DOES INVESTMENT SOURCE DIVERSIFICATION IMPROVE FARM PERFORMANCE? EVIDENCE FROM HORTICULTURAL PRODUCERS IN UZBEKISTAN
DOI:
https://doi.org/10.5281/zenodo.21246598Keywords:
Investment Source Diversification; Horticultural Farms; Farm Performance; Economic Efficiency; Agricultural Finance; Latent Class Analysis; UzbekistanAbstract
This study investigates the relationship between investment source diversification and the
economic performance of horticultural farms in Uzbekistan. Using survey data from 280 producers, an Investment
Source Diversification Index (ISDI) was constructed through Principal Component Factor Analysis, while Latent
Class Analysis was employed to identify heterogeneous financing patterns. The results reveal two statistically
distinct investment groups and show that farms with more diversified financing structures achieve significantly
higher income and profitability. The findings identify investment source diversification as a key determinant of
farm performance and underscore the importance of diversified financing for enhancing investment capacity
and economic efficiency. The study contributes new empirical evidence to the agricultural finance literature and
offers policy insights for the sustainable development of Uzbekistan’s horticultural sector.
References
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.
org/10.2307/2975974
Penrose, E. T. (1959). The Theory of the Growth of the Firm. Oxford: Basil Blackwell.
Stiglitz, J. E., & Weiss, A. (1981). Credit rationing in markets with imperfect information. The American
Economic Review, 71(3), 393–410.
Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1),
–120. https://doi.org/10.1177/014920639101700108
Feder, G., Lau, L. J., Lin, J. Y., & Luo, X. (1990). The relationship between credit and productivity in Chinese
agriculture. American Journal of Agricultural Economics, 72(5), 1151–1157. https://doi.org/10.2307/1242524
Williamson, O. E. (1985). The Economic Institutions of Capitalism: Firms, Markets, Relational Contracting.
New York: Free Press.
Latruffe, L. (2010). Competitiveness, productivity and efficiency in the agricultural and agri-food sectors.
OECD Food, Agriculture and Fisheries Papers, No. 30. Paris: OECD Publishing.
Jolliffe, I. T. (2002). Principal Component Analysis (2nd ed.). New York: Springer.
Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis: With Applications in the
Social, Behavioral, and Health Sciences. Hoboken, NJ: Wiley.
Schimmelpfennig, D. (2016). Farm Profits and Adoption of Precision Agriculture. Economic Research
Report No. 217. Washington, DC: United States Department of Agriculture, Economic Research Service.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2022). Multivariate Data Analysis (9th ed.). Boston,
MA: Cengage Learning.
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/
BF02291575
Bartlett, M. S. (1954). A note on the multiplying factors for various chi-square approximations. Journal of
the Royal Statistical Society: Series B, 16(2), 296–298.
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic
Control, 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. https://
doi.org/10.1214/AOS/1176344136
Vermunt, J. K., & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon
(Eds.), Applied Latent Class Analysis (pp. 89–106). Cambridge: Cambridge University Press.
Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The
Tailored Design Method (4th ed.). Hoboken, NJ: Wiley.
Food and Agriculture Organization of the United Nations. (2023). The State of Food and Agriculture 2023.
Rome: FAO.
World Bank. (2022). Uzbekistan Agriculture Modernization Project. Washington, DC: World Bank.
World Bank. (2023). Transforming Agrifood Systems for Economic Growth and Rural Development.
Washington, DC: World Bank.
Authors’ calculations based on survey data (2026).
Downloads
Published
Issue
Section
License
Copyright (c) 2026 ECONOSCITECH INTEGRATION
This work is licensed under a Creative Commons Attribution 4.0 International License.
