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2020 | OriginalPaper | Buchkapitel

Foreign Involvement and Firm Productivity: An Analysis for Indian Manufacturing, Service, Construction and Mining Sectors

verfasst von : Isha Chawla

Erschienen in: Accelerators of India's Growth—Industry, Trade and Employment

Verlag: Springer Singapore

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Abstract

In recent years, the intensification in the global engagement of Indian firms, by way of exports and outward foreign direct investment (OFDI) has generated significant research that draws on theoretical propositions of the new new trade theory (Helpman, Elhanan, Marc J. Melitz, and Stephen R. Yeaple (2004), “Export versus FDI with Heterogeneous Firms”, American Economic Review, 94:1, 300–316.) (below HMY) that assign a leading role to heterogeneity in firm productivity in explaining self-selection of firms into foreign markets. In HMY, for the export versus OFDI decision, firms with the highest productivity are posited to invest abroad (Goldar, Bishwanath (2016), “Direction of Outward FDI of Indian Manufacturing Firms: Influence of Technology and Firm Productivity”, in Globalization of Indian Industries, Productivity, Exports and Investment, eds. De Beule, F., and K. Narayanan, India Studies in Business and Economics, 71–96, Springer.). Head and Ries (2003) (Head, Keith and John Ries (2003), “Heterogeneity and the FDI versus Export Decision of Japanese Manufacturers”, Journal of The Japanese and International Economies, 17:4, 448–467.) (below HR) note that an empirical complementarity between exports and OFDI could result with differences in fixed costs across destinations. Further, while for manufacturing, predictors such as physical transport cost and sunk cost of OFDI (as in HMY) are considered to be fairly standard, for services, different predictors, based on different considerations such as the need for direct communication with consumers, the difficulty of contracting nonroutine activities to foreign affiliates, near-zero transportation costs and non-commoditized products are proposed to reverse the HMY predictions (Bhattacharya, Rudrani, Ila Patnaik, and Ajay Shah (2012), “Exports versus FDI in Services”, The World Economy, 35:1, 61–78.). This paper examines whether involvement in OFDI is associated with higher productivity levels at the firm level (that is, whether OFDI firms are more productive than firms with purely domestic operations, and those that organize international activities only through exports). Cross-sectional findings of a positive link between firm productivity and foreign involvement could, however, be due to the most productive firms self-selecting into foreign markets, and/or learning effects through foreign engagements. Using Prowess database, over 1995–2010, in addition to manufacturing (57,698 observations) and services (5,145 observations), the under-investigated construction (2,036 observations) and mining (1,196 observations) sector firms are also considered, with no size thresholds. The non-parametric approach of first-order stochastic dominance (Kolmogorov–Smirnov test) is used to examine the nature of productivity differentials between firm categories (based on foreign involvement). In examining the main issue, this paper also discusses some measurement and/or methodological issues. Some modifications are applied towards the construction of real output (gross output (GO), value-added (VA)), and inputs series (combined intermediate inputs, namely, raw materials, energy and services, labour and capital) required for estimating total factor productivity (TFP). While following the widely used Annual Survey of Industries (ASI)-based approach to impute firm-level employment, an attempt is made to overcome the uniform wage criticism by adjusting the labour measure for a ‘wage premium’ based on ownership groups. The measure of physical capital allows for disaggregated growth of investment, and the capital stock measure combines physical and ‘knowledge’ or R&D ‘capital’ stock. In the context of productivity measurement, comparisons are drawn between the alternative methods that attempt to overcome ‘transmission bias’, namely, Levinsohn and Petrin (2003) (Levinsohn, James, and Amil Petrin (2003), “Estimating Production Functions Using Inputs to Control for Unobservables”, Review of Economic Studies, 70, 317–342.) (below LP) and its modification proposed by Wooldridge (2009) (Wooldridge, Jeffrey M. (2009), “On Estimating Firm-Level Production Functions Using Proxy Variables to Control for Unobservables”, Economics Letters, 104:3, 112–114.) (below WLP). Also, in the context of studies that point out that the relative superiority of exporters in comparison to purely domestic firms may also result from several sources of potential bias in productivity estimates (related to the selection of the functional form of the production function, namely, GO vs.VA), attempts are made to explore whether similar concerns are of importance when investigating the relative superiority of OFDI firms (that also export). Next, in the absence of information in the investment outside India data field in Prowess about the percentage holding by Indian firms in their affiliates abroad, while some studies identify an OFDI firm on the basis of existence of positive overseas assets, some use cut-offs on the fraction of OFDI to total assets (as for instance, >1%). In making the cross-sectional comparisons of the estimated productivity distributions, an attempt is made to see whether the stricter basis for classifying foreign investors affects the nature of productivity rankings by firm categories. For firms in the manufacturing and construction sectors, cross-sectional differences in TFP between outward investors that also export, pure exporters, and domestic firms are found to follow the HMY/HR hypotheses, although in contrast to the GO specification, the VA specification suggests an upward bias in the productivity advantage of internationally engaged firms (suggesting that controlling the ‘value-added bias’ is important and it is not sufficient to control only for the ‘transmission bias’). Productivity differentials vary, sometimes considerably by two-digit industry/industry groups. The HMY (and HR) pattern obtains, more so in textiles, coke and refined petroleum products, chemicals, pharmaceuticals, basic metal and fabricated metal, and machinery and equipment n.e.c. than in the rest. In services, TFP comparisons show that pure export firms dominate the purely domestic firms and overseas investors that also export dominate purely domestic firms. However, between the overseas investors that also export and pure exporters, no clear-cut differences could be established unlike a previous study for Indian software services suggesting the stochastic dominance of pure exporters over overseas investors that also export. This suggests that Indian IT firms’ OFDI that is mainly located in developed countries could also be guided by vertical or complex integration strategies, related to the technology-seeking motives and agglomeration economies (due to clustering in specific regions). In mining, only the dominance of pure export firms over purely domestic firms could be established for the latter half of the sample period. Qualified support is thus found for the ‘pecking order’ as predicted by heterogeneous firms’ theories. As the productivity and other firm characteristics of OFDI firms that initially start small are observed to be similar to those with larger positions abroad, if a constraint on financing is found to be an issue for these firms, the government should support a more liberal financial system for OFDI that could also aim specifically at firms with initially small OFDI. EXIM Bank (2017) (Export Import Bank of India (2017), “The Internationalisation of Indian Firms Through Outbound Foreign Direct Investment: Nature, Determinants and Developmental Consequences”, Occasional Paper No. 183. https://​www.​Eximbankindia.​In/​Assets/​Dynamic/​Pdf/​Publication-Resources/​Researchpapers/​Hindi/​82file.​pdf.), for instance, indicates that there is a range over which it is possible to increase firms’ OFDI intensity and increase the benefits from OFDI.

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Fußnoten
1
Extensive margin for exports is the number of firms involved in exports, while intensive margin is the average firm-level exports conditional on exporting. Likewise, for OFDI, extensive margin is the number of firms involved in OFDI, while intensive margin is the average firm-level OFDI flows conditional on doing OFDI.
 
2
In support, the present study finds that Indian foreign investment activity is very much concentrated. In 2009 and 2010, of the sampled firms, the top 1% outward investors from manufacturing account for 64.5% and 68% of the total investment outside India, respectively (Prowess4 database and own calculations).
 
3
World Investment Report (WIR), Annex Tables, UNCTAD 2018.
 
4
Minondo (2014) further finds that the productivity premium is higher in services not related with the internet than in Internet-related services.
 
5
Grublješić and Damijan (2011), however, note that firm size seems to be related to the strong concentration of trade in services on a small number of firms as most exports of services are a function of the number of employees. On the other hand, external trade in knowledge-based services is concentrated with the small- and medium-sized firms (SMEs).
 
6
Most of these studies consider trade in business services that represents the tradable component of services.
 
7
Bogheas and Gorg (2008), however, note that studies that focus on only a couple of the many alternative strategies for global engagement may potentially yield wrong predictions and demonstrate the superiority of capturing a greater variety of organizational forms.
 
8
That the HMY prediction does not hold in the comparison between firms with foreign affiliates and exporters is, however, traced to transition-specific transitory factors related to inherited foreign investments of large inefficient firms. TFP nevertheless has a positive effect on the probability of investing in the first-ever foreign affiliate.
 
9
Zhejiang Province being the largest province in the number of OFDI firms in 2007 and the largest in OFDI in 2010.
 
10
Two alternative approaches for classifying firms’ foreign investments into resource-driven and market-driven OFDI can, however, be used to enhance the empirical precision of the HMY hypothesis. The study distinguishes between the host country approach of HR, whereby low productive firms enter only low-wage but not high-wage countries and the NACE approach that requires similar industry affiliation of the parent company and its subsidiary for market-driven OFDI, and vertical subsidiaries active in upstream (or downstream) industries from their parent’s industry for resource-driven OFDI.
 
11
As studies on exports, OFDI and productivity in services are fewer than in manufacturing, some studies that relate productivity to the likelihood of OFDI are included in this review, even if they do not compare the productivity distributions of firms.
 
12
Service sector firms are, however, assumed to only have the choice of domestic production or OFDI as the dataset does not contain services exports, while manufacturing firms can choose between exports and OFDI. It is also demonstrated theoretically that none of the services firms can exceed the export cut-off productivity level that is sufficiently high enough for them.
 
13
DX covers continuing exporters (firms that export continuously over the sample period) but also firms that switch their export status from domestic to exporter in the current year.
 
14
For details on data sources, data cleaning, variable construction, econometric issues, and methodology of TFP estimation (based on LP), refer to Goldar et al. (2019).
 
15
The ‘ASI-based approach’ involves the computation of an average wage rate (emoluments per employee, at the 2-digit or 3-digit industry level), obtained by dividing Annual Survey of Industries (ASI) data on total emoluments by the total persons engaged. Subsequently, by dividing each firm’s wage bill obtained from the company database by this computed average wage rate, an imputed measure of the employment in the firm is arrived at.
 
16
For consistency with the wage adjustment as performed for manufacturing firms, the reported compensation to labour for group, government and foreign firms is adjusted downwards (by the same percentage as worked out for manufacturing) before imputing employment.
 
17
For Indian manufacturing, Pradhan and Barik (1998) find through a statistical test that primary and intermediate inputs are not separable in the production function, thus weakening the option of using VA for TFP estimation.
 
18
The LP approach is implemented using the levpet command (Petrin et al. 2004).
 
19
The WLP method is implemented using the program available at http://​www.​econ.​umn.​edu/​~petrin/​programs.​html using (ivreg2.do). Under ivreg2, the estimator option gmm2s (that produces the IV/2SLS estimator, standard errors consistent under homoscedasticity) when combined with the cluster option, generates two-step efficient GMM (EGMM) estimates (that is statistics robust to heteroscedasticity and clustering at the firm level). cluster standard errors are robust to both arbitrary heteroskedasticity and arbitrary intra-group correlation. The ivreg2 Stata module developed by Baum et al. (2012), available at http://​ideas.​repec.​org/​c/​boc/​bocode/​s425401.​html, is used for estimation.
 
20
Following S2, however, may cause a firm’s classification to change to a non-exporter and/or a non-overseas investor firm if a change in exports (and/or investment outside India) and/or in sales/total assets causes these ratios to fall below 1% (as for Videocon Industries in 2006) among others, instead of an actual change in the firm’s trajectory between export and/or overseas investment and the domestic market over any given period.
 
21
Unlike the empirical findings wherein few firms export (e.g. Bernard et al. 2007 for US, where exporters represent only 18% of the total population), the relatively large share of exporting firms in the sample reflects the oversampling of the relatively large and medium firms in the database.
 
22
Chawla (2015) shows that DXI category has slightly lower capital–output ratio, combined material, raw material and energy intensity although their services intensity is slightly higher than of D category.
 
23
However, the largest home-based transnational corporations (TNCs) for 2010 as in India country sheet, WIR, UNCTAD (2013) represent manufacturing industries with varying degrees of technological sophistication: Reliance Industries Ltd., Essar Oil Ltd. (coke, petroleum and nuclear fuel), Tata Steel Ltd., Hindalco Industries Ltd., MMTC Ltd., JSW Steel Ltd., Ispat Industries Ltd. (metals and metal products), Tata Motors Ltd., Mahindra and Mahindra Ltd., Bajaj Auto Ltd. ( motor vehicles and other transport equipment), Suzlon Energy Ltd. (machinery and equipment), ITC Ltd. (food, beverages and tobacco), Hindustan Unilever Ltd., Ranbaxy Laboratories Ltd., Tata Chemicals Ltd., Dr. Reddy’s Laboratories Ltd. (chemicals and chemical products), Videocon Industries Ltd., Siemens Ltd., Crompton Greaves Ltd. (electrical and electronic equipment), Apollo Tyres Ltd. (rubber and plastic products) and Ambuja Cements Ltd., Ultratech Cement Ltd. (non-metallic mineral products).
 
24
The test is more robust than the t-test that requires the normality assumption.
 
25
As Girma et al. (2004, p. 319) note, ‘although these tests encompass the possibility that firms of the same productivity level may choose different forms of commerce, the degree of uncertainty in behaviour cannot be too large such that the structure of commerce and firm heterogeneity are no longer meaningfully related’.
 
26
‘The directional hypotheses are evaluated with the statistics: D+ = maxx{F (z) − G (z)}, D  = maxx{F (z) − G (z)} where F(x) and G(x) are the empirical distribution functions for the samples being compared. The combined statistic is: D = max (│D+│,│D│) which identifies the maximum vertical difference between the two empirical cumulative distribution functions. The p-value for this statistic may be obtained by evaluating the asymptotic limiting distributions’ (Stata Base Reference Manual Vol. 2, Release 10, p. 109).
 
27
Similar tables (reporting KS test results), for manufacturing (S2), services (S1, S2), construction (S1) and mining (S1) not reported here, are available in Chawla (2015), results discussed below.
 
28
The mean productivity for the sample DIDXI is not shown for 1995–1999, as due to the small number of firms in this time period, the mean values are subject to larger variations.
 
29
Both columns, however, show that the impact of the negative demand shock for Indian firms in 2008 (Q2) to 2009 (Q2) has been more so for firms with foreign engagements than purely domestic firms.
 
30
Epanechnikov kernel, with varying bandwidth.
 
31
As the HMY model deals with horizontal FDI alone, and although a large fraction of FDI by Indian firms goes to the developed countries for market access (RBI Bulletins), it seems reasonable to test the HMY predictions. Nunnenkamp et al. (2012) also find that the location choice of Indian direct investors is dominated by the motive of market-related factors, much less so for access to raw materials or for superior technologies. In so far as OFDI is also guided by vertical or complex integration strategies, also related to the internationalization of R&D, in the absence of the fraction of OFDI directed by the underlying motives, testing the HMY predictions, may, however yield partial insights.
 
32
The number of observations is reported for GO–LP approach. Under WLP, as noted above, the overall sample size is smaller.
 
33
The year 2001 onwards has also witnessed a significant increase in the number of outward investing firms.
 
34
DXI and DX firms are engaged in industries such as ‘basic telecom services, internet access by the operator of the wireless infrastructure, other wireless telecommunications activities, other telecommunications activities, providing software support and maintenance to the clients (software service and consultancy), news agency activities (television broadcasting media, cable television broadcasting media (DX only), other information service activities n.e.c. (information technology enabled service/BPO), activities of maintaining and operating paging, cellular and other telecommunication networks (DX only)’ (based on Prowess 4). Several firms in the services sector have established large overseas positions, for instance, in 2009/10, while the largest stock of overseas assets was held by Bharti Airtel Ltd., Silverline Technologies Ltd, H O V Services Ltd., Four Soft Ltd. and Mindteck (India) Ltd. had a foreign investment intensity of over 80%. Further, Bharti Airtel Ltd., Reliance Communications Ltd., Tata Communications Ltd., United Breweries Holdings Ltd. (transport, storage and communications), Tata Consultancy Services Ltd., Wipro Ltd., Infosys Ltd., HCL Technologies Ltd., Satyam Computer Services Ltd., Mphasis Ltd., Tech Mahindra Ltd. (business services, the high-skill intensive category of services) list in the largest home-based TNCs for 2010 (WIR, Investment Country Profiles, India, UNCTAD, 2013). Tata Consultancy Services Ltd., Infosys Ltd., Wipro Ltd., Tech Mahindra Ltd., HCL Technologies Ltd. were also the largest service exporters in 2010.
 
35
NIC 58 is not included in the graphical display to bring out any distinct features of this group that is dominated by NIC 62 in terms of firm coverage.
 
36
The mean productivity for the sample DIDXI is not shown for 1995–1999, as due to the small number of firms in this time period, the mean values are subject to larger variations.
 
37
Due to the relatively small number of firms in the services sector for which productivity could be estimated in the 1995–1999 period, the broad trends for this sector are more meaningful for the 2001 onwards time period.
 
38
The density plot for DXI (not shown in the plot) is close to that of DIDXI.
 
39
BPS compares DXI to DX but not DX to D.
 
40
Comparisons of DIDXI with DX and D, respectively, for 1995–99 are not presented due to the small number of DIDXI firms in the same time period.
 
41
Two key characteristics that identify software service companies are near-zero transportation costs for software services that are posited to encourage production at home while software services being non-commoditized, with a range of intangible characteristics, are posited to make customers feel it is risky to buy software services from a distant country, considered to encourage FDI.
 
42
Construction firms involved in exports and outward investment belong to industries such as ‘construction of buildings carried out on own-account basis or on a fee or contract basis, construction and maintenance of motorways, streets, roads, other vehicular and pedestrian ways, highways, bridges, tunnels and subways, construction of utility projects n.e.c., and other civil engineering projects n.e.c.’ (based on Prowess4). For 2010, Larsen and Toubro Ltd., Punj Lloyd Ltd. and Gammon India Ltd. are the largest home-based TNCs from the construction sector (WIR, UNCTAD 2013).
 
43
Over 1995–2010, the estimated average annual growth rate of the real physical capital stock (real NFA) for this sector is comparatively higher (Chawla 2015). If output has not risen in accordance, higher growth of the capital input could partly explain the downward slant in the mean TFP over the years. The yearly fluctuations in mean productivity could reflect the small sample size in each category for which the mean has been computed.
 
45
DX and DXI firms in the mining sector belong to industries such as ‘on shore extraction of crude petroleum and natural gas, mining of iron and other ores, quarrying of granite, mining of clays, salt mining, quarrying, screening, etc., extraction and agglomeration of peat, services incidental to off shore oil extraction, and other operations relating to mining and agglomeration of hard coal’ (e.g. Oil and Natural Gas Corp. Ltd. (ONGC), Sterlite Industries (India) Ltd.). TFP estimates for ONGC could, however, not be obtained as its raw material data is not available. Even though the firm has large overseas stakes in exploration, it is thus not part of the sample of firms.
 
46
Specification S1: DX if export intensity is positive, DIDXI if export intensity is positive and foreign investment intensity is positive. Specification S2: DX if export intensity ≥1%, DXI if export intensity ≥1% and foreign investment intensity ≥1%, DI non-exporter firms with foreign investment intensity ≥1%.
 
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Metadaten
Titel
Foreign Involvement and Firm Productivity: An Analysis for Indian Manufacturing, Service, Construction and Mining Sectors
verfasst von
Isha Chawla
Copyright-Jahr
2020
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-32-9397-7_10