Alert Sign Dear reader, online ads enable us to deliver the journalism you value. Please support us by taking a moment to turn off Adblock on Dawn.com.

Alert Sign Dear reader, please upgrade to the latest version of IE to have a better reading experience

.

With manifestos, hundred-day plans and agendas making headlines recently, one can earnestly hope that all the promises made by political parties will, one day, be executed in their entirety and the impact will be visible for all to see.

This, however, sounds way too optimistic. Policy plans do not always translate into results as hoped for when they are being crafted.

When applied and subsequently met with failure, post-implementation analysis is peppered with generic and often meaningless terms like ‘lack of institutional capacity,’ ‘political economic constraints,’ and ‘non-conducive ecosystem’ that do little to help diagnose the causes of the shortcomings.

I am not attributing failures to lack of political will at the top, as one may think. Let me make my case with a short story.

Blind spots

Before 1997, all teachers in Punjab were hired on a permanent contract by the Punjab Public Service Commission. The process was marred with inefficiencies.

Teachers were posted away from their hometowns without sufficient incentives, and the ensuing discontent led to reverse transfers within a few days of being posted.

Teacher expertise, experience and specialisation were unevenly distributed, which meant that far-flung, rural areas suffered from a shortage of teachers, let alone good teachers.

The result was high student-teacher ratios and multi-grade teaching (one teacher simultaneously teaching several grades), especially in primary and rural schools.

Editorial: The first 100 days...

This, together with several vacant posts and increasing student enrollments, necessitated the introduction of teacher rationalisation policies in the province; policies that aimed to equitably distribute teachers throughout the province.

Years 1998, 2005, 2008, 2010 and 2014 saw the implementation of these policies.

A thorough dissection of the 2014 rationalisation policy reveals that, while the policy was a very comprehensive document, developed by a consortium of international consultants, the implementation staff i.e. the clerks working under the District Coordination Officer did not understand it.

The policy contained a formula for equitable distribution of teachers with finer details that the clerks did not want to invest time in understanding and had no incentive to do so.

Hence, while some districts were able to implement the new policy, most districts relied on the age-old mechanism of near-random allocation they had always been using, resulting in uneven distribution of teachers in terms of experience and expertise once again. This did not allow any room for teachers’ preferences either.

This was one of the several reasons why the results were no different than they had been before the new policy was drawn up.

So while it was a well-intentioned piece of policy crafted by experts, the implementation failed to meet the expectations of its creators.

The policy's focus was the correction of unequal deployment; it did not take into account the fact that its underlying cause was not only the political and economic mesh that defined it, but also how the policy vision was lost further down the implementation ladder, especially since the policy, when designed, had not engaged key stakeholders such as teachers, district education management and implementing officers in districts.

Making policies effective by design

Before one delves into policy design, a rigorous analysis of the underlying causes is critical to assess the factors that contribute to the problem.

It is even more crucial to go beyond the obvious and address these causes so that a sustainable solution can be devised.

Using data is vital to gauge these deep-rooted problems and design a long-lasting policy solution that go beyond just the symptoms. This exercise often results in unexpected conclusions.

Here is one example:

Italian economist Oriana Bandiera (with Andrea Prat and Tommaso Valletti) analysed purchases of 21 generic goods made by 200+ public bodies in Italy. The findings were surprising on various levels.

Firstly, there was a vast difference in the average prices paid for similar goods — a 55% difference between 10th and 90th percentile.

Related: How realistic are PTI’s grand plans?

Goods bought at the same time, in the same location and of the same quality were bought at different prices. The variation may have been expected, but it was the scale that was more concerning.

It is worthwhile to mention that we conducted a comparable exercise in Punjab, which resulted in similar baseline conclusions.

So, why was this happening in Italy? What could be the most obvious hypothesis? It is a natural instinct to blame it on corruption.

However, that was not the case. It was not the bureaucrats’ corruption, but passivity that was the reason.

The experiment showed that 83% of this ‘waste’ was due to bureaucrats' apathy towards the expenditures in their offices.

The bureaucrats being negligent and lazy turned out to be a bigger problem than active corruption.

This revelation changes the nature of any possible policy solution. Data generating from this experiment debunks the commonly held hypothesis.

The analysis further added that these findings could potentially lead to savings of up to 1% of Italy's GDP.

This study's conclusions mean that Italian governments will not be required to raise taxes in order to expand the fiscal space, but can use the data to design smarter policy.

Data can steer the course

These anecdotes powerfully drive home the importance of using data for accurate diagnosis. It is one of the key inputs in breathing life into the hundred-day plans, policy vision documents and reform agendas we discuss and debate.

Once a data-led solution is in place, more data collected on implementation and impact does not only ensure that the ensuing action is effective, but also feeds back into the design to iron out any kinks and further improve the policy.

Read next: Desperately seeking data in Pakistan...

Implementation data from the Benazir Income Support Program gives us similar valuable results that may help cast the programme's net wider, bring more recipients under its coverage and enhance impact, simply by the addition of numbers to the policy mix.

So here is what we have in a nutshell: better diagnosis, smart and human-centred policy solutions and solid implementation may help us fully cash in on the optimism of the policy plans presented during the electoral campaigns — much beyond the election hype and the first hundred days.

These policy solutions will steer this momentum to initiate change and accelerate progress, for change and continuity together is what we need, with data to help us stay the course.


Are you an analyst working on policy reform? Get in touch with us at blog@dawn.com