1. Impact of Regulatory and Institutional Changes on Plant-level Productivity and Technical Efficiency: Evidence from the Indian Manufacturing Sector Sumon Bhaumik, Brunel University Subal C Kumbhakar, SUNY Binghamton, NY

2. 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.

3. 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?

4. 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).

5. 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.

6. 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

7. 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.

8. 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.

9. 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!

10. 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

11. IO and OO measures

12. Stochastic Production Frontier y i = f(x i ;ß) exp{-u i } exp{v i } where f(x i ;ß) exp{v i } is the stochastic frontier, TE i = exp{-u i }.  Since we require that TE i  1, we have  u i  0 is technical inefficiency  Can be estimated econometrically. Inefficiency can be estimated for each producer.

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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 0.89 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

19. 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

20. Textiles (not including apparel)  Median H a : Med(89-90)  Med(00-01)  P-value = 0.00 H a : Med(89-90) > Med(00-01)  P-value = 0.00  Mean H a : Mean(89-90)  Mean(00- 01)  P-value = 0.00 H a : Mean(89-90) > Mean(00- 01)  P-value = 0.00

21. Leather and leather products  Median H a : Med(89-90)  Med(00-01)  P-value = 0.00 H a : Med(89-90) > Med(00-01)  P-value = 0.00  Mean H a : Mean(89-90)  Mean(00- 01)  P-value = 0.00 H a : Mean(89-90) < Mean(00- 01)  P-value = 1.00 H a : Mean(89-90) > Mean(00- 01)  P-value = 0.00

22. Basic metals  Median H a : Med(89-90)  Med(00-01)  P-value = 0.00 H a : Med(89-90) > Med(00-01)  P-value = 0.00  Mean H a : Mean(89-90)  Mean(00- 01)  P-value = 0.00 H a : Mean(89-90) > Mean(00- 01)  P-value = 0.00

23. Non-metallic products  Median H a : Med(89-90)  Med(00-01)  P-value = 0.00 H a : Med(89-90) > Med(00-01)  P-value = 0.00  Mean H a : Mean(89-90)  Mean(00- 01)  P-value = 0.00 H a : Mean(89-90) > Mean(00- 01)  P-value = 0.00

24. Electrical machinery  Median H a : Med(89-90)  Med(00-01)  P-value = 0.08 H a : Med(89-90) > Med(00-01)  P-value = 0.04  Mean H a : Mean(89-90)  Mean(00- 01)  P-value = 0.01 H a : Mean(89-90) > Mean(00- 01)  P-value = 0.00

25. Decomposition  Regressions y 1 =  1 +  1 X 1 - u 1 + v 1 y 2 =  2 +  2 X 2 - u 2 + v 2  Decomposition (y 2 – y 1 ) = (  2 –  1 ) + (X 2 – X 1 )  2 + (  2 –  1 )X 1 - (u 2 – u 1 ) + (v 2 – v 1 )

27. Industry Total Input effectTechnology effectEfficiency effect Effect% of TotalEffect % of Total Effect % of Total (1)(2)(3)(4)(5)(6)(7) Agricultural products2.822.6794.370.207.04-0.04-1.42 Textiles (without apparel)2.822.6895.280.207.20-0.07-2.48 Textile products (incl. apparel)2.402.44101.600.020.89-0.06-2.50 Wood & wood products2.533.13123.75-0.63-24.930.031.18 Paper, paper products and printing 1.801.86103.390.042.17-0.10-5.56 Leather & leather products1.791.6693.040.063.600.063.36 Chemicals2.502.69107.81-0.14-5.41-0.06-2.40 Rubber & plastic products2.472.3796.000.145.62-0.04-1.62 Non-metallic products2.792.88103.37-0.03-1.22-0.06-2.15 Basic metals & alloys2.392.49104.02-0.03-1.10-0.07-2.93 Metals & metal products2.522.4998.860.166.31-0.13-5.16 Non-electrical machinery2.392.2594.190.2610.84-0.12-5.02 Electrical machinery & equipment 2.232.26101.220.000.12-0.03-1.34 Transport equipment2.892.6792.200.3311.25-0.10-3.45 Note: The numbers are percentage difference between the predicted (log) values of value added for 2000-01 and 1989-90.

28. 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.

29.  Thank you for your attention!!  Comments/questions