Introduction
Standard theoretical models of international trade, including the Ricardian and Hecksher-Ohlin-Samuelson (HOS) models, primarily explain trade patterns between countries through the lens of comparative advantage These models assume constant returns to scale in production, suggesting that firm structure is irrelevant Trade patterns across sectors are determined by differences in technology and relative factor endowments, necessitating disparities for trade to occur, where each country typically exports or imports specific commodities Post-World War II empirical research focused on testing these theories against aggregate sectoral and country-level data, often revealing inconsistencies Notably, early studies by Leontief indicated that the U.S., a capital-rich nation, exported labor-intensive goods, contradicting the expectation of capital-intensive exports.
The deviation in labor dynamics can be attributed to the higher skill levels of US workers, indicating that the US is, in reality, a labor-rich country The gravity model serves as a key tool for explaining bilateral trade flows, often without relying on a theoretical framework Notably, the theoretical foundations for the gravity model, as established by researchers such as Anderson (1979) and Deardorff (1998), emerged significantly later than its application in empirical studies, which were largely inspired by the principles of Newtonian forces of attraction and repulsion.
The observed trade patterns reveal significant intra-industry trade, where countries both export and import the same commodities Notably, countries with similar factor endowments engage in more trade with each other compared to those with differing endowments The emergence of new trade theory in the 1980s introduced concepts such as economies of scale and consumer preferences for diverse varieties of the same commodity, explaining the dynamics of intra-industry trade and trade between countries with similar resources In these models, all firms are considered identical, allowing for universal participation in trade The latest advancements in this field are encapsulated in the "new new" trade theory.
1 Samuel C Park, Jr Professor of Economics, Yale University
A study by a fellow at the Indian Council for Research on International Economic Relations (ICRIER) in New Delhi highlights that significant differences among firms impact aggregate output and trade flows Analysis of firm-level production and trade data reveals that only a limited number of firms engage in international trade, exporting merely a small portion of their total production Furthermore, the research indicates that exporters differ markedly from non-exporters, and that trade liberalization contributes to increased average productivity within industries.
According to Bernard et al (2007), only 4% of the 5.5 million firms in the US in 2000 were exporters, indicating significant differences between exporting and non-exporting firms Their research, which spans back to the mid-1990s and utilizes firm-level data across various countries and industries, reveals that exporting firms are generally larger, more productive, and more skilled and capital-intensive, often offering higher wages compared to non-exporting firms.
This article contributes to the expanding body of literature utilizing Indian data, focusing on India's industrialization strategy post-independence in 1947 For nearly forty years, India implemented an approach that protected domestic firms from competition, imports, and inter-firm rivalry through import restrictions and capacity licensing These restrictions led to increased prices for imported intermediates and final goods, affecting the rates of effective protection based on the cost share of intermediates and the tariff rates imposed on both final and intermediate products.
In the mid-1980s, India began a cautious relaxation of its insulation from both import and domestic competition, yet the import substitution policy remained complex Even during strict import restrictions, exporters benefited from various incentives, such as marketable entitlements for scarce imports, favorable exchange rates, and tariff rebates on imported intermediates This allowed exporters to operate at near world prices for their sales and intermediate purchases However, the regime's complexity led to variations across industries and firms, influenced by differences in input-output structures and a discretionary import licensing system Consequently, identical firms were not uniformly treated Early analyses of this intricate system can be found in the works of Bhagwati and Desai (1970) and Bhagwati and Srinivasan.
The post-reform era, as discussed by Srinivasan and Tendulkar (2003) and Panagariya (2008), marked a significant shift in India's economic landscape following a severe macro-economic and balance of payments crisis in 1991 This crisis prompted a departure from the previous insulation strategy, leading to increased import competition and foreign direct investment As a result, aggregate real GDP growth accelerated starting in the 1980s, with a notable rise in exports Consequently, it is crucial to analyze the export incentives for firms in the period following 1991.
Since 1991, India has actively sought regional and preferential trade agreements (PTAs/RTAs), similar to many other nations The existing literature presents mixed findings on these agreements, with some studies suggesting they primarily create trade, while others argue they divert it This paper analyzes the effects of RTAs/PTAs on India's bilateral trade flows through gravity models, adding to the body of research that employs these models for this analysis.
In what follows, we start in section 2 with a brief review of relevant literature Section
This article examines India's trade flows from 1981 to 2006 and evaluates the impact of Regional Trade Agreements (RTAs) It explores the determinants of exports through three sets of firm-level data: (i) labor-intensive manufacturing firms from the PROWESS database, defined by a capital-labor ratio below the average of 15.45, (ii) time-series data on manufacturing firms from 1995 to 2006, and (iii) data from the Confederation of Indian Industry for 2004-05 Additionally, a specially commissioned survey was conducted to gather detailed information on firm characteristics, and the completed questionnaires are currently being analyzed The findings from this survey will be reported in future work, with the paper concluding in Section 5.
Brief Review of Literature
Gravity Models of Bilateral Trade Flows
The gravity model, an empirical framework established in the 1960s, draws inspiration from Newtonian gravitational principles, suggesting that the trade flow between two countries is directly proportional to their GDPs and inversely proportional to the distance separating them This model has evolved to incorporate additional factors that can either promote or impede bilateral trade, such as shared languages or colonial ties, as well as tariffs and transaction costs Recent research has also introduced dummy variables to assess the impact of Regional Trade Agreements (RTA) and Preferential Trade Agreements (PTA) on trade diversion and creation, enhancing the model's applicability in contemporary trade analysis.
The literature on gravity models encompasses extensive theoretical and empirical studies, yet we will focus on three recent empirical works relevant to our analysis of the impact of RTA/PTA membership on trade flows It is crucial to note that one cannot deduce the welfare effects of RTA/PTA membership solely from its trade-diverting or trade-creating characteristics Additionally, since imports and exports cannot be negative, traditional regression models that do not account for this limitation are unsuitable for analyzing trade flows While Newtonian models allow for attraction and repulsion forces to be very small but not zero, bilateral trade flows can indeed be zero, often due to rounding errors This presents challenges for the log-linear form of the gravity equation To address this, we employ a Probit (or Logit) model to assess the likelihood of positive trade flows and a Tobit model to analyze actual flows—whether zero or positive—while also incorporating a conventional regression model for positive flows.
The study by Soloaga and Winters (2001) is the earliest among three gravity model-based analyses assessing the impact of preferential trade agreements (PTAs) on bilateral trade flows They utilize a modified gravity equation to evaluate the distinct effects of PTAs on intrabloc trade, as well as total imports and exports of member countries Their findings indicate that recent PTAs have not significantly enhanced intrabloc trade, with evidence of trade diversion occurring within the European Union (EU) and the European Free Trade Area (EFTA) Additionally, EFTA members display export diversion, leading to welfare costs for non-member countries Their gravity equation modification, which incorporates a sum of three specific terms, allows for a comprehensive assessment of PTA effects on trade flows between countries.
The equation Pki (Pkj) = 1 indicates whether countries i and j are members of the k-th Preferential Trade Agreement (PTA), with b k representing the intrabloc effect on bilateral trade due to preferential trade liberalization This analysis distinguishes between the trade effects of non-preferential and preferential liberalization, where m k measures the impact of country i's PTA membership on its imports from country j, and n k reflects the effect of country j's membership on its exports to country i Together, m k + n k + b k captures the total effect of both countries being PTA members, highlighting the traditional intrabloc trade effect For example, in the context of India and the South Asian Free Trade Area (SAFTA), m k and n k illustrate the combined benefits of India's trade liberalization and its SAFTA membership, while b k quantifies the additional advantages stemming from its partner's membership This approach effectively captures the impact of PTAs, as demonstrated by Soloaga and Winters (2001) in their study of non-fuel imports across 58 countries from 1980 to 1996.
Adams et al (2003) provide a comprehensive review of Preferential Trade Agreements (PTAs), analyzing their theoretical framework and empirical evidence across three distinct waves of PTA formation since the 1950s Their study utilizes a gravity model similar to that of Soloaga and Winters (2001) and encompasses data from 116 countries over 28 years (1970-1997) The authors identify two key findings: first, among 18 recent PTAs examined, 12 have resulted in more trade diversion from non-members than trade creation among members, including notable agreements like the EU, NAFTA, and MERCOSUR Second, while foreign direct investment (FDI) tends to respond positively to non-trade provisions within PTAs, this benefit is counterbalanced by the negative trade diversion effects stemming from the trade provisions of these agreements.
De Rosa (2007) critically evaluates the findings of Adams et al (2003) by employing a modified version of Andrew Rose's (2002) gravity model, while also integrating Soloaga and Winters' (2001) dummies for preferential trade agreement (PTA) membership His analysis spans the years 1970-1999 and includes 20 PTAs, a notable increase from the 18 PTAs analyzed by Adams et al and the 9 PTAs in Soloaga and Winters (2001) Despite not identifying significant methodological flaws in Adams et al (2003), De Rosa reaches a contrasting conclusion, asserting that the majority of the 20 PTAs examined are trade-creating.
Recent studies on the effects of Preferential Trade Agreements (PTAs) have produced inconclusive results regarding whether they are trade diverting or trade creating, a trend consistent with earlier research where outcomes varied based on the countries, data sets, and time periods analyzed Focusing specifically on India's trade flows, we employ a gravity model akin to that of Soloaga and Winters (2001) to assess the impact of 21 PTAs, including several bilateral trade agreements.
The estimated model for India’s export flows Xj t to partner country j in year t is:
( ) jt jt jt jt jt jt k kjt k kit jt
Log X Log GDP Log Pop Log Distance j Log TR
Where GDP jt = GDP of country j in year t.
Pop jt = Population of country j in year t
The European Union (EU), North American Free Trade Area (NAFTA), and MERCOSUR represent significant trade agreements, with MERCOSUR established in 1991 among Argentina, Brazil, Paraguay, and Uruguay Since 2006, Bolivia, Chile, Colombia, Ecuador, and Peru have held associate member status within MERCOSUR.
Distance j = Distance between India and country j Distance is measured as the average of distance between major ports of India and j.
TR jt = Average effective import tariff rate of country j.
RERjt = Real Exchange Rate of country j, units of foreign currency per
Indian rupee (ratio of US dollar per Indian Rupee to US dollar/per unit of country j’s currency)
Lang j = Measure of linguistic similarity between India and country j.
D(t) = Time dummy, taking the value 1 for all observations of year t and zero otherwise.
Pk jt = A dummy taking the value 1 if country j is a member of kth
PTA in year t We consider 11 PTAs including the South Asian Free Trade area (SAFTA).
P kit = A dummy which takes the value 1 if India is a member of kth
PTA in year t. ε jt = Independently and Identically Normally Distributed Random error term with mean zero and constant variance.
This analysis focuses on India, where the time dummy D(t) captures the country's GDP, population, and other time-varying factors Additionally, the parameter β k integrates the parameters b k and n k from the Soloaga and Winters (2001) model.
The import flow model for India closely resembles that of its export flows, with the exception of the tariff variable, which reflects the country's average effective import tariff and is integrated into the time dummy Additionally, the model for total trade flows aligns with that of export flows, although the estimated coefficients for each variable typically vary based on the specific flows being analyzed.
The a priori expected sign of the coefficient α α 1 , 2 and α 6 is positive and that of α 3 and α4is negative There are no prior expected signs for the other coefficients.
Determinants of Export Decision of Firms
Bernard et al (2007) highlighted that, although import and export activities are specific to individual firms, economists often overlook the firm's role in international trade analysis Traditionally, trade theorists have simplified their models by assuming uniformity among firms within an industry However, proponents of the "new new" trade theory have recognized the significant differences between firms, arguing that this heterogeneity influences overall output and trade flows The importance of firm-level data and the role of firms in trade are thoroughly discussed in WTO (2008), Section II-C, 3(a).
Recent empirical studies, such as Bernard et al (2007), highlight key aspects of international trade, revealing that a small percentage of firms engage in exporting, with each serving limited destinations Despite exporting being infrequent, it spans all manufacturing sectors in the U.S., with higher activity in skill-intensive industries—8% of firms in apparel versus 38% in computer and electronics Notably, firms exporting to five or more destinations represent 13.7% of exporters but account for 92.9% of export value, while multiproduct exporters, those selling five or more products, contribute to 98% of export value Additionally, although importing is less common, 41% of exporters also import, and 79% of importers are also exporters.
The article explores the distinction between the extensive margin, which refers to the number of products traded and export destinations, and the intensive margin, representing the value of trade per product per country It highlights the importance of extensive margins in the gravity model of international trade, which illustrates how distance negatively impacts trade flows The findings indicate that as distance to a destination country increases, both the number of exporting firms and the variety of products exported decline, while importers' income positively correlates with these factors Conversely, the intensive margin, or average sales of individual products, tends to rise with distance, suggesting a complex relationship influenced by transportation costs, as indicated by gravity models, contrasting with the traditional "iceberg melting" transportation cost theory proposed by Samuelson.
The iceberg approach posits that a portion of a product is lost during its journey from the production site to its export destination Consequently, to sell one unit at the destination, multiple units must be produced at the origin The loss incurred, which varies based on the fraction that melts away, reflects transportation costs calculated in relation to the unit production cost, independent of the destination price.
The attractiveness of exporting a good increases when its melting fraction is lower and its production cost is higher, relative to its destination price Conversely, if transportation costs are based on the good's weight or bulk rather than production costs, exporting becomes more appealing when the good is lighter or less bulky Additionally, for goods with the same unit weight or bulk, those that command higher prices at the destination are more desirable for export The gravity model more accurately reflects transportation costs associated with weight or bulk compared to the iceberg model.
Research indicates significant differences between exporters and non-exporters, with exporters being more capital and skill-intensive Studies show that US exporters are 14% more productive in value added per worker and 3% more productive in total factor productivity compared to non-exporters Similarly, French exporters exhibit a 15% higher total factor productivity and 31% greater labor productivity than their non-exporting counterparts This raises the question of whether more productive firms self-select into export markets or if exporting drives productivity growth through “learning by exporting.” Most studies across various industries and countries suggest that higher productivity leads firms to enter export markets, with limited evidence of productivity improvement resulting from the act of exporting However, some recent research in low-income countries, such as findings by Van Biesebroeck (2005), indicates that exporting can enhance productivity for manufacturing firms in Sub-Saharan Africa.
Baldwin's "new new" trade theory distinguishes itself from the "new" trade theory by incorporating firms' marginal costs and fixed entry costs, which are necessary for developing diverse products Firms can access the export market by incurring a fixed entry cost that becomes a sunk cost (Melitz, 2003) Roberts and Tybout (1998) highlight that treating entry costs as sunk costs creates an option value for firms to delay entry Their research, which models the export decisions of profit-maximizing firms using data from Colombian firms, effectively differentiates between sunk costs and other factors influencing export behavior An empirical analysis of firm-level panel data confirmed that sunk costs significantly impact export performance, while also revealing that firm characteristics such as size, age, and ownership structure positively correlate with the likelihood of exporting (Roberts and Tybout, 1997; Aitken, Hanson, and Harrison, 1997).
Melitz (2003) highlights the effects of trade liberalization on industry productivity through a selection mechanism, emphasizing how entry costs influence firms' export decisions Once a firm incurs sunk costs, it continues to export even if temporarily unprofitable, drawing productivity from a fixed distribution while facing a constant risk of exit Firms with productivity below a certain threshold will incur losses and exit the market, while only those above the export productivity cut-off find exporting profitable A reduction in trade barriers enhances exporter profits and lowers the export productivity cut-off, leading to increased labor demand from both existing exporters and new entrants This rise in labor demand elevates factor prices, reducing profits for non-exporters and prompting low productivity firms to exit Consequently, output and employment shift towards higher productivity firms, resulting in an overall increase in average industry productivity.
Heterogeneous firm models illustrate how firm diversity influences international trade, demonstrating that the most productive firms tend to self-select into exporting This resource reallocation from less productive to more productive firms enhances overall productivity, leading exporters to expand more quickly than non-exporters (Melitz, 2003) Consequently, both theoretical and empirical research in international trade reveals significant differences between trading and non-trading firms, which are crucial for assessing the benefits of trade.
India is perceived as a nation rich in unskilled labor, which ideally positions it for industries that rely heavily on such labor However, these sectors have struggled under a foreign trade regime that overlooks comparative advantages Domestic factors, including restrictive labor laws and an inadequate education system, have further hindered growth Despite trade liberalization efforts in the 1980s and 1990s, domestic interventions remained largely unchanged A comparison by Srinivasan (2002) highlights that India has significantly lagged behind China in expanding its market share in both global merchandise trade and labor-intensive exports This section will explore the determinants of exports in India's labor-intensive manufacturing sector, as well as the capital/labor ratios of firms engaged in manufacturing activities.
This section examines the factors that enhance the likelihood of export decisions and performance in labor-intensive and all manufacturing sectors The analysis utilizes a binary dummy variable to represent export status, allowing us to estimate the impact of various determinants on export decisions through Probit and Logit models Additionally, we assess linear probability models with industry fixed effects, despite their limitations.
To address the uncertainty surrounding the direction of causality between firm-specific characteristics and export market entry, we lag all firm characteristics and exogenous variables by one year to mitigate simultaneity issues Our model incorporates the influence of firm characteristics, sunk costs, spillovers (both region-specific and industry-specific), as well as government export promotion initiatives.
Our model (probit or logit) is: it it it it X Y
Y = if firm i exports at time t
In this analysis, the probability of a firm exporting (Yit = 1) is determined by its specific characteristics from the previous year (Xit-1), which include factors such as firm size, labor productivity, R&D investment, selling costs, wages and salaries, net fixed assets, and foreign ownership status The lagged export status (Yit-1) serves as an indicator of sunk costs, while μit represents the error term in the model.
Firms’ export performance is measured by their export propensity, expressed as a percentage of total sales, where a value of 1 indicates exports in year t and 0 signifies no exports To analyze this, the Tobit model with binary observations is suitable, as it accounts for both the decision to export and the level of exports relative to sales for those firms that do export This model utilizes balanced panel data to effectively capture these dynamics.
Y it = Yit * if Y it ∗ >0 (the value exported as a percentage of sale by firm i in year t)
Data and Specification of Econometric Models
Gravity Model
The study analyzes annual bilateral trade flows of India from 1981 to 2006, encompassing trade relations with 189 countries Key economic indicators such as GDP, GDP per capita, population, total exports, total imports, and exchange rates were sourced from the World Development database.
The data on India's trade, including exports and imports of goods, is sourced from the World Bank's World Development Indicators (WDI) database and the International Financial Statistics (IFS) Additionally, information on India's total trade in goods, which encompasses both exports and imports, is derived from the International Monetary Fund's Direction of Trade Statistics Yearbook.
India's economic indicators, including GDP, GDP per capita, exports, imports, and total trade, are expressed in million constant (1995) US dollars, with population figures also represented in millions Exchange rates are measured in US dollars per unit of national currency, while tariff rates, including both effective applied rates and Most Favored Nation (MFN) rates, have been sourced from the World Trade Organization (WTO) data from 2008.
The Most Favored Nation (MFN) tariff rates are derived from the UNCTAD Handbook of Statistics database, specifically focusing on the "Average applied import tariff rates on non-agricultural and non-fuel products." These MFN tariffs represent simple averages calculated from the tariffs applicable to manufactured goods, ores, and metals.
The actual classification as per SITC code is
Manufactured goods: 5+6+7+8-68 Ores and Metals: 27+28+68 The codes are defined as per SITC rev.2
5.0 Chemicals and related products 6.0 Manufactured goods classified chiefly by material 7.0 Machinery and transport equipment
27 Crude fertilizers and crude materials (Excluding Coal)
28 Multi ferrous ores and metal scrap
In the analysis of non-ferrous metals, various regression models, including Ordinary Least Squares (OLS), Fixed Effects (FE), Random Effects (RE), and Tobit Random Effects (RE), were applied within a log-linear gravity model framework The Hausman test indicated a rejection of the fixed effects model in favor of the random effects model To estimate the parameters of the gravity model, the Tobit random effects model was employed using the maximum likelihood method, assuming a normally distributed error term.
The regression analysis for export, import, and total trade indicates that key explanatory variables—distance, GDP, population, tariff, and exchange rate—align with expected outcomes Specifically, the negative and significant coefficient for distance suggests that greater geographical separation diminishes bilateral trade, while the positive coefficients for GDP and population indicate that larger economies and populations enhance trade flows This highlights the importance of economic size in influencing trade dynamics Additionally, the similarity of language between trading partners is found to be significant solely in the OLS model.
The exchange rate significantly influences India's exports, with a negative coefficient in the Fixed Effects (FE) model indicating that a decrease in the exchange rate boosts exports Conversely, an increase in import tariffs imposed by other countries leads to a substantial decline in India's exports, exceeding 10% across FE, Random Effects (RE), and Tobit models For imports, the exchange rate shows a significant positive relationship, meaning that an appreciation of the rupee increases imports Additionally, distance, serving as a proxy for transportation costs, negatively impacts both exports and imports, highlighting its role as a critical factor in reducing trade Time dummies are significant across most years, reflecting the influence of various time-related factors, while the Preferential Trade Agreement (PTA) dummy remains significant regardless of its active period.
The study employs an augmented gravity model with dummy variables to assess the effects of 21 preferential trade agreements (PTAs), including bilateral agreements, on India's trade dynamics The analysis reveals that two of the three PTAs involving partner countries exhibit trade diversion, with negative and significant coefficients for intra-bloc trade in the SAFTA and Bangkok Agreement, and a similar negative significance for BIMSTEC across various models The findings suggest that India would benefit more from non-discriminatory trade liberalization with all global partners rather than preferential arrangements with specific PTA members Additionally, the PTA_m variable coefficients for the EU, MERCOSUR, SACU, and ASEAN show positive significance in most regressions, while CARICOM, OECD, and Australia-New Zealand demonstrate positive significance only in OLS, indicating increased import creation within intra-block trade for these agreements.
The positive estimated coefficients of PTAs suggest a general openness among their members However, the negative and significant coefficients associated with specific PTAs, including CIS, NAFTA, EFTA, APEC, ANDEAN, and bilateral agreements like EU-South Africa, EU-Algeria, and EU-Turkey, indicate a notable occurrence of import diversion within these agreements.
The analysis of various preferential trade agreements (PTAs) reveals that memberships in PTAs such as ASEAN, SACU, NAFTA, ANDEAN, EU-Algeria, and Australia-New Zealand negatively impact India's imports, indicating a reduction due to these agreements Conversely, the PTA variables for GCC, APEC, and CARICOM show positive and significant results in the OLS regression model, though they lack significance in other models Additionally, the MERCOSUR and CIS PTAs exhibit positive and significant coefficients in the fixed effects model, highlighting their influence on trade dynamics.
The analysis of RE and Tobit (RE) models with country effects indicates that trade creation occurs under the BIMSTEC agreement, while SAFTA and the Bangkok Agreement demonstrate significant trade diversion effects The coefficient estimates for imports in SAFTA and the Bangkok Agreement are mostly negative, suggesting that trade is redirected away from India’s PTA partners In contrast, BIMSTEC shows a positive and significant impact on imports across OLS, RE, and FE models However, discrepancies in total trade results across OLS, FE, RE, and Tobit (RE) models arise due to multicollinearity, leading to the omission of some explanatory variables Ultimately, the findings confirm that only BIMSTEC fosters trade creation, while SAFTA and the Bangkok Agreement are associated with trade diversion.
Our analysis indicates that India's swift movement towards bilateral Preferential Trade Agreements (PTAs) and Regional Trade Agreements (RTAs) is largely detrimental or insignificant regarding trade flow impacts While the welfare effects of these PTAs cannot be definitively assessed based on trade creation and diversion metrics, the findings raise significant concerns about preferential trade liberalization This suggests that India, along with the global community, should prioritize multilateral non-discriminatory liberalization, such as advancing the Doha negotiations, as a more favorable approach for the global trading system.
Determinants of Exporting Decisions
This study examines the factors influencing export decisions among firms in labor-intensive sectors by analyzing data from 800 companies spanning 1995 to 2006 Utilizing the PROWESS database, which provides firm-level panel data from the CMIE, we focus on six labor-intensive manufacturing activities, including food processing, cotton textiles, leather products, auto-ancillary, bicycles, and gems & jewelry Additionally, we expanded our analysis to encompass all manufacturing sectors, involving a total of 1,365 firms during the same period Our sample comprises both exporters and non-exporters To further explore the impact of ownership and other firm attributes on export probabilities, we incorporated data from the CII for the year 2004-05, which included 3,724 firms across all manufacturing sectors.
The rationale behind the selection of the variables and their possible relations with export propensity are discussed below:
The existing literature highlights the significant role of sunk costs in the decision to export, emphasizing that these costs, which cannot be recovered once incurred, include expenses related to market research, distribution system establishment, and advertising (Baldwin, 1988) Firms face a fixed entry cost to access the export market, which becomes a sunk cost (Melitz, 2003) This dynamic framework suggests that a firm's current export decisions can impact future exporting choices; for instance, a firm may choose to continue exporting despite current losses, anticipating future profitability and considering the sunk entry costs already incurred This persistence in exporting behavior, inferred from data patterns, aligns with Roberts and Tybout's (1998) notion that viewing entry costs as sunk creates an option value for firms, allowing them to wait rather than exit immediately due to negative profits, as future profits may eventually turn positive.
Our analysis suggests the presence of sunk costs, indicated by the consistent patterns of exporting and non-exporting years for firms, rather than random fluctuations To estimate these sunk costs, we utilize the firm's previous export status as a proxy We examine the distribution of exporting sequences and hypothesize that firm characteristics influence the overall duration of exporting but not the specific yearly patterns If firm-specific effects are significant, we anticipate that some firms will export consistently while others will not, as noted by Bernard and Jensen (2001).
Table 2A shows the distribution of firms in labour-intensive activities across all the
Between 2000 and 2006, there were 103 possible sequences of exporting and non-exporting among firms, revealing significant trends in export behavior Notably, 33% of firms consistently exported throughout all seven years, while 30% never engaged in exporting, highlighting a strong persistence in exporting status within labor-intensive sectors Furthermore, firms demonstrated a higher likelihood of exporting for longer durations, with 5.4% exporting once and 8.3% exporting for six years, compared to only 4.38% for three years and 2.35% for four years These patterns indicate distinct sequences of exporting and non-exporting activities among firms.
1110000 and 0000111 are more frequent than those without runs, 0010101 and
When analyzing all manufacturing firms, it was found that a significant portion, 41%, never engaged in exporting, while only 21% consistently exported throughout the analyzed period This contrasts with labor-intensive sectors, indicating a broader disparity in export behavior across different manufacturing firms Additionally, similar to labor-intensive industries, the frequency of firms experiencing sequences of exporting and non-exporting is more common than those without such patterns.
The findings indicate that unobserved firm heterogeneity and sunk costs play a significant role in the export decisions of manufacturing firms, irrespective of their labor intensity.
Foreign ownership significantly varies between exporters and non-exporters, with 30.85% of exporting firms having majority foreign capital participation compared to only 16.22% among non-exporters, according to CII data This indicates a strong correlation between foreign ownership and exporting activity In this context, foreign ownership is defined as a binary variable, assigned a value of 1 if a firm engages in a Joint Venture (JV), has foreign collaboration, or is owned by a foreign parent, and 0 otherwise.
Previous research on export performance consistently indicates that larger firms dominate the exporter landscape, as highlighted by Bernard and Jensen (2001) These larger firms benefit from scale economies, resulting in lower average and marginal costs, which enhances their likelihood of engaging in export activities Additionally, they possess greater resources to manage the costs associated with entering foreign markets Wakelin (1998) notes that fixed costs, such as information and marketing expenses, can disproportionately favor larger firms While economies of scale are crucial for overcoming initial export costs, their significance may diminish in ongoing export activities A non-linear relationship between firm size and export propensity has been identified in studies by Kumar and Sidharthan (1994), Willmore (1992), and Wakelin (1998) In this study, firm size is quantified by the total production value.
Research indicates that R&D intensity significantly boosts a firm's export performance Studies by Veugelers and Cassiman (1999) and Lover and Roper (2001) demonstrate that both R&D expenditure and investment positively influence export intensity Increased R&D spending can improve product quality and streamline production processes, enhancing the chances of entering the export market Therefore, we posit that, all else being equal, R&D has a likely positive impact on exporting activities.
A firm's competitive advantage increases with lower real wages, leading to higher export volumes, particularly in countries like India with an abundant labor supply However, it is not merely the low real wages that create a comparative cost advantage; rather, it is the relationship between low wages and labor productivity that significantly influences export performance This relationship is reflected in the quality of labor, indicating that a higher share of wages, calculated as a percentage of sales, is likely to negatively impact export performance, all else being equal.
Entering foreign markets is closely linked to labor quality, as firms must produce lower-cost or higher-quality products to thrive Labor productivity serves as a proxy for this quality, measured by net value added per worker and the ratio of net value added to total wages The PROWESS database lacks employee count data; however, it provides salary and wage information that allows for employment estimates, following the methodology of Goldar et al (2003) Data from the Annual Survey of Industries (ASI) for labor-intensive sectors such as bicycle manufacturing, auto ancillary, cotton textiles, gems & jewelry, leather, and food processing, spanning 1995-2005, was utilized Emoluments per employee were calculated for 1995-2006, with extrapolation applied to certain industries due to the ASI data series ending in 1995 The firms were categorized based on ASI's three-digit industrial classification, and employment estimates were derived by dividing the CMIE database's salary and wage data by the computed emoluments per employee for each corresponding industry.
In today's global market, a robust distribution network is essential for firms aiming to compete internationally The rise of globalization has significantly expanded global logistics, emphasizing the importance of marketing and advertising within the manufacturing sector Consequently, marketing and sales expenditures serve as indicators of a company's product differentiation and commitment to promoting exports Therefore, it is anticipated that increased selling costs will correlate with a higher likelihood of engaging in export activities.
Energy intensity, defined as the ratio of power and fuel expenditure to sales, significantly impacts export performance Industries with higher energy intensity may be perceived as more productive and competitive in international markets, potentially fostering a positive correlation with exports However, while energy costs can negatively affect overall sales, their primary impact is on export sales Ultimately, we anticipate that the quality effect will be more influential in determining export outcomes.
Companies can achieve technological progress not only by developing their own innovations but also by acquiring new capital or intermediate goods from different industries Capital intensity, which is assessed by the ratio of net fixed assets—total fixed assets minus accumulated depreciation—to sales, plays a crucial role in this process Net fixed assets encompass capital, work-in-progress, and revalued assets, highlighting their importance in driving efficiency and growth.
Roberts and Tybout (1997) discovered that highly productive firms benefit from incurring sunk costs in export markets, as they can better navigate foreign competitiveness Firms facing fixed production costs that operate below the zero-profit productivity threshold tend to exit the industry due to potential losses In contrast, only those firms that exceed the export productivity cut-off are able to export successfully (Melitz, 2003) Therefore, we hypothesize that firms with higher profit margins per unit of sales are more likely to engage in exporting and competing in global markets.
Export Propensity of Firms: A Possible “Hazard” Model
We develop a multinomial logistic model to analyze the likelihood of manufacturing firms in India exporting in any given year, considering their characteristics and prior export history Utilizing data from 1995 to 2006, we classify the firms into four distinct categories to better understand their exporting behavior.
Category 1 = exported in t and did not export in any of the prior years
Category 2 = exported in t and exported at least in one of the prior years Category 3 = did not export in t and not prior to t
Category 4 = did not export in t but at least in one of the prior years.
The probability of exporting for firm i at time t is represented by the equation δ = 1/1 + exp(-η), where η is a function of the firm's characteristics and its exporting history This formulation indicates that η varies over time and among firms However, without strong identifying assumptions, empirical estimation of the model is unfeasible A key assumption is that η, or δ, remains constant over time for each firm, suggesting that only time-invariant characteristics influence this probability This assumption is quite restrictive, as it excludes important time-varying factors such as exporting history and macroeconomic conditions In this simplified model, the probability Pijt of a firm being categorized as j is determined accordingly.
The equation δ = 1/1 exp(+ −η i) can be defined with η i as a linear function, represented by ni = α1 + b1 * X1 + b2 * X2 + b3 * X3 + + b * Xη In this context, the variables denote the average characteristics across all observations for firm i The parameters bj (where j = 0, 1, 2, 3, and 4) can be estimated by maximizing the log likelihood function, which involves summing over multiple observations.
4 1 log , where D ijt is a dummy variable which takes the value 1 if firm is in category j in year t and zero otherwise
We estimated a simplified multinomial Logit model for Pijt, which incorporates time-invariant firm characteristics This approach enhances the analysis by allowing for the consideration of essential firm-specific factors while maintaining model simplicity.
P ijt = 1 by definition, treating the third category as the reference category, we postulate that log odds of category j relative to 3 as
{ } X kit are characteristics of firms i in year t Once α j and {b k } have been estimated, an average of log odds
% (11) can be computed by substituting in (6) the average given by:
(total number of observations)* kt kit t i
From log odds we can recover the probabilities P~ j by noting that
We consider the following four alternative clusters of firm level characteristics:
Model I = Scale, Wage intensity, R&D intensity, Selling cost intensity, Profit intensity,
Net Fixed Asset intensity, Import intensity
Model II = Wage intensity, Selling Cost intensity, Profit intensity, Net fixed Asset intensity, Net Value Added as a percentage of Wages, Import intensity
Model III = Lagex, Wage intensity, wage share, R&D intensity, Selling Cost intensity,
Profit intensity, Net Fixed Asset intensity, Import intensity.
Model IV = Lagex, Energy intensity, Wage intensity, Selling Cost intensity, Profit intensity, Import intensity.
3.3.1 Estimation Results (Maximum Log Likelihood Estimates)
The estimation results reveal significant differences in characteristics among firms across various categories, using category 3 as the baseline Specifically, the multinomial logistic regression analysis shows that firms that have never exported differ markedly from those that have exported at least once Exporting firms, whether in the current year or in previous years, tend to be larger, more research and development intensive, less reliant on low wages, and more profit-oriented compared to non-exporters These findings align with prior research in the field.
Research indicates that firms classified as category 2, which exported in the current year and at least once in previous years, have the highest probability of exporting compared to category 1 firms, which only exported in the current year with no prior export experience While category 1 firms exhibit the lowest probability across most models, category 4 firms—those that did not export in the current year but had prior export activity—show a higher probability of exporting than category 3 firms, which have no export history Overall, firms with prior export experience are significantly more likely to continue exporting in the current year than firms that have never engaged in export activities.
The results reveal that the probability of survival of new firms in the export market is less as compared to those who have been exporting in the prior years
In the 2006-07 period, the engineering sector dominated exports with a significant share of 20.61% It was followed by petroleum products at 15.02%, chemicals and related products at 14.04%, textiles at 12.87%, and gems and jewellery at 12.26% Additionally, machinery accounted for 9.12% of total exports, while electronics contributed a modest 2.29%.
Conclusions
This paper aims to achieve two main objectives: first, to analyze the factors influencing the export decisions of Indian firms, contributing uniquely to the existing literature with a focus on Indian data Second, it examines India's pursuit of preferential trade liberalization, particularly with South Asian neighbors, by employing a modified gravity model to assess bilateral trade flows with 189 trading partners from 1981 to 2006, encompassing 21 preferential trade agreements (PTAs).
Our analysis utilizes two distinct firm-level data sets: the PROWESS data from the Centre for Monitoring the Indian Economy (CMIE) covering the years 1995-2006, and the Confederation of Indian Industry (CII) data for the year 2004-05 Both data sets present significant limitations, notably the voluntary nature of data submission by firms to CMIE and CII, raising concerns about the representativeness of their membership within the broader industry.
Large firms, which significantly contribute to industrial production and foreign trade, are believed to be members of both sectors We employ several models, including Probit, Logit, Tobit, Multinomial Logistic, and a linear probability model, to analyze exporting decisions over time The findings from these models are generally consistent; however, it is important to consider the limitations of the data sets used when interpreting these results.
Our principal findings suggest that the pursuit of preferential trade agreements is counterproductive, as we cannot directly infer welfare effects from trade creation and diversion The coefficient estimates from our gravity model—utilizing OLS, Fixed Effects, Random Effects, and Tobit—indicate that India's optimal approach remains unilateral and multilateral trade liberalization.
Our firm-level data analysis corroborates previous studies, highlighting significant heterogeneity in export decisions among firms Notably, firms that have never engaged in exporting differ markedly from those that have exported for one or more years Exporting firms tend to be larger, invest more in R&D, have lower wage intensity, and achieve higher profitability compared to their non-exporting counterparts This analysis suggests important trends and we encourage further exploration in this area.
Adams, Richard, Phillipa Dee, Jyothi Goli and Greg McGuire (2003) “The Trade and
Investment Effects of Preferential Trading Arrangement-Old and New Evidence” Staff Working Paper Canberra: Australia Productivity Commission.
Aitken, Brian & Hanson, Gordon H & Harrison, Ann E (1977), “Spillovers, foreign investment and export behavior,” Journal of International Economics, Elsevier, 43(1-2), 103-132.
Anderson, J E (1979), “A Theoretical Foundation for the Gravity Equation,”
Baldwin, Richard E (2006) "The Euro's Trade Effect," European Central Bank,
Baldwin, Richard E (1988) "Some Empirical Evidence on Hysteresis in Aggregate
US Import Prices," NBER Working paper series, vol W2483
Bernard, Andrew B., J Bradford Jensen, Redding and Peter K Schott (2007) “Firms in International Trade” NBER Working Paper # 13054.
Bernard, Andrew B and Jensen Bradford (2001) “Why Some Firms Export” NBER
Bhagwati, Jagdish and Srinivasan, T.N (1975), Foreign Trade Regimes and Economic
Development: India, New York, Columbia University Press.
Bhagwati, Jagdish and Desai, Padma (1970), India: Planning for Industrialization,
Cassiman, Bruno & Veugelers, Reinhilde (1999) "Importance of International
Linkages for Local Know-How Flows: Some Econometric Evidence from Belgium," CEPR Discussion Papers 2337.
Centre for Monitoring Indian Economy, Firm Level Database, Prowess
Deardorff, Alan V (1998), Determinants of Bilateral Trade: Does Gravity Work in a
Neoclassical World? In Jeffrey A Frankel, ed., The Regionalization of the World
Economy Chicago: University of Chicago Press
De Rosa, Dean A (2007) “The Trade Effects of Preferential Arrangements: New
Evidence from the Australia Productivity Commission” Working Paper Series no.: WP 07-1 Washington: Peterson Institute for International Economics
Goldar, B.N., V.S Raganathan, Rashmi Banga (2003), “Owenrship and Efficiency in
Enginereering Firms in India 1990-91 to 1999-2000,” Working Paper 15, ICRIER, New Delhi.
Government of India, Annual Survey of Industries (various issues), CSO, New Delhi
Helpman, Elhanan and Paul Krugman (1985) “Market Structure and Foreign Trade:
Increasing Returns, Imperfect Competition and the International Economy”, MIT Press, Cambridge, MA.
Kumar Nagesh and N.S Sidharthan (1994) “Technology, Firms Size and Export behavior in Developing Countries: The Case of Indian Enterprises, Journal of
Linneman, H (1966) “An Econometric Study of International Trade Flows”,
Amsterdam North Holland Publishing Co.
Mayer T and G Ottaviano (2007) “The Happy Few: New Facts on the
Internationalisation of European firms'', Bruegel-CEPR EFIM2007 Report,
Marc J Melitz & Giancarlo I P Ottaviano, 2008 “Market Size, Trade, and Productivity,” Review of
Economic Studies, Blackwell Publishing, vol 75(1), pages 295-316, 01.
Melitz, M (2003) “The Impact of Trade on Intra-Industry Reallocation and Aggregate
Panagariya, A (2008), “India the Emerging Giant”, Oxford University Press:
Roberts, Mark J and James Tybout (1997) “The Decision to Export in Colombia: an
Empirical Model of Entry with Sunk Costs”, American Economic Review, 87(4), 545-64.
Roper, Stephen & Love James H, (2001) "The Determinants of Export Performance:
Panel Data Evidence for Irish Manufacturing Plants," Working Papers NIERC
69, Economic Research Institute of Northern Ireland.
Rose Andrew K (2002) “Do WTO members have more liberal trade policy?” NBER
Soloaga, Isidro and L Alan Winters, (2001) “Regionalism in the Nineties: What
Effect on Trade?” North American Journal of Economics and Finance 12(1), 1-29.
Santos, Silva, J.M.C., and Silvana Tenreyo, (2006) “The Log of Gravity” Review of
Srinivasan, T N (2002) “China and India: Economic Performance, Competition and
Cooperation.” Paper presented at a seminar on WTO Accession, Policy Reform and Poverty, organized by the World Trade Organization, Beijing, June 28-29, 2002.
Srinivasan, T N and Suresh Tendulkar (2003), Reintegrating India with the World
Economy, Washington: Institute for International Economics
Tinbergen, J (1962) Shaping the World Economy: Suggestions for an International
Economic Policy The Twentieth Century Fund New York
UNCTAD (2006) Handbook of Statistics Database, Geneva, United Nations
Conference on Trade and Development.
Van Biesebroeck, Johannes (2005) “Exporting raises productivity in Sub-Saharan
African Manufacturing Firms”, Journal of International Economics, 67(2), 373-391
Wakelin, K., (1998), “Innovation and Export Behaviour at the Firm Level”, Research
Willmore, Larry (1992) "Industrial policy in Central America," Cepal Review, No 48,
WTO (2008), World Trade Report 2008: Trade in Globalizing World, Geneva, World
Table 1: Gravity Models Table 1 A: Export Flows
Exports OLS Fixed Effects GLS Tobit lngdp 0.588***
Imports OLS Fixed Effects GLS Tobit lngdp 0.847***
Table 1 C: Total Trade (Export and Import) Flows
Total Trade OLS Fixed Effects GLS Tobit lngdp 1.591***
Table 2 A: Labour Intensive Activities, Export sequence 2000-06
Table 2 B: Manufacturing Activities: Export sequence from 2000-06
Table 3: Labour Intensive Activities: The Decision to Export
Model I Model II Model II Model IV
Note: standard error in parenthesis;
* significant at 10 percent, ** significant at 5 percent, *** significant at 1 percent
Dependent variable Y = 1 for exporting years
Model I Model II Model II Model IV
Note: standard error in parenthesis;
* significant at 10 percent, ** significant at 5 percent, *** significant at 1 percent
Dependent variable Y = 1 for exporting years
Model I Model II Model III Model IV
Note: standard error in parenthesis
Dependent variable = 0 for the non-exporting years and export as percentage of total sales if they did export in period t
Table 4: Manufacturing Sector Table 4 A: CMIE Data, Logit And Probit Models (Panel)
Note: Std Error in the parentheses
* Significant at 10 percent, ** significant at 5 percent, *** significant at 1 percent Dependent variable Y = 1 for exporting years;
Table 4 B: CMIE Data, Tobit Model (Panel)
Explanatory variables Model I Model II
Note: Std Error in parentheses
* Significant at 10 percent, ** significant at 5 percent, *** significant at 1 percent For Tobit Model: Dependent variable = 0 for the non-exporting years
Export as percentage of total sales if they did export in period t
Table 4 C: Manufacturing Activities (CII data): Probit and Logit Model
Variables Probit Model Logit Model
Note: standard error in parenthesis
Dependent variable = 1 for exporting firms and
Scale is a dummy that takes value 1 if it is a large firm and 0 otherwise
Own is a dummy variable which is equal to 1 if firm either have a JV/Collaboration /foreign parent and 0 otherwise
C P (capital productivity) = total turnover/ investment
Table 4 D: Manufacturing Activities (CII data), Tobit Model
Note: standard error in parenthesis
Dependent variable = 0 for the non-exporting years
Export as percentage of total sales if they did export in period t
Scale is a dummy that takes value = 1 if it is a large firm and = 0 otherwise
Own is a dummy that takes value = 1 if firm either have a JV/Collaboration /foreign parent and 0 otherwise
CP (capital productivity) = total turnover/ investment
Table 5: Multinomial Logistic Model of Log odds Table 5 A: Model I
Log Odds of Category j Relative to Category 3, j = 1, 2 and 4
Category 3 and 1 Category 3 and 2 Category 3 and 4
Here, η 1i = -1.356 + 0.007 *Scale i – 0.035*W i + 0.893* RD i – 0.133* Selcost i + 0.001* P i – 0.000* NFA i + 0.001* Imp i η 2i = -0.673 + 0.016 *Scale i – 0.022*W i + 1.212* RD i + 0.003* Selcost i + 0.001* P i – 0.000* NFA i + 0.001* Imp i η 4i = -0.491 + 0.011 *Scale i – 0.000*W i + 0.253* RD i + 0.004* Selcost i + 0.000* P i – 0.000* NFA i - 0.001* Imp i
The average values of the explanatory variables are:
From the above we get: η 1 = -1.356 + 0.007 *51.98 – 0.035*21.83 + 0.893* 0.064 – 0.133* 13.349 + 0.001* (-71.049) – 0.000* 2.21 + 0.001* 27.13 = -3.47974 η 2 = -0.673 + 0.016 *51.98 – 0.022*21.83 + 1.212* 0.064 + 0.003* 13.349 + 0.001* (-71.049) – 0.000* 2.21 + 0.001* 2.21 = 1.189427 η 4 = -0.491 + 0.011 *51.98 – 0.000*21.83 + 0.253* 0.064 + 0.004* 13.349 + 0.000* (-71.049) – 0.000* 2.21 - 0.001* 27.13 = 0.0870501
From these we can compute the probabilities as:
Pr (Category = 1) = exp (η 1 ) / 1+ exp (η 1 ) + exp (η 2 ) + exp (η 4 ) = exp (-3.47974) / 1+ exp (-3.47974) + exp (1.189427) + exp (0.0870501) = 0.005699
Pr (Category =2) = exp (η 2 ) / 1+ exp (η 1 ) + exp (η 2 ) + exp (η 4 ) = exp (1.189427) / 1+ exp (-3.47974) + exp (1.189427) + exp (0.0870501) = 0.607586
Pr (Category =4) = exp (η 4 ) / 1+ exp (η 1 ) + exp (η 2 ) + exp (η 4 ) = exp ((0.0870501) / 1+ exp (-3.47974) + exp (1.189427) + exp (0.0870501) = 0.201768
Pr (Category =3) = 1 / 1+ exp (η 1 ) + exp (η 2 ) + exp (η 4 ) = 1 / 1+ exp (-3.47974) + exp (1.189427) + exp (0.0870501) = 0.184947
Log Odds of Category j Relative to Category 3
Category 3 and 1 Category 3 and 2 Category 3 and 4
Similarly with the same argument as before the probabilities for different categories for model II are as follows:
Log Odds of Category j Relative to Category 3
Category 3 and 1 Category 3 and 2 Category 3 and 4
Similarly with the same argument as before the probabilities for different categories for model III are as follows:
Log Odds of Category j Relative to Category 3
Category 3 and 1 Category 3 and 2 Category 3 and 4
Similarly as analyzed previously, the probabilities for different categories are:
List of RTAs Covered
SACU GCC BIMSTEC Bangkok EFTA
South Africa Bahrain Bangladesh Bangladesh Norway
Lesotho Kuwait Bhutan Laos Switzerland
Swaziland Oman Nepal Republic of Korea Iceland
Botswana Qatar Sri Lanka Sri Lanka Liechtenstein
ASEAN SAFTA MERCOSUR CIS NAFTA EU
Indonesia India Spain Azerbaijan Canada Austria
Malaysia Bangladesh Portugal Armenia USA Belgium
Philippines Bhutan Brazil Belarus Mexico Bulgaria
Singapore Nepal Argentina Georgia Cyprus
Thailand Sri Lanka Uruguay Kazakhstan Czech Rep
Brunei Pakistan Paraguay Kyrgyz Denmark
Vietnam Maldives Bolivia Moldova Estonia
Lao PDR Chile Russia Finland
Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom
Asia-Pacific economic Cooperation (APEC) Andean Community
People’s Republic of China Venezuela
China Caribbean Community & Common Market Organization for
Japan Antigua & Barbuda & Development (OECD)
Republic of Korea Bahamas Australia UK
New Zealand Dominica Canada Switzerland
Papua New Guinea Grenada Czech Republic Sweden
Russian Federation Jamaica France Portugal
Chinese Taipei Saint Kitts & Nevis Greece Norway
Thailand Saint Lucia Hungary New Zealand
USA Saint Vincent & Grenadines Iceland Netherlands
Export of Principal Commodities (in US $ Million) from India (April- February, 2005-06 and 2006-07)
2 Computer Software in physical form 84.06 46.44 -44.75 0.04
2 Cotton, yarn, fabrics, made-ups, etc 3533.86 3674.37 3.98 3.27
3 Manmade textiles made-ups, etc 1813.95 2104.62 16.02 1.87
XV Cotton Raw Incl Waste 504.63 1107.29 119.43 0.98
Source: Ministry of Commerce & Industry, Govt of India
Note: US Dollar Exchange Rate of April-February 2005-06 is 44.2546 and April-February 2006-07 is 45.4019
Survey Results
The above discussed paper is basically a part of the ongoing project on Global Trading and Financial Systems: Multilateralism of the World Trade Organization versus Regionalism
The research is conducted in two phases: Phase I involves preliminary estimations using secondary data, while Phase II focuses on primary data collected through a survey of firms This survey aims to illuminate the incentives and constraints firms face when entering and exporting to various markets, as well as their connections to productivity and profitability following trade liberalization, particularly with PTA partners Additionally, the study seeks to provide new insights into India's regional and multilateral trade liberalization from a microeconomic perspective.
While a comprehensive analysis of the survey data is pending, we present an overview of the preliminary findings The survey encompasses various locations across India, representing all regions—north, south, east, and west—within specific industry segments.
To achieve a representative sample of firms across various manufacturing sectors, we employed a stratified sampling technique, categorizing the strata by industry segment The sample size for each segment was allocated according to the relative contributions of each industry to the country's manufacturing exports Table 1 below illustrates the number of respondents from each industry segment.
The selected centers for the study included Delhi and various regions of the National Capital Region, along with Mumbai, Pune, and Ahmedabad from the West, Kolkata from the East, and Chennai and Bangalore from the South Although the field operations focused on these major cities, the businesses involved were sourced from multiple locations across the country.
The analysis of data has been approached from both industry perspectives and export intensity, aiming to evaluate the factors influencing export activities Additionally, various other parameters have been investigated in relation to these two viewpoints.
Export performance is represented by export intensity, which is measured as share of exports in total sales turnover expressed in percentage
Export intensity has been divided into four levels: a) below 10% b) 11 to 25% c) 26 to 50% d) over 50%
From these two perspectives we have tried to analyze different parameters such as characteristics of the firms, the incentives and the barriers to export
1.i Ownership Pattern of the Firms
Indian private ownership - The stakes of Indian private investors in all surveyed firms is very high
96 % of the firms holding controlling stakes (over 70%)
Industry Segment - Pharmaceuticals, leather, textile and plastics, Indian private investors hold the controlling stake (over 70%)
In other it is very high in the range of 92-98%
Export Intensity - Across all firms (except plastics) have export quotient of over 50%, have 70% stake by private investors. Lower export quotient in some cases, such as minerals
& Fuels and Metals, have relatively lower stake by private investors.
Private stake ↓ Below 10% 11-25% 26-50% Over 50% Total
Government Ownership - In 97% cases government has no stake
In 2% cases ownership was restricted to less than 30%.
In 1% cases ownership accounts for over 70%
Industrial Segment - Pharmaceuticals, leather, textiles and plastics – no government ownership Export Intensity - Export quotient of over 50% (except metals) have less than 10% government controls
Private stake ↓ Below 10% 11-25% 26-50% Over 50% Total
Foreign Ownership - In 95% of the surveyed firms – no foreign investment
In 3% of the firms – foreign stakes are in the range of 1- 30%
Only 1% firms have higher foreign control (with 31- 50% foreign stake).
Industry Segment - Pharmaceuticals, leather and Plastics – no foreign stake Export Intensity - Export quotient of over 50% has no foreign ownership
Of the total 43% of the sample firms had employee strength of 51 to 100 employees and 39% of over 100 employees Of the remaining 16% have a complement of between 31 and 50 employees and 3% less than 30
The employee strength across industry segments is reported as follows:
The employment landscape showcases a diverse range of company sizes, with a majority of sectors featuring a small percentage of firms employing fewer than 30 individuals Certain industries, such as plastics and pharmaceuticals, predominantly employ between 51 to 100 workers, with 100% and 80% of respondents, respectively, falling within this range Additionally, over half of the companies in the minerals and fuels, metals, and textiles sectors report having more than 100 employees, indicating a significant workforce in these segments.
The table below shows the export intensity across different employment levels.
Export intensity is more dominant in the entire export quotient where employment is more than 100
I iii Age of the Sample Firms
4% of the firms were established before 1950
81% were established during the period from 1950-2000
Rest 15% were established during the period 2000-2007
Export Intensity % Below 10% 11-25% 26-50% Over 50% Total
I.iv Distribution of Firms by Total Assets
In the analysis of firms, 16% were classified as small, 32% as medium, and 48% as large Notably, all firms in the plastics sector reported total assets exceeding Rs 50 crores Other segments with significant total assets included leather at 33% and minerals and fuels at 54% However, the chemicals and pharmaceuticals sectors stood out with much higher proportions, reporting 68% and 80% respectively.
% of Firms small ( 50)
Large firms significantly dominate the export market, with 54% of companies possessing assets over 50 crores reporting an export intensity of less than 10% Additionally, 50% of these firms fall within the export quotient range of 11% to 25%, while 44% operate in the 26% to 50% range, highlighting the varied export performance among larger enterprises.
Export Intensity % Below 10% 11-25% 26-50% Over 50% Total
Small firms (Up to Rs 10cr) 20 19 13 20 16
Large firms (above Rs 50cr) 54 50 44 50 48
About 3/4 th of the responding firms reported to acquire new technology
Industry-segment – about 9 out of every 10 firms in leather admitted to have acquired new technology in last 3 years 84% of Gems & Jewellery, 82% of metals and 80% of pharmaceuticals
Export Performance - Firms with newer technology have higher export quotients
(over 50%) over those with older technology.
Mineral & fuels (100%), Metals (100%) and Gems& Jewellery (100%) have export quotient of more than 50% have acquired new technologies
13% of the firms incurred 0.5% to 1% of their sales as expenditure on R&D while 5% spent up to 0.2% of sales, a lowly 3% of respondents spent between 0.2% and 0.5% of their sales
Industry Segment - The large spender in the highest bracket (>0.5% to 1%) were,
In various industries, the percentage of firms investing in design and R&D varies significantly, with 43% of leather firms allocating funds for these areas, while only 20% of pharmaceutical companies do the same Most sectors fall between 11% and 17%, with firms typically spending between 0.5% and 1% of their sales on design and research activities Notably, the minerals and fuels sector shows the least investment, with only 5% of firms dedicating resources to design and R&D.
More than 78% of companies with significant exposure to foreign markets have been operating for over five years, while 18% have medium-term export experience ranging from three to five years In contrast, only 4% of firms have a short export experience of up to two years.
Export Intensity % Below 10% 11-25% 26-50% Over 50% Total
I viii Total cost to sales
According to the survey, 40% of respondents indicated that their cost of sales was up to 80%, suggesting these firms enjoyed profits exceeding 20% on their sales Additionally, 4% reported total costs between 80% and 90%, while 6% fell within the 90% to 100% range Notably, among the profitable firms, a significant 51% achieved an export quotient of 11% to 25%.
I ix Net profit after tax to sales
Approximately 24% of surveyed companies reported a net profit after tax exceeding 5% of their sales, while 8% achieved less than 2% in net profit Additionally, another 8% of firms recorded net profits ranging from 2% to 5% of their sales.
80% of the firms in pharmaceuticals, 31% in textiles have reported net profit (less tax) to sales in excess of 5%
2.i Export Subsidy under export promotion schemes
76% of the firms surveyed received subsidies under export promotion scheme and 24% of the firms did not receive any such subsidy.
Export Intensity % Below 10% 11-25% 26-50% Over 50% Total
In the export sector, many firms excelled, particularly in gems and jewellery, which achieved an impressive 78% export rate, followed by pharmaceuticals at 75% and leather at 65% Other industries averaged close to 50% among firms that received subsidies, highlighting a significant disparity in export performance across different sectors.
Export Subsidy under Export Promotion Schemes
Industry /Export Intensity Receivers Non-receivers
Industry /Export Intensity Receivers Non-receivers
A significant 71% of businesses in various industries view telephone communication as crucial for their operations While over half of the firms surveyed do not see telecommunication inadequacies as a barrier, 31% acknowledge it as a minor challenge, and 7% regard it as a moderate obstacle.
Approximately 44% of businesses view electricity supply as a minor to moderate challenge, while 35% identify it as a significant issue, with 8% labeling it a serious impediment to their operations Furthermore, nearly half of the firms (44%) perceive the availability of electricity as limited, and 35% report that the quality of the electricity supply is subpar.