TY - JOUR
AU1 - Arnold, Jens, Matthias
AU2 - Javorcik,, Beata
AU3 - Lipscomb,, Molly
AU4 - Mattoo,, Aaditya
AB - Abstract Conventional explanations for the post‐1991 growth of India’s manufacturing sector focus on goods trade liberalisation and industrial delicensing. We demonstrate the powerful contribution of a neglected factor: India’s policy reforms in services. The link between these reforms and the productivity of manufacturing firms is examined using panel data for about 4,000 Indian firms for the period 1993–2005. We find that banking, telecommunications, insurance and transport reforms all had significant positive effects on the productivity of manufacturing firms. Services reforms benefited both foreign and locally owned manufacturing firms, but the effects on foreign firms tended to be stronger. A vital element of India’s rapid economic growth since the early 1990s has been the improved performance of its manufacturing sector. Output in manufacturing grew by 5.7% per year in the period 1993–2005 (Reserve Bank of India, 2008). Previous explanations for the revival of manufacturing emphasise trade liberalisation, more permissive industrial licensing policies and the limited labour market reforms undertaken since 1991 (see review below). In focusing primarily on proximate policies, however, previous analyses have ignored what we demonstrate is a critical factor, policy reforms in services sectors. The neglect of services is surprising, first of all, because finance, transport and telecommunications are important inputs to manufacturing, so the potential for downstream effects is large.1 Moreover, reforms in the 1990s, allowing greater foreign and domestic competition with significantly improved regulation, visibly transformed these services sectors.2 Indian firms were no longer at the mercy of inefficient public monopolies but could now source from a wide range of domestic and foreign private sector providers operating in an increasingly competitive environment. Evidence, presented in Section 2, suggests that firms obtained access to better, newer and more diverse business services. In this article, we address three questions: Has services reform led to an increase in manufacturing productivity? Have reforms in some services had a bigger impact than in others? Have some manufacturers (e.g. foreign firms based in India) benefited more than others? These questions matter significantly for policy; not only is services reform in India incomplete but across the world some of the most intransigent policy restrictions today are in services.3 Convincing evidence that these restrictions penalise the politically cherished manufacturing sector could provide an important impetus to reform, even though improvements in services sectors themselves could directly contribute to faster GDP growth and hence be reason enough to welcome services policy reform.4 Services reform may affect the performance of manufacturing sectors in at least four ways. First, new services may become available thanks to the entry of new and more sophisticated services providers. Examples include new financial instruments and cash flow management tools, multi‐modal transport services or digital value‐added services in telecommunications. Availability of these services may in turn lead to productivity enhancing changes in manufacturing, such as receiving production orders online or setting up online bidding systems for suppliers. Second, services reform may increase availability of services through, for instance, extending internet coverage to rural areas. The improved access may boost performance of smaller or remotely located enterprises. Third, the reliability of existing services may improve as a result of reform. These improvements will then limit disruptions to production and lower operating costs in downstream manufacturing sectors. Fourth, reducing market power in services may enhance innovation incentives in downstream manufacturing if, prior to the reform, part of the innovation rent was appropriated by upstream service providers (Bourlès et al., 2013). Exploring whether there is a systematic link between liberalisation in services sectors and the performance of firms in downstream manufacturing industries requires three types of information: a measure of policy reform in services, a performance measure for manufacturing firms and information on the linkages between different sectors of the economy. In preparation for this study, a large amount of information on the state and the history of services reform was gathered by local consultants employed by the World Bank in India. The information was then condensed into a composite time‐varying policy index for each sector modelled after a similar index compiled by the European Bank for Reconstruction and Development for countries in Central and Eastern Europe and reported in their flagship publication Transition Report2004 (European Bank for Reconstruction and Development, 2004). The index can take on values ranging from 0 to 5 and is available for four sectors: banking, telecoms, transport and insurance for the time period 1991–2004. Constructing the index is one of the contributions of this study, as it can be used in other research on the impact of Indian policy reforms. The performance of manufacturing firms is measured on the basis of total factor productivity estimates obtained from sector‐specific production functions. To take into account the possible simultaneity bias between unobserved productivity shocks and input choices, we follow the procedure outlined by Ackerberg et al. (2006) which builds on the earlier work by Olley and Pakes (1996) and Levinsohn and Petrin (2003). Unlike the latter method, the approach we follow allows for more plausible assumptions about the timing of the firm’s decision regarding input choices and optimisation errors. To examine the link between the performance of services users and services sector reforms, our analysis relates the productivity of manufacturing firms to the state of liberalisation in services sectors weighted by the respective manufacturing sector’s reliance on inputs from each services sector. The reliance of manufacturing sectors on services inputs is assessed based on the national input–output matrix. Our identifying assumption is that the effect of reforms in specific services sectors should be more pronounced in manufacturing sectors relying more heavily on those services inputs. The specification also controls for the level of import tariffs on output and inputs as well as for firm and year fixed effects. The analysis is based on firm‐level data from the Capitaline database, a commercially available database including balance sheets, profit and loss statements, and ownership information on large private and public firms operating in India. Firms included in the database account for 62% of India’s manufacturing output during the period covered by the analysis. Our data set forms an unbalanced panel covering 3,771 firms or 22,558 firm‐year observations during the 1993–2005 period. Our results suggest that policy reforms in services sectors had a significant impact on firms in the manufacturing sector. The aggregate effect of services liberalisation was an increase in productivity of 11.7% for domestic firms and 13.2% for foreign firms for a one‐standard‐deviation increase in the liberalisation index. When the individual services sectors are examined in the same specification, a one‐standard‐deviation change in the banking sector index corresponds to a 6.5% change in productivity for both domestic and foreign firms. A one‐standard‐deviation change in the telecommunications liberalisation index corresponds to a 7.2% increase in productivity for domestic firms and a 9.8% increase in productivity for foreign firms. A similar change in the transport index leads to a 19% improvement in productivity of all firms. Only foreign firms appear to benefit from the insurance reform, enjoying a productivity boost of 3.3%. Our results are confirmed by an instrumental variable approach in which we instrument for reform in India using measures of services reform in Indonesia and China. Indonesia’s services commitments were made in the context of the Uruguay Round negotiations which led to the General Agreement on Trade in Services (GATS). These commitments reflected liberalisation pressure from the industrial countries on developing countries with large markets and significant services protection – attributes Indonesia shares with India. Chinese services commitments were made during that country’s accession to the WTO and were a result of tough bilateral negotiations with the key WTO members, particularly the EU and the US. Since India sees China as a competitor, China’s market‐opening commitments are likely to have influenced Indian policy reform. The findings are also robust to focusing on structural breaks in services reform instead of using the liberalisation index and to employing alternative proxies for reform based on the extent of privatisation and presence of FDI in services sectors. The results also hold when we exclude manufacturing industries supplying equipment to services sectors. Moreover, the results remain unchanged if we control for delicensing and lifting of restrictions on FDI inflows in a given manufacturing sector. Services reform could be expected to have a first order effect on increasing output of the manufacturing sector. Essentially, reduced input costs or relaxation of critical infrastructure constraints should allow firms to expand (whether or not accompanied by TFP improvements). Therefore, our final exercise focuses on the relationship between services reform and manufacturing output. We find a positive and statistically significant relationship between manufacturing output and the overall index of reform, the banking sector reform measures and the telecom reform, though this last effect is less robust. This article proceeds as follows. Section 1 discusses the related literature. Section 2 describes services liberalisation in India between 1990 and 2005 and presents some evidence on its impact. Section 3 describes the data and the construction of the liberalisation index and reviews our estimation procedures, while Section 5 interprets the results. Section 5 examines the link between services liberalisation and output growth in manufacturing. Section 6 concludes. 1. Related Literature A review of the relevant literature reveals that India’s manufacturing revival has been attributed to many things besides its services reforms and research on other countries also tends to overlook services liberalisation in favour of goods trade liberalisation and foreign direct investment. In the few instances where the role of services reform is considered, the focus has been limited to specific services like banking and infrastructure. India’s liberalisation in the 1990s has made it a rich environment for research on the effects of policy reform on manufacturing performance. Considering the 1991 reforms as a single event, Krishna and Mitra (1998) find both price and productivity effects at the firm level. Khandelwal and Topalova (2011) examine reductions in trade protection in individual industries and find that pro‐competitive forces resulting from lower tariffs on final goods, as well as access to better inputs resulting from lower input tariffs, increased firm‐level productivity, with access to inputs having the larger impact. Sivadasan (2009) considers the liberalisation of both the trade and FDI regime in manufacturing and concludes that both increased firm‐level productivity. In a descriptive analysis, Goldberg et al. (2009) show that trade reform spurred imports of previously unavailable products. New imported inputs often originated from more advanced countries and new imported varieties exhibited higher unit values relative to existing imports. Goldberg et al. (2010a) find that lower input tariffs accounted on average for 31% of the new products introduced by Indian firms, which suggest that an important consequence of the input tariff liberalisation was to relax technological constraints through firms’ access to new imported inputs that were unavailable prior to the liberalisation. Other key contributions have looked beyond policy in manufacturing per se, but focused primarily on institutional factors affecting the distribution of benefits from reforms and liberalisation across industries and states. Besley and Burgess (2004) exploit variation in labour regulations across Indian states and find that labour market reforms were a significant determinant of manufacturing output per capita. Aghion et al. (2008) show that the effects of liberalising the system of central controls regulating entry and production activity were stronger in areas where organised labour was relatively weak, arguing that firms were better able to adapt to the new regime in regions where regulations were more pro‐industry. Harrison et al. (2011) find that market‐share reallocations played an important role in aggregate productivity gains immediately following the start of India’s trade reforms in 1991. However, aggregate productivity gains during the overall 20‐year period from 1985 to 2004 were driven largely by improvements in average productivity, which can be attributed to India’s trade liberalisation and FDI reforms. Goldberg et al. (2010b) investigate the impact of liberalisation on Indian firms’ product choice and find little evidence of ‘creative destruction’ in the 1990s, that is Indian firms infrequently discontinued product lines even during a period of trade and structural reform. They argue that remnants of industrial licensing and rigid labour market regulation in the Indian economy prevented firms from adjusting fully to reforms. The emphasis on attributing changes in manufacturing performance to changes in trade, investment and labour market policies in goods characterises much of the existing empirical work on liberalisation in developing countries. For instance, Pavcnik (2002) uses plant level data from Chile to find that trade liberalisation forces exit of the least productive firms while increasing productivity of the remaining firms in the import competing sectors. Empirical research on liberalisation of foreign direct investment has produced mixed results. Aitken and Harrison (1999) find what they term ‘the market stealing effect’ of foreign direct investment which swamps the positive effect of technology transfer on firm productivity in Venezuela. Javorcik (2004) explicitly distinguishes between intra and inter‐industry effects of foreign direct investment using firm level data from Lithuania and finds that foreign direct investment has a positive productivity effect on supplier industries but no significant effect on local competitors in the same industry. Javorcik and Li (2013) show that entry of foreign retail chains boosts the productivity of the supplying industries in Romania. Downstream spillovers arising from policy reform and foreign participation in the services sectors are likely to be different from those arising from foreign direct investment in manufacturing industries. Disruption in the provision of services can result in large delays in production and product delivery, high information costs and an inability to invest in potentially profitable new activities. There has not, however, been much empirical analysis of the downstream effects of services reform. One exception is Banga and Goldar (2004), which undertakes a simple growth‐accounting exercise using Indian industry‐level data and documents a positive relationship between the use of services and output growth and productivity in manufacturing industries, which is consistent with the findings in this study. Another is Bourlès et al. (2013), which relies on industry‐level data and finds that anti‐competitive upstream regulations have significantly curbed productivity growth in OECD countries. Beyond these papers, the few existing studies have focused on specific services sectors, usually banking.5 Rajan and Zingales (1998) show that financial development increases growth. They weight industries by dependence on outside financing (as estimated from US data) and find that firms in industries which are more dependent on external financing gain more from financial development than other firms. Bertrand et al. (2004) demonstrate that banking deregulation in France in 1985 led to improved productivity in manufacturing firms. Entry and exit rates increased following liberalisation, suggesting that less productive firms had been protected by the easy access to credit allocated to large firms by the previously nationalised banking sector. Productivity effects were particularly strong in banking‐dependent sectors. The present study is most closely related to Arnold et al. (2011) which uses firm‐level data to show that increased foreign participation in services provision led to improvement in manufacturing productivity in the Czech Republic in the period 1998–2003. The current article studies the more complex and dynamic Indian context with new data on and measures of services reform. Furthermore, while the previous paper considered the services sector as a whole, in the present study, by separating the liberalisation measures into measures for banking, telecommunications, transport and insurance services, we are able to identify the impact of key reforms in individual sectors. Finally, in contrast to the previous paper, we distinguish between the implications of services liberalisation for domestic and foreign manufacturers. 2. Services Reform in India After decades of state dominance, India’s economic landscape was transformed with the liberalisation of manufacturing in the late 1980s and early 1990s, and the liberalisation of services during the 1990s and 2000s. This Section describes the key reforms in individual services sectors, their determinants and their consequences. We first provide some evidence that the pattern and pace of services reform reflected sector‐specific political forces that were to an extent exogenous to the developments in the downstream manufacturing sector. We then show that the reforms had an impact on the performance of the services sectors. 2.1. The Genesis and Pattern of Reform in Services Sectors In the 1980s, the services sectors in India were dominated by state enterprises, there were restrictions on entry by private domestic and foreign providers, and prices of services were largely fixed by the government (World Bank, 2004). The 1990s saw significant liberalisation, with greater freedom of establishment for domestic and, in some cases, foreign providers, greater operational autonomy for providers and greater reliance on market‐based allocation mechanisms. The pace of policy reform has, however, varied across sectors and been determined primarily by political considerations (Hoekman et al., 2007). Sectors in which privatisation and competition would mean restructuring and large scale lay‐offs saw slower reforms than those in which incumbents could remain profitable and employment would not decline even as foreign and local private competitors entered the market. Reforms were also slower in sectors where it was feared that they could cause a reduction in access to services for poor or rural communities. Most political economy explanations for the pace and pattern of reforms point to considerations in the services sectors themselves rather than in downstream industries.6 Services sectors in India can today be separated into three broad categories: significantly liberalised, moderately liberalised and closed. The telecommunications sector was operated solely by the central government until 1992, when the government began to issue select operating licences to private providers. In 1994, mobile phone service began and the government announced the National Telecom Policy which improved the environment for private investment. In 2002, the government fully opened the long distance sector of the telecom industry to private competition and eliminated all restrictions on the number of service providers, except in areas where limits are dictated by the availability of spectrum. Foreign ownership limitations were also significantly relaxed and now range from 74% to 100% across different segments. To those accustomed to the glacial pace of reform in India, the telecommunications experience seems highly unusual. Discussions with policy makers suggest that technology trumped all other considerations in this sector and India sought to exploit new technological possibilities by rapidly introducing competition.7 Public sector incumbents reincarnated as more or less successful participants with a stake in a competitive and rapidly growing market. The number of telephone subscribers has increased rapidly, with most of the increases taking place in private sector companies (OECD, 2011). The expansion in scale dwarfed any adverse effects of diminished labour intensity – employment grew by as much as a third in the six years following the first significant liberalisation in 1994. It also became evident that better access to services could be achieved than what had been possible with public monopoly, attenuating concerns regarding distributional equity and weakness of regulatory capacity. In the moderately liberalised sectors, Indian firms may remain disadvantaged by the legacies of past policies and hence ill‐equipped to compete. The best example is the banking sector where nationalisation in 1969 of the largest private sector banks led to a sector dominated by public sector banks committed to directing credit to areas identified by the government as priorities.8 Directed lending and interest rate regulations prescribed the credit portfolios which banks were required to hold, challenging the long‐term solvency of many banks (Reddy, 2005). Banks were also required to hold large percentages of their portfolios in government securities bought at concessional interest rates. In 1977, the government began requiring any bank that wanted to open a branch in an area which already had a bank branch to open four branches in (rural) areas with no financial services (Burgess and Pande, 2005). The effect was to generate excessive staffing levels, unprofitable rural branches and large levels of non‐performing loans. The close relationship existing between the banks, the government and central bank created the potential for moral hazard as banks expected government intervention in the event of a failure (Reddy, 2004). Liberalisation of the banking sector was handled by the Reserve Bank of India (RBI) with a focus on maintaining the viability of existing banks while increasing competition and efficiency in the sector (Reddy, 2005). In 1994, liberalisation began with increased approval of private sector banks by the RBI. In 2001, the government began deregulation of the interest rate and, in 2002, foreign participation in the banking sector was allowed up to 49% in private banks. There was also a further increase in the approval rate for the entry of new private banks. At the same time, India has made banking sector liberalisation conditional on improving the competitiveness of public sector banks through measures such as mergers, voluntary worker retirement schemes and the creation of asset management companies to deal with non‐performing assets. A 2004 rule allowed foreign banks to acquire up to a 74% stake in branches listed by the RBI as having weak portfolios; foreign institutions are allowed only a 20% stake in branches which are performing well. Foreign banks may now operate through licensed branches and as fully owned subsidiaries but a few key restrictions remain in the banking sector. There is a cap on the number of licences for branches at 20 per year for both new and existing banks, and the share of foreign bank assets in total banking assets may not exceed 15%. Despite these limitations in the pace of reforms, banking concentration has decreased visibly and the market share of new banks has increased to around 25% (OECD, 2011). The insurance sector has been liberalised more slowly than the other sectors. Prior to liberalisation, the insurance sector was controlled by the Ministry of Finance through publicly owned companies. In 1999, the Insurance Regulatory Development Authority bill was passed which allowed private sector companies to enter the insurance market. Foreign equity participation in the insurance sector is restricted to 26% and foreign firms are allowed entry only through partnerships or joint ventures. The funds of policyholders must be retained within the country and there is compulsory exposure to the rural and social sector, including crop insurance. Entry into the insurance market by private sector providers finally began in 2002 when twelve private sector insurers entered the market. All subsectors of transport services were operated primarily by public sector companies prior to liberalisation. Air transport was run by two publicly owned carriers, states controlled the ports for maritime industries and a large segment of the shipping sector was heavily regulated and dominated by publicly owned companies. In 1997, foreign direct investment up to 40% was allowed in airlines, 74% foreign direct investment was allowed in port construction and private sector companies were allowed to contract for infrastructure maintenance and construction. In air transport, for example, the remarkable increases in passenger traffic can be attributed almost entirely to private entrants (OECD, 2011). Yet transport sectors remain subject to state‐level regulations which vary significantly across states, with haulage particularly susceptible to local political pressures. Professional services including accounting, legal and other services sectors such as retail distribution, postal and rail transport services are formally closed to foreign participation.9 FDI is not allowed in the accounting and legal sectors. Within distribution services, FDI is not allowed in the retail segment but there are no limits in other areas, except the requirement of approval for commission agents, franchising services and wholesale trade. The closed sectors are characterised by domestic firms that are sub‐optimal in size and handicapped by an inhibiting and weak regulatory environment. Many Indian services in closed sectors are highly fragmented by international standards.10 Here, adjustment and employment concerns are the dominant factor impeding liberalisation. A more detailed survey of the liberalisation reforms is provided in online Appendix A. 2.2. The Impact of Reform The elimination of barriers to entry in services co‐incided with dramatic changes both at foreign and domestic providers (Gordon and Gupta, 2004). FDI inflows into services following liberalisation by far exceeded those into other sectors. About 10% of FDI inflows during 1990–2005 went into the transport sector, 9.6% of the inflows were into the telecommunications sector and 9.6% of the inflows were into the financial and other services sector (Indiastat, 2008). At the same time, the services sector grew by an average of 11% per year, with the more liberalised sectors generally growing at relatively faster rates (Figure D1 in online Appendix D and Eichengreen and Gupta, 2011). The share of services in overall value added rose from 39% in 1993 to 50% in 2004 (National Accounts Statistics, 2005). Growth has been particularly strong in the services sectors on which we focus in this study: communication services displayed average annual growth rates of 13.6% in the 1990s, while banking grew by 12.7% on average, transport grew at an average rate of 6.9% and insurance grew at a rate of 6.7% (Gordon and Gupta, 2004). Output per worker in the services sectors in India has increased by over 7.5% per year during the 1990s, clearly outpacing the agricultural or industrial sectors (Bosworth and Collins, 2008). Other evidence suggests that strong total factor productivity growth was at the root of this remarkable performance, not capital deepening or higher markups (Gordon and Gupta, 2004; Bosworth et al., 2007). Indeed, services prices decreased relative to manufacturing prices, as indicated by a slower pace of growth in the services deflator than the overall GDP deflator. The reforms are likely to be responsible for the striking improvements in the performance of services sectors. In maritime transport, the average turnaround time for a container at major ports in India in 1990 was 8 days and at major Mumbai ports the average was 11. This meant that manufacturing companies exporting their products or importing inputs had to factor in more than a week of transit time for their goods, which increased the cash outlays necessary for exporting and importing. By 2005, the average turnaround time at major ports in India had decreased to 3.5 days, with 4.5 days as the average time at Mumbai ports (see Figures D2 and D3 in the online Appendix D). In the 1980s, air transport providers and several of the largest shipping companies were publicly owned. After liberalisation, increasing competition from foreign companies put pressure on Indian carriers to improve their performance. They responded positively and operating efficiency increased. In fact, operating revenue per employee in Indian Airlines increased over 5 times over the period 1990–2004 from 0.5 million Indian Rupees per employee to 2.5 million Indian Rupees per employee. The increased efficiency is likely to have led to the continued growth of India carriers in the period 1990–2005, of nearly 15% yearly in passenger traffic and 11% yearly in cargo traffic (Directorate General of Civil Aviation, 2006). These improvements seem to have benefited manufacturing firms as they allowed them to get their products to markets more efficiently: while 60% of manufacturing firms viewed transport as an obstacle to business operations in 2002, this figure declined to 35% in 2006 (see Table 1).11 Better functioning transport services are likely to have improved the ability of Indian firms to compete in highly variable markets such as textiles and electronics in which the ability to respond quickly to changes in demand is crucial. Table 1 Improvements in Telecom, Transport and Banking Services Between 2002 and 2006 . All respondents . Foreign firms . Domestic firms . . 2002 . 2006 . 2002 . 2006 . 2002 . 2006 . Percentage of respondents reporting it is an obstacle to business operations* Telecommunications 41 23 50 35 41 23 Transport 60 35 62 54 60 35 Access to finance 61 41 41 43 62 40 Cost of finance 68 45 56 51 68 45 No. of days needed to obtain a phone connection (landline)† 50th percentile of respondents 30 4 10 2 30 4 75th percentile of respondents 60 10 30 7 60 10 95th percentile of respondents 365 30 180 20 365 30 . All respondents . Foreign firms . Domestic firms . . 2002 . 2006 . 2002 . 2006 . 2002 . 2006 . Percentage of respondents reporting it is an obstacle to business operations* Telecommunications 41 23 50 35 41 23 Transport 60 35 62 54 60 35 Access to finance 61 41 41 43 62 40 Cost of finance 68 45 56 51 68 45 No. of days needed to obtain a phone connection (landline)† 50th percentile of respondents 30 4 10 2 30 4 75th percentile of respondents 60 10 30 7 60 10 95th percentile of respondents 365 30 180 20 365 30 Notes In response to the question: ‘Please tell us if any of the following issues are a problem for the operation and growth of your business. (A) Telecommunications. (B) Transportation. (C) Access to financing (e.g. collateral). (D) Cost of financing (e.g. interest rates)’. In response to the question ‘Based on the experience of your establishment over the last two years, what is the actual delay experienced (from the day you applied to the day you received the service or approval) (i) Mainline telephone connection’. Source. World Bank investment climate surveys in India in 2002 and 2006. There were 1,818 manufacturing firms surveyed in India in 2002 of which 1,784 were domestic and 34 were foreign. There were 2,195 manufacturing firms surveyed in India in 2006 of which 2,158 were domestic and 37 were foreign. Open in new tab Table 1 Improvements in Telecom, Transport and Banking Services Between 2002 and 2006 . All respondents . Foreign firms . Domestic firms . . 2002 . 2006 . 2002 . 2006 . 2002 . 2006 . Percentage of respondents reporting it is an obstacle to business operations* Telecommunications 41 23 50 35 41 23 Transport 60 35 62 54 60 35 Access to finance 61 41 41 43 62 40 Cost of finance 68 45 56 51 68 45 No. of days needed to obtain a phone connection (landline)† 50th percentile of respondents 30 4 10 2 30 4 75th percentile of respondents 60 10 30 7 60 10 95th percentile of respondents 365 30 180 20 365 30 . All respondents . Foreign firms . Domestic firms . . 2002 . 2006 . 2002 . 2006 . 2002 . 2006 . Percentage of respondents reporting it is an obstacle to business operations* Telecommunications 41 23 50 35 41 23 Transport 60 35 62 54 60 35 Access to finance 61 41 41 43 62 40 Cost of finance 68 45 56 51 68 45 No. of days needed to obtain a phone connection (landline)† 50th percentile of respondents 30 4 10 2 30 4 75th percentile of respondents 60 10 30 7 60 10 95th percentile of respondents 365 30 180 20 365 30 Notes In response to the question: ‘Please tell us if any of the following issues are a problem for the operation and growth of your business. (A) Telecommunications. (B) Transportation. (C) Access to financing (e.g. collateral). (D) Cost of financing (e.g. interest rates)’. In response to the question ‘Based on the experience of your establishment over the last two years, what is the actual delay experienced (from the day you applied to the day you received the service or approval) (i) Mainline telephone connection’. Source. World Bank investment climate surveys in India in 2002 and 2006. There were 1,818 manufacturing firms surveyed in India in 2002 of which 1,784 were domestic and 34 were foreign. There were 2,195 manufacturing firms surveyed in India in 2006 of which 2,158 were domestic and 37 were foreign. Open in new tab In banking, Banerjee and Duflo (2014) find that prior to liberalisation even at the most efficient public sector banks, bank loan approvals in 64% of cases were mechanically made for the same loan amount as prior loans. The rationing of credit by the public sector limited the ability of companies to respond to new business opportunities and finance improvements in products or production processes. Because liberalisation allowed banks to set interest rates at their risk‐adjusted cost of capital and choose diversified loan portfolios, by 2005 the level of investment by banks increased to 4.75 times the size of investment in 1994. The share of investment by foreign and private banks also increased during the period from 11% in 1994 to 24% in 2005. Despite the slow pace of reforms, credit provision and investment have increased across the sector, led by foreign and locally owned private banks (Reserve Bank of India, 2008). As illustrated in Table 1, manufacturing firms in India saw an improvement in their access to finance and cost of financing as a result of the banking sector liberalisation. While 61% of Indian firms reported that access to finance was an obstacle to their business in 2002, only 41 did so in 2006. For the cost of financing, the corresponding figures were 68% and 45% respectively. Before the beginning of the reforms in telecommunications, the sector was controlled by MTNL, a publicly owned company which provided local telephone service, and VSNL, a publicly owned company which provided long distance service. Both companies were plagued by frequent service interruptions, which averaged 19 faults per 100 stations per month in 1991. In addition, service was poorly distributed and access to new lines was difficult.12 Businesses were severely handicapped in their ability to communicate with their customers and suppliers and to coordinate activity across plants. Liberalisation appears to have interacted with technological change to transform the telecommunications market. By 2005, the number of faults had declined to 7.5% and the waiting lists for telephone services had virtually disappeared in urban areas (Figures D4 and D5 in online Appendix D). Even rural customers, projected by critics of the liberalisation reforms to lose from the privatisation, saw increases in access to phone lines. Access to internet services, provided initially only by MTNL, increased quickly when private providers were allowed to enter the market (Figures D6 in online Appendix D). Improvements in telecommunications services, following the opening of the sector to private investment, were reflected in the results of the World Bank survey: in 2002, 41% of manufacturing firms felt that telecommunications availability was an obstacle to business operations, in 2006 only 23% were not satisfied with telecom services (see Table 1). The median respondent’s wait for a mainline phone connection decreased from 30 days to 4 days. Until 2002, private sector competition in the insurance market was proscribed, severely limiting the range of insurance services on offer. Market penetration of insurance quickly increased following the entry of private and foreign insurers. After decades of public monopoly, premiums were equal to only 1.9% of GDP in 1999–2000 but they jumped to 2.86% of GDP by 2002–3 (Insurance Development and Regulatory Authority, 2004). Government projections at the time of liberalisation suggested that market participation by foreign firms in 2005 would reach only 5% of the market, but by November 2005, private firms with foreign shareholding had acquired a 34% market share. This corresponded to limited contraction by Indian public sector incumbents (Department of Public Enterprises, 2003).13 In sum, liberalisation was associated with a metamorphosis of services in India from a narrow range of products of sub‐standard quality and poor distribution, to the current environment in which service providers are highly competitive and offer their consumers, including manufacturing firms, a wide range of new and high quality services products. 3. Empirical Strategy In this study, we investigate whether there is a systematic link between liberalisation in services sectors and the performance of firms in downstream manufacturing industries. This exercise requires three pieces of information: a measure of policy reform in services, a performance measure for manufacturing firms and information on the linkages between different sectors of the economy. 3.1. Measuring Services Reform In order to make the detailed information on services sector reforms in India which was gathered for this study amenable to quantitative analysis, we condense the information into a composite policy index for each sector. In doing so, we have been guided by a similar index compiled by the European Bank for Reconstruction and Development for countries in Central and Eastern Europe and reported in the flagship publication Transition Report 2004. This approach starts from a general template of reforms necessary to achieve a desirable policy environment, which is then adapted to the specific situation of each sector. For each services sector k, the time‐varying services liberalisation index reformkt ranges from 0 to a maximum score of 5. An index value of 0 corresponds to a situation where the public sector is either the only relevant provider of services or has a strong grip on private providers and there is extremely limited scope for the market mechanism. Note that all Indian services sectors considered here fall into this highly restrictive category before the beginning of economic reforms in the early 1990s. A level of 1 indicates at least some scope for private sector participation and some liberalisation of operational decisions, combined with very limited scope for foreign participation (limited, e.g. by low FDI ceilings). To qualify for an index value of 2, there must be only a limited degree of interference with operational decisions by public authorities, substantial price liberalisation and clear scope for foreign participation even if only in narrowly defined segments and as minority shareholders. Still, the state may remain a dominant actor in the sector. An index of 3 implies significant scope for private providers, including foreign ones, clear competitive pressure on the public incumbents from new entrants and explicit possibilities for foreign equity participation. A level of 4 is equivalent to little public intervention and the freedom of operation of private providers, the possibility of majority foreign ownership and the dominance of private sector entities. Finally, a level of 5 (not attained in any of the sectors considered here) would reflect an equal treatment of foreign and domestic providers, a full convergence of regulation with international standards and unrestricted entry into the sector. The details of how the index was constructed are presented in online Appendix B. The index is available for four sectors: banking, telecoms, transport and insurance for the time period 1991–2004. 3.2. Linkages Between Manufacturing Industries and Services Sectors The next question in our analysis is how to aggregate these sector‐specific indices into a single index of services reform. Given that some services are likely to be more important for manufacturing industries than others and that this dependence may vary across different manufacturing industries, an unweighted average of services sector indices is unlikely to be an appropriate measure of the potential impact of upstream services liberalisation on the performance of manufacturing firms. Instead, we use information on the intensity with which services inputs are used in the production of a given manufacturing sector. In particular, we weight each of the reform indices for the four major services sectors (banking, telecom, transport and insurance) by the proportion αjk of inputs sourced by the manufacturing sector j from the services sector k to create the index of services reform: Services_Indexjt=∑kαjkreformkt,(1) where αjk is based on the input–output matrix pertaining to 1993, the first year of our sample.14 Data from a national input–output matrix contain information about the average inter‐industry sourcing behaviour of firms in a given sector of the economy. By using average information, we lose some precision in measuring the reliance of firms on services inputs but we can be less concerned about the endogeneity of this measure. The fact that we use sourcing information from the 1993 input–output matrix should further minimise the scope for endogeneity even at the level of the average firm in an industry.1516 In our analysis, we will also distinguish between the effects of reform in individual services sectors. To do so, we will construct indices capturing the reform in a particular services sector. For instance, we will define Banking_Indexjt=αj,bankingreformbanking,t,(2) where αj,banking reflects the proportion of inputs sourced by the manufacturing sector j from the banking sector, according to the input–output matrix and reformbanking,t is the state of reform in the banking industry at time t. We will follow the same approach to construct indices for telecom, insurance and transport sectors. For the banking sector, an alternative measure of financial dependence will help us to test the robustness of the main measure. This alternative is based on Rajan and Zingales (1998), who compute sector averages of financial dependence based on US data and argue that this is a suitable measure for firms’ technologically induced demand for external finance in an environment with well‐developed financial markets. The measure is based on a comparison between firms’ investment outlays and own cash flow. 3.3. Measuring the Performance of Manufacturing Firms Our goal is to provide a more complete explanation of the improvement in the performance of the Indian manufacturing sector following the post‐1991 economic reforms. We use firm‐level data from the Capitaline database, a commercially available database including balance sheets, profit and loss statements and ownership information on large private and public firms operating in India to measure the performance of manufacturing firms.17 The database covers 62% of India’s manufacturing output during the period considered by the analysis, and includes 11,939 firms, of which 5,236 operate in the manufacturing sector. The data set forms an unbalanced panel due to firm entry and exit covering the period 1993–2005. Firms’ industry affiliations follow India’s National Industry Classification (NIC) which encompasses the manufacturing sectors. After cleaning the data and discarding firms not reporting information on output or production inputs, we are left with 3,771 firms or 22,558 firm‐year observations. A total of 2,224 firms are observed in the data for at least 5 years, while 1,124 are observed for 9 years or longer. A consistent measurement of firm performance is crucial to our analysis. We use the total factor productivity (TFP) as our outcome of interest. To control for a possible simultaneity bias arising from the endogeneity of a firm’s input selection, which will exist if a firm responds to productivity shocks unobservable to the econometrician by adjusting its variable input choices, we follow the method proposed by Ackerberg et al. (2006). Ackerberg et al. build on the widely used estimation procedures proposed by Olley and Pakes (1996) and Levinsohn and Petrin (2003). Unlike the latter method, their approach allows for more plausible assumptions about the timing of the firm’s decision regarding input choices and optimisation errors. We use the Ackerberg et al. (2006)method to estimate sector‐specific production functions and obtain the TFP as the residual from this estimation.18 We group some smaller industries together in order to facilitate the estimation.19 Following the advice of Ackerberg et al., we use value added as the dependent variable in the production function. Value added is defined as the sales of firm i in year t less the value of material, services and energy inputs. All components of value added are expressed in real terms. Capital and labour inputs (expressed in real terms) are included as independent variables. Material and services inputs (in real terms) are used to proxy for the productivity shocks. Nominal output is deflated by a set of wholesale price indices disaggregated at the 2‐digit level, while capital inputs are calculated from detailed data on net values of land, buildings, machinery and computers, all deflated by the relevant sector deflators. In the absence of data on the number of workers employed, the labour input is calculated by normalising the wage bill of each firm by the average wage prevailing in a given 2‐digit sector in a given year.20 Materials are deflated by input–output‐co‐efficient‐weighted sector deflators based on the wholesale price index. Energy inputs are deflated using National Accounts Statistics price indices for ‘Fuel, Power, Light and Lubricants’. Services inputs are aggregated from detailed data on reported expenses on travel, transport, legal services and accounting, and non‐interest banking expenses. These items are deflated using a weighted average of services sector deflators from the national accounts statistics (Central Statistics Office, 2005). Given that our interest is in upstream services reform, a proper accounting for services inputs at the firm level is essential to control for changes in the intensity with which firms use services in their production, in response to enhanced product offerings in the service sectors. Summary statistics for all the variables are presented in Table 2. Table 2 Summary Statistics Variable . Observation . Mean . SD . ln TFP Ackerberg et al. (2006) 22,558 1.53 1.10 ln Output 22,558 2.57 2.01 ln Energy 22,558 −0.12 2.04 ln Capital 22,558 2.52 1.77 ln Labour 22,558 0.45 1.79 ln Material inputs 22,558 2.62 1.90 ln Services inputs 22,302 0.27 1.92 Services Index lagged 22,558 0.18 0.10 Banking Index lagged 22,558 0.06 0.07 Rajan–Zingales Banking Index lagged 22,558 0.71 0.74 Telecom Index lagged 22,558 0.02 0.02 Insurance Index 22,558 0.01 0.02 Transport Index lagged 22,558 0.10 0.04 Foreign Dummy 22,558 0.18 0.38 Tariff lagged 22,558 36.47 17.17 Input Tariff lagged 22,558 16.41 9.38 Delicensing lagged 22,558 0.97 0.15 FDI reform lagged 22,558 0.87 0.33 Variable . Observation . Mean . SD . ln TFP Ackerberg et al. (2006) 22,558 1.53 1.10 ln Output 22,558 2.57 2.01 ln Energy 22,558 −0.12 2.04 ln Capital 22,558 2.52 1.77 ln Labour 22,558 0.45 1.79 ln Material inputs 22,558 2.62 1.90 ln Services inputs 22,302 0.27 1.92 Services Index lagged 22,558 0.18 0.10 Banking Index lagged 22,558 0.06 0.07 Rajan–Zingales Banking Index lagged 22,558 0.71 0.74 Telecom Index lagged 22,558 0.02 0.02 Insurance Index 22,558 0.01 0.02 Transport Index lagged 22,558 0.10 0.04 Foreign Dummy 22,558 0.18 0.38 Tariff lagged 22,558 36.47 17.17 Input Tariff lagged 22,558 16.41 9.38 Delicensing lagged 22,558 0.97 0.15 FDI reform lagged 22,558 0.87 0.33 Open in new tab Table 2 Summary Statistics Variable . Observation . Mean . SD . ln TFP Ackerberg et al. (2006) 22,558 1.53 1.10 ln Output 22,558 2.57 2.01 ln Energy 22,558 −0.12 2.04 ln Capital 22,558 2.52 1.77 ln Labour 22,558 0.45 1.79 ln Material inputs 22,558 2.62 1.90 ln Services inputs 22,302 0.27 1.92 Services Index lagged 22,558 0.18 0.10 Banking Index lagged 22,558 0.06 0.07 Rajan–Zingales Banking Index lagged 22,558 0.71 0.74 Telecom Index lagged 22,558 0.02 0.02 Insurance Index 22,558 0.01 0.02 Transport Index lagged 22,558 0.10 0.04 Foreign Dummy 22,558 0.18 0.38 Tariff lagged 22,558 36.47 17.17 Input Tariff lagged 22,558 16.41 9.38 Delicensing lagged 22,558 0.97 0.15 FDI reform lagged 22,558 0.87 0.33 Variable . Observation . Mean . SD . ln TFP Ackerberg et al. (2006) 22,558 1.53 1.10 ln Output 22,558 2.57 2.01 ln Energy 22,558 −0.12 2.04 ln Capital 22,558 2.52 1.77 ln Labour 22,558 0.45 1.79 ln Material inputs 22,558 2.62 1.90 ln Services inputs 22,302 0.27 1.92 Services Index lagged 22,558 0.18 0.10 Banking Index lagged 22,558 0.06 0.07 Rajan–Zingales Banking Index lagged 22,558 0.71 0.74 Telecom Index lagged 22,558 0.02 0.02 Insurance Index 22,558 0.01 0.02 Transport Index lagged 22,558 0.10 0.04 Foreign Dummy 22,558 0.18 0.38 Tariff lagged 22,558 36.47 17.17 Input Tariff lagged 22,558 16.41 9.38 Delicensing lagged 22,558 0.97 0.15 FDI reform lagged 22,558 0.87 0.33 Open in new tab To establish whether there exists a link between the performance of manufacturing firms and liberalisation of upstream services sectors, we regress the TFP of a manufacturing firm i operating in industry j at time t on the aggregated Services_Indexjt‐1 lagged one period on the disaggregated indices of services reform.21 We control for foreign ownership, trade liberalisation, firm fixed effects and year fixed effects. Our principal estimation equation has the following form: lnTFPijt=γ1Services_Indexjt−1+γ2Tariffjt−1+γ3Inputtariffjt−1+γ4Foreignit+αi+αt+εit.(3) Services sectors were not the only item on the post‐1991 reform agenda in India. Continued reductions in manufactured product tariff rates occurring during the same period may also have influenced manufacturing productivity. To control for changes in tariff rates, we include lagged output tariffs in the same manufacturing sector (Tariffjt‐1) and a weighted measure of input tariffs (Input tariffjt‐1). The weights of the input tariffs are taken from the 1993 input–output matrix, while the aggregation of individual tariff lines to the 2‐digit sector level is achieved using the 1990 import weights. The information on tariffs was obtained from the World Bank’s WITS database.22 As many studies find that foreign affiliates tend to outperform domestic producers (Aitken and Harrison, 1999; Arnold and Javorcik, 2009), we include an indicator for foreign‐owned firms, equal to one if the foreign ownership share in firm i is above 10% at time t (Foreignit). In an expanded specification, we allow for differential effects of services reform on domestic and foreign firms by interacting Foreignit with the Services_Indexjt‐1. The dependent variable is specific to a firm‐year combination but our variables of interest vary at the sector‐year level, therefore, we cluster standard errors at the sector‐year level.23 As a benchmark, we also use OLS to estimate an augmented Cobb–Douglas production function. To make it comparable to the Ackerberg et al. procedure, we regress real firm value added (defined as above) on real labour and capital inputs as well as measures of services reform and other control variables:24 lnVAijt=β1jlnKit+β2jlnLit+β3Services_Indexjt−1+β4Tariffit−1+β5Inputtariffit−1+β6Foreignit+αi+αt+νit,(4) where VAijt stands for the value added of firm i observed in year t (and manufacturing industry j), Kit denotes capital and Lit labour. Note that we allow the co‐efficients on capital and labour inputs to differ across 11 manufacturing sectors. As in specification (3), we include firm and year fixed effects and cluster standard errors at the sector‐year level. Our point estimates for the production function co‐efficients, presented in Table 3, have reasonable values. On average, the labour co‐efficient is 0.73 in the OLS and 0.75 in the Ackerberg et al. (2006) specification, and the capital co‐efficient is equal to 0.27 in both cases. In 9 of 11 industries, the co‐efficient on the capital input is higher in the Ackerberg et al. procedure, which is what we would expect to observe under plausible assumptions (Olley and Pakes, 1996). The average returns to scale are very close to constant (1.00 and 1.01). Table 3 Production Function Coefficients . OLS . Ackerberg et al. (2006) . . Capital . Labour . Sum . Capital . Labour . Sum . Food processing and tobacco products 0.155 0.682 0.837 0.166 0.829 0.995 Textiles 0.345 0.604 0.949 0.357 0.543 0.900 Garments, leather goods and shoes 1.002 0.707 1.709 0.074 0.898 0.972 Wood products, paper products, printing and publishing 0.116 0.864 0.980 0.302 0.780 1.081 Coke, fuel, petroleum and chemicals 0.216 0.616 0.832 0.295 0.811 1.106 Plastic and rubber products 0.326 0.660 0.986 0.261 0.778 1.039 Concrete, cement and glass 0.139 0.735 0.874 0.437 0.651 1.089 Iron and steel 0.211 0.611 0.822 0.257 0.677 0.934 Metal products, machinery and tools 0.056 0.832 0.888 0.145 0.831 0.975 Electrical, lifting, medical and industrial equipment 0.189 0.824 1.013 0.325 0.678 1.003 Motor vehicles and transport systems 0.218 0.870 1.088 0.312 0.745 1.058 . OLS . Ackerberg et al. (2006) . . Capital . Labour . Sum . Capital . Labour . Sum . Food processing and tobacco products 0.155 0.682 0.837 0.166 0.829 0.995 Textiles 0.345 0.604 0.949 0.357 0.543 0.900 Garments, leather goods and shoes 1.002 0.707 1.709 0.074 0.898 0.972 Wood products, paper products, printing and publishing 0.116 0.864 0.980 0.302 0.780 1.081 Coke, fuel, petroleum and chemicals 0.216 0.616 0.832 0.295 0.811 1.106 Plastic and rubber products 0.326 0.660 0.986 0.261 0.778 1.039 Concrete, cement and glass 0.139 0.735 0.874 0.437 0.651 1.089 Iron and steel 0.211 0.611 0.822 0.257 0.677 0.934 Metal products, machinery and tools 0.056 0.832 0.888 0.145 0.831 0.975 Electrical, lifting, medical and industrial equipment 0.189 0.824 1.013 0.325 0.678 1.003 Motor vehicles and transport systems 0.218 0.870 1.088 0.312 0.745 1.058 Open in new tab Table 3 Production Function Coefficients . OLS . Ackerberg et al. (2006) . . Capital . Labour . Sum . Capital . Labour . Sum . Food processing and tobacco products 0.155 0.682 0.837 0.166 0.829 0.995 Textiles 0.345 0.604 0.949 0.357 0.543 0.900 Garments, leather goods and shoes 1.002 0.707 1.709 0.074 0.898 0.972 Wood products, paper products, printing and publishing 0.116 0.864 0.980 0.302 0.780 1.081 Coke, fuel, petroleum and chemicals 0.216 0.616 0.832 0.295 0.811 1.106 Plastic and rubber products 0.326 0.660 0.986 0.261 0.778 1.039 Concrete, cement and glass 0.139 0.735 0.874 0.437 0.651 1.089 Iron and steel 0.211 0.611 0.822 0.257 0.677 0.934 Metal products, machinery and tools 0.056 0.832 0.888 0.145 0.831 0.975 Electrical, lifting, medical and industrial equipment 0.189 0.824 1.013 0.325 0.678 1.003 Motor vehicles and transport systems 0.218 0.870 1.088 0.312 0.745 1.058 . OLS . Ackerberg et al. (2006) . . Capital . Labour . Sum . Capital . Labour . Sum . Food processing and tobacco products 0.155 0.682 0.837 0.166 0.829 0.995 Textiles 0.345 0.604 0.949 0.357 0.543 0.900 Garments, leather goods and shoes 1.002 0.707 1.709 0.074 0.898 0.972 Wood products, paper products, printing and publishing 0.116 0.864 0.980 0.302 0.780 1.081 Coke, fuel, petroleum and chemicals 0.216 0.616 0.832 0.295 0.811 1.106 Plastic and rubber products 0.326 0.660 0.986 0.261 0.778 1.039 Concrete, cement and glass 0.139 0.735 0.874 0.437 0.651 1.089 Iron and steel 0.211 0.611 0.822 0.257 0.677 0.934 Metal products, machinery and tools 0.056 0.832 0.888 0.145 0.831 0.975 Electrical, lifting, medical and industrial equipment 0.189 0.824 1.013 0.325 0.678 1.003 Motor vehicles and transport systems 0.218 0.870 1.088 0.312 0.745 1.058 Open in new tab 4. Results 4.1. Baseline Specification Our baseline regression results from estimating (4) are presented in Table 4. We find that the aggregate services index has a positive and highly significant co‐efficient estimate, suggesting a strong role for services liberalisation in explaining manufacturing firm productivity in India. A one‐standard‐deviation change in the aggregate services index improves manufacturing productivity on average by 9.1%. The change in the index observed between 1993 and 2004 corresponds to an improvement of 23%. We also enter the individual service sector reform indices into the regression one by one. We find positive and statistically significant effects of banking, telecom and transport reforms. For banking, both our standard input–output weighted index and the Rajan–Zingales weighted measure yield similarly significant results. There is no evidence that liberalisation of the insurance industry led to a better performance of manufacturing firms. When we enter the individual sector indices simultaneously (the last column of Table 4), the banking, the telecom and the transport index maintain their positive and significant co‐efficients. The results from this regression suggest that telecom and transport liberalisation have the strongest effects on productivity. A one‐standard‐deviation increase in liberalisation of the telecom industry yields a 8.8% increase in productivity and a one‐standard‐deviation change in transport improves productivity by 14%. Banking reform has a 4.4% productivity effect, while the effect for the insurance sector is not significant at the conventional levels.25 Alternatively, we can focus on the magnitude of the effect corresponding to a one‐unit change in the value of the liberalisation index. For instance, allowing firms greater operational autonomy and enhancing scope of foreign participation (change in the index from one to two) leads to a productivity increase of 1.7% when the banking sector is reformed, 2.7% when the telecom sector is liberalised and 19% when the change pertains to the transport industry. Over the period of our sample, we cannot identify a significant effect from changes in tariff rates on manufacturing productivity.26 We also find that foreign affiliates tend to exhibit higher productivity than domestic firms which is consistent with the conclusions of the existing literature (Aitken and Harrison, 1999; Arnold and Javorcik, 2009). Table 4 Productivity Effects of Services Liberalisation – OLS Approach Services index(t−1) 0.875*** (0.228) Banking index(t−1) 0.765*** 0.620*** (0.246) (0.239) Banking index Rajan–Zingales weights(t−1) 0.164*** (0.033) Telecom index(t−1) 4.594*** 4.215*** (1.354) (1.320) Insurance index(t−1) 0.933 0.322 (0.930) (0.954) Transport index(t−1) 2.921* 3.282** (1.587) (1.548) Tariffs(t−1) 0.001 0.000 0.002 0.000 0.000 0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.002 −0.002 −0.003 0.001 −0.002 −0.004 −0.002 (0.008) (0.008) (0.008) (0.008) (0.008) (0.007) (0.007) Foreign 0.040** 0.041** 0.041*** 0.042*** 0.044*** 0.046*** 0.041*** (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.257 0.256 0.259 0.257 0.255 0.256 0.258 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Services index(t−1) 0.875*** (0.228) Banking index(t−1) 0.765*** 0.620*** (0.246) (0.239) Banking index Rajan–Zingales weights(t−1) 0.164*** (0.033) Telecom index(t−1) 4.594*** 4.215*** (1.354) (1.320) Insurance index(t−1) 0.933 0.322 (0.930) (0.954) Transport index(t−1) 2.921* 3.282** (1.587) (1.548) Tariffs(t−1) 0.001 0.000 0.002 0.000 0.000 0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.002 −0.002 −0.003 0.001 −0.002 −0.004 −0.002 (0.008) (0.008) (0.008) (0.008) (0.008) (0.007) (0.007) Foreign 0.040** 0.041** 0.041*** 0.042*** 0.044*** 0.046*** 0.041*** (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.257 0.256 0.259 0.257 0.255 0.256 0.258 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Notes The estimated specification is described in (4) in the text. The dependent variable is the log of real firm value added. Explanatory variables include capital and labour, all expressed in real terms and logs. Co‐efficients on production inputs are allowed to vary for each of 11 sectors. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab Table 4 Productivity Effects of Services Liberalisation – OLS Approach Services index(t−1) 0.875*** (0.228) Banking index(t−1) 0.765*** 0.620*** (0.246) (0.239) Banking index Rajan–Zingales weights(t−1) 0.164*** (0.033) Telecom index(t−1) 4.594*** 4.215*** (1.354) (1.320) Insurance index(t−1) 0.933 0.322 (0.930) (0.954) Transport index(t−1) 2.921* 3.282** (1.587) (1.548) Tariffs(t−1) 0.001 0.000 0.002 0.000 0.000 0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.002 −0.002 −0.003 0.001 −0.002 −0.004 −0.002 (0.008) (0.008) (0.008) (0.008) (0.008) (0.007) (0.007) Foreign 0.040** 0.041** 0.041*** 0.042*** 0.044*** 0.046*** 0.041*** (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.257 0.256 0.259 0.257 0.255 0.256 0.258 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Services index(t−1) 0.875*** (0.228) Banking index(t−1) 0.765*** 0.620*** (0.246) (0.239) Banking index Rajan–Zingales weights(t−1) 0.164*** (0.033) Telecom index(t−1) 4.594*** 4.215*** (1.354) (1.320) Insurance index(t−1) 0.933 0.322 (0.930) (0.954) Transport index(t−1) 2.921* 3.282** (1.587) (1.548) Tariffs(t−1) 0.001 0.000 0.002 0.000 0.000 0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.002 −0.002 −0.003 0.001 −0.002 −0.004 −0.002 (0.008) (0.008) (0.008) (0.008) (0.008) (0.007) (0.007) Foreign 0.040** 0.041** 0.041*** 0.042*** 0.044*** 0.046*** 0.041*** (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.257 0.256 0.259 0.257 0.255 0.256 0.258 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Notes The estimated specification is described in (4) in the text. The dependent variable is the log of real firm value added. Explanatory variables include capital and labour, all expressed in real terms and logs. Co‐efficients on production inputs are allowed to vary for each of 11 sectors. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab In Table 5, we present the results with our preferred TFP measure estimated using the Ackerberg et al. method. We first apply this method to estimate production functions for each of the 11 sectors separately and then we regress the TFP obtained from these regressions on services and trade liberalisation variables, the foreign affiliate dummy as well as firm and year fixed effects. Using the Ackerberg et al. measure leads to stronger results. The estimated co‐efficients become larger while maintaining or increasing their significance levels. The insurance index, which did not reach conventional significance levels in Table 4, is statistically significant at the 10% level in one specification. The transport index is statistically significant in both specifications. When the individual services sectors are examined together in the last column of Table 5, a one‐standard‐deviation change in the banking sector index corresponds to a 6.6% change in productivity. A one‐standard‐deviation change in the telecommunications liberalisation index corresponds to a 8.4% increase in productivity. A similar change in the transport index leads to a 18.8% improvement in firm performance. No statistically significant effect is found for the insurance sector reform. As before, the coefficients on tariffs do not appear to be statistically significant.27 In additional regressions, not presented here to save space, we split firms into quartiles based on their sales in the first year of the data. We then estimated specifications from Table 5 for each quartile separately. When using the aggregate index, we found that firms in all quartiles benefitted from services reform, though the estimated effect was the smallest in magnitude for firms in the bottom quartile. The same pattern was obtained for the financial sector reform measured using both proxies. In contrast, telecommunications and insurance liberalisation did not appear to have a statistically significant impact on firms in the bottom quartile, though it did benefit larger firms. Transport reform positively affected all quartiles except for the second one. 4.2. Alternative Measures of Services Liberalisation Next, we demonstrate that using measures of services reform other than our services index leads to the same conclusions. Our index captures both policy changes and their implementation. The alternative measures we consider focus on outcomes and are based on: the share of a services industry sales made by private/privatised providers and the share of a services industry sales made by foreign providers.28 As in formula (1), these shares are then weighted by the relevant co‐efficient from the input–output table.29 Privatisation as well as opening sectors to new domestic and foreign entrants are important aspects of any services reform and, thus, progress in this area is a suitable proxy for the success of the reform. Table 5 Productivity Effects of Services Liberalisation – Ackerberg et al. (2006) TFP Measure Services index(t−1) 1.171*** (0.227) Banking index(t−1) 1.046*** 0.911*** (0.249) (0.245) Banking index Rajan–Zingales weights(t−1) 0.194*** (0.032) Telecom index(t−1) 4.765*** 4.037*** (1.281) (1.213) Insurance index(t−1) 1.649* 0.853 (0.952) (0.994) Transport index(t−1) 3.675** 4.300** (1.702) (1.660) Tariffs(t−1) 0.001 0.000 0.003 0.000 0.000 0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.003 −0.003 −0.004 −0.001 −0.003 −0.007 −0.004 (0.009) (0.009) (0.009) (0.009) (0.009) (0.008) (0.007) Foreign 0.027 0.029* 0.030* 0.033** 0.035** 0.041** 0.032** (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.032 0.030 0.035 0.030 0.028 0.029 0.034 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Services index(t−1) 1.171*** (0.227) Banking index(t−1) 1.046*** 0.911*** (0.249) (0.245) Banking index Rajan–Zingales weights(t−1) 0.194*** (0.032) Telecom index(t−1) 4.765*** 4.037*** (1.281) (1.213) Insurance index(t−1) 1.649* 0.853 (0.952) (0.994) Transport index(t−1) 3.675** 4.300** (1.702) (1.660) Tariffs(t−1) 0.001 0.000 0.003 0.000 0.000 0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.003 −0.003 −0.004 −0.001 −0.003 −0.007 −0.004 (0.009) (0.009) (0.009) (0.009) (0.009) (0.008) (0.007) Foreign 0.027 0.029* 0.030* 0.033** 0.035** 0.041** 0.032** (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.032 0.030 0.035 0.030 0.028 0.029 0.034 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab Table 5 Productivity Effects of Services Liberalisation – Ackerberg et al. (2006) TFP Measure Services index(t−1) 1.171*** (0.227) Banking index(t−1) 1.046*** 0.911*** (0.249) (0.245) Banking index Rajan–Zingales weights(t−1) 0.194*** (0.032) Telecom index(t−1) 4.765*** 4.037*** (1.281) (1.213) Insurance index(t−1) 1.649* 0.853 (0.952) (0.994) Transport index(t−1) 3.675** 4.300** (1.702) (1.660) Tariffs(t−1) 0.001 0.000 0.003 0.000 0.000 0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.003 −0.003 −0.004 −0.001 −0.003 −0.007 −0.004 (0.009) (0.009) (0.009) (0.009) (0.009) (0.008) (0.007) Foreign 0.027 0.029* 0.030* 0.033** 0.035** 0.041** 0.032** (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.032 0.030 0.035 0.030 0.028 0.029 0.034 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Services index(t−1) 1.171*** (0.227) Banking index(t−1) 1.046*** 0.911*** (0.249) (0.245) Banking index Rajan–Zingales weights(t−1) 0.194*** (0.032) Telecom index(t−1) 4.765*** 4.037*** (1.281) (1.213) Insurance index(t−1) 1.649* 0.853 (0.952) (0.994) Transport index(t−1) 3.675** 4.300** (1.702) (1.660) Tariffs(t−1) 0.001 0.000 0.003 0.000 0.000 0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.003 −0.003 −0.004 −0.001 −0.003 −0.007 −0.004 (0.009) (0.009) (0.009) (0.009) (0.009) (0.008) (0.007) Foreign 0.027 0.029* 0.030* 0.033** 0.035** 0.041** 0.032** (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.032 0.030 0.035 0.030 0.028 0.029 0.034 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab The results, presented in Table C1 of online Appendix C, are analogous to specifications from Table 5. To save space we present only variables of interest. The top panel shows the results for the privatisation‐based measure. In all four specifications, we find a positive and statistically significant link between the extent of private sector participation in a given services sector and the performance of downstream manufacturing. In the upper middle panel, we repeat the same exercise for the FDI‐based measure. We find a positive and statistically significant co‐efficient for the overall measure, for banking and for transport, but not for telecommunications. In the next panel, we include both proxies in the same specification and find that only privatisation‐based measure is positive and statistically significant. This is not surprising, given that the two measures are highly correlated (the correlation between the foreign ownership and private ownership indices for the combined services sectors within the regression sample is 0.826). The bottom panel presents the results for specifications including the services index as well as the two additional proxies. The services index is positive and statistically significant in the telecommunications and transport regressions, though not in the other two. Again, this is most likely due to the high correlation between the three variables. (The correlation between the foreign ownership index and our standard services index is 0.657, and the correlation between the private ownership index and our standard services index is 0.844 for the general services category in the regression sample.) 4.3. Do Foreign Firms Benefit more from Services Liberalisation? Our finding of a significant productivity premium for foreign‐owned firms is common in the literature. But does ownership also affect the ability of firms to reap the benefits of upstream services reform? Liberalisation allows entry of foreign services firms which may have stronger links with foreign‐owned manufacturing firms and whose local presence could, therefore, provide greater benefits to foreign‐owned manufacturing firms. Moreover, accustomed to doing business in environments with well‐developed services sectors, foreign firms may derive larger benefits from improvements in services industries. In order to test this hypothesis, we estimate an expanded specification which includes interaction effects between the services index and the foreign ownership indicator. The interaction between foreign ownership and services liberalisation is positive and significant for the aggregate measure (see Table 6). This is also true in all cases when services indices enter one by one, confirming our intuition that the productivity effect of services liberalisation is stronger for foreign‐owned firms. This increased effect for foreign‐owned firms is consistent across services sectors, when tested individually, but is not significant for the banking and the transport sector when all services indices enter the same model. This may be because multinational firms are relatively well‐equipped able to procure banking and transport services internationally, and are therefore less reliant on the respective domestic sectors. Table 6 Differential Effect of Services Liberalisation on foreign Firms – Ackerberg et al. (2006) TFP Measure Services index(t−1) 1.106*** (0.236) Services index(t−1) × foreign 0.135** (0.063) Banking index(t−1) 0.932*** 0.896*** (0.264) (0.263) Banking index(t−1) × foreign 0.239** 0.035 (0.115) (0.124) Banking index Rajan–Zingales weights(t−1) 0.182*** (0.034) Banking index Rajan–Zingales weights(t−1) × foreign 0.026** (0.012) Telecom index(t−1) 4.000*** 3.454** (1.391) (1.337) Telecom index(t−1) × foreign 1.442*** 1.198** (0.454) (0.554) Insurance index(t−1) 0.914 0.277 (0.955) (0.955) Insurance index(t−1) × foreign 2.061*** 1.630*** (0.449) (0.508) Transport index(t−1) 3.659** 4.347*** (1.700) (1.656) Transport index(t−1) × foreign 0.258* −0.225 (0.135) (0.160) Tariffs(t−1) 0.001 0.000 0.003 0.000 0.000 0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.003 −0.003 −0.004 −0.001 −0.003 −0.007 −0.004 (0.009) (0.009) (0.009) (0.009) (0.009) (0.008) (0.007) Foreign 0.017 0.021 0.021 0.023 0.024 0.032** 0.021 (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.032 0.030 0.035 0.030 0.028 0.029 0.035 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Services index(t−1) 1.106*** (0.236) Services index(t−1) × foreign 0.135** (0.063) Banking index(t−1) 0.932*** 0.896*** (0.264) (0.263) Banking index(t−1) × foreign 0.239** 0.035 (0.115) (0.124) Banking index Rajan–Zingales weights(t−1) 0.182*** (0.034) Banking index Rajan–Zingales weights(t−1) × foreign 0.026** (0.012) Telecom index(t−1) 4.000*** 3.454** (1.391) (1.337) Telecom index(t−1) × foreign 1.442*** 1.198** (0.454) (0.554) Insurance index(t−1) 0.914 0.277 (0.955) (0.955) Insurance index(t−1) × foreign 2.061*** 1.630*** (0.449) (0.508) Transport index(t−1) 3.659** 4.347*** (1.700) (1.656) Transport index(t−1) × foreign 0.258* −0.225 (0.135) (0.160) Tariffs(t−1) 0.001 0.000 0.003 0.000 0.000 0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.003 −0.003 −0.004 −0.001 −0.003 −0.007 −0.004 (0.009) (0.009) (0.009) (0.009) (0.009) (0.008) (0.007) Foreign 0.017 0.021 0.021 0.023 0.024 0.032** 0.021 (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.032 0.030 0.035 0.030 0.028 0.029 0.035 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab Table 6 Differential Effect of Services Liberalisation on foreign Firms – Ackerberg et al. (2006) TFP Measure Services index(t−1) 1.106*** (0.236) Services index(t−1) × foreign 0.135** (0.063) Banking index(t−1) 0.932*** 0.896*** (0.264) (0.263) Banking index(t−1) × foreign 0.239** 0.035 (0.115) (0.124) Banking index Rajan–Zingales weights(t−1) 0.182*** (0.034) Banking index Rajan–Zingales weights(t−1) × foreign 0.026** (0.012) Telecom index(t−1) 4.000*** 3.454** (1.391) (1.337) Telecom index(t−1) × foreign 1.442*** 1.198** (0.454) (0.554) Insurance index(t−1) 0.914 0.277 (0.955) (0.955) Insurance index(t−1) × foreign 2.061*** 1.630*** (0.449) (0.508) Transport index(t−1) 3.659** 4.347*** (1.700) (1.656) Transport index(t−1) × foreign 0.258* −0.225 (0.135) (0.160) Tariffs(t−1) 0.001 0.000 0.003 0.000 0.000 0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.003 −0.003 −0.004 −0.001 −0.003 −0.007 −0.004 (0.009) (0.009) (0.009) (0.009) (0.009) (0.008) (0.007) Foreign 0.017 0.021 0.021 0.023 0.024 0.032** 0.021 (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.032 0.030 0.035 0.030 0.028 0.029 0.035 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Services index(t−1) 1.106*** (0.236) Services index(t−1) × foreign 0.135** (0.063) Banking index(t−1) 0.932*** 0.896*** (0.264) (0.263) Banking index(t−1) × foreign 0.239** 0.035 (0.115) (0.124) Banking index Rajan–Zingales weights(t−1) 0.182*** (0.034) Banking index Rajan–Zingales weights(t−1) × foreign 0.026** (0.012) Telecom index(t−1) 4.000*** 3.454** (1.391) (1.337) Telecom index(t−1) × foreign 1.442*** 1.198** (0.454) (0.554) Insurance index(t−1) 0.914 0.277 (0.955) (0.955) Insurance index(t−1) × foreign 2.061*** 1.630*** (0.449) (0.508) Transport index(t−1) 3.659** 4.347*** (1.700) (1.656) Transport index(t−1) × foreign 0.258* −0.225 (0.135) (0.160) Tariffs(t−1) 0.001 0.000 0.003 0.000 0.000 0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.003 −0.003 −0.004 −0.001 −0.003 −0.007 −0.004 (0.009) (0.009) (0.009) (0.009) (0.009) (0.008) (0.007) Foreign 0.017 0.021 0.021 0.023 0.024 0.032** 0.021 (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.032 0.030 0.035 0.030 0.028 0.029 0.035 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab The differential impact of liberalisation on foreign firms is remarkably strong in the telecommunications sector. A standard deviation increase in the telecommunications index increases productivity by 7.2% for domestic firms, while it increases productivity by 9.8% for foreign‐owned firms. Given the greater need for coordination across national borders, one may find this result intuitive. As for the insurance reform, only foreign firms seem to be able to appropriate its benefits and see a boost in productivity of 3.3% (see the last column of Table 6). 4.4. Controlling for Other Reforms While many observers have considered decreasing tariff protection to be the key explanation behind the productivity enhancements of Indian firms, recent research suggests that a comprehensive approach may be warranted, encompassing also other policy changes taking place in India (Harrison et al., 2011). Against this background, we extend the set of controls in our baseline specification to include industry‐specific measures of delicensing and FDI reform.30 We do not take into account the labour market reform, most of which occurred before the first year of our sample 1993 (Ahsan and Pagés, 2009). To capture the effects of the delicensing reforms, we use information from Harrison et al. (2011), who extended the data used by Aghion et al. (2008) to 2004, on the basis of press notes from the Ministry of Commerce and Industry. The delicensing variable is a dummy that takes on a value of one if any products in a 3‐digit industry have been delicensed, and zero otherwise. Similarly, the measure of FDI reform was compiled by Harrison et al. (2011) also based on press notes from the Ministry of Commerce and Industry. It takes on a value of one if any products in a 3‐digit industry have been liberalised, and zero otherwise.31 In Table 7, we present the results from the modified specification. We find a positive correlation between delicensing and FDI reform and firm productivity. More importantly for the purposes of this article, our results on services reform are barely affected by this change.32 4.5. Excluding Manufacturing Industries Supplying Services Sectors If services reform leads to expansion of services industries, it may increase the demand faced by manufacturing sectors supplying machinery and equipment to services providers. The increased demand may boost the productivity of manufacturing firms through the realisation of scale economies but this effect would work through a channel different from the one we intend to capture. Table 7 Controlling for Delicensing and FDI Reform – Ackerberg et al. (2006) TFP Measure Services index(t−1) 1.285*** (0.229) Banking index(t−1) 1.212*** 1.010*** (0.249) (0.242) Banking index Rajan–Zingales weights(t−1) 0.190*** (0.031) Telecom index(t−1) 5.025*** 4.097*** (1.328) (1.258) Insurance index(t−1) 2.211** 1.118 (0.978) (0.995) Transport index(t−1) 2.986* 3.569** (1.550) (1.466) Tariffs(t−1) −0.001 −0.001 0.001 −0.001 −0.002 −0.001 −0.000 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.004 −0.004 −0.005 −0.001 −0.004 −0.007 −0.004 (0.008) (0.008) (0.008) (0.008) (0.009) (0.008) (0.007) Delicensing(t−1) 0.243** 0.217** 0.212* 0.231** 0.217** 0.244** 0.279** (0.110) (0.109) (0.109) (0.111) (0.109) (0.110) (0.113) FDI reform(t−1) 0.167*** 0.173*** 0.139** 0.152** 0.164** 0.112* 0.134** (0.064) (0.065) (0.064) (0.066) (0.065) (0.057) (0.057) Foreign 0.030* 0.030* 0.033** 0.036** 0.037** 0.043*** 0.033** (0.017) (0.017) (0.016) (0.016) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.036 0.034 0.038 0.033 0.032 0.032 0.037 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Services index(t−1) 1.285*** (0.229) Banking index(t−1) 1.212*** 1.010*** (0.249) (0.242) Banking index Rajan–Zingales weights(t−1) 0.190*** (0.031) Telecom index(t−1) 5.025*** 4.097*** (1.328) (1.258) Insurance index(t−1) 2.211** 1.118 (0.978) (0.995) Transport index(t−1) 2.986* 3.569** (1.550) (1.466) Tariffs(t−1) −0.001 −0.001 0.001 −0.001 −0.002 −0.001 −0.000 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.004 −0.004 −0.005 −0.001 −0.004 −0.007 −0.004 (0.008) (0.008) (0.008) (0.008) (0.009) (0.008) (0.007) Delicensing(t−1) 0.243** 0.217** 0.212* 0.231** 0.217** 0.244** 0.279** (0.110) (0.109) (0.109) (0.111) (0.109) (0.110) (0.113) FDI reform(t−1) 0.167*** 0.173*** 0.139** 0.152** 0.164** 0.112* 0.134** (0.064) (0.065) (0.064) (0.066) (0.065) (0.057) (0.057) Foreign 0.030* 0.030* 0.033** 0.036** 0.037** 0.043*** 0.033** (0.017) (0.017) (0.016) (0.016) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.036 0.034 0.038 0.033 0.032 0.032 0.037 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab Table 7 Controlling for Delicensing and FDI Reform – Ackerberg et al. (2006) TFP Measure Services index(t−1) 1.285*** (0.229) Banking index(t−1) 1.212*** 1.010*** (0.249) (0.242) Banking index Rajan–Zingales weights(t−1) 0.190*** (0.031) Telecom index(t−1) 5.025*** 4.097*** (1.328) (1.258) Insurance index(t−1) 2.211** 1.118 (0.978) (0.995) Transport index(t−1) 2.986* 3.569** (1.550) (1.466) Tariffs(t−1) −0.001 −0.001 0.001 −0.001 −0.002 −0.001 −0.000 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.004 −0.004 −0.005 −0.001 −0.004 −0.007 −0.004 (0.008) (0.008) (0.008) (0.008) (0.009) (0.008) (0.007) Delicensing(t−1) 0.243** 0.217** 0.212* 0.231** 0.217** 0.244** 0.279** (0.110) (0.109) (0.109) (0.111) (0.109) (0.110) (0.113) FDI reform(t−1) 0.167*** 0.173*** 0.139** 0.152** 0.164** 0.112* 0.134** (0.064) (0.065) (0.064) (0.066) (0.065) (0.057) (0.057) Foreign 0.030* 0.030* 0.033** 0.036** 0.037** 0.043*** 0.033** (0.017) (0.017) (0.016) (0.016) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.036 0.034 0.038 0.033 0.032 0.032 0.037 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Services index(t−1) 1.285*** (0.229) Banking index(t−1) 1.212*** 1.010*** (0.249) (0.242) Banking index Rajan–Zingales weights(t−1) 0.190*** (0.031) Telecom index(t−1) 5.025*** 4.097*** (1.328) (1.258) Insurance index(t−1) 2.211** 1.118 (0.978) (0.995) Transport index(t−1) 2.986* 3.569** (1.550) (1.466) Tariffs(t−1) −0.001 −0.001 0.001 −0.001 −0.002 −0.001 −0.000 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.004 −0.004 −0.005 −0.001 −0.004 −0.007 −0.004 (0.008) (0.008) (0.008) (0.008) (0.009) (0.008) (0.007) Delicensing(t−1) 0.243** 0.217** 0.212* 0.231** 0.217** 0.244** 0.279** (0.110) (0.109) (0.109) (0.111) (0.109) (0.110) (0.113) FDI reform(t−1) 0.167*** 0.173*** 0.139** 0.152** 0.164** 0.112* 0.134** (0.064) (0.065) (0.064) (0.066) (0.065) (0.057) (0.057) Foreign 0.030* 0.030* 0.033** 0.036** 0.037** 0.043*** 0.033** (0.017) (0.017) (0.016) (0.016) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.036 0.034 0.038 0.033 0.032 0.032 0.037 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab To address this issue, we exclude from our sample firms operating in manufacturing sectors that sell more than 5% of their output to the four services industries considered in our study.33 Doing so reduces our sample to 16,751 observations (see Table C2 of online Appendix C). Nevertheless, our conclusions are not affected by this change. We find a positive and statistically significant relationship between reforms in each services industry and the performance of downstream manufacturing firms. A similar relationship holds for the aggregate liberalisation index. 4.6. Instrumenting the Services Liberalisation Index In order to ensure that our finding of services reforms improving manufacturing performance is not driven by reverse causality, we instrument for reform in India using measures of services reform in Indonesia and China. Indonesia’s services commitments were made in the context of the services negotiations during the Uruguay Round which led to the GATS. These commitments reflected liberalisation pressure from the industrial countries on developing countries with large markets and significant services protection (Hoekman, 1996). Since India shares these attributes with Indonesia and was subject to similar pressure, we can presume that there is an association between the pace (i.e. timing) of Indonesia’s services commitments and India’s services policies.34 Chinese services commitments were made during that country’s accession to the WTO, and were a result of tough bilateral negotiations with the key WTO members, particularly the EU and the US.35 Since India sees China as a competitor, progress in China’s market‐opening commitments is likely to have influenced Indian policy reform. In other words, the pace of services reforms in these two countries is likely to be correlated with developments in India and is thus likely to make for suitable instruments, as the test statistics of the first stage regressions confirm. Furthermore, since both Indonesia’s and China’s commitments were primarily outcomes of industrial country pressure to open services markets (apart from domestic considerations), they were exogenous to changes in India’s manufacturing productivity. Accordingly, we measure services liberalisation using the WTO commitments in a given sector. More specifically, we focus on the number of commitments made by a country expressed as a percentage of possible commitments. For the years prior to the first full year of the WTO membership of a given country (e.g. 2002 for China), the number of commitments equals zero. To create an instrument relevant to a particular manufacturing sector, the measure of services liberalisation is multiplied by the proportion αjk of inputs sourced by the manufacturing sector j from the services sector k, as with the services index in (1). In this way we create two instruments: pertaining to China’s commitments; and pertaining to Indonesia’s commitments. Each instrument varies by time, manufacturing industry and services sector. An alternative specification, using instead the commitments of all WTO members yielded similar results (available upon request). The results from IV regressions are reported in Table 8. As expected, the first stage results indicate that Indian services reform responded to services liberalisation in China and Indonesia. The F‐statistics suggest that our instruments perform well. The Sargan test does not cast doubt on their validity with the exception of the specification focusing on the transport sector. The second stage confirms our earlier finding that services reforms have improved manufacturing performance. This gives us confidence that reverse causation is not driving our results. 4.7. Break Regressions While the construction of our services liberalisation index was undertaken with great care and confirmed by extensive consultations with sector experts in India, a composite index is by its very nature prone to measurement imperfections. We, therefore, wish to check the robustness of our findings to more parsimonious approaches to measuring services reform. Although a ‘true’ measure of policy reform does not exist, it may be possible to identify the key structural break points in policy regimes with greater objectivity than is involved in the construction of a composite index that necessarily reflects a judgment of the relative importance of specific reforms. Hence, we check the previous findings by using a simpler measure of structural breaks for each services sector.36 This is done by identifying the year in which a service sector experienced the most transformative policy reform and generating a simple indicator variable that divides years into the ‘before’ and ‘after’ period.37 These policy cornerstones in services sectors are then weighted by the input–output coefficients linking services and manufacturing sectors, in the same way as with the policy index: Breakjt=αjkIkt,(5) where αjk is the share of inputs sourced from services sector k by manufacturing sector j, and Ikt is an indicator variable for services sector k taking on the value of one if an observation pertains to the year of the structural break year or a later period and zero otherwise. Table 8 Productivity Effects of Services Liberalisation – Instrumental variables approach using Ackerberg et al. (2006) TFP Second stage regressions Services index(t−1) 1.277*** (0.260) Banking index(t−1) 1.061*** 0.864*** (0.247) (0.280) Banking index Rajan–Zingales weights(t−1) 0.224*** (0.056) Telecom index(t−1) 5.459*** 4.199*** (1.469) (1.507) Insurance index(t−1) 2.527** 2.646* (1.139) (1.500) Transport index(t−1) 6.891 10.174** (4.206) (4.288) Tariffs(t−1) 0.0009 0.0004 0.0030 0.0004 0.0002 0.0002 0.0019 (0.0018) (0.0017) (0.0019) (0.0018) (0.0018) (0.0016) (0.0018) Input tariffs(t−1) −0.0031 −0.0035 −0.0042 −0.0001 −0.0030 −0.0094 −0.0079 (0.0090) (0.0093) (0.0089) (0.0090) (0.0093) (0.0067) (0.0059) Foreign 0.027 0.029** 0.030* 0.032* 0.034** 0.045*** 0.038** (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.0160) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.032 0.030 0.035 0.030 0.028 0.028 0.029 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 First stage regressions WTO commitments – China 2.970*** 3.746*** 17.665*** 1.471*** 2.645*** 0.675*** (0.229) (0.288) (2.282) (0.199) (0.598) (0.196) WTO commitments – Indonesia 0.564*** 0.210** 1.675 2.117*** 0.398** 4.972*** (0.141) (0.120) (1.665) (0.146) (0.198) (0.941) Tariffs(t−1) 0.0003 −0.0001 −0.0122*** −0.0000 −0.0000 0.0001 (0.0003) (0.0001) (0.0018) (0.0000) (0.0000) (0.0001) Input tariffs(t−1) 0.0006 0.0001 0.0044 −0.0000 −0.0000 0.0004 (0.0005) (0.0001) (0.0074) (0.0001) (0.0001) (0.0003) Foreign 0.003*** 0.001** −0.001 0.000 0.000* −0.001 (0.001) (0.000) (0.009) (0.000) (0.000) (0.000) Test statistics F‐stat 129.470 151.650 34.440 291.620 16.590 50.410 20.690 p‐value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Sargan test 0.068 0.216 0.322 0.763 1.561 6.040 5.345 p‐value 0.795 0.642 0.570 0.382 0.212 0.014 0.254 Second stage regressions Services index(t−1) 1.277*** (0.260) Banking index(t−1) 1.061*** 0.864*** (0.247) (0.280) Banking index Rajan–Zingales weights(t−1) 0.224*** (0.056) Telecom index(t−1) 5.459*** 4.199*** (1.469) (1.507) Insurance index(t−1) 2.527** 2.646* (1.139) (1.500) Transport index(t−1) 6.891 10.174** (4.206) (4.288) Tariffs(t−1) 0.0009 0.0004 0.0030 0.0004 0.0002 0.0002 0.0019 (0.0018) (0.0017) (0.0019) (0.0018) (0.0018) (0.0016) (0.0018) Input tariffs(t−1) −0.0031 −0.0035 −0.0042 −0.0001 −0.0030 −0.0094 −0.0079 (0.0090) (0.0093) (0.0089) (0.0090) (0.0093) (0.0067) (0.0059) Foreign 0.027 0.029** 0.030* 0.032* 0.034** 0.045*** 0.038** (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.0160) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.032 0.030 0.035 0.030 0.028 0.028 0.029 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 First stage regressions WTO commitments – China 2.970*** 3.746*** 17.665*** 1.471*** 2.645*** 0.675*** (0.229) (0.288) (2.282) (0.199) (0.598) (0.196) WTO commitments – Indonesia 0.564*** 0.210** 1.675 2.117*** 0.398** 4.972*** (0.141) (0.120) (1.665) (0.146) (0.198) (0.941) Tariffs(t−1) 0.0003 −0.0001 −0.0122*** −0.0000 −0.0000 0.0001 (0.0003) (0.0001) (0.0018) (0.0000) (0.0000) (0.0001) Input tariffs(t−1) 0.0006 0.0001 0.0044 −0.0000 −0.0000 0.0004 (0.0005) (0.0001) (0.0074) (0.0001) (0.0001) (0.0003) Foreign 0.003*** 0.001** −0.001 0.000 0.000* −0.001 (0.001) (0.000) (0.009) (0.000) (0.000) (0.000) Test statistics F‐stat 129.470 151.650 34.440 291.620 16.590 50.410 20.690 p‐value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Sargan test 0.068 0.216 0.322 0.763 1.561 6.040 5.345 p‐value 0.795 0.642 0.570 0.382 0.212 0.014 0.254 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. All specifications include firm fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab Table 8 Productivity Effects of Services Liberalisation – Instrumental variables approach using Ackerberg et al. (2006) TFP Second stage regressions Services index(t−1) 1.277*** (0.260) Banking index(t−1) 1.061*** 0.864*** (0.247) (0.280) Banking index Rajan–Zingales weights(t−1) 0.224*** (0.056) Telecom index(t−1) 5.459*** 4.199*** (1.469) (1.507) Insurance index(t−1) 2.527** 2.646* (1.139) (1.500) Transport index(t−1) 6.891 10.174** (4.206) (4.288) Tariffs(t−1) 0.0009 0.0004 0.0030 0.0004 0.0002 0.0002 0.0019 (0.0018) (0.0017) (0.0019) (0.0018) (0.0018) (0.0016) (0.0018) Input tariffs(t−1) −0.0031 −0.0035 −0.0042 −0.0001 −0.0030 −0.0094 −0.0079 (0.0090) (0.0093) (0.0089) (0.0090) (0.0093) (0.0067) (0.0059) Foreign 0.027 0.029** 0.030* 0.032* 0.034** 0.045*** 0.038** (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.0160) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.032 0.030 0.035 0.030 0.028 0.028 0.029 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 First stage regressions WTO commitments – China 2.970*** 3.746*** 17.665*** 1.471*** 2.645*** 0.675*** (0.229) (0.288) (2.282) (0.199) (0.598) (0.196) WTO commitments – Indonesia 0.564*** 0.210** 1.675 2.117*** 0.398** 4.972*** (0.141) (0.120) (1.665) (0.146) (0.198) (0.941) Tariffs(t−1) 0.0003 −0.0001 −0.0122*** −0.0000 −0.0000 0.0001 (0.0003) (0.0001) (0.0018) (0.0000) (0.0000) (0.0001) Input tariffs(t−1) 0.0006 0.0001 0.0044 −0.0000 −0.0000 0.0004 (0.0005) (0.0001) (0.0074) (0.0001) (0.0001) (0.0003) Foreign 0.003*** 0.001** −0.001 0.000 0.000* −0.001 (0.001) (0.000) (0.009) (0.000) (0.000) (0.000) Test statistics F‐stat 129.470 151.650 34.440 291.620 16.590 50.410 20.690 p‐value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Sargan test 0.068 0.216 0.322 0.763 1.561 6.040 5.345 p‐value 0.795 0.642 0.570 0.382 0.212 0.014 0.254 Second stage regressions Services index(t−1) 1.277*** (0.260) Banking index(t−1) 1.061*** 0.864*** (0.247) (0.280) Banking index Rajan–Zingales weights(t−1) 0.224*** (0.056) Telecom index(t−1) 5.459*** 4.199*** (1.469) (1.507) Insurance index(t−1) 2.527** 2.646* (1.139) (1.500) Transport index(t−1) 6.891 10.174** (4.206) (4.288) Tariffs(t−1) 0.0009 0.0004 0.0030 0.0004 0.0002 0.0002 0.0019 (0.0018) (0.0017) (0.0019) (0.0018) (0.0018) (0.0016) (0.0018) Input tariffs(t−1) −0.0031 −0.0035 −0.0042 −0.0001 −0.0030 −0.0094 −0.0079 (0.0090) (0.0093) (0.0089) (0.0090) (0.0093) (0.0067) (0.0059) Foreign 0.027 0.029** 0.030* 0.032* 0.034** 0.045*** 0.038** (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.0160) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.032 0.030 0.035 0.030 0.028 0.028 0.029 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 First stage regressions WTO commitments – China 2.970*** 3.746*** 17.665*** 1.471*** 2.645*** 0.675*** (0.229) (0.288) (2.282) (0.199) (0.598) (0.196) WTO commitments – Indonesia 0.564*** 0.210** 1.675 2.117*** 0.398** 4.972*** (0.141) (0.120) (1.665) (0.146) (0.198) (0.941) Tariffs(t−1) 0.0003 −0.0001 −0.0122*** −0.0000 −0.0000 0.0001 (0.0003) (0.0001) (0.0018) (0.0000) (0.0000) (0.0001) Input tariffs(t−1) 0.0006 0.0001 0.0044 −0.0000 −0.0000 0.0004 (0.0005) (0.0001) (0.0074) (0.0001) (0.0001) (0.0003) Foreign 0.003*** 0.001** −0.001 0.000 0.000* −0.001 (0.001) (0.000) (0.009) (0.000) (0.000) (0.000) Test statistics F‐stat 129.470 151.650 34.440 291.620 16.590 50.410 20.690 p‐value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Sargan test 0.068 0.216 0.322 0.763 1.561 6.040 5.345 p‐value 0.795 0.642 0.570 0.382 0.212 0.014 0.254 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. All specifications include firm fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab The structural breaks were determined as follows. The most important reforms in the banking sector occurred in 2001, when there was full deregulation of the interest rates and banks were allowed greater flexibility in choosing borrowers and designing loan terms. Liberalisation of the banking sector allowed for improved allocation of credit and increased investment by private and foreign banks. The most important reforms in the telecommunications sector in India occurred in 2002, when the government terminated the VSNL (publicly owned telecommunications company) monopoly and allowed free entry into the long distance sector. This policy reform in the telecommunications sector quickly led to entry in the sector and intense competition. For transport, the most important reform came in 1997 when increased privatisation in port management was allowed. Approval was granted for up to 74% foreign ownership in port management, foreign and private investment in construction, and increased private and foreign investment in aviation. The effect was to make the transport industry more competitive, which translated into gains in the speed with which processes were completed at ports and deliveries were made. In the insurance industry, 2002 is the most important year of reform, as it marked the registration of 16 new providers and permission for 12 new insurance providers to enter the market. Yet the insurance reforms were slower to be instituted than the other services reforms. The results obtained from replacing the services index in (4) with the variable Breakjt pertaining to individual services industries confirm our earlier findings (Table 9). Important policy changes in services sectors appear to have left their mark on the performance of manufacturing firms dependent on services inputs. Strong productivity effects can be identified from the banking, telecommunications, insurance and transport sectors, and as in the index regressions, the co‐efficients are particularly large for the telecom and transport sectors. Again when measures for several services industries enter jointly, the insurance measure loses its statistical significance. As is evident from Table C3 of online Appendix C, these regressions also confirm that there is a stronger productivity effect on foreign firms than on domestic firms. 4.8. Liberalisation Year Falsification Test In order to ensure that the liberalisation measures identify effects of reforms rather than spurious effects from broader industry‐level productivity trends, we test the liberalisation discontinuity effect on years prior to the reform. If the effect captured by the liberalisation breaks were simply related to industry trends, we would expect the coefficient on years prior to the reform to be as large and significant as the coefficient on our variable of interest. To implement this test, we create a new variable 1yearpriortobreakjt=αjkIPkt,(6) where αjk is the share of inputs sourced from services sector k by manufacturing sector j, and IPkt is an indicator variable for services sector k taking on the value of one in the year prior to the year of the structural break and zero otherwise. We also define an analogous variable for the two‐year period preceding the structural break which we use in an alternative specification. Table 9 Productivity Effect of Services Liberalisation, Structural Break Approach – Ackerberg et al. (2006) TFP measure Banking break 2001 2.626*** 2.269*** (0.641) (0.549) Rajan–Zingales break 2001 0.484*** (0.081) Telecom break 2002 8.126*** 6.226*** (2.347) (2.223) Insurance break 2002 5.218** 3.015 (2.227) (1.937) Transport break 1997 8.103*** 8.528*** (2.628) (2.633) Tariffs(t−1) 0.000 0.003 0.000 0.000 −0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.004 −0.004 −0.003 −0.003 −0.010 −0.009 (0.009) (0.009) (0.009) (0.009) (0.007) (0.006) Foreign 0.029* 0.030* 0.034** 0.035** 0.043*** 0.034** (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.030 0.034 0.029 0.028 0.032 0.036 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 Banking break 2001 2.626*** 2.269*** (0.641) (0.549) Rajan–Zingales break 2001 0.484*** (0.081) Telecom break 2002 8.126*** 6.226*** (2.347) (2.223) Insurance break 2002 5.218** 3.015 (2.227) (1.937) Transport break 1997 8.103*** 8.528*** (2.628) (2.633) Tariffs(t−1) 0.000 0.003 0.000 0.000 −0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.004 −0.004 −0.003 −0.003 −0.010 −0.009 (0.009) (0.009) (0.009) (0.009) (0.007) (0.006) Foreign 0.029* 0.030* 0.034** 0.035** 0.043*** 0.034** (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.030 0.034 0.029 0.028 0.032 0.036 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. Structural break is a binary variable taking on the value of one in the year in which a service sector experienced the most transformative policy reform and in all subsequent years. The variable is equal to zero in years prior to the reform. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab Table 9 Productivity Effect of Services Liberalisation, Structural Break Approach – Ackerberg et al. (2006) TFP measure Banking break 2001 2.626*** 2.269*** (0.641) (0.549) Rajan–Zingales break 2001 0.484*** (0.081) Telecom break 2002 8.126*** 6.226*** (2.347) (2.223) Insurance break 2002 5.218** 3.015 (2.227) (1.937) Transport break 1997 8.103*** 8.528*** (2.628) (2.633) Tariffs(t−1) 0.000 0.003 0.000 0.000 −0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.004 −0.004 −0.003 −0.003 −0.010 −0.009 (0.009) (0.009) (0.009) (0.009) (0.007) (0.006) Foreign 0.029* 0.030* 0.034** 0.035** 0.043*** 0.034** (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.030 0.034 0.029 0.028 0.032 0.036 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 Banking break 2001 2.626*** 2.269*** (0.641) (0.549) Rajan–Zingales break 2001 0.484*** (0.081) Telecom break 2002 8.126*** 6.226*** (2.347) (2.223) Insurance break 2002 5.218** 3.015 (2.227) (1.937) Transport break 1997 8.103*** 8.528*** (2.628) (2.633) Tariffs(t−1) 0.000 0.003 0.000 0.000 −0.000 0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.004 −0.004 −0.003 −0.003 −0.010 −0.009 (0.009) (0.009) (0.009) (0.009) (0.007) (0.006) Foreign 0.029* 0.030* 0.034** 0.035** 0.043*** 0.034** (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.030 0.034 0.029 0.028 0.032 0.036 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. Structural break is a binary variable taking on the value of one in the year in which a service sector experienced the most transformative policy reform and in all subsequent years. The variable is equal to zero in years prior to the reform. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab As is evident from Table 10, we find that in each industry the coefficient on the break in the year of reform is larger and significantly different from the coefficient on the years preceding the reform. The results are somewhat weaker in the second specification for the transport reform (the last column) where the p‐value of the test equal 0.126. Only in three of 10 specifications is the coefficient on the falsification variable positive and statistically significant. Other Robustness Checks A potential concern is that the service indices increase monotonically over time. This makes the empirical strategy susceptible to picking up spurious sectoral trends. If the sectors that are intensive in the more reformed services were more dynamic and productivity grew in these sectors for reasons unrelated to input improvements, we could get the results obtained so far even in the absence of a true effect of services liberalisation on firm performance. To address this concern, we replace year fixed effects with sector‐specific time trends (we use the sector aggregation presented in Table 3). The results, presented in Table 11, confirm most of our earlier findings. We find a positive link between the aggregate measure of services reform and the performance of downstream manufacturing firms. A similar relationship is found for both measures of banking reform. The effect of banking reform remains significant even when entered jointly with the other sectoral reform indices. Table 10 Break falsification test – Ackerberg et al. (2006) TFP Measure . Banking break . Banking break . Banking break (Rajan–Zingales) . Banking break (Rajan–Zingales) . Telecom break . Telecom break . Insurance break . Insurance break . Transport break . Transport break . . 2001 . 2001 . 2001 . 2001 . 2002 . 2002 . 2002 . 2002 . 1997 . 1997 . Break 2.610*** 2.480*** 0.528*** 0.558*** 9.125*** 9.794*** 5.198** 3.890 8.053*** 7.427*** (0.662) (0.706) (0.084) (0.091) (2.528) (2.605) (2.345) (2.417) (2.635) (2.633) Falsification test: 1 year prior to break −0.070 0.180 4.565* −0.099 0.381 (1.171) (0.129) (2.763) (1.836) (1.259) Falsification test: 2 years prior to break −0.330 0.161* 4.070 −3.378* 2.700* (0.854) (0.095) (2.765) (1.961) (1.397) Tariffs(t−1) 0.000 0.000 0.003 0.003* 0.001 0.000 0.000 0.000 −0.000 −0.000 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.004 −0.004 −0.004 −0.004 −0.003 −0.002 −0.003 −0.003 −0.010 −0.010 (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.007) (0.007) Foreign 0.029* 0.029* 0.029* 0.029* 0.033** 0.033** 0.035** 0.036** 0.043*** 0.044*** (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.030 0.030 0.035 0.035 0.030 0.030 0.028 0.028 0.032 0.033 Break coefficient = year(s) prior co‐efficient F‐stat 5.21 10.74 7.2 17.09 2.91 4.39 5.02 7.57 6.59 2.36 p‐value 0.023 0.001 0.008 0.000 0.089 0.037 0.026 0.006 0.011 0.126 . Banking break . Banking break . Banking break (Rajan–Zingales) . Banking break (Rajan–Zingales) . Telecom break . Telecom break . Insurance break . Insurance break . Transport break . Transport break . . 2001 . 2001 . 2001 . 2001 . 2002 . 2002 . 2002 . 2002 . 1997 . 1997 . Break 2.610*** 2.480*** 0.528*** 0.558*** 9.125*** 9.794*** 5.198** 3.890 8.053*** 7.427*** (0.662) (0.706) (0.084) (0.091) (2.528) (2.605) (2.345) (2.417) (2.635) (2.633) Falsification test: 1 year prior to break −0.070 0.180 4.565* −0.099 0.381 (1.171) (0.129) (2.763) (1.836) (1.259) Falsification test: 2 years prior to break −0.330 0.161* 4.070 −3.378* 2.700* (0.854) (0.095) (2.765) (1.961) (1.397) Tariffs(t−1) 0.000 0.000 0.003 0.003* 0.001 0.000 0.000 0.000 −0.000 −0.000 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.004 −0.004 −0.004 −0.004 −0.003 −0.002 −0.003 −0.003 −0.010 −0.010 (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.007) (0.007) Foreign 0.029* 0.029* 0.029* 0.029* 0.033** 0.033** 0.035** 0.036** 0.043*** 0.044*** (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.030 0.030 0.035 0.035 0.030 0.030 0.028 0.028 0.032 0.033 Break coefficient = year(s) prior co‐efficient F‐stat 5.21 10.74 7.2 17.09 2.91 4.39 5.02 7.57 6.59 2.36 p‐value 0.023 0.001 0.008 0.000 0.089 0.037 0.026 0.006 0.011 0.126 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab Table 10 Break falsification test – Ackerberg et al. (2006) TFP Measure . Banking break . Banking break . Banking break (Rajan–Zingales) . Banking break (Rajan–Zingales) . Telecom break . Telecom break . Insurance break . Insurance break . Transport break . Transport break . . 2001 . 2001 . 2001 . 2001 . 2002 . 2002 . 2002 . 2002 . 1997 . 1997 . Break 2.610*** 2.480*** 0.528*** 0.558*** 9.125*** 9.794*** 5.198** 3.890 8.053*** 7.427*** (0.662) (0.706) (0.084) (0.091) (2.528) (2.605) (2.345) (2.417) (2.635) (2.633) Falsification test: 1 year prior to break −0.070 0.180 4.565* −0.099 0.381 (1.171) (0.129) (2.763) (1.836) (1.259) Falsification test: 2 years prior to break −0.330 0.161* 4.070 −3.378* 2.700* (0.854) (0.095) (2.765) (1.961) (1.397) Tariffs(t−1) 0.000 0.000 0.003 0.003* 0.001 0.000 0.000 0.000 −0.000 −0.000 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.004 −0.004 −0.004 −0.004 −0.003 −0.002 −0.003 −0.003 −0.010 −0.010 (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.007) (0.007) Foreign 0.029* 0.029* 0.029* 0.029* 0.033** 0.033** 0.035** 0.036** 0.043*** 0.044*** (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.030 0.030 0.035 0.035 0.030 0.030 0.028 0.028 0.032 0.033 Break coefficient = year(s) prior co‐efficient F‐stat 5.21 10.74 7.2 17.09 2.91 4.39 5.02 7.57 6.59 2.36 p‐value 0.023 0.001 0.008 0.000 0.089 0.037 0.026 0.006 0.011 0.126 . Banking break . Banking break . Banking break (Rajan–Zingales) . Banking break (Rajan–Zingales) . Telecom break . Telecom break . Insurance break . Insurance break . Transport break . Transport break . . 2001 . 2001 . 2001 . 2001 . 2002 . 2002 . 2002 . 2002 . 1997 . 1997 . Break 2.610*** 2.480*** 0.528*** 0.558*** 9.125*** 9.794*** 5.198** 3.890 8.053*** 7.427*** (0.662) (0.706) (0.084) (0.091) (2.528) (2.605) (2.345) (2.417) (2.635) (2.633) Falsification test: 1 year prior to break −0.070 0.180 4.565* −0.099 0.381 (1.171) (0.129) (2.763) (1.836) (1.259) Falsification test: 2 years prior to break −0.330 0.161* 4.070 −3.378* 2.700* (0.854) (0.095) (2.765) (1.961) (1.397) Tariffs(t−1) 0.000 0.000 0.003 0.003* 0.001 0.000 0.000 0.000 −0.000 −0.000 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) −0.004 −0.004 −0.004 −0.004 −0.003 −0.002 −0.003 −0.003 −0.010 −0.010 (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.007) (0.007) Foreign 0.029* 0.029* 0.029* 0.029* 0.033** 0.033** 0.035** 0.036** 0.043*** 0.044*** (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.030 0.030 0.035 0.035 0.030 0.030 0.028 0.028 0.032 0.033 Break coefficient = year(s) prior co‐efficient F‐stat 5.21 10.74 7.2 17.09 2.91 4.39 5.02 7.57 6.59 2.36 p‐value 0.023 0.001 0.008 0.000 0.089 0.037 0.026 0.006 0.011 0.126 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab Table 11 Productivity Effects of Services Liberalisation – Ackerberg et al. (2006) TFP Measure. Adding Industry Time Trends Services index(t−1) 0.985*** (0.295) Banking index(t−1) 1.081*** 1.166*** (0.321) (0.319) Banking index Rajan–Zingales weights (t‐1) 0.122*** (0.037) Telecom index(t−1) 0.982 −0.636 (1.844) (1.871) Insurance index(t−1) 2.540 3.092 (1.762) (1.976) Transport index(t−1) −0.079 0.307 (0.697) (0.682) Tariffs(t−1) 0.000 0.000 −0.000 0.001 0.001 0.001 −0.000 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) 0.001 −0.000 0.000 0.001 0.000 0.001 −0.001 (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) Foreign 0.029* 0.030* 0.031** 0.031* 0.033** 0.032** 0.032** (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.029 0.029 0.029 0.027 0.027 0.027 0.030 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Services index(t−1) 0.985*** (0.295) Banking index(t−1) 1.081*** 1.166*** (0.321) (0.319) Banking index Rajan–Zingales weights (t‐1) 0.122*** (0.037) Telecom index(t−1) 0.982 −0.636 (1.844) (1.871) Insurance index(t−1) 2.540 3.092 (1.762) (1.976) Transport index(t−1) −0.079 0.307 (0.697) (0.682) Tariffs(t−1) 0.000 0.000 −0.000 0.001 0.001 0.001 −0.000 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) 0.001 −0.000 0.000 0.001 0.000 0.001 −0.001 (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) Foreign 0.029* 0.030* 0.031** 0.031* 0.033** 0.032** 0.032** (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.029 0.029 0.029 0.027 0.027 0.027 0.030 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. All specifications include firm and year fixed effects as well as industry time trends. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab Table 11 Productivity Effects of Services Liberalisation – Ackerberg et al. (2006) TFP Measure. Adding Industry Time Trends Services index(t−1) 0.985*** (0.295) Banking index(t−1) 1.081*** 1.166*** (0.321) (0.319) Banking index Rajan–Zingales weights (t‐1) 0.122*** (0.037) Telecom index(t−1) 0.982 −0.636 (1.844) (1.871) Insurance index(t−1) 2.540 3.092 (1.762) (1.976) Transport index(t−1) −0.079 0.307 (0.697) (0.682) Tariffs(t−1) 0.000 0.000 −0.000 0.001 0.001 0.001 −0.000 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) 0.001 −0.000 0.000 0.001 0.000 0.001 −0.001 (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) Foreign 0.029* 0.030* 0.031** 0.031* 0.033** 0.032** 0.032** (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.029 0.029 0.029 0.027 0.027 0.027 0.030 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Services index(t−1) 0.985*** (0.295) Banking index(t−1) 1.081*** 1.166*** (0.321) (0.319) Banking index Rajan–Zingales weights (t‐1) 0.122*** (0.037) Telecom index(t−1) 0.982 −0.636 (1.844) (1.871) Insurance index(t−1) 2.540 3.092 (1.762) (1.976) Transport index(t−1) −0.079 0.307 (0.697) (0.682) Tariffs(t−1) 0.000 0.000 −0.000 0.001 0.001 0.001 −0.000 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Input tariffs(t−1) 0.001 −0.000 0.000 0.001 0.000 0.001 −0.001 (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) Foreign 0.029* 0.030* 0.031** 0.031* 0.033** 0.032** 0.032** (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.029 0.029 0.029 0.027 0.027 0.027 0.030 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. All specifications include firm and year fixed effects as well as industry time trends. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab When we allow for the effects to differ between Indian firms and foreign affiliates (The Table not reported to save space), we also find a positive link between the aggregate measure of services reform and the performance of downstream manufacturing firms. As before, larger benefits appear to accrue to foreign affiliates. A similar pattern is detected for the banking reform. When it comes to the telecom, insurance and transport sectors, the benefits of services liberalisation appear to accrue only to foreign firms. The magnitudes of the effects are similar to those found in Table 6 and are statistically significant at the 1% level. Finally, we examine whether our results are subject to an autocorrelation problem that could lead to the underestimation of standard errors, as discussed by Bertrand et al. (2004). To check for this potential estimation bias, we take their advice and ignore the time‐series information when computing standard errors. We perform the test in three steps. First, we regress the logarithm of TFP on control variables (other than the services variables) and fixed effects and keep the residuals. Second, we divide the residuals into two groups: residuals from the years before the structural break and residuals from the post‐break period and calculate a within‐firm average for each period. In the last step, we regress the two‐period panel of mean residuals on the Breakjt variable defined in (5). We cluster standard errors for each manufacturing industry. We repeat the procedure for a break in each services sector considered in the analysis. As is evident from Table 12, we find positive and statistically significant (at the 1% level) effects for the banking sector, telecoms and insurance reform. Somewhat surprisingly, we obtain a negative coefficient for the transport reform. Given these findings, we feel reasonably confident that our baseline results are not subject to the autocorrelation problem. Table 12 Robustness Check on Autocorrelation – Ackerberg et al. (2006) TFP Measure Banking break 2001 2.859*** (0.686) Rajan–Zingales break 2001 0.412*** (0.061) Telecom break 2002 30.678*** (2.411) Insurance break 2002 15.203*** (2.219) Transport break 1997 −1.453*** (0.512) Observations 6,142 6,142 6,059 6,059 5,440 R2 0.003 0.007 0.026 0.008 0.001 Number of firms 3,771 3,771 3,771 3,771 3,771 Banking break 2001 2.859*** (0.686) Rajan–Zingales break 2001 0.412*** (0.061) Telecom break 2002 30.678*** (2.411) Insurance break 2002 15.203*** (2.219) Transport break 1997 −1.453*** (0.512) Observations 6,142 6,142 6,059 6,059 5,440 R2 0.003 0.007 0.026 0.008 0.001 Number of firms 3,771 3,771 3,771 3,771 3,771 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab Table 12 Robustness Check on Autocorrelation – Ackerberg et al. (2006) TFP Measure Banking break 2001 2.859*** (0.686) Rajan–Zingales break 2001 0.412*** (0.061) Telecom break 2002 30.678*** (2.411) Insurance break 2002 15.203*** (2.219) Transport break 1997 −1.453*** (0.512) Observations 6,142 6,142 6,059 6,059 5,440 R2 0.003 0.007 0.026 0.008 0.001 Number of firms 3,771 3,771 3,771 3,771 3,771 Banking break 2001 2.859*** (0.686) Rajan–Zingales break 2001 0.412*** (0.061) Telecom break 2002 30.678*** (2.411) Insurance break 2002 15.203*** (2.219) Transport break 1997 −1.453*** (0.512) Observations 6,142 6,142 6,059 6,059 5,440 R2 0.003 0.007 0.026 0.008 0.001 Number of firms 3,771 3,771 3,771 3,771 3,771 Notes The dependent variable is the log TFP estimated using the Ackerberg et al. (2006) method for each of the 11 industries listed in Table 2. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. Open in new tab 5. Services Liberalisation and Manufacturing Output Productivity is not the only manufacturing outcome that could be affected by services reform. Services reform could also have a first order effect on output of the manufacturing sector. Essentially, lower input costs and relaxation of critical infrastructure bottlenecks should allow firms to expand (whether or not accompanied by productivity improvements). Therefore, in our final exercise, we examine the relationship between services reform and manufacturing output.38 Output is expressed in real terms, and the empirical specifications mirror those in the baseline Table (Table 4). The results, presented in Table 13, show a positive and statistically significant relationship between manufacturing output and the overall index of services liberalisation and both proxies for the banking sector liberalisation. Manufacturing output also appears to be positively linked to telecommunications liberalisation but the effect is less precisely estimated (it is statistically significant at the 11% level). Insurance liberalisation does not appear to matter, while transport liberalisation seems to have a negative effect (perhaps because it also increases competition from imports). In Table 14, we decompose the manufacturing output growth 1995–2004 into the contribution made by increased use of capital, material and energy inputs, services inputs, labour and total factor productivity.39 All manufacturing sectors have registered an increase in their output, with the growth of output ranging from 22% in garments, leather goods and shoes to 107% in coke, fuel, petroleum and chemicals. In each sector, increased used of services appears to have contributed to output growth, though its contribution was smaller in magnitude than the contribution of capital, materials/energy inputs and in most cases labour. Productivity growth appears to have boosted output in seven of 11 industry groups considered. 6. Conclusions This article suggests that previous explanations for the post‐1991 growth of India’s manufacturing sector have ignored an important factor: the contribution of India’s policy reforms in services. By gathering detailed information on the pace of policy reform in Indian services sectors and constructing a series of reform indices, we demonstrate a significant empirical link between progress in policy reforms in services sectors and productivity in manufacturing industries. When distinguishing the effect of services reform by ownership, we find that foreign‐owned subsidiaries in India display an even greater ability to reap the benefits of services reforms than domestic firms. Our findings are robust to a number of checks, including instrumenting for the pace of reform in Indian services sectors, controlling for trade liberalisation, foreign ownership, sector‐specific time trends and autocorrelation. Table 13 Output Effects of Services Liberalisation Services index(t−1) 0.553*** (0.170) Banking index(t−1) 0.873*** 0.975*** (0.184) (0.188) Banking index Rajan–Zingales weights(t−1) 0.039* (0.021) Telecom index(t−1) 1.358† 1.265 (0.845) (0.869) Insurance index(t−1) −0.356 −1.812*** (0.727) (0.623) Transport index(t−1) −1.611* −1.791** (0.858) (0.814) Tariffs(t−1) 0.000 0.000 0.000 −0.000 −0.000 −0.000 0.000 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Input tariffs(t−1) 0.004 0.004 0.004 0.005† 0.004 0.005* 0.006* (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) Foreign 0.166*** 0.164*** 0.169*** 0.169*** 0.170*** 0.168*** 0.161*** (0.014) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.047 0.049 0.046 0.046 0.046 0.046 0.050 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Services index(t−1) 0.553*** (0.170) Banking index(t−1) 0.873*** 0.975*** (0.184) (0.188) Banking index Rajan–Zingales weights(t−1) 0.039* (0.021) Telecom index(t−1) 1.358† 1.265 (0.845) (0.869) Insurance index(t−1) −0.356 −1.812*** (0.727) (0.623) Transport index(t−1) −1.611* −1.791** (0.858) (0.814) Tariffs(t−1) 0.000 0.000 0.000 −0.000 −0.000 −0.000 0.000 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Input tariffs(t−1) 0.004 0.004 0.004 0.005† 0.004 0.005* 0.006* (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) Foreign 0.166*** 0.164*** 0.169*** 0.169*** 0.170*** 0.168*** 0.161*** (0.014) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.047 0.049 0.046 0.046 0.046 0.046 0.050 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Notes All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. †At the 11% level. Open in new tab Table 13 Output Effects of Services Liberalisation Services index(t−1) 0.553*** (0.170) Banking index(t−1) 0.873*** 0.975*** (0.184) (0.188) Banking index Rajan–Zingales weights(t−1) 0.039* (0.021) Telecom index(t−1) 1.358† 1.265 (0.845) (0.869) Insurance index(t−1) −0.356 −1.812*** (0.727) (0.623) Transport index(t−1) −1.611* −1.791** (0.858) (0.814) Tariffs(t−1) 0.000 0.000 0.000 −0.000 −0.000 −0.000 0.000 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Input tariffs(t−1) 0.004 0.004 0.004 0.005† 0.004 0.005* 0.006* (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) Foreign 0.166*** 0.164*** 0.169*** 0.169*** 0.170*** 0.168*** 0.161*** (0.014) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.047 0.049 0.046 0.046 0.046 0.046 0.050 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Services index(t−1) 0.553*** (0.170) Banking index(t−1) 0.873*** 0.975*** (0.184) (0.188) Banking index Rajan–Zingales weights(t−1) 0.039* (0.021) Telecom index(t−1) 1.358† 1.265 (0.845) (0.869) Insurance index(t−1) −0.356 −1.812*** (0.727) (0.623) Transport index(t−1) −1.611* −1.791** (0.858) (0.814) Tariffs(t−1) 0.000 0.000 0.000 −0.000 −0.000 −0.000 0.000 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Input tariffs(t−1) 0.004 0.004 0.004 0.005† 0.004 0.005* 0.006* (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) Foreign 0.166*** 0.164*** 0.169*** 0.169*** 0.170*** 0.168*** 0.161*** (0.014) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014) Observations 22,558 22,558 22,558 22,558 22,558 22,558 22,558 R2 0.047 0.049 0.046 0.046 0.046 0.046 0.050 Number of firms 3,771 3,771 3,771 3,771 3,771 3,771 3,771 Notes All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry‐year level, are reported in parentheses. ***Denotes significant at the 1% level, **at the 5% level, *at the 10% level. †At the 11% level. Open in new tab Table 14 Decomposition of Manufacturing Output Growth Industry . Output growth . Contribution of . Capital . Materials and energy . Services inputs . Labour . TFP . Food processing and tobacco products 0.67 0.30 0.36 0.03 0.04 −0.06 Textiles 0.46 0.08 0.18 0.01 0.03 0.15 Garments, leather goods and shoes 0.22 0.12 0.07 0.02 0.05 −0.04 Wood products, paper products, printing and publishing 0.56 0.26 0.14 0.03 0.05 0.09 Coke, fuel, petroleum and chemicals 1.07 0.38 0.71 0.03 0.06 −0.11 Plastic and rubber products 0.56 0.19 0.27 0.03 0.02 0.05 Concrete, cement and glass 0.49 0.18 0.05 0.06 0.02 0.18 Iron and steel 0.47 0.20 0.34 0.02 0.02 −0.11 Metal products, machinery and tools 0.61 0.16 0.08 0.02 0.04 0.31 Electrical, lifting, medical and industrial equipment 0.58 0.12 0.09 0.04 0.04 0.29 Motor vehicles and transport systems 0.97 0.26 0.46 0.05 0.05 0.15 Industry . Output growth . Contribution of . Capital . Materials and energy . Services inputs . Labour . TFP . Food processing and tobacco products 0.67 0.30 0.36 0.03 0.04 −0.06 Textiles 0.46 0.08 0.18 0.01 0.03 0.15 Garments, leather goods and shoes 0.22 0.12 0.07 0.02 0.05 −0.04 Wood products, paper products, printing and publishing 0.56 0.26 0.14 0.03 0.05 0.09 Coke, fuel, petroleum and chemicals 1.07 0.38 0.71 0.03 0.06 −0.11 Plastic and rubber products 0.56 0.19 0.27 0.03 0.02 0.05 Concrete, cement and glass 0.49 0.18 0.05 0.06 0.02 0.18 Iron and steel 0.47 0.20 0.34 0.02 0.02 −0.11 Metal products, machinery and tools 0.61 0.16 0.08 0.02 0.04 0.31 Electrical, lifting, medical and industrial equipment 0.58 0.12 0.09 0.04 0.04 0.29 Motor vehicles and transport systems 0.97 0.26 0.46 0.05 0.05 0.15 Open in new tab Table 14 Decomposition of Manufacturing Output Growth Industry . Output growth . Contribution of . Capital . Materials and energy . Services inputs . Labour . TFP . Food processing and tobacco products 0.67 0.30 0.36 0.03 0.04 −0.06 Textiles 0.46 0.08 0.18 0.01 0.03 0.15 Garments, leather goods and shoes 0.22 0.12 0.07 0.02 0.05 −0.04 Wood products, paper products, printing and publishing 0.56 0.26 0.14 0.03 0.05 0.09 Coke, fuel, petroleum and chemicals 1.07 0.38 0.71 0.03 0.06 −0.11 Plastic and rubber products 0.56 0.19 0.27 0.03 0.02 0.05 Concrete, cement and glass 0.49 0.18 0.05 0.06 0.02 0.18 Iron and steel 0.47 0.20 0.34 0.02 0.02 −0.11 Metal products, machinery and tools 0.61 0.16 0.08 0.02 0.04 0.31 Electrical, lifting, medical and industrial equipment 0.58 0.12 0.09 0.04 0.04 0.29 Motor vehicles and transport systems 0.97 0.26 0.46 0.05 0.05 0.15 Industry . Output growth . Contribution of . Capital . Materials and energy . Services inputs . Labour . TFP . Food processing and tobacco products 0.67 0.30 0.36 0.03 0.04 −0.06 Textiles 0.46 0.08 0.18 0.01 0.03 0.15 Garments, leather goods and shoes 0.22 0.12 0.07 0.02 0.05 −0.04 Wood products, paper products, printing and publishing 0.56 0.26 0.14 0.03 0.05 0.09 Coke, fuel, petroleum and chemicals 1.07 0.38 0.71 0.03 0.06 −0.11 Plastic and rubber products 0.56 0.19 0.27 0.03 0.02 0.05 Concrete, cement and glass 0.49 0.18 0.05 0.06 0.02 0.18 Iron and steel 0.47 0.20 0.34 0.02 0.02 −0.11 Metal products, machinery and tools 0.61 0.16 0.08 0.02 0.04 0.31 Electrical, lifting, medical and industrial equipment 0.58 0.12 0.09 0.04 0.04 0.29 Motor vehicles and transport systems 0.97 0.26 0.46 0.05 0.05 0.15 Open in new tab We also investigate the relative contribution of reform in each of the services sectors to the productivity of manufacturing firms and find that liberalisation in the banking, telecommunications and transport sectors had robust productivity effects on manufacturing firms over the period. These findings are intuitive. Liberalisation in the banking sector has improved capital allocation and allowed investment in higher return projects. Liberalisation of the telecommunications sector has interacted with technological change not only to enhance the reliability and reduce the cost of communication but it has also paved the way for entirely new ways of communication and organising production. Liberalisation of the transport sector allows easier and less expensive transport of raw materials and goods for export. However, reforms in several areas of the transport sector in India have been slow and some control over transport remains at the state level. Perhaps because we cannot capture this state‐level variation in our index, the results for the transport sector are slightly weaker. Insurance sector reforms do not appear to have had a strong influence in our data, possibly due to their limited scope so far. Services reforms in India remain incomplete and barriers to domestic and foreign competition exist in many other countries. This article suggests that in addition to retarding the development of the services sectors, these barriers also penalise the manufacturing sector. Wider appreciation of this link may help create broader political support for services reform. It may also provide a useful perspective for international trade negotiations, which have had less success in addressing impediments to services trade and investment than barriers to trade in agriculture and manufacturing. Footnotes 1 " These inputs affect inter alia a firm’s ability to invest in new business opportunities and better production technology, to exploit economies of scale by concentrating production in fewer locations, to manage inventories efficiently and to make coordinated decisions with their suppliers and consumers. Ethier (1982) provides theoretical support for this argument, showing that access to a greater variety of inputs results in higher productivity among downstream industries. Markusen (1989) argues that many producer services are both differentiated and knowledge‐intensive. Knowledge intensity in turn suggests strong scale economies in that knowledge must be acquired at an initial learning cost, after which the knowledge‐based services can be provided at a very low marginal cost. His theoretical results suggest the possibility of significant gains from liberalised trade in producer services. The importance of intermediate inputs for productivity growth has also been emphasised in the theoretical contributions of Grossman and Helpman (1991). Jones (2011) draws attention to how linkages between firms through intermediate inputs result in a multiplier similar to the one associated with capital in a neoclassical growth model. This multiplier is large because of a high share of intermediates in output and thus helps account for differences in incomes across countries. 2 " India implemented significant liberalisation in both goods and services between 1991 and 2005. Major liberalisation began in 1991 as part of an IMF structural adjustment package, designed to combat balance of payments imbalances and continued with the government’s eighth five‐year plan over the period 1992–7. As we discuss below, the pace of reform in services was gradual and sought to balance a variety of economic and political considerations. 3 " Even in industrial countries, the supposed strategic importance of some services has led to the persistence of restrictions (Borchert et al., 2014). For example, witness the barriers to foreign participation in air and maritime transport as well as certain types of communication services in the US, and the difficulty in completing the single market for services in the EU. 4 " Services liberalisation is likely to lead to output growth, as well as labour and productivity growth, in the services sector itself. Examining these effects, however, lies beyond the scope of this study. 5 " There is some work on the economy‐wide effects of services reform. Mattoo et al. (2006) show that services liberalisation leads to higher levels of economic growth. Eschenbach and Hoekman (2006) find similar evidence for Eastern Europe. 6 " Chari and Gupta (2008) provide evidence that the delicensing reforms in India in 1991 categorised certain more concentrated and less competitive industries as strategic and shielded them from foreign competition by maintaining barriers to foreign direct investment. They find that profitable state‐owned enterprises were likely to be protected, particularly in capital‐intensive industries. Lobbying power by state banks and other services companies in India is likely to have been a factor in delaying liberalisation of the services sectors into the mid‐1990s and in excluding them from the general goods liberalisation during the rapid trade reforms which took place in 1991. 7 " The authors discussed the reform experience with B.K. Zutshi, the first Chairman of the Telecom Regulatory Authority of India (TRAI), and H.V. Singh, the Secretary and Director of Economy Policy at the TRAI in December 2006. 8 " The Bank Company Acquisition Act of 1969, quoted in Burgess and Pande (2003), explicitly recognises the goal of expanding credit to priority sectors through government expansion of the banking system. 9 " As an exception to this general rule, single‐brand retailers are allowed. 10 " For example, there are 100,000 chartered accountants in India and 43,000 audit firms, with an average of two chartered accountants per firm as compared to an average of between 350 and 1,500 chartered accountants in the typical affiliates of the ‘big four’ accounting firms. In retail distribution, the penetration of supermarkets in India is only 2% compared to 55% in Malaysia and 36% in Brazil (World Bank, 2004). 11 " World Bank Investment Climate Surveys, India 2002 and 2006. For more details see notes to Table 1. 12 " The communications minister in the 1980s, C.M. Stephens declared in parliament that telephones were a luxury, not a right, and that anyone dissatisfied with their service was welcome to return their phone as there was an eight‐year waiting list of people seeking telephone service (Panagariya, 2008, p. 372). 13 " National Insurance Company Limited, Calcutta, New India Assurance Company Limited, Mumbai and United India Insurance Company Limited Chennai each cut their staffs by 10%, while Oriental Insurance Company Limited, New Delhi cut its staff by 14% (Wharton Business School, 2006). 14 " The input–output matrix includes 66 manufacturing sectors and 16 services sectors. The manufacturing sectors were aggregated to 38 sectors at which sector‐specific price deflators were available. The services sectors include: construction, electricity, gas, water supply, railway transport services, other transport services, storage and warehousing, communication, trade, hotels and restaurants, banking, insurance, ownership of dwellings, education and research, medical and health, other services. Input shares are calculated relative to the total value of inputs sourced. Banking services constitute on average 5% of all inputs, transport 4.4%, telecommunications 1.6% and insurance 1.4%. An alternative normalisation, by gross output, leads to the same conclusions. 15 " Putting potential endogeneity concerns aside, we also experimented with defining weights based on firm‐specific use of telecommunications and, transport and banking services. Doing so does not alter the conclusions of the article. We are unable to identify in the data expenditure on banking and insurance services. 16 " Our index varies across manufacturing sectors and years. About 29% of the variation is due to variation over industries, while 45% is due to variation over time. Thus, industry fixed effects and year fixed effects together explain almost three‐quarters of the variation in the index. 17 " Several firm‐level studies of Indian manufacturing use the Prowess database produced by the Centre for Monitoring the Indian Economy (CMIE). We use the Capitaline database because Prowess does not contain complete information on foreign equity ownership of firms. Both databases use as their source balance sheet‐based financial data drawn from firms’ annual reports and reports filed with regulatory agencies (Contractor et al., 2007). For unlisted firms, Capitaline also relies on own research of the data database provider. 18 " We are grateful to Carolina Villegas‐Sanchez for sharing with us a STATA routine implementing the procedure. 19 " The industry groupings are: food and tobacco; textiles; garments and leather goods; wood, paper and printing; petroleum products and chemicals; rubber and plastics; non‐metallic minerals, iron and steel; metal products; machinery, office, electrical and communication equipment; lifting, medical and industrial equipment; motor vehicles and other transport equipment. 20 " Measuring labour input on the basis of wages implies that differences in the quality of labour are accounted for, as long as wage differences reflect such quality differences. At the same time, if some of the productivity gains are appropriated by workers through higher wages, then measured TFP would be biased downward. We are grateful to an anonymous referee for pointing this out to us. 21 " As this specification does not take into account potential responses that materialise with longer lags, the productivity effects could in principle be underestimated. At the same time, the break regressions reported as a robustness check later in the paper allow for a more general timing of the productivity response to services reform. 22 " The authors are grateful to Rajesh Mehta for providing tariff data for the years in which the figures were missing from WITS. 23 " Clustering at the firm‐level instead would not change the conclusions of the article. As expected, we found that it produces higher significance levels of the estimated co‐efficients. 24 " A specification with output on the left‐hand side and industry‐specific coefficients on material inputs, services inputs and energy leads to very similar results. 25 " If we consider the change in the index occurring during our sample period, the corresponding effects are 5.5% for banking, 8.6% for telecoms and 41.5% for transport. 26 " In a recent paper, Bollard et al. (2010) also find that productivity growth in Indian manufacturing since the 1990s is not robustly related to tariff reductions. It is also possible that we do not find significant effects because most of the tariff cuts took place prior to the time covered by our sample. Note that allowing for a different co‐efficient prior to 1997 (the last year of the period studied by Khandelwal and Topalova, 2011) did not produce any evidence of significant effects either. 27 " In regressions, not reported to save space, we also show that our conclusions are robust to using a translog production function. 28 " More precisely, these variables represent the share of sales in the banking, telecommunications and transport sectors made by firms with more than 10% foreign (or private) ownership. The sales of firms with more than 10% foreign (or private) ownership are weighted by the percentage of foreign (private) ownership in the firm in a given year, and the sum of these weighted sales is then divided by the total sales in the sector in the year. 29 " We did not have equity ownership data or sales data for insurance firms, so the insurance variables are left out of this analysis. Therefore, the general services sector index is the sum of the banking, transport and telecommunications sector index. 30 " According to Harrison et al., by the end of 1991, nearly 85% of industries had been delicensed, with the share increasing to over 90% of industries by the end of the 1990s. FDI liberalisation occurred somewhat more slowly and only in 2000 did all industries became eligible for automatic FDI approval, except those requiring an industrial licence or meeting several other conditions. 31 " We are very grateful to Ann Harrison, Leslie Martin and Shanthi Nataraj for sharing the data with us. Industries have been converted from 3‐digit NIC87 industry codes to 4‐digit NIC98 industry codes. Where direct correspondences were not found, averages were used at the 2‐digit NIC98 level. 32 " Including these additional controls in all other specifications presented in the article would not change its conclusions. 33 " We use the input–output matrix to identify these sectors. They are: manufacture of petroleum products, manufacture of motor vehicles parts and accessories, manufacture of rubber products, paper products and printing, manufacture of office machinery, manufacturing of industrial equipment, manufacture of electronic components and receivers, textile manufacturing. 34 " We could have used Indonesia’s actual policies rather than its Uruguay Round commitments as instruments but we do not have information on the latter. In any case, in most countries, there was a fairly close relationship between relative commitments across sectors and actual policy (Hoekman, 1996). 35 " As a matter of fact, China’s accession to the WTO was held up by the negotiations on services market opening (Mattoo, 2003). China was offering foreign firms access to its insurance sectors but wanted to limit foreign equity share to 50%. A US firm, AIG that already had wholly owned subsidiaries in China, wanted their status to be grandfathered. This was opposed by the EU negotiators who wanted equal treatment for their firms. On 1 October 2001 the Washington Post, reported: ‘The interest of a single U.S. company, insurance giant AIG, was stopping a final agreement on China’s WTO membership’. China joined the organisation on 11 December 2001. The close interplay in China’s case between its accession commitments and actual policy reform implies that the two were even more closely related than in the case of other countries. 36 " Note that it is not possible to do this for the aggregate measure as the timing of structural breaks varies from sector to sector. 37 " The use of these structural policy breaks may also help us to distinguish the impact of reform from the impact of technological progress in sectors like telecommunications. For example, the year of the most significant policy change in telecommunications was 2002, which was much later than the emergence of new mobile communication technologies in the early 1990s. 38 " We are grateful to an anonymous referee for suggesting this exercise. 39 " This is done as follows. We keep firms present in both 1995 and 2004 (we focus on these years to increase the number of observations). In each year (1995 and 2004), we aggregate firm‐level output and input data to the industry level. The input shares (β, γ and φ) are the average input shares found in each industry. The capital share (α) is calculated assuming constant returns to scale. Then our decomposition is as follows, where Y denotes output, K capital stock, M materials, E energy input, S services inputs, L labour input and A is the total factor productivity (calculated as the residual): ΔlnY=αΔlnK+βΔln(M+E)+λΔlnS+ψΔlnL+ΔlnA. 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The views expressed in the paper are those of the authors and should not be attributed to the OECD, to the World Bank, its executive directors or the countries they represent. © 2014 Royal Economic Society
TI - Services Reform and Manufacturing Performance: Evidence from India
JF - The Economic Journal
DO - 10.1111/ecoj.12206
DA - 2016-02-01
UR - https://www.deepdyve.com/lp/oxford-university-press/services-reform-and-manufacturing-performance-evidence-from-india-OUkL2uM003
SP - 1
VL - 126
IS - 590
DP - DeepDyve
ER -