1. Statistician, ESCAP Statistics Division
Bridging economic statistics with people: A role for alternative sources of data?
Zeynep Orhun Girard
Statistician, ESCAP Statistics Division
IAOS, Danang Viet Nam
9 October, 2014
DISCLAIMER: The views presented here are the author’s and do not necessarily reflect the views and position of the United Nations.

2. “No wind favors he who has no destined port”
Michel de Montaigne

3. “We can analyze the data without hypotheses about what it might show
“We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns science cannot. […] Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all”.
Chris Anderson
Editor of Wired Magazine

4. For official statistics to extract value from alternative sources of data like Big data
1) It has to be guided closely by statistical policy
2) with the goal of filling actual methodological and data gaps in different domains of statistics

5. Methodological/policy developments are guiding economic statistics
Macroeconomic statistical frameworks are constantly updated, e.g. SNA
3 key policy-related initiatives are shaping the future of economics statistics
SSF Commission Report
Five recommendations on material wellbeing
Follow-up work on disparities in national accounts, distribution of Household Income, Consumption and Wealth (OECD)
Recommendations on Sectoral and Other Financial and Economic Datasets
Data revolution for targeted policy making
Measurement of progress on sustainable development that complement GDP (SGD17)
- Input-Output analysis
- First econometric model of business cycle and the General Theory
- Report on measurement of national income and the construction of social accounts
SNA published
- Allowed for national statistical policies, recommended IOT and constant prices
- Introduced satellite accounts
- Some non-market production in production boundary
- Concept of employment introduced in the sub-sectoring of household sector
- Use of PPPs for international comparison
- Balance sheets and SAMs
- Chapter on informal aspects of economy
1936
1947
1952
1968
1993
2008
G-20 Data Gaps Initiative
Post-2015 development agenda
We have witnessed a move towards an integrated approach to statistics and an emphasis of the household perspective and the distributional aspects of economic activity

6. Big Data: 3 v’s yes but not only…
Exhaustiveness in scope (n=all)
Granularity
Indexical in identification
Relational
Flexible in fields and scalable in size

7. Big data and economic statistics so far?
Data sources Online search queries/web scraping Substantive areas Housing market, labour market, prices Methodologies/results Correlations and predictive modelling

8. Use of some big data sources for economic statistics
Housing market (Google Trends)
Bank of England: McLaren and Schanbhogue (2011)
Wu and Brynjolfsson (2009)
Labour/employment market (Google Trends and Word Tracker)
D’Amuri and Marcucci (2009)
Askitas (2009)
Ettredge et al. (2005)—Word Tracker
Prices (Scraping and non-traditional enumeration)
Billion Prices
Premise (hybrid)

9. Common points of these studies
Compare aggregate trends of online search data against official/administrative statistics
Emphasize correlation rather than causality
Find that that online search data can predict observed trends within the appropriate lead time (depends on the individuals and area of economic statistics)

10. What can big data do for economic statistics?
Beyond correlations and predictive modelling:
Enhance quality and granularity of economic statistics?
Increase resolution and distributional information, e.g. demographics and geographical location
Enhance availability of economic statistics?
Example: Components of a household balance sheet, e.g. consumer durables

11. Selecting the Main Source of Data
Data requirement X
Traditional Data Source (surveys, administrative records, registers)
Existing dataset
Design new data collection
Alternative Data Source
Big data set
Define measurement objective based on policy question, e.g. distribution of wealth across different quintiles of households at provincial level
Identify approach based on statistical policy
Identify main data source based on FPOS and QAF (Relevance, accuracy, timeliness, punctuality, accessibility, clarity, and comparability and consistency over time) + Cost-efficiency

12. Using big data for distributional aspect
Select dataset Example
Online search keyword, e.g. “insurance” and “repair/garage” for automobiles, yellow pages data for business address searches
Test correlations with any existing official statistics/other data source, e.g. household surveys covering consumer durables
Select variable of disaggregation Example
Location, sex, age, etc.
Test distribution of groups by demographic characteristics
Population Census data and demographic distribution at the national and sub-national levels
Household Income and Expenditure Data for the item in question, e.g. vehicle ownership and its distribution
Apply in analysis Example
Use distribution of vehicle ownership obtained through big data sources on macroeconomic aggregates

13. Using big data for enhancing data availability
Select dataset Example
Value of vehicle owned through purchase and repair data, e.g. insurance databases
Process data Example
Blow up to national (if possible sub-national) level figures
Calculate depreciation
Differentiate household enterprises
Apply in analysis
In construction of balance sheets
Memo item for national accounts

14. Challenges: Big data in official statistics
Shift from planned data collection activities
Possible mismatch between what big data can offer and what the economic policy makers need (comprehensiveness and comparability)
Privacy of individuals and confidentiality of data
Lack of code of conduct covering all stakeholders (public and private)

15. Opportunities: Big data in official statistics
In the policy context we live in we need to integrate different data sources
Alternative sources of data can respond to such needs (exhaustive, relational, flexible and scalable)
Maintaining TRUST of individuals is key
“Fifty-four per cent of global consumers indicated that they would be comfortable with the use of information about them if they believed that the uses would not embarrass them, damage their interests, or otherwise harm them”
(BCG Global Consumer Sentiment Survey 2013)

16. Conclusions Big data to complement official statistics
Conduct research for innovative statistics development;
Provide quality insights through data confrontation and;
Enhance availability of data by closing data gaps.
Statistical policy & actual methodological and data gaps need to guide big data research to allow for meaningful results that can be used
Big data has a potential role to bring in the distributional and household aspect to economic statistics

17. Next steps?
Multiply the number of proposals embedded in methodological and data needs
Conduct studies with official and private sources of data

18. Thanks and for comments/questions:
Zeynep Orhun Girard