Empirical Economics

, Volume 48, Issue 1, pp 493–515 | Cite as

Transaction costs and social networks in productivity measurement

  • Geraldine HenningsenEmail author
  • Arne Henningsen
  • Christian H. C. A. Henning


We argue that in the presence of transaction costs, observed productivity measures may in many cases understate the true productivity, as production data seldom distinguish between resources entering the production process and resources of a similar type that are sacrificed for transaction costs. Hence, both the absolute productivity measures and, more importantly, the productivity ranking will be distorted. A major driver of transaction costs is poor access to information and contract enforcement assistance. Social networks often catalyse information exchange as well as generate trust and support. Hence, we use measures of a firm’s access to social networks as a proxy for the transaction costs the firm faces. We develop a microeconomic production model that takes into account transaction costs and networks. Using a data set of 384 Polish farms, we empirically estimate this model and compare different parametric, semiparametric, and nonparametric model specifications. Our results generally support our hypothesis. Especially, large trading networks and dense household networks have a positive influence on a farm’s productivity. Furthermore, our results indicate that transaction costs have a measurable impact on the productivity ranking of the farms.


Information networks Transaction costs Semiparametric estimation Nonparametric estimation Productivity analysis 

JEL Classification

D22 D23 D24 D85 L14 Q12 



The authors are grateful to Jeff Racine, Martin Browning, Subal Kumbhakar, Chris Parmeter, and two anonymous referees for their valuable suggestions regarding the econometric analysis and for improving the paper. Funding was provided by the European Union’s sixth framework programme within the project Advanced-Eval. Arne Henningsen is grateful to the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) for financially supporting this research.


  1. Aitchison J, Aitken CGG (1976) Multivariate binary discrimination by the kernel method. Biometrika 63(3):413–420CrossRefGoogle Scholar
  2. Bandiera O, Rasul I (2006) Social networks and technology adoption in northern mozambique. Econ J 116(514):869–902CrossRefGoogle Scholar
  3. Beckmann CM, Haunschild PR, Phillips DJ (2004) Friends or strangers? Firm-specific uncertainty, market uncertainty, and network partner selection. Organ Sci 15(3):259–275CrossRefGoogle Scholar
  4. Bradley SW, McMullen JS, Artz K, Simiyu EM (2012) Capital is not enough: innovation in developing economies. J Manag Stud 49(4):684–717CrossRefGoogle Scholar
  5. Burt RS (1984) Network items and the general social survey. Soc Netw 6:293–339CrossRefGoogle Scholar
  6. Buskens V (1999) Social networks and trust. Dissertation, Utrecht University.Google Scholar
  7. Castilla EJ, Hwang H, Granovetter E, Granovetter M (2000) Social networks in Silicon Valley. In: Lee CM, Miller WF, Hancock MG, Rowen HS (eds) The Silicon Valley edge: a habitat for innovation and entrepreneurship. Stanford University Press, Stanford, pp 218–247Google Scholar
  8. Czekaj T, Henningsen A (2013) Panel data specifications in nonparametric kernel regression: an application to production functions. IFRO Working Paper 2013/5, Department of Food and Resource Economics, University of CopenhagenGoogle Scholar
  9. Dekker DJ (2001) Effects of positions in knowledge networks on trust. Tech. Rep. TI 2001–062/1, Tinbergen InstituteGoogle Scholar
  10. den Butter FAG, Mosch RHJ (2003) Trade, trust and transaction cost. Working Paper TI 2003–082/3, Tinbergen Institute, Amsterdam.
  11. Di Matteo T, Aste T, Gallegati M (2005) Innovation flow through social networks: productivity distribution in France and Italy. Eur Phys J B 47:459–466Google Scholar
  12. Fafchamps M (2001) The role of business networks in market development in Sub-Saharan Africa. In: Hayami Y, Aoki M (eds) Community and market in economic development. Oxford University Press, Oxford, pp 186–214CrossRefGoogle Scholar
  13. Hayfield T, Racine JS (2008) Nonparametric econometrics: the np package. J Stat Softw 27(5):1–32Google Scholar
  14. Henning CHCA, Zuckerman EW (2006) Boon and bane of social networking in markets with imperfect information: theory and evidence from Polish and Slovakian rural credit markets. Christian Albrechts University Kiel, Institute of Agricultural EconomicsGoogle Scholar
  15. Henning CHCA, Henningsen G, Henningsen A (2012) Networks and transaction costs. Am J Agric Econ 94(2):377–385CrossRefGoogle Scholar
  16. Hsiao C, Li Q, Racine J (2007) A consistent model specification test with mixed discrete and continuous data. J Econ 140(2):802–826CrossRefGoogle Scholar
  17. Hurvich CM, Simonoff JS, Tsai CL (1998) Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J R Stat Soc Ser B 60:271–293CrossRefGoogle Scholar
  18. Jenssen JI, Koenig HF (2002) The effect of social networks on resource access and business start-ups. Eur Plan Stud 10(8):1039–1046CrossRefGoogle Scholar
  19. Lau CM, Bruton GD (2011) Strategic orientations and strategies of high technology ventures in two transition economies. J World Bus 46:371–380CrossRefGoogle Scholar
  20. Levi M (2000) When good defenses make good neighbors: a transaction cost approach on trust, the absence of trust and distrust. In: Ménard C (ed) Institutions, contracts, and organizations: perspective from new institutional economics, chap 12. Edward Elgar, Chichester, pp 137–157Google Scholar
  21. Li Q, Racine JS (2004) Cross-validated local linear nonparametric regression. Stat Sinica 14(2):485–512Google Scholar
  22. Lin N (1999) Building a network theory of social capital. Connections 22(1):28–51Google Scholar
  23. Luo Y (2003) Industrial dynamics and managerial networking in an emerging market: the case of China. Strateg Manag J 24:1315–1327Google Scholar
  24. Ma S, Racine JS (2011) Inference for regression splines with categorical and continuous predictors, unpublished Working Paper, Department of Economics, McMaster UniversityGoogle Scholar
  25. Ma S, Racine JS (2012) Additive regression splines with irrelevant categorical and continuous regressors. Department of Economics Working Papers 2012–07, McMaster University.
  26. Ma S, Racine JS, Yang L (2012) Spline regression in the presence of categorical predictors. Department of Economics Working Papers 2012–06, McMaster University.
  27. Ménard C (2000) Enforcement procedures and governance structures: What relationship? In: Ménard C (ed) Institutions, contracts and organizations. Perspectives from new institutional economics, chap 17. Edward Elgar Pub., Cheltenham, pp 235–253Google Scholar
  28. Nee V (1998) Norms and networks in economic and organizational performance. Am Econ Rev 88(2):85–89Google Scholar
  29. Nie Z, Racine JS (2012) The crs package: nonparametric regression splines for continuous and categorical predictors. R J 4(2):48–56Google Scholar
  30. Prajapati S, Biswas S (2011) Effect of entrepreneur network and entrepreneur self-efficacy on subject performance: a study of handicraft and handloom cluster. J Entrep 20(2):227–247Google Scholar
  31. R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
  32. Racine JS (1997) Consistent significance testing for nonparametric regression. J Bus Econ Stat 15:369–379Google Scholar
  33. Racine JS, Li Q (2004) Nonparametric estimation of regression functions with both categorical and continuous data. J Econ 119(1):99–130CrossRefGoogle Scholar
  34. Racine JS, Parmeter CF (2014) Data-driven model evaluation: a test for revealed performance. In: Racine JS, Su L, Ullah A (eds) The Oxford handbook of applied nonparametric and semiparametric econometrics and statistics, Oxford handbooks in economics. Oxford University Press, Oxford, pp 308–345CrossRefGoogle Scholar
  35. Racine JS, Hart J, Li Q (2006) Testing the significance of categorical predictor variables in nonparametric regression models. Econom Rev 25:523–544CrossRefGoogle Scholar
  36. Ramsey JB (1969) Tests for specification errors in classical linear least-squares regression analysis. J R Stat Soc Ser B (Methodol) 31(2):350–371Google Scholar
  37. Stam W, Arzlanian S, Elfring T (2014) Social capital of entrepreneurs and small firm performance: a meta-analysis of contextual and methodological moderators. J Bus Venture 29(1):152–173Google Scholar
  38. Uzzi B (1996) The sources and consequences of embeddedness for the economic performance of organizations: the network effect. Am Sociol Rev 94(4):674–698CrossRefGoogle Scholar
  39. Wang MC, van Ryzin J (1981) A class of smooth estimators for discrete distributions. Biometrika 68:301–309Google Scholar
  40. Williamson OE (2000) The new institutional economics: taking stock, looking ahead. J Econ Lit 38:595–613CrossRefGoogle Scholar
  41. Yu SH, Chiu WT (2013) Social networks and corporate performance: the moderating role of technical uncertainty. J Manag Issues 25(1):26–45Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Geraldine Henningsen
    • 1
    Email author
  • Arne Henningsen
    • 2
  • Christian H. C. A. Henning
    • 3
  1. 1.Department of Management EngineeringTechnical University of DenmarkRoskildeDenmark
  2. 2.Department of Food and Resource EconomicsUniversity of CopenhagenFrederiksberg CDenmark
  3. 3.Institute of Agricultural EconomicsChristian-Albrechts University KielKielGermany

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