Berlin 1911, DIW Berlin, German Institute for Economic Research.
    3. Altındağ, Onur & O'Connell, Stephen D. & Şaşmaz, Aytuğ & Balcıoğlu, Zeynep & Cadoni, Paola & Jerneck, Matilda & Foong, Aimee Kunze, 2021. "Targeting humanitarian aid using administrative data: Model design and validation," Journal of Development Economics, Elsevier, vol. 148(C).
    4. Michael J. Weir & Thomas W. Sproul, 2019. "Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment," Sustainability, MDPI, vol. 11(14), pages 1-21, July.
    5. Ginevra Buratti & Alessio D'Ignazio, 2023. "Improving the effectiveness of financial education programs. A targeting approach," Questioni di Economia e Finanza (Occasional Papers) 765, Bank of Italy, Economic Research and International Relations Area.
    6. Caravaggio, Nicola & Resce, Giuliano, 2023. "Enhancing Healthcare Cost Forecasting: A Machine Learning Model for Resource Allocation in Heterogeneous Regions," Economics & Statistics Discussion Papers esdp23090, University of Molise, Department of Economics.
    7. Michael A. Ribers & Hannes Ullrich, 2019. "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Discussion Papers of DIW Berlin 1803, DIW Berlin, German Institute for Economic Research.
    8. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
    9. Resce, Giuliano & Vaquero-Pineiro, Cristina, 2022. "Predicting Agri-food Quality across Space: A Machine Learning Model for the Acknowledgment of Geographical Indications," Economics & Statistics Discussion Papers esdp22082, University of Molise, Department of Economics.
    10. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    11. Ignacio González García & Alfonso Mateos, 2021. "Use of Social Network Analysis for Tax Control in Spain," Hacienda Pública Española / Review of Public Economics, IEF, vol. 239(4), pages 159-197, November.
    12. Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2020. "Local mortality estimates during the COVID-19 pandemic in Italy," Working Papers 14/20, Sapienza University of Rome, DISS.
    13. Garbero, Alessandra & Sakos, Grayson & Cerulli, Giovanni, 2023. "Towards data-driven project design: Providing optimal treatment rules for development projects," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    14. Cerqua, Augusto & Letta, Marco, 2022. "Local inequalities of the COVID-19 crisis," Regional Science and Urban Economics, Elsevier, vol. 92(C).
    15. Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2021. "A Machine Learning Approach to Analyze and Support Anti-Corruption Policy," CESifo Working Paper Series 9015, CESifo.
    16. Radermacher, Jan W., 2023. "Mamma Mia! Revealing hidden heterogeneity by PCA-biplot: MPC puzzle for Italy's elderly poor," SAFE Working Paper Series 382, Leibniz Institute for Financial Research SAFE.
    17. Michael Allan Ribers & Hannes Ullrich, 2023. "Machine learning and physician prescribing: a path to reduced antibiotic use," Berlin School of Economics Discussion Papers 0019, Berlin School of Economics.
    18. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
    19. Hannes Ullrich & Michael Allan Ribers, 2023. "Machine predictions and human decisions with variation in payoffs and skill: the case of antibiotic prescribing," Berlin School of Economics Discussion Papers 0027, Berlin School of Economics.
    20. Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.
    21. Lucchetti, Riccardo & Pedini, Luca & Pigini, Claudia, 2022. "No such thing as the perfect match: Bayesian Model Averaging for treatment evaluation," Economic Modelling, Elsevier, vol. 107(C).
    22. Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022. "Machine learning in the service of policy targeting: The case of public credit guarantees," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
    23. Dutt, Satyajit & Radermacher, Jan W., 2023. "Age, wealth, and the MPC in Europe: A supervised machine learning approach," SAFE Working Paper Series 383, Leibniz Institute for Financial Research SAFE.
    24. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," CESifo Working Paper Series 9037, CESifo.
    25. Kadriye Hilal Topal & Ebru Çağlayan Akay, 2020. "Hanehalkı Tüketim Harcamalarının Mikroekonometrik Analizi: LAD-LASSO Yöntemi," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(33), pages 13-31, December.
    26. Strittmatter, Anthony, 2023. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?," Labour Economics, Elsevier, vol. 84(C).
    27. Di Stefano, Roberta & Resce, Giuliano, "undated". "The Determinants of Missed Funding: Predicting the Paradox of Increased Need and Reduced Allocation," Economics & Statistics Discussion Papers esdp23092, University of Molise, Department of Economics.
    28. Cerqua, Augusto & Letta, Marco, 2020. "Local economies amidst the COVID-19 crisis in Italy: a tale of diverging trajectories," MPRA Paper 104404, University Library of Munich, Germany.
    29. Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.

  4. Andini, Corrado & Andini, Monica, 2018. "Unemployment Persistence And Quantile Parameter Heterogeneity," Macroeconomic Dynamics, Cambridge University Press, vol. 22(5), pages 1298-1320, July.

    Cited by:

    1. Galina Besstremyannaya & Sergei Golovan, 2019. "Reconsideration of a simple approach to quantile regression for panel data: a comment on the Canay (2011) fixed effects estimator," Working Papers w0249, Center for Economic and Financial Research (CEFIR).
    2. Saša Obradoviæ & Lela Ristiæ & Nemanja Lojanica, 2018. "Are unemployment rates stationary for SEE10 countries? Evidence from linear and nonlinear dynamics," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 36(2), pages 559-583.
    3. Galina Besstremyannaya & Sergei Golovan, 2019. "Reconsideration of a simple approach to quantile regression for panel data: a comment on the Canay (2011) fixed effects estimator," Working Papers w0249, New Economic School (NES).

  5. Andini, Monica & Dalmazzo, Alberto & de Blasio, Guido, 2017. "The size of political jurisdictions: A model with some evidence from a fascist consolidation," Journal of Comparative Economics, Elsevier, vol. 45(4), pages 889-909.

    Cited by:

    1. Dalmazzo, Alberto & de Blasio, Guido & Poy, Samuele, 2018. "Local secessions, homophily, and growth. A model with some evidence from the regions of Abruzzo and Molise (Italy, 1963)," Journal of Economic Behavior & Organization, Elsevier, vol. 151(C), pages 284-306.
    2. Blesse, Sebastian & Rösel, Felix, 2017. "Was bringen kommunale Gebietsreformen? Kausale Evidenz zu Hoffnungen, Risiken und alternativen Instrumenten," ZEW Discussion Papers 17-049, ZEW - Leibniz Centre for European Economic Research.
    3. Antonio Accetturo & Michele Cascarano & Guido de Blasio, 2019. "Pirate Attacks and the Shape of the Italian Urban System," DEM Working Papers 2019/15, Department of Economics and Management.
    4. Carlo Ciccarelli & Alberto Dalmazzo & Daniela Vuri, 2021. "Home Sweet Home: the Effect of Sugar Protectionism on Emigration in Italy, 1876‐1913," Papers in Regional Science, Wiley Blackwell, vol. 100(4), pages 925-957, August.
    5. Giulio Cainelli & Carlo Ciccarelli & Roberto Ganau, 2021. "Administrative reforms, urban hierarchy, and local population growth. Lessons from Italian unification," Papers in Evolutionary Economic Geography (PEEG) 2109, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Feb 2021.

  6. Monica Andini & Guido de Blasio, 2016. "Local development that money cannot buy: Italy’s Contratti di Programma," Journal of Economic Geography, Oxford University Press, vol. 16(2), pages 365-393.

    Cited by:

    1. Cerqua, Augusto & Pellegrini, Guido, 2018. "Local multipliers at work," MPRA Paper 85326, University Library of Munich, Germany.
    2. Albanese, Giuseppe & Ciani, Emanuele & de Blasio, Guido, 2021. "Anything new in town? The local effects of urban regeneration policies in Italy," Regional Science and Urban Economics, Elsevier, vol. 86(C).
    3. Emanuele Ciani & Francesco David & Guido de Blasio, 2017. "Local labour market heterogeneity in Italy: estimates and simulations using responses to labour demand shocks," Temi di discussione (Economic working papers) 1112, Bank of Italy, Economic Research and International Relations Area.
    4. Giuseppe Albanese & Guido de Blasio & Andrea Locatelli, 2019. "Place-based Policy and Local TFP," Temi di discussione (Economic working papers) 1253, Bank of Italy, Economic Research and International Relations Area.
    5. Augusto Cerqua & Guido Pellegrini, 2020. "Labor mobility effects of a firm-level shock," Working Papers 1/20, Sapienza University of Rome, DISS.
    6. Ciani, Emanuele & David, Francesco & de Blasio, Guido, 2019. "Local responses to labor demand shocks: A Re-assessment of the case of Italy," Regional Science and Urban Economics, Elsevier, vol. 75(C), pages 1-21.
    7. Annalisa Caloffi, 2017. "System-based, light and complex: industrial and local development policies in the thought of Giacomo Becattini," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 44(4), pages 473-480, December.
    8. Guido De Blasio & Emanuele Ciani, 2015. "European Structural Funds During the Crisis: Evidence from Southern Italy," ERSA conference papers ersa15p435, European Regional Science Association.
    9. Giuseppe Albanese & Guglielmo Barone & Guido DeBlasio, 2021. "The impact of place-based policies on income distribution," Discussion Paper series in Regional Science & Economic Geography 2021-04, Gran Sasso Science Institute, Social Sciences, revised Mar 2021.
    10. Di Stefano, Roberta & Resce, Giuliano, "undated". "The Determinants of Missed Funding: Predicting the Paradox of Increased Need and Reduced Allocation," Economics & Statistics Discussion Papers esdp23092, University of Molise, Department of Economics.
    11. Cerqua, Augusto & Pellegrini, Guido, 2022. "Decomposing the employment effects of investment subsidies," Journal of Urban Economics, Elsevier, vol. 128(C).
    12. Antonio Accetturo & Giuseppe Albanese & Alessio D'Ignazio, 2020. "A new phoenix? Large plants regeneration policies in Italy," Journal of Regional Science, Wiley Blackwell, vol. 60(5), pages 878-902, November.

  7. Andini, Monica & Andini, Corrado, 2014. "Finance, growth and quantile parameter heterogeneity," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 308-322.

    Cited by:

    1. Jenq-Tzong Shiau & Jia-Wei Lin, 2016. "Clustering Quantile Regression-Based Drought Trends in Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1053-1069, February.
    2. Spyridon Boikos & Theodore Panagiotidis & Georgios Voucharas, 2021. "Financial Development, Reforms and Growth," Bank of Lithuania Working Paper Series 98, Bank of Lithuania.
    3. George S. Atsalakis & Elie Bouri & Fotios Pasiouras, 2021. "Natural disasters and economic growth: a quantile on quantile approach," Annals of Operations Research, Springer, vol. 306(1), pages 83-109, November.
    4. Durusu-Ciftci, Dilek & Ispir, M. Serdar & Yetkiner, Hakan, 2017. "Financial development and economic growth: Some theory and more evidence," Journal of Policy Modeling, Elsevier, vol. 39(2), pages 290-306.
    5. Bitar, Mohammad & Pukthuanthong, Kuntara & Walker, Thomas, 2020. "Efficiency in Islamic vs. conventional banking: The role of capital and liquidity," Global Finance Journal, Elsevier, vol. 46(C).
    6. Andini, Corrado & Andini, Monica, 2015. "A Note on Unemployment Persistence and Quantile Parameter Heterogeneity," IZA Discussion Papers 8819, Institute of Labor Economics (IZA).
    7. Agne Setikiene & Mindaugas Butkus, 2021. "The Heterogeneous Impact of Financialisation on Economic Growth in the Long Run," JRFM, MDPI, vol. 14(5), pages 1-30, May.
    8. Neil Lee & Davide Luca, 2019. "The big-city bias in access to finance: evidence from firm perceptions in almost 100 countries," Journal of Economic Geography, Oxford University Press, vol. 19(1), pages 199-224.
    9. Mekki Hamdaoui & Samir Maktouf, 2019. "Overall effects of financial liberalization: financial crisis versus economic growth," International Review of Applied Economics, Taylor & Francis Journals, vol. 33(4), pages 568-595, July.
    10. Aida Caldera Sánchez & Oliver Röhn, 2016. "How do policies influence GDP tail risks?," OECD Economics Department Working Papers 1339, OECD Publishing.
    11. Galina Besstremyannaya & Sergei Golovan, 2019. "Reconsideration of a simple approach to quantile regression for panel data: a comment on the Canay (2011) fixed effects estimator," Working Papers w0249, Center for Economic and Financial Research (CEFIR).
    12. Xue, Xinhong & Wang, Zhongcheng, 2021. "Impact of finance pressure on energy intensity: Evidence from China’s manufacturing sectors," Energy, Elsevier, vol. 226(C).
    13. Nahid Farnaz, 2023. "Does Financial Development Relieve or Exacerbate Income Inequality? A Quantile Regression Approach," Economics Discussion Paper Series 2311, Economics, The University of Manchester.
    14. Galina Besstremyannaya & Sergei Golovan, 2019. "Reconsideration of a simple approach to quantile regression for panel data: a comment on the Canay (2011) fixed effects estimator," Working Papers w0249, New Economic School (NES).

  8. Andini, Monica & de Blasio, Guido & Duranton, Gilles & Strange, William C., 2013. "Marshallian labour market pooling: Evidence from Italy," Regional Science and Urban Economics, Elsevier, vol. 43(6), pages 1008-1022.
    See citations under working paper version above.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 5 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-BIG: Big Data (2) 2018-01-08 2019-02-18
  2. NEP-CMP: Computational Economics (2) 2018-01-08 2019-02-18
  3. NEP-LAB: Labour Economics (2) 2013-07-20 2015-02-22
  4. NEP-URE: Urban and Real Estate Economics (2) 2013-06-16 2013-07-20
  5. NEP-EUR: Microeconomic European Issues (1) 2013-07-20
  6. NEP-LMA: Labor Markets - Supply, Demand, and Wages (1) 2013-07-20
  7. NEP-PPM: Project, Program and Portfolio Management (1) 2013-06-16

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Monica Andini should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.