1. Government Spending (Expenditure) and Economic Growth: Based on First and Second level COFOG Data
Seong-Ho, Jeong

2. Abstract
This study investigates the effect of a specific government spending on the per capita output by using Classification of the Functions of Government (COFOG) data. We analyzed not only the effect of the first level (division) category on the per capita output, but also specified the impact of the second level R&D data (group). The result of the empirical analysis showed that the government spending on the education has the most important role in the economic growth as it has both strong short-run and long-run effect on GDP per capita. Besides, the R&D spending on the education has the significant long-term growth effect in the analysis of the second level R&D data. In sum, the government spending on education is the most influential factor of the economic growth in the sample of the developed countries and they could promote their economic growth via re-composition of their spending portfolio toward human capital accumulation.
Keywords: COFOG, Economic Growth, Governmet Expenditure JEL Classification: H5, E6

3. previous studies & direction
Many previous studies have agreed this idea and examined the effect of various types of government spending on economic growth. To be specific, Barro (1990) and Gemmell et al (2011) divided government spending into two categories, a productive and an unproductive expenditure. They showed that the increase in the share of productive spending changes the long-run output growth rate positive. Devarajan et al. (1996) adopted 10 division of classification of functions of government expenditure (henceforth, COFOG) to see the effect of individual government spending on the economic growth by using IMF’s Government Finance Statistics (GFS) data.
In this article, we adopted the first-level, 10 division, COFOG as in Devarajan et al. (1996) and, taking a step forward, we further utilized second-level COFOG data to analyze the effect of various types of R&D spending on per capita GDP. To this end, we constructed the panel regression models with theoretically cutting-edge factors: 1) considering government budget constraint 2) incorporating short-run and long-run dynamics which are recently suggested by Kneller et al. (1999) and Gemmell et al (2011, 2016).

4. Theoretical underpinnings
Household Problem
𝑉 ℎ, 𝑆 = max 𝑐,𝑎′,𝑙,𝑠 𝑢 𝑐 +𝛽𝐸𝑉 ℎ′, 𝑆′ (2.1)
𝑐+𝑎′=(1− 𝜏 𝑙 + 𝜃 𝑙 )𝑤ℎ𝑙+ 1+ 1− 𝜏 𝑟 + 𝜃 𝑟 𝑟 𝑎−𝑁𝐷.𝑇𝑎𝑥 (2.2)
ℎ ′ = 𝑓 ℎ 𝑠 ;ℎ, 𝑆 (2.3)
𝑠+𝑙=1 (2.4)
Government
𝑁𝑒𝑡.𝑅𝑒 𝑣 𝑡 = 𝑇 𝑡 +𝑁𝐷.𝑇𝑎 𝑥 𝑡 +𝐸 (2.5)
Firm’s Production
𝑌 =𝑍( 𝐸 1 , ⋯,  𝐸 𝐾 , 𝑋)⋅𝐹 𝐻⋅𝐿, 𝐾 (2.6)
GDP Accounting Equation
𝑌=𝐶+𝐼+𝐸+𝑁𝑋 (2.7)

5. Trend of Gov’t Spending
Source: OECD database,
Normalized COFOG and GDP data in 2010 US dollar and calculated government spending share to GDP for each country.

6. Data (GFS vs. SNA)
The COFOG is the abbreviate of the Classification of the Functions of Government and there are two dominant standard ways of reporting: Government Finance Statistics (GFS) suggested by IMF and System of National Accounts (SNA) adopted by OECD. (see Jeong (2017) and IMF (2014) GFSM for more detailed comparison between GFS and SNA) In this article, we focused on the OECD countries which showed more stable economic phases and reliable dataset regarding government expenditure. Thus, we extensively used SNA COFOG data which has more rich information about OECD countries. Bloch et al. (2016) introduced that “the new OECD Public Finance Dataset is motivated by the need for a detailed breakdown of public expenditure and revenues designed specifically for the analysis of the effect of theses spending and revenue items on growth and equity.”

7. COFOG 체계

8. Empirical Specification
Growth Regression (Based on First Level COFOG)
𝑔 𝑖𝑡 =𝛥 𝑦 𝑖𝑡 =( 𝛽 1 log 𝐺𝐷𝑃 𝑃𝑂𝑃 𝑖𝑡−1 + 𝛽 2 𝐶𝑂𝐿 𝑃𝑂𝑃 𝑖𝑡−1 + 𝛽 3 𝑃𝑂 𝑃 𝑅 𝑃𝑂 𝑃 𝑊 𝑖𝑡−1 + 𝛽 4 𝐶 𝐺𝐷𝑃 𝑖𝑡−1 + 𝛽 5 𝐼 𝐺𝐷𝑃 𝑖𝑡−1 + 𝛽 6 𝐸 𝐺𝐷𝑃 𝑖𝑡−1 + 𝛽 7 𝑁𝑋 𝐺𝐷𝑃 𝑖𝑡−1 + 𝛽 8 𝑁𝑒𝑡.𝑅𝑒𝑣 𝐺𝐷𝑃 𝑖𝑡−1 + 𝛽 9 𝑁𝐷.𝑇𝑎𝑥 𝐺𝐷𝑃 𝑖𝑡−1 + 𝛾 𝑘 𝐸 𝑘 𝐸 𝑖𝑡−1 )+ 𝜁 𝑘 𝛥 𝐸 𝑘𝑖𝑡 𝐸 𝑖𝑡 + 𝜇 𝑖 + 𝜔 𝑡 + 𝜖 𝑖,𝑡 (4.1)
R&D-Growth Regression (Based on Second Level COFOG)
𝑔 𝑖𝑡 = 𝛥 𝑦 𝑖𝑡 =( 𝛼 1 log 𝐺𝐷𝑃 𝑃𝑂𝑃 𝑖𝑡−1 + 𝛼 2 𝐶 𝐺𝐷𝑃 𝑖𝑡−1 + 𝛼 3 𝐼 𝐺𝐷𝑃 𝑖𝑡−1 + 𝛼 4 𝐸 𝐺𝐷𝑃 𝑖𝑡−1 + 𝛼 5 𝑁𝑋 𝐺𝐷𝑃 𝑖𝑡−1 + 𝛼 6 𝐶𝑂𝐿 𝑃𝑂𝑃 𝑖𝑡−1 + 𝛼 7 𝑁𝑒𝑡.𝑅𝑒𝑣 𝐺𝐷𝑃 𝑖𝑡−1 + 𝛼 8 𝑁𝐷.𝑇𝑎𝑥 𝐺𝐷𝑃 𝑖𝑡−1 + 𝛼 9 𝑃𝑂 𝑃 𝑅 𝑃𝑂 𝑃 𝑊 𝑖𝑡−1 + 𝛼 11 𝑅𝑛 𝐷 𝑖𝑡−1 𝐺𝐷 𝑃 𝑖𝑡−1 + 𝜙 𝑘 𝑅𝑛 𝐷 𝑘 𝑅𝑛𝐷 𝑖𝑡−1 )+ 𝜓 𝑘 𝛥 𝑅𝑛 𝐷 𝑘𝑖𝑡 𝑅𝑛 𝐷 𝑖𝑡 + 𝜇 𝑖 + 𝜔 𝑡 + 𝜖 𝑖,𝑡 (4.2)

9. Results

10. Discussion and Conclusion
Our main findings are as follows. the expenditure on economic affair and social protection has both negative short- and long-run coefficient. Similarly, government spending on order and safe and housing has negative coefficients even though they are not statistically significant. In contrast, the spending on education has significant and large positive short- and long-term effect, and public service has positive long-run coefficient. In the analysis of second-level R&D data, Housing R&D expenditure has both short- and long-run negative effect while education R&D spending has positive long-term coefficient.
Based on the empirical results, we may suggest the way of re-structuralization in the view of the efficiency of the government spending. Under the limited budget, the government may more focus on the education and public service for the long-term economic growth. Especially, the long-run impact of the education is relatively stronger than others, the government spending regarding education will play key role of further economic growth. This policy implication is still hold when we see the result of the analysis of the second-level R&D data. As the education R&D has long-term positive effect on the economic growth, it is desirable to concentrate more fiscal budget to this category. In sum, the government spending on education is the most influential factor of the economic growth in the sample of the developed countries and they could promote their economic growth via re-composition of their spending portfolio toward human capital accumulation

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