agriregionieuropa Assessing the effect of the CAP on farm innovation adoption. An analysis in two French regions Bartolini Fabio 1 ; Latruffe Laure 2,3 ; Viaggi Davide 1 1 Alma mater studiorum - University of Bologna, Department of Agricultural Economics and Engineering, Italy 2 INRA, UMR1302 SMART, F Rennes, France 3 Agrocampus Ouest, UMR1302 SMART, F Rennes, France 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)  Background  Objective  Methodology  Data used  Results  Conclusions Outline

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Background  New technology adoption and innovation diffusion are two elements of the firm development and growth process  Literature on innovation adoption mechanism has emphasised – the positive effect of the Single Farm Payments (SFP) and Rural Development Payments on the adoption of new technologies – the role of innovation attitude and past innovation adoptions are determinants of the future innovation adoptions

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Objective  To analyse the process of future innovation adoptions and to identify the innovation adoption determinants with focus on – farmers past innovation adoption behaviour – effects of agricultural policy in the promotion the innovation adoption at the farm level

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Methodology Developed in two consequential steps:  Cluster Analysis – Identification of homogenous groups of farmers based on different innovation behaviour using data obtained from past adoptions (past 10 years)  Econometric analysis (Zero Inflated Poisson model) – Analysis of the determinants of future innovation adoption under two different policy scenarios (next 10 years) Scenario 1: Baseline (current 2009 CAP) Scenario 2: NO-CAP (complete abolishment of CAP after 2013) – Dependent variable: sum of the stated innovation adoptions

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Data used (1) Overview  Questionnaire used to collect information of both past innovation adoptions and stated intention about future innovation adoptions on the same farm  Face to face questionnaire  Questionnaire address: – Farm, farmer and household characteristics – Information about the past innovation adopted; source of information used to collect information about innovation adopted; – Stated intentions under policy scenarios Farm strategy (exit, growth, changes in the factor use etc) stated intention about future innovation adoptions  Questions about the stated intentions are repeated for the two policy scenarios  Questionnaire addressed to 295 farmers in two regions in France – 140 respondents in Centre CSA – 155 respondents in Midi-Pyrénées CSA

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Data used(2) Past innovations (used for clustering)  Innovation adoptions (past 10 years) and the timing of adoption of a category of innovation (modified from Sunding and Zilberman 2001) – “Which innovation have you adopted in your farm in the last 10 years. Please specify the year of adoptions and who provided the information about the innovation)” Farming systems innovations Mechanical innovations Biological innovations Agronomic innovations Chemical innovations Biotechnology innovations Marketing innovations Processing innovations

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Data used(3) Future innovations (used ZIP model)  “intention to adopt the following category of innovation in the next 10 years (YES/NO)” Robotisation/precision farming New irrigation systems or input reducing new technology E-commerce/direct selling or other innovation in commercialisation of the farmer’s production Energy crops or production of energy by the farm through solar panel, wind or biogas etc Other innovation, a category let “blank” for adding other innovations that surveyed farmers could intend to adopt in the next years  Sum of the future innovation adoptions (next 10 years)

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Results(1) Cluster analysis  clusters identified following the Rogers (1995) innovation attitude categories  Variables used for the clustering: – number of past innovations adopted – timing of adoption – age of the farm owner ClusterCluster description Farmers (#) Age (average) Innovations adopted last 10 years (#) Innovations adopted last 5 years (#) Innovation adopted last 3 years (#) CL1 Laggards and young CL2 Innovators and young CL3 Innovators and old CL4Laggards and old CL5Late majority

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Results(2) Future innovations (dependent variable)  Sum of stated innovation adopted for both policy scenarios (Innovation intensity) – (0= no innovation >>>> 5 all innovations)  Two separated ZIP models Number of innovation adopted (#) BASELINE NO –CAP scenario (31.2)(38.86) (35.6)(32.64) (18)(18.13) (11.6)(7.25) 4 74 (2.8)(2.07) 5 22 (0.8)(1.04) Total (100)

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Results(3) ZIP models Variable (Description) Parameter estimated under the baseline scenario (Model 1) Parameter estimated under the NO-CAP scenario (Model 2) Innovators and old (dummy) Laggards and old (dummy) Late majority (dummy) Information collected only directly by the farmer (dummy) Share of farm income from agricultural activity in total household income (%) Household lives on the farm (dummy) Educational level lower than secondary school (dummy) External labour used on farm (# of full time equivalents) UAA (ha) Farm type mixed crop livestock (dummy) Legal status: partnership (dummy) Plain (dummy) Hill (dummy) ZERO INFLATED OUTCOME (Logit) Household labour + external labour used on farm (# of full time equivalents) Age of respondent (Ln of age_y) Midi-Pyrénées region (dummy) Share of farm income from agricultural activity in total household income (%) Sources used to collected information about past innovations (#) Late majority (dummy) Laggards and old (dummy) NB variables not significant at 0.10 are omitted

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Conclusions  Preliminary results confirm that the process of innovation adoption does not follow breakthrough, and are not discontinuous, etc,  The storyline about past innovation, the number of past innovations adopted and the timing of adoption, are significant explanatory variables of the new technology adopting process  Results highlight that the CAP strongly affects the innovations – the CAP abolishment increases the exit (also those farmers who state intention to innovate under the baseline scenario) – the CAP abolishment reduces the access to any innovation for those farmers who could be grouped in the category of laggards or late adopters – in a scenario without CAP, the information and the source of information collected strongly affect the innovation adoption  Need to better targeting policy instruments aimed to encouraging innovation adoption or diffusion through financial incentive of innovation  Need of specific instrument aimed to promoting innovation through a development of a system of consultancy specific for the innovations

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