ST A TE - OWNED B A NKS VERSUS P RIV A TE B A NKS IN SOUT H A SI A
fintech/bigtech platforms, 20 and bringing financial education to rural areas. The shortfalls involve costly recapitalizations because of weak governance, operational inefficiencies, credit misallocation, and lack of risk management that can trigger large contingent liabilities. The macrofinancial benefits can involve some stabilization of the credit cycle through countercyclical lending. But this benefit comes at a large cost of greater capital misallocation to less productive firms and large fiscal costs of recapitalization that is needed to backstop lending in downturns and that can crowd out other important public expenditures, such as on health care and education. The proposed reforms are urgently needed for SOCBs in South Asia and beyond to clearly generate net socioeconomic benefits.
Annex 2A. Methodology for Determining Bank Distress Identifying Distress Using Financial Soundness Indicators We define a distress event as the breach of a quantitative threshold. In principle, the threshold could be determined by an economic relationship (identity), predicted/expected value, or even a practical rule of thumb. The threshold value I , together with an actual value of an indicator I, then help determine distance to distress and generate a dummy variable, Di,n,t, identifying observed distress. We identify distress at Indian public sector banks (PSBs) using selected indicators of financial soundness. The main indicator of distress is if the interest coverage ratio (ICR) drops below 1. As robustness checks, we use the return on assets (ROA) dropping below zero; the bank capital adequacy ratio (CAR) against a threshold related to the minimum prudential requirement banks want to keep; and non-zero emergency liquidity assistance (ELA) provided by the central bank to a commercial bank. For ELA, we are missing data because it is difficult to distinguish between regular and emergency
liquidity transactions as reported in the banks’ public financial accounts. The average annual probability of distress (PD) can be estimated as the average probability of distress using historical data on identified distress events:
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1 ∑ N ∑T Di,n,t = 1 | Ii,n,t ≥ I i ;0 , T * N n =1 t =1 (2A.1) PD = i
where Di,n,t is the distress 0/1 dummy variable and i = [private sector banks; public sector banks].
Examining the Distress Factors To assess whether certain bank characteristics could drive bank vulnerability to distress, we run a logit regression for Dn,t on bank characteristics (size; age; bank type, whether public sector or private); funding model of the bank (the loan-to-deposit ratio, net foreign exchange exposure); and macrofinancial shocks (commodity price shocks, portfolio capital flows). All are included in the vector of control variables, Xi,n,t , together with year fixed effects:
p(Di,n,t ) = α Xi,n,t + ε i,n,t . (2A.2) 1 − p(Di,n,t )
We avoid including the indicators, In,t or their transformations that are used to identify distress: that is, Di,n,t. Including those would result in estimating a tautological relationship. By adding year fixed effects, we capture common time factors and any other relevant macroeconomic shocks. This approach also reduces the need to cluster errors. By running the logit regression, we are most interested in uncovering whether stateowned commercial banks (SOCBs) are on average more prone to distress than privately owned banks—conditional on other factors, such as size, funding models, and governance indicators. Here, domestically owned private banks (PVTBs) serve as the control group.
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