Industrial and Aggregate Measurements of Productivity Growth in China, 19822000

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Industrial and Aggregate Measurements of
Productivity Growth in China, 1982-2000

• Jing Cao
• Tsinghua University
• Mun S. Ho, Dale W. Jorgensen
• Harvard University
• Ruoen Ren and Linlin Sun
• BeiHang University
• Ximing Yue
• Renmin University of China

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Main Research Question

• What are the sources of Chinas successful
  economic growth?
• Accumulation of capital?
• Total Factor Productivity Growth?
• Restructuring of the economy from agriculture to
  manufacturing and services?

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Research Goals

• Estimate productivity growth by sectors
• Derive aggregate productivity growth estimates
  using three different methods of aggregation
• Aggregate Production Function
• Aggregate Production Possibility Frontier (PPF)
• Direct Domar-weighted Aggregation
• Use newly developed data based on time-series
  Input-Output Tables and micro data from the
  Survey of Labor Force
• Construct detailed time series data at sector
  level including appropriate deflators

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Time Periods

• Covers the period 1982-2000
• For purposes of analysis, four sub-periods are
  considered
• 1982-84 Characterised by growth in agriculture
  and efficiency gains due to policies including
  the introduction of household registration system
• 1984-88 Growth due to industrial sector reforms
  and open door policy launched
• 1988-94 Social market economy and development
  zones
• 1994-2000 Declining role of SOEs and increasing
  private ownership, tariffs were reduced to be
  ready for WTO entry

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Past studies

• Chow (1993) not including services sector no
  technical change during 1952-80
• Chow and Li (2002) 3.03 TFP growth during
  1978-1998
• Borensztein and Ostry (1996) TFP growth -0.7
  during 1953-78 3.8 during 1979-1994
• Fan, Zhang and Robinson (1999) 4.2 TFP growth
  during 1978-1994
• Woo (1998) 1.1 TFP growth during 1979-1993
• Young (2003) 1.4 in non-agriculture
• Ren and Sun (2005) TFP growth at 2-digit level
  Using Domar weights found 3.83 for the whole
  sector during 1988-94

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KLEMS approach to TFP Growth measurement

• Economy divided into primary, industrial and
  tertiary sectors, in which industrial sectors are
  divided into 26 sectors
• Capital input structures equipment and
  automotive vehicles
• Labor input number of workers classified by
  sex, age and educational attainment
• Intermediate inputs commodities produced
  domestically plus imports put in the production
  process

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Classification of Industries
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Capital and Labor Inputs
Labor Input
Capital Input
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Growth accounting at the industrial sector level

• The gross output of sector j in period t is
  assumed to be produced with a Hicks-neutral
  production function using various types of
  capital, labor and intermediate commodities

• Productivity growth in each sector j from the
  year t-1 to t is given by

• d ln Ajt provides a measure of TFP growth at the
  industrial sectorl level.

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Value-added Functions

• We define a concept of industry value-added. The
  real value added of sector j, , is defined by
  writing output as a weighted share of value added
  and intermediate input

• The following identity is applied

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Time-series I-O table

• An important technical innovation in the growth
  accounting is to construct a internally
  consistent time-series I-O table, labor input,
  capital input data and price deflators.
• Our data set is constructed based on 4 benchmark
  official I-O tables (1981,1987, 1992, 1997)
  including A tables and U tables, consistent to
  the coverage and definitions of 1997 table
• Then a time-series current value U tables are
  constructed for 1981-2000, using Kuroda
  minimization of errors approach.
• Based on current value U tables and sectoral
  price indices, a time series real value U tables
  are constructed.
• Finally, a time series KLEM data are extracted
  based on the time-series real value U tables.

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Industry Output Growth ()1982-2000
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Industry Output Growth ()1994-2000
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Industrial TFP Growth ()1982-2000
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Industrial TFP Growth ()1994-2000
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Domar-Weighted Contribution ()1982-2000
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Domar-Weighted Contribution ()1994-2000
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Growth accounting at the aggregate level

• Three Alternative Aggregation Approaches
• Direct aggregation across industries A simple
  Domar-weighted aggregate of industry-specific
  productivity growth estimates
• Aggregate Production Function Approach Aggregate
  value added is considered as a function of
  aggregate value of capital and aggregate labor
  requires the assumption that there is perfect
  substitution across sectors, same prices for
  labor and capital in all sectors

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Growth accounting at the aggregate level

• Aggregate Production Possibility Frontier
  Relaxes the assumption of the existence of an
  aggregate production function and assumes value
  added is not perfectly substitutable across
  industries but retains assumptions on input side
• We also discusses inter-relationships between
  measures of productivity aggregate level using
  the three different approaches.
• TFP growth under production function approach is
  equal to Domar-weighted sectoral growth plus
  reallocation of value added across sectors plus
  reallocation of labor and capital across sectors.

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Growth in Aggregate Value-added and its Sources
using production possibility frontier
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Aggregate Reallocation Effects ( per year)
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GDP Growth Decomposition
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Main Findings Sectoral productivity change

• TFP growth rates are quite varied across sectors
  and across sub-periods
• Preferred approach is to use the Production
  Possibility Frontier approach allows TFP growth
  estimates to be decomposed into Domar-weighted
  sectoral growth and reallocation in labor and
  capital
• Our results shows that the aggregate TFP is 2.5
  for 1982-2000, and a steady decline in TFP growth
  over the four sub-periods
• Effects due to reallocation of labor and capital
  appear to be negligible when the whole study
  period is considered.

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Some Caveats

• Data problems in the service sector,
• Some data issue in highly regulated sector, such
  as electricity, oil and gas extraction, petroleum
  and refining sectors, sectoral output might be
  underestimated.
• Deverticalization Problem There has been an
  increase in sub-contracting and/or splitting up
  of previously highly vertically integrated
  enterprises, then gross value of output might
  increase.
• We do not have estimates of land input for some
  sectors, but treat it as capital input, this
  would lead to some bias as well.

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THANK YOU!

• Comments Welcome ?