Presentation on theme: "Statistics and National Development Implications of New Structural Economics September 27, 2011 International Forum on Monitoring National Development:"— Presentation transcript:
Statistics and National Development Implications of New Structural Economics September 27, 2011 International Forum on Monitoring National Development: Issues and Challenges Volker Treichel Lead Economist, Operations and Strategy, Office of the Senior Vice-President and Chief Economist
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 New paradigms and approaches have implications for data
Economic Crisis and Crisis in Economics Economic Theory Explain Observed Economic Phenomena Guide Economic Policies or Choices Rethinking Economics Failure to: 4
How has economic development theory evolved? 1950196020102000197019801990 Structuralist Approach Focus on Market Failures: Import Substitution Strategy Miserable results Liberalization Approach Focus on Government Failures: Privatization and Marketization Mixed Results Market based economies with proactive role for government Successful East Asian Tigers: Export Promotion China, Vietnam and Mauritius: Dual-track approach to transition Rethink Development 5
Fast, Sustained growth is a recent phenomenon 7 The fast and sustained income growth in industrialized countries is a result of continuous technological innovation as well as structural change
Major features of Structural Change Kuznets identified four features of modern economic growth: There is a change in the sectoral composition of the economy as the share of the non-agricultural sectors increases and that of the agricultural sector decreases. This sectoral shift is mirrored in the pattern of employment. i.e. the proportion of the labor force employed in the non-agricultural sectors rises, while that in the agricultural decreases. There is redistribution of the population between the rural and urban areas. There is an increase in the relative size of the capital-labor ratio. Major conclusion: “Some structural change, not only in economic but also in social institutions and beliefs, are required without which economic growth would be impossible.” (Kuznets, 1971).
The Power of Structural Change Labor productivity gaps between different sectors are typically very large If Malawi, labor productivity in mining is 136 times larger than that in agriculture. If all of Malawi’s workers could be employed in mining, Malawi’s labor productivity would match that of the United States. In the process of growth, labor and other resources move from less productive to more productive sectors. We need to ensure that labor moves to more productive activities; otherwise structural change will reduce growth.
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. Appropriate policies at each stage of economic development Governance issues for industrial upgrading and structural change Role of agriculture versus other sectors, sectoral priorities and trade-offs An example of policy problems in this area highlights 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?
HOW TO BRING ABOUT STRUCTURAL CHANGE: GROWTH IDENTIFICATION AND FACILITATION 14
“Aim before you fire” The key lesson, from the new structural economics, is that for an industrial policy to be successful, it should target sectors that conform to the economy’s latent comparative advantage. We see this from the historical experience (e.g., next slide). But how to do it? 15
Most Industrial Policies failed Most governments in the developing world used industrial policies but failed. The reason was: – Attempt to develop industries that were too far advanced compared to their of development and went against their comparative advantages – The firms were non-viable in competitive markets and required government policy supports for their initial investment and continuous operations. This led to rent-seeking, corruption, and political capture. 16
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
Facilitating State and Industrial Policy Industrial policy is a useful tool for the state to play the facilitating role: – Type of coordination will be different, depending on industries. – The government’s resources and capacity are limited. The government needs to use them strategically. – To facilitate formation of clusters and obtain agglomeration effects. 18
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.
A Statistical Framework For Understanding Growth And Structural Change (page 1) Understanding Growth and Structural Change Endowments (Stocks) Other InputsOutputsPolicies Social and Demographic Characteristics
A Statistical Framework For Understanding Growth And Structural Change (page 2) Endowments (Stocks) by Sector 1)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 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)Population by age and gender 2)Age-specific morbidity and mortality rates 3)School enrollment and completion rates, achievement levels 4)Migration rates
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.