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
S u b n a t i o n a l G o v e r n m e n t s i n S o u t h A s i a
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.
Estimating Contingent Liability Shocks, Adjustment Costs, and Mitigating Factors Using Data for India 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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