Abstract
We identify a basic dualism within the informal manufacturing sector (IMS) in India between a ‘traditional’/non-capitalist segment, comprising family-based household enterprises that constitute the vast majority of the IMS, and a segment of ‘modern’/capitalist enterprises employing wage labour. We focus on the high-growth decade of 2000–2001 to 2010–2011 to analyse whether there has been a marked tendency of this ‘traditional’ segment to transform into a ‘modern’ segment. We construct a variable, the net accumulation fund, which indicates the ability of an enterprise to accumulate and grow, and explore its evolution, over time and across industries, for enterprises with different production structures and firm-level characteristics. We show that while, on one hand, the average ‘traditional’ enterprise has been able to economically reproduce itself rather than withering away, the dualism between the ‘traditional’/non-capitalist and the ‘modern’/capitalist segments has been reproduced and further reinforced during this period of high economic growth, raising questions about the process of economic transformation as envisaged in much of development literature.
Résumé
Nous identifions un dualisme fondamental dans le secteur manufacturier informel (SMI) en Inde entre un segment “traditionnel”/non capitaliste, comprenant des entreprises familiales, basées sur le foyer, qui constituent la grande majorité du SMI, et un segment “moderne”/d’entreprises capitalistes employant de la main d’œuvre salariée. Nous nous concentrons sur la décennie à forte croissance de 2000-01 à 2010-11 afin d’analyser s’il existe une tendance marquée de ce segment « traditionnel » à se transformer en un segment « moderne » . Nous construisons une variable, le fonds d’accumulation net, qui indique la capacité d’une entreprise à accumuler et à se développer, puis nous étudions son évolution, dans le temps et selon les industries, pour des entreprises ayant des structures de production et des caractéristiques au niveau de l’entreprise différentes. Nous montrons que, si, d’une part, l’entreprise “traditionnelle” moyenne a été capable de se reproduire économiquement plutôt que de dépérir, le dualisme entre les segments “traditionnel”/non capitaliste et “moderne”/capitaliste a été reproduit et renforcé encore pendant la période de forte croissance économique, soulevant des questions sur le processus de transformation économique tel qu’envisagé dans de nombreux ouvrages sur le développement.
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Notes
In the Lewisian literature, the ‘traditional’ segment of the economy is seen to be the one that absorbs the surplus labour force in the economy, is relatively stagnant and is less productive, whereas the ‘modern’ segment is viewed as the dynamic, profit-maximising segment that is relatively more productive and technologically advanced.
It may be argued that traditional household enterprises voluntarily choose to remain small in order to avoid various kinds of labour regulations, which they would have to abide by if their size exceeded a specific threshold level (Perry 2007). For example, in the Indian context, if a firm employs more workers than a particular threshold level, it will be considered to be part of the formal sector of the economy and, under specific circumstances, will be legally bound to enforce various kinds of labour regulations and environmental standards. Implementing such measures might increase the cost of business for the firm. Firms might voluntarily choose to remain under such specific threshold levels in order to avoid these regulations. However, the issue we are exploring here is distinctly different from such cases. Here, we look at the possibilities of transformation within the IMS, i.e. a transition of the ‘traditional’ segment of the IMS into ‘modern’, dynamic enterprises, rather than a transition from the informal to formal sector. The question of voluntarily choosing to remain small in order to avoid costly regulations might be an issue for the larger enterprises in the IMS, e.g. for those at the upper end of the ‘modern’ segment of the IMS. The enterprises in the ‘traditional’ segment of the IMS, on the other hand, do not employ any wage labour and the sizes of such enterprises is nowhere near the threshold levels. Such ‘traditional’ enterprises constitute the overwhelming majority (about 85%) of the IMS.
The official definition recognises the unorganised segment as constituting the following enterprises: “(i) All manufacturing enterprises except those registered under section 2m(i) and 2m(ii) of Factories Act, 19487 and Bidi and Cigar Workers (conditions of employment) Act, 1966. (ii) All manufacturing enterprises except those run by Government (Central Government, State Governments, Local Bodies)/Public Sector Enterprises” (NSSO report 525, 2008, p. 8).
We find that, for all three time points of our analysis (2000–2001, 2005–2006 and 2010–2011), the informal enterprises account for around 95–98% of the estimated population of unorganised enterprises—and around 92–96% of the sample—surveyed by the NSSO.
The NSSO further categorises the establishments into two types: (a) non-directory manufacturing enterprises (NDMEs) employing one to five workers, along with at least one hired worker, and (b) directory manufacturing enterprises (DMEs) employing six to nine workers with at least one hired worker. We have carried out the analysis in this paper with this further categorisation as well, but found that it does not change the results or add any new insights.
The NAF is distinct from the usual notion of NRE or net profit for enterprises where the entire production is carried out using hired labour. For such enterprises, the net profit (which is also the fund for accumulation and reinvestment) is calculated by deducting from their GVA the wages paid to hired workers, as well as rents and interest payments. However, as noted above, the vast majority of informal enterprises carry out production without any hired labour, and even when some wage labour is hired, the household members still participate in the production process without receiving any wages. Hence, to calculate the NAF for such enterprises, we estimate the ‘pseudo-wage’ fund for the unpaid household workers, and then deduct it from the NRE.
Note that the wage fund of the hired workers may also have a savings component, and hence assigning the entire ‘pseudo-wage’ fund as the consumption fund for the household workers may be an overestimation of the consumption fund. However, given the extremely low levels of wages of informal wage workers, the saving component is likely to be negligible.
Given the high level of heterogeneity among the informal firms and, consequently, the high dispersion in the data, the averages are reported at median values throughout this section, unless specified otherwise. For reference purposes, all monetary values can be converted from Indian rupees (INR) to US dollars (USD) following the purchasing power parity (PPP) conversion rate (INR/USD) of 11.23 for the years 2004 and 2005 on an average (11.171 for 2004 and 11.282 for 2005) (OECD 2017).
Note that the establishments in the lowest decile of the NAF distribution, on an average, have negative NAFs. This is due to the fact that, on an average, the wages of hired workers for these establishments as well as the average amount of non-hired family labour working in these enterprises are anomalously higher than those for the entire set of establishments, thereby raising the values of imputed ‘pseudo-wage’ funds and leading to negative NAFs. However, the average GVA for these establishments is much higher than that of OAMEs with negative NAFs for corresponding time points. Therefore, even with negative NAFs, such establishments have a much larger scale of operation.
The eight major NIC codes (NIC 2004 classification) in the Indian IMS are manufacture of wearing apparel, dressing and dyeing of fur (NIC 18); manufacture of food products and beverages (NIC 15); manufacture of tobacco products (NIC 16); manufacture of textiles (NIC 17); manufacture of wood and products of wood and cork, except furniture; manufacture of articles of straw and plating materials (NIC 20); manufacture of fabricated metal products, except machinery and equipment (NIC 28); manufacture of furniture, manufacturing not elsewhere classified (NIC 36); manufacture of other non-metallic mineral products (NIC 26). These eight industries together account for more than 90% of the IMS (varying from 92% to 94% over the three rounds).
One can possibly take a log transformation of the NAF, given the skewed nature of the NAF distribution. However, we refrain from such log transformation in our analysis because: (i) the dependent variable in the regression analysis, the NAF, has many (and quite large) negative values. Out of 384,698 sample observations, 21,190 observations (i.e. approximately 5% of the sample) have a negative NAF. Taking a log would automatically truncate our distribution, but we would ideally not want to lose information on these observations, as they can have important implications for the results; and (ii) while log transformation may bring normality to the distribution, given the large sample size, the ‘large sample properties’ would be applicable for the data, and thus we do not need to assume normality of distribution for any estimations.
Per capita state domestic product for the three time points would control for the overall economic performance of the states. State-level literacy rate and infant mortality rate would control for social infrastructure in the state. The percentage of land under agriculture, share of manufacturing domestic product in the total state domestic product and percentage of workforce in the state employed in manufacturing sector would control for extent of and dependence on agriculture and manufacturing activities in the state. Skill level of workers might be another important variable to control for. However, as pointed out by Raj and Sen (2016), urban versus rural location might be a good proxy for skill. Additionally, skills are expected to vary widely across industry groups, which we control for by adding NIC-level controls in the regression. We are unable to control for state-wise regulatory environments due to unavailability of data for all states for our period of analysis. However, any state- or industry-level policy/regulation would already be controlled for by the state and NIC dummies that account for any state- or NIC-level fixed effects.
We obtain the figures for the differences between the NAF of OAMEs and establishments for 2005–2006 and 2010–2011—INR 26,519 and INR 28,756, respectively—by adding the coefficient for the enterprise type dummy for the base time period (i.e. 15,181) and that for the interaction between the enterprise type dummy and the time dummy (i.e. 11,338 for 2005–2006 and 13,575 for 2010–2011).
While the amount of loans taken by the enterprise would be an important variable that we might need to control for in our regression, we are unable to do so since 93.7% of population in our data do not report any loans. This missing information could either imply that these enterprises do not take loans or have not reported the loans taken. Given this ambiguity, we do not include this variable in our regression (if we were to run the regression without these missing observations, we would only use 6.3% of the population). Additionally, we also ran a regression controlling for land distribution in the state. However, data for Jharkhand are not available for the years 2000–2001 and 2005–2006, thus we do not include this variable as a control. We ran an additional regression, including this control, for all states except Jharkhand, and find that our results hold.
These values are calculated from Specification 2.2 of Regression 2 (i.e. the specification with enterprise type and time interaction dummies and the complete set of controls) that we now run separately for high- and low-growth states. We find that, for low-growth states, the difference in the NAF between establishments and OAMEs in 2000–2001 was INR 15,380 (p < 0.001) and increased by INR 9274 between 2000–2001 and 2010–2011 (p < 0.01), with the difference in the NAF between the two types of enterprises in the low-growth states increasing by 60% over the decade. For the high-growth states, on the other hand, the difference in the NAF between establishments and OAMEs in 2000–2001 was INR 15,011 (p < 0.001) and increased by INR 16,725 between 2000–2001 and 2010–2011 (p < 0.001), with the difference in the NAF between the two types of enterprises in the high-growth states increasing by 111% over the decade. We do not report the entire table here due to scarcity of space. As an extension of this work, it would be interesting to see how the structure of dualism evolved in high- versus low-growth periods in India, as well as across high- and low-growth states over these two periods. However, such an analysis lies beyond the scope of this paper.
We find that, for the tobacco industry, unlike the IMS in general, being subcontracted is positively related to the NAF of the enterprise. The tobacco industry also stands out from other industries in terms of the large-scale outsourcing that is prevalent within the industry. Around 70–80% of informal tobacco manufacturing enterprises are subcontracted enterprises, while only 20–31% of the overall IMS enterprises were subcontracted over the decade.
A similar interpretation can be made for the opposing signs of the explained and unexplained components of the contract dummy. However, the total difference (explained and unexplained) in the NAF between establishments and OAMEs due to subcontracting is negative. As noted herein (and also argued in Basole et al. 2015; Bhattacharya et al. 2013; Raj and Sen 2016), subcontracted enterprises are less productive than non-subcontracted ones, implying that being subcontracted is, overall, an unfavourable characteristic for an enterprise. However, not all values of the decomposition results for the contract dummy are significant.
Other structural factors such as caste and religion of the enterprise owner may also affect the economic dynamics of enterprise. However, we are unable to control for these due to unavailability of data for all three rounds of our analysis.
Cohorts based on these characteristics do not violate the stratification strategy employed by the NSSO in the surveys.
Among the 35 states and union territories in India in 2011, we include all except Chandigarh, Sikkim, Arunachal Pradesh, Nagaland, Manipur, Mizoram, Meghalaya, Daman and Diu, Dadra and Nagar Haveli, Goa, Lakshadweep, Puducherry, and Andaman and Nicobar. Of the eight major NICs mentioned earlier, we exclude the tobacco industry (NIC 16) due to reasons outlined in footnote 13 above.
The loss in statistical significance for some variables may be due to loss of variation in errors on account of aggregation of a fairly large set of data.
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Acknowledgements
We are grateful to Rohin Anhal for unreservedly sharing his expertise and for his detailed comments and several discussions. We thank Anirban Dasgupta for his comments and suggestions on various drafts of this paper. We would also like to thank the two anonymous reviewers for their rich and detailed comments and suggestions.
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Kesar, S., Bhattacharya, S. Dualism and Structural Transformation: The Informal Manufacturing Sector in India. Eur J Dev Res 32, 560–586 (2020). https://doi.org/10.1057/s41287-019-00228-0
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DOI: https://doi.org/10.1057/s41287-019-00228-0