Data Driven Policy and Decision Making

Hello and welcome back to the blog!

Much of the content of this blog has looked at policies Gorbachev attempted to implement that were ultimately unsuccessful and then suggesting ways in which modern technology may have helped change his fortunes for this specific issue. A constant theme is that we are unsure of whether the effect of modern technology would have been profound as the real reasons many of these reforms failed was because they were deemed to go against the party stance on issues such as openness and allowing market forces control the economy. As a result he received much opposition from powerful people within and outside his party who did not want the status quo upset.

Today's focus will not be on any individual policy or reform but rather on how modern technology, information systems and big data technology have and will continue to revolutionize policy making with a focus on economic policy.

Advances in information technology has created a revolution in decision making, as touched upon in my previous blog, across every industry. It has taken much personal bias out of the decision making process and has moved more towards an evidence or data driven approach to decision making. One interesting example of this is how data was used to change policing in New York City in the mid 1990's. They created a new information system to track and map crime by location which helped them to allocate their resources more efficiently. In the subsequent years there was a reduction of above 70% in the murder rate in the city and the system was rolled out to many other cities with similar results. This shows the potential that big data and information systems can unlock.

Gorbachev had a national vision for the USSR he inherited and the role of an elected leader is to employ their ideologies and implement their vision successfully. This requires sound public policy that is transparent, accountable and effective. According to McKinsey, the way to achieve this is through data driven policy making. (Ibrahim, 2012)

A revolution similar to this is underway in government decision making. In the past policy making involves more trial and error and it was difficult to see problems and chart a course for improvement. The move away from paper towards digitization has helped with culminating data and closed many of the gaps in data that existed before. The improved data allows governments to target the problems more accurately. An example of a problem this is the climate change crisis and the policies that have been made to try and fight rising temperatures. This environmental data was previously held by scientists and there was a disconnect between science and policy until the data became indisputable, showing the power of data to affect change. Governments who previously gathered data from paper and inputted into government databases now have many more data collection methods which loosens constraints.(Esty & Rushing, 2007)

Another area in which governments are better able to respond now than in the past is dealing with crisis. Previously the government would have to wait for the problem, be it a terrorist attack or virus outbreak, to manifest. Now thanks to big data they can respond with preventative or to data that is being collected in next to real time. Real time data collection at Chernobyl could have led to a more appropriate action to the problem had it happened today.

Governments dealing with environmental and crisis events can clearly be helped by the information provided to them via big data. Another area that has plenty of applications is in the area of economic policy.

As part of the applied economics department in MIT Alberto Cavallo and Roberto Rigobon run the Billion Prices Project which was founded in 2006 during a time when the Argentine government was manipulating their inflation data. They built a model that more accurately measures the true inflation rate based on prices for goods sold by Argentine retailers. It is an example of the trend of big data being used to gauge how economies are performing. Another example is SpaceKnow which used satellite imaging to gauge the performance of the Chinese manufacturing sector. They take millions of snapshots of over 6,000 industrial sites and uses AI to turn these images into a measure for manufacturing performance.

Argentina thing economies, inflation. This Satellite tool indicated that Chinese manufacturing slowdown is deeper than the official government index indicates. (Wigglesworth, 2018)

In both the above examples this small research team was able to create a more accurate measure for key economic indicators. These would be of huge use to governments as they could make decisions with more accurate information. It is promising and gives a glimpse into the potential of big data in creating economic information for governments.

A more recent example of big data being used to provide insights into the state of the economy that would have been useful in developing economic policy came from Brexit in 2016. When it was announced that Britain had voted to leave the EU many such as Goldman Sachs expected the UK to fall into recession but they proved to be more resilient than most expected. One group that were not surprised by the UK’s economic resilience was Schroders who had developed a digital insights unit the previous year which drew on credit card data that gave an insight into real time into spending habits. From this real time data they saw that the announcement had a negligible impact and told their fund managers this which gave them an edge as to their understanding of the economy. In the hands of policymakers real time data would help them to make the most informed decision possible.

(Wigglesworth, 2018)

The area of data driven policy making has attracted academic attention and it describes it as aiming to make optimal use of sensor data and collaborate and co-create policy with citizens. Anne Fleur van Veenstra and Bas Kotterink have created a Policy Lab, which is an experimental environment for developing and testing policy. This design science approach to policy making involves citizens into the creation of policy. It has three key focuses:

1. To use new non-traditional data sources

2. Co-Creation

3. Experimentation with policy on real life pervasive problems such as poverty, homelessness etc.

(Veenstra & Kotterick, 2017)

This is an exciting new space and one that could have helped Gorbachev in creating more effective policy while also informing every day leadership decisions.

Bibliography

Esty, D. & Rushing, R., 2007. The Promise of Data-Driven Policymaking. [Online] Available at: https://issues.org/esty-2/ [Accessed 26 02 2020].

Ibrahim, M., 2012. Better data, better policy making. [Online] Available at: https://www.mckinsey.com/industries/public-sector/our-insights/better-data-better-policy-making [Accessed 26 02 2020].

Veenstra, A. F. v. & Kotterick, B., 2017. Data-Driven Policy Making: The Policy Lab Approach. TNO Strategy and Policy, pp. 100-111.

Wigglesworth, R., 2018. Can big data revolutionise policymaking by governments?. [Online] Available at: https://www.ft.com/content/9f0a8838-fa25-11e7-9b32-d7d59aace167 [Accessed 25 02 2020].