But a minority do employ hired workers and represent the profit-oriented segment of the disorganized industry. Given the importance of the unorganized manufacturing sector in terms of employment and the number of firms, more studies are needed to examine productivity differences between organized and unorganized sectors, ideally at the unit level. 6This can be done by multiplying the monthly figures by 12 or by the number of months of the year the unit was in operation.
A worker is one who participates full-time or part-time in the enterprise's activity.
Basic descriptive statistics
As a result, the total employment in the unorganized small unit segment is also much larger at 7.4 million compared to that in the organized segment (1.8 million). The median organized sector small unit is also more than twice as productive as measured by value added per worker.
Measuring productivity differences
First of all, note that although units in the 5 to 49 worker range make up only 5% of the NSSO universe, in absolute terms they far outnumber the ASI units in the same segment (about 900,000 as opposed to 100,000). Of course, the fact that the difference in estimated units is much larger than estimated workers indicates that the median unorganized sector unit is much smaller at 6 workers compared to the median organized sector small unit (15 workers). PSM is usually performed to estimate causal average treatment effects (ATEs or ATTs), but our results cannot be given a causal interpretation since, as discussed in section two, units are not randomly assigned to the organized or unorganized sector.
In our example, Group A is the production units of the organized sector, while Group B is the unorganized sector units. In other words, this component measures the expected change in the output of a unit of the unorganized sector if these units had the same resources and characteristics (capital, labor, location, etc.) as organized units. That is, the second component measures the expected change in the average output of the unorganized sector if these units were to organize.
A propensity score (ranging from 0 to 1) is estimated for each observation in the "control" group using a logit model. In our case, this score captures the degree to which a unit in the unorganized sector resembles a unit in the organized ("treated") sector with respect to the same characteristics used for the decomposition exercise described in the previous section. The "average treatment effect on the treated" (ATT) calculated here is the amount by which organized units differ in productivity per unit or per worker from their corresponding unorganized sector counterparts.
Raw differences in productivity across sectors
The third term (interaction) accounts for the fact that differences in endowments and coefficients exist simultaneously between the two groups. The estimate for the effect is given as the mean of the differences between the values of the matched observations. Indeed, looking at the two distributions in Figure 4, it is clear that small units in the organized and unorganized sectors look essentially similar to each other at least in terms of value added per worker.
As expected, the average small unit in the organized sector is larger (18 employees versus 8 employees), better endowed with capital (almost ten times larger) and just over three times more productive than its unorganized counterpart in terms of value added per employee. The fact that the jump in labor productivity is much larger than the jump in wages indicates that profitability is higher in the organized sector. Looking beyond the dichotomy between organized and disorganized, Figure 5 shows that there is significant performance heterogeneity even within the unorganized sector.
In fact, one sees a clear ordering as the divisions move from left to right with increasing degrees of "organization." Table 4 presents the same set of descriptive statistics as in the previous table, this time by degree of organization. First, there is considerable variation within the unorganized sector as seen in the increase in unit size, capital stock and labor productivity across grades 1, 2 and 3. As before, the increase in the wage rate with degree of organization is proportionally smaller than the increase in labor productivity, indicating that profitability grows with organization.
OLS estimates of the organised premium
Intriguingly, the proportion of units located in urban areas increases steadily with organized scale from less than 60% for scale 1 to 84% for scale 3, but then falls back to 62% for rural sector units. organized. A possible explanation for this is that keeping accounts or registration may require entrepreneurs of unorganized units to have a basic level of education that is more prevalent in urban areas, while the location of ASI units follows a completely different logic. The above analysis allows us to estimate the difference in unit value added in the two sectors by controlling for some obvious factors.
Second, it forces the coefficients such as capital and labor elasticities to be the same for both types of units. We address the issue of diversity within the unorganized sector by introducing the variable "degree of organization". See Section 3 for the construction of this variable. Note that each increase in the degree of organization is accompanied by a statistically significant and economically significant jump in value added per unit, controlling for the usual factors.
So, unorganized sector production units that either maintained some accounts or were registered under an Act (but not both) were 6%. Further, the unorganized manufacturing units that did both (maintained accounts and registered) were 12% more productive than those that did neither. This exercise points us to the fact that the average organized premium hides considerable variation within the unorganized sector.
Decomposing the organised-unorganised gap
Of the productivity gap of 1.7 log units, most (63%) is still accounted for by observable characteristics (endowments). That is, the donation component measures the expected change in the output of the unorganized sector unit, if these units had the same characteristics as the organized units. The return component measures the expected change in the average output of the unorganized sector, if these units had organized sectoral coefficients.
We see that, as expected, labor and capital differences account for the majority of the endowment component (76%). This is reminiscent of the literature on farm size and productivity, which generally finds that land productivity is higher on smaller farms, while labor productivity is higher on larger farms (Griffin et al., 2002). Including industry controls, for example, that unorganized sector units are distributed differently across different industries compared to organized sector units.
Finally, note that the returns component may include a number of important but unobserved unit-level characteristics such as managerial skill and labor quality or entrepreneurship as well. The fact that differences in endowments explain 63% of the gap leaves considerable room for the above factors to play a role. But it also means that most of the productivity gap is explained by the fact that unorganized units are less equipped than organized units.
Productivity differentials between matched units
We now discuss the broader implications of the results presented here and also address some limitations of the study. Several previous studies have addressed the issue of productivity (both partial and total) and performance gaps between the formal and informal or, in the Indian context, organized and unorganized sectors. One of our contributions lies in building a combined unit-level dataset covering the entire Indian manufacturing sector at one point in time (2015).
Rather, our purpose is to quantify the extent to which units in the relatively more dynamic part of the unorganized sector come close to productivity performance observed in organized units when we match them on observed characteristics. This exercise is important because it enhances our understanding of the dynamic of the unorganized sector and allows us to assess its potential to drive productivity growth as well as job creation. The results presented here show that differences in observed characteristics or endowments such as factor inputs as well as the state in which the unit is located or operating industry account for the majority of the productivity gap between organized and unorganized units.
But it is also true that factors such as local geography (type of town or city, neighborhood), managerial or entrepreneurial skills, labor quality, and discrimination in factor or product markets account for a significant portion of the productivity gap, unobserved or uncontrolled. . Combining the size of the sector with our findings on its performance suggests one possible policy path for large-scale job creation in India's manufacturing sector. Finally, a distinction must be made between policies aimed at promoting the growth of the modern segment of the unorganized sector and policies aimed at formalizing informal enterprises.
Further, as we saw with the "degree of organization" analysis, there is considerable heterogeneity within the unorganized sector with units registered with any government body tending to perform better. The manufacturing sector has played a key role in almost every successful structural transformation in the last two hundred years. In this paper we have drawn attention to the relatively larger units of the unorganized sector in the Indian manufacturing sector as potential vehicles of transformation.
We have shown that, in terms of partial productivity measures, these unorganized units closely matched observable characteristics with their organized counterparts, bridging a large part of the productivity gap.
