The Impact of Trade Liberalization on SMEs versus Large

PAPER SUBMITTED TO THE 10TH ANNUAL CONFERENCE ON ECONOMIC GROWTH AND DEVELOPMENT, ISI, DELHI CENTER

The Impact of Trade Liberalization on SMEs versus Large Firms*

by

Subhadip Mukherjee**

11/10/2014

*This paper is a part of my PhD thesis, which I will be submitting soon to IIM, Bangalore. I have worked under

the guidance of Prof. Rupa Chanda (Chair), Prof Arnab Mukherjee and Prof Gopal Naik. I am grateful to them

for their valuable suggestions.

** PhD Scholar, ESS Area, IIM Bangalore, email: [email protected]

This study examines the performance of different types of Indian manufacturing firms in the

post-trade liberalization period. Based on firm-level balanced panel data for the 1999-2009

period obtained from the Prowess database, the study determines the differential impact of tariff

and non-tariff reduction over this time frame on the performance of different types of Indian

manufacturing firms, while taking into account firm and industry specific factors. The paper

finds that, trade liberalization has improved firm-level operational and productive efficiency only

for large firms, whereas Indian manufacturing SME firms have remained stagnant throughout the

study period.

Key words: Manufacturing, Small Scale Industry, Total factor productivity, Trade Reform

JEL Classifications: L6, D24, L1, F13

2

The Impact of Trade Liberalization of SMEs versus Large Firms

1. Introduction This paper discusses the impact of trade liberalization on the performance of Indian

manufacturing firms and how it differs across different types of firms (large vs. SME).We

have mainly taken the growth rate of profit after tax as a measure of firm-level profitability

(i.e. operational efficiency) and also calculated total factor productivity as a measure of firm-

level productive efficiency to gauge firm-level performance in the era of trade liberalization.

We examine the impact of a reduction in both tariff and non-tariff barriers (NTB) on these

firm-level performance indicators over time. Moreover, we also find the differences in the

effects of liberalization across various types of firms (large vs. SME).

The plan of the paper is as follows: Section 2 provides a brief overview of the literature,

while Section 3 discusses the data sources, the broad methodology and some issues

concerning the data sources. Section 4 discusses the detailed description of the measurement

of the variables used in the model. Section 5 discusses the detailed description of the fixed

effect models and analyses the results. Section 6 concludes the paper.

2. Literature Review There are several studies, such as, Hasan (2002), Das (2004), Goldar and Kumari (2003),

Balakrishnan et.al (2006), Sivadasan (2009), Topalova and Khandelwal (2011), De Loecker

et.al. (2012), Ghosh (2013), which have examined the impact of trade liberalization on firm-

level as well as industry-level performance of Indian manufacturing sector. However, there

are very few papers, which concentrate on the impact of trade liberalization on the

performance of different Micro, Small, Medium Enterprises (MSME) manufacturing firms in

India.

Mazumdar (1991) examines the effect of protectionist trade policy on competition between

small firms and large and foreign firms in India. A case study of the textile industry in Uttar

Pradesh based on cost benefit analysis shows that small firms have comparative advantage

relative to large firms.1 Protection has hampered the growth of the textile industry in India by

preventing technology growth, competitiveness, export growth and imports of cheap inputs.

However, the findings of this study cannot be used for examining the impact of trade

liberalization on firm performance due to two reasons; one, the study period the author has

1 The study was done based on collected data for the Hand Looms, Power Looms and Mills Entrepreneurs in

textile towns of Uttar Pradesh (World Bank Survey).

3

taken is before 1981 and second the study suffers from a very small size dataset. In another

paper, Nataraj (2011) has tried to examine the impact of tariff liberalization on the

productivity of both formal and informal manufacturing firms. It uses the NSSO database for

firm-level data on informal small firms for the periods 1989-90, 1994-95, and 2000-01 and

the ASI database for firm-level data on formal large firms for the periods 1989-90, 1994-95,

and 1999-2000. The difference-in-difference (DID) estimation shows that unlike the input

tariff, a reduction in the final goods tariff has significantly improved the firm-level

productivity for informal small firms. On the other hand, the reduction in input tariff plays a

comparatively more important role in raising firm-level productivity for formal large firms

compared to the final goods tariff reductions. The author has also done the quantile

regression to examine the effect of tariff liberalization on distribution of productivity and

firm size.2 The estimation indicates that the largest positive impact of a reduction in final

goods tariffs was on the average productivity of informal small firms. On the contrary, the

decline in final goods tariffs does increase productivity among the top quantiles of the

distribution.

Kathuria et al. (2012) use unit-level data on formal and informal manufacturing sectors for 4

years, 1989–1990, 1994–1995, 2000–2001 and 2005–2006 to compare the differential effects

of tariff reforms, industrial de-licensing and the withdrawal of reservation of products for

small firms, on firm-level efficiency in formal versus informal manufacturing sectors. The

stochastic frontier analysis provides strong evidence of a widening gap between the

productivity efficiency of formal and informal sector firms due to economic reforms.3

Although, economic reform has improved firm-level productivity for overall Indian

manufacturing, it has made formal firms more efficient and informal firms less efficient.

Though, the previous two studies provide very important insights in terms of the

effectiveness of tariff reduction (both inputs and output) on firm productivity (both, formal

and informal), they do not capture the impact of different firm, state and industry specific

factors. This can only be done by using firm-level panel data.

Apart from the above papers, which have examined the differential impact of trade

liberalization on small firms in formal and informal sectors, there exist very few studies

2 Roger Koenker and Gilbert Bassett (1978), Regression Quantiles, Econometrica, Vol. 46, No. 1 pp. 33-50

3 Data on the formal manufacturing sector is drawn from the Annual Survey of Industries (ASI), undertaken by

the Central Statistical Organization (CSO) which is the annual census-cum-sample survey of all the formal

manufacturing units for all the industries across all the states, and a large representative survey was done for the

data on the informal manufacturing sector.

4

which identify the different growth prospects of registered Indian MSME manufacturing

firms and how these prospects are impacted by different firm, industry, state-level

characteristics. This section presents some of the papers, which deal with registered MSMEs

in India.

Gang (1992) examines how different macro-economic and industry-specific indicators

influence the share of registered small firms in overall Indian manufacturing industry over

time. The pooled estimations of the share of output and the share of value added of small

firms show that, an expanding economy enables small firms to perform better and they can

overcome the inherent cost disadvantages over large firms only in those industries, which are

less-vertically integrated.4 Moreover, the analysis provides clear evidence of the relatively

greater presence of small firms in the capital-intensive industries. In another paper, Sundaram

(2009) uses a national dataset of household enterprises and enterprises hiring outside workers

(less than 10) for 1989, 1994 and 2000 (NSSO), to see whether the higher presence of banks

in a district intensifies the positive effect of tariff liberalization on firm-level output, capital-

labour ratios and labour productivity for micro enterprises. The analysis provides a clear

evidence of relatively stronger positive impact of tariff liberalization on the performance of

small firms, which belong to the districts with higher bank branches per capita.

Coad and Tamvada (2012) examines the impact of firm size and age on the growth of small

Indian firms. The authors use an extensive firm-level data set consists of 6,71,159 Indian

small firms for the year 2001-02, obtained from the Third All India Census of Registered SSI

units (2001-02). The main findings are that small, young firms tend to grow faster than larger,

older firms do and this holds true for low-tech firms using firewood as their power source.

Moreover, exporting has a positive effect on growth, especially for young firms. From the 3rd

census data, the authors identify various barriers such as, 1) lack of demand, 2) lack of

working capital, 3) lack of raw materials, 4) power shortage problem, 5) labour problems, 6)

marketing problems, 7) equipment problems and 8) management problems, which might have

prevented small Indian firms from growing. The main limitation of the paper is that the

impact of trade liberalization cannot be captured in this cross-section data set. Both the above

papers have used either repeated cross section data from the ASI database or simple cross

section data from the census, which cannot capture firm, industry and state specific effects

over time. For capturing all the above effects, it is important to use panel data in the analysis.

4 Using pooled cross-section time series data on 22 industries over 10 years (1974-1984), collected largely from

ASI data base.

5

Pradhan (2011) analysed the trends and patterns for R&D investment by Indian

manufacturing SMEs by exploring various factors that determine their R&D behaviour. An

extensive firm-level data set, for 16,724 units for the period 1991−2008 was used for this

study. The main finding is that, R& D intensity has a positive and significant relation with

age, size, export, raw materials import, etc. The analysis has revealed that, Indian SMEs

have the lowest rate of doing in-house R&D and their R&D intensities have fallen over time.

One major limitation is that this paper only concentrates on R&D intensity and not other

dimensions of performance for small firms.

As evident from the above literature survey, there are very few empirical studies on the

impact of trade Liberalization on Indian MSMEs, the studies mostly focus on manufacturing

overall. Most either focus on registered big firms or use an inappropriate measurement of

firm size when differentiating between small, medium and large firms. Moreover, existing

empirical studies on MSMEs have concentrated only on the impact of liberalization on R&D

investment by these firms, but not on other performance measures (growth, employment,

productivity, exports, access to credit). The existing literature also does not examine the role

of firm, industry and state level characteristics of Indian MSMEs in explaining the effects of

trade liberalization on these firms. Hence, there are clearly three major gaps in the existing

literature in which, some of them are addressed by this paper in the subsequent sections.

3. Data Sources and Methodology

3.1. Data Sources

For undertaking the above study we mainly use firm-level balanced panel data (842 firms *11

year= 9262) for the period 1999-2009. The firm-level information for different variables (for

example, sales revenue, profit after tax, total assets, labour expenditure, power and fuel

expenditure, raw material expenditure and capital employed) are extracted from the Prowess

database (version 4.12) provided by the CMIE. The industry-level information for different

variables is extracted from Industry Analysis Service and Economic Outlook, the two online

databases provided by the CMIE. All tariff related information is collected from the

TRAINS-WITS online database provided by World Bank. Moreover, we also measure the

NTB data by using the import conditions data from the Director General of Foreign Trade

(DGFT) database, and import data, from the Ministry of Commerce and Industry, Department

of Commerce, Government of India.

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3.2 Methodology

This study has undertaken a fixed effect analysis of all the firm-level performance indicators

to determine the relationship between different trade liberalization indicators (like tariff and

NTB) and different firm level performance indicators, after taking into account for

unobserved firm-level heterogeneity. This approach is also helpful to identify the differential

effects of tariff policy on firm performance across different types of firms based on their size

(mainly two groups, SME and large).

This methodology is firstly applied to all firms and then repeated again for different sub

groups of firms (large firms, SME firms). The firm grouping is done based on investment in

plant and machinery, as per the standard definition provided by the Ministry of Micro, Small

and Medium Enterprises (MSME act 2006).

3.3 Some Issues Concerning Data Sources

Goldberg et al. (2008), Kalirajan and Bhaide (2005) and other authors have highlighted

different advantages of using the Prowess data base over the Annual Survey of Industries

(ASI) data, for firm-level analysis of Indian manufacturing industry. First, instead of repeated

cross section in the ASI, the Prowess data are a panel of firms over a long time period. This

panel nature of the Prowess data makes it possible to follow firm performance over time.

Second, Prowess records comprehensive information of all the financial variables and

contains product-level, industry-level and region-level information at the firm level. Finally,

the data span the period of India’s trade liberalization from 1991 to 2009. Thus the Prowess

data set has a definite advantage over the ASI database for understanding firm performance in

the context of trade liberalization and for understanding the differential impact across firms,

industries and regions. Moreover, due to the availability of information on firm-level

expenses, it is also a very useful data base for understanding the change in production

structure due to trade liberalization. Its only drawback is the absence of employment data.

In terms of the availability of MSME data in the Prowess database, different studies offer

different viewpoints. Alfaro and Chari (2012) in their work has presented evidence of the

presence of small firms in the Prowess data base. The study observes that firm size

distribution of Indian manufacturing industry is represented by a large number of small firms

and a small number of large firms—which can be characterized as the “missing middle” in

Indian manufacturing.5 The main reason behind this is the difference in the classification of

5 Based on the firm-level data from the Prowess database collected by the CMIE from 1989–2005, the study

observes the effects of deregulation of compulsory industrial licensing on the firm-size distribution dynamics

7

Indian manufacturing firms’ in the pre and the post MSME Act of 2006. The detailed

classification of Indian micro, small, medium firms before and after 2006 is given in the

following table.

Table 3.1: Classification of MSMEs in India

Classification Investment Ceiling for Plant, Machinery or Equipments*@

Manufacturing Enterprises Service Enterprises

Micro Up to Rs.25 lakh ($50 thousand) Up to Rs.10 lakh ($20 thousand)

Small Above Rs.25 lakh ($50 thousand) & up to

Rs.5 crores ($1 million)

Above Rs.10 lakh ($20 thousand) & up to

Rs.2 crores ($0.40 million)

Medium Above Rs.5 crores ($1 million) & up to


Above Rs.2 crores ($0.40 million) & up to


* Fixed costs are obviously higher.

Definitions before 2, October 2006

Classification Investment Ceiling for Plant & Machinery or Fixed Assets*

Manufacturing Enterprises Service Enterprises

Micro Up to Rs.25 lakh ($50 thousand) Up to Rs.10 lakh ($20 thousand)

Small Above Rs.25 lakh ($50 thousand) & up to

Rs.1 crore ($0.20 million) —

Medium Not defined Not defined

* Excluding land and building.

@ $1 = Rs.50 (April 2009). Source: Micro, Small, Medium Enterprises in India, an Overview, Ministry of Micro, Small & Medium Enterprises, 2010 (GOI.)

The above table clearly indicates that the before the MSME Act of 2006, there was no such

category which can be called ‘Medium scale enterprises’. Thus, it is not possible to find

evidence of the presence of Medium scale enterprises in the literature. The latter has

restricted their study period to the period up to 2006.

There are few studies like, Khandelwal (2011), Majumdar (1997), Thomas and Narayanan

(2012), Kumar et al. (2001) and Balakrishnan et.al (2006), which categorise the firms into

small, medium and large, based on either sales, market share or on total assets. But, none of

the above studies have followed the guidelines given by the Ministry for defining

manufacturing firms (i.e., based on their investment in Plant & Machinery). In our study we

classify firms into micro, small, medium and large firms based on the guidelines given by the

ministry of MSME in India. . This classification is better than the earlier once as it reflects

the limitation for MSME firms to grow beyond a certain limit in terms of their investments in

plant and machinery. It has been observed that India’s MSMEs are reluctant to increase their

investments in plant and machinery beyond the limit (i.e., the investment guidelines given by

and the reallocation of resources of the Indian manufacturing industry overtime. The quantile regression

estimation of the Cobb-Douglas Production function shows non-linear distributional effects of deregulation on

firm size. Moreover, the Average firm size declines significantly in the deregulated industries. Small firms enter

the sample from the left-hand tail of the size distribution while the incumbent firms get significantly bigger

following deregulation.

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the Ministry of MSME for being called as an ‘MSME’ firm) to avoid discontinuity in getting

benefits from the government for being an MSME firm.

In our study we have taken firm-level data from the Prowess database for five broad

industries, which includes food & agro-based products, Textiles, Leather products, Metals &

Metal products and Machinery and Equipment. These are the industries which cumulatively

account for the maximum share of MSME firms in India. The following Figure gives a clear

indication of the importance of these above industries in determining the overall performance

of the MSME sector in India.

Figure 3.1: Share of different industries in the MSME Sector

Source: Final Report of the Fourth All India Census of Micro, Small & Medium Enterprises 2006-07: Registered Sector.

4. Description of Measurement of Variables Used in the Model

4.1. Productivity Measures

For the measurement of total factor productivity (TFP) of different firms, following Topalova

and Khandelwal (2011), we have used the methodology of Levinsohn and Petrin (2003).

Topalova and Khandelwal (2011) in their paper have measured firm level TFP for the time

period 1989 to 2001. We have extended this to 2009 to also incorporate more recent trade

policy changes, i.e., the Exim Policy, 2004-2009. Guided by the methodology of Levinsohn

and Petrin (2003), we have also taken a firm's raw material inputs as a proxy for the

unobservable productivity shocks and have corrected for simultaneity (i.e., firm’s

simultaneous choice of labour, capital and other factor inputs based on its current

productivity level) in the firm's production function. This methodology is useful in

controlling a part of the error that is correlated with inputs, and ensures that the variation in

9

inputs, which is associated to the productivity term, will be removed from the production

function estimation, through the inclusion of a proxy. Following the assumption of a

Cobb‐Douglas production function, we can represent the estimating equation for the

productivity of firm i in industry j at time t as follows:

(4.1)

Where, y denotes output (measured in terms of firms sales revenue), l denotes labour

(measured in terms of the total amount of compensation to employees), p denotes power and

fuel expenditures, m denotes raw material expenditures, and k denotes capital used (measured

in terms of total capital employed). In the above regression equation, all the variables are

taken in natural log form. The term wijt denotes a firm‐specific, time varying unobservable

productivity shock which may be correlated with the firm's choice of variable inputs, p, m,

and l. Along the lines of Levinsohn and Petrin (2003)’s methodology, we have also inverted

the raw materials demand function, which gives an expression for productivity shock,

wijt=wijt(mijt,kijt). So, in the first stage, by using semi‐parametric techniques we have

estimated the coefficients for the variable inputs, l and p, after substituting the productivity

shock expression in equation (4.1). Next in the second stage, by using Generalized Methods

of Moment (GMM) technique we have obtained the coefficients for k and m.6

Similar to the Topalova and Khandelwal (2011)’s approach, we have also used deflated sales

revenue, capital spending and different input expenditures as proxies for the physical

quantities of output, capital and intermediate inputs, following the literature on productivity

estimation using deflated sales revenue, capital spending and input expenditures as proxies

for physical quantities7. To get deflated sales, compensation to employees, power and fuel

expenditure, capital employed, raw material expenditure, we have used industry specific

wholesale price indices, keeping 2004 as the base year; this differed to 1993-94 that is the

base year for their paper. This is because the study period for Topalova and Khandelwal’s

paper is 1989-2001, whereas our study period is 1999-2009.8 All the industry specific-

6 It can be noted that, to make all the coefficients identifiable in the production function, Levinsohn and Petrin

(2003)’s methodology assumes a Markov process in productivity, where capital adjusts to productivity with one

period lag. Thus the instruments are 1st lag of l and k, and 2

nd lag of m.

7The gross revenue approach of Levinsohn and Petrin (2003) methodology of productivity estimation.

8 Before analysis the data we should deflate the variables by using firm‐specific price deflators (De Loecker,

2009), but due to the unavailability of proper firm-level price deflator, like other studies for example, Topalova

and Khandelwal (2011), we have also deflated by using industry-level deflator.

10

wholesale price indices have been collected from the Economic Adviser, Ministry of

Commerce and Industry, Government of India.

By using all the firm-level panel data on deflated sales revenue and other input expenditures

for the period 1999-2009, we have estimated their respective coefficients by using the

methodology of Levinsohn and Petrin (2003). The above estimation result is given in the

following table for all firms:

Table 4.1: Productivity Estimation Using Levinshon-Petrin Methodology for All Firms

Variables Log (Sales Revenue)

Log(Total Amount of Compensation to Employees) 0.1446831***

(0.0246)

Log(Power and Fuel Expenditures) 0.0524455***

(0.0200)

Log(Total Capital Employed) 0.3638617***

(0.0869)

Log(Raw Material Expenditures) 0.1098807*

(0.0671)

Number of Observations 9262

Number of Firms 842

Sources: Author’s calculation for Total Factor Productivity based on the firm-level Prowess data

After getting all the estimated coefficients, we have calculated total factor productivity (TFP)

for the ith

firm in the jth

industry at time t by using the following equation:

(4.2)

After getting the Hicks-neutral TFP we have also created the productivity index following the

methodology of Aw, Chen and Roberts (2001).9 This is done to make the estimated TFP

comparable across industries. The following table gives the detailed calculation for the

productivity index:

Table 4.2: Calculation of Productivity Index for all Firms

Variable Obs Mean in 1999 Std. Dev.

in 1999 Min in 1999

Max in

1999

Log (Sales Revenue) 842 1.771758 1.557636 -6.04484 7.774695

Log(Total Amount of Compensation to Employees) 842 -1.29511 1.728688 -6.04275 6.028658

Log(Power and Fuel Expenditures) 842 -1.769176 1.811701 -6.73799 5.364671

Log(Total Capital Employed) 842 1.365836 1.506814 -3.01026 7.970963

Log(Raw Material Expenditures) 842 0.7912333 1.870688 -6.7359 6.414844

Mean Log (Sales Revenue) in 1999(Base Period)= 1.771758

Mean Log (Input Expenses) in 1999= 0.1446831* (-1.295112)+ 0.0524455* (-1.769176)+0.3638617*1.385836+0.1098807*0.7912333

Mean Productivity in 1999= Exponential [Mean Log (Sales Revenue in 1999) - Mean Log(Input Expenses in 1999)]

Productivity Index = Productivity - Mean Productivity in 1999

Sources: Author’s calculation for Productivity Index based on the firm-level Prowess data

9 The productivity index is calculated as the logarithmic deviation of a firm from a reference firm's Productivity

in the particular industry in a base year. Following, Topalova and Khandelwal (2011), we have also subtracted

the productivity of a firm with the mean log output and mean log input level in 1998-99 (base year) from the

estimated firm‐level TFP to get the productivity index.

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4.2. Measures of Final Goods Tariff, Input tariff and Effective Rate of Protection (ERP)

We calculate the industry-level effective rate of protection (ERP) by following Topalova and

Khandelwal (2011) to measure the level of protection at industry-level, as this is very useful

to measure the net effect of the tariff liberalization from both the input and the output sides.

The exact formulation of input tariff and ERP for the jth

industry at time t, as defined by

Corden (1966) is given below:

input tariffjt = ∑s αjs final goods tariffst (4.2.1)

ERPjt = (final goods tariffjt – input tariffjt) / 1- ∑sαjs (4.2.2)

Where, αjs is the share of imported input s used in the value of output j,

The above calculation is done for all the 2-digit Industry groups (NIC-2004) based on the

industry level final goods tariff (average MFN rate) data collected from the WITS database

and the Input-Output data collected from the Input-Output table (2004-05) of the OECD-

STAN database.10

From the Input-Output table, we calculate the share of each ith

imported input used in the

value of output of jth

industry at the 2-digit HS level and then grouped them into 12 broad

industry groups and calculated the sum total of the input share of each 2-digit component

industry of these 12 broad industry groups, which are contributing to each other’s respective

final output. Then by using equation (4.2.1), we have calculated the input tariff for 5 broad

industries (i.e. food and agro based industry, textile industry, machinery industry, metal

industry and leather industry), which cumulatively represents highest share in SME firms

total production over the 1999 to 2009 period. After calculating the input tariffs, we also

calculate their ERP by using the formulation given in equation (4.2.2). The calculation of the

input tariff for the leather industry for the year 1999 is given in the following table as an

illustration of the method used:

10

data extracted on 24 Mar 2014 10:18 UTC (GMT) from OECD. Stat

http://stats.oecd.org/Index.aspx?DataSetCode=STAN_IO_TOT_DOM_IMP

12

Table 4.3: Input Tariff Calculation for Leather Industry for the year 1999

Final Product

(Final Goods

Industry)

Inputs used (Input Industries)

Weightage of Input

used (αjs)

(in Percentage)*

Final Goods Tariff for Different

Input Industries in 1999 (in

Percentage)

Input Tariff of

Leather Industry in

1999

Leather

Industry

Food and Agro based Industries 23.36292 38.115

24.3640459

Textiles Industry 3.072147 39.17

Leather Industry 33.30012 34.07

Metal Industry 0.693811 32.645

Machinery Industry 1.126637 29.31

Gems and Jewellery Industry 0.053775 39.02

Wood and Paper Industry 0.385946 31.4525

Chemical Industry 4.015183 29.275

Rubber and Plastic Industry 1.030198 37.67

Transport and Scientific Instruments industry 0.293954 32.735

Mineral Industry 0.00874 19.6

Agricultural Raw Products Industry 2.399705 22.92

∑s αjs 69.7431

*Note: In the table we did not give the exact input share of each 2-digit component industry due to space constraint, rather than given the sum total of the input share of each 2-digit component industry for all the 12 broad industry groups contributing in the production of Leather

industry.

Source: Author’s calculation based on the WITS database

This whole calculation of input tariff and later ERP is done based on the industry-level

output tariff (MFN rate) data collected from the WITS database and the input-output table for

the year 2007-08 from the CSO database, Govt. of India.

4.3. Measures of Inverted Non-Tariff Barriers (NTB)

Non-tariff barriers (NTB) have assumed a lot of importance in India in the last two decades

as tariff rates has declined. Thus, it becomes important to use tariff as well as non-tariff

barriers to measure trade protection. Although, it is very hard to find a good dataset to

measure NTBs, there are few studies (Das (2003), M Pandey (1999)), which have attempted

to measure NTBs for the period 1980-2000, using the import coverage ratio. This

measurement of NTB captures the relative restrictiveness of imports for different industries.

The import coverage ratios are defined as the percentage of products’ imports within a

category that are affected by an NTB. Their formulation of the NTB coverage ratio is given

as follows:

Define wi = mi / mi as the import weight, where mi = imports of the ith

commodity where

mi is the total imports.

Let ni = (1 if there are NTB's

(0 if there are no NTB's.

Then, the NTB coverage ratio is defined as ni wi. An alternative is to calculate simple

averages of the coverage ratios.

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The coverage ratio for each input-output sector has been calculated according to the

following weighting scheme for each 8-digit tariff line and has been assigned a number:

0% if no NTB applies to the tariff line (i.e. if no licensing is required)

50% if imports are subject to special import licenses (SIL)

100% if imports are otherwise restricted or prohibited.

In our study, we use a similar idea but the construction of the variable differs. As the main

objective is to examine the impact of the reduction in non-tariff barriers for various industries

(both partial as well as full) on firm performance, hence, instead of constructing the NTB

coverage ratio, we have taken an inverted version of the NTB measure by reversing the

weighting scheme for each 8-digit tariff line used by Pandey (1999) and Das (2003).11

This is

mainly done to capture both the effects of partial and full liberalization policies across

industries for the period 1999-2009. We use the following weighting scheme for each 8-digit

tariff line:

100% if no NTB applies to the tariff line (i.e. if no licensing is required) (ni=1)

50% if imports are restricted by different import licensing policies (ni=0.5)

0% if imports are fully prohibited only (ni=0)

Then, the Industry-level Inverted NTB coverage ratio is defined as,

Industry Inverted NTB j = ni wi (4.3.1)

Where, j stands for a particular 2-digit Industry and i represents a product line within that

particular industry, wi = mi / mi as the import weight, where mi = imports of the ith

8 digit

level commodity where mi is the total import of the jth

industry.

This above scheme has enabled us to take into account the effects of those imported items (8-

digit HS commodities) whose imports are either free or partially free. This is a value addition

11

The usual NTB index would give 0’s for import free products, hence the reverse formulation.

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to the other previously constructed NTB measures, which do not take into account the effects

of those imported items whose imports are partially restricted.12

Based on the above weighting scheme, we have firstly assigned an appropriate value to each

8-digit product for every year from 1999 to 2009. We have next also calculated their import

share at the 2-digit industry level for each of the years. Then, we have applied these values to

equation (4.3.1) to get the NTB index for the entire 2-digit industry as classified by the HS

system for the study period 1999 to 2009. Finally, we have taken a simple average of these

inverted NTB values at the 2 digit level in each five broad industry group to get the inverted

NTB values for the 5 main groups of industries (Food and Agro based, Textile, Machinery

and Equipment, Metal and Leather Industry).

We have collected the data for import conditions (import policy) for each 8-digit product for

the period 1999-2009 from the Director General of Foreign Trade (DGFT), Government of

India13

. The import data for each 2 and 8 digit industry for the period 1999-2009 has been

collected from the Ministry of Commerce and Industry, Department of Commerce,

Government of India.

4.4. Measures of Various Firm-level Variables used in the Regression Models

We have calculated yearly averages for all the firm-level variables that we have used in our

analysis. This is done for all types of firms separately to understand the differential trends in

all these firm-level variables across various groups of firms (large, SME). The following

Tables 4.4 to 4.11 represent the trends in the yearly average for productivity, profit after tax,

sales revenue, raw materials expenses, compensation to employees, power and fuel expenses,

capital employed and total assets, respectively, for all, large and SME firms. It is important to

note that before calculating the yearly average for all the aforementioned firm-level variables,

we have deflated them by using the industry-level WPI deflator with considering 2004 as the

base year.

12

This is due to the fact that, in other previously constructed NTB measures, both prohibited and restricted

imported items were considered to be fully protected and was assumed to have no imports happening over the

years. 13

http://www.eximkey.com/Sec/DGFT/ImportPolicy

http://www.eximkey.com/Sec/DGFT/ImportPolicy

15

Table 4.4: Average Yearly Productivity across Various Firm Groups (Percent)

Year Average Yearly Productivity for

All Firms

Average Yearly Productivity for

Large Firms

Average Yearly Productivity for

SME Firms

1999 5.488913 4.951562 2.661403

2000 5.804843 5.012674 2.329671

2001 6.113221 5.918555 2.774771

2002 6.74064 6.441208 3.191858

2003 7.368388 6.771215 3.676414

2004 8.248952 7.163035 3.887251

2005 9.417917 8.236653 3.976797

2006 10.88915 9.518386 4.573077

2007 11.89704 10.05698 4.757017

2008 11.98302 9.784218 4.928339

2009 12.26362 9.785468 4.74584

Source: Our calculation based on Prowess database

Table 4.5: Average Yearly Profit after Tax across Various Firm Groups (Rs Millions)

Year Average Yearly Profit after Tax

for All Firms

Average Yearly Profit after Tax

for Large Firms

Average Yearly Profit after Tax

for SME Firms

1999 0.190238 0.454473 -0.04038

2000 0.381122 0.814158 -0.09985

2001 0.045285 0.232857 -0.13455

2002 0.572 1.15713 -0.03941

2003 1.481577 2.53944 0.02994

2004 2.664311 4.543217 0.063684

2005 2.795916 4.611434 0.218225

2006 3.716197 6.151855 0.14639

2007 3.797409 6.30861 0.151369

2008 2.887118 5.249614 0.076077

2009 3.724847 6.722232 0.189571


Table 4.6: Average Yearly Sales Revenue across Various Firm Groups (Rs Millions)

Year Average Yearly Sales Revenue for

All Firms

Average Yearly Sales Revenue for

Large Firms

Average Yearly Sales Revenue for

SME Firms

1999 22.04023 35.48886 3.676583

2000 23.42963 37.6204 3.701094

2001 23.69488 37.95161 3.876959

2002 26.04163 42.0555 3.767576

2003 27.69827 44.4024 3.893273

2004 30.36729 48.39528 4.231145

2005 34.93576 55.44136 4.74452

2006 41.00994 64.77976 5.896584

2007 44.98512 70.38467 6.541405

2008 45.90663 71.726 6.635586

2009 48.31227 76.08836 7.147442


Table 4.7: Average Yearly Raw Materials Expenses across Various Firm Groups (Rs Millions)

Year Average Yearly Raw Materials

Expenses for All Firms

Average Yearly Raw Materials

Expenses for Large Firms

Average Yearly Raw Materials

Expenses for SME Firms

1999 8.551367 13.47961 1.832924

2000 9.043614 14.23139 1.830199

2001 9.38046 14.74773 1.834761

2002 10.37622 16.45234 1.814136

2003 11.22163 17.61444 1.883134

2004 12.73987 19.86311 2.164409

2005 15.34958 24.03374 2.433178

2006 18.49236 28.86508 3.168361

2007 20.9171 32.30306 3.596984

2008 21.75905 33.57626 3.560633

2009 23.02845 35.99348 3.848784


16

Table 4.8: Average Yearly Compensation to Employees across Various Firm Groups (Rs

Millions)

Year Average Yearly Compensation to

Employees for All Firms

Average Yearly Compensation to

Employees for Large Firms

Average Yearly Compensation to

Employees for SME Firms

1999 1.921493 3.168972 0.315938

2000 2.130693 3.516566 0.337755

2001 2.139739 3.435128 0.344504

2002 2.200043 3.648423 0.284168

2003 2.390435 4.014038 0.265017

2004 1.988489 3.310137 0.262444

2005 2.175433 3.60269 0.280632

2006 2.412684 3.990257 0.302686

2007 2.879448 4.78094 0.308975

2008 2.974718 4.929993 0.319016

2009 3.284226 5.442775 0.36157


Table 4.9: Average Yearly Power and Fuel Expenses across Various Firm Groups (Rs Millions)

Year Average Yearly Power and Fuel

Expenses for All Firms

Average Yearly Power and Fuel

Expenses for Large Firms

Average Yearly Power and Fuel


1999 1.121379 1.851698 0.101194

2000 1.256708 2.051068 0.11102

2001 1.268973 2.088098 0.113705

2002 1.368447 2.264364 0.114073

2003 1.337612 2.218634 0.107687

2004 1.329171 2.217669 0.111535

2005 1.516679 2.524896 0.118724

2006 1.589807 2.637157 0.117655

2007 1.650668 2.737164 0.116195

2008 1.67912 2.799437 0.116314

2009 1.80183 3.007773 0.12769


Table 4.10: Average Yearly Capital Employed across Various Firm Groups (Rs Millions)

Year Average Yearly Capital Employed

for All Firms

Average Yearly Capital Employed

for Large Firms

Average Yearly Capital Employed


1999 18.34119 30.68307 2.006612

2000 18.5916 30.98916 2.168623

2001 17.76742 29.48642 2.06843

2002 17.91732 29.86206 1.782889

2003 17.79071 29.70699 1.75159

2004 18.61038 30.92282 1.820629

2005 22.05381 36.36127 2.063771

2006 27.47502 45.36053 2.303422

2007 35.16003 57.42263 2.726083

2008 37.96471 61.59934 2.772753

2009 44.83822 73.09718 3.461786


Table 4.11: Average Yearly Total Assets across Various Firm Groups (Rs Millions)

Year Average Yearly Total Assets for

All Firms

Average Yearly Total Assets for

Large Firms

Average Yearly Total Assets for

SME Firms

1999 25.8783 43.45156 2.660749

2000 26.32947 43.97478 2.968545

2001 26.50965 44.32784 2.928303

2002 27.16204 45.58345 2.463518

2003 27.78081 46.69804 2.432281

2004 29.36143 49.1062 2.549259

2005 34.25365 56.75516 2.990327

2006 41.3122 68.37512 3.480265

2007 51.22641 83.93108 4.090518

2008 55.38639 90.22219 4.087444

2009 65.3755 107.3791 5.102573


17

The above tables indicate that, with respect to all the above variables the SME firms show a

lower trend compare to their large counterparts. This evident an advantageous situation for

large firms over SME firms for the entire study period (1999-2009). The following section

discusses in detail the model specifications, results and findings of our analysis.

5. The Fixed Effect Models: Description, Analysis and Findings

5.1 Model Specifications for Profit after Tax (PAT) and Productivity

In this study one of the main objectives is to determine the differential effects of trade

liberalization on firm performance and how it differs across various groups of firms in terms

of their level of investment in plant and machinery (i.e., large and SME). Thus, this study has

adopted a fixed effect approach to determine these effects after taking into account for

unobserved firm-level heterogeneity. The final fixed effect models for the firm-level growth

rate of profit after tax (PAT) is specified in the following equations (5.1) and (5.2). The

equation (5.1) determines the effects of industry-level tariffs on firm-level growth rates of

PAT, while equation (5.2) determines the effects of industry-level NTB indices. In both the

models the firm-level PAT has been deflated by industry-level WPI deflator. Moreover, the

following models have also been controlled for firm, year and time varying industry effects.

In addition to the firm ( and year ( fixed effects that take into account for different

unobserved firm and year specific heterogeneity, respectively, the inclusion of time varying

industry effects (as denoted by ) has enabled these models to control for different

unobserved time varying industry level heterogeneity such as productivity shock.

Similarly, the fixed effect models for firm-level productivity are specified in the following

equations (5.3) and (5.4). Equation (5.3) determines the effects of varying industry-level

tariffs, while equation (5.4) determines the effects of the industry-level NTB indices, as

calculated earlier, on firm-level productivity. It is important to note that, the fixed effect

18

models on firm-level productivity (Equations 5.3 and 5.4) only take into account firm and

year fixed effects (The time varying industry effects are not included in these models as these

effects measure various industry-level macro economic shocks for example, productivity

shock, which is already present in all these models on productivity, as the dependent

variable).

It could be possible that all the above models might suffer from endogeneity problem due to

the presence of a lagged dependent variable as a regressor in the model.14

Thus, we have also

done a dynamic panel analysis as proposed by Arellano and Bond (1991) to remove this

endogeneity problem from all the above models. Unlike Topalova and Khandelwal (2011), in

our dynamic panel analysis we use two step System GMM method (more efficient) instead of

difference GMM method to remove the endogeneity problem from the model for various

reasons.15

Firstly, using difference GMM in an unbalanced panel data magnifies the gaps in

the dataset by generating missing values for each lag in the dataset, as it instruments the

variables difference term by their lagged levels. On the contrary, system GMM method fills

the missing values with 0 and then instruments the variables difference term by its lagged

levels as well as its lagged differences (Roodman, 2009). Secondly, although in our study we

use balanced panel data, the presence of other firm-level variables such as total asset (which

can be other potential source of endogeneity) in our models, thus system GMM works better

14

Topalova and Khandelwal (2011) in their paper find the evidence of endogeneity between trade policy of a

specific industry and its lagged productivity level during reform period. We have also found endogeneity

between trade policy of a specific industry and its lagged productivity level and it’s lagged absolute value of

PAT, but less predominantly with its (i.e., PAT’s) lagged growth rates (see, Annexure 13). Hence, we find

Arellano and Bond (1991)’s dynamic panel model is more required for the productivity and for the absolute

value of PAT, but less required for its growth rates, to remove endogeneity. It can be clearly visible in the

following results. 15

System GMM was developed by Arellano and Bover (1995) and Blundell and Bond (1998) to overcome the

limitations of Difference GMM

19

than the difference GMM to remove the potential endogeneity from models (Kukenova and

Monteiro, 2009). Finally, as the time span of our study is short (i.e., T=11) and the number of

firms is large (i.e., N=842), the difference GMM method remains inefficient as the lagged

levels of the dependent variable provide only weak instruments for its successive first-

differences, which creates large finite-sample biases (Blundell and Bond, 1998). In addition,

the estimates of the AR (1) coefficients on growth rates of PAT and productivity show that

the series of growth rates of PAT and productivity are moderately persistent, thus the lagged

levels of growth rates of PAT and productivity remain weak instruments for the differences in

the first-differenced GMM model.16

Thus, the system GMM method enables us to reduce

these biases through the incorporation of ‘more informative moment conditions, which are

valid under quite reasonable stationarity restrictions on the initial conditions process’

(Blundell and Bond, 2000).17

This is because, as mentioned earlier the system GMM method

on one side allows lagged first-differences as instruments for level equations, it instruments

the first-differences equations by usual lagged levels on the other.18

Moreover, our models

also satisfy the over identifying restrictions and model specification conditions (i.e., non-auto

correlation) as indicated by Hansen J statistics (1982) and AR statistics (xtabond) for lags 1

and 2 in the subsequent tables, respectively, which represents the results of these

aforementioned dynamic panel models. The dynamic panel analysis also corrects Windmeijer

(2005)’s finite sample correction of the standard errors.

In our fixed effect as well as dynamic panel models on both growth rates of profit after tax

and productivity, we have controlled with firm age, age square, firm size (total asset used as

proxy variable) and industry-level export propensity rate, apart from the main variables of

interest (i.e. lagged tariff or NTB Index). It should be noted that due to the less prevalence of

the endogeneity problem in the firm-level growth rates of profit after tax, as shown in

Annexure 13, the fixed effect estimation for the growth rates of profit after tax with firm, year

and time varying industry fixed effects fits better than the dynamic panel estimations with

lags 1 and 2, while for the productivity estimation the converse is true.

5.2 Results and Interpretations of the Fixed Effect Models

This section discusses the results of the fixed effect models on two firm-level performance

parameters namely, profit after tax and productivity. As discussed earlier, all these models

16

As shown in Tables 5.1 to 5.8 (Columns 5-12), dynamic panel analysed results. 17

Richard Blundell & Stephen Bond (2000): GMM Estimation with persistent panel data: an application to

Production functions, Econometric Reviews, 19:3, 321-340, DOI: 10.1080/07474930008800475(see page 322). 18

For more detail, we refer the reader to Arellano and Bover (1995) and Blundell and Bond (1998).

20

have been used firstly for analyzing the performance of all firms, and then again applied to

analyze the performance of large and SME firms as well. The analysed results of these

various models for all firms and their sub-groups (large and SME firms) are given in each of

the following tables in separate columns. Although, we have taken various versions of these

models in analysing the data, we only discuss the final versions of these models in this

section. The analysed results of the preliminary versions of these models are given in the

appendix (please see Annexure 1 to 8). The following two subsections (5.2.1 and 5.2.2)

discuss the results for growth rate of firm-level profit after tax and productivity, respectively.

5.2.1. Analysis of Firm-Level Profit after Tax

This subsection discusses the results for these models, which determine the effects of various

trade liberalization indicators on the firm-level profit after tax. Table 5.1 and Table 5.2 show

the effects of a reduction in the lagged input and the final goods tariffs, respectively on

growth rates of firm-level profit after tax. Table 5.3 and Table 5.4 respectively, show the

effects of a reduction in the lagged effective rate of protection (ERP) and non-tariff barriers

(NTBs) on growth rate of firm-level profit after tax. In each table, columns 1 to 3 represent

the results from the final versions of the fixed effect models for all firms, large firms and

SME firms, respectively. Columns 5 to 7 show the results for the Arellano and Bond (1991)’s

dynamic panel models with lag1 (i.e., instrumented by successive one period lag of the level

as well as one period lagged difference of the dependent variable and other regressors and

year dummies ) for all firms, large firms and SME firms, respectively. Columns 9 to 11 give

the results for the dynamic panel models with lag 2 (i.e., instrumented by successive two

period lag of the level as well as two period lagged difference of the dependent variable and

other regressors and year dummies). All the firm fixed effect models (columns 1-4)

include firm age, age square, total assets (as a proxy for firm size), industry-level export

propensity rate (measures export intensity of an industry), all year dummies and also all the

time varying industry effects (which controls industry-level macro-economic shocks over

time). On the other hand in the dynamic panel models (columns 5-12), we include

firm age, age square total assets (as a proxy for firm size), industry-level export propensity

rate (measures export intensity of an industry) and all year dummies. It should be noted that

in each of the regressions (columns 1-12) the standard errors are clustered at the firm-level.

The effects of a reduction in the lagged final goods tariff and lagged input tariff on growth

21

rates of firm-level profit after tax for all types of firms are given in the following Tables 5.1

and 5.2, respectively.19

Table 5.1: Growth Rate of Profit after Tax and Lagged Final Goods Tariff

AB 1 AB 2

1 2 3 4 5 6 7 8 9 10 11 12

All Large SME Mixed All Large SME Mixed All Large SME Mixed

VARIABLES Growth Rate of Profit after Tax

Lagged Final Goods

Tariff Industry Wise

-9.960

(7.359)

-15.971**

(7.1868)

5.546

(6.7897)

-4.102

(15.046)

-5.669

(3.659)

-10.766**

(4.505)

2.329

(5.238)

0.119

(3.825)

-2.408

(17.686)

-9.957

(6.262)

5.191

(24.277)

1.562

(4.760)

Lagged Gr_ Profit

after Tax

0.003

(0.004)

-0.000

(0.002)

0.012

(0.013)

0.014**

(0.006)

-3.689

(17.840)

-0.957

(1.820)

5.398

(6.179)

-0.054

(0.419)

Total Assets -0.0745 (0.054)

-0.115 (0.075)

5.853*** (1.651)

0.097 (0.224)

-0.026 (0.027)

-0.055 (0.036)

2.256 (2.284)

0.085 (0.103)

-0.058 (0.234)

-0.095 (0.087)

-8.020 (16.425)

0.079 (0.118)

Age -16.679

(13.0901) -25.424 (17.494)

11.383 (19.615)

-41.560 (41.881)

2.946 (4.017)

3.367 (5.134)

10.755* (6.373)

-5.751 (5.498)

18.564 (91.579)

4.462 (9.525)

-25.779 (58.132)

-1.808 (7.183)

Age Square 0.244*

(0.125)

0.235

(0.219)

0.190

(0.153)

0.315

(0.192)

-0.027

(0.031)

-0.011

(0.043)

-0.112*

(0.064)

0.035

(0.047)

0.181

(0.889)

-0.006

(0.070)

0.278

(0.618)

0.001

(0.056)

Export Propensity

Industry Wise

-11.446

(11.724)

9.120

(6.669)

11.235

(11.120)

-5.040

(4.307)

-0.831

(2.538)

0.625

(3.283)

0.030

(2.039)

-5.186**

(2.241)

-1.175

(9.244)

0.605

(5.050)

-5.636

(10.016)

-4.423**

(2.154)

Firm Fixed Effects YES YES YES YES YES YES YES YES YES YES YES YES

Time Fixed Effects YES YES YES YES YES YES YES YES YES YES YES YES

Time varying

Industry Effects YES YES YES YES

Observations 8420 4710 1840 1870 7578 4239 1656 1683 7578 4239 1656 1683

R-squared 0.0071 0.0096 0.0224 0.0272

Hansen Test

5.11 0.05 2.11 0.57 0.47 0.60 0.18 1.21

AR 1

-2.14** -1.79* -2.29** -2.17** -0.24 -0.00 -0.91 -1.07

AR 2

-0.67 -0.74 -0.65 -0.89 -0.20 -0.54 0.50 -0.18

Number of Firms 842 471 184 187 842 471 184 187 842 471 184 187

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 5.2: Growth Rate of Profit after Tax and Lagged Input Tariff

AB 1 AB 2

1 2 3 4 5 6 7 8 9 10 11 12



Lagged Input Tariff Industry

Wise

-4.446

(8.0519)

-21.447**

(9.651)

-47.675

(49.359)

-14.936

(23.596)

-10.820*

(5.862)

-14.552**

(6.887)

7.208

(12.532)

-18.649

(13.592)

-9.633

(106.151)

-6.252

(19.347)

-25.646

(99.382)

-9.384

(9.967)

Lagged Gr_ Profit after Tax

0.003

(0.004) 0.000

(0.002) 0.012

(0.014) 0.014** (0.006)

-0.202 (18.871)

-1.362 (2.480)

6.070 (7.830)

-0.137 (0.469)

Total Assets -0.074

(0.054)

-0.115

(0.075)

5.853***

(1.651)

0.096

(0.224)

-0.025

(0.023)

-0.046

(0.032)

2.289

(2.259) 0.037

(0.112)

-0.029

(0.111)

-0.092

(0.104)

-10.890

(21.509)

0.045

(0.129)

Age -15.379

(12.213)

-19.971

(16.902)

11.520

(17.177)

-46.181

(36.962)

3.277

(4.367)

4.816

(5.218)

10.565

(6.731)

-7.083

(5.699)

1.799

(95.073)

7.144

(12.177)

-33.861

(78.826)

-3.681

(9.245)

Age Square 0.244*

(0.125)

0.235

(0 .219)

0.190

(0.153)

0.315*

(0.192)

-0.031

(0.035)

-0.028

(0.042)

-0.110

(0.068)

0.053

(0.050)

-0.015

(0.960)

-0.030

(0.088)

0.369

(0.844)

0.020

(0.074)

Export Propensity Industry

Wise

0.800

(6.876)

4.710

(5.977)

-5.747

(23.828)

33.787*

(20.660)

-1.211

(2.827)

0.880

(3.516)

0.248

(2.068)

-6.989***

(2.686)

-1.217

(9.307)

1.549

(5.811)

-10.142

(16.926)

-5.614**

(2.385)



Time varying Industry Effects YES YES YES YES

Observations 8420 4710 1840 1870 7578 4239 1656 1683 7578 4239 1656 1683

R-squared 0.0071 0.0096 0.0224 0.0272

Hansen Test

4.96** 0.07 2.27 0.59 1.68 0.56 0.12 1.22

AR 1

-2.14** -1.79* -2.29** -2.16** -0.02 -0.08 -0.79 -0.87

AR 2

-0.69 -0.75 -0.63 -1.00 -0.01 -0.54 0.42 -0.33

Number of Firms 842 471 184 187 842 471 184 187 842 471 184 187


*** p<0.01, ** p<0.05, * p<0.1

19

Columns 4, 9 and 12 represents the results for those firms (mixed), which have shifted from SME to large and

vies-versa in different points of time. It would be interesting to study those firms in future, but to keep our

analysis more simple we are keeping it for future research.

22

The above two tables (Tables 5.1 and 5.2) show that a reduction in both the final goods tariff

and the input tariff has increased the growth rate of profit after tax for large firms in most of

the specifications. This suggests that the effect of a reduction in the final goods and input

tariffs is positive and helps to increase firm-level profitability for large firms. In the main

specification, where we include both firm and year fixed effects, as well as time varying

industry effects (column 2), the estimated coefficients of the lagged final goods tariff and the

input tariff imply that a one percent reduction in the lagged final goods tariff and input tariff

raises a large firm’s growth rates of profit after tax by 11.97 percent and 21.44 percent,

respectively. However, for SME firms, a reduction in both the lagged final goods tariff and

the input tariff affects negatively and insignificantly on their profits. This makes evident the

disadvantages that India’s SME firms face compared to their large firm counterparts and the

fact that they have not benefited from trade liberalization through increased profitability.

A comparison of the results shown in Tables 5.1 and 5.2 confirm that large firms are the only

gainers from both final goods and input tariff liberalization in terms of increased operational

efficiency. Moreover, the above comparison clearly shows that the positive effect of trade

liberalization through the input channel is stronger than the output channel. This might be the

result of the relatively higher import orientation of large firms compared to SME firms. One

can argue that the reduction in input tariffs has enabled large firms to increase their imports

of higher quality and cheap intermediate inputs, which has ultimately helped them in

improving their operational efficiency as captured by increased profits. On the other hand, the

SME firms that have been characterised by a traditional and low-quality production structure

have not increased their imports of high quality intermediate inputs as much as large firms

and have not been able to obtain these benefits from tariff liberalization through the sourcing

channel.

It is recognised that a reduction in final goods tariffs could lower domestic firms’ mark-ups

over their product costs, which could reduce the product price and increase competition in the

market. Hence, liberalization of final goods tariff could reduce market power as well as the

profit margins of domestic firms. Conversely, a reduction in input tariffs could lower their

cost of production and hence, could improve efficiency by enabling more economies of scale

in their production. This could be done by enabling increased imports of higher quality and

cheaper intermediate inputs. Moreover, input tariff reduction could also increase the

availability of product variety in the domestic market, and thus intensify market competition

23

for import-competing domestic firms. Sivadasan (2009) and others support this point. Thus,

one needs to examine the net effect of a reduction in both types of tariffs (final goods and

input tariffs) on the growth rate of profit after tax for firms.

The following table highlights the net effects of the tariff liberalization from both the input

and output sides on growth rates of firm-level profit after tax. This is captured by examining

the effects of a reduction in the lagged effective rate of protection (ERP) on this variable and

the results are provided in the following Table 5.3.

Table 5.3: Growth Rate of Profit after Tax and Lagged Effective Rate of Protection (ERP)

AB 1 AB 2

1 2 3 4 5 6 7 8 9 10 11 12



Lagged ERP Industry

Wise

-13.3154** (5.9450)

-11.7119** (5.4987)

-6.5975 (10.3421)

-17.706 (16.048)

-2.812 (2.239)

-6.162** (2.866)

0.671 (2.579)

2.227 (2.188)

-4.277 (14.066)

-6.781* (3.948)

5.742 (11.910)

2.316 (3.561)

Lagged Gr_ Profit after

Tax

0.003

(0.004)

-0.000

(0.002)

0.012

(0.012)

0.013**

(0.006)

-5.990

(27.095)

-0.940

(1.646)

5.300

(5.782)

-0.020

(0.401)

Total Asset -0.0745

(0.0549)

-0.1156

(0.0756)

5.8534***

(1.6511)

0.096

(0.224)

-0.021

(0.025)

-0.049

(0.034)

2.154

(2.313)

0.099

(0.098)

-0.091

(0.402)

-0.093

(0.085)

-7.422

(15.167)

0.091

(0.111)

Age -28.0892

(14.6649)

-32.9573*

(19.4952)

6.1820

(23.2026)

-60.230

(39.049)

3.106

(3.995)

3.134

(5.264)

10.548*

(6.084)

-5.187

(5.315)

28.377

(127.990)

3.871

(9.369)

-24.305

(52.451)

-1.175

(6.340)

Age Square 0.2447*

(0.1253)

0.2350

(0.2190)

0.1900

(0.1532)

0.316

(0.192)

-0.029

(0.031)

-0.010

(0.044)

-0.109

(0.060)

0.026

(0.045)

-0.274

(1.224)

0.000

(0.070)

0.260

(0.552)

-0.005

(0.049)

Export Propensity

Industry Wise

3.9598 (16.7328)

21.2383** (8.8424)

-6.7469 (16.6294)

19.450* (11.634)

-0.999 (2.469)

0.051 (3.386)

-0.113 (2.172)

-4.494* (2.329)

-2.742 (14.223)

-0.274 (5.206)

-3.756 (8.841)

-3.833 (2.447)



Time varying Industry

Effects YES YES YES YES

Observations 8420 4710 1840 1870 7578 4239 1656 1683 7578 4239 1656 1683

R-squared 0.0071 0.0096 0.0224 0.0272

Hansen Test

4.99** 0.04 2.02 0.46 0.21 0.60 0.19 1.19

AR 1

-2.14** -1.79* -2.29** -2.17** -0.22 -0.01 -0.96 -1.15

AR 2

-0.67 -0.73 -0.67 -0.93 -0.21 -0.59 0.52 -0.11

Number of Firms 842 471 184 187 842 471 184 187 842 471 184 187


*** p<0.01, ** p<0.05, * p<0.1

The above table shows that the net effect of reduction in both final goods and input tariffs is

positive and significant in improving the growth rate of profit after tax for both large firms

and all firms (particularly for large firms). In the main specification, where we include both

firm and year fixed effects, as well as time varying industry effects (column 2), the estimated

coefficient of the lagged ERP implies that a one percent reduction in the lagged ERP raises a

large firm’s growth rate of profit after tax by 11.71 percent. Moreover, as expected, the

reduction in the ERP is not able to affect significantly the growth rates of firm-level profit

after tax for Indian SME firms.

The above analysis clearly indicates that only large firms have gained from trade

liberalization and that the gains are mainly due to the imported input channel. Hence, trade

24

liberalization has improved profits for large firms by making them more efficient and

competitive in the world market. Improved input sourcing possibilities have enabled them to

consistently produce and export better quality and competitive final goods. Interestingly,

column 2 also indicates a positive and significant role of the industry-level export propensity

in improving the growth rate of firm-level profit after tax for large firms. However, as the

results show, these benefits have not accrued to the small and medium segments which have

failed to increase their profits. This phenomenon is similar to Melitz (2003), which states that

exposure in trade would benefits those firms which are more productive (i.e. the large firms)

and who would gradually capture the whole market through the export of high quality final

goods. On the other hand, the least productive firms (i.e. the SME firms), which usually

produce for domestic market would lose their market share.20

This is corroborated by the fact

that SSI exports as a share of the country’s total exports have remained stagnant at 30-36

percent over the 1999-2008 period.21

This clearly indicates the inability of SME firms to

produce and export better quality final goods and thus their inability to gain from trade

liberalization through improved profitability.

Apart from a huge reduction in various types of import tariffs, there is a significant decline in

the NTBs as well during the study period (1999-2009). Therefore, it is expected that a

reduction in NTBs could also have helped domestic firms in increasing their imports and as a

result, it is improving their performance in terms of their profit after tax. The following Table

5.4 shows the effects of a reduction in NTBs on growth rates of firm-level profit after tax.22

20

Marc J. Melitz 2003, The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry

Productivity, Econometrica, Vol. 71, No. 6 (Nov., 2003), pp. 1695-1725 21

Source: INDIA STAT Database:

http://www.indiastat.com/table/foreigntrade/12/exportbysmallscaleindustriesssikhadivillageindustrieskvi19512011/450692/67718/data.aspx

ACCESS DATE: 09/07/2014, 1:14 pm. 22

The usual NTB indices would give 0’s for import free products; here in our constructed NTB index gives 0’s

for import prohibited products, hence the reverse formulation (i.e. the inverted one).



25

Table 5.4: Growth Rate of Profit after Tax & NTB

AB 1 AB 2

1 2 3 4 5 6 7 8 9 10 11 12



NTB Index

Industry Wise

9.332 (10.918)

38.581** (17.602)

51.309 (42.409)

-22.190 (23.698)

5.154 (15.836)

15.364 (27.824)

3.708 (12.730)

-11.274 (28.323)

-32.295 (212.643)

0.784 (28.095)

26.088 (70.109)

-9.045 (28.381)

Lagged Gr_

Profit after

Tax

0.002

(0.004) -0.000 (0.002)

0.011 (0.013)

0.014** (0.006)

-5.527 (27.724)

-1.469 (2.039)

5.357 (5.724)

-0.078 (0.445)

Total Asset -0.074

(0 .054)

-0.115

(0 .075)

5.853***

(1.651)

0.096

(0.224)

-0.011

(0.021)

-0.024

(0.029)

2.066

(2.306)

0.086

(0.100)

-0.075

(0.408)

-0.089

(0.107)

-8.401

(15.521)

0.073

(0.119)

Age -14.927 (14.686)

-16.256 (16.540)

-52.168 (49.694)

-3.667 (42.655)

4.247 (4.244)

5.748 (5.199)

10.126 (6.217)

-5.866 (5.174)

27.684 (134.598)

8.091 (12.094)

-26.534 (53.071)

-2.323 (7.974)

Age Square 0.244* (0.125)

0.235 (0.219)

0.190 (0.153)

0.315 (0.192)

-0.043 (0.034)

-0.041 (0.041)

-0.104* (0.061)

0.036 (0.043)

-0.271 (1.289)

-0.038 (0.088)

0.286 (0.553)

0.007 (0.064)

Export

Propensity

Industry Wise

5.984 (7.960)

24.754** (10.331)

33.191 (25.562)

-15.101 (9.216)

-0.027 (2.718)

2.707 (3.452)

-0.317 (2.068)

-5.226*** (2.010)

-1.555 (14.235)

2.381 (6.332)

-5.783 (10.525)

-4.634** (1.829)

Firm Fixed

Effects YES YES YES YES YES YES YES YES YES YES YES YES

Time Fixed


Time varying

Industry

Effects

YES YES YES YES

Observations 8420 4710 1840 1870 7578 4239 1656 1683 7578 4239 1656 1683

R-squared 0.0071 0.0096 0.0224 0.0272

Hansen Test

4.47** 0.08 1.95 0.57 0.23 0.53 0.18 1.21

AR 1

-2.14** -1.79* -2.28** -2.17** -0.21 -0.20 -0.97 -1.00

AR 2

-0.71 -0.70 -0.68 -0.87 -0.19 -0.70 0.53 -0.22

Number of

Firms 842 471 184 187 842 471 184 187 842 471 184 187


*** p<0.01, ** p<0.05, * p<0.1

The above table indicates that, the NTB index variable has remained positive and significant

only for large firms.23

In the main specification, where we include both firm and year fixed

effects, as well as time varying industry effects (column 2), the estimated coefficient of the

NTB index implies that a one percent reduction in the NTB, raises a large firm’s growth rate

of profit after tax by 38.58 percent. On the contrary, the NTB reduction does not able to

improve the growth rate of the profit after tax for SME firms. This shows that although, a

reduction in the import restrictions has not led to improve the growth rate of firm-level PAT

for all types of firms, large firms have become more efficient and competitive. Moreover, we

have obtained insignificant results for almost all specifications and for all types of firms

(except column 2 for large firms), as the NTB index variable lacks the power of explaining

the variation in the growth rate of PAT. This may be due to the lack of variation in the NTB

23

Along with the entire aforementioned dynamic panel analysis for growth rate analysis of PAT, we also did the

dynamic panel analysis on the absolute level of PAT. The dynamic panel analysis (columns 4-9) shows that, a

reduction in the NTB has improved the firm level profit after tax (absolute level) for large firms and all firms. In

contrast to that, for SME firms, the inverted NTB does not affect significantly the firm-level profit after tax.

(Refer to Annexure 9 to 12)

26

values across industries and time. The NTB value lies at a very higher level for the entire

study period.24

This subsection has highlighted the relative effectiveness of import tariff and NTB policies

across various groups of firms (large vs. SME) in terms of their operational efficiency gain.

The results have shown that the large firms are the main gainer of the reduction in the tariff

and the non-tariff barriers. The above analysis evident that, the Indian SME firms are lacking

behind in reaping the benefits of trade liberalization, and hence have absolutely failed to

improve their operational efficiency.

5.2.2. Analysis of Firm-Level Productivity

In this sub-section we examine the effects of trade liberalization on firm-level productive

efficiency. There are several studies, which have attempted to examine the effects of trade

liberalization on both firm and industry level productive efficiency in India’s manufacturing

sector. These studies have diverse points of view on the effects of trade liberalization on

Indian manufacturing. One set of studies has shown that, trade liberalization has improved

firm-level productivity, while another set has shown either negative or no effects. Moreover,

previous studies also reveal that the effects of trade liberalization vary across industries,

sectors and also across trade regimes. These studies are very useful for policymakers in

formulating suitable policy instruments to improve firm-level productive efficiency in Indian

manufacturing. However, the main limitation is that apart from very few studies such as

Khandelwal and Topalova (2011) and Nataraj (2011), most of them have mainly focused on

large firms due to the lack of availability of an appropriate database for SME firms. Hence,

these previous studies in their analysis, have failed to include the small and medium

segments, which play an important role in the growth prospects of the Indian manufacturing

sector.

In our analysis, along with large firms, we have also included SME firms, which has enabled

us to capture the effects of trade liberalization on firm-level productive efficiency across

various groups of firms (large vs. SME). Instead of examining the effects for previous trade

regimes (i.e. from 1980 to 2000), we have examined these effects of trade liberalization in the

context of present trade regimes (i.e., our study period is 1999 to 2009). Our study is also an

extension and improvement over Khandelwal and Topalova (2011)’s work, as we have

24

The variation in growth rates of firm-level PAT across various kinds of Indian manufacturing firms can be

better explained by using NTB index at more disaggregated level (such as, for 4 digit industry groups).

However, it goes beyond the scope of this paper; hence I would like to leave it to our future research.

27

examined separately the differential effects of trade liberalization on firm-level productivity

for large and SME firms (followed the definition provided by the Ministry of MSME to

distinguish SME firms from large firms) by analysing a balanced panel data throughout.25

In

addition to firm age, we have also controlled the effects of firm-size (used total asset as a

proxy) within each group (large vs. SME). Moreover, apart from various tariff measures we

have also calculated and included NTB in our analysis as one of the indicator for trade

liberalization. The effects of a reduction in the lagged final goods tariff on firm-level total

factor productivity are given in the following Table 5.5.

Table 5.5: Productivity and Lagged Final Goods Tariff

AB 1 AB 2

1 2 3 4 5 6 7 8 9 10 11 12


VARIABLES Productivity

Lagged Final Goods


-0.161*** (0.050)

-0.099** (0.050)

-0.075* (0.039)

-0.304** (0.134)

-0.015** (0.006)

-0.030*** (0.009)

0.006 (0.040)

-0.011 (0.017)

-0.038 (0.027)

-0.037 (0.039)

-0.020 (0.027)

-0.016 (0.014)

Lagged Productivity

1.099***

(0.037)

1.059***

(0.027)

0.783

(0.615)

1.138***

(0.088)

0 .928***

(0.039)

0.876***

(0.018)

1.200***

(0.293)

1.151***

(0.060)

Total Asset 0.000

(0.001)

-0.000

(0.001)

0.020***

(0.007)

-0.003

(0.002)

-0.000**

(0.000)

-0.000*

(0.000)

0.023

(0.089)

-0.004***

(0.001)

0.000

(0.000)

0.000

(0.000)

-0.031

(0.047)

-0.004

(0.001)

Age 0.179

(0.162)

-0.139

(0.187)

0.195**

(0.093)

0.999*

(0.587)

-0.028*

(0.016)

0.000

(0.010)

-0.017

(0.080)

-0.041

(0.052)

0.021

(0.038)

0.023

(0.025)

-0.116

(0.129)

-0.053

(0.039)

Age Square -0.000

(0.002)

0.003

(0.003)

-0.001**

(0.000)

-0.009

(0.006)

0.000*

(0.000)

-0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

-0.000

(0.000)

-0.000

(0.000)

0.001

(0.001)

0.000

(0.000)

Export Propensity

Industry Wise

-0.090 (0.155)

0.217* (0.121)

-0.116 (0.132)

-0.865 (0.584)

-0.005 (0.007)

-0.008* (0.004)

-0.002 (0.120)

-0.013 (0.048)

0.024** (0.009)

-0.009 (0.012)

-0.125 (0.157)

-0.011 (0.024)



Observations 8420 4710 1840 1870 8420 4710 1840 1870 8420 4710 1840 1870

R-squared 0.0185 0.0138 0.0609 0.0620

Hansen Test

1.35 1.22 4.46** 3.57* 0.59 0.99 2.50 2.76

AR 1

-1.76* -1.18 -1.42 -2.57*** -1.98** -1.22 -1.50 -2.55**

AR 2

-0.99 -1.02 0.67 1.40 -1.03 -1.04 0.53 1.37

Number of Firms 842 471 184 187 842 471 184 187 842 471 184 187

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

The above table indicates that for almost all specifications, a reduction in the lagged final

goods tariff has affected firm-level productivity for all firms and large firms, positively and

significantly. In the main specification, which includes both firm and year fixed effects

(column 2), the estimated coefficient of the lagged final goods tariff implies that a one

percent reduction in the final goods tariff raises large firm’s total factor productivity by

almost 0.10 percent. On the contrary, a reduction in the final goods tariff has failed to

improve firm-level total factor productivity for SME firms (in almost all specification, the

coefficients of lagged final goods tariff are insignificant for SME firms).26

This reveals that

25

Use of firm-level balanced panel data throughout our analysis has also helped us to examine the firm-

dynamics for a fixed set of large and SME firms over 1999 to 2009 period. 26

The results show that, although in the main specification, which includes both firm and year fixed effects

(column 3), a reduction in the lagged final goods tariff has positively and significantly (though at a low

28

large firms have benefited from improved firm-level productivity due to liberalization, but

not the SMEs. It is observed that a reduction in the final goods tariff has increased market

competition by reducing the mark up of domestic producers, which ultimately induces large

domestic firms to become more efficient and competitive. Moreover, the above table (column

2), also highlights a positive and significant role of industry-level export intensity in

improving firm-level productivity for large firms. This is consistent with the literature that

more export-oriented firms are likely to be more productive. The following Table 5.6 shows

the effectiveness of a reduction in the lagged input tariff on firm-level productivity across

various groups of Indian manufacturing firms.

Table 5.6: Productivity and Lagged Input Tariff

AB 1 AB 2

1 2 3 4 5 6 7 8 9 10 11 12



Lagged Input Tariff

Industry Wise

-0.261***

(0.082)

-0.236**

(0.114)

-0.098**

(0.049)

-0.332**

(0.146)

-0.028**

(0.013)

-0.067***

(0.021)

0.003

(0.040)

0.018

(0.028)

-0.025*

(0.013)

-0.051**

(0.020)

0.033

(0.042)

0.002

(0.024)

Lagged Productivity

1.099***

(0.037)

1.060***

(0.027)

0.762

(0.634)

1.139***

(0.088)

0.928***

(0.038)

0.876***

(0.018)

1.217***

(0.304)

1.151***

(0.060)

Total Asset 0.000

(0.001)

-0.000

(0.001)

0.020***

(0.007)

-0.003

(0.002)

-0.000**

(0.001)

-0.000*

(0.000)

0.025

(0.087)

-.003***

(0.001)

0.000

(0.000)

0.000

(0.000)

-0.030

(0.048)

0.002

(0.024)

Age 0.241

(0.201) -0.179 (0.218)

0.246** (0.104)

1.332* (0.741)

-0.027 (0.016)

0.003 (0.010)

-0.015 (0.079)

-0.038 (0.051)

0.027 (0.042)

0.027 (0.029)

-0.115 (0.131)

-0.004*** (0.000)

Age Square -0.000 (0.002)

0.003 (0.003)

-0.001** (0.000)

-0.010 (0.006)

0.000 (0.001)

-0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

-0.000 (0.000)

-0.000 (0.000)

0.001 (0.001)

0.000 (0.000)

Export Propensity

Industry Wise

-0.197

(0.170)

0.142

(0.099)

-0.161

(0.148)

-1.036

(0.646)

-0.006

(0.007)

-0.011**

(0.005)

0.000

(0.12)

-0.010

(0.047)

0.027**

(0.010)

-0.009

(0.008)

-0.127

(0.162)

-0.010

(0.023)



Observations 8420 4710 1840 1870 8420 4710 1840 1870 8420 4710 1840 1870

R-squared 0.0187 0.0152 0.0592 0.0598

Hansen Test

1.36 1.23 4.52** 3.55* 0.57 1.01 2.55 2.74

AR 1

-1.76* -1.18 -1.36 -2.57*** -1.97** -1.22 -1.47 -2.55**

AR 2

-0.99 -1.02 0.66 1.39 -1.03 -1.04 0.53 1.37

Number of Firms 842 471 184 187 842 471 184 187 842 471 184 187


*** p<0.01, ** p<0.05, * p<0.1

The above table indicates that, similar to the final goods tariff, a reduction in the lagged input

tariff has affected positively and significantly the firm-level total factor productivity for all

firms and large firms in all specifications. Moreover, the positive effect of reducing the

lagged input tariff is higher in magnitude than that of reducing the final goods tariff. This

result clearly shows that trade liberalization has had a greater impact on firm performance

through the input channels. In the main specification, where we include both firm and year

fixed effects (column 2), the estimated coefficient of the lagged input tariff implies that a one

percent reduction in the lagged input tariff raises a large firm’s productivity by 0.23 percent,

which is similar to the findings obtained by Topalova and Khandelwal (2011) and Nataraj

significance level with relatively less in magnitude compared to large firms) affected firm-level productivity for

SME firms, the endogeneity removed results (columns 7 and 11) indicate the diverse effects on SME.

29

(2011). Again in this case, a reduction in the input tariff has failed to improve total factor

productivity for SME firms (in almost all specification, the coefficients of lagged input tariff

are positive and insignificant for SME firms).27

Thus, it is evident that large firms are the main gainers from trade liberalization, in terms of

both improved operational and productive efficiency that is likely to be due to their

continuous adoption of new, better quality and more diversified intermediate inputs and

improved techniques in their production process. This confirms the earlier studies such as,

Ethier (1982), Grossman and Helpman (1991) and Rivera‐Batiz and Romer (1991).

The effects of a reduction in the lagged ERP on firm-level total factor productivity are

provided in the following Table 5.7.

Table 5.7: Productivity and Lagged Effective Rate of Protection (ERP)

AB 1 AB 2

1 2 3 4 5 6 7 8 9 10 11 12



Lagged ERP

Industry Wise

-0.064**

(0.027)

-0.009

(0.024)

-0.033

(0.024)

-0.171**

(0.081)

-0.007**

(0.003)

-0.013***

(0.005)

0.004

(0.033)

-0.008

(0.009)

-0.025

(0.020)

-0.022

(0.031)

-0.018

(0.020)

-0.010

(0.008)

Lagged

Productivity

1.099***

(0.037)

1.059***

(0.027)

0.797

(0.603)

1.138***

(0.088)

0.928***

(0.039)

0.875***

(0.018)

1.193***

(0.292)

1.151***

(0.061)

Total Asset 0.000

(0.001)

0.000

(0.001)

0.021***

(0.007)

-0.003

(0.002)

-0.000*

(0.002)

-0.000

(0.000)

0.021

(0.089)

-0.004***

(0.001)

0.000

(0.000)

0.000

(0.000)

-0.031

(0.048)

-0.004***

(0.001)

Age 0.347** (0.147)

0.053 (0.109)

0.260** (0.102)

1.190* (0.625)

-0.027* (0.016)

0.001 (0.009)

-0.019 (0.079)

-0.041 (0.052)

0.020 (0.036)

0.022 (0.024)

-0.115 (0.126)

-0.053 (0.039)

Age Square -0.000 (0.002)

0.003 (0.003)

-0.001** (0.000)

-0.010 (0.006)

0.000* (0.000)

-0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

-0.000 (0.000)

-0.000 (0.000)

0.001 (0.001)

0.000 (0.000)

Export Propensity

Industry Wise

-0.081

(0.144)

0.185*

(0.110)

-0.093

(0.123)

-0.811

(0.561)

-0.006

(0.007)

-0.009*

(0.004)

-0.004

(0.122)

-0.014

(0.049)

0.021**

(0.010)

-0.011

(0.017)

-0.128

(0.155)

-0.012

(0.025)



Observations 8420 4710 1840 1870 8420 4710 1840 1870 8420 4710 1840 1870

R-squared 0.0166 0.0127 0.0559 0.0605

Hansen Test

1.35 1.22 4.42** 3.58* 0.58 1.02 2.50 2.76

AR 1

-1.76* -1.18 -1.46 -2.57*** -1.98** -1.22 -1.51 -2.55**

AR 2

-0.99 -1.02 0.67 1.40 -1.03 -1.04 0.54 1.37

Number of Firms 842 471 184 187 842 471 184 187 842 471 184 187


*** p<0.01, ** p<0.05, * p<0.1

The above table shows that the effect of a reduction in lagged ERP is positive and significant

in improving productivity of all firms. Even though for large firms the effects of lagged ERP

are positive in all specifications, the coefficients have remained insignificant in few

specifications. The result from the dynamic panel analysis, where we controls the effects of

one period lagged productivity (column 6) indicates that, one percent reduction in lagged

ERP raises large firm’s productivity significantly by 0.013 percent. Similar to previous cases,

27

Similar to the final goods tariff, the results show that, although in the main specification, which includes both

firm and year fixed effects (column 3), a reduction in the lagged input tariff has positively and significantly

(though the effect is lower in magnitude compared to large firms) affected firm-level productivity for SME

firms, the endogeneity removed results (columns 7 and 11) show the other way round.

30

for SME firms, the effects of a reduction in lagged ERP on firm-level productivity remains

negative and insignificant in most of the specifications.

The above analysis of the relative effectiveness of tariff liberalization clearly reveals its

success in improving both firm-level profits and productivity for Indian manufacturing firms.

But its success is limited to large firms only. The small and medium enterprises have yet to

obtain any benefits from import tariff liberalization. This might be due to their continuous

focus on traditional production methods, improper asset management, poor access to credit,

technological inefficiency, lack of demand for their products and their fragmented structure.

We next examine the impact of reduction in NTBs on firm-level productivity of different

kinds of Indian manufacturing firms. The following Table 5.8 shows the results obtained for

the impact of a reduction in NTBs on firm-level total factor productivity.28

Table 5.8: Productivity & NTB

AB 1 AB 2

1 2 3 4 5 6 7 8 9 10 11 12



NTB Index

Industry Wise

-0.648**

(0.267)

-0.397

(0.329)

-0.314*

(0.176)

-1.262**

(0.597)

-0.022

(0.033)

-0.034

(0.033)

0.027

(0.124)

-0.127

(0.165)

0.037

(0.067)

0.015

(0.099)

-0.128

(0.198)

-0.105

(0.138)

Lagged

Productivity

1.099***

(0.037)

1.060***

(0.027)

0.779

(0.617)

1.139***

(0.088)

0.927***

(0.038)

0.876***

(0.018)

1.207***

(0.303)

1.151***

(0.061)

Total Asset 0.000

(0.001)

-0.000

(0.002)

0.029***

(0.006)

-0.004

(0.003)

-0.000*

(0.000)

-0.000

(0.000)

0.023

(0.086)

-0.003***

(0.001)

0.000

(0.000)

0.000

(0.000)

-0.030

(0.048)

-0.004***

(0.000)

Age 0.672*** (0.246)

-0.122 (0.151)

0.405** (0.181)

1.981** (0.907)

-0.025 (0.016)

0.007 (0.011)

-0.018 (0.074)

-0.039 (0.051)

0.029 (0.042)

0.031 (0.030)

-0.114 (0.127)

-0.051 (0.038)

Age Square 0.000

(0.003) 0.005

(0.005) -0.001** (0.000)

-0.010 (0.006)

0.000 (0.000)

-0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

-0.000 (0.000)

-0.000 (0.000)

0.001 (0.001)

0.000 (0.000)

Export Propensity

Industry Wise

-0.129

(0.172)

0.223*

(0.127)

-0.160

(0.151)

-0.935

(0.608)

-0.003

(0.007)

-0.004

(0.004)

-0.001

(0.119)

-0.010

(0.050)

0.029***

(0.010)

-0.003

(0.008)

-0.131

(0.164)

-0.009

(0.024)



Observations 9262 5181 2024 2057 8420 4710 1840 1870 8420 4710 1840 1870

R-squared 0.0164 0.0137 0.0641 0.0642

Hansen Test

1.35 1.22 4.46** 3.57* 0.55 1.01 2.57 2.75

AR 1

-1.76* -1.18 -1.41 -2.57*** -1.97** -1.22 -1.49 -2.56**

AR 2

-0.99 -1.02 0.66 1.39 -1.03 -1.04 0.53 1.37

Number of Firms 842 471 184 187 842 471 184 187 842 471 184 187


*** p<0.01, ** p<0.05, * p<0.1

The results show that a reduction in the NTB does not affect significantly firm-level total

factor productivity for large firms. As we have already mentioned, these insignificant results

may be due to the lack of variation in NTB values across industries and time.29

But

28

The usual NTB indices would give 0’s for import free products; here in our constructed NTB index gives 0’s

for import prohibited products, hence the reverse formulation (i.e. the inverted one). 29

The variation in firm-level productivity across various kinds of Indian manufacturing firms can be better

explained by using NTB index at more disaggregated level (such as, for 4 digit industry groups). However, it

goes beyond the scope of this paper; hence we would like to leave it to our future research.

31

interestingly, for SME firms, the estimated coefficient of NTB index is negative and

significant in the main specification, when both firm and year fixed effects are included

(column 3). The estimated coefficient of the NTB index implies that a one percent reduction

in NTB decreases the SME firm’s productivity by 0.314 percent. This clearly indicates the

adverse effects of a reduction in NTB restrictions on firm-level productivity for SME firms.

This could be the result of increased competitive pressure from the external market and the

inability of these previously protected firms to face this competition. Removal of the

restrictions of previously import prohibited industrial and agricultural items has resulted into

a sharp increase in the product variety in the domestic market. This intensifies the product

market competition and this effect is stronger than that of a reduction in tariffs.

5.3 Main Findings of the Fixed Effect Models

The fixed effect models for both growth rates of firm-level profit after tax and total factor

productivity provide evidence of the successful impact of trade liberalization policies (in

terms of both tariff and NTB reductions) on certain aspects of firm performance in Indian

manufacturing. The firm-level growth rates of profit after tax, which measures a firm’s

operational efficiency has shown an improvement due to a reduction in both tariff and non-

tariff barriers. But, as highlighted earlier, these effects are mainly driven by the input side of

trade liberalization policies and are limited to the large firms. The growth rate of profit after

tax for SME firms has not seen any significant improvement due to a reduction in tariff and

non-tariff barriers.

Firm-level total factor productivity, which measures a firm’s productive efficiency, has

shown a significant increase due to a reduction in tariff barriers. However, similar to the

profit after tax, these effects on productivity are limited to large firms and are mainly driven

by the inputs channel. Moreover, in our study, while we have not found any significant

relationship between a reduction in NTB and firms’ productivity for all as well as large firms,

SME firms’ productivity has been found to be affected adversely by a reduction in import

restrictions. This clearly indicates a discrepancy between the two types of firms (large and

SME) in terms of how they have been affected by tariff and NTB liberalization. This might

be due to the varying characteristics in terms of the structure of their operations, their

technological and credit worthiness, marketability, export and import orientation, etc. The

large firms are always in an advantageous position over SME firms with respect to these

32

abovementioned attributes and hence are able to enjoy the monopoly benefits of trade

liberalization.

6. Conclusion

There has been a significant reduction in import tariffs and NTBs over the 1999 to 2009

period. The latter has been an important feature affecting almost all the sectors of Indian

manufacturing industry over this period. One finds that, trade liberalization has helped to

improve firm-level operational as well as productive efficiency considerably for the overall

manufacturing sector. However, this positive effect of trade liberalization has not been

uniform across all segments of manufacturing. Those segments which are characterized by a

very low capital and technology base have failed to reap the benefits of trade liberalization.

Contrary to the large firms, the SME firms have hardly improved their operational as well as

productive efficiency. In fact, they have failed to withstand external competition during the

study period. On the other hand, large firms have successfully extracted most of the benefits

of trade liberalization due to increased availability of better quality and cheaper imported

intermediate inputs in their production process. As the effect of input tariff reduction is more

than that of final goods tariff, large firms have been able to secure the benefits of trade

liberalization, by continuously upgrading their production process with modern techniques

and have become more competitive and efficient. On the other hand, SME firms with very

low scope for modernising production process have remained in a very uncomfortable

position in the era of massive competition. Moreover, as most of the SME firms have focused

only on their traditional production processes, the quality standard of their produced goods

has actually deteriorated over time and hence they have failed to face global competition.

This has worsened their situation as they have not been able to become efficient and

competitive, unlike their larger counterparts.

33

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37

APPENDIX

Annexure 1: Growth Rate of Profit after Tax & Lagged Final Goods Tariff

All Large SME All Large SME

VARIABLES GR_PAT GR_PAT GR_PAT GR_PAT GR_PAT GR_PAT

Lagged Final Goods Tariff Industry

Wise

-8.0397

(6.3157)

-13.6603

(10.1538)

8.8220

(6.8467)

-10.4462**

(5.1521)

-17.8538**

(7.5158)

8.7347

(7.9506)

Total Asset -0.0745 (0.0533)

-0.1159 (0.0802)

5.3800*** (1.6094)

-0.0519 (0.0453)

-0.0828 (0.0610)

5.5962*** (1.6420)

Age -27.9075 (17.9079)

-33.1810 (28.3223)

1.0116 (25.1321)

-28.5149* (16.8984)

-39.2728 (26.3590)

11.0514 (22.6770)

Age Square 0.2174*

(0.1247)

0.1802

(0.2065)

0.1395

(0.1384)

0 .2503*

(0.1292)

0.2292

(0.2190)

0.1543

(0.1450)

Export Propensity Industry Wise 13.0059**

(5.3048)

10.1899

(7.0882)

8.1772

(9.6439)

7.3560

(5.5578)

2.3400

(7.4299)

3.6817

(11.1659)

Firm Fixed Effects YES YES YES YES YES YES

Time Fixed Effects NO NO NO YES YES YES

Time varying Industry Effect NO NO NO NO NO NO

Observations 8420 4710 1840 8420 4710 1840

R-squared 0.0007 0.0009 0.0034 0.0027 0.0035 0.0062

Number of Firms 842 471 184 842 471 184


Annexure 2: Growth Rate of Profit after Tax & Lagged Input Tariff



Lagged Input Tariff Industry Wise -4.8356 (6.9840)

-6.1548 (10.6499)

10.8192 (9.0717)

-10.5468* (6.2158)

-15.8008** (7.96132)

11.4539 (12.9257)

Total Asset -0.0587

(0.0485)

-0.0855

(0 .0703)

5.4481***

(1.5728)

-0.0419

(0.0430)

-0.0620

(0 .05559)

5.5464***

(1.6700)

Age -14.8709 (12.1965)

-9.3394 (17.7449)

-5.3465 (21.6090)

-16.8344 (13.0222)

-16.9333 (18.5445)

5.3363 (21.7585)

Age Square 0.1951*

(0.1151)

0.1562

(0.1961)

0.1542

(0.1343)

0.2324*

(0 .1225)

0.2038

(0.2110)

0.1622

(0.1505)

Export Propensity Industry Wise 8.7178*

(5.0504)

2.6107

(6.1406)

12.6703

(10.0949)

1.4766

(6.1717)

-7.6490

(7.9627)

8.9466

(13.6399)



Time varying Industry Effects NO NO NO NO NO NO

Observations 8420 4710 1840 8420 4710 1840

R-squared 0.0004 0.0002 0.0032 0.0024 0.0028 0.0060

Number of Firms 842 471 184 842 471 184


Annexure 3: Growth Rate of Profit after Tax & Lagged ERP



Lagged ERP Industry Wise -9.1550* (5.2116)

-16.2680** (8.6267)

5.3492 (4.4242)

-9.9477** (4.4089)

-17.7915*** (6.9062)

5.8958 (4.6502)

Total Asset -0.0742

(0.0514)

-0.1224

(0 .0778)

4.9761***

(1.7497)

-0.0590

(0.0466)

-0.1031

(0.0661)

5.5816***

(1.6034)

Age -38.5648*

(19.9976)

-53.8628

(33.3141)

-2.6544

(23.5124)

-36.3064**

(18.2464)

-55.5408*

(30.5980)

10.0039

(19.4614)

Age Square 0.2388*

(0.1299)

0.2235

(0.2205)

0.1528

(0.1311)

0.2548**

(0.1281)

0 .2462

(0.2214)

0.1717

(0.1299)

Export Propensity Industry Wise 18.3608***

(6.4582)

19.7495**

(9.3992)

5.5824

(10.2709)

14.6030**

(6.4877)

15.6003*

(9.3306)

-1.1701

(10.6359)



Time varying Industry effect NO NO NO NO NO NO

Observations 8420 4710 1840 8420 4710 1840

R-squared 0.0011 0.0020 0.0028 0.0030 0.0042 0.0059

Number of Firms 842 471 184 842 471 184


38

Annexure 4: Growth Rate of Profit after Tax & Inverted NTB



Inverted NTB Industry Wise 2.143

(12.322)

2.423

(18.838)

14.039

(13.917)

-6.792

(16.773)

-7.784

(27.426)

-6.320

(17.130)

Total Asset -0.0431 (0.0440)

-0.0681 (0.0583)

5.0974*** (1.7695)

-0.0228 (0.0397)

-0.0357 (0.0483)

5.3362*** (1.6923)

Age -9.3399

(9.9011)

-2.8622

(13.2932)

-24.8563

(16.0673)

-1.7570

(11.6788)

3.2571

(15.2496)

-5.0873

(18.2189)

Age Square 0 .1861*

(0 .1103)

0.1551

(0.1939)

0 .1953*

(0.1187)

0 .2022*

(0.1178)

0.1832

(0.2091)

0.2156*

(0 .1189)

Export Propensity Industry Wise 8.2880*

(5.0326)

1.9334

(6.1575)

11.6364

(9.8888)

3.2374

(5.8049)

-5.4301

(7.5255)

4.6852

(12.4461)



Observations 8420 4710 1840 8420 4710 1840

R-squared 0.0003 0.0002 0.0024 0.0022 0.0025 0.0052

Number of Firms 842 471 184 842 471 184


Annexure 5: Productivity & Lagged Final Goods Tariff


VARIABLES Productivity Productivity Productivity Productivity Productivity Productivity

Lagged Final Goods Tariff Industry

Wise

-0.120***

(0.039)

-0.090*

(0.046)

-0.046

(0.028)

-0.161***

(0.050)

-0.099**

(0.050)

-0.075*

(0.039)

Total Asset -0.000

(0.001)

-0.000

(0.001)

0.016**

(0.007)

0.000

(0.001)

-0.000

(0.001)

0.020***

(0.007)

Age 0.287* (0.149)

-0.024 (0.170)

0.232*** (0.077)

0.179 (0.162)

-0.139 (0.187)

0.195** (0.093)

Age Square -0.001 (0.002)

0.003 (0.003)

-0.001*** (0.000)

-0.000 (0.002)

0.003 (0.003)

-0.001** (0.000)

Export Propensity Industry Wise -0.070

(0.142)

0.204*

(0.111)

-0.095

(0.118)

-0.090

(0.155)

0.217*

(0.121)

-0.116

(0.132)



Time varying Industry Effect NO NO NO NO NO NO

Observations 8420 4710 1840 8420 4710 1840

R-squared 0.0169 0.0117 0.0551 0.0185 0.0138 0.0609

Number of Firms 842 471 184 842 471 184


Annexure 6: Productivity & Lagged Input Tariff



Lagged Input Tariff Industry Wise -0.134***

(0.050)

-0.152*

(0.080)

-0.036

(0.031)

-0.261***

(0.082)

-0.236**

(0.114)

-0.098**

(0.049)

Total Asset -0.000

(0.001)

-0.000

(0.001)

0.017***

(0.006)

0.000

(0.001)

-0.000

(0.001)

0.020***

(0.007)

Age 0.403**

(0.178)

-0.000

(0.166)

0.293***

(0.088)

0.241

(0.201)

-0.179

(0.218)

0.246**

(0.104)

Age Square -0.001

(0.002)

0.003

(0.002)

-0.001***

(0.000)

-0.000

(0.002)

0.003

(0.003)

-0.001**

(0.000)


(0.150)

0.164*

(0.098)

-0.116

(0.119)

-0.197

(0.170)

0.142

(0.099)

-0.161

(0.148)



Time varying Industry Effects NO NO NO NO NO NO

Observations 8420 4710 1840 8420 4710 1840

R-squared 0.0162 0.0123 0.0527 0.0187 0.0152 0.0592

Number of Firms 842 471 184 842 471 184


39

Annexure 7: Productivity & Lagged ERP



Lagged ERP Industry Wise -0.067* (0.024)

-0.024 (0.020)

-0.031 (0.020)

-0.064** (0.027)

-0.009 (0.024)

-0.033 (0.024)

Total Asset 0.000

(0.001) 0.000

(0.001) 0.018** (0.006)

0.000 (0.001)

0.000 (0.001)

0.021*** (0.007)

Age 0.358***

(0.137)

0.103

(0.138)

0.243***

(0.083)

0.347**

(0.147)

0.053

(0.109)

0.260**

(0.102)

Age Square -0.001

(0.002)

0.003

(0.003)

-0.001***

(0.000)

-0.000

(0.002)

0.003

(0.003)

-0.001**

(0.000)


(0.133)

0.177*

(0.096)

-0.078

(0.116)

-0.081

(0.144)

0.185*

(0.110)

-0.093

(0.123)



Time varying Industry effect NO NO NO

Observations 8420 4710 1840 8420 4710 1840

R-squared 0.0159 0.0107 0.0539 0.0166 0.0127 0.0559

Number of Firms 842 471 184 842 471 184


Annexure 8: Productivity & Inverted NTB



Inverted NTB Industry Wise -0.417**

(0.170)

-0.229

(0.182)

-0.188

(0.122)

-0.648**

(0.267)

-0.397

(0.329)

-0.314*

(0.176)

Total Asset -0.000

(0.001)

-0.000

(0.002)

0.026***

(0.007)

0.000

(0.001)

-0.000

(0.002)

0.029***

(0.006)

Age 0.670*** (0.232)

0.156 (0.188)

0.400*** (0.142)

0.672*** (0.246)

-0.122 (0.151)

0.405** (0.181)

Age Square -0.000 (0.003)

0.005 (0.005)

-0.001*** (0.000)

0.000 (0.003)

0.005 (0.005)

-0.001** (0.000)

Export Propensity Industry

Wise

-0.081

(0.155)

0.222*

(0.136)

-0.118

(0.127)

-0.129

(0.172)

0.223*

(0.127)

-0.160

(0.151)



Observations 9262 5181 2024 9262 5181 2024

R-squared 0.0145 0.0118 0.0562 0.0164 0.0137 0.0641

Number of Firms 842 471 184 842 471 184


Annexure 9: Profit after Tax and Lagged Final Goods Tariff

AB 1 AB 2

1 2 3 4 5 6 7 8 9 10 11 12


VARIABLES Profit after

Tax

Profit

after Tax

Profit after

Tax

Profit after

Tax

Profit

after Tax

Profit after

Tax

Profit

after Tax

Profit

after Tax

Profit after

Tax

Profit after

Tax

Profit

after Tax

Profit

after Tax

Lagged Final Goods


-0.055* (0.031)

-0.077 (0 .053)

-0.004 (0.022)

0.068 (0.055)

-0.003 (0.042)

-0.008 (0.074)

-0.001 (0.013)

0.028 (0.056)

0.026 (0.047)

0.044 (0.076)

0.010 (0.014)

-0.152 (0.103)

Lagged Profit after

Tax

0.661*** (0.021)

0.658*** (0.022)

0.327*** (0.058)

0.855*** (0.081)

0.580*** (0.027)

0.565*** (0.015)

0.259** (0.102)

0.896*** (0.041)

Total Assets 0 .056***

(0 .009)

0.061***

(0.008)

-0.018

(0 .021)

-0.035***

(0.003)

Age -0.341**

(0 .135)

-0.485**

(0.218)

0 .019

(0.014)

0.117

(0.113)

-0.093

(0.089)

-0.200

(0.143)

0.027

(0.022)

-0.024

(0.081)

0.002

(0.146)

-0.026

(0.214)

0.057**

(0.028)

-0.315

(0.311)

Age Square 0 .005**

(0 .002)

0 .008**

(0 .004)

-0.000

(0.000)

-0.000

(0.000)

0.004**

(0.001)

0.008**

(0.003)

-0.000

(0.000)

0.001

(0.001)

0.005**

(0 .002)

0.009**

(0.003)

-0.000

(0.000)

0.002

(0.001)

Export Propensity

Industry Wise

0 .024 (0 .053)

0 .124** (0 .057)

0 .020 (0 .022)

-0.051 (0.091)



Time varying


Observations 8610 4750 1930 1930 7749 4275 1737 1737 6888 3800 1544 1544

R-squared 0.1630 0.1977 0.1029 0.1214

Wald Test

8105.09 7739.33 563.54 6265.85 14771.07 15873.62 551.75 9698.09

AR 1

-1.296 -1.2624 -1.6069 -1.392 -1.4278 -1.3216 -1.8385* -1.3762

AR 2

-0.8488 -0.84967 1.1636 -0.0195 -0.8517 -0.9102 1.5159 0.6840

Number of Firms 861 475 193 193 861 475 193 193 861 475 193 193


40

Annexure 10: Profit after Tax and Lagged Input Tariff

AB 1 AB 2

1 2 3 4 5 6 7 8 9 10 11 12



Tax

Profit after

Tax

Profit

after

Tax

Profit after

Tax

Profit

after Tax

Profit after

Tax

Profit

after Tax

Profit after

Tax

Profit

after Tax

Profit

after Tax

Profit

after

Tax

Profit after Tax

Lagged Input

Tariff Industry

Wise

-0.006

(0 .063)

-0.406*

(0 .223)

-0.0026

(0.008)

0.104

(0.087)

-0.152

(0.104)

-0.310*

(0.188)

-0.023

(0.015)

0.299

(0.279)

-0.108

(0.077)

-0.23*

(0.143)

-0.017

(0.014)

-0.123

(0.133)

Lagged Profit

after Tax

0 .660***

(0 .021)

0 .657***

(0.022)

0.324***

(0.056)

0.862***

(0.081)

0.580***

(0 .027)

0.565***

(0.015)

0.255**

(0.103)

0.897***

(0.040)

Total Assets 0 .056***

(0 .009)

0 .061***

(0.008)

-0.018

(0.021)

-0.035***

(0.003)

Age -0.326**

(0 .132)

-0.495**

(0 .227)

0 .023**

(0.011)

0.116

(0.079)

-0.274**

(0.126)

-0.529**

(0.212)

0.000

(0.020)

0.318

(0.257)

-0.213*

(0 .112)

-0.425**

(0.187)

0.009

(0.018)

-0.083

(0.224)

Age Square 0 .0057** (0 .0023)

0.0086** (0 .0040)

-0.000 (0.000)

0.000 (0.000)

0.0049** (0.0020)

0.0084** (0.0038)

-0.000 (0.000)

0.000 (0.000)

0 .005*** (0 .002)

0.009** (0.04)

-0.000 (0.000)

0.001 (0.001)

Export Propensity

Industry Wise

0 .040 (0.038)

0 .320 (0 .198)

0 .003 (0.012)

-0.017 (0.066)



Time varying


Observations 8610 4750 1930 1930 7749 4275 1737 1737 6888 3800 1544 1544

R-squared 0.1630 0.1977 0.1029 0.1214

Wald Test

7591.61 7221.78 535.62 9045.92 13407.91 13837.85 1023.34 7022.02

AR 1

-1.2974 -1.2646 -1.6086 -1.3813 -1.4282 -1.322 -1.8285* -1.3773

AR 2

-0.8467 -0.8470 1.167 -0.0579 -0.8478 -0.9051 1.4927 0.67054

Number of Firms 861 475 193 193 861 475 193 193 861 475 193 193


Annexure 11: Profit after Tax and Lagged Effective Rate of Protection (ERP)

AB 1 AB 2

1 2 3 4 5 6 7 8 9 10 11 12


VARIABLES Profit after Tax Profit after

Tax

Profit after

Tax

Profit

after Tax

Profit

after Tax

Profit after

Tax

Profit

after Tax

Profit

after Tax

Profit after

Tax

Profit after

Tax

Profit after

Tax

Profit after

Tax

Lagged ERP

Industry Wise

-0.072

(0 .060)

-0.150*

(0.078)

0 .001

(0 .004)

-0.015

(0.022)

0.046

(0.042)

0.087

(0.068)

0.005

(0.009)

-0.037

(0.032)

0.062

(0.054)

0.119

(0.088)

0.011

(0.011)

-0.098

(0.062)

Lagged Profit

after Tax

0.661***

(0.021)

0.659***

(0.022)

0.330***

(0.057)

0.853***

(0.081)

0.579***

(0.027)

0.564***

(0.015)

0.261***

(0.101)

0.897***

(0.042)

Total Asset 0 .056***

(0.009)

0.061***

(0 .008)

-0.018

(0 .021)

-0.035***

(0.003)

Age -0.399** (0.175)

-0.665** (0.279)

0 .024* (0.012)

0.067 (0.111)

0.052 (0.152)

0.077 (0.254)

0.041* (0.022)

-0.211 (0.226)

0.147 (0.228)

0.265 (0.363)

0.069** (0.031)

-0.256 (0.262)

Age Square 0.005** (0 .002)

0.008** (0.004)

-0.000 (0.000)

-0.000 (0.000)

0 .004** (0.002)

0.007** (0.003)

-0.000 (0.000)

0.002 (0.001)

0.005** (0.002)

0.008** (0.003)

-0.000 (0.000)

0.002 (0.001)

Export

Propensity

Industry Wise

0 .080 (0.083)

0 .567* (0 .32)

0 .010 (0 .007)

0.052 (0.051)

Firm Fixed


Time Fixed


Time varying


Observations 8610 4750 1930 1930 7749 4275 1737 1737 6888 3800 1544 1544

R-squared 0.1630 0.1977 0.1029 0.1214

Wald Test

8378.23 8329.44 609.88 3998.30 13633.39 16038.58 519.96 8671.91

AR 1

-1.295 -1.2607 -1.6039 -1.3951 -1.4278 -1.3212 -1.8504* -1.3745

AR 2

-0.8515 -0.8541 1.1584 -0.0108 -0.8549 -0.9158 1.5301 0.6788

Number of Firms 861 475 193 193 861 475 193 193 861 475 193 193


41

Annexure 12: Profit after Tax & Inverted NTB

AB 1 AB 2

1 2 3 4 5 6 7 8 9 10 11 12



Tax

Profit

after Tax

Profit after

Tax

Profit

after Tax

Profit after

Tax

Profit after

Tax

Profit after

Tax

Profit after

Tax

Profit after

Tax

Profit after

Tax

Profit after

Tax

Profit after

Tax

Inverted NTB

Industry Wise

0.415

(0.319)

0.798

(0.621)

-0.004

(0.036)

-0.001

(0.158)

0.223*

(0.120)

0.541**

(0.266)

-0.005

(0.050)

0.103

(0.110)

0.277*

(0.169)

0.676*

(0.368)

-0.041

(0.035)

0.054

(0.194)

Lagged Profit

after Tax

0.660***

(0.021)

0.656***

(0.022)

0.326***

(0.061)

0.854***

(0.081)

0.579***

(0.028)

0.561***

(0.015)

0.257**

(0.105)

0.899***

(0.041)

Total Asset 0.056***

(0.009)

0 .060***

(0.007)

-0.014

(0 .017)

-0.031***

(0.003)

Age -0.315** (0 .146)

-0.552** (0.271)

0 .028 (0.021)

0.062 (0.049)

-0.164 (0 .113)

-0.364 (0.226)

0.053* (0.028)

-0.126 (0.143)

-0.123 (0.111)

-0.274 (0.213)

0.043*** (0.016)

0.091 (0.105)

Age Square 0 .005** (0 .002)

0.008** (0 .004)

-0.000 (0.000)

-0.000 (0.000)

0.004** (0.002)

0.008** (0.004)

-0.000 (0.000)

0.001 (0.001)

0.005** (0.002)

0.009** (0.004)

-0.000 (0.000)

0.001 (0.001)

Export

Propensity

Industry Wise

0 .113** (0 .055)

0.220** (0.090)

0.016 (0 .017)

0.050 (0.035)

Firm Fixed


Time Fixed


Observations 9471 5225 2123 2123 7749 4275 1737 1737 6888 3800 1544 1544

R-squared 0.1360 0.1598 0.0366 0.0797

Wald Test

7474.85 7372.60 492.05 4106.16 10382.75 12262.04 707.32 4019.66

AR 1

-1.2956 -1.2613 -1.6186 -1.3951 -1.4285 -1.3223 -1.8299* -1.3752

AR 2

-0.8511 -0.8549 1.1825 -0.0151 -0.8535 -0.9146 1.497 0.6591

Number of

Firms 861 475 193 193 861 475 193 193 861 475 193 193


Annexure 13: Trade Policy Endogeneity: Current Industry Performance

and Subsequent Trade Policy for 1999-2009 Period

Industry-Level Final Goods

Tariff at t Period

Industry-Level Input Tariff at

t Period

Industry-Level ERP at t

Period

Panel 1

Industry-Level Growth Rates

of PAT at t-1 period

-0.033*

(0.014)

-0.016

(0.010)

-0.051*

(0.021)

Constant 23.401***

(0.010)

16.169***

(0.007)

22.387***

(0.016)

R-squared 0.0154 0.0100 0.0209

Observations 45 45 45 Panel 2

Industry-Level PAT at t-1

period

-13.871***

(2.691)

-8.950**

(2.270)

-16.593***

(1.766)

Constant 29.806***

(1.020)

20.083***

(0.860)

30.566***

(0.669)

R-squared 0.4348 0.5208 0.3273

Observations 50 50 50

Panel 3

Industry-Level Productivity at

t-1 Period

-2.816**

(0.977)

-1.830***

(0.332)

-2.965

(1.576)

Constant 42.824***

(6.340)

28.570***

(2.160)

43.517**

(10.227)

R-squared 0.3275 0.3980 0.1909

Observations 50 50 50

The table gives the results of the regressions of industry‐level Growth Rates of PAT (Panel 1), Absolute Value

of PAT (Panel 2), Productivity (Panel 3) in period t-1 on industry‐level Final Goods Tariff (Column 1), Input

Tariff (Column 2), and ERP (Column 3) in period t. Industry‐level productivity is calculated as a real

sales‐weighted average of firm‐level TFP. All regressions include industry fixed effects and are weighted by the

number of firms in each industry for each particular year. Standard errors are clustered at the industry-level.

Significance: * 10 percent; ** 5 percent; *** 1 percent.

42

Annexure 14: Trends in Average Profit after Tax Industry-Wise all firms (Rs Million)

Source: Author’s Calculation based on the Firm-level data obtained from the Prowess database

Annexure 15: Trends in Average Profit after Tax Industry-Wise for Large Firms (Rs Million)


-10123456789

10

-10123456789

10

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Food and Agro based Industries Textile Industry Machinery and Equipments Industries

Metal Industry Leather Industry

Ave

rage

Pro

fit a

fter T

ax In

dust

ry W

ise

Year

Trends in Average Profit after Tax Industry Wise for All Firms (Rs Millions)

-202468

101214161820

-202468

101214161820

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Food and Agro based Industries Textile Industry Machinery and Equiments Industries


Ave

rage

Pro

fit a

fter T

ax In

dust

ry W

ise

Year

Average Profit after Tax Industry Wise for Large firms (Rs Millions)

43

Annexure 16: Trends in Average Profit after Tax Industry-Wise for SME Firms (Rs Million)


Annexure 17: Trends in Average Productivity Industry-Wise for All Firms (Percent)


-1-.75-.5

-.250

.25.5

.751

1.251.5

1.752

-1-.75-.5

-.250

.25.5

.751

1.251.5

1.752

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009



Ave

rag

e P

rofit

after

Ta

x In

dust

ry W

ise

Year

Trends in Average Profit after Tax Industry Wise for SME Firms (Rs Millions)

02468

101214161820

02468

101214161820

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009



Ave

rage

Pro

duct

ivity

Indu

stry

Wis

e

Year

Trends in Average Productivity Industry Wise for All Firms (Rs Millions)

44

Annexure 18: Trends in Average Productivity Industry-Wise for Large Firms (Percent)


Annexure 19: Trends in Average Productivity Industry-Wise for SME Firms (Percent)


02468

101214161820

02468

101214161820

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009



Ave

rage

Pro

duct

ivity

Indu

stry

Wis

e

Year

Trends in Average Productivity Industry Wise for Large Firms (Rs Millions)

02468

101214161820

02468

101214161820

1999

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2001

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2007

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2009

1999

2000

2001

2002

2003

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1999

2000

2001

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2006

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2009



Ave

rage

Pro

duct

ivity

Indu

stry

Wis

e

Year

Trends in Average Productivity Industry Wise for SME Firms (Rs Millions)

Documents

The Impact of Trade Liberalization on SMEs versus Large