My introduction to policy design was as an undergraduate following the Agriculture Tripos at Cambridge University and completing a post graduation in economics at Cambridge. In the Tripos we were trained in how to turn up on a farm, ask the right questions and then create a farm plan designed to not only increase farm productivity but also raise the farm family's income. Our lecturers came mainly from the Farm Economics Branch which completed the economic analysis of farm surveys conducted by the National Agricultural Advisory Service (NAAS) in East Anglia.
The training received at the School of Agriculture at Cambridge was extremely good. However, it was only later in further post-graduate economics studies that I began to realize just how useful it was. Part of the post graduate course was attendance at macro and microeconomic sequences at the Faculty of Economics and core required courses on biometry (advanced statistics) and project evaluation which in essence was project and policy design. There was also a requirement for a dissertation on a topic of choice.
I was somewhat taken aback that courses did not include business planning or policy design. These topics covered in microeconomics and macroeconomics and at Cambridge, macroeconomics being drenched in Keynesianism which at several junctures in the course work appeared to me to be quite ideological as opposed to being based on empirically based microeconomic production functions. I was struck by the same issues with monetarism which was more a topic at Stanford University Department of Economics, for the same reason.
NAAS extension agents advised farmers on latest innovations in machinery, equipment, crop and livestock production techniques and genetic advances. From this information we had access to a vast database of different farm sizes, production combinations and their profitability measure in terms of gross margins. We therefore had a range of references on what could be classified as good, average and poor practice (performance) according to farm sizes, soil conditions and production combinations. We were very fortunate to be tutored by David Wallace the pioneer for the introduction of gross margin analysis in British farming promoted through programmes on the BBC. Linear programming was used to optimize farm plans.
Therefore, when it came to the topic of policy design we had a realistic view of the ability and therefore the likelihood of farms responding to policy incentives. Even as students, we had a good feel for handling such questions as a result of our experience of visiting farms and carrying out optimization plans. The reason this particular experience was of importance was that it was quite common to encounter unexpected reasons why farmers would not take up optimization plans and their range of justifications were always logical. At the beginning of our training, such considerations did not enter our decision analysis calculations because of the assumption farms as businesses would mainly wish to maximize profits. With time we needed to take on board that, in general, farmers knew their business and their own capabilities well and therefore during the farm visit phases we learned to ask the right questions so as to shape our plans to what the farmers wanted and not to what we thought the farmer needed. This reality meant that policy incentives needed to be clearly articulated and easy to apply while also accepting that:
- a significant proportion of enterprises would either not respond to the incentives or only partially
- those that did respond would do so over a considerable time lag and the rate of take up
This overall model is applicable to any supply side sector an in particular manufacturing.
Reviewing OBR studies I have not come across any data or reports that take into account the wide spread of good, average and poor practice in different sectors or on surveys concerning state of the art technologies and associated training requirements in appropriate techniques of application or of estimates on the level of take up of changes to improve productivity. Even if the government budgets do not contain such details, they are unlikely to, it is self evident that if the OBR does not base its evaluation of such a baseline of statistics, it is not in a position to estimate the response of sectors to any types of supply side incentives.
The OBR have attempted to analyse productivity by applying a Cobb-Douglas function something I was exposed to in post graduation courses at Stanford University and which coming from a practical agricultural economic training I considered to be far too abstract and removed from the real economy. This approach operates at all encompassing macro level combining the few variables of Land, Labour and Capital into a production function which associates production or output to combinations of the main factors. This can create 3D production surfaces and isoquant maps of output equivalence with different factor combinations. Since the advent of the Real Money Theory (RMT) it is evident that this production function is missing at least 12 additional variables. The OBR has applied a variant to include energy as a factor to calculate changes in input-output of energy and energy prices to gains in productivity.
Since the OBR needs to evaluate sweeping declarations of supply side initiatives for growth contained in the Treasury mini-budget document "Plan for Growth 2022
" it is necessary for the OBR to apply a more detailed deterministic analysis in order to secure an objective assessment of the mini-budget.
The problem with the mini-budget is that there is no evidence that its preparation followed any known due diligence procedures along the lines of a decision analysis brief (DAB) which would be a demonstration of a coherent structure within which the plans could be justified. However, what we have is sweeping statements and it is up to the OBR to sort this confusion out. Under the terms of reference of the OBR their is no reference to the ability of the OBR to set submission standards for the preparation of budgets to facilitate the work of the OBR. It should not be the role of an independent evaluation body to wade through a proposition that is far from transparent. The government should have presented the analysis format they used to justify the plan so that the OBR would be able to cross-check within a relatively short period by simulating sensitivity analyses based on variations in the due diligence data.
It is evident that this is, as it stands, a sloppy arrangement and the Treasury needs to be required to present budgets in a more transparent and therefore useful fashion.
I will make suggestions on how to establish a workable format and analyses in a following note.
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