Neo-Dualism: Accumulation,Distress,and Proliferation of a Fissured Informality

Abstract

A dominant strand of orthodoxy argues that the problem of the informal sector could be mitigated through the capitalistic growth process. But our observations on India are different—with an expansion of the capitalistic formal sector, as the economy grows, there is a proliferation of fissured informality. Using a structuralist macro-model, we provide certain explanations for this phenomenon, which are also tested empirically using Indian subnational-state and firm-level data. Thus, we explore both the short- and long-run effects of the expansion of the formal sector on the heterogeneous informal economy. While a section of the population is pulled into the advanced informal activities, a vast segment is pushed to petty production. Accordingly, the orthodox transition narrative is questioned and alternative policy and political possibilities are introduced.

JEL Classification: O11, O13, O17, P48

Keywords

capital accumulation income deflation resource expropriation push and pull effects employment diversification migration informal sector dualism nontransition formal-informal conflicts and complementarities development macroeconomics Kalecki India

1. Introduction

A dominant discourse conjectures that, with the growth of the modern capitalistic formal sector, the informal sector would either wither away or be incorporated into the formal sector or, at least, be able to enter into a phase of tortuous transition (Hymer and Resnick 1969; Ranis and Stewart 1993; Fields 2004;¹ Bardhan 2009; La Porta and Shleifer 2014). However, if we look at figure 1, which presents the size of nonfarm informal employment of some major countries of Asia, Africa, and Latin America, it becomes clear that the intensity of informal employment is high, and in many countries, it is still increasing. Most intriguingly, gross domestic products of the majority of these countries, especially of Africa and Asia, have grown considerably during or before the periods under consideration (IMF 2019). Further, even among these, India stands out as a glaring case with very high absolute and relative size of informality, and which is growing, and simultaneously, the growth rate of gross domestic product too is substantially high. This peculiar feature of the Indian economy grabs our attention. We contemplate whether a study of this Indian scenario could generate some novel as well as generalizable insights on proliferation of informality. Thus, we contemplate to go beyond some recent standard positions which propose informality as a product of lack of sufficient growth and expansion of the capitalist circuit, sufficient education and skill, well-defined property right structure, flexibility of labor market and of governance, sufficient safeguard against global competition, and crisis pushing firms toward informalization, etc. (Portes, Castells, and Benton 1989: 27–29; De Soto 2002; Bardhan 2009; La Porta and Shleifer 2014).

Figure 1.

Proportion of informal employment in nonfarm employment (%).

This demands a closer look at the structure of Indian employment and its trends over time, especially when the economy has grown very fast. Table 1, presenting shares of sectoral workforce in the total workforce of India (considering nineteen major subnational states)² over a decade, shows that all the segments of the informal sector (henceforth, INFS:³ with segments such as small-firm/self-employment/own account enterprise⁴ and large-firm/labor-hiring/establishment⁵ across rural-urban locations) are expanding.⁶ But, the share of the agricultural workforce is reducing. Maybe, a part of the workforce moves from agriculture to nonagriculture, mainly to different segments of INFS (over and above crowding of INFS due to population growth expanding the surplus labor force).⁷

Table 1.

Shares (%) of sectoral workforce in the aggregate workforce in India over time.

Year	Informal rural self-employed firm	Informal rural establish-ment	Informalurbanself-employed firm	Informalurbanestablish-ment	Informal sector	Organized manu-facturing sector	Agricultural sector	Other sectors
1999–2000	8.40	1.34	5.31	4.03	19.08	2.03	61.01	17.88
2010–11	9.16	2.51	5.99	5.43	23.09	2.71	51.93	22.27

Source: Calculated from different reports of National Sample Survey Office (NSSO), and Annual Survey of Industries—Central Statistics Office (CSO), Government of India.

Note: Other sectors include mainly formal services and formal-informal constructions.

Some crucial observations can be derived from table 2 too, which presents partial labor productivity levels of different sectors of India (same nineteen states). The productivity level of formal manufacturing is very high compared to agriculture and INFS, and these absolute differences have been increasing over time. Not only that, the absolute differences in productivity levels across INFS segments (rural/urban, self-employment/establishment) have also been increasing.

Table 2.

Partial labor productivity (annual) in India over time (Rs. at constant 1993–94 price).

Year	Informal rural self-employed firm	Informal rural establish-ment	Informalurbanself-employed firm	Informalurbanestablish-ment	Informal sector	Organized manu-facturing sector	Agricultural sector	Other sectors
1999–2000	8248	15427	14760	25161	14137	165871	10463	57668
2010–11	10271	21784	17016	30597	18051	299593	14858	100462

Source: Calculated using NSSO, CSO, and Reserve Bank of India database.

Note: Labor productivity is calculated by dividing sectoral gross value added (GVA) with the sectoral workforce. Other sectors, as above.

A more striking observation is that the labor productivity level of rural self-employed is lower than all other sectors, even agriculture (table 2), but the share of the workforce in this rural self-employed segment is the largest, and it is increasing (table 1). However, informal establishments, especially the urban ones, are in a better position. So, it seems that, in this process of movement of the workforce away from agriculture, both pull and push factors are working. A part of the agricultural workforce (and of an increased population too) moves into high productivity formal manufacturing and mostly urban INFS establishments, maybe, due to pull-factors (like pull from the formal sector, as in Harris and Todaro 1970). On the other hand, perhaps, push-factors (like overcrowding in agriculture, fall in relative income of agriculture, absolute income deflation and forced dissociation of small farmers from means of farming, etc., as in Sanyal 2007) force the agricultural laborers and petty farmers (as well as fresh entrants into the labor force) to get engaged in the less productive rural self-employed segment (and, possibly, in the urban self-employed segment too, as considering the cost of rural-urban migration, effectively, its net per capita value-added could fall below the agricultural average).

Thus, the probable operations of the push- and pull-forces can reallocate the labor, and thereby proliferate the INFS along with its deepening (intra- and intersectoral) divides, strikingly, in a period of high growth. With this crucial backdrop, we posit our basic questions: Is it just a coincidence for India that growth and informality are moving hand in hand? Or, contrary to the orthodox optimism about growth annihilating or absorbing or transforming informality, is it actually a product of growth-pull and associated institutional features (as in Harris and Todaro 1970; Fields 1975; Thakur and Guha 2019)? Or, is it an outcome of a variety of push factors broadly classified as growth-accumulation-dispossession-informalization nexus (Sanyal 2007; Munck 2013; Standing 2011; Bhaduri 2018)? Or something more nuanced, producing a complex economic structure?

To deal with these issues, first, we introduce a theoretical macro-model along structuralist lines (Kalecki 1934, 1954; Bhaduri 1986; Chakrabarti 2011). The static framework and its short- and long-run variants present succinctly a process of accumulation engendering income-deflation, resource-expropriation as also demand-pull, proliferating a heterogeneous informality, and thereby reproducing a new form of dualism. The specific contributions of this theoretical framework are the following:

a. It discusses the structural specificities and relationships involving heterogeneous informal and agricultural sectors along with the modern capitalistic formal sector (henceforth, FS).

b. Contrary to a dominant literature (Ranis and Stewart 1993; Marjit 2003; Fields 2004; Bardhan 2009) presenting a variety of mechanisms through which the FS triggers a transition within the heterogeneous INFS and also brings it close to the FS itself, our model explicates how the FS could, in fact, create informality and thereby, produces not only a new formal-informal dualism (beyond industry-agriculture divide) but also a stark intra-informal duality. Thus, our framework could be a critique of the narrative of transition from traditional to modern.

c. Going beyond the dichotomous doctrines of growth-pulled (Harris and Todaro 1970; Thakur and Guha 2019) and growth-pushed (Sanyal 2007; Bhaduri 2018) migrations away from the traditional farm, and nonfarm, and thereby creations or expansions of rural-urban INFS, we present a more comprehensive framework combining both the dynamics—pull as also push.

d. While a large part of the orthodox, as well as heterodox literature, considers informality as an integral segment of the broad capitalistic network, of course having widely diverse loci, relationships, and impacts (Moser 1978; Portes, Castells, and Benton 1989: 26; Basole and Basu 2011; Breman 2013; Munck 2013; Thakur and Guha 2019), an important strand of heterodox literature considers it as a vast sea of surplus humanity that is redundant for the capitalist economic system (Nun 2000; Davis 2004; Sanyal 2007; Chatterjee 2008). We, on the contrary, deal with a dichotomous INFS: the two subsegments with two distinctly different loci vis-à-vis capital; while a relatively advanced but smaller segment is close to capital (and serving it), the remaining vast petty segment is separated (and redundant).

e. Both the subsegments of our INFS behave in a noncapitalistic way (be it self-employment or hiring labor), survival and decent living being the primary objective of production (and hence, deeply associated with basic resources). Thus, our formal-informal and intra-informal dualities are not just quantitative differences (in terms of size of enterprises). For some of such micro-foundations, we have consulted with a very recent empirical literature (Bhattacharya, Bhattacharya, and Sanyal 2013; Basole, Basu, and Bhattacharya 2015; Chakrabarti 2016; Bhattacharya and Kesar 2018) along with some of the analytical and theoretical writings mentioned earlier.

f. To the best of our knowledge, no other macroeconomic framework simultaneously discusses a process of creation of informality, its heterogeneity at the micro-level, its heterogeneous loci vis-à-vis capital, and its political-economy implications. Ours is an attempt in this regard.

Subsequently, we test empirically the propositions derived from this model. The fundamental empirical results are:

i. There is a clear indication of movement and diversification of workforce from the surplus labor pool and agriculture into both the advanced and petty segments of INFS, respectively.

ii. FS has a significant impact on this dual movement/diversification process, most probably, triggering both the push and pull effects.

iii. FS productivity improvement increases the productivity gap across advanced and petty segments of the INFS.

Finally, while concluding the paper, certain general political-economic implications of this study are discussed, which could be encapsulated as:

i. Modernity itself is creating its own other (outside), reversing the so-called process of transition toward comprehensive capitalism.

ii. This creation of a new form of dualism is a particularity of a general global phenomenon: accumulation by dispossession (Harvey 2003) without proletarianization; this makes political-economic “management” of the outside an imperative.

iii. Although a part of informality is utilized by (global) capital, a much larger part struggles to procreate itself in limbo. This calls for a dual political-economic response. While the former part of INFS could directly bargain with capital, the latter has to organize (cluster) itself for its fast evaporating spaces—economic-geographic-social-cultural.

2. Structure of Our Model Economy

2.1. Basic features of the sectors

We have a short-run closed economy framework. There are three broad sectors in our abstract economy:

(1) modern capitalistic FS driven by accumulation motive; (2) noncapitalistic INFS with two segments—(a) petty INFS (INFS₁) with small, self-employed firms (mostly rural), devoid of surplus over subsistence and (b) advanced INFS (INFS₂) with large, labor-hiring units (overwhelmingly urban), producing surplus for future consumption over subsistence; (3) as a proxy for the generic resources, a dichotomized agricultural sector—(a) capitalist or modern agricultural sector (MAGR) producing high-value-crops like high-quality cereals, fruits, flowers, vegetables, agro-fuel, feedstock and (b) noncapitalist or traditional agriculture (TAGR) producing mainly coarse food-crops and devoid of surplus. This MAGR-TAGR dichotomy essentially represents modern-traditional duality in (constrained) natural resource use.

Now, FS is characterized by excess-capacity and unemployment (Kalecki 1934). FS produces mainly consumption and investment goods for its workers and capitalists. It also produces inputs (like pesticides, fertilizers, tractors, etc.) and consumer goods (like fast-moving-consumer-goods, garments, consumer durables, etc.) for the agricultural sector as a whole.

While capitalistic MAGR behaves just like FS, noncapitalistic TAGR resembles INFS, as discussed below. Further, due to resource, technology, and institutional constraints, TAGR-output is rigid in the short-run (Kalecki 1954),⁸ although not in the long-run. Essentially, TAGR is a proxy for the generic resource-constraint that is binding even in the medium-run. Finally, price-determination for TAGR is a complex process that is discussed later.

In INFS, production takes place using indigenously acquired or produced nonfarm consumer goods, inputs, and tools and using surplus labor (especially in the short-run) as also TAGR output (Chakrabarti 2016). INFS output is used by INFS itself and by TAGR.

Advanced INFS₂ product price is cost-determined, however, including a flexible markup on that cost. As demand expands, price increases, raising the markup; this, in turn, induces an expansion of output and employment, given the supply-side conditions, as detailed later.

Petty INFS₁ price is fully cost-determined without any markup, and output is demand-determined, given the supply-side traits (discussed later).

2.2. Linkages among the sectors

The FS and MAGR are assumed to constitute a composite capitalistic entity. In our following analysis, FS actually stands for this composite entity. Conversely, the noncapitalistic INFS is closely associated with noncapitalistic TAGR.

INFS acquires food and other agro raw materials from TAGR and not from MAGR;⁹ conversely, TAGR purchases only INFS₁ products and not the INFS₂ output (of course, a simplifying assumption).

TAGR output is sold to both INFS₂ and INFS₁ through dual institutional arrangements: while TAGR-INFS₂ interaction is dominated by the traders of INFS₂, TAGR-INFS₁ interaction is closed through the open market. Further, a fixed amount of TAGR output is acquired by the traders of INFS₂ at a (pre)contracted fixed price, and it is held as a buffer stock; this stock is released with a fixed premium,¹⁰ as its demand increases. Thus, the actual petty-farmer of TAGR does not have direct access to the INFS₂ market.¹¹ However, TAGR-INFS₁ interactions are much closer: TAGR sells the other part of its output in the open market, which is directly purchased by the INFS₁ for consumption as well as production, at a market-determined flexible price; conversely, TAGR acquires inputs and outputs directly from the INFS₁, again at a flexible price. The intersectoral exchange rate and clearing of both the markets are elaborated later.¹²

Next, TAGR has to depend on the FS for modern agricultural inputs (machinery, seeds, fertilizer, pesticides). These inputs are sold to TAGR by the FS via powerful dealers/traders.¹³ TAGR also purchases a variety of consumer goods from the FS (garments, processed foods, cosmetics, and many other fast-moving-consumer-goods items)¹⁴.

On the other hand, we assume away direct interactions between INFS₂ and INFS₁ and between FS and INFS₁ to simplify our framework.

Next, there is a specific type of interaction between the FS and INFS₂. In reality, INFS₂ uses some FS commodities, but it is assumed away, as most of the commodities used by INFS₂ are indigenously produced (and also due to the fact that the reverse flow of commodities from FS to INFS₂ and hence, the issue of trade balance may create unnecessary complications of macroeconomic accounting). Therefore, we propose that our FS purchases finished products and various types of inputs from the INFS₂ via market exchange as well as subcontracting, primarily to reduce costs and avoid labor resistance. Thus, a constant fraction of FS income is spent on INFS₂.¹⁵

2.3. A Framework of Formal-Informal-Agriculture interactions

Based on the above-mentioned structures and linkages, we now try to build a comprehensive macroeconomic framework of Formal-Informal-Agriculture interactions, which can be expressed via a flowchart¹⁶ (diagram 1). The corresponding equation system is presented in subsections A1.1, A1.2, A1.3, and A1.4 of appendix section A1.

Diagram 1.

A Flowchart on FS-INFS-agriculture income/expenditure linkages.

2.3.1. Expansion of the FS—short-run push and pull effects

Let us first discuss the short-run impacts of an expansion of FS on the sectoral volumes of different segments of INFS. If FS-investment (in nominal terms) rises, the FS expands, raising the level of demand for INFS₂ output. Consequently, INFS₂ product-price increases. It has the following effects:

With a rise in INFS₂ product-price, there is a cost-push increase in FS product-price too. However, the FS price rise only partially offsets the initial investment thrust within FS, as its output is elastic.

On the other hand, as INFS₂ product-price rises, we have: (a) increased utilization of indigenous (surplus) resources by the INFS₂, (b) increased demand for TAGR output inducing TAGR-supply within INFS₂ through a reduction of the buffer-stock, and (c) increased utilization of (mostly urban) surplus labor. All these processes, arising out of FS-pull, drive up the levels of employment and output in the INFS₂. Furthermore, although INFS₂ product-price rises, resource and labor prices remain the same in the presence of surpluses; and TAGR supply-price is structurally set by the traders, as discussed earlier. Consequently, the INFS₂ gains, in terms of real income too.

Let us now turn to the INFS₁. FS price rise (as stated above) reduces TAGR-farmers’ real income in terms of FS output (that is used for production and consumption in TAGR).¹⁷ Hence, there is a localized distressed-diversification from the TAGR to (mostly rural) INFS₁; this localized pushed-diversification of work is undertaken without spatial migration, either replacing farming or supplementing it. This expands the volume of labor supply to the INFS₁, which may encourage its production by using other underutilized indigenous resources. Next, as the FS price rises, a higher portion of the TAGR-farmers’ nominal income is spent on the INFS₁ product due to a substitution effect. Hence, the net demand for INFS₁ output also increases (pulling labor from TAGR as also from the surplus pool). All these push and pull factors drive up the levels of employment and output in the INFS₁.

Next, we bring in the crucial aspect of TAGR supply-constraint for the INFS₁. As the FS expands, INFS₁ employment and output expand as well (as seen just above). But, we know, TAGR-supply to the INFS₁ is given (after deducting the TAGR buffer-stock for INFS₂ from the aggregate TAGR output that is fixed in the short-run). Consequently, if INFS₁ employment has to rise via work-diversification and surplus labor engagement, the per capita intake of TAGR output by INFS₁ has to fall to maintain food-market equilibrium for the INFS₁.

Stated otherwise, when there is a distress-driven expansion of the INFS₁, within the INFS₁ too, there is a deteriorating standard of living, given the TAGR supply-constraint in particular and the overall resource-constraint, in general.

Thus, in our framework, the push and pull processes triggered by the FS proliferates the INFS. Not only that, while its advanced (mostly urban) segment prospers, the petty (majorly rural) section immiserates.

However, these outcomes are conditional upon certain restrictions, which are discussed briefly in the appendix (subsection A1.6).

2.3.2. Some tentative long-run implications

Extending this short-run framework, we can analyze some long-run issues as well. A major question that could be discussed extending the present framework is the widely debated problem of accumulation by dispossession (Harvey 2003; also Sanyal 2007; Dell’Angelo et al. 2017; Bhaduri 2018) and, more importantly, its probable effects on the INFS.

A continuous process of FS expansion requires a large amount of resources (water-forest-land). Hence, resources need to be transferred/reorganized/reconstituted away from traditional uses (like basic agriculture, forestry, age-old nonfarm activity, etc.). Whatever be the process of resource-drain (force-driven expropriation or market-driven conversion), the outcome is obviously a resource-squeeze for the traditional activities. Consequently, the indigenous population, engaged in traditional farm, and nonfarm activities, is forced to migrate toward the newly spreading INFS in and around cities and also in villages (e.g., sizeable expansion of petty trade, transportation, etc.). However, the final destination of this migrant mass largely depends on certain crucial traits of the migrants and the pattern and extent of pull from the expanding FS as well (affecting the different segments of INFS differently). This migration decision of an individual can be expressed by conceptually extending the Harris-Todaro framework. The basic structure of this extended framework and its certain tentative outcomes are briefly discussed in the appendix (section A2).

However, at present, we go back to our short-run framework and the corresponding comparative static outcomes for an empirical analysis.

3. Some Empirical Verification of the Short-Run Analysis: The Case of India

We can put forward three general propositions from the theoretical analysis pertaining to the short-run:

When the formality expands, the relatively advanced as well as backward segments of INFS expand through the operations of push and pull factors.

These push and pull factors diversify employment and/or drive away labor from TAGR toward INFS alongside absorbing the indigenous surplus labor.

Even if the FS progresses, the petty segments of INFS fail to gain in terms of income, although the conditions of the relatively advanced INFS-firms improve to a certain extent.

First, we present an interesting observation in support of our second premise: an expansion of the economy based on FS growth pushes the distressed agrarian population to diversify and migrate to INFS. The following figures¹⁸ 2 and 3 (and also tables 3 and 4), on India, are self-explanatory. Figure 2 shows, with the growth of the FS, the share of workforce engaged in agriculture is reducing, and the slack is picked up by the INFS. Further, figure 3 shows, with the growth of the FS, the shares of workers in the rural-urban population are expanding for all the segments of INFS.¹⁹

Table 3.

Corresponding correlation among FS, agriculture, and INFS.

	nsdpfs_pop
agrwk_pop	−0.3444***
wkins_pop	0.5899***

Source: Authors’ derivation using NSSO, CSO, and Census data of Government of India.

Note: agwk_pop and wkins_pop denote shares of agricultural workers and INFS workers in the total population. nsdpfs_pop denotes per capita net state domestic product of FS.

***

denotes 1% level of significance.

Table 4.

Corresponding correlation between FS and different segments of INFS.

	wkinoaerl_poprl	wkinoaeur_popur	wkinestrl_poprl	wkinestur_popur
nsdpfs_pop	0.1604	0.1734	0.5775***	0.7361***

Source: Same as table 3.

Note: wkinoaerl_poprl, wkinoaeur_popur, wkinestrl_poprl, and wkinestur_popur denote shares of workers of self-employed rural INFS in rural population, self-employed urban INFS in urban population, rural informal establishment in rural population, and urban informal establishment in urban population, respectively. nsdpfs_pop denotes per capita net state domestic product of FS.

***

denotes 1% level of significance.

Figure 2.

Shares of agriculture and INFS workers (without construction) in total population over per capita net state domestic product of FS²⁰ across 20 major states²¹ of India pooled over 1999–2000 and 2010–11.

Figure 3.

Shares of different segments of INFS worker (without construction) in rural-urban populations over per capita net state domestic product of FS across 20 major states of India pooled over 1999–2000 and 2010–11.

3.1. Influence of FS expansion on sectoral size of INFS: a state-level analysis

To test proposition 1, we introduce some multiple regression²² models (in table 5) using least-square-dummy-variable (LSDV) method based on secondary data. We consider Indian subnational state level data on unorganized manufacturing sector as a proxy for INFS and formal or organized manufacturing sector data as a proxy for FS.²³ All the required data are collected from different sources of Government of India.²⁴ However, we have selected 20 major Indian states, as before, for our analysis.²⁵

Table 5.

Regressions²⁶ on sectoral size of rural and urban INFS₁, INFS_2a, and INFS_2b.

Regression no.	1	2	3	4	5	6
Regressands	Rural INFS₁ workers	Rural INFS_2a workers	Rural INFS_2b workers	Urban INFS₁workers	Urban INFS_2a workers	Urban INFS_2b workers
Regressors
Persons engaged in FS	2.9163*** (.8606)	0.4491*** (.0901)	0.6280*** (.1706)	0.7828*** (.2152)	0.2630*** (.0319)	0.4200*** (.0635)
Share of marginal landholding in total holdings	3952.02 (6531.587)	3797.318*** (613.9244)	1412.592* (721.9344)	1800.629 (1334.919)	−867.7207 (746.5225)	−3002.371** (1309.92)
Share of small landholding in total holdings	54773.02*** (14440.74)	839.541 (1144.121)	4612.865* (2437.59)	5070.628** (2260.923)	785.4429 (1073.39)	3575.603 (2306.666)
Agricultural land	−11.6867 (11.0066)	−0.8871 (1.6679)	−0.3680 (3.2715)	3.2241 (2.9768)	−6.2940*** (2.1456)	−7.3386* (4.1375)
Population	.0162*** (.0029)	.0007 (.0004)	.0011 (.0011)	.0025*** (.0009)	.0020*** (.0005)	.0010* (.0005)
Squared of persons engaged in FS	−2.32e–06*** (6.17e–07)	−2.44e–07*** (6.37e–08)	−3.42e–07** (1.31e–07)	−3.51e–07** (1.52e–07)
Year dummy 1989–90	−23988.77(186439.1)	−30179.91(20027.92)		−60081.22(54015.37)	−18626.13(21996.51)
Year dummy 1994–95	−190869.3(165329.6)	−67299.63***(21471.13)		−94222.46* (51485.77)	−22597.26(23155.51)
Year dummy 2000–2001	−268123.8(162378.7)	−65799.54***(20987.16)	22924.29(26716.68)	−40029.54(48795.6)	9539.056(27645.26)	40255.35(38183)
Year dummy 2005–6	−392451.6**(155477.6)	−56772.67**(23346.55)	14877.56(30245.9)	−80443.97* (48437.5)	−23786.52(22435.39)	34174.62(45225.59)
Constant	−1255737***(326335.8)	−90947.47***(27704.31)	−214597.5***(58592.7)	−180672.6***(59135.01)	21861.11(27242.82)	−23199.98(79692.17)
R ²	0.7265	0.7656	0.6889	0.7844	0.7648	0.6543
Observations	98	99	60	99	99	60

Note: Robust standard errors are in the parentheses.

*, **

, and *** indicate 10%, 5%, and 1% levels of significance, respectively.

In our model, as we proposed to have separate segments of INFS, we consider data on disaggregated INFS as—i) small-sized/self-employment/own-account enterprise; ii) medium-sized/nondirectory-establishment (using 1-5 workers with at least one hired worker on a fairly regular basis); and iii) large/directory-establishment²⁷ (using 6–20 workers with at least one hired worker on a fairly regular basis). We assume, rural-urban medium-sized and large unorganized enterprises²⁸ as advanced/modern INFS or INFS₂ and rural-urban small-sized/self-employed enterprises as petty/backward INFS or INFS₁ (Basole, Basu, and Bhattacharya 2015; Raj and Sen 2016). Thus, we can view small-sized enterprises as INFS₁, and the medium-sized and large enterprises as two different parts of INFS₂ viz. INFS_2a and INFS_2b, respectively. However, we need to mention that, with these regression analyses using pooled data, we actually try to understand the intersectoral relations in a better way than simple correlations; with limited secondary data on INFS, we are not claiming any causal relations between the variables.

With these data and methods, we now address the first theoretical proposition, which essentially says, if the FS expands, given the (short-run) total agricultural output, agrarian land-distribution (and hence, the related aspects like input usage, cropping pattern, etc.), and population, all the segments of INFS expand, depending on the relative strengths of pull by the FS and push from agriculture.

The dependent variable of our regression models is the level of employment in different segments of INFS, and the principal regressor is employment in the FS, which is supposed to influence the sectoral size of INFS positively. The control variables are—(a) agricultural land and (b) shares of area of marginal- and small-holdings in the total farming area and (c) population.²⁹

Agricultural land is used as a proxy for agricultural output. It is an important factor having the potential for negatively influencing the sectoral size of INFS; given the relative price of agriculture vis-à-vis FS, as agricultural output falls, farmers are pushed to diversify toward INFS. We cannot control agricultural income (NSDP-agriculture) as a proxy for output because our theoretical model proposes that an expansion of the FS leads to a reduction in farmers’ income.

The shares of marginal- and small-landholdings in total farming area are used as a proxy for agrarian land-distribution and also for the extent of traditional crop-farming, which are supposed to positively influence the sectoral size of INFS (as elaborated in Chakrabarti and Kundu 2009: 71; Chakrabarti 2016). Lastly, the population of the states is a proxy for state size and also an important positive factor determining the size of INFS workforce.

With these, we can express our regression model as:

\begin{array}{l} E_{i s t} = γ_{1 i} + γ_{2 i} E_f_{s t} + γ_{3 i} L n_a g_{st} + γ_{4 i} S h_m g_{st} + γ_{5 i} S h_s m_{st} \\ + γ_{6 i} P_{s t} + γ_{7 i} T D_{1} + γ_{8 i} T D_{2} + γ_{9 i} T D_{3} + γ_{10 i} T D_{4} + u_{i s t} \end{array}

(A)

i, s, and t stand for i-th segment, s-th state, and t-th year, respectively.

E_ist=Employment of the i-th segment (details given below) of INFS in state ‘s’ and in year ‘t’

i=1, if rural INFS₁

=2, if rural INFS_2a

=3, if rural INFS_2b

=4, if urban INFS₁

=5, if urban INFS_2a

=6, if urban INFS_2b

$E_f$ E_ f = employment in FS; Ln_ag = total agricultural area or land; Sh_mg =Share of marginal-landholding in total farming area; Sh_sm =Share of small-landholding in total farming area; P= population; TD represents four time-dummies respectively for 1989–90, 1994–95, 2000–2001 and 2005–6. γ_1i is the intercept term, and γ_2i, . . ., γ_10i are the respective coefficients of the i-th INFS segment; u_ist represents the error term.

But equation (A) does not show a linear relationship for all the INFS segments. It shows to have a relationship of quadratic nature between INFS employment and FS employment for the first four INFS segments (rural INFS₁, rural INFS_2a, rural INFS_2b, and urban INFS₁). It is confirmed by LOWESS (locally weighted scatterplot smoothing). Hence, for these four segments, in the regression equation, a squared of FS employment term is added as:

\begin{array}{l} E_{i s t} = γ_{1 i} + γ_{2 i} E_f_{s t} + γ_{3 i} L n_a g_{st} + γ_{4 i} S h_m g_{st} + γ_{5 i} S h_s m_{st} + γ_{6 i} P_{s t} \\ + γ_{7 i} T D_{1} + γ_{8 i} T D_{2} + γ_{9 i} T D_{3} + γ_{10 i} T D_{4} + γ_{11 i} E_f_{s t}^{2} + u_{i s t} \end{array}

(B)

Where E_ f²_st = the squared term of the FS employment in state ‘s’ and in time ‘t’; and ‘i’ represents the first four INFS segments only. However, equation (A) holds true for the last two INFS segments, i.e., urban INFS_2a and INFS_2b.

Table 5 represents our regression results that are mostly in line with our expectations. The regression results show that the state-level sectoral size of INFS across its various subsegments is positively explained by the expansion of FS, controlling for the extent and pattern of farming (land and land distribution) and state population. For the disadvantaged INFS₁, in general, especially rural, we have substantially higher corresponding coefficients than all the other INFS segments. Thus, these regression results (along with figures 2 and 3 of section 3 and tables 1 and 2 of section 1) support the first two propositions in the context of our short-run analysis.

3.2. Influence of FS productivity on labor productivity of INFS: A firm-level analysis

Our next concern is to see whether the progress of FS is able to improve the condition of INFS firms across all its segments or not; i.e., in this subsection, we deal with our third theoretical proposition. For this, we develop some OLS regression models (regressions 1–6 of table 6) based on the latest available firm-level data (NSSO, 2005–6) on the unorganized manufacturing sector (a proxy for INFS, as before).³⁰ However, we use state-level data for the corresponding sectoral and aggregate economic activities (same data source, as in previous analysis). Once again, we concentrate on 20 major states of India.³¹

Table 6.

Regressions on firm-level labor productivity (GVA per worker) across rural-urban INFS₁, INFS_2a, and INFS_2b.

Regression no.	1	2	3	4	5	6
Regressands	Labor productivity rural INFS₁	Labor productivity rural INFS_2a	Labor productivity rural INFS_2b	Labor productivity urban INFS₁	Labor productivity urban INFS_2a	Labor productivity urban INFS_2b
Regressors
Fixed asset per worker of INFS firms	.2098*** (.0159)	.1217*** (.0106)	.1345*** (.0145)	.1630*** (.0085)	.0892*** (.0048)	.1637*** (.0112)
Workers in INFS firms	−663.8405** (259.0514)	597.5508*** (177.5792)	34.8092(74.6584)	−18.6471 (256.8356)	433.6721*** (94.1299)	1542.784*** (157.2001)
Labor productivity of FS	−.0018 (.0021)	−.0032 (.0023)	.0182*** (.0050)	.0028 (.0019)	.0037** (.0015)	.0078 (.0116)
NSDP agri / NSDP	10017.73* (5467.857)	−2437.721 (7326.037)	−5232.446 (10564.31)	6109.409 (4421.071)	3572.624 (4524.274)	12758.52 (14981.9)
NSDP/ population	.0213 (.0269)	.0435 (.0427)	−.0596 (.0879)	−.0013 (.0194)	.06676*** (.0209)	.1532 (.1153)
Constant	3491.521** (1633.057)	10763.73*** (2120.23)	15446.44*** (4931.077)	3274.286** (1354.154)	8762.507*** (1459.572)	−790.1838 (5438.025)
R ²	0.1232	0.0935	0.1390	0.1719	0.1262	0.2075
Observations	23626	5262	3001	18695	9136	4127

Note: Cluster standard errors are in the parentheses.

*, **

, and *** indicate 10%, 5%, and 1% levels of significance, respectively.

As in these regressions, the unit of analysis is firm, and we consider some state-level variables as the key regressors, there is a high chance of having correlation in the error terms within a given state. So, we form clusters for the twenty states and run the regressions with cluster standard error to take care of this within-state correlation that can lead to downwardly biased standard errors.

This study helps to understand the variation in firms’ condition (captured through partial labor productivity or GVA per worker) across different segments of INFS with changes in FS activities (i.e., FS labor productivity) when agricultural and state economic activities are controlled.

Thus, the dependent variable³² for these regressions is labor productivity of firms of different segments of INFS and the main regressor is labor productivity of FS. The controlling variables are per capita NSDP (proxy for subnational state’s economic condition), share of agricultural NSDP in aggregate NSDP (proxy for agricultural supply), number of workers engaged in INFS firms (proxy for firm-size), and finally, capital/labor ratio of INFS firms (factor intensity). Therefore, we can introduce our regression model as:

p_{j i s} = α_{1 i} + α_{2 i} \frac{k_{j i s}}{l_{j i s}} + α_{3 i} . l_{j i s} + α_{4 i} p_f_{s} + α_{5 i} \frac{Y_a g_{s}}{Y_{s}} + α_{6 i} \frac{Y_{s}}{P_{s}} + ε_{j i s}

(C)

j, i, and s stand for j-th firm, i-th segment, and s-th state, respectively.

p_jis =labor productivity of the j-th firm of the i-th segment of INFS in state s.

i= 1, if rural INFS₁

2, if rural INFS_2a

3, if rural INFS_2b

4, if urban INFS₁

5, if urban INFS_2a

6, if urban INFS_2b

k = value of the asset of the respective INFS firm; l = number of workers of the respective INFS firm, p_f = state-level average labor productivity of FS firms; Y_ag= NSDP agriculture, Y = NSDP, P = state population; α_1i is the intercept term and, α_2i, . . ., α_6i are the respective coefficients of the variables of the i-th INFS segment; ε_jis represents the error term.

First, the general economic condition of a state should have a positive impact on the productivity of INFS. Similarly, a higher share of agricultural NSDP in NSDP implies that the supply of food and agro-raw materials to INFS is relatively abundant, which can enhance INFS labor productivity. A higher share of agricultural NSDP in NSDP also implies a relatively higher income of the farmers and hence, a demand-pull for the INFS products, which can indirectly induce INFS productivity. The number of workers (the most important factor for INFS firms, not only for production but also for organization) and capital/labor ratio are the internal factors of an INFS firm that should positively impact its productivity.

Now, regressions of table 6 show that the productivity of FS has a significant positive association only with rural INFS_2b and urban INFS_2a. However, it does not have any significant positive relation with the other INFS segments; rather, it may have some negative relation (though not significant) with rural petty INFS. Following our theoretical proposition 3, thus, we can say that the rural and urban petty INFS segments are unable to gain from an improvement in labor productivity of FS, while the INFS large-establishments may get some benefit from it. Therefore, FS activity itself may create a duality within the INFS.

Further, when for all the other INFS segments, larger firm-size shows a positive relation with labor productivity, the self-employed INFS shows a negative relation. Stated otherwise, an increase in the number of workers in self-employed enterprises implies congestion within such firms, lowering their productivity level. This happens, maybe, because, distressed farmers throng the petty INFS.

Thus, regression tables 5 and 6 (along with the observations of tables 1 and 2 and of figures 2 and 3) support the outcomes of our theoretical model. They together show an intriguing phenomenon: an expansion of the FS not only engenders a spread of the INFS but may also create a divide within it.

4. In Lieu of Conclusion: Some General Political-Economic Implications

This paper essentially questions the orthodox narrative of the comprehensive capitalistic transition of the Global South. From the perspective of informality, it tries to show how, instead of engendering the so-called inclusive growth and eventual structural transformation, capital itself, in its pursuit of profit, in/directly and involuntarily/deliberately creates its devalued other/outside; only a small part of which is utilized as well as induced by capital, while the other much larger part proliferates in limbo gasping for spaces. Thus, capital itself produces a neo-dualism—a fractured informality—beyond the historical/traditional one, through income-deflation, resource-expropriation as well as devalued-accommodation alongside exclusion. These FS-INFS and intra-INFS dualities are also aggravated by the policies of a neoliberal State, having distinct bias toward big corporations, adversely affecting the interests of TAGR farmers and of petty INFS population.

These heterogeneous phenomena are not only a particularity for a country like India but also could be observed on a global scale blurring the national boundaries, especially in the developing countries like, Indonesia (Rothenberg et al. 2016), Madagascar (Nordman, Rakotomanana, and Roubaud 2016), Ivory Coast (Günther and Launov 2012), Chile (Amuedo-Dorantes 2004), etc.

This complexity of the emerging local/global economic structure challenges the orthodox policy institutions which try to address/redress this neo-dualism mostly with the instruments of development management (essentially, disciplining the outsiders with a variety of doles). Contrarily, we suggest a dual strategy from the perspective of INFS:

While the formal-informal linkages/negotiations/bargaining could be beneficial for the advanced segments of the informal sector (taking advantage of the dependence of FS on the INFS), the petty segments have to fight with the annihilating power of capital, and in that struggle, cluster form of organization could offer a challenge/respite.

However, for both these strategies, political organization, particularly of/for the ‘informal’ should play a crucial unifying role, in conjunction with the general struggle of the working class. This political organization/movement should take up a dual role: on the one hand, pushing the State to introduce policies toward building formal-informal linkages and also formal clusters of informal firms; on the other, orchestrate union/political bargaining with the FS and politically organize the scattered micro-firms for clustering.

Footnotes

Appendix

Acknowledgements

We are indebted to Anirban Kundu, Anamika Moktan, and Manojit Bhattacharjee for helping us in collecting data. We are grateful to Aparajita Mukherjee, Santadas Ghosh, and the three referees of this journal—Ronaldo Munck, Ron Baiman and Deepankar Basu—for their critical comments and especially suggestions. We have benefited considerably through our interactions with Amit Bhaduri. Earlier versions of this article were presented at Burdwan, Jadavpur, Kalyani, and South Asian Universities and at IIM Calcutta, IHD New Delhi, ISEC Bengaluru, and IIT Kanpur; comments from the participants are gratefully acknowledged.

The first author thanks Professor B. V. Phani for his support and also gratefully acknowledges support from the Syndicate Bank Entrepreneurship Research and Training Centre at IIT Kanpur. The second author thanks ICSSR, Government of India, for financial support for a project on a similar theme. However, the usual disclaimer applies

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Saumya Chakrabarti

1

“[W]e have seen that modern sector enlargement has two beneficial effects on poverty. One is that poverty falls among those workers who are able to move from the informal sector to the formal sector. The other is that poverty falls among the workers who remain informal. . . . [W]ages do rise in. . . [Lewis]. . . model via these two mechanisms, and poverty is correspondingly reduced as Lewis-type growth takes place” (Fields 2004: 13, emphasis added).

2

The selected nineteen states are Andhra Pradesh, Assam, Bihar (including Jharkhand), Goa, Gujarat, Haryana, Himachal Pradesh, Jammu & Kashmir, Karnataka, Kerala, Madhya Pradesh (including Chhattisgarh), Maharashtra, Orissa, Punjab, Rajasthan, Tamil Nadu, Tripura, Uttar Pradesh (including Uttarakhand), and West Bengal.

3

Here, INFS includes manufacturing and services without construction (as per availability of data).

4

Own account enterprise without any hired laborer—on a fairly regular basis (NSSO, India).

5

Enterprise using at least one hired laborer—on a fairly regular basis (NSSO, India).

6

The authors are grateful to Amit Bhaduri for the idea of tables 1 and .

7

This trend of an increasing volume of INFS continues till 2015–16 for both self-employment and establishments (NSSO, 2015–16).

8

There is a substantial fall in some of the crucial food crops’ growth rates after the liberalization of the Indian economy (table A1, appendix section A3); cereal production across Indian states also falls or increases only marginally over the years (table A2, appendix section A3). The loss of basic food output due to resource diversion might have been countered with an increase through technology etc.; still, per capita food availability has fallen (“In 1991, the per capita food grain availability per annum was 186.2 kg and 177.7 kg in 2016” accessed at: https://www.google.com/amp/s/www.downtoearth.org.in/news/food/amp/india-claims-to-be-self-sufficient-in-food-production-but-facts-say-otherwise-62091). Further, aggregate food output might have increased somewhat, but the zones of resource diversion have suffered a loss of critical micro food security (see Chakrabarti and Kundu 2009 and : 12–13, for a detailed discussion of the issue; Patnaik apprehending even a macro insecurity).

9

MAGR produces high-value-crop mainly for urban high-end consumers and export. Its inputs and production-harvesting-sorting-cleaning-storage-transportation-processing-packaging-selling processes have to be highly sophisticated and the latter organized through complex supply chain management using modern technology (via a variety of firm-farm contracts led by corporate capital). Consequently, MAGR is integrated with the FS. Contrarily, TAGR is deeply associated with the INFS; the vast INFS population cannot afford to depend on MAGR for production and consumption. For details on this aspect, we could refer to World Bank (2007); Chakrabarti and Kundu (2009); .

10

Even if this premium rises with a rise in demand for TAGR output from INFS₂ itself, it should not fundamentally affect our basic structure. In an extreme situation, only the markup may fall in INFS₂, if its price rises, due to increased demand from FS, less than proportionately (compared to the rise in TAGR price).

11

“The Dalwai Committee on Doubling Farmers’ Income (Government of India) has pointed out that the share of farmers in consumer’s price is very low; it generally varies from 15 to 40 percent. Studies conducted by the International Food Policy Research Institute and World Bank have confirmed this. The dominant role of middlemen, among others, is primarily responsible for farmers not realizing a reasonable price for their produce, lowering farm income and profitability. This was recognized by the 12th Plan’s Working Group on Agriculture Marketing (India, 2011)” ( https://www.google.com/amp/s/www.thehindubusinessline.com/opinion/agriculture-market-reforms-are-a-must/article10007561.ece/amp/, downloaded July 10, 2019). Further, the distortionary market power of the rural commercial interests, controlling and depressing the farm-gate price, in particular, is elaborated by Harriss-White (2013) and . NSSO, Government of India Report, Situation Assessment Survey of Agricultural Households 2012–13, shows that a large section of the farmers, especially for paddy, sell their produce at a price lower than the government-stipulated minimum support price; and this happens, despite the absence of information asymmetry.

12

This dual market for agriculture with the dual (fix-flex) price is found in the classic paper of .

13

In the post-green-revolutionary society, it is obvious that both the traditional and modern farmers have to depend on seeds, fertilizer, pesticides, and also equipment produced by the FS. In fact, in many cases, the FS products are thrust upon the TAGR petty-farmers using different types of socio-economic networks and political and technological instruments. A dreadful situation pertaining to chemical fertilizer use could be found in (downloaded July 8, 2020).

14

Its enormous importance could be understood from (downloaded July 24, 2020).

15

The FS acquires cheap resources, wage-goods, and even labor from the INFS₂ to maintain/increase its profit (Breman 2013; also can be found in , chap. 4, in a much broader context).

16

There are different types of interactions among the sectors, but here we consider only the dominant streams.

17

Fixity of the price received by TAGR from INFS₂ intermediaries, as mentioned earlier, is essentially a simplifying assumption. What is important is that, although FS price rises and TAGR production becomes costlier, TAGR farm-gate price cannot increase commensurately due to intermediary presence, despite a demand-pull. Consequently, the terms of trade moves against the petty farmers, leading to income deflation. Such an adverse terms of trade has actually persisted in India even at a time of high growth (see table A3 of section A3). In fact, this kind of adverse terms of trade against agriculture could be beneficial, not only for the traders/intermediaries of the INFS₂ but also for the FS, if there be a tacit understanding between the two powerful lobbies. In that case, the FS too could have indirect access to crucial resources and could, in fact, encourage the commercial interests.

18

Extreme outliers are omitted.

19

The outcomes of figure 3 and represent the direct impacts (pull) of the FS only. However, push-impact may modify the outcome slightly. A combination of pull and push impact could be captured to some extent through the following regressions.

20

NSDP of FS does not include NSDP of construction. NSDP of FS is calculated by adding the GVA of organized manufacturing sector and NSDP formal services. We calculated NSDP formal services by subtracting GVA of informal services from NSDP services, data for which are collected from NSSO reports and Reserve Bank of India website.

21

Delhi with the other nineteen selected states mentioned earlier.

22

For the regressions (tables 5 and 6), necessary pre- and post-estimation tests (such as acprplot with LOWESS for linearity, variance inflation factor for multicollinearity, Histogram test for normality, and insignificance of slope dummies in case of least square dummy variable) have been done. For these, we have used and the STATA help manual.

23

Most of the Indian literature uses the terms “informal sector” and “unorganized sector” synonymously while using unorganized manufacturing sector data as a proxy for INFS. Following the Indian definitions, there is very little difference between these two terms “informal sector” and “unorganized sector.” Unorganized sector includes all unincorporated proprietary and partnership enterprises (i.e., informal sector enterprises) and enterprises run by cooperative societies, trusts, private and public limited companies (non-ASI). Besides, there are five quinquennial rounds of NSSO data (1984–85, 1989–90, 1994–95, 2000–01, 2005–06) for the unorganized manufacturing sector, while only three rounds of NSSO data for INFS, as a whole. There is also having scarcity of Indian formal service sector data.

24

The data on the formal/organized manufacturing sector are collected from Annual Survey of Industries, CSO, Govt. of India; the agricultural sector from indiastat.com and Ministry of Agriculture, Govt. of India; population data from Population Census of India; and data on other aggregate activities from Reserve Bank of India database.

25

These twenty states cover 95 percent of the unorganized manufacturing sector’s employment and GVA in 2005–06 (NSSO 2005–06).

26

If we do not include FS into our regression models, the adjusted R² decreases by 20 percent to around 50 percent (except in the case of rural INFS₁), i.e., in general, the explainability of the models reduces by a large extent. This implies FS is crucial in explaining the behavior of INFS.

27

The first two rounds of NSSO data on the unorganized manufacturing sector exclude large enterprises and published data only on small and medium-sized enterprises. The unorganized manufacturing sector data of different sources are not comparable, so the available data on large unorganized manufacturing enterprises from the other sources (like Planning Commission, Government of India) for the pre-liberalization/pre-1991 period are not considered in this analysis.

28

They together are called establishments.

29

The number of observations, mean, and standard deviation of all the variables are given in table A4 of section A3.

30

For this regression analysis, we exclude outliers from the firm-level unorganized manufacturing sector data by using the following method: outliers < (1st Quartile – 1.5*IQR) and outlier> (3rd Quartile + 1.5*IQR); where IQR implies interquartile range.

31

Here, Bihar does not include Jharkhand, Madhya Pradesh does not include Chhattisgarh, and Uttar Pradesh does not include Uttarakhand. However, we have checked that, even if we incorporate these three new small states into our analysis, the results are almost similar.

32

The details of all the variables are given in tables A5 and A6 of section A3.

33

An overwhelming majority of the Indian INFS firms (especially the manufacturing ones) are found to be agro-based. For details, see Chakrabarti (2016: 182).

34

As TAGR farmers have to purchase the INFS₁ products for production and consumption purposes.

35

As TAGR farmers have to purchase modern inputs and consumer merchandise from the FS.

Author Biographies

Kasturi Sadhu is Postdoctoral Fellow in the Department of Industrial and Management Engineering, Indian Institute of Technology, Kanpur, India. She is engaged in empirical and policy-oriented research on the Indian informal sector. She has presented research papers at different institutions and universities at the national and international levels. She has written for Oxford University Press and GlobalDev.

Saumya Chakrabarti is Professor and head of the Department of Economics and Politics at Visva-Bharati (University), Santiniketan, India. He has also taught at St. Xavier’s College Kolkata, the University of Calcutta, and at Presidency University Kolkata. He has been a visiting fellow at Brown University, USA. He has published in journals such as Cambridge Journal of Economics, Review of Radical Political Economics, and Economic and Political Weekly, and has written books published by Prentice Hall and Oxford University Press.

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