Impact of Regulatory and Institutional Changes on Plant-level Productivity and Technical Efficiency: Evidence from the Indian Manufacturing Sector

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Impact of Regulatory and Institutional Changes on
Plant-level Productivityand Technical
Efficiency Evidence from the Indian
Manufacturing Sector

• Sumon Bhaumik, Brunel University
• Subal C Kumbhakar, SUNY Binghamton, NY

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What is productivity?

• Productivity is most widely used in academic and
  nonacademic discussions. It is mostly used to
  mean (average) labor productivity, and is an
  active research area.
• From a macro perspective prosperity of a country
  is identified by its productivity. That is, if
  productivity is high the country is rich (in
  relative sense) because there are more for every
  person.

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Who benefits?

• If productivity of country A is higher than
  country B, it is often argued that country A is
  richer than country B. Who gains from an increase
  in productivity? Producers? Consumers? Both?
  Distributional issue is often neglected.
• Should the objective of a country be to maximize
  productivity? What does it mean economically when
  productivity is maximized?
• Can productivity be raised by government policy,
  such as subsidizing output/input prices,
  deregulation, etc?
• Are there any cost of policy-induced productivity
  gain?

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How to raise productivity?

• Why one country is more productive than another
  country? Is it due to better technology, more
  resources, better trained labor force?
• What does it take for a country to increase its
  productivity? That is, what are the sources of
  productivity change? Are there any cost?
• If the production function is concave,
  productivity can increase through technical
  change (Solow, 1956).

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Micro productivity

• Why should a firm be interested in increasing
  productivity, instead of maximizing profit?
• If productivity is high and wages are also high,
  profit might not be higher.
• Does high productivity mean that producers,
  consumers, and workers are all better off?
• Perhaps a more intuitive approach is to relate
  productivity to profitability, especially in a
  micro-study.

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Role of policy change

• How can a change in government policy affect
  productivity?
• What are the channels through which policy change
  affect productivity? Thorough a shift in the
  technology (neutral or non-neutral)? By making
  the inputs more productive (factor augmenting
  approach)?
• Are there any cost of policy-induced productivity
  gain? For example, subsidy

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Modeling regulatory changes

• Shadow price approach because regulations distort
  input prices.
• Shadow cost function
• Requires price information
• We use a primal approach and in which the
  production technology is allowed to change freely
  between two time periods.

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Our paper

• Estimate plant-level technical efficiency in
  1989-90 and 2000-01
• Decompose output difference between state-owned
  and privately owned firms into the constituent
  factors
• Decompose growth of output across time into its
  constituent factors
• Introduce technical inefficiency into the model.

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What is technical inefficiency?

• Textbook definition of production, cost, profit
  function is based on the concept of max/min. This
  is not followed while estimating these functions.
• Attaining the frontier should be the target but
  many fail
• Extension of the standard neoclassical model
  which assumes away failures!

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Defining inefficiency

• Two measures of technical efficiency are mostly
  used in the efficiency literature. These are
•  
• (i) Input-oriented (I-O) and
• (ii) Output oriented (O-O) technical efficiency

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IO and OO measures
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Stochastic Production Frontier

• yi f(xiß) exp-ui expvi
• where f(xiß) expvi is the stochastic frontier,
  TEi exp-ui.
• Since we require that TEi ? 1, we have
• ui ? 0 is technical inefficiency
• Can be estimated econometrically. Inefficiency
  can be estimated for each producer.

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Empirical strategy

• Production function
• Cobb-Douglas and translog
• Stochastic frontier ? technical efficiency
• Returns to scale
• Cobb-Douglas Same across firms of a certain type
  for each year
• Translog Distribution across firms within each
  category and for each year
• Oaxaca-type decomposition across ownership
  classes
• Oaxaca-type decomposition across years

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Policy initiatives

• 1984-91
• Tax-code simplification
• Trade liberalisation (especially for ICT)
• Broadbanding
• Post-1991
• Licensing policy abandoned
• Trade regime further liberalised
• Tax code rationalisation
• Financial liberalisation
• Interest rate liberalised
• Stock market listing rules eased, CCI replaced by
  SEBI
• Entry barriers to banking sector removed, and
  prudential norms put into place

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What we did

• Data
• Annual Survey of Industries
• 1989-90 and 2000-01
• Plant level data for 2-digit industries
• Estimates
• Returns to scale for each 2-digit industry
• Plant-level technical efficiency
• Decompose growth of output across time
• Characteristics effects
• Coefficients effects
• Technical efficiency effects

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Data

• Annual survey of industries
• Plant-level data
• Information on value of output, value of raw
  materials, employment, cost of labour, productive
  capital, fixed capital, ownership, location
• We control for state, plant age, ownership, etc.
• Examined 14 industry categories
• Want to capture the overall effect of deregulation

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Stochastic frontier production model

• y ? ??X - u v
• y (ln) gross value added
• X factor inputs plant characteristics
• (ln) capital
• (ln) labour
• (ln) plant age
• ownership
• location
• u technical efficiency with half-normal
  distribution
• v N(0, ?2) iid noise term

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Regression estimates I
Coefficient of log capital Coefficient of log labour Returns to scale Technical efficiency
Agricultural products 0.16 0.22 0.76 0.77 0.92 0.99 0.55 0.51
Textiles (w/o apparel) 0.21 0.27 0.68 0.68 0.89 1.05 0.59 0.52
Textile products 0.17 0.11 0.75 0.87 0.92 0.98 0.62 0.56
Wood and wood products 0.17 0.27 0.76 0.71 0.93 0.98 0.60 0.63
Paper, paper products, printing 0.20 0.14 0.74 0.89 0.94 1.03 0.61 0.51
Leather and leather products 0.23 0.13 0.67 0.95 0.90 1.08 0.53 0.59
Chemicals 0.13 0.25 0.73 0.80 0.86 1.05 0.56 0.50
Coding Blue 1989-90, Red 2000-01
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Regression estimates II
Coefficient of log capital Coefficient of log labour Returns to scale Technical efficiency
Rubber and rubber products 0.24 0.36 0.72 0.71 0.96 1.07 0.55 0.51
Non-metallic products 0.13 0.33 0.77 0.70 0.90 1.03 0.60 0.54
Basic metals and alloys 0.18 0.36 0.69 0.66 0.87 1.02 0.56 0.49
Metals and metal products 0.17 0.16 0.77 0.88 0.94 1.04 0.64 0.51
Non-electrical machinery 0.19 0.16 0.73 0.84 0.92 1.00 0.66 0.54
Electrical machinery and equipment 0.19 0.25 0.73 0.77 0.92 1.02 0.53 0.50
Transport equipment 0.16 0.24 0.74 0.78 0.90 1.02 0.68 0.58
Coding Blue 1989-90, Red 2000-01
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Textiles (not including apparel)

• Median
• Ha Med(89-90) ? Med(00-01)
• P-value 0.00
• Ha Med(89-90) gt Med(00-01)
• P-value 0.00
• Mean
• Ha Mean(89-90) ? Mean(00-01)
• P-value 0.00
• Ha Mean(89-90) gt Mean(00-01)
• P-value 0.00

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Leather and leather products

• Median
• Ha Med(89-90) ? Med(00-01)
• P-value 0.00
• Ha Med(89-90) gt Med(00-01)
• P-value 0.00
• Mean
• Ha Mean(89-90) ? Mean(00-01)
• P-value 0.00
• Ha Mean(89-90) lt Mean(00-01)
• P-value 1.00
• Ha Mean(89-90) gt Mean(00-01)
• P-value 0.00

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Basic metals

• Median
• Ha Med(89-90) ? Med(00-01)
• P-value 0.00
• Ha Med(89-90) gt Med(00-01)
• P-value 0.00
• Mean
• Ha Mean(89-90) ? Mean(00-01)
• P-value 0.00
• Ha Mean(89-90) gt Mean(00-01)
• P-value 0.00

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Non-metallic products

• Median
• Ha Med(89-90) ? Med(00-01)
• P-value 0.00
• Ha Med(89-90) gt Med(00-01)
• P-value 0.00
• Mean
• Ha Mean(89-90) ? Mean(00-01)
• P-value 0.00
• Ha Mean(89-90) gt Mean(00-01)
• P-value 0.00

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Electrical machinery

• Median
• Ha Med(89-90) ? Med(00-01)
• P-value 0.08
• Ha Med(89-90) gt Med(00-01)
• P-value 0.04
• Mean
• Ha Mean(89-90) ? Mean(00-01)
• P-value 0.01
• Ha Mean(89-90) gt Mean(00-01)
• P-value 0.00

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Decomposition

• Regressions
• y1 ?1 ?1?X1 - u1 v1
• y2 ?2 ?2?X2 - u2 v2
• Decomposition
• (y2 y1) (?2 ?1) (X2 X1)?2?
• (?2? ?1?)X1 - (u2 u1) (v2 v1)

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Industry Total Input effect Input effect Technology effect Technology effect Efficiency effect Efficiency effect
Industry Total Effect of Total Effect of Total Effect of Total
Industry (1) (2) (3) (4) (5) (6) (7)
Agricultural products 2.82 2.67 94.37 0.20 7.04 -0.04 -1.42
Textiles (without apparel) 2.82 2.68 95.28 0.20 7.20 -0.07 -2.48
Textile products (incl. apparel) 2.40 2.44 101.60 0.02 0.89 -0.06 -2.50
Wood wood products 2.53 3.13 123.75 -0.63 -24.93 0.03 1.18
Paper, paper products and printing 1.80 1.86 103.39 0.04 2.17 -0.10 -5.56
Leather leather products 1.79 1.66 93.04 0.06 3.60 0.06 3.36
Chemicals 2.50 2.69 107.81 -0.14 -5.41 -0.06 -2.40
Rubber plastic products 2.47 2.37 96.00 0.14 5.62 -0.04 -1.62
Non-metallic products 2.79 2.88 103.37 -0.03 -1.22 -0.06 -2.15
Basic metals alloys 2.39 2.49 104.02 -0.03 -1.10 -0.07 -2.93
Metals metal products 2.52 2.49 98.86 0.16 6.31 -0.13 -5.16
Non-electrical machinery 2.39 2.25 94.19 0.26 10.84 -0.12 -5.02
Electrical machinery equipment 2.23 2.26 101.22 0.00 0.12 -0.03 -1.34
Transport equipment 2.89 2.67 92.20 0.33 11.25 -0.10 -3.45
Note The numbers are percentage difference
between the predicted (log) values of value added
for 2000-01 and 1989-90.
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Conclusions

• Conventional wisdom tells us that structural
  reforms increase competition and force companies
  to become more efficient.
• Whether the post-1991 growth is an outcome of
  more efficient use of resources or greater use of
  factor inputs.
• We used plant-level data from 1989-90 and 2000-01
  to address this question. Our results indicate
  that most of the growth in value added is
  explained by growth in the use of factor inputs.
• We also find that median technical efficiency
  declined in all but one of the industries between
  the two time periods, and change in technical
  efficiency explains a very small proportion in
  the change in gross value added.

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• Thank you for your attention!!
• Comments/questions