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:[email protected]).
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