Economic complexity and regional development in India: Insights from a state-industry bipartite network

TL;DR

This study addresses sub-national economic development in India by measuring economic complexity across states using a state–industry bipartite network constructed from MCA firm-level data. It applies the Economic Complexity Index via the reflections method and the nonlinear fitness–complexity algorithm to quantify state diversification and sophistication, linking them to per-capita GSDP. Key findings reveal substantial heterogeneity in capabilities, a positive relationship between complexity and income, and a characteristic triangular structure in the state–industry matrix that signals hierarchical capability accumulation; Maharashtra and Delhi maintain high complexity while states like Karnataka exhibit upward mobility. The work demonstrates the value of firm registry data for complexity analyses in data-constrained settings and provides a quantitative basis for capability-oriented industrial policy in India.

Abstract

This study investigates the economic complexity of Indian states by constructing a state-industry bipartite network using firm-level data on registered companies and their paid-up capital. We compute the Economic Complexity Index and apply the fitness-complexity algorithm to quantify the diversity and sophistication of productive capabilities across the Indian states and two union territories. The results reveal substantial heterogeneity in regional capability structures, with states such as Maharashtra, Karnataka, and Delhi exhibiting consistently high complexity, while others remain concentrated in ubiquitous, low-value industries. The analysis also shows a strong positive relationship between complexity metrics and per-capita Gross State Domestic Product, underscoring the role of capability accumulation in shaping economic performance. Additionally, the number of active firms in India demonstrates a persistent exponential growth at an annual rate of 11.2%, reflecting ongoing formalization and industrial expansion. The ordered binary matrix displays the characteristic triangular structure observed in complexity studies, validating the applicability of complexity frameworks at the sub-national level. This work highlights the usefulness of firm-based data for assessing regional productive structures and emphasizes the importance of capability-oriented strategies for fostering balanced and sustainable development across Indian states. By demonstrating the usefulness of firm registry data in data constrained environments, this study advances the empirical application of economic complexity methods and provides a quantitative foundation for capability-oriented industrial and regional policy in India.

Figures (6)

• Figure 1: Growth dynamics and firm size distribution in India. (a) Number of active firms $N (t)$ in each year $t$. Points represent empirical observations, the red line denotes an exponential fit of the form $N(t) \sim e^{0.112t}$, indicating sustained growth at an annual rate of approximately $11.2\%$. (b) Complementary cumulative distribution function (CCDF) $P(x)$ of the paid-up capital $x$, illustrating the firm-size distribution. The red line represents a power-law fit $P(x) \sim x^{-(\alpha+1)}$ in the intermediate range, with an estimated exponent $\alpha = -1.808$.
• Figure 2: Diversification–ubiquity relationship across Indian states. States are positioned across four quadrants in $k_{s,0}-k_{s,1}$ plane based on their industrial diversity $k_{s,0}$ and the ubiquity $k_{s,1}$ of the industries in which they are active. The red line indicates a linear regression fit. The Pearson correlation coefficient is $r = 0.652$ with a corresponding $p$-value of $6 \times 10^{-4}$. The codes used for the states are provided in the SI Text 1.
• Figure 3: Relationship between economic complexity and income across Indian states. Per capita state gross domestic product vs ECI for the year 2023-24. The straight line represent an exponential fit to the graph. States lying below the fitted trend exhibit lower income levels relative to their complexity, suggesting latent growth potential. The Pearson correlation coefficients between the two variables is found $r=0.599$ with $p-value = 6\times10^{-4}$.
• Figure 4: Temporal evolution of state economic complexity rankings. Plot showing the evolution of Economic Complexity Index (ECI) rankings of Indian states over time. States are ranked from highest (top) to lowest (bottom) complexity in each year. Thin gray lines represent all states, while selected states are highlighted to illustrate distinct developmental trajectories. Persistent high rankings of Maharashtra and Delhi indicate long-standing diversified capability bases, whereas the upward mobility of Karnataka reflects successful accumulation of complex capabilities. In contrast, the declining or stagnant ranks of several states underscore uneven regional capability development. Overall, the figure reveals both stability and mobility in India’s sub-national complexity hierarchy.
• Figure 5: Ordered state-industry matrix revealing hierarchical capability structure. Binary state–industry matrix $M_{sp}$, ordered by decreasing state fitness (top to bottom) and increasing industry complexity (left to right), as obtained from the nonlinear fitness-complexity algorithm. Black cells indicate the presence of a revealed comparative advantage. The emergence of a pronounced triangular structure indicates that high-fitness states are active across both ubiquitous and complex industries, whereas low-fitness states participate only in highly ubiquitous sectors.