Democratizing Development Economics
New Demand for Statistics
Seminar on Shifting Paradigms: Innovative Statistical
Frameworks to Meet Global Challenges
Ann E Harrison
Director, Development Policy, World Bank
18 February 2011
Where has development economics brought
us? Is it serving us well?
(President Robert Zoellick, October 2010)
 The world is a riskier place than we previously
imagined
 New economic powers are emerging to create a multipolar world
 But not everyone has benefited, and we can no longer
assume that there is a single model for development
 This is a time for new paradigms and new approaches
Open Data, Open Knowledge,
Open Solutions
 The flow of knowledge is no longer North to South or
West to East or rich to poor
 We must share the knowledge and tools we create as
widely as possible (data.worldbank.org)
 In a networked, interconnected world we can reach out
to new sources of information
 But we also continue to value and support the expert
sources of knowledge represented here
New Framework for Research at the World Bank
 Transformation: stimulating structural transformation
 Opportunities: broadening opportunities
 Risks: increasing risks and vulnerability
 Results: focusing on results in policies and aid impacts
Growth and Transformation:
Understanding Structural Change
• Understanding the relationship between
structural change and broader development
goals, including poverty reduction
• Role of states, markets and the private sector in
promoting structural transformation and
upgrading. Related governance issues.
• Two examples of policy problems in this area
highlight the statistical and data challenges
The Process of Economic Integration (Imbs and
Wacziarg)
• Sectoral diversification in early stages of
development is accompanied by geographic
agglomeration.
• Sectoral concentration in later stages of
development accompanied by geographic deagglomeration.
• Reduced range of activities produced across all
regions. Location of activity does not seem to
matter. Regions become increasingly similar.
• How to accelerate this process?
Data Needs from Imbs and Wacziarg
• Long time series on sectoral production and
employment at a disaggregated level: very
difficult to obtain
• Details at the sectoral level by subregion
• While trade data is typically available at a
disaggregated level, production or sectoral data
much more problematic
Uganda: the puzzle (Gollin and Rogerson)
• Large fractions of Uganda's population live in rural areas. 85%
live in the rural areas and 73 % work in agriculture.
• The agricultural sector appears to have low productivity,
relative to non-agriculture. Agriculture accounts for 20% of
GDP, suggesting that output per worker in non-agriculture is
greater by a factor of 12!
• Poverty is relatively concentrated in rural areas (93%).
Puzzles:
• Why are so many people concentrated in a sector where they
are so (relatively) unproductive?
• Within the sector, why do so many people live in semisubsistence?
What if Sub-Saharan Africa had high income country
sectoral distributions? (from McMillan and Rodrik)
What if Sub-Saharan Africa economies had High Income countries’ sectoral distribution?
GHA
KEN
MUS
0
100
200
Increase in economy-wide labor productivity
(as % of observed econ-wide L prod. in 2005)
300
Data Needs for Understanding why Transformation
does not happen in Africa
• Sectoral composition of output and employment
in urban and rural settings
• Sectoral data on capital stocks and other inputs,
enabling productivity calculations
• Details on output and input (wages) prices
• Data on infrastructure (roads), irrigation. One
explanation for the puzzle is that high
transportation costs make food expensive in
cities, limiting the size of urban populations.
But sectoral production and input data are scarce:
“Estimating sectoral measured TFP requires data on
total output, employment, capital stocks, and
intermediate input usage, all in real terms, by
sector..The set of countries and sectors for which
this measured TFP can be computed is not
large..There are only 12 countries with all the
required data in at least some sectors…”
--From Levchenko and Zhang, February 2011
A Statistical Framework For Understanding
Growth And Structural Change (page 1)
Endowments
(Stocks)
Understanding
Growth and
Structural Change
Other Inputs
Outputs
Policies
Social and
Demographic
Characteristics
A Statistical Framework For Understanding
Growth And Structural Change (page 2)
 Endowments (Stocks) by Sector
1)
2)
3)
4)
5)
Natural resource stocks (mineral; marine; forests; soil; water)
Human capital (schooling, skills)
Physical capital (machinery, equipment, structures)
"Hard" infrastructure (transportation, information and
communication, water and sanitation)
"Soft" infrastructure (social cohesion, inequality, institutional
capacity, business environment)
 Other Inputs by Sector
1)
2)
3)
4)
Labor force (employment by industry, skill levels, gender)
Raw & intermediate materials (energy, material inputs)
Capital services (depreciation)
Financial (investment, domestic and international credit markets)
A Statistical Framework For Understanding Growth
And Structural Change (page 3)
 Outputs
1) Output and value added by industrial sector
2) Output and value added by household sector
3) Prices of inputs, outputs: needed to calculate multifactor productivity
(TFP)
4) Exports and imports of goods and services by sector
 Policies
1) Taxes and subsidies
2) Interest rates
 Social and demographic characteristics
1)
2)
3)
4)
Population by age and gender
Age-specific morbidity and mortality rates
School enrollment and completion rates, achievement levels
Migration rates
Some Additional Data Gaps
• A great need for informal sector data
(employment, output, productivity): 80 -90% of
India’s manufacturing employment is informal!
• Disaggregated data on factor incomes to calculate
self-employment, labor and capital shares in
national income (Is labor’s share really falling?)
• Firm-level censuses on areas other than
manufacturing: services, agro-industry
We cannot understand the world without good data:
Structure of economy in Kenya (2005) without accounting for
informality and self-employment (from McMillan and Rodrik)
Agriculture
Kenya ( ? ) 2005
Mining
Manuf.
Utilities
Construction
Wholesale and Retail Trade
Transport, Storage and Communications
Finance, Insurance and Bus. Serv.
Other Services
20
40
60
80
Employment (%)
"cspsgs"
"con"
"man"
"wrt"
"agr"
"tsc"
"fire"
"pu"
"min"
100
Kenya 2005 when accounting for self-employment and
informality.
Kenya2005
Agriculture
Mining
Manuf.
Utilities
Construction
Wholesale and Retail Trade
Transport, Storage and Communications
Finance, Insurance and Bus. Serv.
Other Services
20
40
60
80
Employment (%)
"wrt"
"agr"
"man"
"cspsgs"
"tsc"
"min"
"fire"
"pu"
"con"
100
The Role of Official Statistics
 The global statistical system is built upon the work of
National Statistical Offices
 NSOs ensure quality and consistency and accessibility of
valuable public goods
 Many of these datasets are already produced by NSOs or
other official agencies
 Others will require new frameworks and data collection
efforts (such as the work already underway by OECD,
UNECE, and Eurostat to develop indicators of green
growth and sustainable development)
From Open Data to Open Development
 We cannot understand the world without good data
 As governments and the private sector act on new
knowledge and pursue new policies, the demand for data
will grow
 Building the capacity of national statistical systems to
respond to these challenges is part of the transformation
process
 We have seen many advances in national and
international statistics over the past decade, and we will
be there to work with you in the decade ahead – that is
the spirit of Open Development.