Understanding manufacturing informality employment and productivity linkages with the formal economy

1 YSI Asia Convening, August 2019, VNU University of Economics and Business, Hanoi Paper Theme: Urban and Regional Economics Understanding Manufacturing Informality: Employment and Productivity Linkages with the Formal Economy Author: Abhinav Verma, B.Sc (Hons) Economics and Finance, Ashoka University, India Abstract: This paper on spatial economic relationships explores the possibility that the formal and informal sectors of an economy are deeply interconnected through buyer-supplier networks and through agglomeration effects Looking at data on Indian manufacturing, this paper investigates employment and productivity linkages between formal and informal firms and refutes the classical theory of Economic Dualism Linkages are estimated by using National Sample Survey and Annual Survey of Industries datasets that contemporaneously survey the informal and formal economy respectively at a nationally representative level Data from 2001 and 2005 is used, and the unit of analysis is at the state-industry level An OLS regression with fixed effects shows that a 1% increase in average form formal firm size in a particular state-industry category leads to a 0.083% increase in individual informal firm size In terms of productivity, the impact of formal productivity in state-industry on informal productivity is positive but not significant Acknowledgements: I would like to thank my faculty advisor at Ashoka University, Dr Anisha Sharma for her continued guidance, feedback, and support while I was completing this paper I would also like to thank those that took the time to discuss my topic and provide insights into areas of research Among them, I would like to mention Mrs Sanchita Mitra: National Program Manager of SelfEmployed Women's Association (SEWA) Bharat and Dr Ajit Ghose: Honorary Professor at the Institute for Human Development, New Delhi Finally, I am truly grateful to my friends who took the time to proofread my paper and provide invaluable feedback I Introduction: Manufacturing is an important lever of growth and a source of employment for low-skilled workers However, like in many emerging economies, Indian manufacturing has a large, unproductive, and persistent informal sector Informal manufacturing is responsible for 80% of India’s manufacturing employment while contributing only 20% of output; these shares have remained stable over the last decades (Raj and Sen, 2016) If the majority of manufacturing jobs are coming from the informal economy, it becomes important to study its relationship with the rest of the economy, and in particular with the formal sector What is the nature of the relationship between formal and informal firms? What kinds of informal firms have stronger linkages to formal firms? Does the growth of formal firms affect employment patterns in the informal economy? Classical theories of development see informality as a transient phase – with growth, large modern manufacturing enterprises take the place of less productive, small informal enterprises The Dual Economy Model, drawing on the work of Harris-Todaro (1970) and Lewis (1954) states that the rural agricultural sector has surplus labour that is queuing to work in the urban formal sector which has a higher marginal product (Bardhan and Udry, 1999) Rural migrants that don’t get formal jobs make up the informal economy Unlike the formal sector, the primitive informal sector is incapable of saving and investment As capital flows to the formal sector, more jobs are generated and the size of the informal economy shrinks However, the transition from farm to factory hasn’t been as straightforward in developing countries Manufacturing in developing countries has a large informal component; this is true even if the country in question has a high growth rate (Moreno-Monroy et al., 2014) In South Asian alone, the informal economy accounts for 82% of total employment in the non-agricultural sector.1 This paper explores the reasons for the failure of dualism, and argues that the formal and informal economy are deeply interconnected Using NSSO data, it is seen that the informal economy is diverse; it does have subsistence based firms like dualism models, but it also has more productive capitalist type firms that are capable of saving and investing Moreover, it is possible that agglomeration effects are at play where informal firms that operate in close proximity to formal firms can imitate these more advanced enterprises and benefit from increases in formal employment and productivity Agglomeration effects were first proposed by Marshall (1919) These are positive externalities associated with clustering of firms, which makes it easier to move labour, goods, and ideas (Ellison et al., 2010) Agglomeration effects have largely been used to explain the clustering of formal firms (Combes and Gobillon, 2015) However, it can be argued that these effects might be more pronounced in the informal economy which is heavily dependent on informal buyer-supplier networks Linkages between formal and informal firms can exist in a number of different ways A formal firm can form a vertically integrated supply chain with an informal firm – this involves the In sub-Saharan Africa it is 66%, in East and South-East Asia (excluding China) it is 65%, and in Latin America it is 51% (ILO, 2016) formal firm subcontracting work to informal firms that can cheaply produce goods with household labour In other situations, informal firms might sell to middlemen who scout the informal economy and sell its products to formal firms Informal firms can also transact with formal firms in open-market style transactions through sub-sector networks where prices and quality standards are dictated by formal firms (Chen, 2005; Daniels 2004) Most of the work on formal-informal linkages has focused on the role subcontracting (Mukim, 2011) However, agglomeration effects imply that formal-informal interactions occur through other channels as well This paper estimates such linkages between formal and informal manufacturing, both in terms of employment and productivity In the datasets used, only 27.36% of the informal firms engage in subcontracting Nevertheless, positive and significant employment linkages are found, suggesting that formal-informal linkages extend beyond subcontracting linkages It is seen that a 1% increase in average form formal firm size in a particular state-industry category leads to a 0.083% increase in individual informal firm size In terms of productivity, the impact of formal productivity in state-industry on informal productivity is positive but not significant What these linkages mean for the evolution of informal manufacturing in India? A rich dataset that collects data on the different characteristics of informal firms makes it possible to study how these linkages vary with the type of informal firm It is seen that informal firms in cities have weaker employment linkages with formal firms, suggesting that informal firms that try to compete with formal firms to capture the same market eventually get crowded out Empirical studies in West Africa and India have found that formal firms tend to subcontract work to more modern capital intensive informal firms (Bohme and Thiele, 2014; MorenoMonroy et al 2014) In line with their results, it is seen that formal firms have stronger employment linkages with informal firms that have a higher capital stock This might explain the trend of increasing capital intensity of informal firms seen in India (Natrajan et al., 2010) This suggests that labour intensity in the informal economy will continue to fall as informal firms seek formal firms to partner with Though formal firms tend to partner with capital intensive informal firms, employment linkages are stronger in the case of informal firms that are not registered with local authorities as well as informal firms that have shrunk in the recent past This might imply that formal firms seek to partner only with smaller informal firms that not have sufficient bargaining power In summary, formal firms crowd out informal firms that try to compete with them and subordinate other informal firms to produce cheap inputs for them Taken together, these results indicate that formal-informal linkages can negatively impact the informal economy’s ability to generate jobs The rest of the paper is organized as follows Section II defines informality in the context of Indian manufacturing Section III explores reasons for the persistence of the informal economy and explores the possibility of formal-informal linkages Section IV describes the datasets used Section V lays out the paper’s estimation strategy to estimate formal-informal productivity and employment linkages The results are also discussed in this section Section VI concludes At the end of the paper, an appendix that contains definitions of variables used and detailed regression results II Understanding the Informal Economy What is informality? Before delving deeper into the processes that perpetuate informality, it is useful to understand what informality is The distinction between formal and informal was first made by the ILO in 1972 based on the study of urban employment patterns in Ghana (Bromley, 1978) Informal employment is characterised by low level of skill, capital, and productivity Though the terms ‘unorganised’ and ‘informal’ are often used interchangeably, the former has more to with the regulation of firms while the latter has to with regulation of labour (NSC, 2012) In manufacturing for example, an Indian firm is unorganised if it is not registered as per the Indian Factories Act of 1948 (NSC, 2012)2 The unorganized sector accounts for over 80% and 99% of Indian manufacturing employment and establishments, respectively, while contributing to only 20% of output (Ghani et al., 2015) Is informality bad? Reigning in informality can expand the nation’s tax base Naturally, informality tends to be correlated with abysmal tax collection rates 0.32% enterprises of the total businesses registered in Philippines are VAT registered, 1.23% in Mexico, 4.2% in Turkey and 1.02% in South Africa (ILO, 2016) Lower tax collection means there is lesser money to spend on public services This also results in a sort of subsidy to informal firms, allowing them to produce at a lower marginal cost than taxpaying formal firms However, it is not fair to judge the informal economy as a disease that needs to be eliminated If the informal economy did not exist, it is possible that there might be massive open unemployment Therefore improving the performance of informal firms can actually result in an improvement of the standard of living in a country Raj and Sen (2016) find a negative correlation between labour productivity of informal firms and urban poverty Heterogeneity of Informal Firms The Act requires all firms engaged in manufacturing to register if they employ 10 workers or more and use power, or if they employ 20 workers or more (NSC, 2012) Figure 1: Histogram of frequency of informal firms for different size classes, over time The average firm size deceased from 2.15 to 2.02 people from 2000-01 to 2010-11 Source: (Raj & Sen, 2016, p 95)] The informal sector is not an amorphous pool of workers As seen in figure 1, there is significant variation in informal firm size, with a very large number of small informal firms Informal firms can also be group into different categories as defined in India Unorganized manufacturing firms in India are categorized as Own Account Manufacturing Enterprises (OAMEs), Non-directory manufacturing enterprises (NDMEs), and Directory manufacturing enterprises (DMEs) – in increasing order of firm size OAMEs have no hired workers with family members engaged in the production process When it comes to the performance of informal firms, there are modern and productive firms that are out to maximize profits, but there are also traditional subsistence-based informal firms This distinction can be made clearer by summarizing informal firm characteristics depending on whether or not they are registered with any local authority In table 1, using t-tests on the available NSSO data for 2001 and 2006, it is seen that registered firms employ more workers, use more capital, have a higher output per worker, and are more likely to sell to enterprises and middlemen as opposed to selling to individuals This heterogeneity is important to acknowledge, as it is likely that different kinds of informal firms have different relationships with formal firms Table 1: Registered Informal Firms Not Registered (1) Workers Employed 1.723 Workers Hired 0.244 Output Per Worker (INR) 15,647.21 Capital (INR) 42,610 Maintains Accounts (y/n) 0.014 Registered (2) 3.784 2.242 43,639.18 3,57,640 0.182 T-test: Ha [1-2] < 0.000 0.000 0.000 0.000 0.000 Formal vs Informal Manufacturing Figure 2: Labour Productivity Levels by Enterprise Type Unorganised firms are subcategorised Own Account Manufacturing Enterprises (OAMEs), Non-directory manufacturing enterprises (NDMEs), and Directory manufacturing enterprises (DMEs) – in increasing order of firm size Source: (Raj & Sen, 2016, p 137) Though the informal manufacturing firms play an important role in employment generation, they are significantly less productive than formal manufacturing firms As is apparent in figure 2, the process of growth becomes less inclusive with the presence of informality – in Indian manufacturing, the output per worker was 8.8 times higher for the formal sector in 1984; despite the 1991 reforms, the productivity gap went up to 14.7 by 2005 Natrajan et al (2010) find that there was a steady decline in unorganised firms’ total factor productivity (TFP) over 1994-2005 while total factor productivity grew in organised manufacturing This is troubling as it implies a diverging trend in formal and informal productivity The low productivity that is characteristic of the informal economy has clear ramifications on the standard of living of workers Further, despite high growth rates since the 1990s, there has not been a commensurate reduction in poverty (Raj and Sen, 2016) Without access to formal legal and financial institutions, informal firms also find it hard to scale up Globally, an average formal manufacturing firm employs 126 people, while an average informal manufacturing firm employs only (La Porta and Shleifer, 2014) III Why does Informality Persist? La Porta and Shleifer (2014) note that “an average surveyed informal firm has been in business for nearly a decade without attempting to become formal.” (p 117) Moreover, firms not formalise unless forced to so Attempts to pay firms to formalise and even the implementation of surprise inspections by municipal inspectors have not significantly increased the likelihood of registering (La Porta and Shleifer, 2014) However, a review of the literature makes it clear that formalisation does not mean an increase in firm productivity In fact, it is the more productive firms are the ones that find it worthwhile to formalise Further, informal firms might persist because of their relationship with formal firms and the rest of the economy Better Firms Formalize There is overwhelming evidence that suggests that the better firms formalize Empirical evidence from Hsieh and Olken shows that the average return to capital is not high for small firms in poor countries (98) From this pool, older firms that have grown larger and have tried and tested business models, are more likely formalize (De Mel et al., 2008) Many have tried to solve the endogeneity problem and evaluate the impact of formalization on firm profitability There is definitely heterogeneity in the ability of entrepreneurs in the informal economy 3; the story that unfolds is that firms with more able entrepreneurs are more likely to benefit from formalizing McKenzie and Sakho use the distance to the tax offices as an instrument for formalization and find that there is a strong and significant effect of entrepreneurial ability on profits (McKenzie and Sakho, 2007) Fajnzylber et al (2010) compare firms established shortly before and after the Brazilian SIMPLES program that made formalisation easier and find ability to play an important part too In a similar vein, Raj and Sen (2016) find that on average, more educated workers have larger and more productive firms Even when it comes to the registration of informal firms with local authorities, it is seen that registered informal firms larger and more productive (see Table 1) Existence of Formal-Informal Linkages So why don’t the more productive firms simply wipe out their informal competitions? It is possible that there exists a symbiotic relationship between formal and informal firms A key idea here is of agglomeration economies – the benefits that firms gain from locating close to each other – was first proposed by Alfred Marshall in 1919 Proximity to similar firms facilitates the reduction three types of transportation costs – those associated with moving goods, people, and ideas (Ellision, Glaeser, and Kerr, 2010) The literature on agglomeration largely focuses on the clustering of formal firms However, there are reasons to believe that clustering exists and might be more pronounced in informal firms Mukim (2011) makes a case for such agglomeration effects by arguing that buyer-supplier linkages and access to networks might well be more important to informal firms that not have access to formal institutions For instance, informal firms might need formal firms to sell their products in non-local markets They might also need working capital loans from their suppliers, given that they don’t have easy access to formal credit Moreover, formal firms that have to pay taxes might seek to reduce their costs by partnering with informal firms While analysing clustering of informal firms around formal firms, it is important to note that there can be conflicting forces at play There are the Marshallian forces that predict co3 Workers that voluntarily transiting into self-employment from formal sector earn substantially more than those who transition involuntarily (Maloney, 2004) location of firms However, there can also be negative effects of clustering, where competition to capture a market can negatively impact the performance of some firms Both Mukim and Ghani find evidence of the negative impact of clustering on small informal firms that can’t compete with larger firms Mukim (2011) finds that localization or clustering of firms within the same industry makes it less likely that new small-scale informal firms will come up in a district Ghani et al (2015) note that one-person firms don’t put up with competition very well From 1989 to 2010, the share of 1-person establishments is higher in districts that are smaller, less dense, and less urban, have less literate populations, worse infrastructure, larger share of SC/ST population, and larger female workforce participation Nature of formal-informal linkages What kind of firms formal firms partner with? There are two contrary viewpoints – stagnation and modernization hypotheses (Moreno-Monroy et al 2014) The stagnation hypothesis states formal firms interact with more labour intensive traditional informal firms in order to minimize labour costs Formal firms have all the bargaining power which they effectively use to push down their input costs This results in a subordinate relationship where informal firms are unable to accumulate a surplus and their survivalist characteristics get accentuated In contrast, the modernization hypothesis claims that formal firms choose to partner with relatively modern informal firms in order to reduce their wage bill while ensuring that certain quality standards are met Thus, formal-informal interactions can set informal firms on a path to modernization There is empirical evidence to support the modernization hypothesis An empirical study conducted in six West African countries indicate that formal firms tend to partner with informal firms that have more capital stocks (Bohme and Thiele, 2014) For the first time in India, Moreno-Monroy et al (2014) use manufacturing data find evidence to support this claim.4 Raj and Sen (2016) also find evidence that formal firms are using informal firms not just as a source of cheap labour, but also as providers of specialized inputs IV Data Used This paper uses repeated cross-sectional surveys on both formal and informal manufacturing firms from years: 2000-01 and 2005-06 Formal and informal firms are surveyed contemporaneously during these years Data on informal firms comes from NSSO Unorganised Manufacturing Surveys conducted every five years These surveys are a rich source of information on informal firms as they record a variety of firm-level characteristics, making it possible to study heterogeneous effects Data on formal firms comes from the Annual Survey of Industries Collectively, these datasets provide a detailed picture of the manufacturing landscape of India To study formal-informal linkages, formal firm data is In their approach, they create a state-industry panel of aggregate data on formal sector subcontracting and informal employment A continuous index of modernity was created for each state-industry On interacting formal sector subcontracting with the modernity index, they find that informal employment growth is skewed towards more modern state-industries aggregated at the state-industry level Informal firm-level traits are then regressed on average formal traits by state-industry District level controls like the total population and education level have also been added in from the 2011 Census Please refer to the appendix for a complete codebook of variables used In order to pool data across surveys, the datasets had to be modified in a number of ways Between 2000 and 2005, the system of National Industry Classification (NIC) codes that are used to categorise industries underwent a change Further, a different system of state codes was used in the 2000 NSSO survey Both these variables were concorded across the years to ensure comparability It was also seen that new districts were carved out of old ones between 2000 and 2005 New districts were merge with their parent districts Finally, the 2005-06 datasets were adjusted for inflation using WPI multipliers The datasets were then cleaned by replacing missing values with zeros, in order to perform operations on the variables.5 Negatives and zero values of capital and gross value added were replaced with before log values were calculated The data was then winsorized by making cuts at the 1% and 99% levels Following Natrajan et al (2010) and Ghani et al (2015), data for only 15 of India’s major states was kept This accounted for around 80% of the unit-level observations in each dataset Totally, there are 226,090 unit-level informal firm observations, of which 73.46% are from 2001 and 26.54% are from 2006 Formal firm data was collapsed at the district-2 digit industry level and the state- digit industry level To study agglomeration effects, analysing the impact of formal firm characteristics at the district-industry level on informal firm employment would have been ideal However, only 47.54% of the unit-level informal firm observations were merged with formal district-industry data points On the other hand, 99.87% of unit-level informal firm observations were merged with formal state-industry data points Hence, only the impact of formal characteristics at the state-industry level was studied One of the limitations of matching informal firms with state-2 digit industry aggregates is that inter-industry linkages cannot be measured Studying linkages within broad 2-digit NIC industry categories is problematic because firms might be similar in some dimensions but might be dissimilar in others It is not necessary that all the industries under a 2-digit NIC code employ similar workers or sell to similar markets On the other hand, there might be strong inter-industry linkages For example, textiles and apparels are classified as separate 2-digit industries A potential solution is to add formal firm aggregate vales from each industry in a state to the regression equation Matching industries based on input-output tables can also fix this problem It is important to note that this has the potential to bias the results if values are systematically missing, say if the respondents were unwilling to provide an answer or were unable to recollect specific details Since most of the missing values ought to have positive values (like wages), it is possible that the results could be biased downwards 10 V Quantifying Formal-Informal Linkages Employment Linkages As discussed earlier, there are reasons to believe that there are strong formal and informal linkages Studying the impact of formal firm traits on informal firm employment and productivity can improve our understanding of how the informal economy evolves over time To study employment linkages, the average formal firm size for each state-industry was calculated (SIm_l_labour sit) Average formal firm size is used here, since total formal employment wouldn’t convey much about the performance of formal firms A large figure could result from many small formal firms or fewer larger ones For each state-industry, average formal firm labour intensity (labour/capital) was also calculated Informal firm level employment was then regressed on average formal firm size and labour intensity for a particular state-industry Informal firm controls and district level controls were sequentially added This helps answer the question: controlling for labour intensity, how does an increase in the average formal firm size in a state-industry affect informal firm level employment? To study how formal-informal employment linkages vary with informal firm type, certain informal variables of interest were interacted with SIm_l_laboursit Estimation Strategy: l_labourjidt = β0 + β1SIm_l_labourintensitysit + β2SIm_l_laboursit + ∑k>1βkPjidt + ∑v>1βvPjidt*SIm_l_laboursit + ∑g>1βgQjidt + ∑m>1βmZdt + γs + λi + δt + εjidt Subscripts: Firm j, Industry i at the digit NIC level, State s, District d, Time t Informal firm variables of interest P: These include whether a firm has a higher than average capital stock, whether it is located in an urban area, whether it produces tradable goods, whether it engages in contract work, whether it sells to and buys from enterprises and middlemen as opposed to dealing with individuals, whether it has a female proprietor, whether it is located within the household, whether it is growing, new or stagnant, and whether it registered with local authorities Formal firm variables at the state-industry level: ● SIm_l_labourintensitysit: Log of average labour intensity ● SIm_l_laboursit: Log of average firm size Informal firm controls Q: Variables included record whether a firm produces throughout the year i.e is a ‘perennial firm’, whether it faces a shortage of capital, whether it has an uninterrupted supply of electricity, whether it maintains accounts, and whether it has received any sort of assistance 10 11 District level controls Z: Log of total population, share of population that is urban, share of SC/ST population, share of population that is literate, share of population that is part of the workforce, share of females that is in the workforce Fixed effects: ● γs – State Fixed Effects ● λi – Industry Fixed Effects at the Digit NIC level ● δt – Year Specific Dummies Standard errors clustered at the state level See appendix for detailed definitions of variables Results: Please see the appendix for regressions with controls sequentially added The following is a discussion of the coefficients, keeping endogeneity issues in mind Table 2: Employment Linkages Variable State-Industry Formal Mean Log Labour (SILL) Coefficient 0.0830* High Capital * (SILL) 0.0985*** Contract * (SILL) 0.0395** Urban * (SILL) -0.0283* Tradable * (SILL) 0.00156 Sells to Enterprise/ Middleman * (SILL) 0.0699** Buys from Enterprise/ Middleman * (SILL) 0.00790 Female Proprietor * (SILL) -0.0595* Located Within Household * (SILL) -0.0896*** Growing Firm * (SILL) -0.0350*** New Firm * (SILL) -0.0655*** Stagnant Firm * (SILL) -0.0361*** Registered with Local Authorities * (SILL) -0.108*** Observations R-squared 2,25,815 0.464 It is seen that a 1% increase in average form formal firm size in a particular stateindustry category leads to a 0.083% increase in individual informal firm size A positive and significant value supports the hypothesis that agglomeration effects exist between formal and informal firms This is significant at the 10% level However, this might be because the standard errors are clustered at a high level i.e state level Others like Ghani et al (2015) cluster their standard errors at the district-industry level Since I control for population, this relationship is not driven by the size of the labour force However, the level of infrastructure 11 12 in a district has not been controlled for This is a concern as better and cheaper infrastructure can promote the development of larger formal and informal firms State fixed effects control for infrastructure at the state level, but not changes in the level of infrastructure across time As one might assume, firms that are engaged in subcontracting tend to have stronger linkages with formal firms A 1% increase in average form formal firm size in a particular state-industry leads to a 0.1225% increase in individual informal firm size Interestingly, formal firm growth has positive net impact on a large variety of informal firms, though only 27.36% of the sample is engaged in subcontracting This suggests that there are formal-informal linkages beyond just subcontracting An important informal variable of interest is High Capital which measures whether or not informal firms have a higher than average capital stock In line with the findings of Bohme and Thiele (2014) and Moreno-Monroy (2014), firms that have a larger capital stock tend to have stronger linkages to formal firms Employment linkages are more than twice as strong in the case of informal firms that have a higher than average capital stock A 1% increase in average formal firm size results in a 0.1815% increase in the size of high capital informal firms This suggests that formal firms are indeed interacting with more modern, capitalist-type informal firms This becomes clearer while analysing interactions with other variables of interest For instance, linkages tend to be stronger in the case of firms that either buy from or sell to enterprises or middlemen, as opposed to dealing directly with individuals Less productive firms that are located within the household or have a female proprietor tend to have weaker linkages What is surprising to see is that linkages are much weaker for informal firms that are registered In fact, a 1% increase in average formal firm size corresponds to a 0.025% decrease in the size of informal firms that are registered Thus registered informal firms actually shrink as formal firms grow in size Moreover, firms that are growing, new, or stagnant have weaker linkages than the base group of firms that owners reported to be contracting (shrinking) It is important to dig deeper and determine the direction of causality – are formal-informal linkages exploitative as Tokman hypothesized or badly performing informal firms seek out formal firms to partner with? The weaker linkage that registered informal firms seem to have might indicate that informal firms with less bargaining power enter subordinate relationships with formal firms Finally, it is seen that firms in urban areas tend to have weaker employment linkages with formal firms For urban firms, a 1% increase in formal firm size results in a 0.0547% increase in informal firm size in a particular state-industry This is in line with the findings Mukim (2011) and Ghani et al (2015) that localization or clustering of firms within the same industry can have negative externalities resulting from competition This could imply that as formal firms in cities grow, they absorb informal firms It could also be the case that urban formal firms simply put informal firms out of business, and these informal workers easily find other jobs in the city 12 13 Overall, the results indicate that formal firms have stronger linkages with more modern and more productive informal firms This is in line with the modernisation hypothesis However, it is troubling to note that registered informal firms shrink as formal firms grow As seen in Table 1, registered informal firms tend to employ more people and are more productive Productivity linkages An average value for formal output per worker was calculated at the state-industry level (SIm_l_outputperworker sit) Like in the case of employment linkages, informal firm productivity was regressed on this average, and district and informal firm controls were sequentially added To evaluate heterogeneous effects, informal firm variables of interest were interacted with SIm_l_outputperworker sit Estimating Equation: l_outputperworkerjidt = β0 + β1SIm_l_outputperworker sit + ∑k>1βkPjidt + ∑v>1βvPjidt*SIm_l_outputperworkersit + ∑g>1βgQjidt + ∑m>1βmZdt + γs + λi + δt + εjidt Subscripts: Firm j, Industry i at the digit NIC level, State s, District d, Time t Results: Please see the appendix for regressions with controls sequentially added Table 3: Productivity Linkages Variable State-Industry Formal Mean Log Output/Worker (SILP) High Capital * (SILP) Coefficient 0.00847 -0.00411 Contract * (SILP) 0.0135* OAME * (SILP) 0.0443** NDME * (SILP) 0.0147 Urban * (SILP) -0.00551 Tradable * (SILP) -0.0242 Sells to Enterprise/ Middleman * (SILP) -0.0110 Buys from Enterprise/ Middleman * (SILP) -0.00576 Female Proprietor * (SILP) -0.00525 Located Within Household * (SILP) 0.0116 Growing Firm * (SILP) -0.0111 New Firm * (SILP) 0.0143 Stagnant Firm * (SILP) 0.00460 Registered with Local Authorities * (SILP) -0.0252* Observations 2,25,787 13 14 R-squared 0.66 Here, it is seen that the average formal firm productivity at the state-industry level is not related to informal firm-level productivity: the coefficient on SIm_l_outputperworker is positive but not significant This could be the case because of a number of reasons First, data on output per worker is quite noisy In involves aggregating many variables to calculate gross value added – many of these values are missing or could be misreported Using labour productivity is problematic also because it ignores the role of other inputs in the production process Using total factor productivity might be a better alternative Second, it is possible that information spillovers take time The mechanism of productivity spillovers isn’t very clear Even if this involves learning from workers that move between firms, such learning is bound to take time A potential solution would be to use ASI data for the years preceding the NSSO surveys of informal firms Another possible reason for the absence of a significant coefficient is that information slipovers are pronounced in industries like finance that require more human capital (Ellison et al., 2010) Moreover, such spillovers usually occur over smaller geographic areas To check this, a similar regression was run where formal firm productivity was averaged at the district-industry level It was seen that a 1% increase in average formal output per worker corresponded to in a 0.0165% increase in informal firm-level labour productivity, but the results were not significant Nevertheless, these linkages cannot be ruled out based on the district-industry regression because over 50% of informal firms were not matched with district-industry formal firm averages VI Conclusion This paper studies the persistence of the informal economy with a special focus on the role of formal-informal linkages The prevalent theory of Economic Dualism fails to adequately explain why informality persists In contrast to the legalist school of thought that claims that high costs of formalisation result in informality, it is seen that only the more productive firms find it worthwhile to formalise Firm formalisation is not a cure-all The informal economy is here to stay and it plays a significant role employment generation; the focus should be on improving informal firm productivity rather than forcing informal firms to formalise Apart from its role in employment generation, it is seen that informal firms are part of a larger interconnected manufacturing sector Linkages between formal and informal firms can take many different forms, from subcontracting, selling to middle-men, and participating in sub-sector networks (Chen, 2005; Daniels 2004) Using ASI and NSSO data from 2001 and 2006, I study both productivity and employment spill-overs that occur due to these linkages I find that there are positive and significant employment linkages between formal and informal manufacturing in India It is seen that a 1% increase in average form formal firm size in a particular state-industry category 14 15 leads to a 0.083% increase in individual informal firm size This relationship exists even though more than 70% of the firms in the sample not engage in direct subcontracting, suggesting that formal-informal linkages extend beyond subcontracting linkages In terms of productivity, the impact of formal productivity in state-industry on informal productivity is positive but not significant Analysing the heterogeneity of formal-informal employment linkages shed light on what kinds of informal firms formal firms prefer to partner with Linkages are stronger with more productive informal firms that are located outside the household Therefore, it appears that formal firms partner with more productive, capitalist-type informal firms Firms which have a higher than average capital stock have much stronger employment linkages This is in line with empirical evidence on subcontracting which states that formal firms subcontract work to more modern informal firms that use more capital Though this supports the modernisation hypothesis, there is evidence to indicate that formal firms exploit informal firms As formal firms increase in size, informal firms that are registered seem to be shrinking, while informal firms that are not registered appear to grow in size It is thus important to determine the direction of causality when it comes to formal-informal linkages and informal firm productivity Does partnering with formal firms results in any productivity gains for informal firms, or is this purely an extractive relationship? I also find that agglomeration economies exist in cities: urban informal firms are larger and more productive However, urban informal have weaker employment linkages with formal firms, possibly due to formal informal competition Ghani et al (2015) note that the growth in informal employment since 1995 has be driven by one-person urban informal firms If this trend continues – as is likely according to the Harris-Todaro (1970) model of migration – what does this mean for the performance of informal firms? Raj and Sen (2016) find that since 2000, the average informal firm size has been shrinking It is possible that competition from formal firms will wipe out large informal firms and result in the growth of smaller informal enterprises This will lead to a further decline in average informal firm size, and negatively impact the informal economy’s potential to generate jobs It is important to remember that this paper does not comment on productivity spill overs However, even if there are significant productivity spill overs, it is unlikely that this will result in higher revenues for informal firms that sell to formal firms due to intense competition in the informal economy Though the informal economy is less productive than its formal counterpart, it plays an important role in employment generation Analysing formal-informal linkages makes it possible to comment on the evolution of the informal economy In summary, my results indicate that formal firms crowd out informal firms that try to compete with them and subordinate other informal firms to gain access to cheap informal labour Taken together, these results indicate that formal-informal linkages can negatively impact the informal economy’s ability to generate jobs Therefore, better public policy is necessary to shift resources to the informal economy in order to enable it to absorb the growing workforce There is a consensus that protecting small 15 16 informal firms is bad and that it causes informal firms to become less productive over time – the de-reservation of products for small-scale industry post 1991 was a welcome move Therefore, possible steps to improve informal productivity can include expanding access to microcredit, investments in physical infrastructure, legal reforms to allow make it easier for informal firms to transact with other entities, and spending on education and vocational training On the other hand, moves like demonetisation will only serve to damage the informal economy and worsen poverty Apart from shutting down informal firms and forcing their owners into poverty, demonetisation also led to a demand deflation as a large portion of demand is driven by the informal economy (Himanshu) The reverse charge mechanism of the Goods and Services Tax (GST) will also make it harder for informal firms to access markets through formal firms Instead of solely focusing on formalisation, governments need to abandon their hostility towards informal firms The need of the hour is to proactively reduce the formal-informal productivity gap and improve the standard of living of the millions of people that eke a living out of the informal economy Word Count: 5350 (excluding footnotes, captions, and tables) 16 17 References Amin, M (2009, 5) What are the benefits to firms from formalization? 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The Informal Sector in Post-Reform India Delhi: Oxford University Press Sarkar, S., & Mazumdar, D (2017) Employment Problem in Indian and the Missing Middle In U Kapila, Indian Econoomy Since Independence (pp 819-834) Delhi: Academic Foundation Sen, K (2014, April 24) The puzzle of declining labour intensity in organised Indian manufacturing Retrieved from International Growth Centre : https://www.theigc.org/blog/the-puzzle-of-declining-labour-intensity-in-organised-indianmanufacturing/ Tokman, V (1978) An Exploration into the Nature of Informal—Formal Sector Relationships In R Bromley, The Urban Informal Sector (pp 1065-1076) Oxford: Pergamon Press Townsend, R (2006) Credit, Intermediation and Poverty Reduction Chicago: University of Chicago Why we need to rethink the informal economy (2015, 5) Retrieved from World Economic Forum: https://www.weforum.org/agenda/2015/06/why-we-need-to-rethink-the-informaleconomy/ 19 20 Appendix A Definition of variables: Subscripts: Firm j, Industry i at the digit NIC level, State s, District d, Time t NSSO Survey of Unorganized Manufacturing: Informal firm variables of interest P: • Nature of informal enterprise o OAMEjidt: Frim with no hired workers o NDMEjidt: Firm with upto workers, at least one of whom is hired o DMEjidt: Firm with more than workers, at least one of whom is hired • registjidt: Firm is registered with local authorities • urbanjidt: Firm is urban • tradablejidt: Firm belongs to an industry classified as ‘tradable’ Tradable industries have a higher than average ratio of exports + imports to gross output • contractjidt: Firm produces for formal firms based on a contract • sellentmmjidt: Firm sells to enterprises or middlemen, as opposed to selling to individuals • buyentmmjidt: Firm buys from enterprises or middlemen, as opposed to buying from individuals • femaleownershipjidt: Firm has a female proprietor Base group includes firms with male proprietors and partnership firms • withinHHjidt: Firm produces within the household premises • Status of firm in the last years Literal question asked to respondent o growingjidt o newjidt o stagnantjidt o contractingjidt Informal firm controls Q: • perennialjidt: Firm operates throughout the year • capshorjidt: Firm is experiencing a shortage of capital • electricityjidt: Firm has an uninterrupted supply of electricity • accmaintjidt: Firm maintains accounts • assistjidt: Firm has received some sort of assistance Annual Survey of Industries: Formal firm variables at the state-industry level: • SIm_l_labourintensitysit: Log of average labour intensity • SIm_l_laboursit: Log of average employment • SIm_l_outputperworker sit: Log of average output per worker Formal firm variables at the district-industry level: • DIm_l_labourintensitysit: Log of average labour intensity 20 21 • • DIm_l_laboursit: Log of average employment DIm_l_outputperworker sit: Log of average output per worker 2011 Population Census: District level controls Z: • logtotalpopdt: Log of total population • urbanpopsharedt: Share of population that is urban • scstsharedt: Share of SC/ST population • literacyratedt: Share of population that is literate • demodivdt: Share of population that is part of the workforce • femworkersharedt: Share of females in the workforce Regression Results: Table A.I Determinants of Informal Firm Size: District controls added in sequentially Determinants of Informal Firm Size (1) (2) VARIABLES l_labour l_labour urban tradable contract sellentmm buyentmm femaleownership withinHH growing new stagnant perennial capshor electricity accmaint regist assist logtotalpop urbanpopshare scstshare literacyrate demodiv femworkershare Constant 0.0370** -0.102*** -0.00205 0.317*** 0.0875*** -0.349*** -0.185*** 0.134*** 0.0533** 0.00414 -0.178*** -0.00599 0.0298*** 0.413*** 0.434*** 0.224*** 0.656*** 0.0340** -0.102*** -0.00433 0.314*** 0.0875*** -0.347*** -0.184*** 0.137*** 0.0531*** 0.00535 -0.180*** -0.00358 0.0308*** 0.411*** 0.435*** 0.225*** 0.0102 0.0949* 0.186* -0.0435 -0.356 0.202 0.537* 21 22 Observations R-squared 226,083 0.420 226,083 0.421 Table A.II Determinants of Informal Firm Output per Worker: District controls added in sequentially Determinants of Informal Productivity (1) (2) VARIABLES l_outputperworker l_outputperworker OAME NDME urban tradable contract sellentmm buyentmm femaleownership withinHH growing new stagnant perennial capshor electricity accmaint regist assist logtotalpop urbanpopshare scstshare literacyrate demodiv femworkershare Constant Observations R-squared -0.438*** -0.114*** 0.252*** -0.0372 0.0322 0.0477 0.110*** -0.693*** -0.391*** 0.489*** -0.225*** 0.270*** 1.602*** 0.0232 0.0746*** 0.536*** 0.283*** 0.169*** 10.67*** -0.431*** -0.113*** 0.245*** -0.0447 0.0265 0.0436 0.109*** -0.691*** -0.388*** 0.491*** -0.230*** 0.270*** 1.599*** 0.0259 0.0771*** 0.531*** 0.288*** 0.171*** 0.0189 0.141 0.304** -0.606*** 0.275 -0.668 10.70*** 226,083 0.658 226,083 0.659 Table A.III Employment Linkages with Formal Firm Traits at the State-Industry Level: Informal firm controls and district controls added sequentially VARIABLES Employment Linkages With Formal Firms (1) (2) l_labour l_labour (3) l_labour 22 23 urban tradable contract sellentmm buyentmm femaleownership 0.122* -0.109 -0.175** -0.0407 0.0584 -0.0170 0.126* -0.0936 -0.173** -0.0311 0.0457 -0.0489 0.126* -0.0923 -0.173** -0.0320 0.0520 -0.0449 withinHH 0.275** 0.262** 0.263** growing 0.284*** 0.295*** 0.297*** new 0.337*** 0.346*** 0.342*** stagnant 0.170*** 0.177*** 0.179*** regist 0.877*** 0.814*** 0.811*** SIm_l_labourintensity 0.000304 0.000951 0.000935 SIm_l_labour 0.0823* 0.0812* 0.0830* 1.highcap#c.SIm_l_labour 0.104*** 0.0987*** 0.0985*** 1.contract#c.SIm_l_labour 0.0382** 0.0398** 0.0395** 1.urban#c.SIm_l_labour -0.0300* -0.0276* -0.0283* 1.tradable#c.SIm_l_labour 0.00443 0.00188 0.00156 1.sellentmm#c.SIm_l_labour 0.0752** 0.0700** 0.0699** 1.buyentmm#c.SIm_l_labour 0.00805 0.00939 0.00790 1.femaleownership#c.SIm_l_labour -0.0654** -0.0591* -0.0595* 1.withinHH#c.SIm_l_labour -0.0943*** -0.0893*** -0.0896*** 1.growing#c.SIm_l_labour -0.0318** -0.0350*** -0.0350*** 1.new#c.SIm_l_labour -0.0642*** -0.0662*** -0.0655*** 1.stagnant#c.SIm_l_labour -0.0362*** -0.0358*** -0.0361*** 1.regist#c.SIm_l_labour -0.111*** -0.109*** -0.108*** -0.187*** 0.00466 0.0314** 0.330*** 0.206*** perennial capshor electricity accmaint assist logtotalpop urbanpopshare scstshare literacyrate demodiv femworkershare Constant 0.0217 0.144 -0.188*** 0.00504 0.0316** 0.329*** 0.207*** 0.0122 0.0948** 0.0926* 0.0213 -0.339 0.232 -0.0315 Observations R-squared 225,815 0.446 225,815 0.463 225,815 0.464 Table A.IV Productivity Linkages with Formal Firms Traits at the State-Industry Level: Informal firm controls and district controls added sequentially Productivity Linkages with Formal Firms (1) (2) (3) 23 24 VARIABLES OAME NDME urban tradable contract sellentmm buyentmm femaleownership withinHH growing new stagnant regist SIm_l_outputperworker 1.highcap#c.SIm_l_outputperworker 1.contract#c.SIm_l_outputperworker 1.OAME#c.SIm_l_outputperworker 1.NDME#c.SIm_l_outputperworker 1.urban#c.SIm_l_outputperworker 1.tradable#c.SIm_l_outputperworker 1.sellentmm#c.SIm_l_outputperworker 1.buyentmm#c.SIm_l_outputperworker 1.femaleownership#c.SIm_l_outputperworker 1.withinHH#c.SIm_l_outputperworker 1.growing#c.SIm_l_outputperworker 1.new#c.SIm_l_outputperworker 1.stagnant#c.SIm_l_outputperworker 1.regist#c.SIm_l_outputperworker perennial capshor electricity accmaint assist logtotalpop urbanpopshare scstshare literacyrate demodiv femworkershare Constant Observations R-squared l_outputperworker -0.439*** -0.0918** 0.301*** -0.0471* 0.0580** 0.0734 0.142*** -0.716*** -0.412*** 0.551*** -0.224*** 0.286*** 0.376*** 0.00814 -0.00268 0.0116 0.0423* 0.0142 -0.0109 -0.00981 -0.0181 -0.00797 -0.00553 0.0106 -0.0222** 0.00483 -0.00279 -0.0347* l_outputperworker -0.449*** -0.109*** 0.255*** -0.0301 0.0300 0.0485 0.112*** -0.688*** -0.396*** 0.496*** -0.231*** 0.268*** 0.287*** 0.0114 -0.00448 0.0129* 0.0464** 0.0168 -0.00641 -0.0261 -0.0117 -0.00611 -0.00554 0.0122 -0.0141* 0.0111 0.00147 -0.0235 1.604*** 0.0214 0.0757*** 0.531*** 0.168*** 12.15*** 10.68*** l_outputperworker -0.442*** -0.107*** 0.247*** -0.0383 0.0241 0.0441 0.111*** -0.686*** -0.394*** 0.497*** -0.236*** 0.266*** 0.292*** 0.00847 -0.00411 0.0135* 0.0443** 0.0147 -0.00551 -0.0242 -0.0110 -0.00576 -0.00525 0.0116 -0.0111 0.0143 0.00460 -0.0252* 1.601*** 0.0239 0.0783*** 0.527*** 0.170*** 0.0197 0.141 0.301** -0.614*** 0.357 -0.708 10.68*** 225,787 0.610 225,787 0.659 225,787 0.660 24 ... II defines informality in the context of Indian manufacturing Section III explores reasons for the persistence of the informal economy and explores the possibility of formal- informal linkages Section... informal linkages Studying the impact of formal firm traits on informal firm employment and productivity can improve our understanding of how the informal economy evolves over time To study employment. .. informal firms The need of the hour is to proactively reduce the formal- informal productivity gap and improve the standard of living of the millions of people that eke a living out of the informal