Modeling the contribution of district-level cotton production to aggregate cotton production in Punjab (Pakistan): an empirical evidence using correlated component regression approach

This study aims to determine the relative importance of major cotton-producing districts with reference to their aggregate cotton production in Punjab using the time series data from 1982-2021. The empirical analysis is conducted with a correlated component regression approach, which is comparatively more suitable to multicollinear and high-dimensional data sets like those examined in this study. The standardized regression coefficients' absolute value determines each district's rank and relative importance. The degree of geographic concentration of the district-level impact is examined by the Herfindahl-Hirschman Index (HHI). The empirical analysis is analyzed for two-time windows. The first-time window includes crop years from 1982 to 2001, and the second window covers crop years from 2002 to 2021. The Findings of the study indicate that all the included districts had a positive and statistically significant impact on Punjab provincial aggregate cotton production in both the study periods, with the exception of districts Sargodha and Mianwali, which had a negative impact on Punjab-aggregate-cotton-production from 1982 to 2001. Furthermore, the estimated results of the correlated component regression show a decline in the degree of geographic concentration during 2002-2021, as indicated by a 36-point decrease in the value of standardized coefficient HHI. This indicates that technological advancements and policy factors have contributed to greater geographic variation in the relative importance of each district during 2002-2021 compared to 1982-2001. The cumulative sum of residuals (CUSUM) and cumulative sum of square residuals (CUSUMSQ) for both time periods show that the estimated parameters of the correlated component models are structurally stable.