Non-agricultural employment can be further divided into several industries such as manufacturing, retail, real estate, banking, etc. We aggregated these industry sectors into four categories (Business Activity, Services, Construction, and Mining), and as before, we then did a k-means clustering based on the percentage of population employed in each of these categories. We were thus able to label each district in terms of the dominant nature of employment in the district. A good clustering was obtained for k = 4, and these 4 categories represent high manufacturing industry presence (Type-4), high services industry presence (Type-3), moderate industry presence (Type-2), and low industry presence (Type-1). The maps below show the change in various discretionary and social infrastructure variables, with respect to the type of industries in the district. We can clearly make out that Type-3 and Type-4 districts (having services and manufacturing industries) are more likely to grow faster in all the variables except the main source of water. Put together with the earlier hypotheses, this shows that discretionary variables tend to improve with improvements in formal employment in the manufacturing and services sectors by generating disposable income. These sectors are likely to employ more literate people and hence we see a strong correlation between literacy and formal employment.
Districts with Low Industrial Presence |
Districts with Moderate Industrial Presence |
High Manufacturing Districts |
High Services Districts |