University of Virginia
delay payments owed in the aftermath of demonetisation, and reported higher income, sales and expenses as compared to non-connected firms.
The role of political connections in running businesses is widely known. Politically connected firms operate in all countries across the world, including those with strong institutions and low levels of corruption. This nexus between business and government, however, has always been an area of active policy interest and debate.
While a large body of research1 has examined whether political connections matter (and has shown that they do), we know relatively less about how they matter for a firm, especially in times of macroeconomic distress, when economic growth is low and resources are scarce. In theory, political connections could help firms exert their influence over the bureaucratic machinery during a crisis and divert scarce resources towards them. On the other hand, the political system could instead leverage these connections to drain resources from firms, as rent-seeking incentives become more acute during an economic downturn. In addition to this, a second question that has received even lesser attention
(primarily due to data constraints)is that of the mechanisms through which political connections impact firm performance. For example, can, and do connected firms access resources and systematically alter their borrowings and liabilities portfolio during a crisis? How do they invest these resources? Does it lead to differential changes in firm performance and growth after the crisis? In a recent study (Chen et al. 2022), we explore the answer to these questions in the aftermath of demonetization in India in 2016.
Measuring political connections and firm outcomes
Rigorously examining the above questions presents two key challenges: the first is measuring political connections, and second, getting detailed data on firms to understand how they matter. A key contribution of our study is to assemble a comprehensive database of the political connections of firms in India. Our data includes comprehensive lists of politicians, bureaucrats, and details of members on a firm's Board of Directors, from various rich data sources. We use machine learning methods to extract information on work relationships between directors (of firms) and politicians or bureaucrats from references made in more than five million news articles (crawled2 daily from seven leading media outlets in India between 2011-2016).
Put together, we are able to define a firm as politically connected if one or more of its directors is, or was: (i) either a politician or bureaucrat; (ii) kin or close relatives of a politician or bureaucrat; (iii) connected through work interactions as reported in the media. Similar in spirit to Faccio (2006), our method improves on the precision of measuring political connections, as compared to other commonly used measures in the economics literature (such as proximity by social, gender or regional identities), by combining machine learning techniques to construct these networks from disparate data sources which otherwise are difficult to measure.
Data on firm outcomes is obtained from the Prowess Data of the Centre for Monitoring of the Indian Economy (CMIE). Prowess is a database of over 40,000 firms that includes all firms traded on the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE), and thousands of unlisted Public and Private Limited Companies. We use a panel of firms from 2012-2019. There are two key advantages of the Prowess over other firm datasets (like the Annual Survey of Industries, for example). First, the Prowess gathers details on the Board of Directors in a firm, which allows us to measure their political connections as described above. Second, it provides very detailed data on not only aggregate information (like output, sales, expenses, etc.), but also granular information on long- and short-term assets, liabilities, and borrowings portfolios of firms.
How do political connections matter?
Our empirical strategy relies on comparing various firm outcomes (such as sales and expenses) between politically connected and non-connected firms, before and after demonetisation using a Synthetic Difference-in-Differences strategy (SDID)3. We first examine if connected firms were systematically able to alter their liabilities, borrowings, and asset portfolios. We find that connected firms (as compared to their non-connected counterparts) were able to:
i) Increase their short-term liabilities (that is, liabilities
expected to be paid off within a year) after demonetisation. In
particular, they were able to delay payments to their creditors and
suppliers, as well as potentially delay short-term interest and debt
payments.
ii) Cut down on long-term borrowings (those not expected to
be repaid within a year), and instead shift the composition of their
borrowing portfolio towards more immediate short-term borrowings.
Moreover, there is suggestive evidence that these firms were more likely
to access unsecured bank borrowings(that is, loans given without any
collateral or security).
iii) Increase the total assets held
after demonetisation. This increase is reported both for short and
long-term investments of these firms, as well as investments in
acquiring intangible assets (such as computer software, patents,
marketing rights, etc.).
Do firms with political connections do better?
How do these portfolio changes impact aggregate firm outcomes? We find that politically connected firms reported 8-11% higher income, sales, and expenses as compared to non-connected ones after demonetisation. Moreover, as shown in Figure 1, this effect was persistent for up to three years following the crisis. Lastly, we find a 3-5% increase in firm productivity (as measured by total factor productivity ratio (TFPR)), which we attribute largely to changes in price markups charged by a firm as opposed to gains in a firm’s production efficiency.
Figure 1. Differential impact of demonetisation on income (left panel), sales (centre panel) and expenses (right panel) of politically connected firms as compared to non-connected firms
Notes: i) The graphs plot the regression coefficients estimating the relative difference between connected and non-connected firms for sales, income, and expenses. ii) The red horizontal line at 2015 – the year before demonetisation – indicates the base year.
Discussion of findings
While there is a large body of research documenting the importance of political connections, our analysis sheds light on the channels through which they play a central role in altering firm decisions during an economic downturn. In the context of India's demonetisation episode, we find that politically connected firms were able to access short-term credit, especially from banks that were already reeling under a substantial depletion of cash and credit. Moreover, they were able to delay payments owed to suppliers, vendors, and creditors, along with delaying short-term interest and debt payments, which resonates with the large-scale supply chain disruptions and delays that were noted by various commentators, experts, and policymakers. Overall, these firms reported higher incomes, expenses and sales that were persistent over a longer period of time.
Conclusion
Understanding the role of political connections during a crisis is especially relevant today, given that the world has experienced two of the worst economic downturns since the Great Depression in a span of a decade – the Global Financial Crisis in 2008, and more recently, the Covid-19 pandemic. Our analysis suggests that firms can leverage these connections to access scarce resources during a crisis. However, exactly how firms use them in their interactions with different stakeholders through requests, reputation, threats or future reciprocation is beyond the scope of this study, but a very promising avenue for future research.
Notes:
- For example, see Fisman (2001), Sapienza (2004), Khwaja and Mian (2005), Heitzet al., (2021), Faccioet al. (2006), and Acemoglu et al. (2016).
- A Web crawler (or a crawler) is an Internet bot that systematically browses the World Wide Web, and is a technique used to extract a large amount of data. The automation of this process is helpful for extracting data from a website and developing data for machine learning applications.
- Synthetic Difference-in-Differences (SDID) methodology (Arkhangelsky et al. 2021) combines insights from difference-in-differences and synthetic control methods. It therefore allows for selection through additive unit and time-specific differences, but re-weights units to match the pre-trends between treated and control units, thus generating comparable counterfactuals.
Further Reading
- Acemoglu, Daron, Simon Johnson, Amir Kermani, James Kwak and Todd Mitton (2016), “The Value of Connections in Turbulent Times: Evidence from the United States”, Journal of Financial Economics,121(2): 368-391.
- Arkhangelsky, Dmitry, Susan Athey, David A Hirshberg, Guido W Imbens and Stefan Wager (2021), “Synthetic Difference-in-Differences,” American Economic Review, 111(12): 4088–4118.
- Chen, Y, G Chiplunkar, S Sekhri, A Sen and A Seth (2022), ‘Political Connections of Firms During A Crisis’, Darden Business School Working Paper No. 4058355.
- Faccio, Mara (2006), “Politically Connected Firms”, American Economic Review, 96(1): 369-386.
- Faccio, Mara, Ronald W Masulisand John J McConnell (2006), “Political Connections and CorporateBailouts”, The Journal of Finance, 61(6): 2597-2635.
- Fisman, Raymond (2001), “Estimating the Value of Political Connections”, American Economic Review, 91(4): 1095-1102.
- Heitz, Amanda, Yuan Wang and Zigan Wang (2021), “Corporate Political Connections and Favorable Environmental Regulatory Enforcement”, Management Science.
- Khwaja, Asim Ijaz and Atif Mian (2005), “Do Lenders Favor Politically Connected Firms? Rent Provision in an Emerging Financial Market”, The Quarterly Journal of Economics,120(4): 1371-1411.
- Sapienza, Paola (2004), “The Effects of Government Ownership on Bank Lending”, Journal of Financial Economics, 72(2): 357-384.
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