BIG DATA, OFFICIAL STATISTICS, AND PUBLIC POLICY
2016/2017, Semester 1
Lee Kuan Yew School of Public Policy (Lee Kuan Yew School Of Public Policy)
Modular Credits: 4
Empirical evidence is key to sound public policy formulatiom, monitoring, and evaluation. Official statistics, as trusted, organized information, have served this purpose for centuries; their production is institutionalized and governed by internationally-agreed ethics and practices. Unstructured information, including Big Data and Geoinformation, has emerged recently, offering public policy new empirical basis for making decisions. This has been described as ‘Data Revolution’ by international organizations. This course is designed for practitioners in the field of public policy to gain an indepth understanding of the design and intricacies of structured information (official statistics) and unstructured information such as Big Data and Geoinformation.
Identify information sources and examine their adequacy in public policy making, monitoring and evaluation.
Gain perspectives on the use of information in decision making in public policy.
Obtain in-depth understanding of the latest discussion on ‘data revolution’ and ‘open data’ initiatives, the measurement limitations of structured and unstructured data, the multi-mode approach to information management, and the latest trends in data utilization.
Provides students a sound basis to understand and assess various sources of data, including both structured and unstructured information, and to see how such information could be used in the context of public policy.
11 August 2016: Shopping week
From Data to Policy: The Logic of Evidence-based Public Policy and Decision Making
Examine the use of information in public policy formulation, monitoring and evaluation and the rationale for the push for evidence-based decision making globally. Concepts such as ‘data revolution’ and ‘open data’ will be introduced. The changing paradigms of supply and demand of information will be examined.
Scott, C. (2005). Measuring Up to the Measurement Problem: The role of statistics in evidence-based policy making. Paris21 Occasional Paper.
Cartwright, N., Hardie, J. (2012). Evidence-Based Policy: A Practical Guide to Doing It Better. Oxford University Press.
Economics and Statistics Administration (2014). Fostering Innovation, Creating Jobs, Driving Better Decisions: The Value of Government Data. US Government.
The Secretary-General’s Independent Expert Advisory Group on Data Revolution for Sustainable Development (2015) A World That Counts: Mobilizing the Data Revolution for Sustainable Development. United Nations.
18 August 2016
Official Statistics and Public Policy: Institutional Arrangements and global Support
Discuss the intricacies of how official statistics is organized nationally and globally and the implications of such arrangements.
Ward, M. (2004).
Quantifying the World: United Nations Ideas and Statistics
. Indiana University Press.
Holt, D. T. (2008). Official statistics, public policy and public trust.
Journal of the Royal Statistical Society: Series A (Statistics in Society)
, 171(2), 323-346.
Dilnot, A. (2012). Numbers and Public Policy: The Power of Official Statistics and Statistical Communication in Public Policymaking.
, 33(4), 429-448.
25 August 2016
Measuring Economic Activities in a Globalized World
Examine the basis and adequacy of official statistics in measuring national economic activities within and across national borders.
Understanding Economic Statistics: An OECD Perspective
. OECD Publishing.
Gutierrez, C. M., Glassman, C. A., Landefeld, J. S. & Marcuss, R. D. (2007).
Measuring the economy. A primer on GDP and the national income and products accounts
. Bureau of economic analysis (BEA), US Dept. of Commerce, September.
Stratford, K. (2013). Nowcasting world GDP and trade using global indicators.
Bank of England Quarterly Bulletin
, 53(3), 233-242.
Measuring Globalisation: OECD Economic Globalisation Indicators 2010
Selected articles from ‘The Economists Explains’
1 September 2016
Measuring the household sector: Poverty, Income Inequality and Cost of Living
Scrutinize the measurement of household economic attributes and the related indicators which are often targets of public policy.
Divided We Stand: Why Inequality Keeps Rising
. OECD Publishing.
OECD Framework for Statistics on the Distribution of Household Income, Consumption and Wealth
. OECD Publishing.
Cowell, F. (2011).
. Oxford University Press.
Meyer, B. D. & Sullivan, J. X. (2012). Identifying the disadvantaged: Official poverty, consumption poverty, and the new supplemental poverty measure.
The Journal of Economic Perspectives
, 26(3), 111-135.
: 7 September 2016
Measuring Social and Demographic Change
How do we measure social and demographic changes will be discussed and their implications for public policy will be assessed.
MacDonald, H. & Peters, A. (2011).
Urban Policy and the Census
. ESRI publishing.
United Nations Statistics Division. (2005). Principles and methods of Population and Housing Census. United Nations Publications.
United Nations Statistics Division. (2010). The World's Women 2010: Trends and Statistics.
. No.19. United Nations Publications.
15 September 2016
Measuring Global Development Goals: From Millennium Development Goals to Sustainable Development Goals
Discuss the measurement challenges of designing and evolving a global development agenda by the United Nations and implications for national policy formulation. The new Sustainable Development Goals will be adopted in September 2015 by world leaders in a special session of the General Assembly.
Sachs, J. D. (2012). From millennium development goals to sustainable development goals.
, 379(9832), 2206-2211.
Sachs, J. D. (2013). High stakes at the UN on the Sustainable Development Goals.
, 382(9897), 1001-1002.
Loewe, M. (2012).
Post 2015: how to reconcile the Millennium Development Goals (MDGs) and the Sustainable Development Goals (SDGs)?
. German Development Institute.
Egelston, A. E. (2013).
Sustainable Development: A History
. Springer Netherlands.
How’s Life? Measuring Well-being.
29 September 2016
Quality, Ethics and Errors in Official Statistics: Implications for Public Policy
Examine the performance of official statistics in various global economic crises and how official statistics deal with the issues of errors, quality and ethical standards.
Seltzer, W. & Anderson, M. (2001). The dark side of numbers: The role of population data systems in human rights abuses.
, 68(2), 481-513.
Gutmann, M. P., Witkowski, K., Colyer, C., O'rourke, J. M., & Mcnally, J. (2008). Providing spatial data for secondary analysis: Issues and current practices relating to confidentiality.
Population Research and Policy Review
, 27(6), 639-665.
Kaufman, C. E. & Ramarao, S. (2005). Community confidentiality, consent, and the individual research process: Implications for demographic research.
Population Research and Policy Review
, 24(2), 149-173.
6 October 2016
Data Revolution: Big Data and Public Policy
Discuss the emergence of unstructured data as a new source of information for public policy, including its contributions and limitations.
Mayer-Schonberger, Viktor; Cukier, Kenneth (2014).
Big Data: A Revolution That Will Transform How We Live, Work, and Think
. Eamon Dolan/Mariner Books.
Misuraca, G., Mureddu, F., & Osimo, D. (2014). Policy-Making 2.0: Unleashing the Power of Big Data for Public Governance. In M. Gascó-Hernández (Ed.),
(Vol. 4, pp. 171-188), Springer New York.
13 October 2016
Location Information Platform and Geoinformation for Public Policy
The integration of geoinformation with other empirical evidence for public policy is gaining pace for locality development through the adoption of location information platforms.
Thomas, C. & Sappington, N. (2009).
GIS for decision support and public policy making
. Redlands: ESRI Press.
Cromley, E. K. & McLafferty, S. (2012).
GIS and public health
. Guilford Press.
UK Office for National Statistics (2016) In Depth Review of Developing Geospatial Information Services based on Official Statistics, paper for Conference of European Statistician 27-29 April, 2016. ECE/CES/2016/7
20 October 2016
Using Unstructured and Spatial Data for Policy Formulation
Discuss critically the use of Big Data and Geoinformation in the context of public policy with examples and applications, and the issues concerning confidentiality, privacy, and accuracy.
The White House, Big Data and Privacy: A Technological Perspective, A report of the President’s Council of Advisors on Science and Technology. 1 May 2014.
27 October 2016
Measuring the Unmeasurable: New Data Challenges for Public Policy
Empirical data are being demanded for new policy issues which are not easy to measure such as national well-being and sustainable development, and this poses a big challenge to the information providers.
Conceição, P., & Bandura, R. (2008).
Measuring subjective wellbeing: A summary review of the literature.
United Nations Development Programme (UNDP) Development Studies, Working Paper.
Ahmad, N. (2013).
Measuring Trade in Value Added, and Beyond.
Paper presented at the Measuring the Effects of Globalization, Washington DC.
3 November 2016
The Future of Information in Public Policy: Privacy vs Practicality considerations
Examine the impact of rapid information flow (including social media and real-time data) on the formulation, monitoring and evaluation of public policy and the issues of privacy and confidentiality.
OMB Memorandum (M-13-13) Open Data Policy – Managing Information as an asset.
OMB Memorandum (M-06-02) Improving Public Access to and Dissemination of Government Information.
High quality empirical evidence is paramout to sound public policy formulation, monitoring, and evaluation. Official statistics, as trusted, organized information, have served this purpose for centuries; their production is institutionalized and governed by internationally-agreed ethics and practices. Unstructured information, including Big Data and Geoinformation, has emerged recently, offering public policy new empirical basis for making decisions. This has been described as ‘Data Revolution’ by international organizations. This course is designed for practitioners in the field of public policy to gain an in-depth understanding of the design and intricacies of structured statistical information (official statistics) and unstructured information such as Big Data and Geoinformation. The integration of these information sources will provide decision makers a significant competitive advantage in making sound empirically-based policy judgements.
see course description file sent to work bin.
Participation in discussion: 20%
Active class participation is expected and will be evaluated. It is important to prepare well for class, to raise pertinent issues, and to actively participate in class discussion.
Policy Brief: 30%
The United Nations will convene a World Data Forum in December 2016 in New York. You are asked by your government to draft a short policy paper to be presented in this Forum. This short ‘policy paper’ for your government should not be more than 15 pages, and should focus on an important data policy issues. You could consider focusing on the following issues:
a) Should your country adopt an open-data policy?
b) How to measure the Sustainable Development Goals? Do you agree with the targets and indicators?
c) How to modernize your national statistical/information system?
d) How would you advise on the collection, use and dissemination of the SDG or development indicators?
Policy Paper: 50%
Select a substantive area of interest (such as health, ageing, urbanization) and develop a policy paper for your government to improve the information system for this area. The paper could focus on a) the availability and use of information in policy making, b) the relevance of open data for this policy, c) regulating the new information regime of the future, d) the future development of the statistical system. This is a think piece, and you have the opportunity to develop your ideas.
Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week