The effect of sequentiality and heterogeneity in network formation games

In the benchmark model of Bala and Goyal (2000) on network formation, the equilibrium network is asymmetric and unfair as agents have different payoffs. While they are prominent in reality, asymmetric networks do not emerge in the lab mainly because of fairness concerns. We extend this model with a sequential linking decision process to ease coordination and with heterogeneous agents. Heterogeneity is introduced with the presence of a special agent who has either a higher monetary value or a different status. The equilibrium is asymmetric and unfair. Our experimental results show that thanks to sequentiality and fairness concerns, individuals coordinate on fair and efficient networks in homogeneous settings. Heterogeneity impacts the network formation process by increasing the asymmetry of networks but does not decrease the level of fairness nor efficiency

On the sequential formation of networks

The network formation model described in this research paper is based on the connection model of Bala and Goyal. We extend this model by introducing two properties: heterogeneity of players and sequentiality of the network formation process. These two characteristics modify the emergence of equilibrium and efficient networks. Our main theoretical finding is that sequentiality is the main determinant of the equilibrium network while heterogeneity is secondary. The equilibrium often requires that the last player of the game incurs a large part of the linking costs. On the contrary, the leader does not create links at the equilibrium. In the following of the paper we build an experimental protocol. Our purpose is to analyse whether our model predicts be-haviour in the laboratory and whether individuals are more attracted by the equilibrium or by efficiency.Le modèle de formation de réseau décrit dans ce document de recherche est basé sur le modèle de connexion de Bala et Goyal. Nous étendons ce modèle en introduisant deux propriétés : l'hétérogénéité des acteurs et la séquentialité du processus de formation du réseau. Ces deux caractéristiques modifient l'émergence de réseaux d'équilibre et de réseaux efficaces. Notre principal résultat théorique est que la séquentialité est le principal déterminant du réseau d'équilibre alors que l'hétérogénéité est secondaire. L'équilibre exige souvent que le dernier joueur du jeu supporte une grande partie des coûts de liaison. Au contraire, le leader ne créer pas de liens à l'équilibre. Dans la suite de l'article, nous construisons un protocole expérimental. Notre but est d'analyser si notre modèle prédit le comportement en laboratoire et si les individus sont plus attirés par l'équilibre ou par l'efficacité

An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies

Eco-efficiency is a useful guide to dairy farm sustainability analysis aimed at increasing output (physical or value added) and minimizing environmental impacts (EIs). Widely used partial eco-efficiency ratios (EIs per some functional unit, e.g. kg milk) can be problematic because (i) substitution possibilities between EIs are ignored, (ii) multiple ratios can complicate decision making and (iii) EIs are not usually associated with just the functional unit in the ratio's denominator. The objective of this study was to demonstrate a 'global' eco-efficiency modelling framework dealing with issues (i) to (iii) by combining Life Cycle Analysis (LCA) data and the multiple-input, multiple-output production efficiency method Data Envelopment Analysis (DEA). With DEA each dairy farm's outputs and LCA-derived EIs are aggregated into a single, relative, bounded, dimensionless eco-efficiency score, thus overcoming issues (i) to (iii). A novelty of this study is that a model providing a number of additional desirable properties was employed, known as the Range Adjusted Measure (RAM) of inefficiency. These properties altogether make RAM advantageous over other DEA models and are as follows. First, RAM is able to simultaneously minimize EIs and maximize outputs. Second, it indicates which EIs and/or outputs contribute the most to a farm's eco-inefficiency. Third it can be used to rank farms in terms of eco-efficiency scores. Thus, non-parametric rank tests can be employed to test for significant differences in terms of eco-efficiency score ranks between different farm groups. An additional DEA methodology was employed to 'correct' the farms' eco-efficiency scores for inefficiencies attributed to managerial factors. By removing managerial inefficiencies it was possible to detect differences in eco-efficiency between farms solely attributed to uncontrollable factors such as region. Such analysis is lacking in previous dairy studies combining LCA with DEA. RAM and the 'corrective' methodology were demonstrated with LCA data from French specialized dairy farms grouped by region (West France, Continental France) and feeding strategy (regardless of region). Mean eco-efficiency score ranks were significantly higher for farms with 30% maize in the total forage area before correcting for managerial inefficiencies. Mean eco-efficiency score ranks were higher for West than Continental farms, but significantly higher only after correcting for managerial inefficiencies. These results helped identify the eco-efficiency potential of each region and feeding strategy and could therefore aid advisors and policy makers at farm or region/sector level. The proposed framework helped better measure and understand (dairy) farm eco-efficiency, both within and between different farm groups

The relationship of dairy farm eco-efficiency with intensification and self-sufficiency. Evidence from the French dairy sector using Life Cycle Analysis, Data Envelopment Analysis and Partial Least Squares Structural Equation Modelling

We aimed at quantifying the extent to which agricultural management practices linked to animal production and land use affect environmental outcomes at a larger scale. Two practices closely linked to farm environmental performance at a larger scale are farming intensity, often resulting in greater off-farm environmental impacts (land, non-renewable energy use etc.) associated with the production of imported inputs (e.g. concentrates, fertilizer); and the degree of self-sufficiency, i.e. the farm’s capacity to produce goods from its own resources, with higher control over nutrient recycling and thus minimization of losses to the environment, often resulting in greater on-farm impacts (eutrophication, acidification etc.). We explored the relationship of these practices with farm environmental performance for 185 French specialized dairy farms. We used Partial Least Squares Structural Equation Modelling to build, and relate, latent variables of environmental performance, intensification and self-sufficiency. Proxy indicators reflected the latent variables for intensification (milk yield/cow, use of maize silage etc.) and self-sufficiency (home-grown feed/total feed use, on-farm energy/total energy use etc.). Environmental performance was represented by an aggregate ‘eco-efficiency’ score per farm derived from a Data Envelopment Analysis model fed with LCA and farm output data. The dataset was split into two spatially heterogeneous (bio-physical conditions, production patterns) regions. For both regions, eco-efficiency was significantly negatively related with milk yield/cow and the use of maize silage and imported concentrates. However, these results might not necessarily hold for intensive yet more self-sufficient farms. This requires further investigation with latent variables for intensification and self-sufficiency that do not largely overlap- a modelling challenge that occurred here. We conclude that the environmental ‘sustainability’ of intensive dairy farming depends on particular farming systems and circumstances, although we note that more self-sufficient farms may be preferable when they may benefit from relatively low land prices and agri-environment schemes aimed at maintaining grasslands

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