Democratizing Development EconomicsNew Demand for Statistics

18 February 2011

Seminar on Shifting Paradigms: Innovative Statistical Frameworks to Meet Global Challenges

Ann E HarrisonDirector, Development Policy, World Bank

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 multi-polar 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 de-agglomeration.

• 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 semi-

subsistence?

What if Sub-Saharan Africa had high income country sectoral distributions? (from McMillan and Rodrik)

0 100 200 300Increase in economy-wide labor productivity

(as % of observed econ-wide L prod. in 2005)

MUS

KEN

GHA

What if Sub-Saharan Africa economies had High Income countries’ sectoral distribution?

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)

Understanding Growth and Structural Change

Endowments (Stocks)

Other Inputs

Outputs

PoliciesSocial and

Demographic Characteristics

A Statistical Framework For Understanding Growth And Structural Change (page 2)

Endowments (Stocks) by Sector1) Natural resource stocks (mineral; marine; forests; soil; water)2) Human capital (schooling, skills)3) Physical capital (machinery, equipment, structures) 4) "Hard" infrastructure (transportation, information and

communication, water and sanitation)5) "Soft" infrastructure (social cohesion, inequality, institutional capacity,

business environment) Other Inputs by Sector

1) Labor force (employment by industry, skill levels, gender)2) Raw & intermediate materials (energy, material inputs)3) Capital services (depreciation)4) 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 sector3) Prices of inputs, outputs: needed to calculate multifactor productivity

(TFP)4) Exports and imports of goods and services by sector

Policies 1) Taxes and subsidies2) Interest rates

Social and demographic characteristics1) Population by age and gender2) Age-specific morbidity and mortality rates3) School enrollment and completion rates, achievement levels4) 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)Se

ctor

al Pr

oduc

tivity

as

% o

f Ave

rage

Pro

duct

ivity

20 40 60 80 100Employment (%)

"cspsgs" "con" "man" "wrt " "min"

"agr" "t sc" "f ire" "pu"

Kenya ( ? ) 2005Agriculture

Mining

Manuf.

Utilities

Construction

Wholesale and Retail Trade

Transport, Storage and Communications

Finance, Insurance and Bus. Serv.

Other Services

Kenya 2005 when accounting for self-employment and informality.

Sect

oral

Prod

uctiv

ity a

s %

of A

vera

ge P

rodu

ctivit

y

20 40 60 80 100Employment (%)

"wrt " "agr" "man" "cspsgs" "con"

"t sc" "min" "f ire" "pu"

Kenya2005Agriculture

Mining

Manuf.

Utilities

Construction

Wholesale and Retail Trade

Transport, Storage and Communications

Finance, Insurance and Bus. Serv.

Other Services

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.