Impact of gender participation in non-farming activities on household income and poverty levels in Pakistan

Work. 2015;52(2):345-51. doi: 10.3233/WOR-152103.

Abstract

Background: In the rural areas of Pakistan, the majority of farm households have small landholdings of less than 2 hectares. Both male and females are engaged in farming and non-farming activities. However, in Pakistan the gender-wise participation in farming activities is not much documented.

Objectives: The main objective of the current study is to estimate the impact of male and female participation in non-farming activities on a household's income level and poverty status in Pakistan.

Methods: The current study is based on a cross-sectional data set collected from 325 households through a purposive random sampling technique. A detailed comprehensive questionnaire was prepared for data collection. The data were analyzed by employing the propensity score matching approach.

Results: The empirical results indicate that both male and female participation in non-farming activities has a positive impact on household welfare in Pakistan by raising income levels and thus contributing to poverty reduction. However, the impact is greater when the males of a household take part in these activities rather than the females.

Conclusions: In the past only a few studies have focused on gender-based participation in non-farming activities. The non-farming sector is an important one in rural areas, especially in developing countries like Pakistan. More opportunities need to be created for both men and women in rural areas of Pakistan to find off-farm work, in order to increase household income and reduce poverty levels.

Keywords: Non-farming opportunities; propensity score matching; rural areas.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Cross-Sectional Studies
  • Educational Status
  • Employment / statistics & numerical data*
  • Female
  • Humans
  • Income / statistics & numerical data*
  • Male
  • Pakistan
  • Poverty / statistics & numerical data*
  • Rural Population / statistics & numerical data*
  • Sex Factors*
  • Surveys and Questionnaires