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Can Fiscal Decentralization Alleviate Government Consumption Volatility?

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Abstract

This paper assesses the effect of fiscal decentralization on government consumption volatility using data for 97 developed and developing countries from 1971 to 2010. The results suggest that a higher degree of fiscal decentralization leads to lower government consumption volatility. This result holds for the sub-sample of advanced economies, while it is not confirmed for those less-developed. This mechanism seems to work mainly through a lower volatility of the non-discretionary spending, which typically belongs to the central government’s policy. We also confirm existing findings according to which country size lowers government spending volatility. Thus, given a minimum level of development, fiscal decentralization reforms can reduce spending volatility by distributing power to sub-central governments, particularly in smaller countries which are usually more prone to volatility.

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Notes

  1. The positive link between growth and volatility can also occur when countries invest more thanks to high saving rates motivated by higher volatility (e.g., for precautionary reasons), thus leading to higher economic growth (Mirman 1971).

  2. For other empirical studies on this issue see, for example, Fatas and Mihov (2005), Furceri (2007), Afonso and Furceri (2008).

  3. More precisely, some evidence suggests that decentralization based on grants would reduce the sub-central governments’ tax effort and inflate their spending, with adverse consequences on local deficits and debt that can easily deteriorate country’s fiscal position (e.g., Stein 1999; OECD 2009). Indeed, money transfers from other levels of government (especially those not earmarked) are likely to be spent with more discretion and in a less accountable way (see Weingast 2014). Finally, with high fiscal decentralization the number of veto players normally increases. This, in turn, may lead to higher government fragmentation making more difficult the control of total deficit, so reducing country’s fiscal discipline.

  4. A negative relationship between fiscal decentralization and government size also arises from the traditional theory of fiscal federalism, assuming a benevolent central policy-maker that can better take into account heterogeneous individuals’ preferences by decentralizing the supply of local public goods. The main implication of this so-called ‘Decentralization hypothesis’ is that fiscal decentralization will again lead to greater efficiency overall and then to a leaner public sector.

  5. The WB database of Fiscal Decentralization Indicators is mainly derived from the IMF’s Government Finance Statistics (GFS). The fiscal variables in the WB dataset are standardized across countries and this represents an important strength of these indicators. The shortcoming of that dataset is, however, that the decentralization indicators are available in the WB’s Statistical Information Management and Analysis (SIMA) only up 2000. Inevitably this standardization leads to a loss of detail and data richness that must be kept in mind when using IMF-GFS data to assess decentralization. As an example, in 1996 only 46 over 149 countries—normally covered by the GFS on a yearly basis—reported expenditures at both national and sub-national government levels. In short, even though the framework for the GFS is very comprehensive, available data are much less comprehensive for the purpose of constructing decentralization indicators.

  6. We use two alternative measures for robustness purposes, finding that the choice of how to measure government consumption volatility does not alter the results. In particular, results hold when using the standard deviation of the cyclical component of real government consumption obtained by applying the Hodrick-Prescott filter to annual data with two alternative smoothness parameters, 100 and 6.25 (see Ravn and Uhlig 2002). We avoid checking the results using the coefficient of variation as a third alternative due to its well-known problems in terms of potentially misleading negative values for countries with highly volatile government spending.

  7. Including inter-governmental transfers spending (revenues) into sub-national expenditures (revenues) definition would provide an overestimation of the effective degree of expenditure (revenue) and, ultimately, fiscal decentralization as the numerator of both indices would contain resources not spent within the territory nor collected by each specific level of government. In detail, inter-governmental transfer expenditure (revenue) represents transfers payable to other levels of governments (to each level of government by other levels of government). However, these figures do not take account of either transfers payable to (from) the supranational level of government or transfers between the central government level and social security which are considered to be internal. Hence, even by excluding such spending and revenue transfers, the decentralization indicators are able to take into account the vertical relation between governments that should play a more important role than the horizontal relationship among them in terms of aggregate spending volatility. Indeed, grants and transfers are also used to finance investment spending that would otherwise overwhelm the fiscal capacity of sub-national governments and in the case of rigid revenue-sharing arrangements, they are automatic from the central government (de Mello 2000). Finally, interregional transfers can smooth shocks to regional revenue and prevent regional/provincial spending from varying across time (see e.g., Tochkov 2007 for China; Sacchi and Salotti 2015 for OECD countries). This issue, however, goes beyond the scope of our paper.

  8. Some attempts to overcome this shortcoming are those of Stegarescu (2005) and Gemmell et al. (2013), who built tax decentralization indices able to capture the real autonomy of sub-central governments over their tax decisions, avoiding overestimation measurement problems. However, data coverage is limited in terms of countries and years.

  9. This horizontal aggregation also disregards the differences in competencies among them (as the revenue decentralization data do not distinguish between tax revenues that are legislated and collected locally and those that accrue to the sub-central governments through revenue sharing schemes). Moreover, any information about the spending composition and the policy mix is actually lacking, thus making us unable to distinguish between the potentially different effects of, e.g., decentralized education versus health expenditure.

  10. These variables are obtained by regressing (on a country-by-country basis) the annual growth of government consumption on its lagged value and annual output growth, adding inflation, squared inflation and a time trend as control variables. We instrument output growth using two of its lags and (the logarithm of) oil prices. Then, we computed the 5-years standard deviation of the fitted values—to be interpreted as volatility due to automatic stabilizers—and the 5-years standard deviation of the residuals—as a proxy for discretionary spending volatility (See the Appendix for details). It should be noted that by focusing on government consumption, the analysis does not capture stabilization provided by transfers. Unfortunately, data coverage on government transfers is limited in terms of countries and years, and thus preventing us to include them in the analysis.

  11. The analysis does not consider the role of revenues. While focusing also on the revenue side of the governmental actions may offer interesting insights, it is empirically more challenging to disentangle between the automatic and the discretionary component of revenue than for spending (Fatas and Mihov 2003). Moreover, even though it can be hypothesized that the volatility of local expenditure and of local revenues may be jointly determined, the existing literature does not contain clear indications on whether tax or expenditure decisions come first as argued by Blanchard and Perotti (2002). In any case, all these considerations seem to be more valid at the same governmental level of the budget, i.e. the volatility of local revenue sources may be influenced by the volatility of local spending and vice versa (see Sacchi and Salotti 2015). In particular, local governments may respond to the assignment of new spending responsibility by manoeuvring the taxes that they control. Also, central governments may assign new tasks to local governments (requiring changes in spending) and at the same time manipulate the intergovernmental grants to ensure adequate financing. A more difficult channel to be considered is why with less automatic stabilizers at the central level, the volatility of revenues at the sub-national level would increase.

  12. Indeed, the fd_oecd coefficients become not significant when excluding post-2000 data (regressions not reported for the sake of brevity but available upon request).

  13. Since the measure of discretionary spending is obtained starting from the residuals of a regression, its variability is more limited than that of the measure of automatic stabilizers (constructed starting from the predicted values of such regression). This may explain the different results shown in Tables 4 and 5. We thank an anonymous referee for pointing this out.

  14. There is a general agreement on using the ratio between public expenditures and GDP and this is mainly due to the fact that considering tax revenues only is likely to underestimate the actual government size since it would disregard all public spending and activities financed by debt and deficit.

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Acknowledgments

We would like to thank Wolfram Berger, Carmine Trecroci, Alberto Zanardi, participants in the XXV conference of the Italian Association of Public Economics (Pavia, September 2013), and the XXI Meeting on Public Economics (Girona, January 2014), the editor George Tavlas as well as two anonymous referees for useful comments. The usual disclaimer applies.

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Authors and Affiliations

  1. International Monetary Fund, Research Department, 700 19th Street, N.W., Washington D.C., 20431, USA

    Davide Furceri

  2. University of Palermo, Palermo, Italy

    Davide Furceri

  3. Universitas Mercatorum, Rome, Italy

    Agnese Sacchi

  4. Governance and Economics research Network, GEN, Ourense, Spain

    Agnese Sacchi

  5. European Commission, Joint Research Centre (JRC), Institute for Prospective Technological Studies (IPTS), C/Inca Garcilaso, 3, E-41092, Seville, Spain

    Simone Salotti

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  1. Davide Furceri
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Correspondence to Davide Furceri.

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The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.

The views expressed in this paper are those of the author and do not necessarily reflect those of the IMF or IMF policy.

Appendices

Appendix: Data Sources

  • GC_vol i,t (standard deviation of annual growth of real government consumption spending over 5-years’ periods): government consumption data are taken from Penn World Tables 7.1 (Alan Heston, Robert Summers and Bettina Aten, Penn World Table Version 7.1, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, Nov 2012).

  • GC_vol i,t (same as the previous one, but distinguishing between automatic and discretionary government consumption volatility): the series are obtained by firstly estimating the following model (taken from Badinger 2009) with annual data country-by-country:

    $$ \Delta \ln G{C}_{i,t}={\alpha}_i+{\beta}_i\Delta \ln G{C}_{i,t-1}+{\chi}_i\Delta \ln {Y}_{i,t}+{\boldsymbol{\updelta}}_i\mathbf{control}{\mathbf{s}}_{i,t}+{\varepsilon}_{i,t}^{GC}, $$

    where GC is real government consumption, Y is real GDP, W is a vector of controls (inflation, squared inflation, a time trend). Due to endogeneity concerns, ΔlnY i,t is instrumented with all other right-hand-side variables plus two lags of output growth and the logarithm of oil price (source: Dow Jones & Company).

    The 5-year average of the standard deviations of the fitted values is taken as the measure of automatic stabilisers volatility. The 5-year average of the standard deviations of the residuals is taken as the measure of discretionary policy.

  • fd i,t : World Bank data cover 97 countries for the period 1972–2000 and come from the World Bank Fiscal Decentralization Indicators, mainly derived from IMF’s Government Finance Statistics. OECD data cover 29 countries for the period 1971–2010 and come from the OECD Fiscal Decentralisation Database . Both sources offer expenditure decentralization (ed_wb and ed_oecd) and revenue decentralization data (rd_wb and rd_oecd). By simply averaging the two we obtain two additional measures of fiscal decentralization (fd_wb and fd_oecd).

  • reg_inst set: data are taken from the website of A.K. Rose

  • demo set: these data come from the World Development Indicators (WDI) published by the World Bank.

  • macro set:

    • a) gdp i,t & b) inflation i,t : these data are taken from the WDI;

    • c) open i,t : trade openness data come from Penn World Tables 7.1.

    • d) fin_open i,t : financial openness data are taken from the most up-to-date version of the External Wealth of Nations Mark II database (Lane, P.R., Milesi-Ferretti, G.M. 2007. The external wealth of nations mark II. Journal of International Economics 73, 223–250).

    • e) fin_depth i,t : financial depth data are taken from the most up-to-date version of the dataset. Financial institutions and markets across countries and over time: data and analysis. World Bank Policy Research Working paper 4943.

  • govsize: government consumption share of real GDP per capita. Source: PWT 7.1

  • inst_quality: ICRG (International Risk Country Guide) indicator of quality of government, taking into account corruption, law and order, and bureaucracy quality. Source: The QOG Basic Dataset, University of Gothenburg.

List of Countries

The 29 countries for which OECD decentralization data can be used: Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Ireland, Israel, Italy, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, the United Kingdom, the United States.

World Bank data are also available for these other countries: Albania, Argentina, Aruba, Australia, Azerbaijan, Bahrain, Bangladesh, Belarus, Benin, Bolivia, Botswana, Brazil, Bulgaria, Burkina Faso, Cameroon, Chile, China, Colombia, Democratic Republic of Congo, Republic of Congo, Costa Rica, Croatia, Cyprus, Dominican Republic, Ecuador, El Salvador, Ethiopia, Fiji, the Gambia, Guatemala, Honduras, India, Indonesia, Iran, Japan, Jordan, Kazakhstan, Kenya, Kyrgyz Republic, Latvia, Lithuania, Madagascar, Malawi, Malaysia, Mauritius, Moldova, Mongolia, Morocco, Nicaragua, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Romania, Russian Federation, Senegal, Somalia, South Africa, Sri Lanka, Sudan, Swaziland, Tajikistan, Thailand, Trinidad and Tobago, Tunisia, Turkey, Uganda, Uruguay, Venezuela, Zambia, Zimbabwe.

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Furceri, D., Sacchi, A. & Salotti, S. Can Fiscal Decentralization Alleviate Government Consumption Volatility?. Open Econ Rev 27, 611–636 (2016). https://doi.org/10.1007/s11079-016-9392-1

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