31pgs, This article aims at investigating the impact of financial supports from agricultural policy on farm-size dynamics. Since not all farms may behave alike, a non-stationary mixed-Markov chain modelling (M-MCM) approach is applied to capture unobserved heterogeneity in the movements of farms across economic size (ES) classes. A multinomial logit specification is used for transition probabilities and the parameters are estimated by the maximum likelihood method and the Expectation-Maximisation (EM) algorithm. An empirical application to an unbalanced panel from 2000 to 2018 shows that French farming consists of ‘almost stayers’, with a high probability of remaining in the same ES class over time, and ‘likely movers’, which present a higher probability of a change in size. The results also show that the impact of subsidies and other economic factors depends greatly on the type that a farm belongs to. These findings confirm that individual characteristics of farmers may be relevant for policy efficiency and more attention should thus be paid to unobserved farm heterogeneity in both policy design and the assessment of their impacts on farm-size dynamics.
Yu Jin (author), Huffman, Wallace E. (author), and Department of Economics, Shanghai University of Finance and Economics
Department of Economics, Iowa State University
Format:
Journal article
Publication Date:
2016
Published:
Wiley Periodicals, Inc.
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 16 Document Number: D10455
17 pages., Via online journal., This article provides new estimates of the marginal product of public agricultural research and extension on state agricultural productivity for the U.S., using updated data and definitions, and forecasts of future agricultural productivity growth by state. The underlying rationale for a number of important decisions that underlie the data used in cost‐return estimates for public agricultural research and extension are presented. The parameters of the state productivity model are estimated from a panel of contiguous U.S. 48 states from 1970 to 2004. Public research and extension are shown to be substitutes rather than complements. The econometric model of state agricultural TFP predicts growth rates of TFP for two‐thirds of states that is less than the past trend rate. The results and data indicate a real social rate of return to public investments in agricultural research of 67% and to agricultural extension of 100+%. The article concludes with guidance for TFP analyses in other countries.
25pgs, We combine farm accounting data with high-resolution meteorological data, and climate scenarios to estimate climate change impacts and adaptation potentials at the farm level. To do so, we adapt the seminal model of Moore and Lobell (2014) who applied panel data econometrics to data aggregated from the farm to the regional (subnational) level. We discuss and empirically investigate the advantages and challenges of applying such models to farm-level data, including issues of endogeneity of explanatory variables, heterogeneity of farm responses to weather shocks, measurement errors in meteorological variables, and aggregation bias. Empirical investigations into these issues reveal that endogeneity due to measurement errors in temperature and precipitation variables, as well as heterogeneous responses of farms toward climate change may be problematic. Moreover, depending on how data are aggregated, results differ substantially compared to farm-level analysis. Based on data from Austria and two climate scenarios (Effective Measures and High Emission) for 2040, we estimate that the profits of farms will decline, on average, by 4.4% (Effective Measures) and 10% (High Emission). Adaptation options help to considerably ameliorate the adverse situation under both scenarios. Our results reinforce the need for mitigation and adaptation to climate change.
