Abstract
We use unique panel data on the evolution of transparent budget procedures in the U.S. states over the past three decades to explore the political and economic determinants of fiscal transparency. Our case studies and quantitative analysis suggest that both politics and fiscal policy outcomes influence the level of transparency. More equal political competition and power sharing are associated with both greater levels of and increases in fiscal transparency during the sample period. Political polarization and past fiscal conditions, in particular state government debt and budget imbalances, also appear to affect the level of transparency.
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Notes
Petrie (2003) provides thoughtful suggestions on how to make the implementation of transparency reform more effective.
Trust increases when transparency means “tell them what you’re going to do.” If politicians have a history of having nothing to hide, they are more likely to have the people’s trust when they ask them to take something on faith. Transparency reassures people that politicians have not abandoned long-term goals; the confidence this fosters can give politicians greater flexibility.
Greater disclosure makes actions more predictable when transparency means “give them details and justifications.”
In the political market, voters get a clearer view of performance and can make more effective use of votes. In financial markets, participants are not misled and risks are reduced (Glennerster and Shin, n.d.). Generally, better observability is welfare-improving, as it reduces transaction costs in the broadest sense.
Lowry (2001) provides such an interpretation of balanced budget rules, conditional on market behavior. In monetary policy, an incumbent wishing to be seen to fight inflation can do so more effectively if the standard for what will count as fighting inflation is unambiguous.
This potential for “harmful” competition may be a reason to support secret voting (Dal Bo, 2005).
Alt and Lassen (2006b) find support for this hypothesis in a cross-section of advanced OECD economies for the 1990s. They use this, and a measure of legal origin (common vs. civil law), to instrument for transparency in an analysis of the effects of transparency on government debt.
For example, the introduction of balanced budget rules in the nineteenth century took place as a result of fiscal crises following from the construction of railroads and canals (Heckelman and Wallis, 1997).
Alt and Lassen (2006b) present a transparency index based on similar principles for 19 OECD countries.
Around the same time, Rhode Island took many other steps to open up the legislative process to public scrutiny. For a summary of these reforms, see http://www.rilin.state.ri.us/studteaguide/RhodeIslandHistory/chapt9.html
If control of the legislature is split between parties, we code the case as divided government. We return to this in Table 5.
It is defined as −1*abs(share of seats in lower house held by Democrats −0.5)*abs(share of seats in upper house held by Democrats −0.5).
Recall that a polarization measure is available only for states represented by more than one party in Congress. States that elect only one party to Congress will, besides typically having small populations, have some degree of polarization and may even be more polarized than states electing more than one party, when everything else is equal. For consistency, they are represented in our analysis by a dummy variable because a score of zero indicates minimal polarization, as well as only one party in Congress.
Alt and Lassen (2006b) show that, in a sample of OECD countries, lower transparency leads to higher debt.
In fact, what Table 3 shows is that, absent fiscal shocks, the effect of divided government is stronger than it appeared in Table 1. This may be because divided government is slow to respond to fiscal shocks (Alt and Lowry, 1994). We will also reexamine the other competition indicators, whose effects were stronger in Table 1, for evidence of interactions.
All robustness test results are available from the authors upon request.
The estimation was carried out using Stata’s xtabond2 procedure (Roodman, 2003) with the noleveleq option. There was no sign of overidentification or second-order autocorrelation in the first-differences. For computational reasons, the instrument matrix was constructed using xtabond2’s collapse option, which drastically reduces the dimension of the instrument matrix, which becomes large in Arellano-Bond estimation of long panels. In long panels, the collapse correction reduces efficiency, which implies that the reported standard errors are conservative, but at the same time it counters problems with bias arising from the number of instruments approaching the number of observations.
We also experimented with a random effects Tobit model, with no qualitative effect on the results.
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The authors thank Gian Maria Milesi-Ferretti; Ilyana Kuziemko; and participants at the Colloquium on Law, Economics, and Politics at New York University, and the IMF Annual Research Conference for helpful suggestions. Lassen thanks the Economic Policy Research Unit network for funding.