Technical Regulations, Intermediate Inputs andProductivity of Firms: Evidence from India∗

Rahul Singh†

June 15, 2017

Abstract

This paper estimates the productivity losses from introduction of restrictiveTechnical Barriers to Trade (TBT) measures on final goods and intermediateinputs for manufacturing firms in India. I match firm level data from Prowessto the database of TBT measures that have been raised as a concern by mem-ber countries with India in the dedicated committees of WTO. Incidence ofrestrictive TBT measures in an industry does not have a significant impact onfirm-level productivity. Higher TBT incidence on intermediate inputs to theindustry, on the other hand, is associated with an economically and statisti-cally significant decrease in firm-level productivity. The effects are primarilydriven by reduced access to imported inputs. On average, a 10% increase inincidence of technical standards in input share of output leads to a decreasein productivity of 5.5% for firms that import their inputs. I also documentheterogeneity in the impact of restrictive TBT measures by firm and industrycharacteristics. The results are robust to a series of robustness checks andinstrumental variables estimation.

∗I would like to thank Rupa Chanda, Mia Mikic, Cosimo Beverelli, Vidhya Soundararajan, Pavel Chakraborty,Arun Jacob for helpful comments. I am grateful to Pavel Chakraborty and Shanthi Nataraj for sharing data ontariffs, and delicensing and FDI liberalization respectively.†Rahul Singh: Doctoral Candidate, Indian Institute of Management, Bangalore, Bannerghatta Road,Bengaluru,

560076 (e-mail:rahul.singh14@iimb.ernet.in).

1 Introduction

The drastic decline in tariffs on account of trade liberalization episodes in developing

countries has been followed by an increase in the incidence of regulatory non-tariff

measures. The WTO negotiation rounds have increasingly focused on harmonization

of these regulatory measures across countries to reduce their impact on trade flows.

While the proximate reasons for implementation of these regulatory measures may be

related to addressing health, safety, product quality and environmental concerns in an

economy, some measures may also negatively affect trade flows. They may increase

the fixed and/or variable cost of exporting thereby reducing trade flows. This in turn

has implications for domestic markets of the country maintaining these regulatory

measures. Pro-competitive effects of increased competition from foreign goods will

not materialize if the regulatory measures are restrictive to import flows. Reduced

access to imported intermediate inputs will adversely affect the productivity of users

of these inputs as they would no longer benefit from better technology embodied in

foreign inputs.

This paper attempts to estimate the productivity losses for Indian manufacturing

firms due to introduction of restrictive Technical Barriers to Trade (TBT) measures

by India. I combine firm level data from Prowess with the database of TBT measures

that have been raised as a concern by member countries with India in the dedicated

committees of WTO. I focus on TBT measures that have been raised as a concern

against India in the committees of WTO as these are likely to be most restrictive to

flow of imports.1

I estimate the productivity losses from introduction of restrictive TBT measures

on final goods and intermediate inputs following the standard methodology in the

literature studying the effects of trade liberalization and productivity (Pavcnik, 2002;

Amiti and Konings, 2007; Fernandes, 2007; Topalova and Khandelwal, 2011). I

construct firm-level productivity measures from the estimates of the coefficients of

the production function at the industry level using the methodology of Levinsohn

1TBT measures can also increase trade if they address market failures like information asym-metries between buyers and sellers and do not discriminate against foreign firms.

1

and Petrin (2011). Next I study the impact of incidence of restrictive TBT measures

in an industry and its intermediate inputs on productivity of manufacturing firms.

I find that the incidence of restrictive TBT measures in an industry does not have

a significant impact on productivity of firms. On the other hand, incidence of these

measures on intermediate inputs is associated with an economically and statistically

significant decrease in productivity. On average, a 10% increase in incidence of

restrictive TBT measures in inputs share of output leads to a decrease in firm-level

productivity of 3.8%.

As TBT measures are “behind the border” measures, they apply to both domestic

and foreign firms exporting to India. This raises a concern that the effects of these

measures may be driven by their effect on domestic firms through channels other than

imports of intermediate inputs. To address this concern, I interact the incidence of

restrictive TBT measures on intermediate inputs with the importing status of a firm.

I find that the effect of restrictive TBT measures on intermediate inputs is greater

for importing firms than other domestic firms competing with them. On average,

a 10% increase in incidence of restrictive TBT measures on inputs as a share of

output decreases the productivity of importing firms by 5.5% while the effect on

non-importing domestic firms is not significant.

I also study heterogeneity in the impact of restrictive TBT measures across firm

and industry characteristics. Productivity losses differed across firms based on own-

ership. The results are driven by effects of these measures on private domestic firms

while government and foreign owned firms were not impacted by increased TBT

measures on inputs. While final goods and intermediate goods industry were both

negatively impacted by the incidence of these measures on their inputs, the effect

was larger for final goods industries. Productivity losses were also much higher for

industries with higher import share of sales at the start of the sample.

The Indian government was also implementing other industrial reforms like deli-

censing and FDI liberalization during the sample period. FDI liberalization seems

to have had a significant positive impact on productivity 2. However the effect of

restrictive TBT measures on intermediate inputs on productivity of firms is robust

2The data on delicensing and FDI liberalization are from Harrison et al. (2013)

2

to controlling for these industrial reforms.

I also address the potential endogeneity of introduction of these measures. If

restrictive TBT measures were introduced selectively for industries with low pro-

ductivity, it would lead to reverse causality and spurious correlation between these

measures and firm productivity. I instrument for restrictive TBT measures on final

goods as well as inputs. The construction of the instrumental variables rests on the

assumption that similar industries within a broader industry group are likely to have

similar incidences of these measures (Fontagne et al., 2015). The results are robust

to instrumental variable estimations.

This paper makes important contributions to the literature. This is the first

paper to study the impact of restrictive restrictive TBT measures on productivity

of domestic firms in the maintaining country. I am also able to isolate the impact

of these measures separately for importing firms which is novel for firm level studies

of productivity in India. The results also highlight the importance of including

restrictive TBT measures in analysis of effects of tariff changes on productivity of

firms.

The rest of the paper is structured as follows. Section 2 discusses the related

literature, describes the data and presents stylized facts about restrictive TBT mea-

sures. Section 3 discusses the empirical methodology. Section 4 presents the results.

Section 5 concludes.

2 Related literature, data and stylized facts

2.1 Related literature

This paper is related to the literature studying the effect of sanitary and phyto-

sanitary (SPS) measures and TBT measures on imports to the maintaining country.

Firm level studies find that TBT measures have a negative impact on trade both

through the extensive and intensive margins (WTO, 2012). Shepherd (2007) finds a

negative association between restrictive TBT measures and the number of varieties of

goods imported to the Europen Union in the textiles, clothing and footwear sectors.

3

Using the World Bank Technical Barriers to Trade Survey database of 619 firms in

17 developing countries Chen et al. (2006) find that restrictive TBT measures have

a significant negative impact on exports for firms in the sample. Fontagne et al.

(2015) find that restrictive SPS measures have negative impact on the extensive and

intensive margins of export from the EU to the SPS maintaining country. This paper

goes a step further and studies the negative impact of restrictive TBT measures on

productivity of firms in the maintaining country. I focus on a specific channel-

restrictive TBT measures’ effect on import of intermediate inputs - linking these

measures imposed by the maintaining country to the productivity of its domestic

firms.

The link between access to imported inputs and productivity is well established

in several models of international trade. The productivity gains result from increased

access to variety of intermediate inputs, technologically superior inputs, and learning

effects (Ethier, 1982; Markusen, 1989; Grossman and Helpman, 1991; Rivera-Batiz

and Romer, 1991). This channel is particularly important for firms in developing

countries which are technologically constrained. Trade models also predict produc-

tivity gains from pro-competitive effects of trade. The aggregate industry produc-

tivity increases because of exit of least productive firms (Melitz, 2003). There is also

increase in firm level productivity as firms are forced to move down their average

cost curves (Helpman and Krugman, 1985).

This paper complements the literature studying the productivity impacts of trade

liberalization episodes in developing countries (Pavcnik, 2002; Schor, 2004; Amiti

and Konings, 2007; Fernandes, 2007; Topalova and Khandelwal, 2011)3. The main

findings of this literature are that tariff liberalization has a positive impact on pro-

ductivity of firms with input tariffs having a larger impact than output tariffs . For

example, Amiti and Konings (2007) find that a reduction in input tariff of 10% leads

to an increase in productivity by 12% for Indonesian importing firms, much larger

than the effects of reduction in output tariffs. Topalova and Khandelwal (2011)

report similar findings for Indian firms during the trade liberalization in the early

3Tybout et al. (1991), Tybout and Westbrook (1995),Krishna and Mitra (1998) and Sivadasan(2009) also study effect of trade liberalization on productivity of firms in developing countries

4

1990’s. They find that productivity gains for Indian firms due to reduced input tariffs

were higher than that from reduced output tariffs. Also Goldberg et al. (2009) find

that 86% of imported inputs during the reform period comprised of either previously

unavailable products or new varieties within existing products.

Given the above findings, it is natural to ask whether introduction of restrictive

TBT measures following trade liberalization in the 1990’s resulted in productivity

losses for Indian firms by affecting the import of intermediate inputs. If firms had

adjusted their technology following trade liberalization and were dependent on im-

ported inputs for production, it is expected that reduced access to these inputs would

adversely affect the productivity of firms. This effect would be particularly severe if

inputs available in the domestic market after liberalization were still inferior in qual-

ity compared to imported inputs4. The data also allows me to separately estimate

the impact of these regulations on importing firms, which to my knowledge has not

been done in previous studies on firm level productivity in India5.

2.2 Specific Trade Concerns Database

TBT measures relate to technical regulations, standards, and conformity assessment

procedures not covered under sanitary and phyto sanitary (SPS) measures 6. Tech-

nical regulations refer to mandatory compliance requirements for products and their

related processes and production methods. Standards, on the other hand, refer to

rules and guidelines related to product characteristics and their related processes

and production methods which are voluntary. Conformity assessment procedures

are procedures undertaken to determine if the products meet the requirements of the

technical regulations and standards7. TBT measures are generally aimed at address-

4Even if same quality inputs were available in domestic markets, effect of reduced variety ofinputs could still affect productivity

5Amiti and Konings (2007) estimate the effects of input tariff reductions on importing firms forIndonesian firms

6SPS measures relate to sanitary (human and animal safety) and phytosanitary(plant safety)measures and members are expected to follow the provisions of the Agreement on the Applicationof Sanitary and Phytosanitary Measures.

7The precise definitions and explanatory notes are provided in the Annex I of the Agreementon Technical Barriers to Trade. The text of the agreement is available at https://www.wto.org/

5

ing issues related to human safety or health, plant safety or health, environment,

consumer safety and awareness 8.

For the purpose of my analysis, it is important to identify those technical regula-

tions that are considerable barriers to import flows to India.The WTO notifications

database covers all regulations that have been notified by the maintaining country

to the WTO. However, not all regulations may be notified and it fails to identify the

regulations which are barriers to trade. Survey data on non tariff measures also do

not capture all measures that are restrictive to trade flows 9.

This paper uses the WTO database on specific trade concerns (STCs) which

records concerns raised in the dedicated committees of the WTO. Member countries

of the WTO are obliged to conform to the provisions of the Agreement on Technical

Barriers to Trade. The dedicated committee on TBT measures was established as a

forum for member countries to raise concerns with specific TBT measures introduced

by other member countries10 . The countries raising the concern have to provide

information about the issues with the TBT measure, products covered by the measure

and objective of the measure concerned. The unique feature of this data is that it

systematically identifies the technical regulations that are restrictive to trade flows.

Countries are likely to raise a concern only if the TBT measure acts as a significant

barrier to trade for exporters in these countries. Thus, the STC database overcomes

the shortcomings of other databases on non tariff measures used in previous studies11.

The STC database spans from 1995-2011 and provides information on: (1) mem-

english/docs_e/legal_e/17-tbt.pdf8TBT measures may be implemented for other reasons such as harmonization of regulations and

trade facilitation. Details are available at https://www.wto.org/english/tratop_e/tbt_e/tbt_info_e.htm

9 The TRAINS dataset, which has been used extensively in previous studies on NTMs, hasnot been revised since 2001. Also the TRAINS database does not identify whether a measure isrestrictive to trade flows or not.

10The main functions of the TBT committee are to review concerns with specific TBT measuresand strengthen the implementation of the TBT agreement. More details are available athttps://www.wto.org/english/tratop_e/tbt_e/tbt_e.htm

11Fontagne et al. (2015) use SPS concern data from WTO to study the differential effects of SPSconcerns raised by EU on exporting French firms.

6

ber countries raising the concern and countries maintaining the TBT measure; (2)

year in which the concern was raised ; (3) HS-4 digit products covered by the TBT

measure;(4) resolution status of the concern; (5) objectives of the TBT measure;

and (6) issues that the countries raising the concern have with the TBT measure.

Interestingly none of the concerns are reported as resolved in the dataset12. This

suggests that regulations once implemented are difficult to withdraw and can have a

large impact on import flows to the maintaining country.

Overall there are 318 concerns that have been raised in the TBT committee

during 1995-2011. I focus on the 18 STCs raised against India for the analysis in

this paper. Fig 1 shows the number of HS4 product lines covered under STCs by at

least one concern for all countries and India from 1995-2011. The figure shows that

the number of product lines covered by STCs has risen exponentially in the sample

period. Around 1000 HS4 product lines were covered by at least one STC in the

sample period. For India this figure is around 400. Table 1 shows the yearly number

of new STCs and number of HS4 product lines covered by at least one concern in

that year for all countries and India. Columns 1 and 2 show that India introduced

restrictive TBT measures covering a large number of products in the years 2001,

2002, 2007, 2009 and 2011. There were no STCs raised against India from 1995-

2001. Considering all countries, columns 3 and 4 show that the both the number of

STCs and the HS4 product lines covered by them have been increasing over time.

The objectives for the TBT measures raised as STCs against India are shown in

Table 2. A particular STC may have multiple objectives associated with it. Human

health and safety, and consumer safety or protection are the most common objectives

for the STCs against India with 14 and 12 measures, respectively, reporting these

objectives. Environment protection and quality issues are reported as objectives of 4

and 2 STCs respectively13. The issues related to the concerns also vary considerably.

Some concerns are raised to seek clarification regarding the regulation or to raise

transparency issues with the regulation. Other issues raised in the concerns relate

12A closer look at the concerns raised against India suggests that all of the TBT measures werestill in force in 2011. This clearly suggests that at least for India there was no misreporting for theresolution of STCs.

13The objectives for the STCs against India are reported for 16 out of 18 measures.

7

to the discriminatory nature of the regulation, question the legitimacy and rationale

for the regulation, or deem the regulation as an unnecessary barrier to trade. Table

3 reports the number of STCs associated with the different issues raised by member

countries against India14.

An example of a TBT measure restrictive to trade flows was the concern raised

by Australia, Canada, Japan, EU and the USA against India in 2001. The TBT

measure in question related to mandatory labeling and quality standards requirement

for 133 product categories15. The measure came into force in 2001 but there was

no notification reported to the WTO. The member countries raising the concern

reminded India of its obligations under the Agreement on Technical Barriers to Trade

to notify the WTO members before introduction of any measure which may be

restrictive to trade. The Indian representative submitted that the measure was not

a barrier to trade as it applied to domestic products as well. However, the members

concerned objected to the requirement for all exporters of the 133 product categories

to mandatorily register and obtain compliance certificates from the Bureau of Indian

Standards (BIS). They submitted that the measure covered a broad range of products

and there was uncertainty regarding compliance procedures as they were unclear

and not transparent. They further submitted that the TBT measure constituted an

unnecessary barrier to trade. The concern remained unresolved during the sample

period.

An example of a restrictive TBT measure on intermediate inputs imposed by

India was the concern raised by EU, the USA, Korea and Japan in 2006 related

to mandatory registration and certification from BIS for all exporters of pneumatic

tyres and tubes for automotive vehicles16. The members raising the STC submitted

that the TBT measure was in violation of Article 5.1.2 of the TBT agreement and

constituted an unnecessary obstacle to trade. The measure did not accept prevailing

14The data reports issues raised for 17 out of 18 STCs against India. Again one STC may beassociated with more than one type of issues raised by countries raising the concern.

15The 133 product categories correspond to 125 HS4 product lines. See document G/TBT/M/25for minutes of TBT committee on this issue.

16see document G/TBT/N/IND/20 for the notification of the measure. DocumentsG/TBT/M/41 to G/TBT/M/51 include the minutes of the TBT committee discussions on thisissue.

8

standards in other countries and that of the United Nations Economic Commission

for Europe (UNECE). The members were concerned that conformity to the measure

involved high costs and significant adjustments and modifications of the production

lines as compared to other markets. The Indian representative clarified that the

measure had different requirements than other prevailing standards due to the cli-

mate and road conditions of India and were necessary to ensure consumer safety.

The members raising the concern reminded India that other countries with similar

climate and road conditions as India accepted the “e-mark” certification conforming

to UNECE regulations. Another objection to the measure was the higher license

fees applied to importers as compared to domestic products. The measure thus vi-

olated the Article 5.2 of the TBT agreement and was discriminatory. The concern

remained unresolved during the sample period. These examples show that TBT mea-

sures having legitimate public policy objectives may nonetheless negatively impact

trade.

2.3 Prowess Dataset

The firm level data on manufacturing firms in India for the years 1996-2011 comes

from the Prowess database provided by the Centre for Monitoring the Indian Econ-

omy. The dataset reports firm level information from income statements and bal-

ance sheets for publicly listed firms and from yearly surveys of unlisted firms. The

database has information on more than 41000 companies. For the analysis in this

paper I focus on the companies in the manufacturing sector. The dataset reports in-

formation on around 6900 manufacturing firms. The firms are categorized according

to NIC 2008 industrial classification in the Prowess dataset. I use the correspondence

between NIC2008 to NIC 2004 classification to map each firm to the corresponding

NIC 2004 industry. There are 104 industries across 22 manufacturing sectors repre-

sented in the sample. One potential drawback of the dataset is that it is not suitable

to study the entry and exit of firms. In the context of this paper this implies that it

would be difficult to disentangle the effects of reallocation of resources on aggregate

industry productivity and within firm productivity changes. To overcome this I es-

9

timate the baseline specification on a subset of firms for which data is available for

all years(Topalova and Khandelwal, 2011).

A unique feature of the dataset is that it provides information on yearly imports

by individual firms. The total imports are further classified into raw materials,

capital goods, stores and spares and final goods17. The data on imports is crucial

to my analysis as it enables me to estimate the effect on importers separately from

the non importer firms. Recall that the TBT measures apply to both domestic and

imported products. Thus the productivity losses may be driven by the effect of these

measures on domestic firms which do not import18. This would raise concerns about

the hypothesized relationship between reduced access to imported inputs due to TBT

measures and firm level productivity.

3 Empirical Strategy

Following the literature on productivity effects of trade liberalization, this paper

proceeds in two steps for the empirical analysis. First I construct firm level measures

of total factor productivity (TFP). Next I estimate the effect of introduction of

restrictive TBT measures on productivity of firms.

3.1 Productivity

I construct firm level TFP using the Levinsohn and Petrin (2011) methodology to

correct for simultaneity in the production function. If input levels are correlated

with unobserved productivity shocks then the production function coefficients will

be biased. To correct for the simultaneity bias, Levinsohn and Petrin (2011) use raw

materials as a proxy for unobserved firm level productivity shocks in the production

17The Annual Survey of Industries (ASI) dataset provides data on major imported inputs forsome years. However total imports are not reported and thus is not suitable for the analysis in thispaper. The percentage of firms importing are similar in the two datasets which suggests that theProwess dataset is accurately capturing the importing status of the firms.

18There can be indirect effects of reduction in imported inputs on non importing firms. Howeverit is not possible to disentangle the direct effects of TBT measures on non importing domestic firmsand the indirect effects of reduced imported inputs on these firms

10

function estimation. Assuming Cobb-Douglas production function, the estimation

equation for firm i in industry j at time t is given by:

yijt = α + β1lijt + β2pijt + β3mijt + β4kijt + ωijt + εijt (1)

where y is output, l is labor input, p is power and fuel input, m denotes raw material

inputs, and k is capital used.19

The simulataneity problem arises due to unobserved firm level productivity shock

ωijt, which is correlated with the choice of variable inputs, l, p and m. Levinsohn

and Petrin (2011) show that under the assumption that the raw material demand

function is monotonic in firm level productivity for all k, i.e., conditional on capital

higher productivity firms also use higher raw materials as inputs, raw materials

serve as a valid proxy.20 The demand function for raw materials inputs is mijt =

mjt(ωijt, kijt). By inverting the demand function for raw materials, productivity can

be expressed as a function of raw materials and capital, ωijt = ωjt(mijt, kijt). The

coefficients on the variable inputs, proxy variable and capital are recovered using

a two stage procedure. The inverted demand function is substituted in equation

(1) and the coefficient on labor and power and fuel are estimated in the first stage.

This is followed by estimation of coefficients on proxy variable and capital in the

second stage.21The industry level production function parameters are consistently

estimated by this method. The firm level TFP measure is calculated by subtracting

the predicted output for a firm from its actual output at time t:

tfpijt = yijt − β̂1lijt − β̂2pijt − β̂3mijt − β̂4kijt (2)

In the absence of data on physical quantities of output, variable inputs and capi-

tal, I use the deflated values of sales revenue, variable input expenditures and capital

19All variables are expressed in natural logarithm and are deflated with industry level deflators.See appendix for details on variables and deflators.

20Other variable inputs such as electricity, power and fuel expenditures can also serve as validproxies subject to the assumption of monotonicity being satisfied.The results are robust to usingpower and fuel expenditures as proxy instead of raw materials. The results are available on request.

21See Levinsohn and Petrin (2011) for a detailed explanation of the estimation procedure

11

expenditures for each firm as proxies for the physical quantities. I use industry level

price deflators to convert the nominal values in real terms for all variables. Firm level

price deflators are unavailable in the dataset and hence the TFP measures may cap-

ture price-cost markups apart from technical efficiency (Katayama et al., 2009).22

However the TFP measure captures technical efficiency if price-cost markups are

correlated with technical efficiency (Topalova and Khandelwal, 2011).

3.2 TBT Measures and Productivity

In the second stage, I study the effects of restrictive TBT measures on firm level

productivity. Using the firm level estimates of TFP from equation (2), I estimate

the equation:

tfpijt = α0 + αi + αj(2),t + β1concernoutputj,t−1 + β2concern

inputj,t−1

+ β3concerninputj,t−1importerijt + β4importerijt

+ β5tariffj,t−1 + νijt

(3)

where concernoutputj,t−1 is a dummy variable equal to 1 if the industry j is covered by

a concern in time t− 1 , concerninputj,t−1 is a lagged measure of incidence of concern on

intermediate inputs for industry j, importerijt is an indicator of importing status of

a firm i equal to 1 if the firm imports raw materials or capital goods in time t, and

tariffj,t−1 denote the lagged measures of tariffs for industry j.23. All specifications

include firm level fixed effects αi and industry (2 digit)-year αj(2),t fixed effects. Firm

fixed effects control for unobserved heterogeneity in the determinants of productivity

that are firm specific. The industry-year fixed effects control for shocks over time

that affect productivity which may vary across the 2 digit industries.

I map the HS4 product lines to NIC 2004 industry codes to measure the incidence

of STC in industry j at time t, concernoutputj,t−1 .24 Similar to construction of input

22See De Loecker (2011) for discussion of issues related to using industry level price deflators23All specifications include output and input tariffs or effective rate of protection (ERP) as

measures of tariffs24All industry level variables are mapped to NIC 2004 classification. See appendix for details on

12

tariffs, I compute the concerninputj,t−1 as the weighted average of incidence of STCs on

the intermediate inputs for an industry j. The concerninputj,t−1 is given by:

concerninputj,t−1 =

∑s

αjs · concernoutputs,t−1 (4)

where αjs is the share of input s in total output for industry j, concernoutputs,t−1 is equal

to 1 if input s for industry j is covered by a STC in year t − 1. All specifications

include both input and output tariffs or the effective rate of protection (ERP), as

defined by Corden (1966), to control for the effects of tariffs on firm level productivity.

The ERP is calculated as

ERPj,t−1 =(tariff output

j,t−1 − tariffinputj,t−1 )

1−∑

s αjs · tariff outputs,t−1

(5)

The incidence of a STC in an industry may lower the import competition and firms

may not benefit from the pro-competitive effects of trade. In such a scenario, firms

will not be forced to increase efficiency in order to compete with the foreign products.

Also the reallocation of resources to most productive firms within the industry may

not materialize. Thus the productivity of firms may stagnate or even decrease due

to the introduction of restrictive TBT measures in an industry(β1 ≤ 0).

There is strong evidence that firm level productivity increased substantially fol-

lowing trade liberalization in the 1990s in India. The productivity increases were

mainly due to reduction in input tariffs which led to greater access to imported in-

puts (Topalova and Khandelwal, 2011). If firms had adjusted their production tech-

nology to use more of higher quality imported inputs, incidence of restrictive TBT

measures may lead to losses in productivity. Further the losses would be higher for

importing firms than non importers. Thus the specifications include a measure of

incidence of STCs on inputs for an industry, concerninputj,t−1 and its interaction with

a firm level indicator of importing firms, concerninputj,t−1importerijt. The coefficient of

interest is β3 and I hypothesize that it is negative suggesting that importing firms

variables.

13

suffered the highest losses from introduction of restrictive TBT measures. The tariffs

continued to decline throughout the sample period and hence I control for tariffs in

all specifications. It is expected that the effect of tariffs on productivity is negative

(β5 < 0).

The coefficient on concerninputj,t−1 , β2 will depend on the direct effect of the TBT

measure on non importers and the indirect effects of reduced access to imported

inputs on these firms. 25 The direct effects of restrictive TBT measures on domes-

tic firms is ambiguous. If TBT measures address market failures like information

asymmetry between buyers and sellers, they may increase the productivity of firms

as firms would benefit from economies of scale. On the other hand if firms have to

significantly adjust their production methods to comply with these measures, it may

lead to short term productivity losses. Unfortunately the data does not allow me to

disentangle the direct and indirect effects of TBT measures on non importing firms.

3.3 Endogeneity of STCs

The first concerns were raised against India only in 2001.26. This raises a concern that

restrictive TBT measures may have substituted for other trade policy instruments for

protection of industries by the government. Fig 2 shows that while tariffs continued

to decline even after 2001, the number of industries covered by STCs and the average

STCs on inputs rose significantly. This suggests that it is important to control for

tariffs in the specifications.

Fig 3 shows the same trend with delicensing and FDI reforms for industries.

FDI liberalization was completed for most industries by early 2000s and this was

accompanied by a significant increase in incidence of STCs on industries and their

inputs. I include delicensing and FDI reforms to control for the possible correlation

between these measures and STCs.

If industries were selected for introduction of TBT measures based on their

productivity, it would lead to a spurious correlation between STCs and produc-

25Recall that TBT measures apply to domestic as well as imported products. See Amiti andKonings (2007) for a discussion on indirect effects of input tariffs on non importers.

26The STC dataset spans the period 1995-2011

14

tivity of firms. Following Topalova and Khandelwal (2011) I regress concerninputj ,

concerninputj , tariff output

j and tariff inputj in period t+ 1 on productivity on industry

level productivity in period t. The industry level productivity is calculated as real

sales weighted average of firm level productivity. If measures of trade protection

were adjusted to productivity of industries, it is expected that current industry level

productivity should be correlated with future measures of trade protection. Table

5 reports the results for the periods 1995-2011 (column (1)), 1995-2001 (column

(2)) and 2002-2011 (column (3)). The correlation between future tariffs and current

industry level productivity is significant and negative in columns (1) and (3) sug-

gesting that tariffs were adjusted based on productivity of industries and that lower

productivity industries had higher tariffs associated with them. On the other hand,

the correlation between concerninputj and industry productivity is insignificant in all

three periods. This suggests that industry level productivity was not a determining

factor for incidence of STCs on inputs for industries. For concernoutputj , the coefficient

on productivity is significant and positive in column (3) implying higher incidence of

STCs on more productive industries. Thus the test suggests that the main variable

of interest for this study, concerninputj may not suffer from endogeneity issues while

the results may show a spurious positive effect of concernoutputj on the productivity

of firms.

4 Results

4.1 TBT Measures and Productivity

The results from estimating equation (3) are reported in Table 6. Odd numbered

columns include tariff outputj and tariff input

j while even numbered columns include

ERPj,t−1 as measures of industry level tariffs. In columns 1 and 2 I regress TFP

on concernoutputj . The coefficient on concernoutput

j is insignificant suggesting that

incidence of STC has no significant effect on productivity of firms in that industry.

However, the data does not allow me to delineate the direct effects of incidence of

STCs from the indirect effects (see section 3.2). In columns 3 and 4, I add concerninputj

15

to the previous specification. The coefficient on concernoutputj remains insignificant

while the coefficient on concerninputj is negative and highly significant. The point

estimate suggests that on average, a 10% increase in incidence of STCs on input

share of output for an industry results in a loss of productivity of around 3.8%.

As discussed earlier, TBT measures apply to both domestic and imported prod-

ucts. Thus it is necessary to delineate the effects of these measures on firm level

productivity due to reduced access to imported inputs from the direct effects in the

domestic market. If the productivity losses are driven by reduced access to high

quality imported inputs, there should be higher losses for importing firms who are

dependent on the use of these inputs. Thus, I interact concerninputj with an indicator

of importing status of a firm, importerijt in columns 5 and 6. Firms are considered

as importers if they import raw materials, capital goods or spare parts in that year.27

The coefficient on the interaction term is negative and highly significant suggesting

that importers indeed suffered higher productivity losses from introduction of re-

strictive TBT measures. On average, a 10% increase in incidence of STCs on input

share of output for an industry results in a loss of productivity of around 5.5% for

importing firms. The coefficient on concerninputj is -0.12, although it is no longer

significant. This suggests that non importing firms were not significantly affected by

introduction of these measures.

The coefficient on importerijt is negative and significant. As the specifications

use firm fixed effects, coefficients on all firm level indicators only capture variation

over time. However, once the importer dummy is replaced by import shares, the

coefficient is no longer significant.28 As expected, the coefficients on tariff outputj

and ERPj,t−1 is negative and significant. However the coefficient on tariff inputj is

not precisely estimated, which could be due to high correlation between output and

input tariffs in the sample.29

Melitz (2003) shows that intra-industry reallocation of resources from exit of the

least productive firms leads to increase in aggregate industry productivity. This

27I exclude firms which only import final goods.28Results are available on request.29Coefficient on tariff input

j is negative and highly significant once output tariff is excluded.

16

selection channel may not materialize in the presence of restrictive TBT measures

which reduce competition from foreign firms. To see if the results are not driven by

this channel, I follow Topalova and Khandelwal (2011) in estimating equation (3)

on a balanced panel of firms in column 7.30The coefficient on the interaction term

is very similar to that in columns 5 and 6 suggesting that the results are not driven

by the lack of intra-industry reallocation of resources in industries with incidence of

STCs.

The results suggest that an important channel by which restrictive TBT measures

affect firm level productivity is through reduced access to possibly high quality and

varied imported inputs. Following the trade liberalization of the 1990s Indian firms

had adapted their production technology to using imported inputs. Reduced access

to these imported inputs led to productivity losses for these firms. Also the impact

on non importing firms is not significant suggesting that the productivity losses were

driven by the importing channel as hypothesized.

4.2 TBT Measures and Trade Reforms

The Indian government was implementing additional trade reforms like FDI liberal-

ization and delicensing of industries in the sample period. Almost all industries were

liberalized by the early 2000s just as the incidence of restrictive TBT measures start

in the sample. To see if there are differential effects of incidence of these measures

across liberalized versus non liberalized industries, I include industry level measures

of FDI reforms, FDIt and delicensing status, delicenset . FDIt and delicenset data

are from Harrison et al. (2013) and have been extended based on various Govt. of

India publications. FDIt is a dummy variable equal to one if any products within

the industry are liberalized and zero otherwise. delicenset is a dummy variable equal

to one if any products within the industry are delicensed and zero otherwise.

Table 7 reports the results. Column 1 includes delicenset and column 2 also

includes the interaction of delicenset with concerninputj . The coefficient on the in-

30The prowess data is not suitable for studying entry and exit of firms. Exit of firms from thedataset does not necessarily imply their exit from the market.

17

teraction between concerninputj and importerijt remains negative and statistically

significant and is virtually unchanged from that in the baseline specification. The

coefficient on the interaction between delicenset and concerninputj in column 2 is in-

significant suggesting there were no heterogeneous effects based on delicensing status

of industries. The coefficient on delicenset is negative and significant suggesting that

delicensing was associated with decrease in productivity in the sample period. Only

a few industries were still under the licensing regime by 1995 and these were subse-

quently delicensed in the sample period. The negative coefficient on the delicensing

indicator is driven entirely by the effects of delicensing on these few industries.

In column 3 I add FDIt and in column 4 I also add its interaction with concerninputj .

Again the coefficient on the interaction between concerninputj and importerijt remains

virtually unchanged from that in the baseline specification. The coefficient on inter-

action between FDIt and concerninputj is insignificant suggesting that there was no

heterogeneity in impact of restrictive TBT measures based on the FDI liberalization

of industries. The coefficient on the FDIt term is positive and significant implying

increases in productivity for firms in liberalized industries in the sample.

4.3 TBT Measures and Industry Characteristics

In this section I examine the heterogeneity in the impact of restrictive TBT measures

based on industry characteristics. Table 8 reports the results. In columns 1 to 5

I examine the differential effects of these measures on intermediate goods and final

goods industry. Nouroz (2001) classifies industries based on the characteristic of their

produced goods into basic, intermediates and capital goods (intermediate goods), and

consumer durable and consumer non durable goods (final goods). Columns 1, 2 and 3

report estimates of equation (3) for basic, intermediates and capital goods industries

respectively while columns 4 and 5 report results for consumer durables and non

durables respectively. In the intermediate goods industries, importers in basic and

intermediates were most affected by restrictive TBT measures. The coefficient on

the interaction term between concerninputj and importerijt is negative and significant.

The coefficient is negative but not statistically significant for capital goods industries.

18

The coefficient on the interaction term is negative and highly significant for final

goods industry. Consumer non durables in particular show the largest response to

incidence of restrictive TBT measures on their inputs. The results suggest that both

intermediate and final goods industries were affected by incidence of restrictive TBT

measures on their inputs and the effect was larger for final goods industries.

If the productivity losses are driven by reduced access to imported inputs, one

would expect the effects to be more pronounced for importing firms in industries

which intensively use imported inputs. To check this, I construct an indicator vari-

able for industries import intensity at the start of the sample period. Industries

are characterized as import intensive if the average imports to sales ratio is above

the median value for all industries. Firms in industries which imported more inputs

following trade liberalization in the early 1990s should then be more affected by the

incidence of TBT measures on their inputs as these firms are more likely to have ad-

justed their production technology to use imported inputs. Columns 6 and 7 report

the results from estimating equation (3) for high and low import intensity industries.

Indeed I find that firms in import intensive industries suffered higher productivity

losses than low import intensive industries. The effects are almost double in magni-

tude for import intensive industries. This result further increases confidence in the

hypothesized relation between incidence of restrictive TBT measures on inputs and

productivity losses for importers.

4.4 TBT Measures and Firm Characteristics

In this section I examine the differential effects of restrictive TBT measures across

firms of different sizes, ownership categories and exporting status. Firms are classified

as private domestic, government and foreign based on their ownership. Firms are

classified as small, medium or large if their average sales over the sample period is

below median value, greater than the median value excluding the top 2 percentile

and in the top two percentile of the distribution respectively. Firms are classified as

exporters if they have positive exports in that year.

Table 9 reports the results. Columns 1 to 3 report results of each subgroup of

19

firms based on firm size. The results suggest that firms of all sizes were negatively

affected by incidence of restrictive TBT measures on their inputs. The magnitude

of the coefficients do vary indicating that large firms were relatively more affected

by these measures. However the coefficient is negative and significant for all three

categories of firm sizes.

Columns 4 to 6 report results for private domestic, foreign and government owned

firms respectively. Due to few observations for foreign owned firms columns 4 to 6

include industry and year fixed effects to conserve degree of freedom.31. The results

suggest that incidence of restrictive TBT measures on intermediate inputs had a

negative and significant impact on private domestic firms. The coefficients on the

interaction term is not significant for foreign and government owned firms. This

finding could be driven by the fact that foreign owned firms already have access to

better technology inputs through licensing and are better able to adjust to restricted

access to imported intermediate inputs. Columns 7 and 8 report results for exporting

and non exporting firms respectively. The coefficient on the interaction term is very

similar for both groups of firms suggesting that importers who also exported did

not have any additional losses in productivity compared to importers who did not

export.

4.5 Financial Crisis

This section addresses the concern that the main results could be driven by the effects

of the financial crisis, which started in late 2007, leading to currency depreciation and

huge reduction in trade flows. Table 10 reports the results. Columns 1 and 2 report

results from estimating equation (3) using data upto 2007 excluding the financial

crisis period. The coefficient on concerninputj is negative and statistically significant

and the magnitude is very similar to that reported in Table 6. Column 2 includes

the interaction of concerninputj and importerijt. The coefficient on the interaction

term is again negative and significant and remains virtually unchanged. As before

31The regressors are jointly not significant from zero in presence of firm and industry-year fixedeffects

20

the effect on non importing firms is not significant.

Columns 3 to 6 report results for the full sample. I include an indicator variable,

Crisist, which equals 1 for the years 2008 to 2010 and 0 otherwise.32 Column 3 in-

cludes the interaction term between concerninputj and Crisist. The coefficient on the

interaction term is not significant suggesting that there was no significant difference

in the effects of restrictive TBT measures during the crisis. In column 4 I interact

the crisis dummy with the interactive concerninputj importerijt variable to see if there

were differential effects on importers due to the financial crisis. The coefficient on

the interaction term is positive and significant suggesting that for importers the ef-

fects of restrictive TBT measures on inputs were offset to some extent during the

crisis. This could be driven by the increased prices of inputs driven by depreciation

of the currency during the financial crisis However, the main results remain qualita-

tively unchanged with the coefficient on concerninputj importerijt being negative and

significant and slightly lower in magnitude to the earlier estimates.

Columns 5 and 6 include the interaction of trade weighted real exchange rates,

REERt, with the importing and exporting status of firms to control for the effect

of currency depreciation on productivity of importers and exporters.33 The coef-

ficients on the interaction terms are negative and significant.34 The coefficient on

concerninputj importerijt is lower in magnitude but remains negative and significant.

This suggests that the earlier estimates were partially driven by the effects of ex-

change rate movements on productivity of importers. However even after controlling

for exchange rates, the results suggest that productivity of importers was negatively

affected by introduction of restrictive TBT measures. On average, a 10% increase

in incidence of restrictive TBT measures on input share of output for an industry

resulted in a productivity loss of around 4.9%. In column 6, I include the exchange

rates and the interactive crisis variables and the coefficient on the interaction between

32The effects of the financial crisis were transmitted to developing countries with a slight lag.Also trade volumes only recovered to their pre-crisis level by 2010.

33 The trade weighted real exchange rates are from the Reserve bank of India publications andare computed using trade volumes with 36 countries.

34See citetAmiti for a discussion of effects of exchange rate movements on productivity for im-porters and exporters.

21

concerninputj importerijt and crisis dummy drops in magnitude and is only significant

at the 10% level. The coefficient on the concerninputj importerijt term is still nega-

tive and significant suggesting that there were productivity losses for importers even

after controlling for the effects of financial crisis and exchange rate movements on

productivity.

4.6 Robustness Checks

This section presents the results of a series of robustness checks performed to check

the validity of the main result. Table 11 reports the results from the baseline specifi-

cation using alternative productivity measures. Columns 1 and 2 report results from

using TFP calculated using OLS estimation assuming a Cobb Douglas production

function. The coefficient on concerninputj is negative and significant in column 1 al-

though the magnitude is lower compared to that reported in Table 6. In column 2

the coefficient on the interaction term, concerninputj importerijt is again negative and

significant with lower values than that in Table 6. In columns 3 and 4, I use a mea-

sure of labor productivity as the dependent variable. Prowess data does not report

the number of employees for all firms and hence I use the real output divided by the

value of compensation to the workers as a measure of labor productivity. The results

are similar to that reported in Table 6 although the magnitude of the coefficient on

the interaction term, concerninputj importerijt is lower. In columns 5 and 6, I estimate

the TFP of firms with the Levinsohn-Petrin methodology without foreign firms. This

corrects for any bias that may arise in the production function estimations if foreign

firms have a different production technology than domestic firms. Again the results

are qualitatively similar to that reported in Table 6.

The identifying assumption for recovering the coefficient estimates of capital and

raw materials in the Levinsohn-Petrin method is that productivity follows a Markov

process and capital adjusts to productivity with a lag. Thus, not including the lagged

value of productivity in estimating equation (3) is inconsistent with the identifying as-

sumptions of the Levinsohn-Petrin method. I estimate equation (3) including lagged

values of the TFP as an independent variable using the Arellano and Bond (1991)

22

GMM technique for dynamic panels with orthogonal deviations to preserve sample

size.35 Table 12 reports the results. Odd and even numbered columns instrument for

the lagged TFP with one and two lags respectively. The related specification tests

are also reported. The results are qualitatively similar to that reported in Table 6

suggesting that the main results are not due to serial correlation in productivity of

firms.

The use of industry level price deflators mean that the coefficients on concerninputj

and concerninputj importer may be capturing the response of price-cost mark-ups in

addition to changes in technical efficiency. To see if the effects are not entirely driven

by changes in price-cost markups, I add a Herfindahl concentration index, HHI , com-

puted as the sum of squared market shares in each 4 digit NIC 2004 industry. Firms

in highly concentrated industries may be able to charge higher mark-ups. In column

1 of Table 13, the coefficient on HHI is not significant suggesting that there are no

significant differences in average productivity of firms in high and low concentrated

industries. Column 2 interacts HHIt with the interactive concerninputj importer term

to check if the productivity effects of restrictive TBT measures on inputs are different

for firms in high and low concentration industries. The coefficient on the interaction

term is not significant suggesting that there is no heterogeneity in the impact of

these measures across high and low concentration industries. In column 3 I inter-

act a firm’s market share with concerninputj importer to see if there was differential

impact on productivity based on a firm’s market share. Again the interaction term

is not significant. These results suggest that price-cost markups are not driving the

main results and changes in technical efficiency of firms is also being captured by the

estimates.

As the hypothesized relation between restrictive TBT measures and productivity

works through import of inputs, it is expected that the effects should be driven by

imports of raw material and capital goods. A unique feature of the prowess dataset is

that it provides information on the type of goods imported (See section). I explore the

importing channels through which restrictive TBT measures affects firm productivity

in columns 4 to 7. The importer dummy is replaced in equation (3) by an indicator

35See Arellano and Bond (1991) and Arellano and Bover (1995) for details.

23

variable equal to 1 if the firm imports that type of good (raw materials, capital,

spares or final goods) in that year. The results suggest that indeed the importers

of raw materials and capital goods were the worst affected by introduction of these

measures. The coefficients on the interaction terms are similar for raw materials and

capital goods and are negative and highly significant. I also find that importers of

spare parts also suffered productivity losses although the magnitude of the coefficient

is almost halved from that on importers of raw materials and capital goods. Finally

the dataset also has firms which import final goods. As per the hypothesized channel,

the effects of restrictive TBT measures on inputs on productivity should be the same

for importers of final goods as non importers. This then acts as a placebo test for my

hypothesis. The coefficient on the interaction term in column 7is indeed insignificant

suggesting there are no additional productivity effects on importers of final goods

as compared to non importers. The test also helps address a less plausible concern

that the effects are driven by some other firm level characteristic which differs for

importers and non importers. If that was the case, the results should be qualitatively

similar for all importers irrespective of the type of good imported. The above test

suggests that the above concern is not driving the main results in this sample.

4.7 Instrumental Variable Estimations

In the test for endogeneity of tariffs, concerninputj and concernoutput

j earlier there is no

evidence that concerninputj is endogenous while tariffs and concernoutput

j are possibly

endogenous (see section 3.3). The results from the above test combined with the

fact that I use firm and industry-year fixed effects in almost all specifications makes

it less plausible that there is a serious endogeneity issue with respect to incidence

of restrictive TBT measures on inputs. Nevertheless, to address concerns about

endogeneity of restrictive TBT measures, I instrument for concernoutputj , concerninput

j

and concerninputj importer and the results are presented in Table 14.

Assuming that similar industries within a broader industry group are likely to

have similar incidence of restrictive TBT measures, I construct the instrumental

variables for concernoutputj and concerninput

j (Fontagne et al., 2015). The intuition

24

behind this is that industry and product characteristics determine whether industries

are regulated by a TBT measure. For example polluting industries are more likely to

be covered by a TBT measure with environmental concerns as its objective. Similarly

if the objective is consumer safety or health certain products would be more likely

to be covered by such TBT measures. Finally, similar industries within a broader

industry group may share these characteristics which links the incidence of TBT

measures on these industries. concernoutputj is instrumented by a dummy variable

equal to 1 if there is incidence of restrictive TBT measure on any 4 digit NIC 2004

industry within the broader 3 digit industry to which j belongs (excluding industry

j). The instrumental variable for concerninputj is constructed as follows. First, for

each distinct 3 digit NIC 2004 industry mapped to a particular IO sector s, I create

an indicator variable equal to 1 if there is incidence of TBT measure on that 3 digit

industry outside of the IO sector s. The incidence of TBT measure on the IO sector

s is equal to 1 if the sum of the indicator variable for all distinct 3 digit NIC 2004

industries mapped to s is greater than 0. This variable then replaces concernoutputs,t−1 in

equation (2) to arrive at the instrumental variable for concerninputj . Following Amiti

and Konings (2007) I instrument for importer using a dummy variable equal to 1 if

the firm imported any of its inputs at the start of the sample.

The instruments provide a good fit in the first stage for all specifications. Columns

1 and 2 instrument for concernoutputj , columns 3 and 4 instrument for concerninput

j ,

column 5 instruments for both concernoutputj and concerninput

j and finally column 6 in-

struments for concernoutputj ,concerninput

j and the interaction term concerninputj importer.

The coefficient on concernoutputj is not significant suggesting there is no impact of in-

cidence of restrictive TBT measures on an industry on the productivity of firms. The

coefficient on concerninputj in columns 3,4 and 5 is similar in magnitude to that re-

ported in Table 6. The coefficient on the interaction term in column 6 is negative and

significant although the magnitude is lower than that reported in Table 6. Thus the

key results are robust to instrumental variable estimation and the main result that

restrictive TBT measures on inputs negatively affects the productivity of importing

firms remains valid.

25

5 Conclusions

While the productivity effects of trade liberalization episodes in developing countries

have been extensively documented in the literature, we know much less about the

effects of post liberalization industrial policy of governments in these countries. I fill

this gap in the literature by examining how reduced access to intermediate inputs

from the introduction of restrictive TBT measures on intermediate inputs negatively

affects firm level productivity. Further I am also able to isolate the effects on import-

ing firms from non importing firms. I show that there is significant productivity loss

for importers from reduced access to imported intermediate inputs. The estimates

suggest that on average, a 10% increase in incidence of restrictive TBT measures

on input share of output for an industry results in a loss of productivity of around

5.5% for firms that import their inputs. I also find that incidence of these measures

on an industry does not have a significant effect on the productivity of firms. The

results are robust to a series of robustness checks and instrumental variable estima-

tions. I also document heterogeneity in the impacts of these measures across firm

and industry characteristics.

The results suggest that regulatory measures aimed at addressing market failures

in the domestic market can have unintended consequences for firm performance in

developing countries if they negatively affect the import flows of intermediate inputs.

Using a dataset that identifies the regulatory measures that are most restrictive to

trade flows, I provide preliminary evidence that Indian firms had indeed adjusted

their production technology to use more of imported inputs post liberalization. The

main policy implication from the findings in this paper is that regulatory measures

should be designed such that the import of intermediate inputs are not affected.

While the results establish that the link between restrictive TBT measures and

firm level productivity works through the import of intermediate inputs, the data

does not allow further examination of the exact mechanisms by which firm level

productivity was affected. Also the data is not suited to disentangle the direct and

indirect effects on non importing firms. I leave this task for future research.

26

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020

040

060

080

010

00

1995 2000 2005 2010Year

products_total products_India

Figure 1: Products Covered by TBT Concerns

30

0

20

40

60

80

1995 2000 2005 2010Year

average input_concern (%) average output_concern (%)output_tariff input_tariff

Figure 2: TBT Concerns and Tariffs

31

0

20

40

60

80

100

1995 2000 2005 2010Year

average input_concern (%) average output_concern (%)delicense fdi

Figure 3: TBT Concerns and Trade Reforms

32

.5

.6

.7

.8

.9

2000 2005 2010Year

concern (%) concern_intermediates (%)concern_finalgoods (%)

Figure 4: TBT Concerns for Intermediate and Final Goods

33

Table 1: Yearly Incidence of STCs

Year All Countries India

concerns products concerns products

(1) (2) (3) (4)

1995-2000 52 578 0 0

2001 15 317 2 125

2002 20 436 2 171

2003 15 471 0 0

2004 14 29 1 14

2005 12 337 0 0

2006 24 459 2 7

2007 27 329 4 142

2008 32 333 0 0

2009 46 363 3 193

2010 29 683 1 6

2011 31 514 3 237

Source: Author’s calculation based on STC dataset.

Table 2: Objectives of STCs

Objectives Concerns

Human Health and Safety 12Consumer Safety or protection 10Environment 4Quality 2

Source: Author’s calculation based on STC dataset.

34

Table 3: Issues raised in STCs

Objectives Concerns

Unnecessary barrier to trade 13Transparency 11Clarification 9Standards 5Discrimination 4Legitimacy and Rationale 4

Source: Author’s calculation based on STC dataset.

Table 4: Endogeneity of Trade Policy

1995-2011 1995-2001 2002-2011

(1) (2) (3)

A:Outputconcernt+1IndustryProductivityt 0.0755∗ 0.0986∗∗ 0.140∗∗

(0.0433) (0.0475) (0.0580)

B:Inputconcernt+1IndustryProductivityt -0.000253 0.00861 0.00437

(0.0129) (0.0165) (0.00817)

C:Inputtarifft+1IndustryProductivityt -0.917 0.305 -1.824∗∗∗

(0.597) (0.368) (0.670)

D:Outputtarifft+1IndustryProductivityt -0.904 0.735 -1.333∗

(0.650) (0.755) (0.702)

Industry FE Yes Yes YesYear FE Yes Yes YesObservations 1614 637 976

Significance ∗ 10%, ∗∗ 5%, ∗∗∗ 1%

35

Table 5: Basic Results

Full Sample Balanced

(1) (2) (3) (4) (5) (6) (7)

concernoutputj,t−1 0.00827 0.0116 0.0114 0.0149 0.00665 0.0102 -0.0551∗∗

(0.0185) (0.0185) (0.0185) (0.0185) (0.0182) (0.0183) (0.0269)

concerninputj,t−1 -0.449∗∗∗ -0.430∗∗∗ -0.212∗ -0.193∗ -0.259∗

(0.106) (0.107) (0.109) (0.110) (0.157)

concerninputj,t−1 × importerijt -0.388∗∗∗ -0.388∗∗∗ -0.317∗∗∗

(0.0509) (0.0509) (0.0982)

tariff inputj,t−1 0.171 0.151 0.136

(0.134) (0.133) (0.133)

tariffoutputj,t−1 -0.396∗∗∗ -0.407∗∗∗ -0.392∗∗∗

(0.107) (0.107) (0.107)

ERPt−1 -0.178∗∗∗ -0.176∗∗∗ -0.170∗∗∗ -0.213∗∗

(0.0605) (0.0606) (0.0607) (0.0886)

importerijt -0.0175 -0.0176 -0.0496(0.0149) (0.0149) (0.0308)

Firm FE Yes Yes Yes Yes Yes Yes YesIndustry(2 digit) × Year FE Yes Yes Yes Yes Yes Yes YesObservations 56688 56688 56688 56688 56688 56688 15344Adjusted R2 0.962 0.962 0.962 0.962 0.962 0.962 0.982

All regressions include firm age and age squared as controls. Robust standard errors are in parentheses and areclustered at the firm level. Significance ∗ 10%, ∗∗ 5%, ∗∗∗ 1%

36

Table 6: TBT Measures and Trade Liberalization

Delicense FDI Reforms

(1) (2) (3) (4)

concernoutputj,t−1 0.00937 0.00867 0.00861 0.00773

(0.0183) (0.0183) (0.0183) (0.0183)

ERPt−1 -0.156∗∗∗ -0.139∗∗ -0.184∗∗∗ -0.166∗∗∗

(0.0604) (0.0603) (0.0604) (0.0603)

concerninputj,t−1 -0.200∗ 0.761 -0.181∗ 0.873

(0.110) (0.544) (0.110) (0.557)

concerninputj,t−1 × importerijt -0.389∗∗∗ -0.391∗∗∗ -0.383∗∗∗ -0.385∗∗∗

(0.0508) (0.0508) (0.0509) (0.0509)

delicenset -0.271∗∗ -0.264∗∗

(0.111) (0.111)

concerninputj,t−1 × delicenset -0.967∗

(0.535)

FDIt 0.0429∗ 0.0478∗

(0.0253) (0.0252)

concerninputj,t−1 × FDIt -1.060∗

(0.549)

importerijt -0.0173 -0.0168 -0.0203 -0.0198(0.0149) (0.0149) (0.0149) (0.0149)

Firm FE Yes Yes Yes YesIndustry(2 digit) × Year FE Yes Yes Yes YesObservations 56688 56688 56125 56125Adjusted R2 0.962 0.962 0.957 0.957

Table reports regressions of firm level productivity on lagged TBT measures onfinal goods and intermediate goods, effective rate of protection and measuresof delicensing and FDI reforms for industries. FDIt and delicenset data arefrom Harrison et al. (2013) and have been extended based on various Govt.of India publications. FDIt is a dummy variable equal to one if any productswithin the industry are liberalized and zero otherwise. delicenset is a dummyvariable equal to one if any products within the industry are delicensed andzero otherwise. Robust standard errors are in parentheses and are clustered atthe firm level. Significance ∗ 10%, ∗∗ 5%, ∗∗∗ 1%

37

Tab

le7:

TB

TM

easu

res

and

Indust

ryC

har

acte

rist

ics

Inte

rmed

iate

Good

sF

inal

Good

sIm

port

Inte

nsi

ty

Basi

cIn

term

edia

tes

Cap

ital

Good

sD

ura

ble

sN

on

-du

rab

les

Hig

hL

ow

(1)

(2)

(3)

(4)

(5)

(6)

(7)

concernoutp

ut

j,t−

1-0

.0494

0.2

47∗∗

∗0.0

102

0.0

697

-0.0

194

0.0

0742

-0.0

00583

(0.0

513)

(0.0

584)

(0.0

297)

(0.0

691)

(0.0

382)

(0.0

303)

(0.0

295)

ER

Pt−

1-0

.0240

-0.6

49

-0.5

07∗∗

0.2

62

-0.4

96∗∗

∗-0

.114

-0.4

53∗∗

∗

(0.0

714)

(0.4

30)

(0.2

17)

(0.5

85)

(0.1

65)

(0.0

716)

(0.1

61)

concernin

put

j,t−

1-0

.369∗

-1.6

87∗

0.1

92

-2.4

67∗∗

0.0

985

-0.1

08

0.0

0918

(0.2

14)

(0.8

99)

(0.3

94)

(1.2

17)

(0.2

40)

(0.1

92)

(0.3

75)

concernin

put

j,t−

1×

imp

ort

erij

t-0

.166∗∗

-0.4

95∗∗

∗-0

.202

-0.2

35∗∗

-0.8

02∗∗

∗-0

.447∗∗

∗-0

.127

(0.0

805)

(0.1

27)

(0.1

60)

(0.1

10)

(0.1

15)

(0.0

600)

(0.1

08)

imp

ort

erij

t-0

.0160

-0.0

367

0.0

192

-0.0

0866

0.0

00800

-0.0

243

-0.0

316

(0.0

293)

(0.0

448)

(0.0

479)

(0.0

389)

(0.0

241)

(0.0

194)

(0.0

242)

HH

I t0.3

54

-0.9

89∗∗

∗0.1

13

0.1

64

-0.1

98

-0.1

71

0.1

91

(0.2

93)

(0.3

27)

(0.2

17)

(0.2

24)

(0.1

49)

(0.1

69)

(0.1

26)

FD

I t0.1

33

0.0

803

-0.0

733

-0.1

67

0.0

646∗∗

0.0

945∗∗

-0.0

371

(0.1

25)

(0.0

835)

(0.0

837)

(0.1

42)

(0.0

309)

(0.0

419)

(0.0

446)

del

icen

set

00.0

802

00

-0.2

55∗∗

0-0

.234∗∗

(0.2

14)

(0.1

14)

(0.1

14)

Fir

mF

EY

esY

esY

esY

esY

esY

esY

esIn

du

stry

(2d

igit

)×

Yea

rF

EY

esY

esY

esY

esY

esY

esY

esO

bse

rvati

on

s11407

7284

6532

8629

22273

36941

16506

Ad

just

edR

20.9

59

0.9

58

0.9

75

0.9

53

0.9

44

0.9

49

0.9

64

Tab

lere

port

sre

gre

ssio

ns

of

firm

level

pro

du

ctiv

ity

on

lagged

TB

Tm

easu

res

on

fin

al

good

san

din

term

edia

tegood

sby

ind

ust

rych

ara

cter

isti

cs.

All

regre

ssio

ns

incl

ud

efi

rmage

an

dage

squ

are

das

contr

ols

.R

ob

ust

stan

dard

erro

rsare

inp

are

nth

eses

.S

tan

dard

erro

rsare

clu

ster

edat

the

firm

level

.T

he

class

ifica

tion

of

ind

ust

ries

into

Inte

rmed

iate

good

san

dF

inal

good

sis

base

don

Nou

roz

(2001)

class

ifica

tion

of

good

sin

toB

asi

c,In

term

edia

tes

an

dC

ap

ital

(Inte

rmed

iate

),an

dC

on

sum

erD

ura

ble

san

dC

on

sum

ern

on

-d

ura

ble

good

s(F

inal

Good

s).

Th

ein

du

stri

esare

class

ified

as

hig

him

port

inte

nsi

ve

ifth

esh

are

of

ind

ust

ryim

port

sin

tota

lin

du

stry

sale

sin

1995

was

ab

ove

the

med

ian

valu

e,el

seas

low

imp

ort

inte

nsi

ve.

Sig

nifi

can

ce∗

10%

,∗∗

5%

,∗∗

∗1%

38

Tab

le8:

TB

TM

easu

res

and

Fir

mC

har

acte

rist

ics

Fir

mO

wn

ersh

ipE

xp

ort

ing

Sta

tus

Dom

esti

cF

ore

ign

Gover

nm

ent

Exp

ort

ers

Non

Exp

ort

ers

(1)

(2)

(3)

(4)

(5)

concernoutp

ut

j,t−

10.0

415∗

0.1

58

0.1

01

0.0

140

0.0

216

(0.0

222)

(0.1

46)

(0.0

926)

(0.0

209)

(0.0

321)

ER

Pt−

1-0

.0981

0.1

12

-0.1

30

-0.0

671

-0.2

13∗∗

(0.0

682)

(0.6

48)

(0.2

41)

(0.0

633)

(0.0

964)

concernin

put

j,t−

10.0

301

-0.1

38

-1.2

92∗

-0.3

58∗∗

-0.0

425

(0.1

19)

(2.0

07)

(0.6

71)

(0.1

40)

(0.1

62)

concernin

put

j,t−

1×

imp

ort

erij

t-0

.629∗∗

∗-0

.00929

0.3

29

-0.2

34∗∗

∗-0

.198∗∗

∗

(0.0

880)

(1.8

63)

(0.3

79)

(0.0

764)

(0.0

619)

imp

ort

erij

t-0

.440∗∗

∗-0

.424∗∗

-0.6

86∗∗

∗-0

.0125

-0.0

412∗∗

(0.0

252)

(0.1

92)

(0.1

62)

(0.0

239)

(0.0

175)

FD

I t0.0

491

0.2

24∗∗

0.2

13

0.0

849∗∗

∗0.0

0292

(0.0

302)

(0.1

12)

(0.1

99)

(0.0

302)

(0.0

421)

del

icen

set

-0.3

87∗∗

∗-0

.759∗∗

-0.3

69∗∗

-0.2

59∗∗

(0.1

03)

(0.3

22)

(0.1

48)

(0.1

26)

Fir

mF

EN

oN

oN

oY

esY

esIn

du

stry

(2d

igit

)×

Yea

rF

EN

oN

oN

oY

esY

esIn

du

stry

FE

Yes

Yes

Yes

No

No

Yea

rF

EY

esY

esY

esN

oN

oO

bse

rvati

on

s48830

859

1639

27321

24953

Ad

just

edR

20.7

24

0.8

63

0.7

57

0.9

72

0.9

13

Tab

lere

port

sre

gre

ssio

ns

of

firm

level

pro

du

ctiv

ity

on

lagged

TB

Tm

easu

res

on

fin

al

good

san

din

term

edia

tegood

sby

firm

chara

cter

isti

cs.

All

regre

ssio

ns

incl

ud

efi

rmage

an

dage

squ

are

das

contr

ols

.R

ob

ust

stan

dard

erro

rsare

inp

are

nth

eses

an

dare

clu

ster

edat

the

firm

level

.F

irm

sare

class

ified

as

dom

esti

c,fo

reig

nor

gover

nm

ent

base

don

ow

ner

ship

.S

ign

ifica

nce

∗10%

,∗∗

5%

,∗∗

∗

1%

39

Table 9: TBT Measures and Financial Crisis

Pre-Crisis Full Sample1995-2007 1995-2011

(1) (2) (3) (4) (5) (6)

concernoutputj,t−1 0.0398∗∗ 0.0335∗ 0.00753 0.00838 0.00830 0.00816

(0.0179) (0.0178) (0.0183) (0.0182) (0.0182) (0.0182)

ERPt−1 -0.272∗∗∗ -0.265∗∗∗ -0.172∗∗∗ -0.174∗∗∗ -0.171∗∗∗ -0.170∗∗∗

(0.0574) (0.0574) (0.0602) (0.0602) (0.0601) (0.0601)

concerninputj,t−1 -0.380∗∗∗ -0.128 -0.177∗ -0.216∗∗ -0.237∗∗ -0.243∗∗

(0.0999) (0.105) (0.106) (0.106) (0.110) (0.107)

concerninputj,t−1 × importerijt -0.387∗∗∗ -0.384∗∗∗ -0.340∗∗∗ -0.304∗∗∗ -0.281∗∗∗

(0.0526) (0.0509) (0.0497) (0.0501) (0.0498)

concerninputj,t−1 × Crisist -0.0370 -0.0993 -0.0824

(0.0690) (0.0894) (0.0894)

concerninputj,t−1 × importerijt × 0.111 0.0844

Crisist (0.0901) (0.0896)

importerijt × REERt -0.192 -0.124(0.126) (0.121)

exporterijt × REERt -0.707∗∗∗ -0.715∗∗∗

(0.121) (0.121)

importerijt × Crisist -0.0895∗∗∗ -0.0764∗∗∗

(0.0298) (0.0291)

exporterijt -0.0771∗∗∗ 0.643∗∗∗ 0.651∗∗∗

(0.0105) (0.124) (0.124)

importerijt -0.0932∗∗∗ -0.0324∗∗ -0.0202 -0.000720 0.177 0.116(0.0122) (0.0137) (0.0149) (0.0148) (0.128) (0.124)

FDIt 0.0700∗∗∗ 0.0677∗∗∗ 0.0503∗∗ 0.0475∗ 0.0467∗ 0.0464∗

(0.0232) (0.0231) (0.0251) (0.0250) (0.0250) (0.0250)

delicenset -0.201∗ -0.206∗ -0.283∗∗ -0.278∗∗ -0.284∗∗ -0.287∗∗

(0.109) (0.110) (0.112) (0.112) (0.112) (0.112)

Firm FE Yes Yes Yes Yes Yes YesIndustry(2 digit)* Year FE Yes Yes Yes Yes Yes YesObservations 38556 38556 56125 56125 56125 56125Adjusted R2 0.961 0.962 0.957 0.957 0.957 0.957

All regressions include firm age and age squared as controls. Robust standard errors are in parentheses andare clustered at the firm level. Column 1 reports results excluding years 2008-2010. Crisis is a dummy variableequal to 1 for years 2008-2010 and 0 otherwise. Significance ∗ 10%, ∗∗ 5%, ∗∗∗ 1%

40

Table 10: Alternative Productivity Measures

Log(real output per No ForeignOLS unit compensation) firms

(1) (2) (3) (4) (5) (6)

concernoutputj,t−1 0.0442∗∗∗ 0.0425∗∗∗ 0.0399∗ 0.0361 0.0120 0.00686

(0.0129) (0.0129) (0.0219) (0.0220) (0.0186) (0.0184)

ERPt−1 -0.215∗∗∗ -0.213∗∗∗ -0.342∗∗∗ -0.337∗∗∗ -0.200∗∗∗ -0.193∗∗∗

(0.0524) (0.0523) (0.0752) (0.0749) (0.0592) (0.0591)

concerninputj,t−1 -0.0728 0.0104 -0.310∗∗ -0.120 -0.380∗∗∗ -0.136

(0.0708) (0.0759) (0.132) (0.144) (0.111) (0.116)

concerninputj,t−1 × importerijt -0.127∗∗∗ -0.290∗∗∗ -0.375∗∗∗

(0.0360) (0.0591) (0.0519)

importerijt 0.0172∗∗ 0.0434∗∗∗ 0.139∗∗∗ 0.199∗∗∗ -0.0962∗∗∗ -0.0186

(0.00756) (0.0109) (0.0134) (0.0181) (0.0113) (0.0154)

HHIt 0.0565 0.0547 -0.176 -0.180 0.218∗∗ 0.214∗∗

(0.0635) (0.0636) (0.111) (0.111) (0.0944) (0.0949)

delicenset -0.0101 -0.0112 0.0408 0.0381 -0.275∗∗ -0.278∗∗

(0.0866) (0.0867) (0.0932) (0.0937) (0.113) (0.113)

FDIt 0.0635∗∗∗ 0.0626∗∗∗ 0.0631∗∗ 0.0609∗ 0.0730∗∗∗ 0.0700∗∗∗

(0.0203) (0.0203) (0.0320) (0.0319) (0.0266) (0.0265)

Firm FE Yes Yes Yes Yes Yes YesIndustry(2 digit) × Year FE Yes Yes Yes Yes Yes YesObservations 56125 56125 56125 56125 55276 55276Adjusted R2 0.736 0.736 0.846 0.846 0.956 0.957

All regressions include firm age and age squared as controls. Robust standard errors are in parentheses and are clustered atthe firm level. Significance ∗ 10%, ∗∗ 5%, ∗∗∗ 1%

41

Table 11: Arellano Bond Estimations

AB1 AB2 AB1 AB2 AB1 AB2

(1) (2) (3) (4) (5) (6)

TFPt−1 -0.154 0.347∗∗∗ 0.233 0.359∗∗∗ 0.192 0.354∗∗∗

(0.350) (0.0568) (0.215) (0.0572) (0.225) (0.0568)

concernoutputj,t−1 0.00715 0.0120 0.0128 0.0134 0.00909 0.0104

(0.0192) (0.0117) (0.0133) (0.0115) (0.0138) (0.0116)

concerninputj,t−1 -0.508∗∗ -0.239∗∗∗ -0.303∗∗ -0.236∗∗∗ -0.134 -0.0829

(0.222) (0.0745) (0.139) (0.0733) (0.112) (0.0720)

concerninputj,t−1 × importerijt -0.275∗∗∗ -0.225∗∗∗

(0.0786) (0.0379)

ERPt−1 -0.140∗∗∗ -0.136∗∗∗ -0.138∗∗∗ -0.133∗∗∗

(0.0457) (0.0410) (0.0474) (0.0411)

tariff inputj,t−1 0.0736 0.191∗∗

(0.161) (0.0907)

tariffoutputj,t−1 -0.361∗∗∗ -0.287∗∗∗

(0.122) (0.0734)

importerijt -0.0693∗∗∗ -0.0616∗∗∗ -0.0128 -0.0137(0.0154) (0.00863) (0.0125) (0.0109)

Firm FE Yes Yes Yes Yes Yes YesIndustry(2 digit)* Year FE Yes Yes Yes Yes Yes YesAR(1) -0.0196 -7.939 -1.735 -8.148 -1.491 -8.126AR(2) -1.392 -0.112 -0.514 -0.0452 -0.679 -0.123Observations 40032 40032 40032 40032 40032 40032

Table reports the Arellano Bond estimations. Odd and even numbered columns instrument the laggedvalue of productivity by one and two lags respectively.All regressions include firm age and age squaredas controls. Robust standard errors are in parentheses and are clustered at the firm level. Significance ∗

10%, ∗∗ 5%, ∗∗∗ 1%

42

Tab

le12

:R

obust

nes

sC

hec

ks

Mark

-up

Imp

ort

Ch

an

nel

Pla

ceb

o

Ind

ust

ryco

nce

ntr

ati

on

Mark

etsh

are

Raw

mate

rial

Cap

ital

good

sS

pare

sF

inal

good

s

(1)

(2)

(3)

(4)

(5)

(6)

(7)

concernoutp

ut

j,t−

10.0

0907

0.0

139

0.0

102

0.0

113

0.0

0892

0.0

0933

0.0

140

(0.0

182)

(0.0

181)

(0.0

182)

(0.0

182)

(0.0

180)

(0.0

182)

(0.0

182)

ER

Pt−

1-0

.181∗∗

∗-0

.184∗∗

∗-0

.179∗∗

∗-0

.184∗∗

∗-0

.179∗∗

∗-0

.184∗∗

∗-0

.185∗∗

∗

(0.0

602)

(0.0

601)

(0.0

602)

(0.0

602)

(0.0

599)

(0.0

602)

(0.0

602)

concernin

put

j,t−

1-0

.220∗∗

-0.2

10∗

-0.2

21∗

-0.2

56∗∗

-0.3

10∗∗

∗-0

.362∗∗

∗-0

.411∗∗

∗

(0.1

12)

(0.1

10)

(0.1

13)

(0.1

10)

(0.1

06)

(0.1

08)

(0.1

07)

concernin

put

j,t−

1×

imp

ort

erij

t-0

.303∗∗

∗-0

.327∗∗

∗-0

.317∗∗

∗

(0.0

501)

(0.0

531)

(0.0

523)

concernin

put

j,t−

1×

imp

ort

erij

t×

hig

h0.1

72∗

con

centr

ati

ont

(0.0

909)

concernin

put

j,t−

1×

imp

ort

erij

t×

0.4

35

mark

etsh

are

t(0

.467)

concernin

put

j,t−

1×

Imp

ort

erR

Mt

-0.2

88∗∗

∗

(0.0

472)

concernin

put

j,t−

1×

Imp

ort

erC

Gt

-0.2

61∗∗

∗

(0.0

396)

concernin

put

j,t−

1×

Imp

ort

erS

St

-0.1

39∗∗

∗

(0.0

436)

concernin

put

j,t−

1×

Imp

ort

erF

Gt

-0.0

658

(0.0

706)

imp

ort

erij

t×

RE

ER

t-0

.193

-0.2

17∗

-0.1

95

(0.1

26)

(0.1

26)

(0.1

26)

Exp

ort

ert×

RE

ER

t-0

.706∗∗

∗-0

.692∗∗

∗-0

.709∗∗

∗-0

.775∗∗

∗-0

.599∗∗

∗-0

.880∗∗

∗-0

.964∗∗

∗

(0.1

21)

(0.1

20)

(0.1

21)

(0.1

20)

(0.1

19)

(0.1

20)

(0.1

14)

HH

I t0.1

33

0.0

485

0.1

22

0.1

31

0.1

36

0.1

41

0.1

38

(0.0

921)

(0.0

963)

(0.0

952)

(0.0

922)

(0.0

924)

(0.0

917)

(0.0

916)

imp

ort

erij

t0.1

79

0.1

92

0.1

85

-0.0

453∗∗

∗-0

.0633∗∗

∗-0

.0698∗∗

∗-0

.0802∗∗

∗

(0.1

28)

(0.1

28)

(0.1

29)

(0.0

139)

(0.0

103)

(0.0

102)

(0.0

102)

Exp

ort

ert

0.6

42∗∗

∗0.6

28∗∗

∗0.6

44∗∗

∗0.7

12∗∗

∗0.5

35∗∗

∗0.8

18∗∗

∗0.9

01∗∗

∗

(0.1

24)

(0.1

23)

(0.1

24)

(0.1

23)

(0.1

21)

(0.1

23)

(0.1

16)

Fir

mF

EY

esY

esY

esY

esY

esY

esY

esIn

du

stry

(2d

igit

s)*Y

ear

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Ob

serv

ati

on

s56125

56125

56125

56125

56125

56125

56125

Ad

just

edR

20.9

57

0.9

57

0.9

57

0.9

57

0.9

57

0.9

57

0.9

57

Rob

ust

stan

dard

erro

rsare

inp

are

nth

eses

an

dare

clu

ster

edat

the

firm

level

.Im

port

sare

furt

her

div

ided

into

fou

rca

tegori

es:

fin

al

good

s(F

G),

store

san

dsp

are

s(S

S),

cap

ital

good

s(C

G)

an

dra

wm

ate

rials

(RM

).im

port

erij

tan

dim

port

erij

tare

du

mm

yvari

ab

les

wh

ich

equ

al

to1

ifth

efi

rmim

port

s/ex

port

sin

that

yea

r.S

imilar

du

mm

ies

are

con

stru

cted

for

fou

rca

tegori

esof

imp

ort

s.A

llre

gre

ssio

ns

incl

ud

efi

rmage

an

dage

squ

are

d,

mea

sure

sof

del

icen

sin

gan

dF

DI

refo

rms

as

contr

ols

.A

llre

levant

inte

ract

ion

term

sare

incl

ud

edin

the

regre

ssio

ns

an

dh

ave

bee

nom

itte

dfo

rb

revit

y.C

olu

mn

s(4

)to

(7)

incl

ud

eth

ein

tera

ctio

nb

etw

een

the

resp

ecti

ve

imp

ort

erd

um

my

an

deff

ecti

ve

exch

an

ge

rate

san

dh

ave

bee

nom

itte

dfo

rb

revit

y.S

ign

ifica

nce

∗10%

,∗∗

5%

,∗∗

∗1%

43

Table 13: Instrumental Variable Estimation

(1) (2) (3) (4) (5) (6)

concernoutputj,t−1 -0.515∗∗∗ -0.477∗∗∗ -0.384∗∗ -0.402∗∗

(0.164) (0.157) (0.156) (0.158)

concerninputj,t−1 -0.383∗∗ -0.454∗∗∗ -0.445∗∗ -0.242

(0.172) (0.168) (0.182) (0.197)

concerninputj,t−1 × importerijt -0.325∗∗

(0.147)

ERPt−1 0.172 -0.178∗∗∗ 0.113 0.130(0.139) (0.0614) (0.136) (0.138)

importerijt -0.0978∗∗∗ -0.0992∗∗∗ -0.0989∗∗∗ -0.0321(0.0107) (0.0106) (0.0107) (0.0335)

delicenset -0.321∗∗∗ -0.285∗∗ -0.320∗∗∗ -0.325∗∗∗

(0.113) (0.112) (0.113) (0.114)

FDIt 0.0921∗∗∗ 0.0532∗∗ 0.0758∗∗∗ 0.0740∗∗∗

(0.0288) (0.0253) (0.0286) (0.0285)

HHIt 0.120 0.140 0.0802 0.0720(0.0985) (0.0944) (0.101) (0.102)

Firm FE Yes Yes Yes Yes Yes YesIndustry(2 digit) × Year FE Yes Yes Yes Yes Yes YesFirst Stage F stat [37.85] [39.5] [1105.08] [1421.83] [19.72; [14.27;

841.46] 563.19;305.7]

Observations 56688 56125 56688 56125 56125 56125Adjusted R2 0.960 0.955 0.962 0.957 0.956 0.956

Table reports instrumental variable estimations for TBT measures on final and intermediate goods. Ro-bust standard errors are in parentheses and are clustered at the firm level. Columns 1 and 2 instru-ment for concernoutput

j,t−1 . Columns 3 and 4 instrument for concerninputj,t−1 . Column 5 instruments for both

concernoutputj,t−1 and concerninput

j,t−1 . Column 6 instruments for concernoutputj,t−1 , concerninput

j,t−1 and concerninputj,t−1

× importerijt. Significance ∗ 10%, ∗∗ 5%, ∗∗∗ 1%

44