Reaching new heights of policy success

For over two decades, various government committees—spanning different political dispensations—recommended the corporatization of the 200-year-old Ordnance Factory Board (OFB) to modernize operations.

- AJAY KUMAR

In 1985, the UK corporatized its Royal Ordnance Factories, the precursor to the OFB. Despite this clear model, India decided to corporatize OFB only in 2020, under Prime Minister Narendra Modi, highlighting the challenges of policymaking.

Another example is the protracted journey of India's data protection law, which has been under discussion since 2015 but remains unimplemented after nine years.

Policymaking's inherent complexities can sometimes delay decision-making, impacting overarching development objectives. By addressing these challenges, we can enhance the quality of public policy, driving meaningful change and ensuring sustained long-term growth.

However, resistance to change poses a significant challenge to effective policymaking. Individuals, organizations, and communities resist change due to fears of job loss, financial risk, or loss of influence. For instance, labour unions opposed computerization out of job security concerns. The older a practice, the more entrenched these interests, making reforms harder.

Government policy changes are further complicated by an entrenched culture of extensive documentation—laws, rules, executive instructions, and circulars. This documentation reinforces the status quo and creates a cascading effect, forming an intricate web of interdependencies. Each document often references others, which in turn spawns additional documentation. This interconnectedness creates a complex system where any modification can have ripple effects across several others, the full extent of which is often unclear. Artificial intelligence (AI) models should be developed to analyze government regulations and identify all potential interconnections arising from policy changes.