Reasons for Debt Specialization: Understanding the Perspectives of Small and Large Organizations

Abstract

Debt specialization (DS) has become widespread among organizations in recent years. However, the reasons for its existence and prevalence have yet to be fully examined, especially among small and large firms. This paper aims to empirically determine whether both small and large companies pursue DS strategies for similar reasons. We use seven years’ panel data for 2009–15 for 419 nonfinancial companies in Pakistan, listed on the Pakistan Stock Exchange. The results of the comparative analysis confirm the existence of DS across organizations. Small firms follow DS to reduce expected bankruptcy costs, economize information asymmetries and decrease agency conflicts due to limited ingress to the debt market. Large companies include fewer types of debt to reduce operational risk and flotation costs and for building a good reputation. We suggest several theoretical justifications for these results, based on tradeoff and agency cost theory.

The relationship between the level of debt specialization and financial constraint of Brazilian firms over the time

“…Thus, for financial constrained firms, it was found that the younger the firms (shorter time of listing), the higher the HHI tends to be, causing them to become more specialist in the composition of their debt structure. This result corroborates with Khan et al (2017Khan et al ( , 2021 and Póvoa and Nakamura (2014) given that age is considered a significant reputation signal on the market and it can reduce some agency problems, information asymmetries, and financial distress costs. Another significant difference lies on profitability variable.…”

Core Predictors of Debt Specialization: A New Insight to Optimal Capital Structure

“…The model selection criteria of core predictor identification are based on the Chang [59] and Frank [2] process: (1) regress HHI on all variables of the category and record their coefficient and t statistics; (2) eliminate the variable having minimum t statistics among the group variables and regress HHI on the remaining; (3) repeat the process on each of the variables until only one left; (4) record cumulative R 2 and BIC value on each step and select the model with lowest BIC as an optimal model; (5) report own R 2 which is the R 2 of the worst-performing variable, calculated through a simple univariate regression analysis; and (6) this process is continued and first applied to the whole sample. Then, randomly divided the overall sample data into ten equal groups and repeated the selection process on each of the subsamples; (7) finally, applied the selection process on annual subsamples independently. We also identified the optimal model based on minimum BIC specification in steps ( 6) and ( 7) from each sample group.…”

“…Previously, very few studies are conducted, mainly in the United States [7][8][9] and in a few emerging economies [7][8][9], to confirm its existence. However, the cross-country studies of capital structure divulge that previously identified capital structure determinants, established in the United States, are unable to explain capital structure decisions outside the United States, both in developed [10] and developing countries [11].…”

Debt Market Trends and Predictors of Specialization: An Analysis of Pakistani Corporate Sector

“…Size and age are considered essential factors in determining the debt composition (Povoa and Nakamura 2014). Larger and mature companies generally employed diversified types of loans, whereas small and new firms rely on few debt choices (Khan et al 2017b). The capital structure theories also provided support for these factors.…”

“…Johnson (1997) admitted 73% of the firms lend from long-term debts. Barclay and Smith (1995) stated 26% of organizations adopt a single priority structure; similarly, Khan et al (2017b) claimed 24% (93%) small, and 23% (98%) of the large firms obtain more than 60% (30%) loan from one type of debt.…”