This document summarizes a presentation on how quality statistics support evidence-based policymaking. It discusses how statistical analysis can identify issues, support policy development, and facilitate monitoring and evaluation. It emphasizes that quality data is needed at all stages of the policy process. The document also outlines some of Jamaica's development challenges and how its long-term development plan uses statistics and indicators to track progress. It notes some risks like data errors and misuse of statistics if users lack statistical literacy.

1. STATISTICS IN SUPPORT
OF POLICY
Presenter : Colin Bullock
Planning Institute of Jamaica
October 29, 2015

2. Outline
• Policy failure reinforcing the imperative for quality
statistics
• The dimensions of integrating statistical analysis with
policy
• The importance of quality statistics to policy
• Jamaica’s policy environment and challenges
• The consequences of misuse or ignorance
• The importance of analyst familiarity with data sources
• Strengthening capacity to generate and utilize quality
statistics.

3. Background
• Global thrust towards developing a culture of
evidence-based decision increasing the importance
of data and statistics.
• Underpinned by a results-based management
framework which situates information as
fundamental tools for planning, implementation
and monitoring and evaluation.
• Predicated on identified weaknesses in the
processes of policy formulation, resource
allocation, project and programme design,
implementation, management, monitoring and
evaluation.

4. Background
• Evolving focus of development and the methods
and tools utilized in achieving development
goals and targets.
• Millennium Development Goals (MDGs) and the
Sustainable Development Goals (SDGs) for the
post-2015 development agenda.
• Today, the definition of development includes
focus on inclusiveness, sustainability.
• Pursuit of change in the social, economic, socio-
cultural and political spheres and in
environmental resilience to man and climate
change.

5. Definitions
Statistics
• The science of learning from (or making sense out of) data.
• The theory and methods of extracting information from
observational data for solving real-world problem.
• The science of collecting and analyzing numerical data in
large quantities, especially for the purpose of inferring
proportions in a whole from those in a representative
sample.
Data
• numbers, words or images that have yet to be organized or
analyzed to answer a specific question.

6. Snapshot of Statistical
Analysis
6
Statistical
Analysis Data
Analysis
Data
Collection
Forecasting

7. Statistical Analysis Tools
and Methods• Threshold 21
– Designed to support long-term planning, which integrates,
economics, social and environment factors.
• Computable General Equilibrium
– Used to evaluate the impact of economic and policy shocks -
particularly policy reforms. It is a completely-specified model
of an economy including all production activities.
• Input-Output
– Used to assess/predict the impact of changes in one industry
on other industries
• Econometric models
– Used to forecast future developments in the economy

8. Importance of Quality Data and
Statistics• Evidence-based decision making, supported by
quality data and statistics has been recognized
as a necessity to formulate policies that are
technically sound, politically relevant and
results-based oriented.
• It has been predicated on identified weaknesses
in the processes of :
– Policy formulation
– Resource Allocation
– Project and Programme Design
– Implementation
– Management
– Monitoring and Evaluation

9. How Statistics Support
Policy
• Quality data and statistics support policymakers by:
 identifying issues that require policy intervention
 providing the evidence to support the development of or adjustment
of policy
 facilitating monitoring and evaluation of the impact of policy
• Stages in Evidence-Based Policymaking:
 Identification
 Assessment
 Implementation
 Monitoring and evaluation

10. Evidence-Based Policy
Making

11. • For a government to plan and monitor the impact of its
policies, it must be able to benchmark data and see year on
year progress. Comparing progress across countries is
important – shared indicators and statistical frameworks help
countries see how they are doing in comparison to others.
• Tracking progress on new goals will increase the demands on
often hard-pressed National Statistical Offices to collect and
analyse data in new areas. This in turn will require increasing
resources for the statistical system and building statistical
capacity, with the support of the international community.
Tracking Progress

12.  Fifty two years after independence Jamaica is still
challenged by:
 anaemic growth ( average growth of approximately 1.7 per
cent 1962-2013)
 poverty (largely rural)
 unplanned urbanization and
 high unemployment
 An interdependence between protracted low
economic growth and a heavy burden of public
indebtedness.
12
Evidence-Based Policy
Making
Jamaica’s History of
Underperformance

13.  Macroeconomic Instability
 High debt levels
 Uncompetitive Business Environment
 Bureaucracy
 Physical insecurity
 Factor and Transaction Costs (including energy)
 Long term declines in labour productivity and total factor
productivity
 Social exclusion
 Crime and Violence
 Environmental degradation
Jamaica’s Main Constraints
to Economic Growth

14. Vision 2030 Jamaica National
Development Plan– Jamaica’s first long-term National Development plan
– Developed using the Threshold 21 model which
supports:
• the integration of economic, environmental and
social elements of the society
• provides scenarios of future long term outcomes;
and
• project consequences of different strategies
across a range of indicators.
– Implemented through a series of 3-yearly Medium Term
Socio-Economic Policy Frameworks (MTFs)
– Underpinned by a results-based monitoring and
evaluation mechanism that establishes specific
indicators and targets to measure and track
performance.
• version of the

15. Summary Policy Alignment
Framework
Medium Term Economic Programme
New Agreement with the International
Monetary
Fund
Medium Term Socio- Economic Framework
(MTF)
2009 – 2012 2012 -2015 and 2015-18
Growth Inducement Strategy
Vision 2030 National Development Plan

16. Role of Policymaker
• Statistics must be seen as a tool available to policymakers to
support the evidence used in the formulation of policy.
• Effective decision making requires an evidence-based approach that
works together with analytical skills and political support.
• Policy makers must be equipped with the right skills to discern
between evidence which is reliable and useful, and that which is
not.
• Identify gaps in the transmission process and act swiftly to plug
these areas to ensure that statistics, evidence, policy and
development goals are aligned and functioning efficiently.

17. Taking a closer Look at
Statistics
• Statistics is woven into the fabric of evidence-
based policymaking, overlapping at each
stage, to create a seamless transmission of
quality statistics to policymakers to inform
decision making.

18. Quality Data

19. Assessing the Quality of
Statistics
Questions to consider in assessing the quality of data:
• What are the priority issues?
• Which statistics/indicators are needed to measure the
issues?
• Do the statistics/indicators exist?
• Are the data readily available?
• What are the data sources?
• How are the data collected?
• What is the data coverage?
• What is the periodicity of data collection?
• What is the quality of the data?

20. Statistics in the 21st
Century
• Technological advancements has changed how we interact
with statistics.
• The rise of mobile technology has facilitated improvements in
the timeliness and accuracy of data collection.
• Example
– “The Rapid Mobile Phone-based survey (RAMP)”, which relies on
a mobile phone-based software, allows surveys to be done
remotely using software that allows data to be uploaded onto
mobile phones or tablets.
– Completed survey are fed back to a server that allows real time
viewing of the data being collected.

21. Statistics in the 21st Century :
Data Collection

22. Visual Statistics
• Numerous software packages have been created and adopted by
practitioners in the areas of research, especially in the fields of economics,
sociology, political science and medicine.
• The capabilities of some these software packages include data
management, statistical analysis, graphics, simulations, regression
analysis, and custom programming.

23. Types and Sources of
Errors
• Any measurement process that generate statistical data is subject to error. Hence,
It is important for a researcher to be aware of these errors, in particular non-
sampling errors, so that they can be either minimised or eliminated from the data
collected.
• There are two types of errors:
1. Non-sampling errors
 These are errors that arise during the course of all data collection
activities.
2. Sampling errors.
 These are errors that arise because data has been collected from a part,
rather than the whole of the population. It refers to the difference
between the estimate derived from a sample survey and the 'true' value
that would result if a census of the whole population were taken under
the same conditions

24. Misuse, Non-use and
Misinterpretations• Notwithstanding errors that can occur during the statistical process,
statistics can be misused. The misuse of statistics can produce subtle, but
serious errors in description and interpretation.

25. Misuse, Non-use and
Misinterpretations

26. Statistical Literacy
• Skepticism and misunderstanding of statistics is commonly
associated with the quotation.
"There are three kinds of lies: lies, damned lies, and statistics".
• It is paramount for citizens to understand material presented
in publications such as newspapers, television, and the
Internet to be able to differentiate between unreliable and
reliable statistics.
• This is particularly important as the frequency and methods
in which people interact with statistics is evolving at a rapid
pace.

30. The Great Debate 1

31. The Great Debate 2

32. • Time series of global mean sea level
(deviation from the 1980-1999 mean)
in the past and as projected for the
future.
• For the period before 1870, global
measurements of sea level are not
available.
• The grey shading shows the uncertainty
in the estimated long-term rate of sea
level change.
• The red line is a reconstruction of
global mean sea level from tide
gauges, and the red shading denotes
the range of variations from a smooth
curve. The green line shows global
mean sea level observed from satellite
altimetry.
• The blue shading represents the range
of model projections for the SRES A1B
scenario for the 21st century, relative
to the 1980 to 1999 mean, and has
been calculated independently from the
observations.
Estimates, Projections and Forecasts

33. Sea level rise
• Global sea level rose about 17 centimeters (6.7 inches)
in the last century. The rate in the last decade, however,
is nearly double that of the last century.
• Due to the close proximity to the equator, Caribbean sea
level rise may be higher than in other regions.

34. • Caribbean coral reefs ‘will be lost
within 20 years’ without protection
• A comprehensive analysis by 90
experts of more than 35,000
surveys conducted at nearly 100
Caribbean locations since 1970
shows that the region’s corals have
declined by more than 50%.

35. • The earth’s average temperature has
increased by 0.74°C over the past
century.
Global
Warming

36. Projected land loss from sea
level rise at Hope Bay,
Portland.

37. Melting Ice
Caps

39. Overcrowded
Schools

40. Overcrowded
Hospitals

41. And on the Other Hand
Under-populated schools
Underutilized clinics
Abandoned and vandalized
community centres
Neglected sports facilities
Empty transportation
centres

42. 0.00
50.00
100.00
150.00
200.00
250.00
PercentageofGDP
Debt to GDP Profile
Domestic Debt External Debt Total Stock of Debt
Domestic Debt
External Debt
TOTAL STOCK OF
DEBT

43. Constraints of Debt Service (Budget)

44. Hydro-meteorological Events 2001-2012

45. A Forecast of The Way Forward
• The need to improve the dialogue between policymakers
and the statistician community
• Whilst it may be unrealistic for professional decision-
makers and practitioners to be competent doers of
statistics, it is both reasonable and necessary for
policymakers to be able to understand and use statistics
in their professional practice

46. A Forecast of The Way Forward…continued
• Statisticians often need to ‘translate’ statistics
into a language that is useful to the users of
evidence, without distorting or misrepresenting
data.
• Improve ownership and uptake of evidence, in
both policy and practice, developing better,
ongoing interaction between evidence providers
and evidence users.

47. A Forecast of The Way Forward…continued
• Strengthen national and regional information and
statistical and analytical services relevant to sustainable
development polices and programmes.
• Analysts, forecasters and policymakers to support
adequate resource base for statisticians.
• Statisticians to be less passive in accepting results and
implications of resource inadequacy.

48. Conclusion
• Successful integration of evidence with policy
process depends on
 quality statistics
Sound data collection, processing, forecasting
and analysis
ensuring that policy makers have a discriminatory
familiarity with source data
H0 : The cost of neglecting quality statistics
exceeds that of investing in and properly
utilizing quality statistics.