DP2020-13

Technology Diffusion throughForeign Direct Investment:
A Unit-Level Analysis of theI n d i a n M an u f a c t u r i n g I n du s t r y
Azusa FUJIMORIManabu FURUTA
Takahiro SATO

Revised July 31, 2020

Technology Diffusion through Foreign Direct Investment:
A Unit-Level Analysis of the Indian Manufacturing Industry$Azusa Fujimori*
Department of Management, Osaka Seikei University
Manabu Furuta
Department of Economics, Aichi Gakuin University
Takahiro Sato
Research Institute for Economics and Business Administration, Kobe University
Abstract
This study examines technology diffusion resulting from foreign direct investment (FDI) in
the domestic manufacturing sector in India. We employ unit-level panel data (where a unit
refers to an enterprise within the manufacturing sector) from 2000 to 2007, covering all
medium- and large-size manufacturing enterprises in India, obtained from India’s Central
Statistics Office. We attempt to empirically capture evidence of FDI technology spillover
effects through two key mechanisms: horizontal spillover (technology diffusion within thesame industry) and vertical spillover (technology diffusion between foreign firms and theircustomer or suppliers). Vertical spillover effects can be further divided into backward
linkages (technology diffusion from foreign firms to upstream industries), and forward
linkages (technology diffusion from foreign firms to downstream industries). In addition,technology diffusion can be the result of both short- and long-term spillover effects. The
results of the empirical analyses highlight the presence of short- and long-term horizontal
spillover effects, both of which negatively affect the total factor productivity performance ofdomestic manufacturers. Moreover, we find an inverse relationship between the growth of
FDI and total factor productivity in upstream industries in the short term; however, thischanges to a positive relationship in the long term. Furthermore, the results show no evidenceof FDI spillover effects to downstream sectors.
Keywords: Technology Diffusion; Foreign Direct Investment; Total Factor Productivity;
Backward Spillover Effect; Manufacturing Industries; Unit-Level Data
JEL Classification: C81, F21, O53$ We are grateful for the financial support from JSPS KAKENHI Grant Number 17H01652.
* Corresponding Author. E-mail: fujimori-a@osaka-seikei.ac.jp

1

1 Introduction
The innovation and diffusion of technology is crucial for economic growth in developing
countries. A key source of technology diffusion is through spillovers from foreign direct
investment (FDI). FDI spillovers resulted in rapid economic growth in East Asian countries
in the late 20th century, referred to as the East Asian Miracle (World Bank 1993). Several
empirical studies have demonstrated the presence and effects of FDI spillovers by analysing
aggregate industry-level data (e.g., Blomström and Persson 1983; Blomstrom 1986; Kokko
1994, 1996) and firm-level data (e.g., Javorcik 2004; Blalock and Gretler 2008; Javorcik andSpatareanu 2008, 2010; Liu 2008).
However, some empirical studies have not found significant FDI spillover effects, such as
Akinlo’s (2004) aggregate industry-level analysis and firm-level analyses by Haddad andHarrison (1993), Aitken and Harrison (1999), and Bosco (2001).
Considering these divergent findings, we examine FDI spillover effects in the manufacturingsector in India. FDI increased significantly in India after the 1991 Indian economic crisis andsubsequent economic reform, supported by an International Monetary Fund stabilisation
program. India became an attractive FDI destination due to its large labour force, low
production costs, and high technological innovation potential. India attracted substantial FDI
inflow following this period of reform and this inflow of FDI was a key factor in India’s rapid
economic recovery and growth (National Council of Applied Economic Research 2009).
Studies by Kathuria (2001, 2002) focus on the effects of FDI spillovers in Indian
manufacturing industries using firm-level data from the Centre for Monitoring Indian
Economy of companies traded on the National Stock Exchange of India and the Bombay
Stock Exchange. A more comprehensive analysis is offered by Fujimori and Sato (2015), who
present empirical evidence of FDI spillovers in India during the liberalisation period from1995 to 2004 using aggregate industry-level data.
Following the arguments in previous studies, we analyse empirical evidence of technology
spillovers from FDI within the Indian manufacturing industry with a focus on the causal
relationship between the level of FDI and total factor productivity (TFP) of domestic
enterprises in India from 2000 to 2008.
The remainder of this study is presented as follows: The theoretical background of the study
is provided, followed by an analysis of the data, variables, and the empirical model. Next, theresults of the analysis are presented, and the characteristics of spillover effects in Indian
manufacturing industries are discussed. Finally, the results of the analysis are summarised
together with policy-relevant conclusions.

22 Theoretical Background
Endogenous economic growth theory favours the acceptance of FDI; as Barro and Sala-iMartin (2004) show, accepting FDI from developed countries raises the probability oftechnological innovation in host countries. Furthermore, technological innovation positively
affects productivity and economic growth and micro foundation of this relationship are given
by Fujimori and Sato (2015).
According to the Görg and Greenaway (2004), by accepting FDI from developed countries,
domestic firms in the host country can reap benefits such as technology transfer, management
expertise, and export market access. The process of technology diffusion begins, as Saggi(2002) and Keller (2010) suggest, when FDI accepting producers directly acquire advanced
technology from foreign-affiliated companies, which increases their productivity.
This advanced technology then transfers to other non-FDI accepting producers in the same
industry through imitation, reverse engineering, or the mobility of highly skilled personnel.
Subsequently, advanced technology gradually spreads within the industry, which is known as
the horizontal spillover effect.
In addition, advanced technologies also diffuse through inter-industry linkages. In
developing countries, FDI accepting companies often purchase intermediate goods from local
suppliers. In this situation, foreign-affiliated companies transfer advanced technology to these
suppliers to improve the quality of their products. Conversely, FDI accepting companies can
supply intermediate goods or services to local suppliers, facilitating the transfer of advanced
technology to these suppliers by providing quality intermediate goods. This is referred to as
the vertical spillover effect.
However, FDI can result in negative effects, one of the most typical being the crowding out
effect, where FDI inflow displaces domestic investment. This phenomenon can suppress theactivity of domestic manufacturing firms, reduce technological advancement in domestic
industries, and negatively affect economic growth (Aitken and Harrison 1999). This is often
criticised as the domination of developing country economies by foreign-affiliated companies.
Previous empirical studies that examined FDI spillover effects have captured both thepositive and negative effects; however, the presence of a significant relationship between
increasing FDI inflow and technological development has not yet been established.
3 Method
3.1 Data Sources
Our empirical analysis is based on census data for the manufacturing sector in India, divided
3

into units, which represent the various sub-industries of the manufacturing sector. The unitlevel data were obtained from the Annual Survey of Industries (ASI), which contains
economic data for India’s industrial sector and is published annually by the Central Statistics
Office. The ASI contains unit-level data for India’s ‘organised’ manufacturing enterprises,
defined as enterprises with more than 10 employees (power-assisted) or more than 20
employees (non-power-assisted)1. All enterprises categorised as part of the organised sector
are required to register under The Factories Act, 1948. The ASI data is divided into two
categories: census sector, which contains large-scale enterprises with more than 100
employees, and sample sector, which contains small-scale manufacturing enterprises not
categorised as census enterprises. For the purpose of constructing unit-level panel data, thisstudy only employs the census category. Also, this study mainly focuses on the situation of
medium and large size of manufacturing units.
Annual FDI stock data was unavailable; therefore, we approximated this variable using an
accumulated value of FDI inflow from 1991. FDI inflow data were obtained from India’s
Ministry of Commerce. The sample underlying this empirical analysis is an unbalanced panel
dataset from 2000/01 to 2007/082.
3.2 Empirical ModelTo estimate FDI spillover effects on TFP, we employ a two-step procedure by first
calculating the fitted value of TFP and then estimating the causality effect of FDI variables
on TFP. To measure the fitted value of TFP, we consider the Cobb–Douglas production
function in the logarithmic form as follows:
ln𝑌𝑖,𝑗,𝑡 = 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + 𝛽1 ln𝐾𝑖,𝑗,𝑡 + 𝛽2 ln𝐿𝑖,𝑗,𝑡 + 𝑢𝑖,𝑗,𝑡(1)

where 𝑌𝑖,𝑗,𝑡 stands for gross value added, 𝐾𝑖,𝑗,𝑡 is the capital stock (the value of fixed capitalin each unit), and 𝐿𝑖,𝑗,𝑡 is the labour input, which is the man-days of employment in each
unit. The subscripts i, j, and t refer to the ith manufacturing unit operating j’s sector at year 𝑡.
Both 𝑌𝑖,𝑗,𝑡 and 𝐾𝑖,𝑗,𝑡 are deflated to make real values. Regarding the deflation of Y, weemploy the double deflation method as follows:1

Small manufacturing enterprises based on these definitions are categorised as part of the unorganised
sector. Relevant economic data is included in the National Sample Survey, which is published by India’s
National Sample Survey Office.
2 Annual data is determined according to the Indian fiscal year (1 April to 31 March) rather than calendar
years.

4

𝑌𝑖,𝑗,𝑡 =𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑂𝑢𝑡𝑝𝑢𝑡(𝑁𝑜𝑚𝑖𝑛𝑎𝑙)
𝑊ℎ𝑜𝑙𝑒𝑠𝑎𝑙𝑒 𝑃𝑟𝑖𝑐𝑒 𝐼𝑛𝑑𝑒𝑥−

𝑇𝑜𝑡𝑎𝑙 𝐼𝑛𝑡𝑒𝑟𝑚𝑒𝑑𝑖𝑎𝑡𝑒 𝐺𝑜𝑜𝑑𝑠 𝐼𝑛𝑝𝑢𝑡(𝑁𝑜𝑚𝑖𝑛𝑎𝑙)𝑃𝑟𝑖𝑐𝑒 𝐼𝑛𝑑𝑒𝑥 𝑜𝑓 𝐼𝑛𝑡𝑒𝑟𝑚𝑒𝑑𝑖𝑎𝑡𝑒 𝐺𝑜𝑜𝑑𝑠(2)

where the Wholesale Price Index is obtained from the Reserve Bank of India and the Price
Index of Intermediate Goods is the weighted average of the price index of each intermediate
good3.
In the first step, we estimate the output elasticity of capital (β1 ) and labour (β2 ) from Eq. (1)and then obtain the TFP of each manufacturing unit as follows:
̂𝑖,𝑗,𝑡 =𝑇𝐹𝑃

𝑌𝑖,𝑗,𝑡̂2
̂1 𝛽𝛽𝐾𝑖,𝑗,𝑡 ∙𝐿𝑖,𝑗,𝑡(3)

In the Cobb–Douglas production function regression, it is necessary to consider theendogenous problem between the observable inputs and unobservable elements such as
productivity shocks. To manage this problem, we conduct a production function regression
according to the methods of Ackerberg, Caves, and Frazer (2015), hereafter referred to as
ACF; Levinsohn and Petrin (2003), hereafter referred to as LP; Olley and Pakes (1996),
hereafter referred to as OP; and Wooldridge (2009), hereafter referred to as WRDG. Fuel cost
is used in this estimation as a proxy variable of unobservable macroeconomic shocks.
In the second step, we estimate the spillover effects using the fitted values of TFP of unit i,which are obtained via the regression analysis in the first step. According to the estimation
procedure of Liu (2008), the baseline regression equation is expressed in logarithmic form
as follows:
̂𝑖 ,𝑗,𝑡 = 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + δ𝑡𝑖𝑚𝑒 + ϑ1 ℎ𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙𝑗,𝑡 + ϑ2 𝑏𝑎𝑐𝑘𝑤𝑎𝑟𝑑𝑗,𝑡 + ϑ3 𝑓𝑜𝑟𝑤𝑎𝑟𝑑𝑗,𝑡ln𝑇𝐹𝑃+ϑ4 ℎ𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙𝑗,𝑡 ∗ 𝑡𝑖𝑚𝑒+ ϑ5 𝑏𝑎𝑐𝑘𝑤𝑎𝑟𝑑𝑗,𝑡 ∗ 𝑡𝑖𝑚𝑒+ ϑ6 𝑓𝑜𝑟𝑤𝑎𝑟𝑑𝑗,𝑡 ∗ 𝑡𝑖𝑚𝑒 + 𝑢𝑖,𝑡(4)

where unit i belongs to industry j, ℎ𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙𝑗,𝑡 is the short-term intra-industry spillover
effect of j’s sector, 𝑏𝑎𝑐𝑘𝑤𝑎𝑟𝑑𝑗,𝑡 is the short-term spillover effect from industries
downstream of j’s industry, 𝑓𝑜𝑟𝑤𝑎𝑟𝑑𝑗,𝑡 is the short-term spillover effect from industries
upstream of j’s sector, ℎ𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙 ∗ 𝑡𝑖𝑚𝑒 is the long-term intra-industry spillover effect,and 𝑏𝑎𝑐𝑘𝑤𝑎𝑟𝑑 ∗ 𝑡𝑖𝑚𝑒 is the long-term spillover effect from downstream industries.
horizontalj,t represents the ratio of FDI stock to domestic capital in each industry and can becalculated as follows:3

The weights of each intermediate goods are calculated according to the 2003/04 Input–Output Tables.

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𝐹𝐷𝐼

𝑗,𝑡
ℎ𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙𝑗,𝑡 = 𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝐶𝑎𝑝𝑖𝑡𝑎𝑙

(5)

𝑗,𝑡

For Domestic Capital, we use the accumulated value of invested capital since 1991 in eachindustry, obtained from the ASI. backwardj,t and forward are created according to Javorcik(2004) and Javorcik and Spatareanu (2008, 2010) as follows:
𝑏𝑎𝑐𝑘𝑤𝑎𝑟𝑑𝑗,𝑡 = ∑𝑘≠𝑗 σ𝑗𝑘 ∗ ℎ𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙𝑘,𝑡(6)

𝑓𝑜𝑟𝑤𝑎𝑟𝑑𝑗,𝑡 = ∑𝑙≠𝑗 𝛿𝑖𝑙𝑗𝑡 ∗ ℎ𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙𝑙,𝑡(7)

where σ𝑗𝑘 is the proportion of sector j’s output supplying to sector k in total output, and 𝛿𝑖𝑙𝑗
is the proportion of inputs purchased by unit i in sector j from sector l in total inputs sourced
by unit i in sector J. σ𝑗𝑘 and 𝛿𝑖𝑙𝑗𝑡 are calculated using the 2003/04 input and output matrices
published by the Central Statistics Office, and the ASI, respectively. To address the possible
endogeneity of FDI, we use lagged FDI variables as explanatory variables in Eq. (4).
Each variable in the ASI is constructed based on the 2004 National Industrial Classification
at the two-digit level and classified in terms of categories defined by the Secretariat for
Industrial Assistance.
3.3 Descriptive Statistics
The descriptive statistics are presented in Table 1. We estimate that the average unit has 400
employees, assuming an average of 300 working days per year. We analyse only the ASI
census sector; therefore, we expect the average unit size to be larger than the average size forthe whole organised sector. The descriptive statistics for the fitted value of TFP are calculated
by LP, OP, and WRDG and show similar results. The fitted value of TFP calculated by ACF
is relatively lower but does not differ significantly from the other calculations.
horizontal represents the proportion of FDI stock to the total capital stock of the whole sector
and shows an average value of 0.27, indicating that the percentage of FDI in domestic capitalstock in the organised sector is around 30%. The vertical spillover FDI variables, backward
and forward, are calculated as a weighted average of horizontal; therefore, the values of bothvariables should remain within the range of horizontal.
<Table 1>

64 Empirical Results
The results from the first-step regression of the Cobb–Douglas production function are
shown in Table 2 and indicate that the coefficients of elasticities of both capital (β1 ) andlabour (β2 ) are statistically significant across all cases. Moreover, the coefficients of eachestimation method are different; in particular, the coefficients obtained from the OP and
ACF estimations demonstrate an increasing return to scale in most of the sub-industries.
<Table 2>
̂ 𝑖,𝑗,𝑡 is strongly influenced by the estimation method as demonstrated
Furthermore, ln𝑇𝐹𝑃
by the large range of the standard deviation. Overall, the fitted values estimated by the ACF
and OP methods are low relative to the results estimated by the LP and WRDG methods.
In Tables 3 and 4 we present the coefficients from the second-step estimation. The
coefficient of the short-term horizontal spillover effect is negative and statistically significant
at the 1% level, while the coefficient of the long-term horizontal spillover effect is also
negative and statistically significant in all regression analyses except with the year dummy
variable in the OP and LP methods. In the one-year lag model (Table 4), the coefficients ofthe short-term spillover effects are negative but not statistically significant across all theregression analyses.
In contrast, the results of the examination of backward spillover effects are more unified.
The results of all the regression analyses show that short-term spillover coefficients arenegative, long-term spillover coefficients are positive, and all are statistically significant.
These results are replicated in the one-year-lag model, except when using the ACF method.
These estimation results imply the existence of strong FDI backward spillover effects in the
Indian manufacturing sector.
The analysis of forward spillover effects yields varying results depending on the estimation
model. The coefficients are negative and significant in the short term and positive in the longterm without the year dummy; however, the results are opposite when adding the year
dummy; the coefficients are positive and significant in the short term and negative in the longterm. Moreover, In the one-year-lag model, the coefficients are only significant when using
the ACF method.
In sum, the non-lagged model results indicate that increasing FDI decreases the TFP levelof intra-industry manufacturing units in both the short and long terms. The results of the oneyear lag model also suggest that an increase in FDI has a negative effect on intra-industry
TFP in the short term, and any evidence of long-term horizontal spillover effects are notcaptured. However, the coefficients of backward spillover effects in both models imply that7

increasing FDI enhances the TFP level of manufacturing units in upstream sectors in the longterm. Robust evidence of FDI spillover is not detected in downstream sectors.
<Table 3><Table 4>
5 Discussion
The results of our empirical analyses highlight three key issues.
First, an increase in FDI appears to negatively affect TFP growth through horizontal
linkages. We interpret this as an indication that the expansion of foreign affiliates’ market
share reduces the total output of local firms, resulting in relatively high fixed costs for local
firms and a consequent reduction in TFP growth. Therefore, a negative spillover effect isobserved (Aitken and Harrison 1999; Javorcik 2014).
Second, while backward linkages affect TFP growth negatively in the short term, positive
significant spillover effects are captured in the long term. The short-term results indicate agestation period of investment; however, the long-term results require a more detailed
analysis. Final assembly sectors can procure their input materials locally or from overseas. In
India, many foreign affiliate companies invest in local industries, traditionally as a result ofstrict local content requirements imposed by the Indian government until 2000. In this context,
foreign subsidiaries created domestic supply chain networks in India and enhanced
productivity and technology in the local manufacturing sector.
An example is provided by the automotive industry (Chatterjee 1990; Bhargava 2010).
Suzuki Motors, one of the leading foreign companies to advance into India before economic
liberalisation, broadly invested and transferred technology to local component industries in
the 1980s. Following Suzuki’s investment pattern, other foreign subsidiaries procured
components from local suppliers. The quality of the final products was influenced by thetechnological capacity of local suppliers; therefore, foreign companies had an incentive totransfer technology to upstream suppliers. This evidence supports the results of our empiricalanalysis.
Finally, no significant evidence of forward spillover effects is detected in our results. Thismay be due to the timeframe of our data; the linkages between the manufacturing sector andthe technology sector may have been relatively weak before the 2010s.

84 Conclusion
In this study we examined the channels of technology diffusion through FDI using a casestudy of the manufacturing sector in India during the liberalisation period from 2000 to 2008.
The results of the empirical analyses posit a significant relationship between FDI andtechnological progress in the manufacturing sector. Importantly, we highlight the strong
evidence of technology spillover from foreign firms to upstream suppliers.
Our findings are consistent with Fujimori and Sato (2015), who highlight the existence oflong-term backward spillover effects. As an overall conclusion, the acceptance of FDI
contributes to technological progress and, as such, is beneficial to India’s long-term economic
growth.
We find no evidence of technology diffusion to downstream industries in this study. We,
however, expect future research that will examine FDI spillover effects after the 2010s. The
amount of FDI inflows into information and communications technology (ICT) industries
have been increasing since the 2010s. Therefore, the industrial linkages to the upstream
sectors as well as downstream sectors must be more deepen than the 2000s.

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Table1 Descriptive Statistics
Gross Value Added: Y (Million Rupees)
Man-Days of Employment: L (Days)
Fixed Capital: K (Million Rupees)
ln TFP (LP)ln TFP (OP)ln TFP (WRDG)
ln TFP (ACF)horizontal
backward
forward

Number of Obsevations

Mean

Std. Dev.

Min

Max

87,62987,62987,62987,62987,62987,62987,62987,62987,62987,629165
116,902179
6.28775.54486.56093.7016
0.27160.28250.57151,030
348,026
1,810
1.90471.72092.1314
1.58570.67770.24670.83490.0012420.000001-5.8312-10.7291-11.8082-6.19420.00000.04380.1327100,000
16,400,000
176,000
13.3175
12.119915.2251
11.6605
7.12251.22594.4515

Notes: Total Factor Productivity (TFP) is estimated by four methods denoted by the following: LP is the Levinsohn
and Petrin (2003) method, OP is the Olley and Pakes (1996) method, WRDG is the Wooldrige (2009) method, andACF is the Ackerberg, Caves and Frazer (2015) method.

12

Table 2 Results of First-Step Regression Analyses on the Indian Manufacturing Industry
Dependent Variable: ln Y

(1)

(2)

(3)

OP
Code
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36

LP

WRDG
ln L
ln K

(4)
ACF
ln L

Industries

Number of Observations

ln L

ln K

ln L

ln K

Food
TobaccoTextilesApparelLeather
Wood
Paper

19625
2232
15096
6645
2598
1469
2294

0.661***
0.786***
0.495***
0.555***
0.561***
0.771***
0.612***

0.263***
0.0822**0.369***
0.306***
0.377***
0.215***
0.414***

0.668***
0.750***
0.363***
0.353***
0.535***
0.638***
0.448***

0.248***
0.0599***0.406***
0.352***
0.399***
0.231***
0.297***

0.661***
0.749***
0.345***
0.349***
0.532***
0.650***
0.431***

0.248***
0.0655**0.388***
0.339***
0.416***
0.239***
0.263***

0.750***
0.823***
0.559***
0.607***
0.659***
0.840***
0.626***

0.385***
0.218***
0.399***
0.276***
0.347***
0.244***
0.414***ln K

Publishing

2227

0.715***

0.202*

0.475***

0.229**

0.473***

0.273***

0.793***

0.353***

Coke/Petroleum

905

0.790***

0.422***

0.613***

0.377**

0.613***

0.366***

0.943***

0.382***

Chemicals

11289

0.457***

0.260***

0.418***

0.226***

0.404***

0.214***

0.560***

0.461***

Rubber/Plastics

3614

0.604***

0.352***

0.390***

0.480***

0.386***

0.362***

0.659***

0.419***

Non-metallic mineral

9287

0.606***

0.180***

0.461***

0.210***

0.459***

0.207***

0.762***

0.174***

Basic metals

4256

0.544***

0.364***

0.607***

0.371***

0.602***

0.249***

0.769***

0.364***

Metal products

4195

0.625***

0.279***

0.401***

0.278**

0.418***

0.208***

0.728***

0.240***

Machinery

3607

0.719***

0.348***

0.628***

0.446***

0.628***

0.432***

0.775***

0.351***

Office machinery

377

0.283***

0.587***

0.365***

0.526***

0.334***

0.267**

0.643***

0.289*

Electrical machinery

3997

0.663***

0.457***

0.572***

0.471***

0.572***

0.513***

0.747***

0.346***

Television/Communication

1014

0.702***

0.365***

0.639***

0.465***

0.646***

0.361***

0.753***

0.365***

Medical/Watches

1492

0.604***

0.353***

0.595***

0.329***

0.585***

0.369***

0.691***

0.385***

Motor vehicles

285

0.714***

0.823***

0.503***

0.658***

0.494***

1.005***

0.789***

0.321*

Other transport

2450

0.589***

0.354***

0.509***

0.403***

0.488***

0.460***

0.602***

0.409***

Furniture

3297

0.704***

0.244***

0.610***

0.291***

0.602***

0.293***

0.796***

0.391***

Notes: Regression analyses are undertaken using four methods denoted by the following: LP is the Levinsohn andPetrin (2003) method, OP is the Olley and Pakes (1996) method, WRDG is the Wooldrige (2009) method, and ACF
is the Ackerberg, Caves and Frazer (2015) method.
Standard errors are reported in parentheses. * Denotes significance at the 10% level, ** at the 5% level, and ***at the 1% level.

13

Table3 Results of Second-Step Regression Analyses on the Indian Manufacturing Industry
Dependent variable: ln TFP

horizontal
horizontal ×time
backward
backward ×time
forwardforward ×time
Constant
Year Fixed EffectObservations
R-squared
Number of Panel

(1)
OP

(2)
OP

(3)
LP

(4)
LP

(5)
WRDG

(6)
WRDG

(7)
ACF

(8)
ACF

-0.0284*** -0.0270*** -0.0333*** -0.0320*** -0.0522*** -0.0518*** -0.0359*** -0.0353***(0.00725) (0.00724) (0.00743) (0.00742) (0.00769) (0.00768) (0.00719) (0.00719)-0.00640*** 0.00379 -0.00896*** 0.00167 -0.0202*** -0.00980*** -0.0243*** -0.0151***(0.00235) (0.00235) (0.00241) (0.00241) (0.00250) (0.00249) (0.00233) (0.00233)-2.075*** -1.345*** -2.624*** -1.892*** -3.009*** -2.318*** -1.082*** -0.261***(0.0817)(0.0895)(0.0838)(0.0916)(0.0866)(0.0949)(0.0810)(0.0888)0.240*** 0.0999*** 0.282*** 0.140*** 0.318***0.182*** 0.190*** 0.0460***(0.00694) (0.00861) (0.00711) (0.00882) (0.00735) (0.00914) (0.00688) (0.00855)-0.125*** -0.0209* -0.121***-0.0144 -0.111*** -0.00611 -0.116*** -0.0137(0.0103)(0.0111)(0.0106)(0.0114)(0.0110)(0.0118)(0.0103)(0.0110)0.0155*** -0.00110 0.0144*** -0.00251 0.0128*** -0.00353** 0.0139*** -0.00316**(0.00145) (0.00155) (0.00149) (0.00159) (0.00154) (0.00164) (0.00144) (0.00154)
5.794*** 5.585***
6.631*** 6.419*** 6.977***6.779*** 3.776*** 3.560***(0.0145)(0.0168)(0.0148)(0.0172)(0.0153)(0.0178)(0.0144)(0.0167)
NO
YES
NO
YES
NO
YES
NO
YES
87,6290.046
28,117

87,6290.068
28,117

87,6290.050
28,117

87,6290.072
28,117

87,6290.052
28,117

87,6290.072
28,117

87,6290.046
28,117

87,6290.066
28,117

Notes: Regression analyses are undertaken using four methods denoted by the following: LP is the Levinsohn andPetrin (2003) method, OP is the Olley and Pakes (1996) method, WRDG is the Wooldrige (2009) method, and ACF
is the Ackerberg, Caves and Frazer (2015) method.
Standard errors are reported in parentheses. * denotes significance at the 10% level, ** at the 5% level and *** atthe 1% level.

14

Table 4 Results of Second-Step Regression Analyses on the Indian Manufacturing Industry with Lagged
Explanatory Variables
Dependent vriable: ln TFP

horizontal (-1)
horizontal (-1)×time
backward (-1)
backward (-1)×time
forward (-1)
forward (-1)×time
Constant
Year Fixed EffectObservations
R-squared
Number of Panel

(1)
OP

(2)
OP

(3)
LP

(4)
LP

(5)
WRDG

(6)
WRDG

(7)
ACF

(8)
ACF

-0.0121 -0.0280*** -0.0134 -0.0295*** -0.0239** -0.0395*** -0.0182* -0.0313***(0.0104)(0.0104)(0.0106)(0.0106) (0.0109) (0.0109) (0.0104) (0.0104)-0.00859** -0.00247 -0.00873** -0.00257 -0.00627* -0.000238 -0.00496 0.000502(0.00356) (0.00355) (0.00362) (0.00361) (0.00373) (0.00372) (0.00356) (0.00355)-1.909*** -0.338*** -1.986*** -0.408*** -2.082*** -0.525*** -1.714*** -0.149(0.107)(0.122)(0.109)(0.124)(0.112)(0.128)(0.107)(0.122)0.228*** 0.0342*** 0.236*** 0.0411*** 0.242*** 0.0500*** 0.211***0.0177(0.00914) (0.0117) (0.00930) (0.0119) (0.00957) (0.0123) (0.00915) (0.0117)-0.123*** -0.0146 -0.120*** -0.0121 -0.117*** -0.0113 -0.126*** -0.0248*(0.0135)(0.0145)(0.0137)(0.0148) (0.0141) (0.0152) (0.0135) (0.0145)
0.0187*** 0.000106 0.0180*** -0.000604 0.0175*** -0.000917 0.0199*** 0.00150(0.00187) (0.00201) (0.00191) (0.00205) (0.00196) (0.00211) (0.00188) (0.00202)
5.864*** 5.627*** 6.633*** 6.395*** 6.922*** 6.687*** 3.912*** 3.668***(0.0170)(0.0199)(0.0173)(0.0203) (0.0178) (0.0209) (0.0170) (0.0199)
NO
YES
NO
YES
NO
YES
NO
YES
54,9150.046
17,762

54,9150.069
17,762

54,9150.046
17,762

54,9150.069
17,762

54,9150.044
17,762

54,9150.065
17,762

54,9150.044
17,762

54,9150.067
17,762

Notes: Regression analyses are undertaken using four methods denoted by the following: LP is the Levinsohn andPetrin (2003) method, OP is the Olley and Pakes (1996) method, WRDG is the Wooldrige (2009) method, and ACF
is the Ackerberg, Caves and Frazer (2015) method.
Standard errors are reported in parentheses. * denotes significance at the 10% level, ** at the 5% level and ***at the 1% level.

15