Birth versus worth: Impact of the caste system on entrepreneurship in India

 

Sampreet Goraya

Stockholm School of Economics

sampreet.goraya@hhs.se

The prevalence of the caste system has restricted social mobility in India. This article further looks at how caste disparities have contributed to the misallocation of resources across firms. It quantifies the differences in productivity and financial conditions for low- and high-caste entrepreneurs, and finds that this has macroeconomic implications on wealth and income inequality and aggregate total factor productivity. 

Historically, the caste system sorted people into different occupations and restrained social mobility, suppressing the entrepreneurial prowess of a vast section of society. While mobility restrictions for caste groups have weakened over time and are presently less restrictive, the caste system remains a salient feature of India. 

There is a large body of literature which argues that the misallocation of resources is substantial in developing countries, and that this can explain a large fraction of cross-country differences in aggregate productivity (Banerjee and Duflo 2005, de Mel et al. 2008, Hsieh and Klenow 2009). Several market-oriented distortions, such as financial frictions, labour market regulation and size-dependent policies, among others, have been proposed as being responsible for resource misallocation. However, we lack systematic evidence about the quantitative importance of informal institutions in generating aggregate misallocation. 

My research explores the hypothesis that “birth, and not worth” – that is, caste instead of productivity of individuals – determines the way in which resources are allocated in the Indian economy and quantifies its implications on aggregate productivity and output. 

The study

My recent study (Goraya 2022) uses firm-level data to provide evidence that is consistent with the presence of high levels of caste-driven resource misallocation. The empirical analysis exploits data from the Micro, Small and Medium Enterprises (MSME) survey (2006-07)¹, which provides a representative sample of MSMEs together with an exhaustive list of balance sheet variables, and information on the caste of the enterprise owner and employees – a feature missing in other commonly used firm-level datasets in India. Three main insights come out of this data, and are outlined below. 

Relative productivity and size of low-caste entrepreneurs

On average within a narrowly defined sector², low-caste (LC) and middle-caste (MC) entrepreneurs have 25-30% and 13-22% higher capital productivity or average revenue product of capital (ARPK)³ respectively, relative to high-caste (HC) entrepreneurs with similar characteristics. Furthermore, non-HC entrepreneurs are also characterised by lower credit-to-capital and credit-to-output⁴ ratios relative to HC entrepreneurs. 

Second, most of the cross-caste dispersion in ARPK is driven by small entrepreneurs. Moving from the smallest to the largest entrepreneur in the economy, ARPK for LC firms declines from being 52% higher than that of HC firms for the smallest entrepreneurs to approximately 12% higher for the largest entrepreneur. 

Third, cross-caste ARPK differences negatively correlate with regional financial development (credit-to-output ratio). The observed differences in ARPK across castes fall as the credit-output ratio increases. It is observed that LC firms have an ARPK that is double the value of HC firms in the states with the lowest financial development (such as Bihar, Jharkhand, and Uttar Pradesh) whereas no such differences are observed in states with well-functioning financial markets. 

Financing conditions for low- and high-caste entrepreneurs

I use a model of entrepreneurship where entrepreneurs face caste-specific borrowing constraints (based on Buera et al. 2015). The model predicts that non-HC entrepreneurs face stricter financial frictions as their credit-output ratios are much lower relative to HC entrepreneurs.  This pushes non-HC entrepreneurs to substitute capital with labour, that in turn decreases their capital-labour ratio and increases ARPK. 

Further, financing constraints hinder firm creation among low castes and create cross-caste income and wealth disparities. The model attributes a significant proportion of cross-caste income and wealth inequality to asymmetric access to finance; however, the model underestimates the level of cross-caste inequalities relative to the data. This may hint towards other forces (including historical disadvantages and labour market discrimination, among others) that are absent in the model and require further investigation. 

The model helps us to understand regional entrepreneurial differences across castes. Regional financial development is interpreted as a shock to credit supply that affects all castes proportionately in the model. It predicts a declining cross-caste ARPK difference – and increasing capital-labour ratio⁵ – with regional financial development, as it particularly benefits relatively more constrained entrepreneurs, who are concentrated among non-HC castes. 

The macroeconomic costs of caste-specific distortions

The model is also used to estimate the aggregate cost of caste-specific borrowing constraints. It finds that when non-HC entrepreneurs have a borrowing capacity that is similar to their HC counterparts, the aggregate total factor productivity (TFP)⁶ increases by 3.6%, and output per worker by 6.6% in the entrepreneurial sector. Further, the model is used to decompose TFP gains at the extensive and intensive margins⁷. First, the reallocation of capital from unproductive HC entrepreneurs to more productive non-HC entrepreneurs increases the allocative efficiency of the economy; therefore, as a result, cross-caste ARPK differences fall to zero, and the overall dispersion in ARPK declines substantially. These changes result in a 2.26% rise in TFP. 

Second, the reduction in borrowing constraints should induce the entry of more non- HC entrepreneurs as entrepreneurship is relatively more profitable when borrowing constraints are less binding. Moreover, the influx of entrepreneurs increases the demand for capital and labour. This implies an increase in the cost of productive inputs, which further leads to the exit of unproductive entrepreneurs (more likely to be wealthy and HC entrepreneurs). This improvement further increases TFP by 1.34%. 

Conclusion and scope for further research

Misallocation of resources is rampant across developing economies. However, the sources of misallocation are still under investigation, and several firm-level distortions have been proposed. My work suggests that the caste system in India is one example of such distortions, and quantifies its importance in explaining aggregate TFP losses. 

Improved access to finance for LC entrepreneurs would trigger more entrepreneurship, expansion of credit-constraint firms, and spur savings and wealth creation. This in turn would increase labour demand and create more jobs. This is of particular importance in India, where a large fraction of households partake in low-income self-employment. 

Given the findings of this study, a logical next step would be to pin down the exact reasons for low credit allocation to non-HC entrepreneurs and support high-growth low-caste entrepreneurs. Apart from benefiting LC households, this will create welfare gains for most of the population through high output per worker and thus high wages, regardless of their caste.

Notes: 

1. The survey was conducted by the ministry of MSME.
2. Sectors are defined according to National Industrial Classification (NIC) codes. NIC codes are a statistical standard for developing and maintaining a comparable data base for various economic activities, developed with an intent to ascertain and analyse as to how each economic activity is contributing towards national income.
3. ARPK is a ratio of gross value added and the stock of fixed assets. It captures the output produced per unit of capital.
4. Credit-to-capital is the ratio of amount of outstanding loans and fixed assets; credit-to-output is the ratio of amount of outstanding loans and gross value added. Both of these ratios measure the indebtedness of firms/entrepreneurs.
5. Improved access to finance reduces the cost of capital. This incentivises firms to invest more in capital relative to labour, therefore increasing capital-labour ratio and causing ARPK to decline.
6. Total factor productivity captures the efficiency with which inputs are converted into output..
7. Extensive margin refers to the reallocation of households across occupations; intensive margin refers to the reallocation of capital across entrepreneurs.

Further Reading

• Banerjee, A and E Duflo (2005), ‘Growth theory through the lens of development economics’ in P Aghion and S Durlauf(eds.), Handbook of Economic Growth, Volume 1.
• Buera, Francisco J, Joseph P Kaboski and Yongseok Shin (2015), “Entrepreneurship and Financial Frictions: A Macro development Perspective”, Annual Review of Economics, 7(1): 409-36.
• Hsieh, Chang-Tai and Peter J Klenow (2009), “Misallocation and manufacturing TFP in China and India”, The Quarterly Journal of Economics, 124(4): 1403-1448.
• Goraya, SS (2022), ‘How Does Caste Affect Entrepreneurship? Birth Vs Worth’, Working Paper.
• de Mel, Suresh, Christopher Woodruff and David McKenzie (2008), “Returns to Capital in Microenterprises: Evidence from a Field Experiment”, The Quarterly Journal of Economics, 123(4): 1329-1372. 

 

 

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