Estimating Contingent Liability Shocks, Adjustment Costs, and Mitigating Factors Using Data for India

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Finally, there is no apparent relationship between fiscal deficits and the debt trajectory using the AG accounts debt data. Figure 4.6 (panel b) disaggregates the year-on-year change in the debt-to-GDP ratio of aggregate provincial debt into changes in the fiscal deficits and the stock-flow adjustment (SFA)— with the latter measured as the residual. The figure highlights that most of the variation in debt levels cannot be explained by the fiscal deficit.

Apart from public debt, there is also no regular reporting of risks that may arise from guarantees and contingent liabilities at the provincial level. Provinces do not systematically record the amount of guarantees and letters of comfort provided, yet experience shows that contingent liability shocks can exert long-term effects on provincial finances. For example, when the Bank of Punjab suffered some PRs 16.8 billion in losses due to nonperforming loans in FY2008, the government of Punjab—which owned 51 percent of the Bank of Punjab at the time—made capital injections equivalent to PRs 10 billion in FY2010 and PRs 7 billion in FY2011. Subsequently, in FY2015 and FY2017, the government issued two letters of comfort totaling PRs 14.2 billion to the State Bank of Pakistan to guarantee the provisioning requirement against an agreed amount of nonperforming loans. Even though the guarantees have matured and have not been triggered, budgeting for such large contingent liabilities can crowd out public spending on more important and immediate development priorities. It is unclear whether other provincial governments have also lent support to their respective commercial banks,20 but similar shocks cannot be ruled out in the future.

Unfunded pension liabilities are also a significant source of implicit contingent liabilities for provinces. In Punjab, the government estimates that unfunded accrued pension liabilities stood at PRs 3.8 trillion as of the end of June 2016.21 Although the Punjab government created the Punjab Pension Fund to partially fund future pension liabilities, the gap between the fund’s total assets and projected liabilities remain significant. Similarly, the General Provident Fund (GPF), available for government employees, is an emerging fiscal risk. In Sindh, it is expected that the unfunded GPF liability will more than double, from PRs 100 billion in FY2014 to PRs 228 billion by 2030, posing significant risk to the sustainability of public finances.22 The governments of Khyber Pakhtunkhwa and Balochistan similarly have their own pension and provident investment funds, but had not yet assessed the size of unfunded liabilities at the time of writing.23 In the case of Khyber Pakhtunkhwa, the provident fund is an exclusive liability of the government because employee contributions are not collected.

Fiscal risks also emanate from the power sector. Although most of the guarantees are provided by the federal government, provincial governments also play a role in financing infrastructure investments in their respective jurisdictions. Out of the PRs 75 billion in guarantees issued by the government of Punjab, for example, PRs 70 billion accrues to the power sector. These guarantees come in the form of (1) credit guarantees of loans issued by special purpose vehicles for the construction of power plants and (2) commitment to financial support in the case of project cost overruns. While these guarantees are part and parcel of financing much-needed capital investments—and do not result in financial outflows unless they are called24 — delays in the implementation of such projects could pose financial liabilities for the provincial government.25 Recording and disclosing them regularly would help both the provincial and federal governments better manage potential fiscal risks.

Among South Asian nations, India has the longest history and the richest sources of data available to analyze subnational fiscal risks. These data make it possible to implement an econometric framework that estimates (1) the probability of contingent liability shocks;

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(2) the adjustments that occur after the shocks; and (3) their impact on relevant economic outcomes such as investment. To this end, this section first lays out the institutional background for subnational borrowing in India before discussing the methodology, data, and results of an econometric analysis of contingent liability shocks.

institutional Background

Indian states enjoy fiscal autonomy to incur liabilities, either directly domestically or through on-lending of external borrowing by the central government. The Indian institutional framework regulates subnational borrowing through three mechanisms. First, the Finance Commission, a constitutional body primarily tasked with determining the distribution of central funds to states, incentivizes fiscal responsibility through the intergovernmental transfer system. For instance, the Thirteenth Finance Commission proposed a subnational debt relief scheme for subnational loans from the central government that was extended to states that had reduced their fiscal deficit. Second, states’ fiscal position and debt levels are also regulated as part of state-level fiscal responsibility laws, following the passing of the central Fiscal Responsibility and Budget Management (FRBM) Act in 2003. These laws typically limit fiscal deficits to less than 3 percent of GDP and in most cases prescribe an overall subnational debt ceiling. Third, SNGs require approval from the central government to incur liabilities whenever they are indebted with the central government—which, in practice, applies to all states.

Indian states borrow through so-called state development loans, which are dated securities issued by state governments. State development loans are auctioned through the Reserve Bank of India (RBI) on a weekly basis, with the issuing states providing the details of envisioned terms and conditions for their borrowing prior to the auction. The RBI also issues notifications in leading newspapers before the auction to assist in marketing. State development loans are valued at a marginal premium over central government securities, and yields vary by state, because there is no explicit central government guarantee on state borrowing. However, the variation in yields across states is limited and only marginally reflects states’ fiscal situation, partially owing to the wide-spread perception that state securities enjoy an implicit guarantee by the central government. RBI also manages the borrowing and, through an automated debit mechanism, ensures repayment of states’ liabilities.

In 2018, states borrowed primarily from private markets (figure 4.7). In addition, 25.6 percent of subnational debt was owed to pension, savings, and other funds. States also borrow from state-owned banks and enterprises, such as the State Bank of India and the National Bank for Agriculture and Rural Development, which accounted for slightly less than 10 percent of total borrowing in 2018. Loans from the central government, which include states’ external borrowing, accounted for 3.8 percent of total borrowing in 2018.

Historically, the development of state debt and fiscal risks over the last two decades can be broadly divided into three subperiods. The first, from the late 1990s to about 2004, was a period of fiscal slippage. In this period, the absence of regulation and central oversight meant that states exposed themselves to significant contingent and noncontingent liabilities, resulting in fiscal deficits and rising debt (figure 4.8, panel a). Fiscal pressure was compounded through the issuance of power bonds by state governments, which increased liabilities by 22.8 percent in FY2004. With concerns about fiscal risks mounting, the central Fiscal Responsibility and Budget Management Act was passed in 2003, and the Twelfth Finance Commission initiated incentives schemes for subnational fiscal responsibility in 2004, which resulted in a second period of gradual consolidation, until about 2012. More recently, states’ debt has started increasing again, from about Rs 18 trillion (measured at 2011 prices) in 2012 to Rs 30 trillion in 2018 (figure 4.8, panel a), or about 25 percent of GDP. Jharkhand and Nagaland