India shining? A two-wave study of business constraints upon micro and small manufacturing firms in India

Abstract

The national marketing slogan ‘India Shining’ was launched in 2004 by the Indian government to showcase rapid economic growth; around the same time, a number of new policy measures to promote small businesses were introduced to help them deal with an increasingly globalised environment. This article analyses survey data collected by the World Bank from owners and managers of 1300 Indian firms in 2002 and 608 firms in 2006 respectively, to explore how prevailing business constraints limit the performance of micro (fewer than 10 ) and small (10 to 49 employees) manufacturing businesses in 2002 and 2006. Using capacity utilisation to measure performance, the study finds that both micro and small firms fared somewhat worse in 2006 than in 2002.

Keywords

business constraints capacity utilisation Indian manufacturing sector micro firms small businesses

Introduction

India ranks as the second largest emerging economy (Jain, 2006), but the Indian economy has been characterised by bureaucratic procedures and regulations that impede business operations and performance. In the 1990s the country embarked on a process of reform through major changes in government policies (Ahuja et al., 2006) in the areas of licensing, import tariffs and foreign direct investment. The subsequent two decades from 1990 to 2010 were marked by liberalisation in most sectors of the economy. This period coincided with faster growth, with gross domestic product (GDP) increasing at a compounded annual growth rate (CAGR) of 6.6 percent, and the value added by manufacturing growing at a CAGR of 6.9 percent. The reforms undertaken since 1991 are widely credited with increasing growth (Panagariya, 2005), although critics such as DeLong (2004) caution against such attribution. However, the increase from the so-called ‘Hindu rate of growth’¹ (3–4%) lent support to the national marketing slogan of ‘India Shining’ launched in 2004.

In this article, we examine the performance of the smallest manufacturing businesses at two points in time, 2002 and 2006, which mark a period of significant policy activity and rapid industrial growth in India. Using survey data collected by the World Bank at these two points in time, we characterise the business environment facing two types of manufacturing firms: micro, those with less than 10 employees; and small, with 10–49 employees.² The article relates the organisational performance of these firms, as measured by their capacity utilisation, to features of the business environment in both 2002 and 2006.

Following the ‘Big Bang’ liberalisation of 1991, progressive reforms were initiated from 2000 to 2005 to improve the business environment for small businesses in India. By comparing data from the two surveys in 2002 and 2006, this study tests whether small manufacturing businesses actually experienced such improvements and whether their performance improved as a result. The comparison of performance between the two surveys (2002 and 2006) highlights the role of small firms in the rapid growth of the manufacturing sector overall during the period. As such, we respond to the call of Bruton, Ahlstrom and Obloj (2008) for research on entrepreneurship in India, with special attention to the historical context shaping this emerging economy. India is a vast country with stark differences between its constituent regions: it adopted a mixed economy with strong government control in the first four decades of its existence as a sovereign state (Srinivasan, 2004). Thus, the economic liberalisation of the last two decades is a major transition, with profound consequences for the people of India and the rest of the world. The country’s administrative structure – a federation with decision rights shared between the union and state governments; and its politics, a multi-party democracy at both central and state levels – limit the state’s ability to unilaterally design and execute long-term strategies for development (in contrast with other Asian economies; see Srinivasan, 2006). A variety of institutions, central and state, exist to facilitate industrial development, but coordination among them remains elusive (World Bank, 2006). These distinctive features of the Indian economy make it likely that the relations between policy, business environment and the performance of small/entrepreneurial firms are unique to this context. In order to explore the research question, this article is structured as follows. It begins with a description of the Indian context, followed by a review of the literature on institutional environments, business constraints and capacity utilisation as they relate to the hypotheses. The method section explains how the data were derived from a public database compiled by the World Bank. It then describes the methods of data analysis and presents the results. The article concludes by discussing the findings and highlighting the contributions as well as limitations of the current work, along with directions for future research.

Context: economic reform in India

After four decades of a government-led mixed economy, in the 1990s the Indian government lowered the barriers to business operations and opened up the economy to international competition. The resulting changes in policies were multidimensional and affected different aspects of the institutional environment, from regulations to taxes, and from ethical issues to capital access. The policy changes were implemented progressively over a period of time, allowing industry to adapt gradually. Before 1991, only items listed explicitly on a ‘positive’ list could be imported in the Open General License category; this changed to import restrictions being placed only on items on a ‘negative’ list. The New Industrial Policy of 1991 also lifted constraints placed by the earlier competition law (the Monopolies and Restrictive Trade Practices Act 1969), and eased entry restrictions, reduced the public sector monopoly and removed nearly all industrial licensing requirements except for those specified on health, safety, security and environmental grounds. The level of foreign equity investment permitted (with the approval of the Reserve Bank of India) in almost all industries was raised to 51 percent. Import licensing was removed for almost all intermediate and capital goods. Tariff rates were rationalised and reduced. The number of products reserved for small businesses was reduced. The Indian currency, the rupee, was devalued by 22 percent to make exports more globally competitive.

One consequence of these trade reforms was the greater export of goods and services to other countries and higher levels of imports (Goldberg et al., 2009), with reforms extending to all sectors of the economy (Government of India, Ministry of Finance, nd). The reforms also affected all dimensions of the economy: fiscal and administrative, the financial sector, international trade and investment, the industrial sector, infrastructure, labour, agriculture and the privatisation of government-owned firms (Ahuja et al., 2006). The effects of macro-environmental reforms have been acknowledged, with impacts on different economic outcomes such as productivity, internationalisation, innovation and entrepreneurship and employment and welfare (see for example, Ahuja et al., 2006; Das, 2008; Unel, 2003). Overall, the reforms of 1991 and the subsequent fine-tuning of policies appear to have had mainly favourable consequences in terms of aggregate output growth from 1990 to 2010. In particular, 2002 to 2006 saw the fastest growth, where GDP expanded at 8.8 percent CAGR, and the manufacturing sector grew at 9.9 percent CAGR. Figures 1a and 1b plot the growth in India’s GDP and the value added by the manufacturing sector from 1990 to 2010, and confirm that the most rapid increase came between 2002 and 2006.

Figure 1a.

India’s GDP (in current US$) from 1990–2010.

Figure 1b.

India’s manufacturing value added (in current US$) from 1990–2010.

Regarding the manufacturing sector in particular, a 2003 International Monetary Fund (IMF) report (Unel, 2003) found that productivity increased after the 1991 reforms. However, Krishna and Mitra (1998) found a profit-reducing impact of trade liberalisation reforms on Indian manufacturing firms, while Sivadasan (2009) found evidence of plant-level productivity improvement in industries that were liberalised (and attracted foreign direct investment) relative to those that were not. Besley and Burgess (2004) studied the effect of labour market reforms and found that pro-worker policies tended to depress productivity in manufacturing. Similarly, Aghion et al. (2008) found stronger effects of liberalisation on entry and productivity in the presence of pro-industry (as opposed to pro-organised labour) regulations.

Policies towards small businesses

Small and medium-sized enterprises (SMEs) form the backbone of the Indian manufacturing sector and constitute an engine of economic growth. Indian SMEs, employing 60m people, contribute 45 percent of the nation’s manufacturing output and 40 percent of total exports, accounting for almost 90 percent of industrial units and 40 percent of added value in manufacturing (Raju, 2008). Micro, small and medium-sized enterprises (MSMEs) more than quadrupled from 7.351m units in 1992–1993 to 31.152m units in 2008–2009 (Ministry of Micro, Small and Medium Enterprises, 2011). The most recent census of the sector (Ministry of Micro, Small and Medium Enterprises, 2007) shows that micro and small firms constitute 95.05 percent and 4.74 percent respectively; 29 percent of all MSMEs are manufacturing firms employing 51 percent of the workforce. Table 1 presents a profile of Indian MSMEs from two successive census reports.

Table 1.

Micro, small and medium enterprises: comparison between third census (2001–2002) and fourth census (2006–2007).

Characteristics	3rd census (2001–2002)	4th census (2006–2007)
Number in million (units):	10.50	26.1
Manufacturing enterprises in millions (%)	4.446 (42.26%)	7.453 (28.56%)
Service enterprises in millions (%)	6.075 (57.74%)	18.647 (71.44%)
Employment (millions)	24.933	59.461
Type of organisation:
Proprietary	10.084 (95.8%)	24.663 (94.49%)
Partnership	0.203 (1.93%)	0.178 (0.68%)
Private company	0.0071 (0.68%)	0.137 (0.052%)
Public limited company	Nil	0.073 (0.28%)
Cooperatives	0.015 (0.14%)	0.134 (0.51%)
Others	0.148 (1.65%)	0.916 (3.51%)
Per unit employment (registered sector only)	4.48	5.93

Source: Ministry of Micro, Small and Medium Enterprises (2007).

While the reforms made it possible for MSMEs, at least theoretically, to obtain imported raw materials more easily and to target export markets with their finished products, opening up the domestic market to larger firms, new entrants and foreign competitors posed a more immediate threat to India’s hitherto protected small businesses. The number of products reserved for this sector declined rapidly from 821 to 343 in 1998–1999, and finally to zero in 2001–2002 (Micro, Small and Medium Enterprises, nd).

In order to moderate the impact of economic liberalisation on the sector, the government unveiled a number of initiatives: 1991 saw the introduction of a new policy for small, tiny and village enterprises to boost output, employment and exports (Ministry of Micro, Small and Medium Enterprises, 1991). After refinements to the policy in successive annual budgets, the next major initiative came in 2000, with a comprehensive policy package for the small-scale and tiny sector, which streamlined rules and regulations and provided fiscal, credit, infrastructure, technology and marketing support (Ministry of Micro, Small and Medium Enterprises, 2000). In 2003–2004, the Reserve Bank of India moved to increase the flow of credit (in the form of higher loan amounts, lower collateral requirements) through the Small Industries Development Bank of India. The limit for loans to finance technology upgrading was increased again the following year, as was the amount of subsidy provided by the government (Government of India, 2006–2007) and in 2005, a performance and credit rating scheme was introduced to encourage SMEs to improve their credit profiles. On the global stage, the longstanding multi-fibre agreement that provided preferential access to global garment markets through a quota system was dismantled in 2005, unleashing a wave of competition for additional market share (Ernst et al., 2005). These and other relevant policy initiatives are summarised in Table 2. It is clear that in this rapidly evolving policy environment, small businesses in India need to be adept at entrepreneurial coordination (Coase, 1937) for survival and success.³

Table 2.

Timeline of SME-relevant policies from 2000–2006.

Year	SME-relevant policy initiative
1991	Policy for small, tiny and village enterprises with separate package for the promotion of tiny enterprises introduced for the first time
2000	Comprehensive policy package launched for the small scale and tiny sector to provide fiscal, credit, infrastructure, technology and marketing support as well as streamlining rules and regulations
2003–2004	Reserve Bank of India mid-year review of monetary and credit policy – measures to improve credit delivery to small-scale industries sector: raised loan limit from Rs.1.5m to Rs.2.5m without the requirement of collateral; rationalised interest rate on the deposits of foreign banks placed with the Small Industries Development Bank of India towardstheir sector shortfall (reduced interest rate from 6.75% to the prevailing bank rate)
2003–2004	Under the Micro Finance Programme of the Small Industries Development Bank of India, the Government of India to provide funds under the Portfolio Risk Fund so that only 2.5% (instead of 10%) of the loan has to be placed as a fixed deposit
2004–2005	The ceiling for loans under the Credit-Linked Capital Subsidy Scheme (a technology upgrading scheme of the Ministry of Small Scale Industries) was raised from Rs.4m to Rs.10m; the rate of subsidy raised from 12% to 15%
2004	National Commission for Enterprises in Unorganized Sector formed
2005	Performance and credit rating scheme introduced to encourage micro and small enterprises to improve their credit ratings; Ministry of Micro, Small and Medium Enterprises to subsidise 75% of the fee charged by the Rating Agency
2005	Multi-fibre Agreement (which provided preferential access to the global garment market through quotas) eliminated
2005	Scheme of Fund for Regeneration of Traditional Industries launched for regeneration of traditional industries
2005–2006	National Manufacturing Competitiveness Programme launched, with particular focus on SMEs
2006	Micro, Small and Medium Enterprises Development Act enacted to promote, develop and enhance competitiveness of MSMEs

The reforms – macro and SME-specific – changed the focus from the protection of small businesses to promoting their stability and growth. After 1991 the Indian government attempted to set apart funds earmarked for lending to SMEs, provided more loan guarantees to provide easier access to commercial loans, and established agencies to provide market development assistance (to help SMEs target domestic and overseas markets). In this sense, the Indian government appears to have adopted a ‘helping hand’ model (Frye and Shleifer, 1997).

Literature review

Institutional environment and business constraints

A firm applies for registration to start operation, acquires permits to buy and build facilities, hires and pays workers according to prevailing norms, draws credit from financial institutions, enforces contracts (as necessary) with suppliers, debtors and customers and assesses and pays taxes to local and federal governments. Thus, the smooth operation of any business, small or large, depends on the existence and cooperation of numerous institutions. A developed capital market provides access to capital by giving investors sufficient information about firms’ activities (Bartlett and Bukvic, 2001; Hashi, 2001; Pissarides et al., 2000). A functioning regulatory system makes it possible for firms to compete fairly (Bartlett and Bukvic, 2001; Brunetti et al., 1998; Hashi, 2001), while a well-developed legal system enables the enforcement of contracts and dispute resolution. When the tax system is designed and implemented carefully, it affects all firms equitably (Bartlett and Bukvic, 2001; Bohata and Mladek, 1999; Hashi, 2001). Infrastructure, such as transportation, electricity and telecommunications, affects the core of business operations, particularly for the manufacturing sector (Hulten et al., 2006). Finally, broad ethical norms, such as the level of bribery and corruption (Bohata and Mladek, 1999), affect the overall costs of operating a business.

North defined institutions as ‘the rules of the game in a society or … the humanly devised constraints that shape human interaction’ (1990: 1). Of all institutions, perhaps the most significant is government, as it is responsible for establishing and enforcing the rules and regulations of operation (Fogel et al., 2008). This is especially true for India’s micro and small business sector: for the nearly 30m micro and small businesses scattered all over India and operating in diverse industries, the government, acting through its many agencies, builds and provides access to infrastructure, enforces law and order and levies and collects taxes. In addition, transportation and electrical power are a state monopoly in many parts of India, especially in rural areas, and government-linked companies continue to be major telecommunications providers, even after deregulation. Wholly or partially government-owned banks often lend capital to small businesses which lack the credit profile to interest private banks. While community resources and informal networks might provide solutions to the gaps left by government policy, the reach of such policies is unrivalled by other actors. Thus these policies have the potential to shape the institutional context within which businesses operate, and assist them to achieve higher performance.

Poorly developed institutions, which are common in emerging countries, create obstacles (North, 1997; Yeager, 1999) such that businesses experience constraints that limit profits and growth. Such constraints might be rooted in:

bureaucratic red tape that add delays and costs (and sometimes can be circumvented through bribery and corruption);

gaps in infrastructure – e.g. poor roads, power cuts;

rigidities in labour laws – especially in matters of wages and retrenchment;

difficulties in raising capital – even when government funds are set apart; or

high rates of taxation – which reduce the funds available for business growth.

In the specific case of India, specific barriers to small firm growth have been highlighted. Siggel and Agrawal (2009) point to infrastructure as a barrier to growth; and in their large-sample study, Arnold et al. (2010) highlight that transportation in particular, as a part of infrastructure, has constrained the growth of manufacturing firms. Ayyagiri et al. (2006) found finance, crime and political instability to pose significant obstacles to firms. Constraints within the institutional environment affect smaller businesses to an even greater extent: for example, Beck et al. (2005) explored how financial and legal constraints and corruption affect firm growth, concluding that these constraints disproportionately affect small firms. Other constraints might be avoided by the smallest firms (Aterido et al., 2009): many of the micro firms in India are unregistered and manage to avoid regulators and tax authorities. Since constraints limit performance, easing business constraints should be reflected in improved firm performance.

Firm performance

The performance of small firms may be measured in different ways, including sales, employment, profits and capacity utilisation. For manufacturing firms, capacity utilisation as a measure of performance responds to the demand for a firm’s product, as well as its ability to coordinate its production using the resources available. It is well placed to detect changes in the level of business activity in a firm, whether due to market adversity (e.g. increased competition), supply constraints (i.e. the availability and prices of inputs and labour) or the availability of working capital. Apart from its accounting significance as the base over which fixed costs are apportioned, capacity utilisation also measures how well the resources allocated to a business are being utilised. A low level of capacity utilisation represents unused resources and, to that extent, the opportunity cost foregone by the owner or investor. Porter (1985) lists capacity utilisation as one of the 10 major cost drivers of a firm. The positive link between capacity utilisation and firm profitability has been empirically asserted by Banker et al. (1993), Capon et al. (1990), D’Aveni (1989) and Hammesfahr et al. (1993). At the macro-economic level, capacity utilisation provides a window into business cycles, and also impacts upon labour productivity and level of employment (Greenwood et al., 1988).

Capacity utilisation can be interpreted in a technical sense, relative to the maximum output obtainable from a production facility under realistic assumptions; or in an economic sense, relative to the economically optimal level of output. The measures derived from the two conceptualisations, engineering and economic, do not always correlate well (Azeez, 2001; Berndt and Morrison,1981; Erumban, 2005). Levels of capacity utilisation in the Indian manufacturing sector have been studied recently by Goldar and Renganathan (2008), who noted a decline within the organised manufacturing sector from 1995 to 2001, with some recovery thereafter. Ray (2011, 2012) found only decline over the same time period in both the pharmaceutical and aluminum industries.

Hypotheses

Based on the foregoing discussion of business constraints, firm performance and the process of economic reform in India, three hypotheses are suggested below. While the direction of the hypotheses is similar for both micro (fewer than 10 employees) and small (10–49 employees) firms, we expect differences between the two groups at the level of individual parameters and the strength of the relationships. In the literature review it was found that only Aterido et al. (2009) have investigated systematic differences between micro and small firms; it is our desire to advance this line of work.

The first hypothesis concerns the relation between business constraints and the performance of micro and small manufacturing businesses in 2002 and 2006. Given the performance-limiting nature of the business constraints, the following alternate (i.e. non-null) hypothesis is proposed:

H1_a: The level of business constraints had a negative effect on the performance of micro and small manufacturing businesses in 2002 and 2006.

We expect different sets of constraints to matter most to the performance of micro and small firms. In particular, following Aterido et al. (2009), we expect the performance of small firms to be most sensitive to policies, the legal/ethical environment and taxation, while micro firms are likely to feel the constraints of capital and infrastructure most keenly. We expect the relation between environment and performance to be stronger for small firms than for micro firms.

The second hypothesis relates to the change in the level of business constraints from 2002 to 2006. The intent of policy reform, at the macro and SME levels, to ease business constraints for small manufacturing firms leads to the following alternate (non-null) hypothesis:

H2_a: The business constraints facing micro and small manufacturing firms in India were lower in 2006 than they were in 2002.

We expect the easing of business constraints to be perceived similarly by micro and small firms, because both groups are subject to similar policies under Indian MSME legislation and thus experience the same changes in these policies.

The final hypothesis concerns the change in performance of micro and small firms from 2002 to 2006. Based on the expectation that performance-limiting business constraints were lowered during this period, the alternate hypothesis is framed as follows:

H3_a: The performance of micro and small manufacturing firms in India improved over time from 2002 to 2006.

We expect the performance of both micro and small firms to improve with the reduction of business constraints, although the extent of improvement is likely to be different (because the constraints affect the two groups differently). In general, small firms should benefit more from the easing of the business constraints, as their performance is likely to be tied more closely to the business constraints in the first place (Figure 2).

Figure 2.

Research model.

Method

Data collection

Data for this study were drawn from the World Bank’s Enterprise Surveys (WBES), which have been administered so far to more than 130,000 firms in 125 countries (World Bank and International Finance Corporation, 2012). Two waves of data were collected through face-to-face interviews with business owners or managers in India: in 2002 (from 1827 firms) and 2006 (from 4234 firms). The 2006 survey data were collected from 36 Indian cities in 16 states, in order to capture the geographical diversity of a large country. These data constitute some of the most recent information available on Indian SMEs across all regions and industry sectors. The fourth All-India Census of Micro, Small and Medium Enterprises of the Indian government was conducted also during the same time (in 2006), and the results were published in 2011. Currently, a third survey by the World Bank is in progress, and data are expected to be released in 2013.

Table 3 shows the representation of different industry sectors in the SME population of India, including both registered and the (far more numerous) unregistered units, and the present study’s 2002 and 2006 samples. The firms in these samples come from 10 major industry groups: auto and auto components, chemical and pharmaceuticals, electronics, food, garments, leather, metals and machinery, paper, plastics and wood and furniture.

Table 3.

Industry-wise composition of the MSME population and samples.

Industry group	NIC 2004	Registered units		Unregistered units		2002 sample		2006 sample
		Micro	Small	Micro	Small	Micro	Small	Micro	Small
Auto and auto	34	5224	600	25,000	0	62	145	26	26
components	35	14,574	1102	72,000	0
Chemicals and pharmaceuticals	24	45,760	5161	189,000	0	111	147	27	47
Electronics	31	33,233	1580	261,000	0	97	130	26	36
Electronics	32	15,581	398	125,000	0
Food	15	214,378	8284	2,288,000	1000	63	59	21	30
Garments	18	209,630	3945	2,952,000	0	42	134	31	27
Leather	19	25,628	926	123,000	0	36	19	27	10
Metals and	28	134,608	5366	701,000	1000	68	55	94	86
machinery	29	66,548	6136	342,000	0
Paper	21	11,947	1446	33,000	0	0	0	3	5
Plastics	25	33,251	3651	205,000	1000	0	0	15	23
Textiles	17	97,597	8202	736,000	1000	39	93	11	31
Wood and	20	54,034	1143	585,000	0	0	0	4	1
furniture	36	96,831	2665	1,061,000	1000
Others	n.a.	132,040	10703	725,000	0	0	0	0	1
Total		1,190,864	61308	10,423,000	5000	518	782	285	323

When retaining only manufacturing firms that employ fewer than 50 permanent employees and removing outliers (fewer than 1% of the data) who reported wages or sales per employee at least two orders of magnitude different from the average, a usable sample of 1300 firms remained in the 2002 sample, and 608 firms in 2006. The smaller sample in 2006 is due to the fact that the World Bank’s data collection effort in 2006 was distributed over manufacturing and service firms; the 2002 survey focused solely on manufacturing firms. The two waves of the survey, conducted by different units of the World Bank, share about 100 items in common (out of a total of 540 items), and these common items constitute the subject of the present analysis.

Sample

This study follows the definition of a micro firm used by Heshmati (2001) who, in turn, adopted it from Eurostat (1994). Aterido et al. (2009) showed the importance of distinguishing between micro (fewer than 10 employees) and small firms (10–49 employees). Following the same logic, separate analyses were conducted for micro and small firms in the samples (rather than pooling all the data together).

Table 4 shows the main characteristics of the sample of small manufacturing businesses in 2002 (1300 firms) and 2006 (608 firms). The average age of the firms was 14.25 years in 2002, and 17.07 years in 2006 (which confirms that the firms in the 2006 sample started business around the same time as those in the 2002 sample; indeed some, but not all, firms were visited for both surveys). Both sets of firms had mainly male owners, used predominantly domestic inputs and sold mostly within India. More than two-thirds of the firms were members of associations. The average firm had slightly more than 15 permanent employees and about three temporary workers. Micro firms (with fewer than 10 employees) formed 78 percent of the 2002 sample, and 77 percent of the 2006 sample. Therefore, in nearly all respects, the 2002 and 2006 samples are comparable.

Table 4.

Characteristics of 2002 and 2006 samples.

	2002	2006
Manufacturing industry:	Total: 1300	Total: 608
Auto and auto components	207	52
Chemicals and pharmaceuticals	258	74
Electronics	227	62
Food	122	51
Garments	176	58
Leather	55	37
Metals and machinery	123	180
Paper	0	8
Plastics	0	38
Textiles	132	42
Wood and furniture	0	5
Other manufacturing	0	1
Average age (years of operation)	14.25	17.07
Male owner	97%	89%
Share of domestic inputs	97%	98%
Share of domestic sales	93%	92%
Membership of association	73%	66%
Number of permanent workers	15.50	15.31
Micro firms (0–10 permanent workers)	78%	77%
Small firms (11–50 permanent workers)	22%	23%
Number of temporary workers	2.66	3.44

Data analysis

To arrive at a parsimonious set of factors to describe the business environment, an exploratory factor analysis was undertaken of the 17 business obstacles measured in the WBES. Factor analysis not only reduces the dimensionality of the data, but also yields orthogonal components of the business environment (termed ‘business constraint factors’ in this article), reducing the potential for multicollinearity when the factors are used jointly to explain variance in outcome variables, such as performance. Because the computation of factor scores is sensitive to missing data (no scores are computed if any one of the 17 items is missing for a firm), the missing values of these items (‘obstacles’) were replaced with the mean values for the item. This affected 1.2 percent of all the data and did not materially change the analysis and interpretation of the factors. The factors extracted with mean imputation are almost identical to their counterparts extracted with list-wise deletion of data (minimum r = 0.998).

In order to test the first hypothesis, regression models of capacity utilisation on the business constraint factors were developed (separately for micro and small manufacturing firms), while controlling for the industry sector and age of each firm, for each of the two years, 2002 and 2006. To test the second hypothesis, independent sample t-tests were conducted on the business constraint factors to compare the mean levels of the factors in 2002 and 2006 for micro and small manufacturing firms. The third hypothesis was tested with a regression of capacity utilisation of micro and small manufacturing firms on time (elapsed between the calendar years 2002 and 2006), with the industry sector and age of each firm as control variables.

Results

The descriptive statistics of capacity utilisation for micro and small firms in different industries in the 2002 and 2006 samples are presented in Table 5. The capacity utilisation data in this study are based on the engineering approach (as a proportion of installed capacity), as owners of small businesses might find it difficult to compute the economically optimal level of output and use it to answer questions about capacity utilisation at their plants. While earlier studies estimated capacity utilisation using historical output data, the World Bank surveys posed direct questions on estimated capacity utilisation to business owners and managers. It is reassuring that the average levels of capacity utilisation reported in the surveys (in the 60–80% range) overlap well with those estimated by Mukherjee and Misra (2012) and Saikia (2012) using econometric methods.

Table 5.

Capacity utilisation across industries, firm size and time periods.

% capacity utilisation		2002			2006
Industry		Micro	Small	Total	Micro	Small	Total
Auto and auto components	Mean	75.1	81.8	79.8	70.7	67.1	68.9
	S.D.	21.4	18.3	19.5	17.7	15.6	16.6
	n	62	144	206	26	26	52
Chemicals and pharmaceuticals	Mean	80.7	75.9	78.0	63.7	67.3	66.0
	S.D.	18.8	20.1	19.6	15.5	25.2	22.1
	n	107	144	251	27	46	73
Electronics	Mean	76.1	76.9	76.6	62.3	65.0	63.8
	S.D.	20.8	22.0	21.4	26.9	23.5	24.8
	n	94	127	221	26	34	60
Food	Mean	80.0	78.8	79.4	66.8	66.2	66.4
	S.D.	19.5	19.8	19.6	25.5	27.2	26.3
	n	61	58	119	20	30	50
Garments	Mean	74.9	79.6	78.5	75.5	79.7	77.4
	S.D.	15.9	16.9	16.7	15.1	14.6	14.9
	n	42	132	174	31	27	58
Leather	Mean	83.6	81.1	82.7	56.3	60.0	57.3
	S.D.	13.5	13.8	13.5	17.4	25.7	19.7
	n	32	18	50	27	10	37
Metals and machinery	Mean	81.2	74.1	77.9	63.0	70.2	66.5
	S.D.	20.9	20.9	21.1	17.6	16.7	17.5
	n	64	55	119	94	85	179
Paper	Mean				73.3	70.0	71.3
	S.D.				20.8	6.1	12.2
	n				3	5	8
Plastics and non-metallic	Mean				65.4	67.6	66.7
	S.D.				21.2	25.6	23.7
	n				15	23	38
Textiles	Mean	84.3	75.2	77.9	79.1	76.1	76.9
	S.D.	21.7	23.8	23.5	15.6	15.4	15.3
	n	39	93	132	11	31	42
Wood and furniture	Mean				63.8	50.0	61.0
	S.D.				16.0		15.2
	n				4	1	5
Other manufacturing	Mean					100	100
	S.D.
	n					1	1
Total	Mean	79.1	77.9	78.4	65.6	69.5	67.7
	S.D.	19.7	20.1	19.9	19.5	20.9	20.3
	n	501	771	1272	284	319	603

Despite possible right-censoring (none of the capacity utilisation figures reported in the survey exceed 100%), the distribution of the dependent variable is approximately normal, as shown in Figure 3, allowing us to fit linear models.

Figure 3.

Distribution of the dependent variable: capacity utilisation.

The factor analysis of the 17 business obstacles measured in the WBES, performed using the principal components extraction method and varimax orthogonal rotation, yielded a set of five factors (eigenvalues > 1) that account for 64.2 percent of the total variance. The (rotated) component matrix in Table 6 shows the loading of the 17 obstacles on the five business constraint factors. The names for the five business constraint factors are indicated in the top row of the table: policies (permits, trade and labour regulations, macroeconomic stability/uncertainty), legal/ethical environment (law and order, informal practices and corruption), access to capital (access to and cost of finance and land), taxation (rates and administration) and infrastructure (transport, electricity and telecommunications).

Table 6.

Factor analysis of business obstacles into business constraint factors.

Business obstacle	Business constraint factors
	1: Policies	2: Legal/ethical	3: Capital	4: Taxes	5: Infrastructure
Licensing and operating permits	.653	.214	.274	.168	.155
Customs and trade regulations	.646	.073	.066	.383	.125
Labour regulations	.633	.132	.107	.377	.144
Macroeconomic instability (inflation, exchange rate)	.608	.493	.167	–.003	.020
Skills of available workers	.576	.153	.241	.078	.169
Economic and regulatory policy uncertainty	.573	.414	.295	.089	–.018
Crime, theft, disorder	.156	.765	.126	.087	.224
Anti-competitive/informal practices	.262	.739	.169	.022	.123
Corruption	.126	.732	.094	.359	.038
Access to finance (e.g. collateral)	.253	.166	.841	.091	.060
Cost of finance (e.g. interest rates)	.223	.262	.800	.146	.038
Access to land	.160	–.023	.410	.281	.383
Tax rates	.148	.133	.203	.806	.143
Tax administration	.323	.184	.078	.772	.106
Transport	.267	.163	.093	.091	.733
Electricity	–.241	.127	.097	.241	.721
Telecommunications	.340	.080	–.037	–.039	.678
% variance explained	17.03	13.95	11.18	11.11	10.93
Cumulative variance explained %	17.03	30.98	42.16	53.27	64.2

Table 7 shows the bivariate correlations between the five business constraint factors and the dependent variable, capacity utilisation, for micro and small firms in 2002 and 2006. As might be expected for constraints on performance, more than half of the correlation coefficients are negative, but only a couple of them attain statistical significance.

Table 7.

Bivariate correlations of the business constraint factors with capacity utilisation.

	Factor 1: policies	Factor 2: legal/ethical	Factor 3: capital	Factor 4: taxes	Factor 5: infrastructure
2002, micro firms	0.046	–0.004	0.018	–0.097*	0.052
2002, small firms	–0.106**	–0.051	–0.010	–0.216**	–0.031
2006, micro firms	0.082	0.029	–0.105	0.085	–0.076
2006, small firms	–0.083	–0.054	–0.083	0.097	0.035

p<0.01, *p<0.05; n = 1875.

Notes: The factors, extracted using principal components and varimax rotation, are mutually orthogonal. The business constraint factors generally are not significantly associated with the age of the firms.

Table 8 shows the regression model of capacity utilisation on the business constraint factors, with industry sector and age included as control variables. Separate models are estimated for micro and small firms in each of the two years, 2002 and 2006. Support for H1 was found (i.e. significant adverse effects of the constraints on performance) only in the case of small firms in 2002: policies, legal/ethical environment and taxes had significant negative impact on performance. Unexpectedly, the performance of micro firms in 2002 was negatively related to their (perceived) tax burden. This is surprising, given that most of the micro firms are unregistered (Ministry of Micro, Small and Medium Enterprises, 2011) and might fall outside the tax net. We would have expected them to be less affected by tax issues.

Table 8.

Regression of capacity utilisation on business constraint factors. controlling for industry and age.

	Model 1	Model 2	Model 3	Model 4
	2002	2002	2006	2006
	Micro firms	Small firms	Micro firms	Small firms
Policies	1.293	–2.194**	0.793	–2.026
Legal/ethical	0.308	–1.888**	0.561	–2.365
Capital	0.206	–0.234	–1.132	–1.513
Taxes	–2.186*	–4.325**	0.587	1.566
Infrastructure	0.255	–0.823	–2.250	0.206
Age	0.118	–0.017	–0.315**	0.021
R ²	0.044	0.073	0.133	0.082
Adjusted R²	0.017	0.057	0.080	0.030
N	472	744	281	316
Industry effect F-statistic	1.596	1.408	2.374*	1.529

p<0.01, *p<0.05.

None of the constraints had a significant impact on firm performance in 2006. It appears that by 2006, the negative effects of business constraints on performance were much weakened. Although half of the regression coefficients are negative, none make it to statistical significance.

Of the control variables, age has a negative effect on micro firms in 2006: that is, younger firms fare better. There is also significant inter-industry variation in capacity utilisation among micro firms in 2006: the leather sector has the lowest level, and garments the highest.

Table 9 shows the levels of the five business constraint factors in 2002 and 2006 for micro and small firms. In support of H2, significant improvements were found in the policies and legal/ethical environment facing both Indian micro and small manufacturing firms from 2002 to 2006. Contrary to H2, the perceived tax burden became significantly heavier for small firms, indicating a deterioration of their business environment. Constraints on capital eased for small firms, but became more acute for micro firms. The constraint posed by infrastructure did not change significantly between 2002 and 2006. Overall, the support for H2 was mixed.

Table 9.

Level of business constraint factors in 2002 and 2006.

	Factor 1: policies	Factor 2: legal/ethical	Factor 3: capital	Factor 4: taxes	Factor 5: infrastructure
Micro firms:
2002
Mean	.103	.180	–.052	.040	.036
N	518	518	518	518	518
S.D.	.981	1.002	.873	.982	1.060
2006
Mean	–.595	–.485	.152	.063	.049
N	285	285	285	285	285
S.D.	.932	.795	1.313	1.032	.922
t-value (801 d.f.)	9.817**	9.649**	–2.637**	–.309	–.168
Small firms:
2002
Mean	.224	.217	.026	–.117	–.001
N	782	782	782	782	782
S.D.	.904	1.021	.918	.962	.993
2006
Mean	–.182	–.387	–.115	.163	–.099
N	323	323	323	323	323
S.D.	1.063	.829	1.039	1.055	.974
t-value (1103 d.f.)	6.439**	9.423**	2.224*	–4.276**	1.503

p<0.01, *p<0.05.

Note: Positive t-values (initial–final) indicate a lowering (improvement) of the business constraint factor from 2002 to 2006. Negative t-values indicate an increase in the level of the constraint, i.e. a deterioration in that aspect of the business environment.

Table 10 shows the regression model of capacity utilisation on elapsed time from 2002 to 2006 for micro and small firms separately, with industry sector and age included as control variables. The significant negative coefficient for time in both models indicates that the capacity utilisation of both micro and small manufacturing firms declined over time from 2002 to 2006. Figure 4 shows this decline graphically, using a means plot.

Table 10.

Regression of capacity utilisation on time, controlling for industry and age.

	Model 1	Model 2
	Micro firms	Small firms
Time	–3.29**	–1.98**
Age	–0.02	–0.02
R ²	0.11	0.05
Adjusted R²	0.10	0.04
N	753	1060
Industry effect F-statistic	6665.31	4038.49

p<0.01, *p<0.05.

Figure 4.

Change in performance of micro and small businesses in 2002 and 2006.

Contrary to H3, the performance of both micro and small manufacturing firms, as measured by capacity utilisation, fell sharply from 2002 to 2006. Therefore, we conclude that while policy reforms led to the relaxation of some business constraints from 2002 to 2006, the performance of SME manufacturers declined significantly in this period. In relative terms, small firms (with 10–49 employees) lost less ground than micro firms (with fewer than 10 employees). In order to test the robustness of the results, sales were used as a measure of performance: a similar decline in performance was found.

Discussion

Major national policy reforms in India began in 1991 and gained momentum in the 2000s. Policies specifically targeting small businesses also emerged between 2000 and 2005. The present study analysed secondary data from the WBES to see if these policy changes relaxed the business constraints faced by micro and small manufacturing firms from 2002 to 2006, and whether the relaxation of these constraints contributed to better firm performance.

As mentioned before, using factor analysis, data on 17 obstacles were reduced to five business constraint factors: policies, legal/ethical environment, capital, taxation and infrastructure. Keeping in mind that the WBES data are measures of perception rather than objective reality, other measures of business constraint, perceptual or factual, were explored from other sources (as suggested by Doern, 2009).

The World Governance Indicators Project (World Bank Group, 2012) provides perceptual measures of governance for 213 countries from 1996 to 2010, based on a wide range of views. Each indicator ranges from -2.5 (weak) to 2.5 (strong). Among the World Governance Indicators’ measures were the following:

regulatory quality – defined as the government’s ability to formulate and implement sound policies and regulations that permit and promote private sector development – improved slightly in India from -0.37 in 2002 to -0.21 in 2006;

control of corruption – the measure of legal/ethical environment, defined as the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as ‘capture’ of the state by elites and private interests – improved slightly in India from -0.49 in 2002 to -0.28 in 2006.

Further, summary data from the World Bank on the financial sector of India shows that the lending interest rate remained almost constant from 2002 (11.9%) to 2006 (11.2%). The KPMG (2002, 2006) tax rate survey reports show that corporate tax rates fell slightly during the period from 35.7 percent in 2002 to 33.7 percent in 2006. The percentage of total roads paved – a common measure of infrastructure used by the World Bank – hardly changed in India, from 47.4 percent in 2002 to 47.7 percent in 2006.

These alternate measures of the business constraint factors in 2002 and 2006 are shown in Table 11. We note small improvements on all of the five business constraints.

Table 11.

Alternate measures of business constraint factors in 2002 and 2006.

	Factor 1: policies	Factor 2: legal/ethical	Factor 3: capital	Factor 4:taxes	Factor 5: infrastructure
Source	World Governance Indicators	World Governance Indicators	World Bank	KPMG Tax Rate Survey	World Bank
Measure	Regulatory quality (–2.5 = weak to 2.5 = strong)	Level of corruption (–2.5 = weak to 2.5 = strong)	Lending interest rate %	Corporate tax rate	% of total roads paved
2002	–0.37	–0.49	11.9	35.7	47.4
2006	–0.21	–0.28	11.2	33.66	47.7

The changes in the first two business constraints – policies and legal/ethical environment – match the statistically significant improvements observed in the WBES data. The slight easing of lending rates matches the experience of small firms, which enjoyed easier access to capital in 2006. However, such an improvement in the availability and cost of capital was not experienced by micro firms, for whom things actually got worse from 2002 to 2006. The change in the infrastructure constraint is not statistically significant in the WBES data, mirroring data from other sources.

The only major disagreement between the WBES data and the alternate sources lies in the path of the taxation-related business constraint. Although nominal tax rates did not increase (as noted by the KPMG survey), WBES respondents, particularly small firms, felt a heavier tax burden in 2006 than in 2002. One potential explanation is that the ongoing reform of India’s tax laws steadily reduced the number of exemptions and concessions, thus forcing more small firms to pay nominal rates (Rao, 2005). The other possibility is that the enforcement of tax laws became stricter between 2002 and 2006 to recoup the revenue lost to falling tariffs in an age of globalisation. Tighter enforcement, aided by computerised information processing (Rao, 2005), increases the pressure for compliance and can explain heightened perceptions of tax burden, even if rates remain unchanged. Yet another possible explanation is provided by Guha (2007), who studied the relationship between company size and the effective corporate tax rate for Indian private manufacturing companies, finding that smaller companies faced a higher effective tax rate. Therefore, despite the slight lowering of nominal tax rates from 2002 to 2006, the effective tax rates for small firms may not have fallen. Whatever the reasons, the micro and small firms responding to the WBES of 2006 clearly felt – more so than their 2002 counterparts – that taxes posed a major constraint to their business. In her analyses of the reforms in the Indian tax system, Poirson (2006) concluded that firms that rely on internal sources of funds, or face problems borrowing, would continue to face high marginal rates. Micro and small firms are more likely to personal rather than borrowed funds, which could explain their complaints about high taxes. Given the slight relaxation in business constraints, why did the performance of micro and small manufacturing firms in India decline from 2002 to 2006? A number of candidate explanations merit mention.

Increased competition arising from the rapid entry of new domestic firms and the accompanying increase in production capacity are likely to have contributed to the decline in capacity utilisation. Between the third census of 2001–2002 and the fourth of 2006–2007, the number of manufacturing SMEs in India increased from 4.446m to 7.453m, implying a compound annualised growth rate of almost 11 percent. Even with the market steadily growing to absorb the additional output of the growing sector, some lag between capacity growth and demand growth is only to be expected.

A major aspect of India’s reforms has been the opening up of the economy, which provides opportunities to exploit foreign markets and use foreign inputs. However, the sample in the present study of micro and small firms consisted of manufacturers that sold mainly in the domestic market (92–93%) and used predominantly domestic inputs (97–98%) in both 2002 and 2006. These firms did not venture abroad thereby, missing the benefits associated with the opening up of the Indian economy. Instead, they faced increased competition from foreign firms and/or foreign products entering the Indian market, as the protection historically granted to Indian SMEs (e.g. in the form of products reserved for domestic small businesses) was reduced. Anecdotal evidence suggests that cheap imports from neighbouring countries such as Bangladesh, China and Sri Lanka overwhelmed many local manufacturers, especially in the textile industry, following the end of the Multi-Fiber Agreement regime. Our findings also agree with Kumar and Sengupta (2008), who point out that the manufacturing sector of India is only a weak global player, with low levels of foreign direct investment and research and development spending, and relatively less skilled personnel.

Regulatory policy constraints negatively affected the performance of small firms, and had no effect on micro firm performance in 2002. Perhaps the changes in permits, trade and labour regulations and macroeconomic stability or uncertainty are more relevant for relatively larger firms with more than 10 employees, and these aspects of the regulatory environment actually hinder their operations (consistent with Aterido et al., 2009). It is possible that for micro firms, informal governance mechanisms such as those based on trust, reputation and relationships are more important than formal mechanisms such as the regulatory environment and taxes (see Allen et al., 2007). This study found that age had a negative effect on micro business performance in 2006. This reinforces the findings of Coad and Tamvada (2011), who also found age to have a negative effect on growth (their performance measure); it is possible that the newer firms are more tuned to today’s global pressures, and thus are better equipped to compete.

Conclusion

Contributions of the study

This study, which uses secondary data to examine the effect of national context on small businesses in India, makes some important contributions. First, the sheer size and coverage of the data collected by the WBES from door-to-door surveys of business owners and managers affords a glimpse into the overall state of Indian business in 2002 and 2006. We note that the data include representation of micro firms, most of which are unregistered and operate in the informal sector. The World Bank’s door-to-door survey methodology reaches these firms that are often missed by official statistics. In addition, the data are collected in real time (no recall is involved) and are therefore, free from retrospective bias.

Besides capacity utilisation, the WBES also gather information on the value of sales and the number of employees in each firm: because the value of the final products and the labour intensity of the production processes vary widely, sales and employment figures are more susceptible to sectoral variations and hence, harder to aggregate. We did replicate some of our analyses with sales as a dependent variable (instead of capacity utilisation), and obtained results that are similar in direction but less significant, presumably due to the lower statistical power of the study’s sample in the face of greater variability.

Given the paucity of studies on entrepreneurial small businesses in India (Bruton et al., 2008), this research fills an important gap in the literature. The WBES data cast light on how SMEs in India navigate a changing business environment. The study finds evidence that the much-discussed regulatory reforms translated into a more benign policy and legal/ethical environment for micro and small firms. However, it also discovers the limit of such reforms in terms of access to capital, taxation and infrastructure, where micro firms continued to suffer from capital constraints and small firms from the (perceived) tax burden, even after policy changes. Finally, it highlights a non-obvious finding that, in spite of many reforms, micro and small firms in India could not keep up with their larger counterparts during the period of rapid growth from 2002 to 2006. While the spectacular growth of GDP and total manufacturing output during the period of this study attracted the label of ‘India Shining’, micro and small firms seem to have been left behind.

Limitations of the study

The use of secondary data in this study imposes some limitations. First, the data are not panel data, so the information in the two waves of the survey comes from overlapping but different sets of firms. Some, but not all, of the firms surveyed in 2002 were revisited in 2006. This restricts our ability to make causal inferences, as the study did not follow the same firms through the two waves of survey data. Due to incomplete information about the sample firms in 2002 and/or 2006, there was substantial reduction in the number of variables that could be used for the statistical analyses. The measure of firm performance, capacity utilisation, may be right-censored, although the approximate normality of the variable was confirmed (see Figure 3). The broader focus of the 2006 WBES to include service businesses led to a reduction in the coverage of manufacturing firms. The 2006 sample in the present study was less than half the size of the 2002 sample, and sometimes we were left with effects in these data that pointed in the expected direction, but failed to attain statistical significance. A larger sample in 2006 would have increased the power of the analysis and sharpened the findings.

Implications of the study and suggestions for future research

The findings from this study have implications for research, business and policy. Future research could examine data on a panel of small businesses over time in order to understand better the longitudinal impact of India’s policy reforms on performance. In addition, researchers can delve more deeply into firm-level reasons to explain why some small businesses are able to adapt to business obstacles than others. Owners/managers of micro and small businesses in India need to be alert to the specific effects of policy reforms. For example, with the reduction in the number of products that are reserved for small businesses, and the lowering of import restrictions and tariffs, small business managers must consider internationalisation strategies such as exporting or contract work for larger firms (Woldesenbet et al., 2012). Also, they should seek external sources of management expertise to navigate the turbulent environment; the banks that lend to them can be particularly helpful in this matter (Han et al., 2012). Finally, policymakers can note that the disruption caused to SMEs by the opening up of markets was not completely offset by SME-targeted policies thereafter. Smallbone and Welter (2001), citing the Bolton Committee (1971) report, point out that policies that are intended to be neutral in their effect may turn out not to be so, due to the differences in the size of firms. In other words, size-neutral policies do not always have size-neutral outcomes. Although this study observed a decrease in some constraints faced by micro and small manufacturing businesses in India from 2002 to 2006, this did not translate into better performance by these businesses, highlighting the need to do more, and soon, to stem the decline of this sector. Perhaps in response to these needs, the Micro, Small and Medium Enterprises Development Act 2006 undertook further reforms, this time specifically targeting the SME segment of Indian industry. Provisions of the 2006 Act included the promotion of quality management systems, awareness of intellectual property, management development, technical support through mini-tool rooms and marketing assistance for MSMEs. In future work we hope to study the efficacy of these measures in reviving the MSME sector in India, by using data from third round of the WBES currently ongoing in India.

Footnotes

Acknowledgements

The authors wish to thank the World Bank for providing free access to data from its World Bank Enterprise Surveys.

Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Notes

Author biographies

Shobha S. Das is an Associate Professor in the College of Business & Economics, Qatar University. Her research interests are in the areas of strategy and entrepreneurship in developed and emerging economies.

Amit Das is an Associate Professor in the College of Business & Economics, Qatar University. He studies governance across countries and over time.

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