Purpose: Cotton is the backbone of Pakistan economy, as country is the 4th largest producer of cotton in the world. Despite this importance there is steep decline in cotton production over time due to climate change. The need to evaluate the potential of adaptation in improving cotton yield has necessitated this study.
Design/Methodology/Approach: This study is based on the farm household survey of four cotton producing districts, two from each Punjab and Sindh that were purposively selected from heat stress regions of Pakistan. Data were analyzed through multinomial endogenous switching regression model and treatment effect framework.
Findings: Farm management practices were evaluated for their significance in reducing adverse impacts of climatic extremes on cotton yield. Adaptation in the combination of first three strategies observed to be the most successful strategies in increasing yield.
Implications/Originality/Value: For effective adaptation access to credit and extension, education, farming experience, and sources of information revealed to be important predictors
This study investigates the effect of working capital management on profitability of 148 diverse manufacturing firms listed on Karachi Stock Exchange, Pakistan for the period January 2006 to December 2011. The fixed effect and random effect models results revealed that firms’ aggressive strategy of financing negatively affect the profitability. Moreover, tight credit policy, efficiency of stock-in-trade management, early payment policy and conservative strategy of investment in current assets are found to have significant positive effect on profitability of firms. Findings of the study suggested that profitability of firms can be improved by devising optimal working capital management policies and also emphasized the investigation of factors that must be considered by management while formulating appropriate working capital management policies.
The study is aimed to investigate the effect of working capital management on the operational liquidity position of diverse manufacturing firms listed on Karachi Stock Exchange, Pakistan. Descriptive statistics, Pearson’s correlation analysis and Pooled (OLS) analysis techniques are applied on the balanced panel data set of 148 manufacturing firms for the period (Jan. 2006 to Dec. 2011). It is found that tight credit policy, efficiency of inventory management, delayed payment policy and overall efficiency of working capital management have significant positive effect on firm’s operational liquidity position. But conservative strategy of investment in current assets and aggressive strategy of short terms financing are found to have negative effect on operational liquidity position of firms. The study suggested that operational liquidity position of listed manufacturing firms can be improved by using appropriate policies and strategies of working capital management. This study can be extended by investigating the differences in working capital management efficiency and working capital policies across various industries and their effect on various measures of liquidity position of firms.
Purpose:Cotton is the backbone of Pakistan economy, as country is the 4th largest producer of cotton in the world. Despite this importance there is steep decline in cotton production over time due to climate change. The need to evaluate the potential of adaptation in improving cotton yield has necessitated this study.
Design/Methodology/Approach:This study is based on the farm household survey of four cotton producing districts, two from each Punjab and Sindh that were purposively selected from heat stress regions of Pakistan. Data were analyzed through multinomial endogenous switching regression model and treatment effect framework.
Findings:Farm management practices were evaluated for their significance in reducing adverse impacts of climatic extremes on cotton yield. Adaptation in the combination of first three strategies observed to be the most successful strategies in increasing yield.
Implications/Originality/Value:For effective adaptation access to credit and extension, education, farming experience, and sources of information revealed to be important predictors
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