Productivity and wage gaps between informal and formal firms in India: Trends and determinants

ABSTRACT

This study examines the productivity and wage gaps between informal and formal sector firms in India. Using a comprehensive dataset covering a 15-year period, we explore the influence of access to finance, infrastructure, and labor regulations on these gaps. Employing standard Oaxaca decomposition method, we analyze the role of these factors in explaining the informal–formal gaps in wages and productivity. Our results reveal significant and expanding gaps in both productivity and wages between informal and formal firms. Decomposition analysis suggests firm characteristics and infrastructure as primary factors widening the gaps, while access to finance and flexible labor regulations help narrow them. The findings point to the importance of targeted interventions needed to augment the growth and development of the informal manufacturing sector. Policies that enhance access to finance, improve infrastructure, and promote favorable labor markets can contribute to narrowing the gaps.

KEYWORDS:

• Productivity gap
• wage disparities
• informal sector
• access to finance
• infrastructure
• labor regulations
• manufacturing sector
• India

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ More details on the construction of covariates are presented in Table A1 in the Appendix.

² The Factories Act governs working conditions in the formal sector, setting guidelines for working hours, leave with wages, and holiday provisions. Employers must strictly adhere to these regulations or face stringent penalties (Kathuria et al., 2013).

³ In the Indian context, the terms “unorganized sector” and “informal sector” are often used interchangeably.

⁴ The choice of the study period is determined by the availability of unit-level data on these firms.

⁵ Due to incomplete coverage of all states and union territories in surveys throughout our study period, we limited the analysis to firms in 18 major Indian states, collectively accounting for the majority of manufacturing activities. These states represent approximately 96 percent of manufacturing firms, 95 percent of total manufacturing employment, and 91 percent of the total gross value added by manufacturing firms in India. Our selection of states is also justified by the unavailability of information on our labor regulation measure for minor states and union territories.

⁶ Needless to say, real values of gross value added and emoluments are used in our estimations, with appropriate deflators applied to convert the nominal values to real values.

⁷ The estimation of the specification using either of the measures results in qualitatively similar results.

⁸ Our study focuses on regulations impacting labor adjustment mechanisms in manufacturing firms, particularly the Industrial Disputes Act (IDA), 1948. Notably, Chapter “VB” of the IDA imposes restrictions on layoffs, closures, and retrenchments for nonseasonal industrial establishments with a minimum of 100 workers, requiring prior government permission and specifying notice, compensation, and penalties for noncompliance. The Constitution of India gives provisions to state authorities to regulate certain areas, allowing them to enact and amend laws independently. Prior studies by Besley and Burgess (2004) and the Organization for Economic Co-Operation and Development (2007) have attempted to codify state-level differences in labor regulations. Gupta et al. (2009) used this measure to create a composite measure of labor regulations, classifying states as flexible if changes in labor regulations favor employers and as rigid if they favor workers. We used these classifications in our analysis.

¹⁰ The convergence graph in Figure 3 excludes the outlier states and union territories (for example, Himachal Pradesh, Goa and Dadra, and Nagar Haveli). We have also generated convergence graphs, including major states (Figure A2) and all states (Figure A3), presented in the Appendix. The findings remain robust across these alternative figures. We thank the referee for suggesting this approach.

¹¹ As Figure 3 may not capture the dynamics of changes in 2006 and 2011, we plotted the convergence graph using the data for all subperiods (2001–2006, 2006–2011, and 2011–2016). The results reinforce our finding that productivity and wage inequality are primarily driven by slower convergence in the formal sector. The figures, which are not presented here due to space constraints, can be obtained from authors on request.

¹² Our estimation measures the gap as informal minus formal, resulting in a negative value. A larger negative value signifies a greater gap in labor productivity. Consequently, a negative coefficient for a covariate indicates a widening effect on the gap, whereas a positive coefficient indicates a narrowing effect.