This study estimates a set of fixed effects/random effects models to ascertain the long-run relationships between poverty, income inequality, and growth using pooled data from eight household income and expenditure surveys conducted between 1992/93 and 2007/08 in Pakistan. The results show that growth and inequality play significant roles in affecting poverty, and that the effect of the former is substantially larger than that of the latter. Furthermore, growth has a significant positive impact on inequality. The results also show that the absolute magnitude of net growth elasticity of poverty is smaller than that of gross growth elasticity of poverty, suggesting that some of the growth effect on poverty is offset by the rise in inequality. The analysis at a regional level shows that both the gross and net growth elasticity of poverty are higher in rural areas than in urban areas, whereas the inequality elasticity of poverty is higher in urban areas than in rural areas. At a policy level, we recommend that, in order to reduce poverty, the government should implement policies focusing on growth as well as adopting strategies geared toward improving income distribution.

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This study investigates the impact of globalization (defined as phasing-out of the multifiber arrangements [MFA]/agreement on textile and clothing [ATC]) on working conditions of textile and apparel workers in general and female workers in particular in Pakistan. We found that the impact of the elimination of the MFA/ATC on workers of the textile and apparel sector is negative and statistically insignificant. The working conditions of workers in the textile and apparel sector are not different relative to other sectors. The working conditions of females deteriorated as a result of the phasing out of the MFA/ ATC in relation to the male working environment and compared with other industries. Our findings are robust in the sense that the direction of the impact remains the same and statistically significant even after performing sensitivity analysis. We also controlled for provincial heterogeneity, and the results showed minor correction in magnitude but remain negative and statistically significant. We also addressed treatment selection bias by performing propensity score analysis and found similar results.

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