Agriregionieuropa Farm level impact of rural development policy: a conditional difference in difference matching approach Salvioni C. 1 and Sciulli D.

Slides:



Advertisements
Similar presentations
Agriregionieuropa A regional analysis of CAP expenditure in Austria Wibke Strahl, Thomas Dax, Gerhard Hovorka Bundesanstalt fuer Bergbauernfragen, Vienna.
Advertisements

Community Strategic Guidelines DG AGRI, October 2005 Rural Development.
Impact analysis and counterfactuals in practise: the case of Structural Funds support for enterprise Gerhard Untiedt GEFRA-Münster,Germany Conference:
Community Strategic Guidelines DG AGRI, July 2005 Rural Development.
How to measure the CMEF R2 Indicator about Gross Value Added in agricultural holdings without reliable accounting data ? A methodological proposal applied.
Biodiversity/HNV indicators and the CAP Zélie Peppiette Rural Development Evaluation Manager DG AGRI, European Commission UK seminar on HNV farming policy,
Measuring the Impact of the RDP Issues being addressed at an EU level with regards to measuring the impact of the Rural Development programmes B. Schuh.
RURAL DEVELOPMENT POLICIES IN BULGARIA Nedka Ivanova UNWE, Sofia, Bulgaria.
Evaluation of the impact of the Natural Forest Protection Programme on rural household incomes Katrina Mullan Department of Land Economy University of.
Propensity Score Matching Lava Timsina Kristina Rabarison CPH Doctoral Seminar Fall 2012.
The World Bank Human Development Network Spanish Impact Evaluation Fund.
BUSINESS AND FINANCIAL LITERACY FOR YOUNG ENTREPRENEURS: EVIDENCE FROM BOSNIA-HERZEGOVINA Miriam Bruhn and Bilal Zia (World Bank, DECFP)
CONCEPTS of VALUE. FACTORS OF VALUE UTILITY –THE ABILITY OF A PRODUCT TO SATISFY HUMAN WANTS. RELATES TO THE DAMAND SIDE OF THE MARKET. SCARCITY –THE.
Agriregionieuropa A CCOUNTING FOR MULTIPLE IMPACTS OF THE C OMMON A GRICULTURAL P OLICIES IN RURAL AREAS : AN ANALYSIS USING A B AYESIAN NETWORKS APPROACH.
Agriregionieuropa A metafrontier approach to measuring technical efficiency The case of UK dairy farms Andrew Barnes*, Cesar Reverado-Giha*, Johannes Sauer+
Agriregionieuropa The CAP and the EU budget Do ex-ante data tell the true? Franco Sotte Università Politecnica delle Marche – Ancona (Italy) 122 nd European.
Agriregionieuropa Methodological and practical solutions for the evaluation of the economic impact of RDP in Latvia M.oec. Armands Veveris Latvian University,
Agriregionieuropa Mapping changes on agricultural and rural areas: an ex-post evaluation of the EU membership for Hungary Monasterolo, I., Pagliacci, F.
Agriregionieuropa An empirical analysis of the determinants of the Rural Development policy spending for Human Capital Beatrice Camaioni 1, Valentina Cristiana.
1 Sebastian Stępień, PhD Poznań University of Economics Department of Macroeconomics and Food Economy The EU Common Agricultural Policy and the interest.
Modeling the efficiency of the agri-environmental payments to Czech agriculture in a CGE framework incorporating public goods approach Zuzana Křístková.
Agriregionieuropa Dynamic adjustments in Dutch greenhouse sector due to environmental regulations Daphne Verreth 1, Grigorios Emvalomatis 1, Frank Bunte.
Agriregionieuropa Assessing the effect of the CAP on farm innovation adoption. An analysis in two French regions Bartolini Fabio 1 ; Latruffe Laure 2,3.
Agriregionieuropa Evaluating the CAP Reform as a multiple treatment effect Evidence from Italian farms Roberto Esposti Department of Economics, Università.
Empirical validity of the evaluation of public policies: models of evaluation and quality of evidence. Marielle BERRIET-SOLLIEC 1, Pierre LABARTHE 2*,
Agriregionieuropa Closing session Few final considerations Giovanni Anania University of Calabria (Italy) & Spera 122 nd European Association of Agricultural.
122 nd EAAE Seminar Ancona 17 – 18 February nd EAAE Seminar Ancona Capturing impacts of Leader and of measures to improve Quality of Life in rural.
Agriregionieuropa A minimum cross entropy model to generate disaggregated agricultural data at the local level António Xavier 1, Maria de Belém Martins.
Agriregionieuropa Exploring the perspectives of a mixed case study approach for the evaluation of the EU Rural Development Policy Ida Terluin.
Agriregionieuropa Evaluating the Improvement of Quality of Life in Rural Areas Cagliero R., Cristiano S., Pierangeli F., Tarangioli S. Istituto Nazionale.
FARMS MULTIFUNCTIONALITY AND HOUSEHOLDS INCOMES IN SUSTAINABLE RURAL DEVELOPMENT Session 4: Income and Employment of the Rural Household By Marco Ballin.
Matching Methods. Matching: Overview  The ideal comparison group is selected such that matches the treatment group using either a comprehensive baseline.
Health Programme Evaluation by Propensity Score Matching: Accounting for Treatment Intensity and Health Externalities with an Application to Brazil (HEDG.
Off-farm labour participation of farmers and spouses Alessandro Corsi University of Turin.
SEDA IMPACT EVALUATION WESTERN CAPE (SOUTH AFRICA) Varsha Harinath (the dti) Francisco Campos (World Bank) Finance and Private Sector Development IE Workshop.
73 rd EAAE Seminar Ancona, June rd EAAE Seminar Ancona, June rd EAAE Ancona, Franco Sotte Dipartimento di Economia Università.
THE IMPACTS OF THE EU SUBSIDIES ON THE PRODUCTION OF ORGANIC FARMS Marie Pechrová Czech University of Life Sciences Prague, Faculty of Ecoomics and Management.
Rural Development Plan for England (RDPE) – improving the environment through agri-environment Rosie Simpson, Natural England.
Page 1 NGUETSE TEGOUM Pierre NAKELSE Tebila OUEDRAOGO Issaka Combining Qualitative and Quantitative Methods in Assessing the Impact of Agro-pastoral Projects.
Agriculture’s Dual Challenge of Delivering Food While Protecting the Environment Tamsin Cooper A Future for a Strong CAP – European Symposium.
1 Improving the competitiveness of the agricultural and forestry sector Imre Wayda Senior counsellor Ministry of Rural Development 27th June 2011.
Deborah Roberts Arkleton Centre for Rural Development Research University of Aberdeen, Scotland Partners: Federal Institute for Less-Favoured and Mountainous.
Beyond surveys: the research frontier moves to the use of administrative data to evaluate R&D grants Oliver Herrmann Ministry of Business, Innovation.
Wageningen International Introduction agri environment measures Pleven Agri environment in the Netherlands Background Natura 2000 and agricultere Common.
AFRICA IMPACT EVALUATION INITIATIVE, AFTRL Africa Program for Education Impact Evaluation David Evans Impact Evaluation Cluster, AFTRL Slides by Paul J.
Applying impact evaluation tools A hypothetical fertilizer project.
What is randomization and how does it solve the causality problem? 2.3.
Spatial impacts and sustainability of farm biogas diffusion in Italy Oriana Gava, Fabio Bartolini and Gianluca Brunori 150th EAAE Seminar ‘The spatial.
Assessing the Impact of CAP Reforms: policy issues and research challenges AgSAP Conference Egmond aan Zee, March 2009 Tassos Haniotis Head of Unit,
Randomized Assignment Difference-in-Differences
Bilal Siddiqi Istanbul, May 12, 2015 Measuring Impact: Non-Experimental Methods.
What can a CIE tell us about the origins of negative treatment effects of a training programme Miroslav Štefánik miroslav.stefanik(at)savba.sk INCLUSIVE.
1 Joint meeting of ESF Evaluation Partnership and DG REGIO Evaluation Network in Gdańsk (Poland) on 8 July 2011 The Use of Counterfactual Impact Evaluation.
Effects of migration and remittances on poverty and inequality A comparison between Burkina Faso, Kenya, Nigeria, Senegal, South Africa, and Uganda Y.
MATCHING Eva Hromádková, Applied Econometrics JEM007, IES Lecture 4.
Do European Social Fund labour market interventions work? Counterfactual evidence from the Czech Republic. Vladimir Kváča, Czech Ministry of Labour and.
The Evaluation Problem Alexander Spermann, University of Freiburg 1 The Fundamental Evaluation Problem and its Solution SS 2009.
Agriregionieuropa associazioneAlessandroBartola studi e ricerche di economia e politica agraria Groupe de Bruges International Conference "The Common Agricultural.
Assessing the Impact of Informality on Wages in Tanzania: Is There a Penalty for Women? Pablo Suárez Robles (University Paris-Est Créteil) 1.
The “Health Check” of the CAP reform: Impact Assessment DG for Agriculture and Rural Development European Commission.
Looking for statistical twins
Directore General for Agriculture and Rural Development
Presentation at the African Economic Conference
Impact evaluation: The quantitative methods with applications
Matching Methods & Propensity Scores
Matching Methods & Propensity Scores
Matching Methods & Propensity Scores
Evaluating Impacts: An Overview of Quantitative Methods
Relevance of GNB for CAP monitoring and evaluation system
The CAP post-2013: statistical needs in the field of rural development
Presentation transcript:

agriregionieuropa Farm level impact of rural development policy: a conditional difference in difference matching approach Salvioni C. 1 and Sciulli D. 2 1 DASTA, University of Chieti-Pescara, Pescara, Italy and SPERA 2 DMQTE, University of Chieti-Pescara, Pescara, Italy and SPERA 122 nd European Association of Agricultural Economists Seminar Evidence-Based Agricultural and Rural Policy Making Methodological and Empirical Challenges of Policy Evaluation February 17 th – 18 th, 2011, Ancona (Italy) associazioneAlessandroBartola studi e ricerche di economia e di politica agraria Centro Studi Sulle Politiche Economiche, Rurali e Ambientali Università Politecnica delle Marche

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy)  Aim of the paper  EU Rural development objectives  Policy evaluation –Assessing the counterfactual: matching methods –Propensity score method –Diff-in-diff method  Data  Results  Work in progress: the LFAs scheme  Future work Plan of presentation

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy)  A key issue in policy evaluation is the establishment of a baseline or counter-factual scenario to determine “additionality”, i.e. the additional net impact that particular policy measures have had on a variable of interest. Propensity Score Mathing (PSM) methods can provide a tool to identify whether significant and causal differences in outcome variables occur between farms receiving and those not receiving a subsidy.  The aim of this paper is to evaluate the impact of the implementation of the first Italian Rural Development Programme ( period). Aim of the paper

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) EU RD policy objectives A menu of measures (22+4) that can be structured around 3 axes aiming at a)improving the competitiveness of agriculture and forestry by encouraging farmers to structural changes (Axis 1); b)improving the environment and the countryside (Axis 2); c)improving the quality of life in rural areas and encouraging diversification of economic activity (Axis 3) General goals of RD policy:  To increase employment and GDP growth (Lisbon Strategy)  Sustainable development (Goteborg Strategy)

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Assessing the counterfactual: matching methods  Quasi-experimental (non-random) method used to construct control groups when it is not possible to obtain participating (“treatment”) and non- participating (“non-treatment”) groups through experimental design. It consists of constructing a comparison group using matching comparisons.  Matching involves identifying non–programme participants comparable in essential characteristics to participants. Both participating and non-participating groups should be matched on the basis of either a few observed characteristics or a number of them that are known or believed to influence both participation and programme outcomes.

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Propensity Score Matching PSM is a tool for identifying a suitable comparison group which can then be compared to the treatment group. Matching is done by using the propensity score, i.e. predicted probability of participation given observed characteristics p(X), which is estimated as a function of individual characteristics based on a statistical model (logit or probit model). This method allows one to find a comparison group from a sample of non-participants closest to the treatment group in terms of observable characteristics.

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Conditional Difference-In-Difference/CDiD Method  CDiD compares a treatment and a comparison group (Heckman et al. 1997) –before (first difference) –after the intervention (second difference). –Conditioning on the propensity score p(X)  The mean difference between the “after” and “before” values of the impact indicators for each of the treatment and comparison groups is calculated. The impact of the programme is the change in the value of the second difference compared to the first difference.  This method can be combined with propensity score method to adjust for pre-treatment differences that affect the parameter in question (e.g. economic growth, employed people).  Differently from PSM, CDID solves the problem of time-invariant unobserved factors

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Data The analysis is based on a panel of more than 3000 Italian family farms drawn from the Italian FADN sample. Sample: stratified according to criteria of geographical region, economic size (ESU) and farming type (FT). Field of observation: commercial farms (economic size greater than 4 ESU - 4,800 euro) 5 waves balanced panel of farms containing only those holdings for which information where collected in all years of the period … Volontary survey (not random sample) Pre-treatment (no RDP payments) Treatment group: 341 farms (13.32%) Control group: 2200 farms outcomes Not available (yet)

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Type VariablesMeanStd. Dev.MeanStd. Dev. Outcomes ( ) D FAWU ** D AWU D UAA * D TAA *** D Added value*/AWU D Profit/FAWU ratio *** Covariates (2003) Age of the operator *** Male operator ** North-West North-East *** Centre * South Islands * Plane * ESU < FT olive FT wine FT field crops *** FT citrus FT livestock *** Environ. protected areas *** Pluriactivity ***

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Horizontal axis: intervals of the propensity score; height of each bar on the vertical axis : fraction of the relevant sample with scores in the corresponding interval. The figure shows that the overlapped region is quite wide, hence it is not needed to eliminate a relevant number of observations. Propensity score histograms by treatment status

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Average Treatment Ettect of the treated (ATT) – RDP

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Results  Not significant impact on AWU: RDP did not produce a direct impact on agricultural employment.  Significant positive impact on FAWU. Family labor substituted to waged labor force – Self-exploitation (accepting returns to owned labour and land lower than the market wage and rent) to cope with external economic pressures and survive economic crisis; – Maximization of farm family income (cope with high unemployment in rural areas – diversification increases on farm labor opportunities).  Significant positive impact on FNVA/AWU and profits/FAWU: RDP did produce a direct positive impact on agricultural and rural GDP.  Not significant impact on farm land, either total and cropped.

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Work in progress: the LFAs scheme

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Average Treatment Effect of the treated – LFA Our findings suggest that the measure promoted the extensivation of farm production, while not negatively affecting farmers’ income. These evidences appear to be encouraging in respect with the use of payments for ecosystem services (PES), that is payments, such as the compensatory allowances paid under the LFAs scheme, to compensate farmers for maintaining or promoting the use of sustainable farming systems in environmentally sensitive areas, in view of preserving farmland landscapes and conserving fragile environments.

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Future work  Update the analysis in order to capture the long standing effect of RD policy.  A further direction of research is to enlarge the set of outcome variables in order to evaluate the environmental impact of measures.  In addition, it would be of interest to disentangle the complementarities between measures, for example LFAs compensatory allowances and agri- environmental measures.

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Thank for your attention