This study analyzes high frequency productivity data from Indian garment manufacturing, exploring how caste-based social networks affect individual and group productivity. With nearly 35,000 worker-days, a 1 percentage point increase in the same-caste workers boosts daily individual productivity by at least 0.09 points. Notably, the least efficient worker's productivity rises by almost 0.17 points with a 1 point increase in caste homogeneity. These findings, robust to unobservable factors, suggest production externalities driven by within-network peer effects as potential explanations.

Purpose

This paper aims to understand the causes of low adoption of hybrid rice technology. The paper also assesses the impact of adoption of hybrids and modern varieties on crop yield, vis-à-vis the old or traditional varieties.

Design/methodology/approach

Using unit-level data from a large-scale survey of farm households (19,877 paddy cultivators), the authors applied multi-nomial regression method to understand the factors for adoption of hybrid rice and instrumental variable method of regression to estimate its impact.

Findings

AbstractEmployment income data are coarsened as a result of questionnaire design. In the previous chapter we saw that Statistics South Africa (SSA) ask two employment income questions: an exact income question with a showcard follow-up. In public-use datasets, this results in two income variables: a continuously distributed variable for exact income responses and a categorical variable for bounded income responses with separate categories for nonresponse. It is the task of the researcher to then generate a single income variable that effectively deals with this mixture of data types.