Networks and Transaction Costs

Based on the well-known fact that social networks can provide effective mechanisms that help to increase the trust level between two trade partners, we apply a simple game-theoretical framework to derive transaction costs as a high risk of opportunistic behavior in a repeated trade relation determined by the density and size of trading networks. In the empirical part of the paper we apply a two stage procedure to estimate the impact of social network structures on farm’s transaction costs observed for different input and output markets. At a first stage we estimate a multiple input-multiple output stochastic Ray production function to generate relative shadow prices of three inputs and two outputs traded by farms. At a second stage a structural equation system is derived from the first order conditions of farm’s profit maximization to estimate simultaneously the of commodity specific transaction cost functions for all traded farm inputs and outputs. Estimation results based on a sample of 315 Polish farms imply a significant influence of social network structures on farm’s transaction costs. Moreover, estimated transaction costs correspond to a reasonable amount of farm specific shadow prices.Resource /Energy Economics and Policy,

Using Non-parametric Methods in Econometric Production Analysis: An Application to Polish Family Farms

Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb-Douglas or the Translog production function is used. However, the specification of a functional form for the production function involves the risk of specifying a functional form that is not similar to the “true” relationship between the inputs and the output. This misspecification might result in biased estimation results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used for estimating production functions without specifying a functional form and thus, avoiding possible misspecification errors. We use a balanced panel data set of farms specialized in crop production that is constructed from Polish FADN data for the years 2004-2007. Our analysis shows that neither the Cobb-Douglas function nor the Translog function are consistent with the “true” relationship between the inputs and the output in our data set. We solve this problem by using non-parametric regression. This approach delivers reasonable results, which are on average not too different from the results of the parametric estimations but many individual results are rather different.Farm Management,

Sample Selection Models in R: Package sampleSelection

This paper describes the implementation of Heckman-type sample selection models in R. We discuss the sample selection problem as well as the Heckman solution to it, and argue that although modern econometrics has non- and semiparametric estimation methods in its toolbox, Heckman models are an integral part of the modern applied analysis and econometrics syllabus. We describe the implementation of these models in the package sampleSelection and illustrate the usage of the package on several simulation and real data examples. Our examples demonstrate the effect of exclusion restrictions, identification at infinity and misspecification. We argue that the package can be used both in applied research and teaching.

Investment Utilisation, Adjustment Costs, and Technical Efficiency in Danish Pig Farms

In this paper, we present a theoretical model for adjustment costs and investment utilisation that illustrates their causes and types and shows in which phases of an investment they occur. Furthermore, we develop an empirical framework for analysing the size and the timing of adjustment costs and investment utilisation. We apply this methodology to a large panel data set of Danish pig producers with 9,281 observations between 1996 and 2008. The paper further contributes with a thorough discussion of the calculation and deflation of capital input from microeconomic data. We estimate an output distance function as a stochastic frontier model and explain the estimated technical inefficiencies with lagged investments, farm size and age of the farmer. We allow for interaction effects between these variables and derive the formula for calculating the marginal effects on technical efficiency. The results show that investments have a negative effect on farm efficiency in the year of the investment and the year after accruing from adjustment costs. There is a large positive effect on efficiency two and three years after the investment. The farmer’s age and the farm size significantly influence technical efficiency, as well as the effect of investments on adjustment costs and investment utilisation. These results are robust to different ways of measuring capital.investment utilisation, adjustment costs, stochastic frontier analysis, technical efficiency, pig production, Denmark

An empirical investigation of the demand for bananas in Germany

We use econometric methods to investigate consumer demand for bananas and for other fruit in Germany. Monthly household survey data for the period 1986-1998 are analysed. Demand for bananas is significantly responsive to own price, suggesting that policy-induced price increases generate the usual dead-weight losses. Demand is also responsive to income changes, indicating that there is scope for further market expansion as incomes grow. There is evidence that other categories of fruit are both gross and net substitutes for bananas.bananas, fruit, dynamic demand system, Germany, Demand and Price Analysis,

systemfit: A Package for Estimating Systems of Simultaneous Equations in R

Many statistical analyses (e.g., in econometrics, biostatistics and experimental design) are based on models containing systems of structurally related equations. The systemfit package provides the capability to estimate systems of linear equations within the R programming environment. For instance, this package can be used for "ordinary least squares" (OLS), "seemingly unrelated regression" (SUR), and the instrumental variable (IV) methods "two-stage least squares" (2SLS) and "three-stage least squares" (3SLS), where SUR and 3SLS estimations can optionally be iterated. Furthermore, the systemfit package provides tools for several statistical tests. It has been tested on a variety of datasets and its reliability is demonstrated.