Thursday, April 28, 2022

Budgets as theories

A government has a new climate policy. It outlines how climate investments will be spread through a number of different ministries, and implemented by those ministries using a range of modalities. Some funding will be channelled to various multilateral organisations. Some will be spent directly by the ministries. Some will be channelled on to the private-sector. At some stage in the future this government wants to evaluate the impact of this climate policy. But before then it is been suggested that an evaluability assessment might be useful, to ask if how and when such an evaluation might be feasible.

This could be a challenge to those with the task of undertaking the evaluability assessment. And even for those planning the Terms of Reference for that evaluability assessment. The climate policy is not yet finalised. And if the history of most government policy statements (that I have seen) has any lessons it is that you can't expect to see a very clearly articulated Theory of Change of the kind that you might expect to find in the design of a particular aid programme.

My provisional suggestion at this stage is that the evaluability assessment should treat the government's budget, particularly those parts involving funding of climate investments, as a theory of what is intended. And to treat the actual flows of funding that subsequently occur as the implementation of that theory.  My na├»ve understanding of the budget is that it consists of categories of funding, along with subcategories and sub- subcategories, et cetera. In other words a type of tree structure involving a nested series of choices about where more versus less funds should go.  So, the first task of an evaluability assessment would be to map out the theory i.e. the intentions as captured by budget statements at different levels of detail, moving from national to ministerial and then to small units thereafter. And to comment on the adequacy of these descriptions and and gaps that need to be addressed.

This exercise on its own will not be sufficient as an explication of the climate policy theory because it will not tell us how these different flows of funding are expected to do their work. One option would be to follow each flow down to its 'final recipient', if such a thing can actually be identified. But that would be a lot of work and probably leave us with a huge diversity of detailed mechanisms. Alternatively, one might do this on sampling basis, but how would appropriate samples be selected?

There is an alternative which could be seen as a necessity that could then be complemented by a sampling process. This would involve examining each binary choice, starting from the very top of the budget structure and asking 'key informants" questions about why climate funding was present in one category but not the other, or more in one category than the other.  This question on its own might have limited value because budgeting decisions are likely to have a complex and often muddy history, and the responses received might have a substantial element of 'constructed rationality' . Nevertheless the answers could provide some useful context. 

A more useful follow-up question would be to then ask the same informants about their expectations of differences in performance of the amount of climate financing via category X versus category Y.  Followed by a question about how they expect to hear about the achievement of that performance, if at all.  Followed by a question about what they would most like to know about performance in this area. Here performance could be seen in terms of the continuum of behaviours, ranging from simple delivery of the amount of funds as originally planned, to their complete expenditure, followed by some form of reporting on outputs and outcomes, and maybe even some form of evaluation, reporting some form of changes.  

These three follow-up questions would address three facets of an evaluability assessments (EA): a) The ToC - about expected  changes, b) Data availability , c) Stakeholder interests.  Questions would involve two types of comparisons: funding versus no funding, and more versus less funding. The fourth EA question, about the surrounding institutional context, typically asks about the factors that may enable and/or limit an evaluation of what actually happened (more on evaluability assessments here).

 There will of course be complications in this sort of approach.. Budget documents will not simply be a nested series of binary choices, at each level their work may be multiple categories available rather than just two.  However informants could be asked to identify 'the most significant difference 'between all these categories, in effect introducing an intermediary binary category. There could also be a great number of different levels to the budget documents, with each new level in effect doubling the number of choices and associated questions that need to be asked. Prioritisation of enquiries would be needed, possibly based on a 'follow the (biggest amount of) money 'principle.  It is also possible that quite a few informants will have limited ideas or information about the binary comparisons they are asked about.  A wider selection of informants might help fill that gap.  Finally there is the question of how to 'validate" the views expressed about expected differences in performance, availability of performance information and relevant questions about performance.  Validation might take the form of a survey of a wider constituency of stakeholders within the organisation of interest, of the views expressed by the informants.

PS: Re this comment in the third para above: "And to treat the actual flows of funding that subsequently occur as the implementation of that theory"  One challenge the EA team might find is that while it may have accessed to detailed budget documents, in many places it may not yet be clear where funds have been tagged as climate finance spending. That itself would be an important EA finding.

To be continued...

Sunday, April 24, 2022

Making small samples of large populations useful

I was recently contacted by someone who is working for a consulting firm that has a contract to evaluate the implementation of a large-scale health program covering a huge number of countries.  Their client had questioned their choice of 6 countries as case studies.  They were encouraging the consultancy firm to expand the number of country case studies, apparently because they thought this would make this sample of country cases more representative of the population of countries as a whole.  However, the consulting firm wasn't planning to aggregate results of the six country case studies and then make a claim about generalisability of findings across the whole population of countries.  Quite the opposite, the intention was that each country case study would provide a detailed perspective on one or more particular issues that was well exemplified by that case.

In our discussions, I ended up suggesting a strategy that might satisfy both parties in that it addressed to some extent the question of generalisable knowledge at the same time was designed to exploit the particularities of individual country cases.  My suggestion was relatively simple, although implementing might take a bit of work making use of whatever data is available on the full population of countries.  The suggestion was that for each individual case study the first step in the process would be to identify and explain the interesting particularities of that case, within the context of the evaluation's objectives.    Then the evaluation team would look through whatever data is available on the whole population of countries, with the aim of identifying a sub-set of other countries that had similar characteristics (perhaps both generic {political, socio-economic indicators} and issue specific) with the case study country. These would then be assumed to be the countries where the case study findings and recommendations could be most relevant. 

A shown in the diagram below, it is possible that the sub-set of countries relevant to each case study county might overlap to some extent. Even when one case study country is examined it is possible that it might have more than one particularity of interest, each of whose analysis might be usefully generalised to a limited number of other countries. And those different sub-sets of countries may themselves overlap to some extent (not shown below).  

Green nodes = case study countries
Red nodes = remainder of the whole population 
Red nodes connected to green nodes = countries that might find green node country case study findings relevant
Unconnected red nodes = Parts of whole population where case study findings not expected to have any relevance

Another possibility, perhaps seen as unadvisable in normal circumstances, would be to identify the relevant countries to any case study analysis after the fact, not necessarily or only before.  After the case study had actually been carried out there would be much more information available on the relevant particularities of the case study country that might make it easier to identify which other countries these finding were most relevant to. However the client of the evaluation might need to be given some reassurance in advance. For example, by ensuring that at least some of these (red node) countries were identified at the beginning, before the case studies were underway.

PS: It is possible to quantify the nature of this kind of sampling. For example, in the above diagram
Total number of cases =  37  (red and green). 
Case study cases = 5 (14%)  of all cases
Relevant-to-case-study cases = 17 (46%) of all cases
Relevant-to->1-case-study cases = 3 (8%) of all cases
Not-relevant-to-case-study* cases = 15 (40%) of all cases 

*Bear in mind also that in many evaluations case studies will not be the only means of inquiry. For example, there are probably internal and external data sets that can be gathered and analysed re the whole set of 37 countries.

Conclusion: We should not be thinking in terms of binary options. It is not true that either  a case is part of a representative sample of a whole population, or it is representative and of interest only to itself. It can be relevant to a sub-set of the population.