Lack of fiscal transparency and economic growth expectations: an empirical assessment from a large emerging economy

Abstract

This paper provides empirical evidence of the lack of fiscal transparency’s effect (fiscal opacity) on the government’s budget deficit on economic growth expectations. Based on the Brazilian data from 2004 to 2018 and using signal-to-noise ratios, we built fiscal opacity indicators that measure the agents’ level of ignorance regarding the government’s budget deficit. The evidence from several regression models indicates that an increase in fiscal opacity undermines short-term expectations of economic growth (current year, 12 months ahead, and next calendar year). Moreover, the impact of fiscal opacity on growth expectations is more significant when we consider the manufacturing sector. The findings suggest that discretionary fiscal policy can damage economic growth expectations.

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Notes

  1. 1.

    For an overview of central bank transparency and its importance in monetary policy management, see, for example, Geraats (2002), and Dincer and Eichengreen (2014).

  2. 2.

    Alt and Lassen (2006) and Arbatli and Escolano (2015) present evidence that fiscal transparency is associated with better fiscal results.

  3. 3.

    Fiscal transparency is the result of several principles (see IMF, 2019). Throughout this study, the lack of fiscal transparency refers to the difficulty for private agents to anticipate the execution of the government’s budget.

  4. 4.

    Since the government’s effort to seek fiscal balance occurs mainly through restraining spending and increasing revenues, the deficit measure we used in this study corresponds to the government’s budget deficit (primary result).

  5. 5.

    In 2013, the CBB launched a new information governance policy that has become a benchmark for the public sector. As a result of this policy, in 2018, the CBB won the FinTech RegTech Central Banking Data Management Initiative Award.

  6. 6.

    To achieve growth expectations 12 months ahead, we use the method proposed by Dovern et al. (2012), that is, \(E\left[ {Y_{t,T + 12m} } \right] = \left( {\frac{12 - m + 1}{12}} \right)E\left[ {Y_{t,T} } \right] + \left( {\frac{m - 1}{12}} \right)E\left[ {Y_{t,T + 1} } \right]\).

  7. 7.

    The use of Taylor’s rule for setting the interest is part of the Brazilian inflation targeting framework (see Bogdanski et al. 2000). Hence, the monetary policy interest rate is endogenous to a measure that considers the departures of inflation expectations to the target.

  8. 8.

    Table 10 (appendix) provides descriptive statistics for all variables used in the different specifications.

  9. 9.

    For an analysis of the use of a similar measure on expectations of economic growth in Brazil, see de Mendonça and de Deus (2019).

  10. 10.

    Based on the results from the three unit root tests under consideration, at least two tests show that the series under analysis are I(0).

  11. 11.

    In addition to the lagged regressors, we also used as instrumental variables in the models: the market expectations of public sector net debt for the next 12 months (E(DEBTt+12m)); the market expectations of the monetary policy interest rate (Selic) for the next 12 months (E(SELICt+12m)); and the market expectations of inflation for the next 12 months (E(INFt+12m)). The maximum lag applied for each instrument was 12 (1 year) to avoid bias in the results. Moreover, the number of instruments used for all models is less than 15% of the total observations). A list of instruments for each model is available upon request to the authors.

  12. 12.

    As pointed out by de Mendonça and de Deus (2015) and Hallerberg et al. (2007), it is common to analyze the impact of institutional and governance variables on fiscal forecast error through fiscal contracts and decision-making power delegation. However, these variables are time-invariant, and thus, they are not adequate for an analysis that considers time series models.

  13. 13.

    Ibovespa is the main performance indicator of the stocks traded in B3 and lists major companies in the Brazilian capital market.

  14. 14.

    There is evidence that an increase in economic activity positively affects economic growth, see Dalgaard (2002).

  15. 15.

    To observe the importance of reducing fiscal risk by, for example, avoiding explosive paths of public debt/GDP ratio for economic growth, see Reinhart and Rogoff (2010), and Eberhardt and Presbitero (2015).

  16. 16.

    For empirical evidence of the effect of a currency devaluation on short-term economic growth, see Habib et al. (2017).

  17. 17.

    For an analysis of the positive effect of inflation targeting credibility on economic growth, see de Guimarães e Souza, de Mendonça, and Andrade (2016). One of the transmission mechanisms that explain the perverse effect of the lack of monetary credibility on growth is in the behavior of interest rates. High credibility reduces the monetary authority’s need to make changes in the interest rate to control inflation, see de Mendonça and de Guimarães e Souza (2009).

  18. 18.

    See de Mendonca and Almeida (2019) for an analysis of the importance of government approval for business confidence.

  19. 19.

    For an analysis of the negative impact of the global financial crisis of 2008 on economic growth, see Chen et al. (2019).

  20. 20.

    Change in expectations of the exchange rate is significant in the service sector but not in the manufacturing sector. Although exchange devaluation can spur the manufacturing sector through an increase in exportation, there are adverse effects that can void it. A good example is the automotive industry. Exchange devaluation increases production cost by increasing the prices of imported inputs and reducing the supply of iron in the domestic market. Regarding the service sector, a good argument for the significant positive effect is the benefit of tourism in the country.

  21. 21.

    Weights were specified using the option “Normalized Loading,” meaning that the scores formed from this decomposition rule have variations equal to the corresponding eigenvalues.

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Acknowledgements

Author Helder Ferreira de Mendonça has received research grants from the National Council for Scientific and Technological Development (CNPq - Brazil).

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Appendix

Appendix

See Appendix Tables 10, 11, 12, 13.

Table 10 Description of the variables, sources of data, and descriptive statistics
Table 11 Unit root tests (ADF, DF-GLS, and Ng-Perron)
Table 12 Asymmetric effect of fiscal forecast errors on expectations of economic growth
Table 13 Principal component analysis

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de Mendonça, H.F., Calafate, V.R.L. Lack of fiscal transparency and economic growth expectations: an empirical assessment from a large emerging economy. Empir Econ (2021). https://doi.org/10.1007/s00181-020-02000-4

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Keywords

  • Lack of fiscal transparency
  • Economic growth expectations
  • Government’s budget deficit

JEL Classification

  • E61
  • H68