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Although operating reserves can aid nonprofit organizations in alleviating periods of fiscal stress, they are not widely used. This study examines organizational factors that impact the level of operating reserves in nonprofit organizations. It also explores the relationship of operating reserves with organizational demographics and financial health variables using a six-year (1998-2003) unbalanced panel regression model containing 460,437 observations. Findings demonstrate a positive relationship between operating reserves and administration ratio, profit margin, operating margin, and organization age. Conversely, the size of operating reserves is negatively related to leverage ratio, donations, and organization size. Revenue diversification, however, shows a mixed relationship with operating reserves among different types of nonprofit indicating complexity in risk-reducing strategy. This study contributes to understanding factors relevant to the presence, or absence, of nonprofit operating reserves.

Despite the enormous size of the nonprofit sector, there has been very little empirical research done on the capital structure of nonprofit organizations, and no one has examined the potential effects of borrowing on individual contributions. Using a representative sample of nonprofits, the empirical analysis first determines whether secured or unsecured borrowing by nonprofits influence future contributions. The results for the full sample support a “crowding-out” effect. When the analysis is repeated on a subsample of nonprofits that are older, larger, and more dependent upon donations, the results are more ambiguous: secured debt has little or no effect, while unsecured debt has a “crowd-in” effect. The empirical analysis is then expanded to test whether nonprofits with higher than average debt levels have different results than nonprofits with below average debt levels. The results suggest that donors do remove future donations when a nonprofit is more highly leveraged compared to similar organizations.

This study examines the fiscal impact of tax and expenditure limitations (TELs) on state spending by expanding the popular, narrow view of examining TELs and taking into account the scope, purpose, and restrictiveness of individual state TELs. Using an efficient estimator, called fixed effect vector decomposition I employ a set of panel data from all fifty states for the period 1997 - 2006. While a number of studies have been inconclusive about the impact of state TELs on spending, this study finds that having a TEL is not what matters. Rather, the impact of TELs depends on the actual features of the individual TEL. Further, TELs impact different categories of spending in different ways and, under the right conditions, TELs can have the desired impact and effectively reduce state spending.

The purpose of this paper is to use data on municipal bond sales in the US primary market for the period 1984–2007 to explore the effect of debt levels on the cost of borrowing for state governments.

This paper employs OLS and two-stage least squares regression model using instrumental variables.

The empirical analysis finds that despite the steady increase of state debt in recent years, there is no evidence that the market penalizes states with high-debt levels relative to other states.

The findings urge states to exercise prudence when making borrowing decisions because high-debt levels have the potential to crowd out spending for essential services and can lead to budget imbalances in the long term.

The DataArts dataset, although it covers mostly arts organizations, has emerged as an alternative source of data for nonprofit research. Most existing studies use the IRS 990 data, which is considered a reliable source for research. We evaluate the reliability of the DataArts dataset by comparing the consistency of the values reported to the DataArts Cultural Data Profile (CDP) and to the 990 forms. We: 1) examine correlations between the same measures in each dataset, 2) assess the cumulative distribution of differences between the two datasets and 3) compare the results of the same empirical model conducted with the DataArts dataset and 990 data, respectively. We conclude that the DataArts dataset is an adequate and reliable source of financial and performance information, but researchers should be aware of a few limitations.