Endogenous network production functions with selectivity

Horrace, William C.; Liu, Xiaodong; Patacchini, Eleonora

doi:10.1016/j.jeconom.2015.06.005

Cited by 31 publications

(23 citation statements)

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“…Once the spatial regimes are identified, we also control for spatial dependence by estimating the spatial model with regimes (2.3). In the context of production function models the endogenous spatial interactions i.e., ρ capture the spatial dependence structure between farms' production systems (Horrace et al 2016). For example, farmers' decisions on production innovations that spill-over among neighboring farms.…”

Section: The Estimation Of a Production Function And Potential Endogementioning

confidence: 99%

Modelling spatial regimes in farms technologies

2018

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We exploit the information derived from geographical coordinates to endogenously identify spatial regimes in technologies that are the result of a variety of complex, dynamic interactions among site-specific environmental variables and farmer decision making about technology, which are often not observed at the farm level. Controlling for unobserved heterogeneity is a fundamental challenge in empirical research, as failing to do so can produce model misspecification and preclude causal inference. In this article, we adopt a two-step procedure to deal with unobserved spatial heterogeneity, while accounting for spatial dependence in a cross-sectional setting. The first step of the procedure takes explicitly unobserved spatial heterogeneity into account to endogenously identify subsets of farms that follow a similar local production econometric model, i.e. spatial production regimes. The second step consists in the specification of a spatial autoregressive model with autoregressive disturbances and spatial regimes. The method is applied to two regional samples of olive growing farms in Italy. The main finding is that the identification of spatial regimes can help drawing a more detailed picture of the production environment and provide more accurate information to guide extension services and policy makers. Keywords Unobserved heterogeneity • Spatial dependence • Cobb-Douglas production function • Olive production JEL codes D24 • C14 • Q12

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Section: The Estimation Of a Production Function And Potential Endogementioning

confidence: 99%

Modelling spatial regimes in farms technologies

2018

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“…(), Horrace et al . () and Hsieh and Lee () extends control function methods to a networks context. The selection correction term is a non‐linear function of the predicted network, and thus of variables determining link choice.…”

Section: Dealing With Endogeneity Of Network Formationmentioning

confidence: 99%

“…For example, Horrace et al . () consider the performance of a sports team, where the network is taken to be the set of players that play in the same game for one team. The team size is fixed, and relatively small, so that the network formation process can be modelled as the choice of selecting a fixed number of players from a longer list.…”

Section: Dealing With Endogeneity Of Network Formationmentioning

confidence: 99%

Credibly Identifying Social Effects: Accounting for Network Formation and Measurement Error

Advani

Malde

2018

Journal of Economic Surveys

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Understanding whether and how connections between agents (networks) such as declared friendships in classrooms, transactions between firms, and extended family connections, influence their socio-economic outcomes has been a growing area of research within economics. Early methods developed to identify these social effects assumed that networks had formed exogenously, and were perfectly observed, both of which are unlikely to hold in practice. A more recent literature, both within economics and in other disciplines, develops methods that relax these assumptions. This paper reviews that literature. It starts by providing a general econometric framework for linear models of social effects, and illustrates how network endogeneity and missing data on the network complicate identification of social effects. Thereafter, it discusses methods for overcoming the problems caused by endogenous formation of networks. Finally, it outlines the stark consequences of missing data on measures of the network, and regression parameters, before describing potential solutions.CREDIBLY IDENTIFYING SOCIAL EFFECTS 1017 unobserved dimensions could influence within-group interactions, and through this the actual outcome. Ignoring variation in interactions within such groups can lead to misleading conclusions and policy design, as shown in recent work by Carrell et al. (2013).More recently, a growing body of research within empirical economics uses data which directly measure interactions between pairs of agents (network data hereon) to sidestep these issues. This growth has been spurred by the increasing availability of such data, as well as the development of methods to identify and estimate social effects with such data. Starting with Bramoullé et al. (2009) andDe Giorgi et al. (2010), methods have been developed to overcome the reflection problem. They show how information on network structure can be used to break the simultaneity, and obtain the necessary exclusion restrictions for parameter identification. These methods, reviewed in detail by Advani and Malde (2014), Topa and Zenou (2015) and Boucher and Fortin (2015), impose strong restrictions on the network formation process and the quality of the data.In particular, the network is assumed to be exogenous conditional on observed agent-and network-level characteristics, and to be fully and perfectly observed by the researcher. Both assumptions are unlikely to hold in practice. In a schooling context, for example, personality traits which are rarely observed by a researcher might influence both a child's choice of friends and her schooling performance. Estimates of the influence of a child's friends' outcomes on her outcomes will be biased if her choice of friends is not accounted for. Similarly, accurately collecting fine-grained information on all connections is very costly and logistically challenging, making it rare to observe a complete, perfectly measured network. This has important implications for identification of social effects using restrictions based on the network struct...

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“…In such examples, the absence of a link is due to the unobserved terms of the two agents being correlated in a specific way rather than the absence of correlation between these terms. Recent literature, including Goldsmith-Pinkham and Imbens (2013 ), Blume et al (2015) , Arduini et al (2015) and Horace et al (2015) among many others, has begun considering solutions to this issue. Many of these methods are reviewed in a longer working paper version of this article (see Advani and Malde 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Methods to identify linear network models: a review

Advani

Malde

2018

Swiss J Economics Statistics

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In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper, we review the literature studying econometric methods for the analysis of linear models of social effects, a class that includes the ‘linear-in-means’ local average model, the local aggregate model, and models where network statistics affect outcomes. We provide an overview of the underlying theoretical models, before discussing conditions for identification using observational and experimental/quasi-experimental data.

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Endogenous network production functions with selectivity

Cited by 31 publications

References 50 publications

Modelling spatial regimes in farms technologies

Modelling spatial regimes in farms technologies

Credibly Identifying Social Effects: Accounting for Network Formation and Measurement Error

Methods to identify linear network models: a review

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