Valuing Ecosystem Services for Agricultural TFP: A Review of Best Practices, Challenges, and Recommendations

This paper provides a brief overview of methods to incorporate ecosystem service values into measures of agricultural total factor productivity (TFP), both in theory and in practice. This includes a review of the academic literature, a summary of related economic index theory, and a comparison of agency guidelines. We consider areas of consensus between the agencies and the research literature, as well as open debates surrounding the implementation of a standardized ecosystem accounting framework to integrate with existing TFP measures. This helps to bridge the gap between theoretical approaches to measurement and valuation in the research literature and their implementation in practice by national accounting agencies. Better connecting theory to practice also serves to highlight common challenges in the field, including questions of definition, scope, and scale for ecosystem services, as well as data collection and dissemination. We end with a summary of recommendations for moving forward

Assessing the Productivity Consequences of Agri-Environmental Practices When Adoption Is Endogenous

We address the general problem of selection bias, endemic to analyzing the effects of any policy where adoption is voluntary, with empirical application to environmental policies for agriculture. Many voluntary practices for mitigating the environmental impacts of agriculture provide external benefits while lowering productivity. Policy analysis of the productivity consequences is complicated by the fact that decision-makers can choose their own policy levers, an action that ruins any notion of random assignment. We introduce an identification strategy to correct this kind of endogeneity, combining classic methods from stochastic frontier analysis and selection models. Applying it to micro-level data from Finnish grain farms, we find that more efficient producers are more likely to enroll in subsidized practices. And, because these practices tend to reduce yield, frontier analysis without the endogeneity correction greatly understates productivity losses. In other words, naively basing the frontier estimator on the subset of less productive farms leads to downward bias in the resulting frontier estimates. In fact, average inefficiency more than doubles after the correction in this case. An outlier investigation also suggests that the lowest decile of farms are responsible for most of the selection bias in the uncorrected model.nonPeerReviewe

Assessing the productivity consequences of agri-environmental practices when adoption is endogenous

We address the general problem of selection bias, an issue endemic to policy analysis when adoption is voluntary, with an empirical application to environmental policies for agriculture. Many voluntary practices for mitigating the environmental impacts of agriculture provide external benefits while lowering productivity. Policy analysis of the productivity consequences is complicated by the fact that decision makers can choose their own policy levers, an action that ruins any notion of random assignment. We introduce an identification strategy to correct this kind of endogeneity, combining classic methods from stochastic frontier analysis and selection models. Applying it to micro-level data from Finnish grain farms, we find that more efficient producers are more likely to enroll in subsidized practices. And, because those practices tend to reduce yield, frontier analysis without the endogeneity correction greatly understates the productivity loss. In other words, naïvely basing the frontier estimator on the subset of less-productive farms leads to downward bias in the frontier estimates. In fact, average inefficiency more than doubles after the correction in this case. An outlier investigation suggests that the lowest decile of farms are responsible for most of the selection bias in the uncorrected model.peerReviewe

Production effects of wetland conservation: evidence from France

This study takes a production function approach to examine the effects of farm wetland area for a set of producers in the Limousin region of France. By combining data from a recent survey of regional wetland areas with detailed farm-level production panel data, we find that maintaining wetland areas poses significant costs to farmers, in terms of foregone production value. These results help to explain the relatively low participation rate in agri-environmental schemes targeted to wetlands by farmers in this region. This represents a new application of the production function approach to estimate the cost of maintaining wetlands on working agricultural land, and is one of few studies to examine agricultural wetland costs outside of the US. This framework could be used to further inform payment incentives for agrienvironmental schemes more generally.La productivité des zones humides agricole est étudiée par la spécification d’une fonction de production, estimée sur un ensemble de producteurs agricoles de la Région française du Limousin. L’analyse des données de panel rassemblant les comptabilités d’une centaine d’exploitation suivies pendant trois ans montre que le maintien agricole des zones humides implique des coûts significatifs en termes de pertes de production. Ces résultats aident à comprendre la relative faiblesse de l’adoption par les agriculteurs des mesures agroenvironnementales ciblant les zones humides de cette région. Ce travail est une nouvelle application de l’approche par la fonction de production pour estimer le coût de maintien des zones humides, et l’une des rares applications sur ce thème, hors des Etats-Unis. La méthodologie peut être utilisée pour l’établissement de paiements incitatifs dans le cadre de programmes agri-environnementaux ou pour des services environnementaux en général

Production effects of wetland conservation: evidence from France

This study takes a production function approach to examine the effects of farm wetland area for a set of producers in the Limousin region of France. By combining data from a recent survey of regional wetland areas with detailed farm-level production panel data, we find that maintaining wetland areas poses significant costs to farmers, in terms of foregone production value. These results help to explain the relatively low participation rate in agri-environmental schemes targeted to wetlands by farmers in this region. This represents a new application of the production function approach to estimate the cost of maintaining wetlands on working agricultural land, and is one of few studies to examine agricultural wetland costs outside of the US. This framework could be used to further inform payment incentives for agri-environmental schemes more generally

Reconstructing Nonparametric Productivity Networks

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown