| Data Management in Asia KLEMS Asia KLEMS Database Management Workshop July Seoul National University, Hoam International Convention Center Abdul A. Erumban Groningen Growth and Development Centre Faculty of Economics and Business Univeristy of Groningen, the Netherlands The WIOD project is funded by the European Commission, Research Directorate General as part of the 7th Framework Programme, Theme 8: Socio-Economic Sciences and Humanities. Grant Agreement no:

| ›Asia KLEMS, what and why? ›EU KLEMS and WIOD experience ›Two studies using WIOD 2

| Asia KLEMS, what?  Asian regional research consortium  Aiming to construct growth and productivity accounts for all Asian countries  promote building database and conduct international productivity comparison among Asian countries  Motivated by EU KLEM and follows the KLEMS methodology  Early initiative by Hak K. Pyo, Kyoji Fukao and Tsutomu Miyagawa (participated in EU KLEMS project)  A spinoff of World KLEMS project (Dale Jorgenson, Bart van Ark, Marcel Timmer) 3

| Asia KLEMS, why?  Understanding and suggesting policies that would enhance competitiveness of the region  Analyze the determinants of productivity change and relative levels  Comparison with EU KLEMS  describe the evolution of industry productivity in Asia, within the Asia, and in comparison with the EU and the US  Analytical research projects in the areas of:  analysis of productivity, prices, industry structures, technology and innovation indicators  labour markets and skills  technological progress and innovation  Link to productivity research using firm level databases 4

| ›Data management in Asia KLEMS: some lessons from EU KLEMS and WIOD 5

| Background for EU KLEMS  policy interest  monitoring and evaluating the Lisbon and Barcelona agendas, complementary to existing indicators (e.g. Eurostat structural indicators)  Divergence between EU and US productivity performance  Impact of new entry of 10 member states on EU economic performance  developments in academic world:  Growth regression literature is running out of steam  Growth accounting methodologies have become more sophisticated  Need for better data to test hypotheses on, e.g., skill-biased technological change and role of non-technological innovations  statistical developments in productivity measurement and national accounts  Eurostat Handbooks on Price & Volume Measures and Input-Output Manual  Publication of OECD Productivity and Capital Measurement Manuals  Developments in SNA (e.g. role for intangibles and importance of capital services)  Increased availability of firm level databases for productivity measurement

| What is in EU KLEMS?  Inter-industry accounts  Gross output (basic prices)  intermediate inputs (purchaser prices)  broken down into energy inputs, material inputs and service inputs  value added  prices for output and intermediate inputs to deflate value series  Labor Accounts  total number of persons engaged (full-time and part-time)  average and total number of actual hours worked  Breakdown of labour quantity  gender, age (3-4 categories), education (3 or more categories)  total labour compensation and wage share by gender, age, education  Capital Accounts  Investment in seven asset categories  IT equip., communication equip., other machinery, transport equip., software, non- res. structures, dwellings  Estimation of capital stocks and rental prices.  Price indices for investment and user cost to obtain capital services.  Measures of total capital compensation  Industry level: ±31 - ±60

| Motivation of WIOD project  Increasing interdependencies of national economies (e.g. global value chains)  This has impact on:  Income inequality within and across countries  Decoupling of production and consumption (e.g. of pollutants such as CO 2 )  Interpretation of international trade surplus and deficits  A consistent framework to analyse these interdepencies between countries and industries

| What is in WIOD?  a time-series of input-output tables with supply broken down by origin: domestic or imported (by partner country)  Satellite accounts:  Socio-economic accounts  EU KLEMS extension to other countries in WIOD  Gross output and value added (consistent with National Accounts) in current, constant, and international prices  Capital formation and capital stock, but no capital services  Labor by skill types (education based)  Low-skilled, Medium-skilled, High-skilled  Environmental (energy, emissions to air and water, natural resources)  Coverage: Period from 1995 to 2006 | 27 EU countries and 13 other major countries | 35 industries and 59 products

| Relevant KLEMS Data sources: EU KLEMS & WIOD  Inter-industry Accounts  Input-Output Tables and NAS from individual countries, Eurostat, OECD  Industry detail on the basis of census/industry survey information  Labour Accounts  National Accounts from individual countries, Eurostat and OECD  Industry detail on the basis of labour force statistics  Alternative sources: social security statistics, household surveys - Non-EU Labor input data from sources consistent to NAs - Economic censuses; Employment and labor force statistics; Households surveys  Capital Accounts  National Accounts from individual countries, Eurostat and OECD  Extended to obtain greater industry detail on the basis of commodity flow tables, production and trade statistics -PPPs based on input-output approach (Timmer and Inklaar, 2012) -All data ensures Internal, intertemporal, and international consistency

|  Asia KLEMS follow the EU KLEMS standards, but +/- Asia specific issues  Land, ICT, depreciation  Produce ‘consistent’ KLEMS data from disparate data  It is fine to have different policies on different aspects, depending upon the specific situations of countries (e.g. land may be an important capital asset in India, but need not be elsewhere!). But still need some consistency. Data construction and management: Lessons, challenges for Asia KLEMS

| Data construction and management: Lessons, challenges for Asia KLEMS 12 EU KLEMS and WIOD worked with a clearly defined project design

|  Interindustry Accounts  Availability of annual IO-tables (ind x ind)  Construct on basis use/supply tables  Intrapolation in between benchmarks  Inconsistency between GVO and GVA measures from IO- tables and NA  NAS is periodically revised, while IO’s are not  Solve breaks in series due to industry classification differences  Measurement of quality change in price indices  Use of hedonic prices for ICT output Methodological and data research in EU KLEMS: Lessons for Asia KLEMS

|  Labour Accounts  Consistency of employment from enterprise/ establishment based surveys, household surveys and national accounts  Small samples can lead to volatile series - can introduce errors  Analysis of average working hours (e.g., with time use surveys)  Detailed comparison of categories of educational attainment:  High, Medium, Low  Country specific divisions  Occupational classifications, e.g., for IT workers  Labour costs  non-wage labour costs  Self-employed – wage imputation Methodological and data research in EU KLEMS: Lessons for Asia KLEMS

|  Capital Accounts  Specific capital assets:  Measurement of software  Splitting assets (e.g. machinery and transport equipment in private sector in India)  Land, inventories and infrastructure  Hedonic price measures for ICT capital inputs  Age of physical capital stock: harmonised or national?  Age-efficiency profiles: harmonised or national?  Role of taxation differences (Erumban 2008)  Rate of return – internal or external, aggregate or industry specific Methodological and data research in EU KLEMS: Lessons for Asia KLEMS

| Statistical vs. analytical modules of database  Statistical module of the database:  Ensure data consistency with those published by NSIs  According to rules and conventions on national accounts, supply and use tables, commodity flow methods, etc. (SNA 1993, ESA 1995)  Ensure that data meet statistical standards of NSI‘s, but consistent with the KLEMS framework that will make international comparison feasible.  Analytical module of the database  Produces additional data and fills gaps using alternative techniques (e.g. growth accounting) which presently will go beyond the NSI practices  Consider alternative or pioneering assumptions regarding statistical conventions on,  for example, the output and price measurement of ICT good sand non-market services, measurement of skill levels, construction of capital stock and capital services, or capitalization of intangible assets.

| Practical Problems: some examples  Aggregation and disaggregation of sectors:  A raw data category may have to be split into several KLEMS categories or aggregated to one KLEMS item  Use weights from available official information or any other sources  Ensure that the results aggregate to the observed total.  Employ available indicators (“pattern data”) of how to split.  Balancing  Estimate investment by industry and by asset type.  Observe detail in only 1 dimension at a time.  Estimates aggregated by type to equal the observed totals by industry.  Estimates aggregated by industry to equal the observed totals by type.  Employ available indicators to estimate the detailed breakdown.

|  Concording  Converting data for time-series breaks in national industry codes  Converting data from national industry code to KLEMS definitions  imputing labor by ‘quality-level’ using occupation data  Defining the correct ISCED classification comparable to National.  Wage share in value added  Self-employed compensation  Initial capital stock (e.g Harbgerger approach)  Robustness checks 18 Practical Problems: some examples

|  Specialized tools to estimate Capital stocks, labour services, capital services, TFP, Domar weighting, alternative deflation (e.g. pre-written program/worksheet module/syntax)  Documentation  reference manual  users guide  Storing the data  Keep all raw data and files containing critical data manipulation steps -in addition to final maps  Construct a logical directory structure in which to store your data  Use meaningful names and a standard naming convention for data files Some practical issues

|  Store and document ‘Assumptions’ and Methods  Use meta data: To have 100% confidence in your data it is important to have metadata – information about data. It is essential to know what the data actually refers to, when it was collected, how and by whom, etc. This could be included in a summary text file located with each file and/or folder  Timeframe of the project?  EU KLEMS project was a 3-year  Delivery standards  Each table in EUKLEMS had the following information  Description of source (publisher, NSO) and version (release date)  Classification Meta-data associated with each dimension of national database  Concordance with EUK dimension lists Some practical issues

| General lay-out of Analytical module  1. Collection of basic data  A. Basic data by country  2. Making meta-data (hierarchies, concordances)  B. Data in readable format  3. Combining datasources: (dis)agg, concor, balancing  C. Input data KLEMS: full time-series and industries  4. Applying productivity tools: harmonization and experiment  D. Output data KLEMS  5. Selection of series for analytical database  E. Public analytical database

| Application of WIOD-two examples  Slicing up Global value chain Marcel Timmer, Abdul A. Erumban, Bart Los, Robert Stehrer and Gaaitzen de Vries, memeo GGDC  Deconstructing the BRICS (going beyond WIOD) Gaaitzen J. de Vries, Abdul A. Erumban, Marcel P. Timmer, Ilya Voskoboynikov and Harry X. Wu  Journal of Comparative Economics,

| Slicing up Global Value Chains  Motivation  Increasing integration of world economy and fragmentation of the production processes  In almost all countries the share of intermediate imports rose between 1995 and 2008  Case studies: e.g. the iPod (Dedrick, Kraemer and Linden, 2010)

| iPod value chain (in $) Linden, Dedrick & Kraemer, 2009 made in China

| What we do  global value chain income: the value that is added in various stages of production  decompose the value of expenditure on manufacturing goods into incomes for production factors in any country that are directly and indirectly needed for the production of these goods  Leontief (1936, 1941); Johnson and Noguera (2012) and Bems, Johnson and Yi (2011) 25

| Global value chain incomes in advanced & emerging countries, (in million 1995 US$) Note: Advanced countries =EU-15, Japan, Korea, Taiwan, Australia, Canada and the U.S.; Emerging countries = all other countries in the world. GVC income in advanced and emerging countries is equal to world expenditures on manufacturing products at basic prices 26 Stagnation in advanced countries But high growth in emerging countries since 2004

| GVC income, 1995 and 2008 GVC income in EU increasing; In 2008, China almost on par with US and East Asia 27 Note: GVC income in global manufacturing, in million 1995 US$. East Asia includes Japan, South Korea and Taiwan.

| Global value chain incomes in advanced and emerging countries, by factor type, (in million 1995 US$)- Advanced countries 28 advanced regions are steadily specializing in high skill intensive activities. GVC income for highskilled workers increased, while those for medium- and low- skilled workers declined. The share of capital in value added remained relatively stable

| Global value chain incomes in advanced and emerging countries, by factor type, (in million 1995 US$)-Emerging economies Notes: Factor income earned by high-skilled labour (HS), capital and by mediumand low-skilled labour (MS + LS). Skill categories based on workers classified by educational attainment levels 29  GVC incomes increased for all workers  But more than half of total income is due to capital.  Part of these gross profits will not end up in local hands however, as we donot account for ownership

| ›The increasing fragmentation of global production is benefitting the workers in emerging countries, but still largely the capital.  The ultimate benefit still depends upon the ownership of capital ›Low and Medium scale workers in advanced countries are loosing out 30

| Deconstructing the BRICs : Structural Transformation and Aggregate Productivity Growth 31 Development entails structural change…  reallocation of labor across sectors, in particular from primary goods to manufacturing and services, featured prominently in early analysis of economic growth (e.g. Kuznets, 1966)  ‘New structural economics’ reiterates the importance of economic structures in economic analysis and the design of appropriate policies (Lin, 2010)  Structural change contributes to productivity growth in Asia (McMillan and Rodrik, 2011) in general and in India and China (Bosworth and Collins, 2008); while not in Africa and Latin America (McMillan and Rodrik, 2011)

|  In developing countries, the informal sector accounts for the majority of employment and a substantial share of value added (Schneider, 2000)  If formal and informal activities within sectors are not distinguished, the role of structural change for growth may not be accurately measured.  Extending the WIOD data - Brazil and India  Brazil: informal defined in terms of contract status. Also, autonomous workers, comprising own-account workers and employers of unregistered firms are considered part of the informal sector.  India: workers in the unregistered segment of the economy But for countries in Asia, economic dualism may also play an important role

| When economic dualism is taken into account, structural change is growth enhancing in Brazil, but not in India 33 Reallocation of labour across sectors is contributing to aggregate productivity growth in India, whereas in Brazil it is not (McMillan and Rodrik 2011]. However, this result is overturned when a distinction is made between formal and informal activities within sectors.

|  Advanced economies have not been able to benefit from the increasing global demand for manufacturing goods,  Emerging economies benefitted most from the deepening of production networks between advanced and emerging regions.  Activities intensive in the use of high-skilled labor and capital increased, but use of low and medium-skilled workers declined. WIOD analysis-new lessons

| WIOD analysis-new lessons  Increasing formalization of the Brazilian economy since 2000 appears to be growth-enhancing, while in India the increase in informality after the reforms is not growth-enhancing.  Increasing dualism in the Indian economy with high productivity levels and growth rates in the formal sector, partly achieved by economizing on the use of labor through outsourcing labor-intensive activities to small informal firms (Pieters, Monroy and Erumban, 2011).  Low TFP in aggregate manufacturing may be a manifestation of the unproductive informal sector  Call for more reforms in labor market, to remove many rigidities 35

| Thank yu for your attention