Abstract
Nongovernmental stakeholders, including taxpayers and investors of government bonds, have an interest in government financial information. Fiscal transparency entails both access to and understandability of government information. Analyses of a government borrower’s financial information, often facilitated by market intermediaries, directly affect investor return on the bond market. In contrast, taxpayers may lack the incentive or capacity to comprehend government financial data prepared based on complex accounting rules. State fiscal monitoring programs compile and analyze local government financial data to track fiscal stress. This paper examines how housing and municipal bond markets respond to the New York monitoring program implemented in 2013, which uses existing, account-level local government financial data to assign simple labels signifying local government insolvency. Housing prices decreased following significant fiscal stress designations, but not statistically significantly following the other more modest stress labels. In contrast, the bond market priced in government financial information even before the state monitoring.
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Notes
It is important to distinguish between debt used to finance regular government operation and debt used to finance capital projects. While the former generates public services today, the later enables public services in the future as future taxpayers can continuously benefit from the infrastructure investment. Therefore, the capitalization of debt should only occur in the former situation.
Local governments in New York have different fiscal year end dates and are divided into several groups based on their fiscal year calendars. The state reviews local governments with earlier fiscal year end date first and publishes label designations for each group accordingly. The exact date of a label designation is known. New York City is excluded from the state’s review.
The Comptroller’s office does make technical assistance, such as financial planning tools and online training, available to all local governments.
Localities follow the same accounting standards in preparing their financial data, a process that ensures comparability.
Because the state assigns fiscal labels based on cutoffs along an index score, a natural design to estimate the effect of label assignment is regression discontinuity (RD) analysis comparing observations just below and above a cutoff score. However, this study does not adopt a RD design for a few reasons. First, only around 50 localities are assigned a label each year and the number close to a particular cutoff is even smaller. Even though there are hundreds or thousands of property or bond sales, the number of localities around a cutoff, not the number of sales, determines the sample size. Therefore, we do not have sufficient statistical power for a precise local RD analysis. Second, for each indicator, the state assigns scores based on discrete thresholds, and thus a RD analysis could focus on the indicators as opposed to the index score as the running variable. However, this approach would capture only response to indicators as opposed to the stress label. As discussed later in the paper, while stress labels are widely covered in the media, specific indicators are technical and may not draw as much attention.
Counties may also receive labels. Because later analysis on housing prices assigns the label of the smallest jurisdiction to each house, county labels are not applicable and thus excluded from the figure as well.
The few localities that were not reviewed did not submit financial data in compliance with state rules by the review deadline. These localities are excluded from the analysis.
Only GO bonds are included because the yield of nonGO bonds may depend not on the financial condition of issuer government but rather the creditworthiness of the debt-financed projects. Only long-term bonds with maturity longer than a year are included because short-term bond is a stress factor analyzed by the state monitoring system.
A property can be located in a county, a city or town, and a village at the same time. We assign to each property the state designation for the smallest jurisdiction the property is located in, that is, village if applicable and town or city otherwise. The rationale is that those jurisdictions are the closest to the taxpayers and may provide services most essential to them. A later robustness check also controls for labels of overlapping local governments including school districts.
Housing characteristics include the number of full bathrooms, the number of bedrooms, whether having a full basement, a fireplace, or central air conditioning, the overall physical condition and the construction grade as determined by the assessor, quadratic form of the square footage of the living area, and property age.
While time fixed effects included in the regression can control for changes in the overall market environment, the shape of the yield curve could change over time. To address this possibility, we use yield spread to a synthetic riskless bond as the dependent variable (Yang 2019).
Bond characteristics include issue size, maturity, tax and insurance status, issuing method, and credit ratings. Credit rating agencies may respond to information conveyed through the state monitoring program and adjust ratings. If that is the case, the rating variables may pose as an overcontrol. As a robustness check, results from regressions not controlling for credit ratings (not reported) are similar.
Indeed, research finds that financial intermediaries such as the credit rating agencies rely on similar financial indicator analysis to understand the creditworthiness of local government debt (Johnson et al. 2012). It is worth noting that not all municipal bonds have a credit rating, unless the issuer chooses to pay for one. In our sample, about 92 percent of the bonds carry ratings from at least one rating agency, as shown in Appendix Table 9.
First, the bond market charged borrowers with higher fund balance to expenditure ratios (indicator 2) less, as localities with more savings are better positioned against fiscal shocks and more likely to pay back debt. Second, the market charged a risk premium on borrowers with high debt service costs (indicator 9), as existing debt represents competing claims over the government’s financial resources.
Interestingly, the school district labels are not statistically significantly associated with house sale prices. While this contradicts findings from Thompson (2016) that in Ohio, school district stress labels reduce housing prices, the two states’ monitoring programs are different. Labeled school districts in Ohio must take corrective actions, and on average, cut spending and increase taxes, as Thompson (2016) shows. No such actions are required in New York. Therefore, the decline in housing prices observed in Ohio could be due to the reduction in public services, as opposed to solely an informational shock. But if school district labels in New York provide no informational shock, why would general-purpose government labels? This may be because school performance is highly correlated with school finances and homebuyers already have good access to information on school performance and finances through, for example, the state’s education department and online school rating platforms.
The finding also holds with alternative measures of fiscal variables, such as logged general fund revenue and spending, or logged total revenue and spending.
The regressions control for locality and year fixed effects as well as county-specific time trends, with standard errors clustered at the locality level.
The threshold is chosen so that the two groups of observations are similar in size and thus one should have a similar chance of detecting statistically meaningful relationships.
If the housing price decline following a significant stress label was a result of an information shock, there may be heterogeneity in the impact depending on how much access a local community has to the state monitoring information. As the media provides for an important source of such information, we estimate the heterogeneous effect of fiscal labels based on the number of daily newspapers in a county as reported by Gentzkow et al. (2011). Specifically, we add to the baseline specification interactions between fiscal label variables and number of newspapers. The coefficient estimates of the interaction terms are not statistically significant, and thus provide no support for heterogeneous information shocks based on newspaper coverage. This finding remains true if newspaper coverage is measured using the number of circulation or population weighted circulation.
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Acknowledgement
I thank participants at the 2018 DC Policy Day Conference, the DC-Area Public Finance and Budgeting Seminar, the 2019 Public Choice Society Annual Conference, and the 2019 University of Illinois Public Finance Conference for their helpful comments. All errors are mine.
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Yang, L.(. Fiscal transparency or fiscal illusion? Housing and credit market responses to fiscal monitoring. Int Tax Public Finance 29, 1–29 (2022). https://doi.org/10.1007/s10797-020-09654-x
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DOI: https://doi.org/10.1007/s10797-020-09654-x