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Thanks to Santiago Acosta Ormaechea, Marcio V. Da Cruz, Mark De Broek, Xavier Debrun, Salvatore Dell’Erba, Eva Jenkner, Serhan Cevik, Sebastian Weber, and Veronika Zavacka for very useful comments and suggestions. All errors remain our own. Special thanks go to Jeta Menkulasi and Asad Zaman for discussions and the precious help in collecting data.
In theory the presence of bail-out guarantees of central governments to sub-national governments could also have the effect of increasing the yield paid by central governments. See Jenkner and Lu (2014) for an analysis of this channel.
Spreads of SNGs’ also take negative values in sporadic cases for some German Laenders.
As long as the investor resides in the same state as the issuer.
It is worth noticing that alternative specifications could be used to isolate country-specific variability from global shocks. For instance, Beber et al. (2009) specify the right-hand-side variables in deviation from cross country averages to capture the effects of global factors. Dell’Erba and Sola (2013), instead, estimate a spreads equation using variables in levels and introducing principal components as additional regressors to control for global shocks.
The VXO is the S&P 100 implied volatility computed by the Chicago Board Options Exchange (CBOE).
The p-value of the Hausman test is about 0.25.
A parallel line of investigation has instead analyzed whether common pool problems emerge in countries characterized by higher decentralization. Eichler and Hofmann (2013) and Jin and Zou (2002) have found that indeed decentralization tends to worsen the central government fiscal position.
Heckman selection models aim at addressing selection bias (see Heckman 1979). In our model, the selection bias is determined by the fact that we can only look at SNGs that have actually issued bonds.
Results available upon request.
Data from Capital Data Bondware are available from 1980, but to ensure consistency with the secondary market data I limit the sample to the period 2000-2010.
Data sources are: Bureau of Economic Analysis and the Census for the U.S.; Statistics Canada for Canada; Australian Bureau of Statistics and the Commonwealth Budget Documents for Australia; the Federal Ministry of Finance, the Federal Statistical Office and the Statistical Office of the Laender for Germany and the Bank of Spain for Spain.
It is about -0.02 and not statistically significant at any reasonable level.
As for the liquidity measure computed with secondary market data, this is supposed to capture the size of a bond issued by a given sub-national in year “t” relative to the total size of the sub-national bonds market in the country in year “t”.
Similar results are found also in studies which analyze risk premia of central government bonds (i.e. Ardagna et al. 2007).
Because of data limitations at local level, nominal rather than real GDP is used in the analysis. However, results are similar when using GDP deflated by CPI where data are available.
The effect of fiscal rules per se cannot be estimated because of the presence of fixed effects.
It would be interesting to control also for a measure of roll over needs, like the residual maturity of outstanding debt. However such data are not available for sub-national governments.
The statistical significance of the coefficients of regression (1) is also tested using bootstrapped standard error and different assumptions on the error term (i.e. GLS). These modifications do not affect the levels of significance of the coefficients. Results are available upon request.
Expressing the right-hand side variables in deviations from the benchmark is already one possible way to avoid non-stationarity issues. Unfortunately however, because of the short time dimension in the sample, a formal test of non-stationarity is not possible.