Short- and long-term relation between economic development and government spending: the role of quality of institutions

ABSTRACT

This paper tests the Wagner’s assumption of the one-sided directional flow moving from economic growth to public spending considering an international database over the 1996–2012 period. By using indicators on the level of country control of corruption, government effectiveness, political stability, rule of law, regulatory quality and voice and accountability, the paper analyses the economic performance-public spending nexus controlling for the quality of the institutions. The empirical evidence supports the existence of the Wagner’s law, showing that, in the short-run, public spending positively reacts to a positive shock in national income, with a lower magnitude for democratic countries. In the long run, the error-correction model shows the convergence between public spending and national output occurring less quickly for non-democratic, low-income and to a smaller extent for non-OECD countries. Institutional quality, such as effort in controlling corruption and the presence of regulations that permit and promote private sector development, may help reducing the amount of per capita public spending and making it more productive. Higher expenses in compositional amenities such as public services for the elderly may explain why public spending per capita will increase the most in economies with a higher share of the population that need healthcare facilities.

KEYWORDS:

• Wagner’s law
• public spending
• economic growth
• quality of government

JEL CLASSIFICATION:

• O40
• H54

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Notes

¹ Studies using time series analysis examine the effect of the national income growth on the expansion of government expenditures over time for a particular country. The panel data analysis usually investigates the relationship between national income and government expenditures across different countries..

² See also Poot (2000) for a synthesis of the 1983–1998 published literature on the empirical evidence regarding the interaction between government policies and growth..

³ This database or at list part of its variables have been used in several papers, within different topics and relying both on country, regional and individual level (See for instance, among others, Charron and Lapuente (2018) and Charron, Dijkstra, and Lapuente (2014)).

⁴ The database covers 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The six aggregate indicators are based on over 30 underlying data sources reporting the perceptions of governance of a large number of survey respondents and expert assessments worldwide. These individual indicators can be used to make comparisons of countries over time, as all of the underlying sources use reasonably comparable methodologies from 1 year to the next. Details on the underlying data sources, the aggregation method, and the interpretation of the indicators can be found in Kaufmann, Kraay, and Mastruzzi (2010)..

⁵ From Figures 2 and 3, it seems that the dynamics of the quality of the government seems almost constant. This means that the first differences are almost zero, which may cast doubt on the estimation results of Equation (3). However, this is mainly due to the scale of the figures. To show that there is, instead, dynamics of the quality of the government, we show the descriptive statistics of the quality of the government proxies year by year for all countries, democracies and non-democracies, OECD and non-OECD countries as well as for high and low-income countries in Tables C, D, E, F and G in the On-line Appendix..

⁶ Information from https://datahelpdesk.worldbank.org/knowledgebase/articles/906519 has been used to divide countries according to the income level of their economy. Low-income economies are those with GNI per capita, calculated using the World Bank Atlas method, of $1,045 or less in 2014. Lower-middle-income economies are those with a GNI per capita, calculated using the World Bank Atlas method, of more than $1045 but less than $4,125. Middle-income economies are those with a GNI per capita, calculated using the World Bank Atlas method, of more than $1,045 but less than $12,736. Upper-middle-income economies those with a GNI per capita, calculated using the World Bank Atlas method, of more than $4,125 but less than $12,736. High-income economies are those with a GNI per capita, calculated using the World Bank Atlas method, of $12,736 or more..

⁷ In particular, we analyze the nexus between public spending and growth below and above the median of our preferred welfare indicator. To measure the level of welfare of the countries we again rely on the QoG dataset using a variable called ‘Poverty and economic decline’ which strains the ability of the state to provide for its citizens if they cannot provide for themselves. Poverty and economic decline strain the ability of the state to provide for its citizens if they cannot provide for themselves and can create friction between the ”haves” and the ”have nots”. Includes pressures and measures related to economic deficit, government debt, unemployment, youth employment, purchasing power, GDP per capita, GDP growth, inflation..