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Total Factor Productivity Growth in Indonesian Manufacturing, 1975-1995: Issues in Measurement

Total Factor Productivity Growth in Indonesian Manufacturing, 1975-1995: Issues in Measurement. Dec. 2005 Virginie Vial v.vial@economic-institute.org. Why does TFP Growth matter ?. Study of productivity relevant for understanding industrial development

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Total Factor Productivity Growth in Indonesian Manufacturing, 1975-1995: Issues in Measurement

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  1. Total Factor Productivity Growth in Indonesian Manufacturing, 1975-1995: Issues in Measurement Dec. 2005Virginie Vialv.vial@economic-institute.org

  2. Why does TFP Growth matter ? • Study of productivity relevant for understanding industrial development • Indonesia large, fast growing S-E Asian economy from 1968 to 1997 • Inform the intensive versus extensive economic growth debate for S-E Asia • Inform the controversy about the efficiency of industrial policy in Indonesia

  3. Indonesian manufacturing TFP Growth: Key findings • Economic growth 9% p.a. 1976-95 • Consensus on aggregate average TFPG for 1976-95 at ca. 2.7% p.a. • But seems to be more of a coincidence than consensus, as sub-period and 2-digit TFPG estimates diverge greatly!

  4. Short history • 1949, Indonesian Independence • 1949-1965 Sukarno era of economic stagnation • 1965 Suharto comes into power • 1968-1980 Oil boom period • 1981-1983 Oil crisis • 1984-1988 Deregulation period • 1989-1995 Investment boom

  5. Average TFP growth rates at the 2-digit industry level, 1975-81

  6. Average TFP growth rates at the 2-digit industry level, 1981-85

  7. Average TFP growth rates at the 2-digit industry level, 1984-91

  8. Average TFP growth rates at the 2-digit industry level, 1989-95

  9. Why do more research on this topic? • Need to understand divergence of TFPG estimates • Possibly improve current methodologies • Find a way to estimate establishment-level TFP to propose a microeconomic explanation to aggregate TFPG and inform the debate over industrial policy

  10. Purpose of the paper • Review existing TFPG estimates and explain discrepancies • Propose new methodology for TFPG estimation at establishment level for 1975-95 • Construct new capital stock series • Estimate new elasticities of value added with respect to inputs • Construct aggregate with Divisia Index Number

  11. The data: key features (1/2) • Very rich and reliable dataset for Developing Countries standards • 160 variables • Covering 24 years (1975-1998) • Average of 20,000 observations per year • Covering all manufacturing establishments with 20 employees or more

  12. The data: key features (2/2) • Produced by the Indonesian Bureau of the Census • Unpublished data • Population census rather than sample • Establishments traced over time with ID number • Ideal for industrial demographic purposes

  13. The data: Peculiarities (1/2) • 2 versions of the dataset exist: • Raw Statistik Industri (RSI) • Backcast Statistik Industri (BSI) • RSI contains only original variables with 160 variables and an average of 12,000 observations per year • Mid-1980s, “Discovery” of a large number of establishments = Improvement of census coverage • Growth rates calculated on RSI figures are biased

  14. The data: Peculiarities (2/2) • To overcome this problem, the BSI has been constructed • BSI provides a “backcast” series of selected variables for establishments discovered after they had started operations • Suited for growth calculations and industrial demographic studies • However only provides 4 variables: employment, output, intermediate inputs and value added • Average of 20,000 observations per year for 1975-1996

  15. The data: relevant variables for TFP estimation • BSI, 1975-1996 • Gross Output • Value Added • Number of Workers • Intermediate Inputs • RSI, 1975-1998 • Wages • Investment (annual changes in fixed assets) • RSI, 1988-1998 • Fixed assets

  16. Reasons for TFPG discrepancies among authors • Use of RSI rather than BSI (Osada, 1994) • Different vintages of BSI, here BSI 1996 is used, while Timmer (1999), Aswicahyono et alii (1996) use BSI 1993 • The most important difference is the construction of the capital stock series

  17. Previous methodologies of capital stock estimation: PIM (1/2) • Most authors use the Perpetual Inventory Methodology (PIM) to estimate the capital stock series • Step 1: Estimate a benchmark capital stock for 1975 using ICVAR (incremental value added ratio) or ICOR (incremental capital-output ratio), assuming the steady state. • ICVARs or ICORs are calculated at the 2-digit level and averaged for 1975-1977, then applied to value added or gross output in 1975 to obtain initial benchmark capital stock

  18. Previous methodologies of capital stock estimation: PIM (2/2) • Step 2: The current capital stock is then calculated as the initial capital stock, plus new investment, minus sales and scrapping of old capital stock • Methodology widely accepted at the aggregate level • BUT does not easily apply at the establishment level

  19. Previous methodologies of capital stock estimation: Fixed assets (1/2) • Goeltom (1995) uses fixed assets figures as a benchmark capital stock, substracting and adding investments to create a capital stock series at the establishment level • But fixed assets and investment figures are inconsistent and can lead to negative capital stock figures • Discarding those observations remove a large part of the population

  20. Previous methodologies of capital stock estimation: Fixed assets (2/2) • Okamoto & Sjöholm (1999) study plant-level TFP of the Indonesian automotive sector and use existing fixed assets data for the period 1990-1995 • They seem to work with RSI and exclude establishments not reporting fixed assets figures

  21. New methodology of capital stock estimation: What need to be done • Previous establishment-level study of Indonesian manufacturing suggest that the fixed assets series is the one to use • Data only available for 1988-1998 • Data only available for RSI • Need to “backcast” fixed assets figures for 1975-1987 • Need to “backcast” fixed assets figures for establishments in BSI not present in RSI

  22. New capital stock series: Estimation (1/2) Using observed RSI figures, the following regression is run over the period 1988-95, using Random Effect GeneralisedLeast Square regression on panel data: Where is the unobservable individual specific effect, and is the remainder disturbance.

  23. New capital stock series: Estimation (2/2) Random Effect GLS regression of establishment-level capital stock on establishment-level output, 1988-1995

  24. Aggregate annual average rates of growth (aggregation with Divisia Indices)

  25. Previous methodology for elasticities estimation • Most studies estimate TFP using a value added production function • Previous authors use the share of wages in value added as the elasticity of value added with respect to labour α • The elasticity of output with respect to capital is then calculated as (1- α)

  26. Are these elasticities plausible? • α is found to be quasi-constant over time • α has and average value of 0.2 • Fairly implausible compared to the world benchmark at 0.6/0.7 • Sarel (1997) finds an econometric estimate for Indonesia at 0.65 • Authors agree that labour and wages are fairly under-reported in BSI/RSI

  27. New methodology for elasticities estimation • Following Levisohn & Petrin, 2003, we use an estimator which uses electric consumption as a proxy in order to control for the correlation between input level and the unobserved firm-specific productivity process

  28. Variables used • All variables used are collected variables from RSI for the period 1988-95 • We do not introduce backcast estimates in the elasticities estimation • Variables : Value added, number of workers, net fixed assets, electricity consumption

  29. Levisohn & Petrin (2003) estimator (1/2) • The production function is written as: Where And is the transmitted productivity component, while is the error term.

  30. Levisohn & Petrin (2003) estimator (2/2) 1st stage estimates and up to the intercept using OLS: • 2nd stage estimates in minimising: • Code for Stata software can be found at: http://gsbwww.uchicago.edu/fac/amil.petrin/research/

  31. Levisohn-Petrin value added-based estimator

  32. New TFP Growth estimates: Establishment level • TFP Growth is first calculated with the growth accounting methodology at the establishment level using parameters estimated previously

  33. New TFP Growth estimates: Aggregate manufacturing • Aggregation at the aggregate manufacturing levelis carried out with Divisia Index Number Methodology (weighted sum) • Indices of TFP Growth are compared both with previous aggregate results and with simple average figures

  34. Comparing results with previous studies

  35. Comparing establishment-average and aggregate TFPG

  36. Avenues for future research (TFP) • Use the 5 different type of assets available both in the backcasting of assets and in the estimation of elasticities (land, buildings, vehicles, machinery, other) • Conduct the analysis at a more disaggregate level (2- to 3-digit)

  37. Avenues for future research (industrial demography) • Demographic map of Indonesian manufacturing including establishment-level TFPG estimates • Impact of entry and exit on aggregate TFPG • Determinants of exit • Determinants of establishment TFP heterogeneity

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