complementing conventional with innovative data...

Complementing Conventional with Innovative Data Sources

Arturo Martinez Jr.

Statistician

Asian Development Bank

International Workshop on Sustainable Development Goals Indicators

26 to 29 June 2018

Outline of Discussion

Conventional Data and SDGs

Overview of SAE methodologies

Complementing with Innovative Data

Data for Development TA

SDG Monitoring

• Many countries in Asia and the Pacific have examined the link between their respective development targets and SDGs � strategic priorities

• Countries have also done data availability assessment � (i) immediately available, (ii) can be made available using conventional data sources, (iii) needs more effort

Conventional Data Sources for SDGs

• Administrative Reporting Systems- Education- Health- Trade and Industry (e.g., business permits, letters of credits)- Civil Registration

• Censuses- Population and Housing- Agriculture- Enterprise

• Surveys- Households- Enterprises- Farm Holdings

Uses of Conventional Data Sources

• About 20-50% of SDG indicators can be derived from household surveys and censuses

0% 20% 40% 60% 80%

SDG 01

SDG 02

SDG 03

SDG 04

SDG 05

SDG 06

SDG 07

SDG 08

SDG 09

SDG 10

SDG 11

SDG 12

SDG 13

SDG 14

SDG 15

SDG 16

SDG 17

Proportion of SDG Indicators derived from HH Surveys

Limitations of Conventional Data Sources

- 10,000 20,000 30,000 40,000

BGD (2010-11)

PHL (2015)

VNM (2010)

THA (2015)

MYS (2012)

NPL (2010-11)

LKA (2012-13)

AFG (2013-14)

KHM (2016)

MMR (2009-10)

GEO (2016)

PNG (2009-10)

LAO (2007-08)

TKM (2003)

ARM (2016)

MNG (2011)

TLS (2011)

FJI (2008-09)

BTN (2012)

SLB (2012-13)

VUT (2010)

MDV (2009-10)

WSM (2013-14)

TON (2009)

FSM (2005)

KIR (2006)

PLW (2006)

COK (2005-06)

TUV (2010)

Total Number of Households ('000)

Population - HH

- 20,000 40,000 60,000 80,000

Sample Size

Sample Size - HH

Source: Compiled data from ADB Portal

of Statistics Resources and International

Household Survey Network

Sample size of latest household surveys vs. total number of households of select ADB DMCs

Availability of Disaggregated Data

Source: Serrao (2017) – Presentation on SDG Data Compilation

Situation in Asia and the Pacific

• Major surveys and censuses in some countries conducted only if donor funds are available in many countries

– donor dependence 70-80% budget in some countries

• Poor coverage and quality of administrative reporting systems

• both economic and social increasing the dependence on surveys

• For disaggregated data, surveys alone may not sufficient

• administrative data such as from civil registration and administrative registries need strengthening for long term sustainability

Small Area Estimation

• Let’s focus on indicators that are compiled through surveysG but surveys are unable to provide reliable estimates at very granular level!

• A small area is small geographical area or a population segment for which reliable statistics of interest cannot be produced due to survey’s limitations

• Small area estimation techniques borrow strength from other ‘auxiliary information’

Limitations of SAE methods

• Good auxiliary data usually required

• Gap between census and survey years can increase model error

• Only time invariant variables can be added in the model if the gap between census and survey periods is too wide

Big data can help address the limitations of SAE

Variety

Velocity

Volume

Big data can be much

faster in providing

granular auxiliary data

than conventional data

sources.

How can big data be useful for data disaggregation?

Source: Marchetti et al. (2015) – Small Area Model-Based Estimators Using Big

Data Sources

• To create proxy indicators

• To generate new covariates for small area models

• To validate small area estimates

Create Proxy Indicators

Video source: https://www.youtube.com/watch?v=rXFVejLDGAA


R² = 0.36670

1

2

3

4

5

0 2 4 6 8 10 12

ln (

Po

ve

rty R

ate

(%

))

ln (sum of nighttime lights)

Relationship between Poverty and Nighttime Lights - PHL 2009

R² = 0.44450

1

2

3

4

5

0 1 2 3 4 5

ln (

Po

ve

rty R

ate

(%

))

ln (average of nighttime lights)


R² = 0.35780

1

2

3

4

5

0 2 4 6 8 10 12

ln (

Po

ve

rty R

ate

(%

))

ln (sum of nighttime lights)


R² = 0.48160

1

2

3

4

5

0 1 2 3 4 5

ln (

Po

ve

rty R

ate

(%

))

ln (average of nighttime lights)



R² = 0.0004

0

10

20

30

40

50

60

-2000 0 2000 4000 6000 8000

Dif

f, P

ove

rty R

ate

(%

)

Diff, sum of nighttime lights

Difference - 2012 and 2009 Poverty and Nighttime Lights , PHL

R² = 0.0002

0

10

20

30

40

50

60

-10 -5 0 5 10 15 20 25D

iff,

Po

ve

rty R

ate

(%

)

Diff, sum of nighttime lights

Difference - 2012 and 2009 Poverty and Nighttime Lights , PHL

Generate new covariates

New RAI Estimates at the Subnational Level

SDG 9.1.1. Proportion of the rural population who live within 2 km

of an all-season road

• Indicator can be generated

using satellite images

• Method to calculate rural

access index (RAI)

requires the following data:• Population distribution

(e.g. Landscan and

WorldPop gridded

population maps)

• Road network (e.g.

OpenStreetMap)

• Road condition (e.g.

satellite image, user

reports from apps)

Validate Small Area Estimates

Source: Marchetti et al. (2015) – Small Area Model-Based Estimators Using Big

Data Sources

• Socioeconomic indicators derived from big data can be compared to similar measures obtained from survey data

• E.g. poverty estimates based on satellite image vssmall area estimates based on household expenditure surveys

Other uses of Big Data: Now-casting Food Prices in Indonesia Using Social Media Signals

Source: UN Global Pulse

Other Uses of Big Data: Algal Bloom Early Warning Alert System

Source: Group on Earth Observations, 2017

Key Considerations

• Some types of big data may not be representative of the whole population of interest (self-selection bias)

• Other types of big data are held by private sector

• Needs a different technological infrastructure

Data for Development Technical Assistance

Aims to build the capacity of DMCs in compiling disaggregated data for select indicators of the SDGs using combination of traditional and innovative forms of data in accordance with the SDGs’ “leave no one behind” principle’s granular data requirements.

ADB is collaborating with UNESCAP, PARIS21

and other development partners


Country-Specific Case Studies on Data

Disaggregation and Big Data Analytics

• Issue: What is the benefit of

complementing conventional with

innovative data sources?


�Technical Manual on Disaggregation of Official Statistics and SDGs

�Strategically-designed training workshops targeted to NSO staff

�Online Course Modules on SAE and Big Data Analytics

Thank you very much!

email: [email protected]

ADB's Statistics Capacity Building Efforts and Some Lessons

First statistics capacity building project in 1970s (for Singapore on national accounts)

Approximately 100 technical assistance projects on various topics since then

Statistics management and strengthening of national statistical systems

Development of statistics master plan

Strengthening of selected areas in statistics (national accounts, financial statistics, social statistics, etc. )

Improving data collection strategies (household surveys, administrative reporting system, dissemination practices)

Established partnerships with other development agencies in the region.

ADB's Statistics Capacity Building Efforts and Some Lessons

International Comparison Programme for Asia and the Pacific

Updating and Constructing the Supply and Use Tables for Selected Developing Member Economies

Statistical Business Registers (SBR) for Improved Information on Small, Medium-Sized, and Large Enterprises

Evidence and Data for Gender Equality (EDGE)

Innovative data collection methods for agricultural and rural statistics

Implementing Information and Communication Technology Tools to Improve Data Collection and Management of National Surveys in Support of the Sustainable Development Goals

complementing conventional with innovative data...

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