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

1. 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

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 yi = f(xi;ß) exp{-ui} exp{vi} where f(xi;ß) exp{vi} is the stochastic frontier, TEi = exp{-ui}. • Since we require that TEi 1, we have • ui 0is 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. 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

17. Regression estimates I Coding: Blue – 1989-90, Red – 2000-01

18. Regression estimates II Coding: Blue – 1989-90, Red – 2000-01

19. Median Ha: Med(89-90)  Med(00-01) P-value = 0.00 Ha: Med(89-90) > Med(00-01) P-value = 0.00 Mean Ha: Mean(89-90)  Mean(00-01) P-value = 0.00 Ha: Mean(89-90) > Mean(00-01) P-value = 0.00 Textiles (not including apparel)

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

21. Median Ha: Med(89-90)  Med(00-01) P-value = 0.00 Ha: Med(89-90) > Med(00-01) P-value = 0.00 Mean Ha: Mean(89-90)  Mean(00-01) P-value = 0.00 Ha: Mean(89-90) > Mean(00-01) P-value = 0.00 Basic metals

22. Median Ha: Med(89-90)  Med(00-01) P-value = 0.00 Ha: Med(89-90) > Med(00-01) P-value = 0.00 Mean Ha: Mean(89-90)  Mean(00-01) P-value = 0.00 Ha: Mean(89-90) > Mean(00-01) P-value = 0.00 Non-metallic products

23. Median Ha: Med(89-90)  Med(00-01) P-value = 0.08 Ha: Med(89-90) > Med(00-01) P-value = 0.04 Mean Ha: Mean(89-90)  Mean(00-01) P-value = 0.01 Ha: Mean(89-90) > Mean(00-01) P-value = 0.00 Electrical machinery

24. 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)

26. Note: The numbers are percentage difference between the predicted (log) values of value added for 2000-01 and 1989-90.

27. 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.

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