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Profit Gap Analysis on the Small Scale Production of Shallot: A Case Study in a Small Village in East Java Province of Indonesia


  • Sujarwo
  • Saghaian, Sayed H.


This study attempts to contribute to poverty alleviation through increasing efficiency of input allocation which can raise profit of the small scale farmers without changing the technology they use. Accordingly, this study addresses the problem of allocative inefficiency and profit gap of the farmer’s shallot production. Double-log production function and polynomial cost function are applied to measure the profit gap analysis. The empirical results from double-log production function confirm that land, labor, fertilizer, and pesticide are allocated by farmers inefficiently. Furthermore, three simulations for efficient inputs allocation and profit gap analysis are taken into account based on the costs level spent by the farmers. The result shows that profit gaps are 4.72 percent, 13.96 percent and 17.92 percent for low, middle, and high input costs level, respectively.

Suggested Citation

  • Sujarwo & Saghaian, Sayed H., 2013. "Profit Gap Analysis on the Small Scale Production of Shallot: A Case Study in a Small Village in East Java Province of Indonesia," 2013 Annual Meeting, February 2-5, 2013, Orlando, Florida 142550, Southern Agricultural Economics Association.
  • Handle: RePEc:ags:saea13:142550

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    1. Baltagi, Badi H. & Li, Qi, 1995. "Testing AR(1) against MA(1) disturbances in an error component model," Journal of Econometrics, Elsevier, vol. 68(1), pages 133-151, July.
    2. Murat Isik & Stephen Devadoss, 2006. "An analysis of the impact of climate change on crop yields and yield variability," Applied Economics, Taylor & Francis Journals, vol. 38(7), pages 835-844.
    3. Rafael E. De Hoyos & Vasilis Sarafidis, 2006. "Testing for cross-sectional dependence in panel-data models," Stata Journal, StataCorp LP, vol. 6(4), pages 482-496, December.
    4. Kaddour Hadri, 2000. "Testing for stationarity in heterogeneous panel data," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 148-161.
    5. Mendelsohn, Robert & Nordhaus, William D & Shaw, Daigee, 1994. "The Impact of Global Warming on Agriculture: A Ricardian Analysis," American Economic Review, American Economic Association, vol. 84(4), pages 753-771, September.
    6. David M. Drukker, 2003. "Testing for serial correlation in linear panel-data models," Stata Journal, StataCorp LP, vol. 3(2), pages 168-177, June.
    7. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    8. Pesaran, M. Hashem, 2004. "General Diagnostic Tests for Cross Section Dependence in Panels," IZA Discussion Papers 1240, Institute for the Study of Labor (IZA).
    9. Eitzinger, J. & Stastna, M. & Zalud, Z. & Dubrovsky, M., 2003. "A simulation study of the effect of soil water balance and water stress on winter wheat production under different climate change scenarios," Agricultural Water Management, Elsevier, vol. 61(3), pages 195-217, July.
    10. Christopher F Baum, 2001. "Residual diagnostics for cross-section time series regression models," Stata Journal, StataCorp LP, vol. 1(1), pages 101-104, November.
    11. S. Kambua Chema & Leonie A. Marks & Joseph L. Parcell & Maury Bredahl, 2006. "Marketing Biotech Soybeans with Functional Health Attributes," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 54(4), pages 685-703, December.
    12. Sarker, Md. Abdur Rashid & Alam, Khorshed & Gow, Jeff, 2012. "Exploring the relationship between climate change and rice yield in Bangladesh: An analysis of time series data," Agricultural Systems, Elsevier, vol. 112(C), pages 11-16.
    13. Frees, Edward W., 1995. "Assessing cross-sectional correlation in panel data," Journal of Econometrics, Elsevier, vol. 69(2), pages 393-414, October.
    14. Bruce A. McCarl & Xavier Villavicencio & Ximing Wu, 2008. "Climate Change and Future Analysis: Is Stationarity Dying?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(5), pages 1241-1247.
    15. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," Review of Economic Studies, Oxford University Press, vol. 47(1), pages 239-253.
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    Shallot production; double-log production; polynomial cost function; efficient input allocation; profit gap; Production Economics;

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