EBPM has finally begun in Japan
In 2017, the government announced the Basic Policy on Economic and Fiscal Management and Reform 2017, the so-called Basic Policies 2017, which clearly states “the promotion of evidence-based policymaking.” In August 2017, the EBPM Promotion Committee was held as a system to promote EBPM throughout the government.
In fact, the movement for evidence-based policymaking has been taking place for about 20 years in Europe and the U.S., and it has become a global trend in recent years, with evidence-based policymaking having been actively introduced under the Obama administration in the U.S.
The background to this is that millions of individual data on consumers and workers, or so-called micro-data, have been accumulated to become big data, and information technology to analyze them has developed to facilitate data utilization.
In the U.S., experts called data scientists are actually in the government or have created a specialized company to be involved in policymaking.
In other words, EBPM is based on the idea of quantifying and digitalizing events to understand their causal relationships and utilize this information to build and improve institutions.
In short, EBPM is the formulation of policies after analyzing facts objectively, without falling into reliance on precedents or a conflict of ideologies.
The effectiveness of EBPM can be seen in the policies of leading countries. Symbolically, it is also used to formulate policies to alleviate poverty in developing countries.
Japan has finally begun to move in the direction of promoting this movement. In response to the government’s Basic Policies, each ministry and agency is moving to introduce EBPM.
In the fields of food, agriculture and rural sectors, EBPM first appeared in the Food, Agriculture and Rural Area Policies in FY2018, and more recently, the promotion of EBPM was clearly stated in the New Basic Plan for Food, Agriculture and Rural Areas (Cabinet Decision of March 31, 2020).
Making Use of Agricultural Big Data in EBPM
Moreover, given that Japan’s food self-sufficiency rate is not high, rebuilding agricultural policies is a major challenge for us. In this sense, too, expectations are high for the promotion of EBPM.
However, in order to promote EBPM, there needs to be big data, an accumulation of micro-data, in the agricultural sector. In fact, Japan has been collecting world-class rich agricultural and forestry statistics, such as in the Census of Agriculture and Forestry and the Agricultural Management Statistics Survey.
In particular, the Census of Agriculture and Forestry is conducted every five years and is a complete survey. In short, in the case of agriculture, all farmers in the country are subject to this survey, except for those who engage in gardening as a hobby, and the number of samples has reached about 2 million.
Furthermore, this survey has been conducted ever since 1950, therefore, the total amount of data accumulated is enormous.
However, the purpose of the data was to understand the current state of agriculture and forestry and was not intended for research. Thus, data utilization itself was very difficult.
In other words, although we have accumulated such a vast amount of data, the data were mostly used only as aggregate data.
Things changed when the Statistics Act was revised in 2007. As a result of this, data use which was previously considered unintended has become easier.
Of course, as the data are individual data and their handling requires extreme caution, it is only natural to have regulations to prevent personal information being leaked and other accidents.
However, it is very significant that valuable data, which were previously treated as mere aggregate data, can now be utilized.
So, how can this data be utilized based on EPBM? I think that it can be used to conduct a more rigorous analysis based on causality in policy evaluation.
As a matter of fact, there has not been much effort in the past to conduct a rigorous evaluation of the policies that have already been implemented and apply it to subsequent policy formulation.
For example, the overwhelming majority of cases of Japanese farm management are family businesses, and there is a policy to promote and support incorporation, improve productivity, and incubate local core farmers. When evaluating the effectiveness of this policy, it is not sufficient to simply compare the productivity and profits of farmers that became incorporated with those that did not.
This is because incorporated farmers are, in the first place, farmers of a certain size who can show performance. A simple comparison of farmers who cannot be incorporated is not a rigorous evaluation of this policy.
However, by analyzing big data, which is the accumulation of individual data, I think it will be possible to accurately ascertain the causal relationship between the policy and the results. If a policy can be rigorously scrutinized based on the data, it can be said that it is an evidence-based evaluation.
If we can carry out that much work, we will be able to understand what we have achieved and what we have not achieved in that policy, and then we will be able to understand what kind of policy we should formulate next.
It is important to compatible data analysis with field survey
For example, when we have to start a new initiative to change the status quo, it is rare in the agricultural sector as well as other industrial sectors and the business world to analyze past initiatives using data, or in some cases, to obtain data on new initiatives and examine the causal relationships between initiatives and their results.
On the contrary, we often rely on successful precedents and incorporate them without strictly evaluating them.
Of course, especially in the business world, as we often don’t have time to spare, such a method is inevitable to achieve short-term results.
However, the field of information technology is rapidly developing, and the analysis of big data is becoming easier.
For example, the intuition and rules of thumb of a person in charge are actually important in making proposals based on them, but in this age of complex organizations and stakeholders, it is difficult to convince the people around you on those grounds alone.
After all, it is effective to analyze data using information technology and econometric analysis and to draw up a plan based on evidence, and it is also easy to obtain the consent of those around you.
And being obsessed with short-term results alone could lead to neglecting long-term challenges.
For example, in the agricultural sector, environmental issues and climate change are important challenges. Nevertheless, we tend to focus on urgent issues and current problems, and less on environmental issues, which are long-term issues.
However, owing to climate change, the growth of crops that have grown well until now has deteriorated, and many farmers are increasingly suffering from natural disasters caused by abnormal weather.
Solutions to long-term issues cannot be offered immediately, but I think we will be able to find more effective methods by accumulating data and collecting evidence. And it should be the same in the business world.
I think that quantification and data analysis are ways to visualize problems that have been difficult to grasp and make it easier to seek solutions. EBPM, which is based on the above, is a global trend and must be actively adopted in Japan.
However, there are things that cannot be understood or captured by data alone.
It is important, too, to understand what cannot be grasped by numerical values through observation and investigation at rural fields. It’s like two wheels of a cart that can’t run if one wheel is missing; it is important to compatible field survey with data analysis.
I think it is important for EBPM to achieve this balance. Also, I think this kind of thinking is necessary in the business world, too.
* The information contained herein is current as of November 2020.
* The contents of articles on Meiji.net are based on the personal ideas and opinions of the author and do not indicate the official opinion of Meiji University.
* I work to achieve SDGs related to the educational and research themes that I am currently engaged in.
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