Democratizing PDIA knowledge one practitioner at a time

written by Salimah Samji

We now have 569 development practitioners in 64 countries who have successfully completed a version of our free PDIA course.

Since we began our online journey in November 2015, we have learned, iterated and adapted our course three times, essentially PDIA-ing our way forward. More than 80% of each cohort has completed our course evaluation, which has enriched our understanding of how our content was received, as well as helped us identify learning gaps. To address some of these gaps, we went against conventional MOOC wisdom and increased the length of our courses in our last offering, by adding 2 weeks to Principles and 4 weeks to Practice. As we had hoped, this change did improve the learning and did not significantly change the attrition rates or the overall rating of the course.

PDIA online course _81817

Here’s what we have learned in the third iteration:

  • Groups in the Practice course were able to hold their members accountable and they learned how to work together. 30 groups working on problems in 15 countries successfully completed the course in June 2017.
  • The practical parts of the course continue to be rated as most useful. In particular, problem construction, deconstruction, crawling the design space, authorization, isomorphic mimicry, reflections, and multi-agent leadership were listed as key takeaways for both the Practice and Principles courses. In their words:
    • Construction and the deconstruction of problems. Because it helped me focus on smaller element of a complex problem by the help of a fishbone, which in return helped me solved complex problem with ease.”
    • Authorisation – the clear articulation of what authorisation is, why it’s important to have, and why conflicting or unclear lines of authority can cause program failure or lack of authority to deliver a program.”
    • Crawling the Design Space Worksheet helped me to apply the concepts and tools in a real problem.”
    • Reflections, which allowed me to apply the learnt knowledge to my particular local context and experience.”
    • The real-life based experience, very hands on approach and some of the fundamental issues and reflections such as a new perspective on leadership in development contexts or tacit knowledge and ways to acquire it or how change happens in practice.”
  • The participants of the Practice course, a course for self-created groups working on a problem of their own, also listed change space analysis/AAA, iteration, teamwork, small steps, patience/persistence/grit, and contextualization as key takeaways highlighting the fact that they had understood the true meaning of doing PDIA. In their words:
    • The fishbone diagram served as a starting point for deconstructing and constructing our problem(s) and then served as a basis for the rest of the course, namely designing our entry points and interventions.”
    • The identifying change space enabled one know that even if there is small change space, there is still something one can do.”
    • The team exercises and bonding it brought about, the content of the course, concepts and ideas about leadership, change space etc. Above all, working contextually to solve problems.”
    • The PDIA tools are tangible outputs that I can easily explain how to use when approaching future problems. However, I also see the value in writing the reflections, as this cemented the important of the feedback process in learning from what worked and what didn’t.”
    • I have learned that it may be scary to thread through the unknown, but it is the best approach to find best fit solutions to an identified problem.”
    • The iterations helped bring theory into focus by practicing what we understood intellectually.”
    • The iterations. I believe it is the most important aspect of PDIA and without it chances of failure are great.”

We are delighted to announce that we will be offering the Practice of PDIA beginning September 3, 2017. Registration opens on Wednesday August 23. Stay tuned for more details!


Sequencing in the construction of State capacity: Walk before you can run

Guest blog by Ajay Shah

In thinking about the State, there are two useful principles:

  1. We should embark on things that we can do (i.e. don’t take on things that we don’t have the ability to do); and
  2. We should walk before we run (i.e. do simple things, achieve victory, then move on to a more complex problem).

These simple and obvious ideas have interesting implications for how we think about doing public policy and public administration in a place like India, where there is a crisis of State capacity, where numerous policy initiatives break down in the implementation.

Premature load bearing

Pritchett, Woolcock and Andrews, 2010 talk about `premature load bearing’ as a source of implementation shortfall. Their metaphor is a bridge that is built to a certain limited capability. If you run a truck on the bridge which has a weight that is too high, the bridge comes crashing down. In this example, the concept of `load’ and `load bearing capacity’ is quite clear.

Example. We commission a government facility that registers land transactions. This is inadequately sized. The staff collapses under the crushing pressure of a large number of transactions. A black market develops where some people pay bribes to get ahead. The staff is partly super-busy fire-fighting, and have no time to think about fixing the broken system. The staff is also happy to receive a steady flow of bribes and lacks incentive to fix the broken system. The load was too high, the capacity was inadequate, and the system collapsed.

What is load in public administration?

With a bridge, the load is clear: the mass of the vehicles that run on the bridge. How do we think about load in the public policy context? What are easy problems vs. what are difficult problems? Pritchett and Woolcock, 2003 suggest two dimensions of load: high transactions and high discretion. The example above (land titling office that collapsed) is a simple example where the question was of transaction processing capability. That is an easy one to comprehend. But we should look beyond this simple engineering perspective of counting the number of transactions. A public administration problem is more difficult when front-line officials have more discretion.

There is a valuable third dimension to thinking about load, which is to think about the stakes. What is at stake? What could a corrupt official stand to gain? It is easier to run a system where the stakes are low. As an example, the personal gains to a school teacher from being absent half the time at school are relatively small. While it’s hard to make school teachers show up to work and to teach well (it is a transaction-intensive discretion-intensive service), this is not that hard, as the stakes are low. But the gains to a bad tax official can be 1000 times larger than the gains to a bad school teacher.

Thinking about the incentives of the civil servant takes us to thinking about load that comes from the magnitude of the principal-agent problem between the objectives of the organisation and the objectives of the individuals that man it. The load that is placed upon a system is the extent to which the objectives of the individuals diverge from the objectives of the organisation.

Example: Parking enforcement

Every economics student is exposed to the problem of enforcing self-service parking meters. Cars are supposed to pay Rs.10 for parking. Suppose we do not police compliance pervasively. Suppose there is only a 0.01% probability of getting caught. Let’s set the fine at Rs.1000. Risk averse persons will then prefer to pay the fee for sure (i.e. pay Rs.10) instead of taking the risk of losing Rs.1000.

The attractive thing about this fable is that it shows us the path to a small traffic police force. Instead of having a large number of policemen watching all cars, we can get by with limited enforcement. We can have a small government and yet get the job done. At first blush, you would think things are always easier in public administration with a small police force, which calls for a smaller number of transactions.

The story changes significantly when we worry about the principal-agent problem between the police department and the front-line enforcer of the fine. Do we have the ability to create checks and balances where the police will actually collect a fine of Rs.1000? Or will this collapse in pervasive corruption?

The magnitude of the fine is the load that the system is placed under. When the fine is small, e.g. Rs.100, the policeman has the choice of taking a bribe of Rs.50 and catering to his personal interest, or insisting on the fine of Rs.100. This is a small divergence between self-interest and the objectives of the organisation. But when the fine is Rs.1000 or Rs.10,000, the gap between the two enlarges. The system is placed under greater load.

Suppose the user charge is u, the probability of getting caught is p1and the fine is F. In the textbook, we try to make p1 small, so as to have a small police force, and compensate by ratcheting up F=u/p1. However, large values of F are a large load upon the system. We have to ask ourselves whether we have designed a public administration mechanism that is able to deal with a large F.

What is load bearing capacity?

We are asked to build a bridge that will be strong enough to take 10 main battle tanks weighing 60 tonnes each. Now we must pull together an elaborate array of design features in the bridge, so that it is able to cope with this load.

In similar fashion, once we know about the number of transactions, the extent of discretion of front-line officials, and about the stakes, we have a characterisation of the load. What are the elements that shape load bearing capacity?

The simplest question is transaction processing capacity. If there are 100 land market transactions a day, we must build a facility that will have commensurate capacity. The second dimension is discretion: if systems can be designed which reduce discretion, this will increase the load-bearing capacity. In some situations, IT systems can remove discretion and thus remove one dimension of the load.

The most important element of load bearing capacity is to think about the stakes, i.e. the maximisation of the official. Public administration is about establishing processes so that the organisation achieves its goals even though individuals have divergent personal interests. The task of management is to reshape incentives so that the narrow self-interest of employees gets aligned with the objectives of the organisation. The quality of the processes, and the checks and balances that have been designed, determine the load-bearing capacity.

Let’s go back to the parking fine problem. What shapes the thinking of the policeman? There is a probability p2 that he gets caught if he asks for a bribe. From his point of view, if p2 is high and F is low, then it’s safer to just enforce the fine. As the fine gets bigger, he is more tempted to ask for a bribe. Under good conditions of public administration, p2 is high. If a cop in London asks for a bribe, there’s a good chance that he gets caught. Our job in public administration is to build the checks and balances so that p2 goes up. The load-bearing capacity of the system is reflected in p2.

There is a tension between making a problem easier by reducing the number of transactions vs. making a problem easier by reducing the stakes. E.g. when a parking fine goes from Rs.1000 to Rs.100, the number of fines (i.e. the number of transactions and citizen-interfaces) has to go up by 10 times.

Consequences of premature load bearing

When an organisation is asked to deal with load that goes beyond its load-bearing capacity, what results is a rout: `a collapse of organisational coherence and integrity’. While lip service to the goals of the organisation continues, on the ground, there is an every day reality of a large divergence between the behaviour of individuals in the organisation versus the objectives of the organisation.

Once an organisation collapses in this fashion, it shifts into a low level equilibrium of pervasive rent collection. All that goes on is the abuse of coercive power of the State in favour of laziness and corruption by the persons manning and wielding the instruments of power. These rents can often get ingrained into a new political arrangement, and political incentives for preservation of the status quo. Pritchett et. al. thus encourage us to watch out for premature load bearing, particularly because it can lead to sustained and persistent implementation shortfall, and create an incumbent set of players who are the biggest obstacles to fixing things.

We see this with entrenched bureaucracies in many areas in India. Premature load bearing led to an organisational rout, and in the wreckage we now have incumbents that man the State machinery who are zealously defending the corruption and laziness.

Walk before you can run

We should build the parking enforcement system through the following constructive strategy:

  1. First, we would setup a parallel and independent measurement system to obtain data about the extent to which cars are not paying the user charge and the perception in the eyes of citizens that when they are caught, they pay a bribe instead of the fine.
  2. Next, we would build a large police force (i.e. high p1) and a low F. We would put down all kinds of monitoring and checks and balances, in order to overcome the principal-agent problem. We would design accountability mechanisms to create pressure on the leadership and at every level of the police force, so as to get p2 up.
  3. We would fight with implementation shortfall until the survey evidence shows us that the offenders are paying the fine and not the bribe.
  4. Only then would we announce We know how to run this system for a certain F and p1.
  5. Only then would we take the next step, of reducing the police force by 25% and increasing the fine to 1.25F. This will be assisted by behavioural changes among the police and citizens, who would have gotten more ingrained in good behaviour, where misbehaviour is more likely to set off alarms.
  6. We would do this, one step at a time, pushing up the fine by 25% at each step, and continuously watching the survey evidence to look at the incidence of bribe-taking. At a step where the survey evidence shows that bribe-taking has gone up unacceptably, we would stop and undertake deeper reforms of the public administration mechanisms so as to push up p2 until the extent of bribe-taking goes back to an acceptable level.

The four hardest problems in State building

The stakes are sky high in four areas:

  • The criminal justice system,
  • The judiciary,
  • Tax collection, and
  • Infrastructure + financial regulation.

In these four areas, the personal gains that staffers in government can get, in return for sacrificing the objectives of the organisation, are thousands of times larger than their wage income. They all involve a large number of transactions, and there is inescapable discretion in the hands of front-line officials. Here, creating the public administration machinery to make civil servants behave correctly is the hardest. These four areas are the most challenging problems in State building.

This has two implications:

  1. Particularly in these areas, we should learn to walk before we can run. At first, policy pathways should involve low load. In order to do this, we should push towards low transaction intensity, low discretion and low stakes.
  2. These four problems should take up the highest priority as the big hairy audacious goals of State building. The top management has to prioritise time and resources for these big four problems.

Example: Punishments in the criminal justice system

Every now and then, we have outrage in India about crime. After a great deal of hand wringing, the outcome too often is: Let’s increase the punishment.

The criminal justice system (laws, police, prosecution, prisons, courts) is one of the hardest problems in public administration. The policeman and the public prosecutor are able to talk with the accused and threaten that if the law is enforced, a certain punishment will flow, and ask for a bribe in exchange for not enforcing the case. The bigger the punishment, the bigger the divergence between personal benefits and the objectives of the organisation.

Ratcheting up punishments in response to failures of enforcement is thus precisely wrong. The criminal justice system is failing when given low load (e.g. 2 years imprisonment for rape). If the load is increased (e.g. 4 years imprisonment for rape) then this places a higher load upon a broken system. This will result in an inferior criminal justice system.

First we have to make the criminal justice system work with low punishments. There is a lot to be done, in building the criminal justice system, with reforms of laws, lawyers, police, public prosecutors and prisons. We should keep punishments low, and make this work in terms of processes, delays, arbitrariness, etc.

Only after that can we consider the tradeoffs in higher punishments. This consideration trumps all others. You may have a strong moral belief that a certain crime deserves a certain punishment. You may be able to demonstrate that the minimum level of punishment required to achieve deterrence is quite high. You may see mainstream practices in developed countries and think that gives a ballpark estimate for what a certain punishment should be. All these considerations are irrelevant. The maximal punishment that should be used is the one that we are able to pull off, in terms of the load bearing capacity of the criminal justice system. Only after we have established a high load bearing capacity can we bring other considerations into play, and potentially ratchet up to larger punishments.

There are other good arguments in favour of low punishments. One is Occam’s razor of public policy: we should desire the lowest punishment which gets the job of deterrence done. James Scott has a meta-principle: Prefer to do things that you can undo if you discover you were wrong. He has a footnote on this saying this is a good reason to not have a death penalty. A certain fraction of people that we convict are always Type 1 errors (innocent but convicted); the harm that we impose is lower when punishments are lower.

Example: Design of the Goods and Services Tax in India

Satya Poddar and I have an article on `walk before you can run’ applied to the design of the Indian GST.

Example: Armed forces

An article from October 2016.

Example: bankruptcy reform

An article from June 2017.


Most management is about principal-agent problems. Most public administration is about the principal-agent problem between citizen and State employees. To be a public policy thinker, we have to ask three questions. What’s the market failure? What’s the minimal intervention that can address this market failure? Do we have the ability to build this intervention, in the light of public choice theory? The third test is a big barrier in India. Many things that sound reasonable and are done by many countries are not feasible in India. We in India have yet to learn about establishing accountability mechanisms through which we obtain high performance agencies. We are at the early stages of this journey.

As Kaushik Basu says, there is libertarianism of choice and there is libertarianism of necessity. All too often, in India, the right answer is to do less in government, out of respect for limited State capacity. I think of `do less’ as two dimensions.

The first is to not do certain things at all e.g. it’s impossible for India to build unemployment insurance. This will permit scarce resources (money, management capacity) to be focused on a few core issues. As an example, Jeff Hammer emphasises that in the field of health, we should use our limited State capacity to emphasise public health.

The second is to get started with low loads. In an environment of pervasive implementation shortfall, we should first score victory with policy pathways that place low load upon public agencies. We should ask for high performance organisations which are able to deliver results when given easy problems : low transaction intensity, low discretion and low stakes. Only after we know how to walk should we consider running.

There is very limited capacity in terms of the top management which will build and run these systems. We have to focus on the few highest priorities that are worth pursuing. The big four are: criminal justice system, judiciary, tax collection and infrastructure + financial regulation. They should be the top priority in State building, and have the highest claim on scarce top management time and resources. In each area of these four areas, our strategy should be:

  1. Build an overall strategy;
  2. Create independent measurement so as to track the performance of the system e.g. survey-based measurement of the incidence of corruption in tax administration;
  3. Rescale the objectives of policy to minimise the load : i.e. favour pathways that involve low number of transactions, low discretion for officials and low stakes.
  4. Design for load-bearing capacity. This is about adequate sizing for the required number of transactions, setting up processes which reduce discretion, and creating checks and balances to get accountability. This involves many design elements, as has been done for macro/finance by FSLRC: clarity of objective, minimising conflicts of interest, limited powers and discretion, precision of rules, procedural and transactional transparency, accountability mechanisms including the rule of law.
  5. Achieve demonstrable success on low load problems;
  6. Consider increased load only after success has been achieved at low load.

How does this link up to the debate about small steps versus grand schemes, the tension between  incrementalism and transformative change? We should do transformative change like the GST, but we should start at a low load GST — low rate, flat rate, comprehensive base. We should first learn how to build the load-bearing capacity for this low-load GST, before contemplating a higher rate or multiple rates or a Balkanised base.


I am grateful to John Echeverri-Gent, Lant Pritchett and Vijay Kelkar for stimulating conversations on these issues.


Capability traps? The mechanism of persistent implementation failure. Lant Pritchett, Michael Woolcock, Matt Andrews. Working Paper, 2010.
Has a section on premature load bearing.

Solutions when the solution is the problem: Arraying the disarray in development. Lant Pritchett, Michael Woolcock. World Development, 2003.
Introduces the 2×2 scheme for classifying problems as hard when there is discretion and transaction intensity.

Improving governance using large IT systems. Ajay Shah. In S. Narayan, editor, Documenting reforms: Case studies from India, Macmillan India, 2006.
IT systems can be used to remove discretion and thus make some problems easier.

How do you know when to use PDIA?

written by Salimah Samji

We often get asked the question “why do you need to use PDIA for a problem that we already know how to solve?” The answer is simple. You don’t. If people have already crawled the design space and figured out how to solve a type of problem, then by all means, you should just apply the known solution.

We have developed two ways to help you determine whether PDIA makes sense for your problem or activity.

  • The first one asks four questions in order to determine the typology of your problem and the kind of capability required to solve it. If, for example, your activity is “implementation intensive” or “wicked hard”, PDIA might be a worthwhile. For more watch this video by Lant Pritchett or read chapter 5 of the Building State Capability book.
  • The second one looks at what capabilities exist to tackle a specific problem in a given context. We use an exercise to illustrate this whereby one is challenged to construct their journey from St.Louis, Missouri to the West coast in the United States in two different contexts. The first is the year 2015 and the second is the year 1804. The capabilities required in these two contexts are radically different, as will be the approach to solve the challenge. If your problem looks like the 1804 challenge (the lack of a map, etc.), then PDIA might be the right approach for you. For more watch this video by Matt Andrews or read chapter 6 of the Building State Capability book.

We use both of these in our PDIA online course and we have found that the visual and experiential nature of the 1804 exercise really helps drive this point home.

So you can imagine my delight when I saw that Chris Blattman highlighted both of these frameworks in his lecture notes on building state capability for his political economy of development course this week. He also wrote “This week’s lecture draws heavily on one of the most important books on development I’ve ever read: Building State Capability by Harvard’s Matt Andrews, Lant Pritchett, and Michael Woolcock.”


How did China Create “Directed Improvisation”?

written by Lant Pritchett

Yuen-Yuen Ang, a Professor of Political Science at University of Michigan came to speak at Harvard the other day and I was lucky enough to hear her presentation.  Her most recent book is How China Escaped the Poverty Trap, which is an original and insightful take on what is perhaps the biggest development puzzle of my lifetime:  how did China escape from long-term stagnation and political chaos into the fastest and longest and most poverty reducing burst of economic growth in the history of humankind?

Her framing of the fundamental problem is the conventional wisdom is that good institutions lead to greater wealth through higher levels of productivity and that greater wealth leads to better institutions.  This obviously leads to a “chicken and egg” problem (metaphorically, this is not about real chickens (or cash)).  Her unconventional insight is that this means the first challenge of development is to harness ‘weak/wrong/bad’ institutions to create markets. 

History has shown us that this first step of the chicken and egg problem of harnessing weak institutions to create markets is not at all an easy task.  Gerschenkron’s “advantages of backwardness”—the “advantage that, because the leading countries have pioneered technologies and industries, this makes it possible for the followers to progress more rapidly than their predecessors—are belied by the empirical experience of “Divergence, Big Time.”  Most countries that began behind in the post World War II era have remained behind, and disturbingly, many gotten even further behind.  And this divergence is not just in per capita income.  The “big stuck” in state capability documented in our book demonstrates that (a) many countries in the world have weak state capability and (b) the measured progress in state capability is very slow.

There have been three popular approaches to the “harnessing weak institutions to create markets” problem.  One has been the idea to first “build institutions” by the creation of the forms and structures of governmental organizations (e.g. post offices, Central Banks, police forces) with the hope form will create function—or, more prosaically, “fake it till you make it.”  Another has been more or less “big bang” efforts of market driven reform, on the presumption that freeing markets will lead to stronger institutions (or that both can be built simultaneously).  A third has been to “integrate” and draw on the strength of existing institutions (like countries joining the EU).

Professor Ang’s insight is that China recognized the weaknesses of each of those approaches and wanted to begin a process of “adaption” and for that they needed to create conditions conducive to “directed improvisation.”  In PDIA lingo this is the challenge of “authorizing positive deviation.”

Her insight is that the central government in China had to balance between control that was “too loose” and control that was “too tight.”  She shows that the central government issued directives of three types.  One clearly prohibited local governments from doing some things.  It was no question of “anything goes.”  Another type of directive clearly mandated that all local governments had to do certain things.  But the third type specified objectives but was deliberately vague about how to accomplish the objective and made it clear innovation was allowed but did not specify exactly what was allowed.  This created a space in which local governments could create their own innovations and attempts that the central government might have never thought of, while at the same time, allowing the central government the space to claw back if things were headed in bad directions.  This, she argues was central to China’s ability to (in our words) “crawl the design space” incrementally towards a market system.  The organizational and institutional forms created on the path—like Township and Village Enterprises—were, as to be expected, unique hybrid forms.  These allowed much of the functionality and dynamism of a market economy even before there were private firms and clear delineation of property rights.

This insight about creating conditions in which local governments were allowed to pursue “directed improvisation” and not strictly limited to what was forbidden and what was mandated reminded me of the old saw about different types of approaches.  In some countries “everything which is not expressly forbidden is allowed”, in others “everything which is not expressly allowed is forbidden”, some in which “everything is forbidden, even that which is expressly allowed” and those in which “everything is allowed, even that which is expressly forbidden.”  The challenge of building prosperous economies and capable states is creating the conditions in which there is possibility of innovation and improvisation, while not pushing systems into chaos.

There are three points in the more general context of development.

First, this “directed improvisation” is not the same as “directed experimentation.”  That is, one might say that the path towards innovation is a series of centrally directed and tightly controlled “experiments” that are evaluated rigorously.  But that approach has three important limitations.  First, it can easily push responsibility for innovation away from the local back to the “top” and leave design to a centralized process.  But precisely the reason for a decentralized approach to innovation is to get ideas no one would have thought of.  Second, this process is too slow as a means of exploring the design space.  As Nadel and Pritchett (2015) show, with even a modestly large design space and modestly sensitivity of success to design there are too many alternatives for a typical “design-implement-evaluate” process to explore.  What is needed are many options being explored simultaneously, even with the capacity for real time adoption of design.  Third, this approach implicitly assumes that organizational learning and organization capability building are two separate processes and that an organization can “juggle without the struggle.”  In fact, the process of doing the innovation is often what creates the capability to implement.

Second, the main challenge of development is not what donors or philanthropists or any other outsides do or don’t do, the main challenge is how governments (and societies more generally) can create conditions of a “good struggle” in which the authorization of positive deviation leads currently weak/bad/wrong institutions both to be able to delivery functionality of specific types and creates a long slog path to better institutions.

Third, even though donor and external agencies are not the principal actors, they can be a force for good or actually an obstacle.  External funders have particular problems with directed improvisation.  There are powerful pressures for donors to fund activities against a strict log-frame that specifies exactly what will be done by whom and when.  This is a management and accountability structure to ensure against malfeasance so that all resources are disbursed only against the prescribed activities.  But if all budget is locked into specific categories and even specific activities at the beginning of the project/program it is very hard to create space for a “search-frame” approach as the actors don’t have free resources.

Professor Ang is making important advances in understanding how development can be made possible in her approach to Complexity and Development 2.0.

Motivating teams to muddle through

written by Anisha Poobalan

In theory, PDIA seemed like the most logical, straightforward way to go about solving a problem. A team is formed, they deconstruct the identified problem and then attack each causal area, learning and adapting as they go. Being in the field, meeting with the teams weekly, hearing about the obstacles cropping up at each turn, I realize how frustrating and discouraging this work can be. The first challenge is to get the government officials working, but then comes the task of motivating them to keep at it. The temptation to just give up and revert to the status quo grows greater with each pushback they face.

Motivation is central to this work and motivation is difficult. Each team responds to different methods of motivation at different stages on their journey. Various strategies might boost a team for a week or two before they slow down again. In the past two months, the teams were motivated by presentations to high level authorities, responsibility sheets, healthy inter-team competition, inspiring stories from successful economies, brutally honest conversations, site visits, and more. A common factor in all these strategies is the accountability it creates. Creating a culture in which mid-level civil servants are inspired, empowered and then held accountable for delivering real outputs, is necessary if they are to remain motivated.

Throughout the project, teams voiced concerns at their lack of authorization. They doubted that superiors would support their work and proposals and this demotivated them. One team worried that policy makers would not incorporate their proposals and inputs from external consultants might outweigh the teams’ findings. Another team questioned their authority to directly engage with investors and yet another team worried about their inability to influence change. Over the past two months, teams have presented and received the support of several high-level policymakers, ministries and stakeholders. Much to the teams’ surprise, their superiors are keen to expedite approvals, empower the teams, and take ownership of the proposals made. Real work led to engagement which led to authorization and this high-level support and expectation has motivated the teams beyond belief.

Inter-team meetings and synergies motivate and create accountability as well. The teams eventually understood how dependent they were on each other and success for one team meant success for the whole group. If one team was slacking or faced a road block, the output from another team may not be demanded or used to its full potential. For example, when two inter-dependent teams met for the first time, they realized that although theoretically, the output from the first team was world-class, real world experience and engagements were necessary to inform these results. That was a gap the second team had learned to and now had the capability to fill. This meeting helped link their new, or in some cases latent, capabilities. This growing interdependence has created accountability for each team to deliver. As one team continued to work, they identified a gap in the economy that would challenge their success in the future. They were overwhelmed by the severity of the problem and realized they did not have the bandwidth to address this themselves. Much to their relief however, at the next launchpad session they found that another team was already addressing this issue and the team could assure external parties that the challenge was being addressed. The team worked harder at filling this gap once they knew another group was depending on them to succeed. These are big steps in a system that lacks synergy and suffers from severe coordination failure.

Navigating the local landscape in any context is difficult, but some of these officers have struggled with repeated coordination failure for almost 30 years. This leads to frustration, discouragement and cynicism about change. One of the teams experienced this when trying to share a summary document with another government agency. They had to share this document to get support from higher level officials and expedite their work. What should have taken two days, took over two weeks. A disheartening but useful lesson, this team is learning to plan ahead, follow up and prepare for such delays in their timeline. Another team is still waiting on the approval for a document submitted around six months ago. The time and energy spent on inter- and intra-agency coordination is frustrating but the teams have made considerable progress despite the difficulties. Their persistence and continued efforts are inspiring and we hope that these notes will encourage you to persevere in your own challenging contexts.

Building capability: the true success of PDIA

written by Anisha Poobalan

The PDIA team has been working in Sri Lanka for the past six months with five talented and motivated government teams. This work is challenging and demands hard work by government officials and yet through short, repeated iterations, real progress is achieved. The teams update a facilitator every two weeks while also preparing for their next two week ‘sprint’. Once a month, the teams meet together at a ‘Launchpad’ session to update each other, evaluate their progress, adapt their action plans accordingly and set out for the next month of hard work. I have the privilege of sitting in on team meetings every week. This work takes time, it takes perseverance and it requires trust, and the task of attacking some of the most challenging areas in government is frustrating but absolutely worth it with each breakthrough. While impossible to articulate completely, this post attempts to reflect the ground reality of practicing PDIA in order to build state capability.

Emergence, in complexity theory, is the process by which lessons learned from new engagements and activities lead to a unique recombination of ideas and capabilities that result in unpredictable solutions. Emergence is evident in each PDIA team. For example, as one team made progress on their problem, they identified a constraint that needed to be addressed if they were to succeed. Another team had a similar realization and eventually the idea for a potential solution cropped up and an entire team was formed around it. As one of the team members noted, the more we engage, the more opportunities arise and connections are made and we will get lucky soon!

As the teams prepare for their lucky moment and produce tangible products, the individual capability built is the true success of this work.  As one team leader said, ‘We haven’t done something like this in the 30 years I have been [working] here!’ At the first launchpad session, a team member told me about experiences they had had at similar workshops. ‘We meet and discuss various topics and then leave. But I think this will be different, we must actually do something.’ Faced with a new challenge, undertaking a task he had no experience in, this member is now an expert and motivates the others along. From the onset, he has been determined to achieve his targets and has proven to the rest in that team, that hard work and genuine interest can lead to unexpected, impressive learning and results.

Another team member, an experienced yet skeptical team leader, did not leave the first launchpad session quite as confident. She didn’t believe this work would lead to real results and doubted they would have the necessary political backing. A few months later, she is now the most motivated, engaged, focused member on his team. ‘So many people come to collect information, then they put down their ideas in a document and give it to us to act on. This just ends up on a shelf. It’s better not to talk, but to do something – so we are happy! Especially the support from the higher-level authorizers has given us confidence to keep working’. This team embarked on a journey from confusion to clarity. They had to trust this approach, take action and gradually fill the information gaps they did not even know existed a few months before. It has been frustrating, and yet they continue in good faith that with each piece of information gathered they are closer to a clear, achievable vision for their project. The capability to create project profiles like this has grown in this team and will be useful to their colleagues across government. These capabilities are the results of hard work, intentional engagement, and consistent expansion of authority.

Some people ask, ‘So what makes a good team? What departments should they come from or what expertise should they have?’ My answer to that is simple: a successful team comprises of people who are willing to work; government officials willing to trust a completely new approach and work hard. Hard working teams are essential to the success of PDIA and while expertise, seniority, and experience may be considered necessary, without genuine hard work, any team, no matter how talented, will fail. Here in Sri Lanka, each team is unique, with varying weaknesses and strengths they have learned to work around. Some teams lack strong leadership which forces team members to take greater responsibility and ownership in decision making and motivation. Other teams have strong leadership so some members took on less responsibility and at points didn’t contribute at all to achieving the teams’ goals. Some teams have capable workers frustrated by their lack of authority, and others have the authority but lack capability. There are teams that perform well with organized deadlines and targets, while others struggle to set deadlines beyond the coming week. Each team’s composition has adapted as the work evolved, and each team has achieved great things through their diverse skill sets, past experience, commitment to real work and time-bound action.

I hope these field notes help give a sense of what PDIA is like on the ground and how this approach, although difficult and emotionally draining, can lead to new, or make use of latent, capabilities within government.

If you are interested in learning more about the Sri Lanka work, you can read the targeting team working paper.

Initiating action: The action-learning in PDIA

written by Matt Andrews

I recently wrote a blog in response to a question I was asked by a colleague about how we move from the foundation or framing workshop in PDIA processes—where problems are constructed and deconstructed—into action, and beyond that, action learning. In this post I will offer some ideas on how we do that.

First, we push teams to action quickly: We ensure that the teams working in the framing workshops can identify clear next steps to act upon in the days that follow the workshop. These next steps need to be clear and actionable, and teams needs to know that action is expected.

Second, we don’t aim for ‘perfect next steps’—just action to foster learning and engagement: The steps team identify to start with often seem small and mundane, but our experience indicates that small and mundane steps are the way in which big and surprising products emerge. This is especially the case when each ‘next step’ yields learning (with new information, and experiential lessons) and expands engagement (with new agents, ideas, and more). This is because the problems being addressed are either complicated or complex, and are addressed by expanding engagement and reach (which fosters coordination needed to confront complicated problems, and interaction vital to tame complexity) and leads to learning (which is crucial in the face of the uncertainty and unknowns that typify complex problems).

Third, we create time-bound ‘push periods’ for the next step action assignments:  After the framing workshop, the PDIA process involves a set of action iterations where teams go away and take the ‘next step’ actions they identify, agreeing to meet again at a set date and time to ‘check-in’ on progress. Each iteration is called a ‘push period’ in which team members push themselves and others to take-action and make progress they otherwise would not.

Fourth, we convene teams for ‘check-ins’ after their push periods, and ask questions: The team reassembles after the push period, with PDIA facilitators, at the ‘check-in’ date—and reflects on four questions: ‘What was done? What was learned? What is next? What are your concerns?’ Note that the questions start by probing basic facts of action (partly to emphasize accountability for action, and also to start the reflection period off with a simple report—a basic discussion to precede deeper reflection, which often needs some context). We then ask about ‘what was learned’, where we focus on procedural and substantive lessons (about all their experiences—whether frustrating or inspiring), and learning about the context.

Fifth, facilitating learning requires nudging and pushing: We find that you often need to push participants to ask deep questions about their lessons.

  • For instance, someone may say “we tried to get Mr X to work with us, and he did not respond positively, so we learned that he does not want to work with us.”
  • We would follow up by asking, “why do you think Mr X did not respond?”
  • Often this leads to a new set of questions or observations about contexts in which work is being done (including, very importantly, the politics of engagement). In the example, for instance, the ‘why’ question raised discussion about how people engage in the government (and if the team reached out to Mr X in the right manner) and the politics of the context (the interests of Mr X and how these might be playing into his non-response).

This process facilitates learning by the teams and by my PDIA facilitators. Both the teams and our facilitators produce written documents (short, but written) about what was learned. Over time, we can keep coming back to these lessons to ensure everyone gains a better understanding of procedural, substantive, and context issues.

As a note: People often ask where we address ‘politics’ in PDIA. That requires another blog post, but hopefully you see, in the description here, the basic process of what we call Political Economy Engagement (PEE), which we prefer to Political Economy Analysis (PEA). The action steps in PDIA always involve pushes into—or engagements with-the context and yield responses that allow one to learn about politics (who stands where, who has power, how it is exercised, etc.)

Finally, we push teams to the next steps quickly, again—which is where they ‘adapt’: You will notice that the last two questions we ask are about next steps and issues to address in taking these steps. We do not let teams get bogged down by tough lessons, but push them to think about what they can do next, adapting according to the lessons they have learned; we focus on what is important and what is possible ‘next’, given what has been learned; and we try to build and maintain momentum, given the belief that capability and results emerge after accumulated learning and engagement from multiple push periods.

In conclusion, When considered as one full iteration, the blend of programmed action with check-in questions and reflection is intended to foster action learning and promote adaptation and progress in solving the nominated problems.  The combination of learning while producing results (through solving problems) is key to building new capability.


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Some linkages to theory, literature and management practice

  1. Why we focus on learning and engagement in this process: In keeping with complexity theory, the principle idea is that action leading to novel learning and engagement and interaction fosters emergence, which is the key to finding and fitting solutions to complex problems. Further in keeping with theory, the idea here is that any action can foster learning, and it is thus more important to get a team to act in small ways quickly than to hold them away from action until they can identify a big enough (or important enough) next step.
  2. Why we refer to ‘push periods’: The Scrum version of agile project management processes have similar time-bound iterations, called Sprints, which are described as ‘time-boxed’ efforts. We refer to ‘push-periods’ instead of sprints, partly to reflect the real challenges of doing this in governments (where CID focuses its PDIA work). Team members are pushing themselves to go beyond themselves in these exercises, and the name recognizes such.
  3. How we draw on action learning research, and our past experiments: Our approach builds on PDIA experience in places like Mozambique, Albania and South Africa, which has attempted to operationalize action learning ideas of Reg Revans (1980) and recent studies by Marquardt et al. (2009). These combined efforts identify learning as the product of programmed learning (which everyone is probably familiar with, and is often provided through organized training), questioning, and reflection (L=P+Q+R), which the PDIA process attempts to foster in the structure of each iteration (with action to foster experience, a check-in with simple questions about such experience, and an opportunity for reflection—facilitated by an external ‘coach’ figure). The questions asked in the PDIA check-in are much more abbreviated than those suggested by Revans and others, largely because experience with this work in busy governments suggests that there are major limits to the time and patience of officials, and asking more questions can be counter-productive (and lead to non-participation in the reflection process). The questions posed to teams are thus used to open opportunities for additional questions: like ‘who needed to be engaged and was not?’ or ‘why did you not do what you said you would?’ or ‘what is the main obstacle facing your team now?’ As the team progresses through iterations, they start to ask these more specified questions themselves, and come into the check-in reflection session with such questions in their own minds.

If you are interested in reading the Sri Lanka working paper, you can find the full version here