Measuring the influence of networks on transaction costs using a non-parametric regression technique

TY - UNPB

T1 - Measuring the influence of networks on transaction costs using a non-parametric regression technique

AU - Henningsen, Géraldine

AU - Henningsen, Arne

AU - Henning, Christian H.C.A.

PY - 2013

Y1 - 2013

N2 - All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs. One of the major factors in transaction costs theory is information. Firm networks can catalyse the interpersonal information exchange and hence, increase the access to non-public information so that transaction costs are reduced. Many resources that are sacrificed for transaction costs are inputs that also enter the technical production process. As most production data do not distinguish between these two usages of inputs, high transaction costs result in reduced observed productivity. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks and dense information networks and household networks have a positive impact on a farm’s productivity. A bootstrapping procedure confirms that this result is statistically significant.

AB - All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs. One of the major factors in transaction costs theory is information. Firm networks can catalyse the interpersonal information exchange and hence, increase the access to non-public information so that transaction costs are reduced. Many resources that are sacrificed for transaction costs are inputs that also enter the technical production process. As most production data do not distinguish between these two usages of inputs, high transaction costs result in reduced observed productivity. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks and dense information networks and household networks have a positive impact on a farm’s productivity. A bootstrapping procedure confirms that this result is statistically significant.

M3 - Working paper

T3 - IFRO Working Paper

BT - Measuring the influence of networks on transaction costs using a non-parametric regression technique

PB - Department of Food and Resource Economics, University of Copenhagen

CY - Frederiksberg

ER -