Assembly Elections

154 HIDDEN DEBT

reduction of 32.2 percent in the year after the shock and then returns gradually to trend.

What Factors Can Explain and Mitigate Contingent Liability Shocks?

To guide policy, a central question is which factors explain contingent liability shocks and how these factors can be reformed to mitigate the occurrence and repercussions of these shocks. To investigate this question, we focus on five factors: political incentives during elections, transparency, legal frameworks and fiscal rules, markets, and fiscal capacity and intergovernmental frameworks.

Political Incentives Related to Elections

To identify approaches to mitigating fiscal shocks, it is important to understand whether policy makers can influence the timing of when these shocks occur and whether the shocks are affected by political incentives. To address these questions, we examine the likelihood of contingent liability shocks around elections. We chose elections because they, arguably, shape the main incentives of policy makers. Elections may influence the timing of when contingent liabilities are realized. For instance, policy makers may be prone to adopt a more lenient fiscal policy in the run-up to an election. They can take on debt of state-owned enterprises to secure jobs in the short term. Alternatively, policy makers may instead delay the shock until after elections because the adjustments required in response to a contingent liability realization and the impact on the local economy may cause negative political fallout.

In our data analysis, we focus on state legislative assembly (Vidhan Sabha) elections because they largely determine the state-level governments in India—which hold authority over fiscal policy. The econometric analysis provides evidence of the interrelationship between elections and fiscal policy. Figure 4.15 shows that the likelihood of a

contingent liability shock increases significantly in the year before an election, peaks in the year of and after the election,

and then gradually reverts to the trend. This thus provides direct evidence that contingent liability realizations, as defined here, respond to the political incentives provided by elections.

Transparency

In addition to elections, increasing transparency is an alternative measure to hold policy makers accountable and align their incentives with fiscal responsibility. To assess the effect of transparency measures on contingent liability realizations, we use the gradual adoption of debt transparency measures across Indian states as a case study (figure 4.16). Such measures range from the publication of debt and guarantee data in the annual financial statements and budgets to the publication of dedicated reports analyzing debt and (in some cases) outstanding guarantees.

To systematically assess the effectiveness of increased transparency, we collected information on states’ publication of debt and guarantee-related information from the websites

FIGURE 4.15 Occurrence of Contingent Liability Shocks around Indian State Legislative Assembly Elections

0.4

0.3

0.2

0.1

0

−0.1

−3 −2 −1 0 1 2 3 4

Time relative to election (years)

Source: Blum and Yoong 2020. Note: The figure plots the coefficient estimates βs of the following regression:

3 CL Shockit 0 Election +s it j t its∑ α β γ µ ε= + + + +

s=−3

Each coefficient measures the relative likelihood of a contingent liability (CL) shock occurring s years before and after an election, compared to states with no such election. The x-axis plots the time relative to election, with –1 denoting the year before an election and 1 denoting the year after, for instance. The y-axis plots the values for the corresponding βs.