Regional Price Dynamics Of Flour In Madagascar: Evidence From Econometric Analysis

Dupont Herilala NOMENJANAHARY, Tsirihanitra E.P. RANDRIANARISON, Tolontsoa RAKOTOSON, Andrimihaja Harimisa RAVELOSON

Abstract


This study investigates the regional price dynamics of flour in Madagascar using an econometric approach based on panel data. The dataset, sourced from the National Institute of Statistics (INSTAT), covers multiple regions and provides detailed information on price levels expressed in Malagasy Ariary (MGA). Descriptive evidence shows that the average price of flour ranges between approximately 3,400 and 3,600 MGA, with significant dispersion across regions and over time. Econometric analysis highlights the presence of moderate price variability, with regional deviations from the national average reaching up to ±30 percent in certain periods. The results further indicate that while price movements are generally positively correlated across regions, the degree of co-movement varies considerably, suggesting incomplete market integration. The persistence of regional price gaps and the heterogeneity in price transmission point to the existence of spatial frictions, including transportation constraints and localized supply conditions. Overall, the findings provide strong evidence of regional disparities in flour markets in Madagascar and underscore the importance of improving market connectivity to enhance price convergence and efficiency.

Keywords


Econometric modeling; Price dynamics; Market integration; Spatial dispersion; Madagascar

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References


Fackler, P.L. and Goodwin, B.K. (2001). Spatial price analysis. In: Gardner, B. and Rausser, G. (eds.), Handbook of Agricultural Economics, Vol. 1. Elsevier. DOI: https://doi.org/10.1016/S1574-0072(01)10007-3

Ravallion, M. (1986). Testing market integration. American Journal of Agricultural Economics, 68(1), pp.102–109. DOI: https://doi.org/10.2307/1241654

Minten, B. and Kyle, S. (1999). The effect of distance and road quality on food collection, marketing margins, and traders’ wages: Evidence from Madagascar. World Development, 27(2), pp.313–331. DOI: https://doi.org/10.1016/S0305-750X(98)00110-6

Barrett, C.B. (2001). Measuring integration and efficiency in international agricultural markets. Review of Agricultural Economics, 23(1), pp.19–32.DOI: https://doi.org/10.1111/1058-7195.00043

Abdulai, A. (2000). Spatial price transmission and asymmetry in the Ghanaian maize market. Journal of Development Economics, 63(2), pp.327–349. DOI: https://doi.org/10.1016/S0304-3878(00)00115-2

Stifel, D. and Minten, B. (2008). Isolation and agricultural productivity. Agricultural Economics, 39(1), pp.1–15. DOI: https://doi.org/10.1111/j.1574-0862.2008.00310.x

INSTAT (2023). Price statistics database. National Institute of Statistics of Madagascar. Available at: https://www.instat.mg/

Baulch, B. (1997). Transfer costs, spatial arbitrage, and testing for food market integration. American Journal of Agricultural Economics, 79(2), pp.477–487. DOI: https://doi.org/10.2307/1244145

Conforti, P. (2004). Price transmission in selected agricultural markets. FAO Commodity and Trade Policy Research Working Paper. DOI: https://doi.org/10.22004/ag.econ.23772

INSTAT (2023). Statistical data on prices. National Institute of Statistics of Madagascar. Available at: https://www.instat.mg/

Cleveland, W.S. (1993). Visualizing Data. Hobart Press. DOI: https://doi.org/10.2307/2532898

Wooldridge, J.M. (2010). Econometric Analysis of Cross Section and Panel Data. MIT Press. DOI: https://doi.org/10.7551/mitpress/9780262232586.001.0001

Baltagi, B.H. (2021). Econometric Analysis of Panel Data. Springer. DOI: https://doi.org/10.1007/978-3-030-53953-5

Hamilton, J.D. (1994). Time Series Analysis. Princeton University Press. DOI: https://doi.org/10.1515/9780691218632

Hunter, J.D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), pp.90–95. DOI: https://doi.org/10.1109/MCSE.2007.55

Seabold, S. and Perktold, J. (2010). Statsmodels: Econometric and statistical modeling with Python. Proceedings of the 9th Python in Science Conference. DOI: https://doi.org/10.25080/Majora-92bf1922-011




DOI: http://dx.doi.org/10.52155/ijpsat.v57.2.8152

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