Which Will Overcome? The Productivity or Risk Premium

Subject Areas : Environmental policy and management

Javad Shahraki ¹ , Shahram Saeedian ^{2 *}

1 - Assistant Professor, Economics Department, University of Sistan and Baluchestan,Iran
2 - Graduate Student of Agricultural Economics, Economics Department, University of Sistan and Baluchestan,Iran

Received: 2013-05-23 Accepted : 2013-07-01 Published : 2014-03-01

Keywords: Nigeria, consumers, Hedonic Pricing, cowpeas,

Abstract :

The study investigates consumers’ preference for cowpea reflected in the Nigerian markets through price discounts and premiums that consumers pay for different cowpea characteristics. The price data used for this study were obtained through a market survey. A common data collection protocol was employed. Every month, between October 2009 to December 2010, five cowpea samples per seller were bought from randomly selected sellers in six markets and the prices noted. In the laboratory, the non-price data, such as, 100 grain weight, number of bruchid holes per 100 grains, eye colour and texture of the testa were obtained. A hedonic pricing regression model was used to analyze data collected. Hedonic pricing methods provide a statistical estimate of premiums and discounts. Results indicate that eye colour is the most important determinant of cowpea market prices. Cowpeas with brown colour commands a clear premium in all but one market. The consumers discount prices for insect damage in most markets. In general, this study signals the need for cowpea breeders to identify cost effective ways of breeding for brown coloured cowpea (Ife-brown specie) which was noted to attract price premium.Risk-averse farmers are prudent to use different inputs because every input has a distinct effect on output fluctuations and production risk as well. This paper examines the effect of input using growth on producer welfare of date farmers in Sistan and Baluchestan province which is the second greatest producer and exporter of date in Iran. It is well known that input using growth impresses both productivity and risk premium. These two factors contribute to producer welfare so that increasing the productivity will boost the welfare and an addition to risk premium shall detract the welfare of risk-averse farmers. Results showed that technical change has reduced both productivity and production risk in 2011/2012 and the welfare increased as 912727.21. But, in 2010/2011, productivity and risk premium had a positive growth and finally the producer's welfare experienced a reduction as 1041478.41.

References:

1- Amemiya, T. (1977). The maximum likelihoodand the Non-linear three-Stage least squares estimatorin the General Nonlinear Simultaneous EquationModel. Econometrica, 45, 955-968. [DOI via Crossref]

2- Antle, J.M. (1987). Econometric estimation ofproducers' risk attitudes, American Journal of AgriculturalEconomics, 69, 509-522. [DOI via Crossref]

3- Balk, B.M. (2003). The residual: On monitoringand benchmarking firms, industries, and economieswith respect to productivity, Journal of ProductivityAnalysis, 20:5-47. [DOI via Crossref]

4- Brink, L. & McCarl, B.A. (1978). The tradeoffbetween expected return and risk among cornbeltfarmers. American Journal of Agricultural Economics,60, 259-263. [DOI via Crossref]

5- Buccola, S.T. (2004). Productivity growth whenoutput is risky. (Department of Agricultural and ResourceEconomics, Oregon State University), Paperbased on earlier version presented at the NorthAmerican Productivity second workshop ɪɪ, Schenectady,NY, June 20-22, 2002.

6- Buccola, S.T., & Mc Carl, B.A. (1986). Smallsampleevaluation of Mean-Variance productionfunction estimation. American Journal of AgriculturalEconomics, 68, 732-738. [DOI via Crossref]

7- Dillon, J.L. & Anderson, J.R. (1971). Allocativeefficiency, traditional agriculture and risk. AmericanJournal of Agricultural Economics, 53, 26-32. [DOI via Crossref]

8- Food and Agriculture Organization Of the UnitedNations (FAO). Retrieved from http://faostat.fao.org/site/339/default.aspx/ 2012.

9- Just, R.E. & Pope, R.D. (1979). Production functionestimation and related risk considerations. AmericanJournal of Agricultural Economics, 61, 276-284. [DOI via Crossref]

10- Imbens, G. & Wooldridge, J.W. (2007). What's newin Econometrics? NBER Summer Institutes lectures.Retrieved from: http://www.nber.org/confr/2007/si2007/wneprg.html, Last accesssed: 4 May 2013.

11- Karbasi, A., Daneshvar, M.H. & Toopkanlu, Z.(2006). Survey on effective factors on productionrisl of irrigated and drought Cumin in KhorasanProvince. Journal of Agricultural Science and Industries,19(2), 57-64.

12- Love, H. A. & Buccola, S.T. (1991). Joint riskpreference-technology estimation with a primal system.American Journal of Agricultural Economics,73, 765-774. [DOI via Crossref]

13- Lovell, C.A.K. (1996). Applying efficiencymeasurement techniques to measurement of ProductivityChange. Journal of Productivity Analysis,7, 329-340. [DOI via Crossref]

14- Ministry of Jihad-e-Agriculture, (2004). Cultivationand production database. Agricultural CropsInformation. retrieved from http://www.dbagri.maj.ir/Zrt/year/2012.

15- Moghaddasi, R. & Yazdani, S. (1997). Risk inproduction function and investigating the effectivefactors. Paper presented in The First Iranian AgriculturalEconomics Conference, Zabol University.

16- Musanejad, M., Rahimi, H. & Chizari, A. (2001).Determining the efficiency and production risk ofpotato in Fars province. Iran, Journal of AgriculturalScience, 1, 36-44.

17- Naghshine Fard, M., Mohammadi, H., Bagheri,M., Kafilzadeh, F., Pishbin, S. & Bargian, A. (2007).Determining the effect of inputs on production riskusing generalized stochastic production function,case study of sugar beet farmers of Fars province ofIran. Journal of Sugar Beet, 22(1), 91-100.

18- Nelson, C.H., & Preckel, P. (1989). The conditionalBeta distribution as a stochastic production function.American Journal of Agricultural Economics, 71,370-378. [DOI via Crossref]

19- Orea, L. & Wall, A. (2012). Productivity andproducer welfare in the presence of production risk.Journal of Agricultural Economics, 63(1), 102-118,[DOI via Crossref]    [DOI via Crossref]

20- Paris, Q. (1979). Revenue and cost uncertainty,generalized Mean-Variance, and the Linear complementarityproblem. American Journal of AgriculturalEconomics, 61, 268-275. [DOI via Crossref]

21- Sabouhi, S.M. (2012). Application of mathematicalprogramming in agricultural economics witha focus on using the Excel software. Iran, ZabolUniversity and Noor-e-Elm.

22- Saeedian, Sh., Sabouni, S.M., Shahraki, J.,Moradi, E. & Sepahi, M. (2013). The relationshipbetween Meta technology ratios and varietal differencesin date production. International Journal ofAgricultural Management and Development, 3(2),45-62.

23- Sharzei, Gh. & Zibaee, M. (2002). The effect ofinput usage on production risk of cotton in Farsprovince. Journal of Agricultural Science and Industries,15(2), 49-55.

24- Torkamany, G. & Ghorbani, M. (1998). Theeffect of inputs on production risk using generalizedstochastic production function. Journaal of AgriculturalSciences of Iran, 28(2), 37-42.

25- Wiens, T.B. (1976). Peasant risk aversion andallocative behaviour: A quadratic programming experiment.American Journal of Agricultural Economics,58, 629-635. [DOI via Crossref]