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Are bigger governments better providers of public goods? Evidence from air pollution

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Abstract

Theories explaining government size and its consequences are of two varieties. The first portrays government as a provider of public goods and a corrector of externalities. The second associates larger governments with bureaucratic inefficiency and special-interest-group influence. What distinguishes these alternatives is that only in the former is governmental expansion generally associated with an increase in social welfare. In the latter, the link between government size and public goods provision (or social welfare) is negative. We study the empirical significance of these competing claims by examining the relationship between government size and a particular public good, namely environmental quality (notably, air quality measured by SO2 concentrations), for 42 countries over the period 1971–1996. We find that the relationship is negative, even after accounting for the quality of government (quality of bureaucracy and the level of corruption). This result may not prove conclusively that the growth of government has been driven by factors other than concern for the public good, but it creates a presumption against the theory of government size that emphasizes public good provision.

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Notes

  1. Peacock and Wiseman (1961) argue that growth of government might be due to the existence of a ratchet effect in response to wars, with Higgs (1987) including any national emergency or economic crisis, in this theory. That is, once government spending increases due to a war or depression, it does not fall back to its original levels. Henrekson (1990), however, does not find empirical support for this hypothesis.

  2. While a third subcategory is often mentioned, namely the government as an executor of income-wealth redistribution (Meltzer and Richard 1981, 1983), it seems that this subcategory is not distinct but rather subsumed in one of the previous two. According to classical liberals, income redistribution belongs to the second, while according to socialists to the first category.

  3. It should be noted though that many externalities identified by governments are in practice not true externalities, and thus legitimate targets of government action. And second, even for correctly identified externalities, government intervention often makes things worse.

  4. See Mueller (2003) for a comprehensive summary of the results of such studies.

  5. Earmarks are spending projects that are requested directly by individual members of Congress and are not subject to competitive bidding.

  6. Note that although SO2 travels across borders, it is largely a national problem relative to other air pollutants, such as CO2, which are global and their reductions require the cooperative efforts of many nations due to positive externalities.

  7. Naturally, the cost in terms of public resources may well differ across different measures (for instance whether a production quota is imposed or whether tradeable pollution permits are issued), but in general it will be nonzero.

  8. An increase in the share of government spending in GDP by 10% is associated with an increase in SO2 concentrations by 1.10 percentage points.

  9. Multicollinearity is not a problem since the mean VIF for the model is 1.67 and the VIFs for all variables are smaller than 0.06.

  10. Results are not shown here.

  11. Desai (1998) shows that corruption contributes significantly to environmental degradation in developing countries.

  12. Note, however, that there exist instances where interest groups promote environmental quality in order to sell their products. For example, Brandt and Svendsen (2004) argue that the US wind turbine industry advocated CO2 reductions because it served their own interests.

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Correspondence to Vally Koubi.

Appendix

Appendix

1.1 A.1 Country list

Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China, Colombia, Czechoslovakia, Denmark, Egypt, Finland, France, Germany, Ghana, Greece, Hong-Kong, India, Indonesia, Iran, Iraq, Ireland, Italy, Japan, Kenya, Malaysia, Netherlands, New Zealand, Pakistan, Peru, Philippines, Poland, Portugal, South Korea, Spain, Sweden, Switzerland, Thailand, United Kingdom, United States, Venezuela.

1.2 A.2 Description of variables and data sources

The data set was constructed with data taken from the following sources:

SO 2 concentrations::

log of the median of SO2 concentrations at site j, city i, in country k, at time t; average from all sites for each country and year. GEMS/AIR, US Environmental Protection Agency (US EPA) (http://www.epa.gov/airs/aexec.html)

Government size::

total nominal government spending divided by nominal GDP. International Monetary Fund, International Financial Statistics

Corruption::

corruption index ranging from 0 to 6, with 6 indicating low and 0 indicating high levels of corruption. Knack, Steve and Philip Keefer (2006). IRIS-3: File of International Country Risk Guide (ICRG) Data. 3rd Edition, College Park, Maryland (http://ssdc.ucsd.edu/ssdc/iri0001.html/)

Bureaucratic quality::

bureaucratic quality index ranging from 0 to 6, with 6 indicating high and 0 indicating low levels of bureaucratic quality. Knack, Steve and Philip Keefer (2006). IRIS-3: File of International Country Risk Guide (ICRG) Data. 3rd Edition, College Park, Maryland (http://ssdc.ucsd.edu/ssdc/iri0001.html/)

Polity::

index ranging from −10 (mostly autocratic) to 10 (mostly democratic). Polity IV (http://www.cidcm.umd.edu/inscr/polity)

Income::

3-year average of lagged GDP per capita. The Penn World Tables, NBER (ftp://ftp.nber.org/pwt56/), and International Monetary Fund, International Financial Statistics

Activity::

real GDP/km2 (GDP/population × population/km2). The Penn World Tables

Capital::

capital to labor ratio (the amount of the physical capital per worker). The Penn World Tables

Trade::

ratio of the sum of exports and imports to GDP. The Penn World Tables

Population::

Global Population Distribution Database, The Consortium for International Earth Science Information Network (CIESIN) (http://grid2.cr.usgs.gov/globalpop/1-degree/description.html)

Precipitation::

coefficient of variation of monthly precipitation (the standard deviation of monthly precipitation in a given year divided by the monthly precipitation average in that year). Global Historical Climatology Network (GHCN), National Climatic Data Center of the US National Oceanic and Atmospheric Administration (ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v1/)

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Bernauer, T., Koubi, V. Are bigger governments better providers of public goods? Evidence from air pollution. Public Choice 156, 593–609 (2013). https://doi.org/10.1007/s11127-012-9916-1

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