|By:||Marielle Brunette (Laboratoire d’Economie Forestière, INRA – AgroParisTech)
Philippe Delacote (Laboratoire d’Economie Forestière, INRA – AgroParisTech)
Serge Garcia (Laboratoire d’Economie Forestière, INRA – AgroParisTech)
Jean-Marc Rousselle (INRA, UMR 1135 LAMETA)
The impact of the safety-net use of Common-pool resources (CPR) on the individual investment into and extraction from the commons is analyzed in this paper. Agents of the community first choose to invest in their private project and in the CPR; second, they choose how much to extract from their private project and the commons. The model compares two types of risk management tool: CPR as risk-coping and risk-diversification mechanisms. It also compares two types of risk: risk on a private project and risk on CPR investment by other community members. The theoretical predictions are empirically tested with experimental economics. In this view, we propose an original CPR game composed of two periods, an investment one and an extraction one. Our result clearly shows that risk reduction in the private project unambiguously decreases investment in the CPR, while it does not impact CPR extraction. We also show that a risk-coping strategy is well understood as more flexible and influenced by the outcome in terms of private project yield.
|Keywords:||Common-pool resource, Common-pool resource game, deforestation, experimental economics.|
|JEL:||Q15 Q23 D71 D81|
Cappelen, Alexander W., James Konow, Erik Ø. Sørensen, and Bertil Tungodden. 2013. “Just Luck: An Experimental Study of Risk-Taking and Fairness." American Economic Review, 103(4): 1398-1413. DOI: 10.1257/aer.103.4.1398; Online Appendix (307.82 KB) | Download Data Set (215.97 KB)
Shankar, S. (2012). Production economics in the presence of risk*. Australian Journal of Agricultural and Resource Economics, 56(4), 597-620. Wiley;
This paper provides an overview of the literature on production under the influence of risk. Various specifications of stochastic production function such as models with additive and multiplicative uncertainty, Just and Pope model, output-cubical, state-allocable and state-general models are discussed. Further, criteria determining optimal producer behaviour are derived for deterministic production technology and for various kinds of state-contingent technologies such as output-cubical, state-specific, state-allocable and state-general technologies. Finally, a brief discussion is presented about the drawbacks of each of these specifications of technology.
- Arrow, K.J. (1953). Le des valeurs boursiers pour la repartition la meillure des risques. Technical report, Cahiers du Seminair d’Economie, Centre Nationale de la Recherche Scientifique (CNRS), Paris.
- Briec, W. and Cavaignac, L. (2009). An extension of the multi-output statecontingent production model, Journal of Economic Theory 39, 43–64.
- Chambers, R.G. and Quiggin, J. (1998). Cost functions and duality for stochastic technologies, American Journal of Agricultural Economics 80(2), 288–295.
- Chambers, R.G. and Quiggin, J. (2000). Uncertainty, Production, Choice and Agency: The State-Contingent Approach. Cambridge University Press, Cambridge, UK.
- Chambers, R.G. and Quiggin, J. (2002). The state-contingent properties of stochastic production functions, American Journal of Agricultural Economics 84(2), 513–526.
- Chambers, R.G. and Quiggin, J. (2003). Price stabilization and the risk-averse firm, American Journal of Agricultural Economics 85(2), 336–347.
- Chavas, J.-P. (2008). A cost approach to economic analysis under state-contingent production uncertainty, American Journal of Agricultural Economics 90(2), 435–446.
- Debreu, G. (1952). A social equilibrium existence theorem, Proceedings of the National Academy of Sciences of the United States of America (38), 886–893.
- Hirshleifer, J. and Riley, J.G. (1992). The Analytics of Uncertainty and Information. Cambridge University Press, Cambridge.
- Jaramillo, P.E., Useche, P., Barham, B.L. and Foltz, J.D. (2010). The state contingent approach to farmers’ valuation and adoption of new biotech crops: Nitrogen-fertilizer and drought tolerance traits. July 2010 Annual Meeting. Agricultural and Applied Economics Association, Denver, CO.
- Just, R.E. (2003). Risk research in agricultural economics: opportunities and challenges for the next twenty-five years, Agricultural Systems 75, 123–159.
- Just, R.E. and Pope, R.D. (1978). Stochastic specification of production functions and economic implications, Journal of Econometrics 7(1), 67–86.
- Kumbhakar, S.C. (2002). Specification and estimation of production risk, risk preferences and technical efficiency, American Journal of Agricultural Economics 84(1), 8–22.
- Love, H.A. and Buccola, S.T. (1991). Joint risk preference-technology estimation with a primal system, American Journal of Agricultural Economics 73(3), 765–774.
- Love, H.A. and Buccola, S.T. (1999). Joint risk preference-technology estimation with a primal system: reply. American Journal of Agricultural Economics, 81(1), 245–247.
- Nauges, C., O’Donnell, C. and Quiggin, J. (2009). Uncertainty and technical efficiency in finnish agriculture. Number (53rd) Conference, Cairns, Australia. Australian Agricultural and Resource Economics Society.
- O’Donnell, C.J. and Griffiths, W.E. (2006). Estimating state-contingent production frontiers, American Journal of Agricultural Economics 88(1), 249–266.
- O’Donnell, C.J., Chambers, R.G. and Quiggin, J. (2010). Efficiency analysis in the presence of uncertainty, Journal of Productivity Analysis 33, 1–17.
- Quiggin, J. and Chambers, R.G. (1998). Risk premiums and benefit measures for generalized-expected-utility theories, Journal of Risk and Uncertainty 17(2), 121–137.
- Rasmussen, S. (2003). Criteria for optimal production under uncertainty. the state-contingent approach, The Australian Journal of Agricultural and Resource Economics 47(4), 447–476.
- Rasmussen, S. (2004). Optimizing production under uncertainty: generalisation of the state-contingent approach and comparison of methods for empirical application. Unit of Economics Working papers 24184, Royal Veterinary and Agricultural University, Food and Resource Economic Institute.
- Sandmo, A. (1971). On the theory of the competitive firm under price uncertainty, The American Economic Review 61(1), 65–73.
- Shephard, R.W. (1953). Cost and Production Functions. Princeton University Press, Princeton, NJ.
- Shephard, R.W. (1970). Theory of Cost and Production Functions. Princeton University Press, Princeton, NJ.
- Yaari, M.E. (1969). Some remarks on measures of risk aversion and on their uses, Journal of Economic Theory 1(3), 315–329.
|By:||Lora R. Todorova (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)
Bodo Vogt (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)
This paper experimentally examines the relationship between self-reporting risk preferences and behavioral choices in the subsequently played dictator, ultimatum and investment games. The results from these experiments are used to discern the motivational bases of behavioral choices in the ultimatum and investment games. The focus is on investigating whether strategic considerations are important for strategy selection in the two games. We find that self-reporting risk preferences does not alter the dictators’ offers and trusters’ investments, while it significantly decreases the proposers’ offers and leads to a substantial decrease in the amount trustees give back to their partners. We interpret these results as evidence that the decisions of proposers in the ultimatum game and trustees in the investment game are strategic.
|Keywords:||coordination game, dictator game, ultimatum game, investment game, questionnaire, risk scale, risk preferences|