Sectoral Favors in Corporate Income Taxation and ...
Cross-Sector Tax Discrimination and Economic Performance:
A Cross-Country Analysis
Young Lee*
Hanyang University
Taeyoon Sung**
Yonsei University
Taejong Kim***
KDI School of Public Policy and Management
June 2008
Abstract
Using corporate financial statement data in 70 countries, this paper measures tax burden in each sector and analyzes the impact of tax burden on the growth of sector and overall economy. We find that sectors and countries with lower effective tax rates tend to grow faster. In addition to the level of tax rates we also examine the effect of cross-sector differences in tax burden within a country, which can be seen as a proxy for industrial policy through taxation. We find that industrial policy through taxation hampers the growth of other sectors and fails to promote the economic growth, though it can promote the growth of the favored sector. We also find that negative effect of industrial policy is stronger in OECD countries, implying that the room for government intervention becomes smaller as the economy and the market develops.
Key words: Corporate Income Taxation, Industrial Performance, Economic Growth, Preferential Corporate Taxation
JEL classification: H21; E62
________________________
* Corresponding Author. Professor Young Lee, College of Economics and Finance, Hanyang University, Seoul 133-791, Korea. E-mail: younglee@hanyang.ac.kr
** Professor Taeyoon Sung, School of Economics, Yonsei University, Seoul, 130-722, Korea. E-mail: tsung@yonsei.ac.kr
*** Professor Taejong Kim, KDI School of Public Policy and Management, Seoul, 130-868 Korea. E-mail: tjkim@kdischool.ac.kr
I. Introduction
We attempt to measure cross-sector discrimination in corporate income taxation and analyze its impact on both industry-level performance and overall economic growth. For this purpose, we utilize a large-scale international panel data of firm-level financial statements. Given the data set, we can estimate effective marginal tax rates cross industries, and examine the relationship of the estimated rates with economic performance. In particular, since we have estimated effective marginal tax rates even at the industry level, we can examine the implications of cross-sector differences, as well as the absolute level of corporate tax burden, on economic performance.
The discriminatory application in corporate taxation, such as preferential tax rates, depreciation rules, and tax credits for investment and R&D expenditures, is a widely-adopted policy measure across countries. This measure can be interpreted to represent a kind of industrial policy through taxation. Theoretically, the overall impact of cross-sector tax discrimination on economic growth is ambiguous. First, the discrimination policy in taxation can distort resource allocation, and thus hampering growth.[1] On the other hand, the policy may compensate for positive externalities from tax-favored sectors to the rest of the economy. Thus, in this case, a prerequisite for the effectiveness of tax favors is the ability of the government to identify appropriate industries and set tax rates at optimal levels. Thus, figuring out whether the discriminative tax policy promotes economic growth calls for an empirical investigation.
The rest of the paper is organized as follows. In Section 2, we begin by reviewing the literature on the relationship between taxation and economic growth, and survey various concepts of the effective tax rate. The review also summarizes the literature on corporate tax burden and economic growth. Section 3 measures effective marginal tax rates across sectors for each country. To capture the effective marginal tax rate in each industry, we estimate coefficient from regressing corporate income taxes on before-tax earnings of firms in a given sector. For this purpose, we employ the OSIRIS dataset, a large-scale international longitudinal database of corporate financial statements.[2] Section 4 analyzes how estimated industry-level tax differences affect industry-level growth. Finally, Section 5 studies their impact on overall economic growth. The analysis in Section 5 incorporates the impact of statutory tax rates as well as the estimated effective tax rates. For the analysis in Section 5, we combine the estimated effective tax rates, the aggregate data from the World Bank's World Development Indicators, and statutory tax rates coming from the Worldwide Summary of Corporate Taxes from Price Waterhouse. Section 6 provides concluding remarks.
2. Literature Review
As already mentioned, distortion in taxation can disturb efficient resource allocation, and thus, hamper capital accumulation and growth in the overall economy (i.e., see Feldstein, 1974, 1978; Chamley, 1981; Becker, 1985; Judd, 1985; Jones and Manuelli, 1990; Rebelo, 1991). However, if taxation appropriately compensates for positive externalities from some sectors to the rest of the economy, taxation may contribute to efficient allocation of resources and economic growth.
Thus, the theoretical perspectives call for empirical investigation on the effect of taxation on growth. However, in literature, empirical studies report mixed results. For example, King and Rebelo (1990) and Jones, Manuelli, and Rossi (1993) report that corporate income taxation reduces economic growth. On the other hand, some studies, such as Lucas (1990) and Stokey and Rebelo (1995), suggest that the effects are either insignificant or may even work in the opposite direction. They are mostly calibration studies based on US data.
Ambiguity in calibration results also call for empirical studies that attempt to trace the impact of taxation in the growth regression framework by using the cross-country data. These studies include Skinner (1987), Koester and Kormendi (1989), Easterly and Rebelo (1993), Dowrick (1996), Agell, Lindh, and Ohlsson (1997), Bibbee, Leibfritz, and Thornton (1997), Mendoza, Milesi-Ferretti and Asea (1997). These studies suggest either negative or insignificant impact of overall corporate income taxation on economic growth. More recently, Lee and Gordon (2005) utilized a newly-available data on statutory corporate income taxes, and find a negative and significant effect of corporate income tax rates on economic growth. Not only were the results robust to the inclusion of other growth factors, but also their instrumental variable and fixed effects estimates confirmed the basic results.
In contrast to the earlier studies on the relationship tax and economic growth, this paper focuses on effective tax rates and cross-sector differences in corporate income taxation, and their impact on economic performance. Hence, this study supplements the existing literature in the sense that corporate taxation may affect overall growth not just through the aggregate tax rate overall, but also through the preferential treatment of certain industries. Cross-sector tax discrimination may disturb the equalization of before-tax rates of return across industries, and thus hamper the efficient resource allocation and capital accumulation.
3. Measuring Industry-Level Tax Burden
The main data analyzed in this paper come from the OSIRIS database provided by Bureau van Dijk Electronic Publishing. OSIRIS reports financial data for 47,180 business corporations from 1978 to 2006 from 138 countries. Due to missing values and entry & exit of firms, the actual number of firms in the data is smaller. For example, the number of firms with non-missing value of before-tax earnings is around 30,000 in 2005 from 107 countries. By using the financial data of business corporations, we construct industry-level and country-level variables. We generate four 5-year sub-samples: late 1980's (1986-2000), early 1990's (1991-1995), late 1990's (1996-2000), and early 2000's (2001-2005). For these four periods, we construct industry-level and country-level time-series, cross-section data. Our data, industry-level or country-level, is an an unbalanced panel.
Our key variable, effective tax rate (hereafter, ETR), comes from regressing corporate income taxes on before-tax corporate earnings. We followed method used in Koester and Kormendi (1989) which estimate effective marginal tax rates for a country by regressing government's tax revenue on GDP for a sample of countries. To prevent outliers from exercising undue influence on our estimates, we adopt median regressions for our purpose. Reported before-tax earnings are adjusted for possible loss-carry-forward and loss-carry-backward. When we estimate ETR, we use only firms with positive (loss-carry-adjusted) earnings and positive tax payments, which results in around 30% decrease in the number of firms. Using only firms with normal operation improves the fit considerably.
Our estimated ETR reflect not just statutory marginal tax rates (hereafter, STR) but also a myriad of provisions such as rules applying to investment and R&D tax credits and special tax exemptions.[3] Our estimation is an attempt to capture the overall impact of statutory tax rates, tax credits and special tax exemptions.
Our country-level estimates of ETR are strongly correlated with statutory corporate tax rates. The correlation coefficient between ETR and STR is 0.50 with p-value 0.000. shows how ETR and STR are related for each country in the 2001-2005 sub-sample. If the estimated effective tax rates coincide with statutory tax rates, observations come along with the 45-degree. Although there are some cross-country differences, overall, the estimated ETRs turn out to be consistent with STRs.
Not all the observations appear exactly on the 45-degree line. Some countries appear above the 45-degree line, while other countries are located below the 45-degree line. For the countries above the 45-degree line, the estimated effective tax rates are higher than statutory tax rates. Given that our statutory tax rates are rates by the central government, we can observe this pattern if local government tax burden on corporate income is substantial. This pattern is observed in several OECD countries, such as Norway, Switzerland, Japan, Germany, Italy, and Ireland. We also observed that ETR is higher than STR in some countries with zero statutory rates, such as Jordan, Bermuda, Cayman Islands, Saudi Arabia, and Kuwait.
On the other hand, when various tax exemptions are provided, the estimated effective tax rates can be lower than statutory tax rates. In this case, observations can appear below the 45-degree line. We observe that ETR is lower than STR by more than 10%p in Egypt, Ecuador, Paraguay, Latvia, Belgium, Taiwan, Sri Lanka, Israel, Philippines, Luxembourg, Austria, and Pakistan.
We examine how effective tax rates changed over time for three groups of countries: all, OECD countries, and non-OECD countries. Since our data is an unbalanced panel, we cannot use a simple average of estimated ETR, and need to control for missing values. Using country-level data, we run simple regressions of estimated ETR on period dummies and country dummies. shows that, on average, our estimated ETR decreased by 8%p from 32% in the late 1980s to 24% in the early 2000s.[4] Decrease in effective tax rates is more salient in non-OECD countries. The average ETR in non-OECD countries dropped by 11%p, while those in OECD countries dropped only by 5%p. STR also dropped considerably over the same period. One interesting pattern is that the difference in ETR between OECD and non-OECD countries becomes much larger than that in STR, suggesting that tax credits and tax exemptions become used more widely in non-OECD countries over time.
We estimate ETR not just for countries but also for industries in a given country. Our classification of industry is based on Standard Industry Code (SIC). Starting from one-digit SIC code, we combine or divide them to have similar numbers of observation and to have several key industries as a separate category. We classify firms into 16 industries: agriculture, forestry, and fishing (two-digit SIC code between 1-9), mining (10-14), construction (15-19), other manufacturing (20-27, 31-34), chemical (28-30), industry machine & transportation equipment (35, 37), electricity (36), transportation (40-47), communication (48), utility, i.e. electric, gas, and sanitary services (49), wholesale trade (50, 51), retail trade (52-59), depository (60), non-depository (61-69), service (70-79, 81-89), and health (80).
presents ETR at the industry level for three groups of countries: all, OECD, and non-OECD. As in the investigation of over-time changes in ETR, we use a simple regression to examine ETR by industry.[5] Those figures are calculated using estimated coefficients of industry dummies in the regressions of ETR on industry dummies, period dummies, and country dummies. It is observed that on average, non-depository, service industries, and communication are less heavily taxed than construction, wholesale, depository, and utility. There are some differences between OECD and non-OECD countries. Agriculture and retail are more heavily taxed in non-OECD, while construction is more heavily taxed in OECD.
Estimating effective tax rates for individual sectors allows us to evaluate the cross-sector differences in effective tax rates within a country. We use the standard deviation of ETR across sectors within a country as a measure indicating a kind of industrial policy through taxation. The choice of the standard deviation as a proxy for cross-sector tax discrimination merits discussion. If a country decides to lowers (raises) tax rates for industries with rates higher (lower) than the average rate while preserving the average tax rates, it would represent a reduction in cross-sector discrimination and be well captured by a decrease in the standard deviation of ETR. On the other hand, a country reduces tax rates for each industry by the same %p, the level of ETR will be lower but standard deviation of ETR will not be changed.
The standard deviation of ETR is especially low in countries such as the U.S. (0.03), UK (0.06), Denmark (0.07), France (0.07), and Finland (0.07). Among OECD countries, Norway (0.16), Austria (0.15), and Belgium (0.15) are countries with large standard deviation of ETR. Note that these countries with higher standard deviation tend to have higher level of ETR.[6] Since we include both ETR and the standard deviation of ETR as independent variables in regressions, the standard deviation captures (not across industry) across country and period variability of ETR after controlling for the average level of ETR.
5. Preferential Tax Treatment and Industry Growth
Do preferential treatments in taxation promote growth in the favored industries? How do the preferential treatments affect capital formation and employment in the favored industries? How do industrial policy through taxation affect the overall growth of industry? This section provides empirical results to answer these questions.
Industry growth of the following five variables are used as dependent variable: after-tax earnings, before-tax earnings, net sales, total assets, and employment. The first two variables measure profits, the third variable measures output, and the last two variables measure inputs. Since corporate income taxes are basically on equity-financed capital, it is expected to affect assets much more strongly than employment. In the calculation of industrial growth, we do not drop firms with negative earnings, because we are measuring growth of industry not of firms. As mentioned earlier, we have four 5-year sub-samples. Combined with the classification of industries into 16 sectors, the maximum number of observation is 4,480 period-industry cells for 70 countries (=4*16*70) in total. Among 4,480 possible cells, we can estimate ETR and industrial growth for 1,486 cells. Dropping top and bottom 2% observations in the value of growth rate of before-tax profits leave us with 1,411 observations. We did so because there are some observations with extreme values of growth rates. In median regressions for the whole sample without dropping these observations with extreme values, we find qualitatively the same results.
In addition to ETR and standard deviation of ETR, we added log of initial GDP per capita to control for the tendency of low-income countries’ growing faster. We also added industry dummies, period dummies, and country dummies. Our specification for industrial growth regressions is:
[pic]
where subscript i indicates country, subscript j industry, and subscript T period,
[pic] is growth rates of industry,
[pic] is estimated effective tax rates,
[pic] is standard deviation of ETR within a country,
[pic] is log of initial GDP per capita in constant US dollar,
[pic] is industry dummies,
[pic] is period dummies, and
[pic] is country dummies.
In addition to pooled OLS, we also use instrumental variable (IV) estimation method. Surely, one cannot dismiss a possible effect from industry growth to tax rates. It is possible that high industry growth allows a lower tax rates if the government needs a fixed amount of tax revenue. We use as instruments the weighted average ETR in other countries, weighting by the inverse of the distance between the two countries. In a similar way, we also construct IV for standard deviation of ETR. Lee and Gordon (2005) use the similar IV in the investigation of the impacts of statutory tax rates on economic growth. The correlation in the tax rates in nearby countries is remarkably high in the data.[7] Tax competition would make the weighted average of ETR and standard deviation of ETR elsewhere a good instrument for the local tax rates and its standard deviation.
provides summary statistics of the variables for industry-level regressions. ETR is on average 28%, ranging from 0% to 98%. Standard deviation of ETR within a country is on avearge 0.095. Growth of before-tax and after-tax earnings are on average around 20-22%. Among various measures of industry growth, before-tax and after-tax earnings are most closely correlated with correlation coefficient 0.89. The correlation coefficient between sales and assets is 0.81, while that between sales and employment is only 0.28.
presents OLS regression results for investigating the relationship between ETR and industry growth. We include industry and period dummies in all regressions. Column (1) shows that industries with lower values of ETR have high growth rates, as expected. In Column (2), we added standard deviation of ETR within a country to find that both the level and standard deviation of ETR are significantly negative. In column (3) we added the log of the initial GDP per capita as an independent variable. In all regressions with log of initial GDP per capita, it takes the expected sign.
The level of ETR is found to be significantly correlated with various measures of industry growth except for being insignificant on employment. Our OLS results indicate that a 10 percent point reduction in ETR would increase growth rates for before-tax earnings, after-tax earnings, net sales, and assets by 3.07%p, 2.86%p, 2.97%p, and 2.47%p respectively.
Standard deviation of ETR has a negative coefficient in the regressions for before-tax and after-tax earnings, implying that more cross-sector differences can hamper industrial performance in terms of earnings. However, as for net sales, the coefficient turns out to be insignificant, although it is negative. It is interesting to observe that growth rates of assets are correlated strongly negatively with ETR and standard deviation of ETR, while growth rates of employment are significantly correlated with neither ETR nor standard deviation of ETR. This suggests the impacts of corporate income tax are mostly on capital not on employment.
IV estimation results without country dummies are reported in . ETR and standard deviation of ETR become larger (in absolute value) and more significant in IV estimation results. In fact, ETR and standard deviation of ETR become statistically significant except for regressions for employment. Columns (1) and (3) of imply that a one-standard-deviation decrease in ETR is associated with around 5.5% increase in before-tax and after-tax earnings. A decrease in one standard deviation of ETR by 0.04 is estimated to be associated with around 7.3-9.4% increase in before-tax and after-tax earnings.
In , we also examine possible differential effects of ETR on growth by country groups. We find that the level of ETR is significantly negatively correlated with growth in non-OECD countries, while this correlation is not observed in OECD countries. This result is consistent with the findings of Lee and Gordon (2005) using the statutory tax rates. However, more interestingly, the negative impacts of standard deviation of ETR are estimated to be much larger and more significant in OECD countries. Perhaps this indicates that the cost of industrial policy becomes larger in countries with more developed markets. Policy implication of this finding, of course, is that the industrial policy should be avoided in the developed market economy.
reports regressions results when country dummies are added as independent variable. Generally, the estimated effects of ETR and standard deviation of ETR becomes smaller. However, ETR still remains strongly negatively correlated with industry growth, and negative impact of standard deviation of ETR on growth of earnings remains statistically significant for whole and OECD countries.
6. Cross-Sector Tax Discrimination and Economic Growth
Lower corporate income tax rates are likely to contribute to faster economic growth by promoting entrepreneurship and investment. In fact, Lee and Gordon (2005) present evidence that economic growth is faster in countries with lower statutory corporate income tax rates. Djankov et. Al (2008) find that effective corporate tax rate have a large adverse impact on aggregate investment, FDI, and entrepreneurial activity. In addition to the aspect of lower corporate income taxes, this section examines how cross-sector discrimination in taxation affects overall economic growth. Industry-level tax discriminations, as measured by dispersion in ETR across industries, are an indication of cross-sector preferential corporate taxation in a given country.
Industry-level tax favors may in principle promote economic growth when they are appropriately targeted at industries with positive externalities and benefit of these positive externalities is larger than the cost from distorting efficient allocation of resources across industries and between corporate and non-corporate sector. However, if the targeting of government misses the mark due to either its incompetence or political pressures or both, the overall impact on economic growth can be negative.
As mentioned before, the country-level data is an unbalanced panel of country and periods. Our country-level data consists of 161 observations: 28 countries in the late 1980s, 37 countries in the early 1990s, 41 countries in the late 1990s, and 55 countries in the early 2000s. The dependent variable is the growth rate of GDP per capita within each period. As independent variables, we include other key determinants of economic growth identified in the literature as well as ETR and standard deviation of ETR. We include log of the initial GDP per capita, average inflation rates, and average trade openness. The specification used for the investigation of economic growth is:
[pic]
where subscript i indicates country, and subscript T period,
[pic] is growth rate of GDP per capita,
[pic] is estimated effective tax rates for each country-period,
[pic] is standard deviation of ETR within a country ,
[pic] is log of initial GDP per capita in constant US dollar,
[pic] is average inflation rate using CPI,
[pic] is average of (imports+exports)/GDP, and
[pic] is period dummies.
reports basic descriptive statistics for variables of the country-level data. On average, the country grows at 2.8%. The average ETR is 28%, which is virtually the same as the average ETR in the industry-level data. The average statutory tax rate is 32%.[8]
reports regression results. Column (1) shows that the raw correlation between ETR and economic growth is statistically significant and negative. The correlation between ETR and economic growth becomes 20% smaller when other determinants of economic growth are added. Estimated coefficients of initial GDP per capita, inflation rates, and trade openness take the expected sign. Columns (3) and (4) show that using STR instead of ETR produces a very similar result. Interestingly, explanatory power of ETR in terms of [pic] is larger than that of STR. This is confirmed in column (5), where ETR is statistically significant but STR is not significant when both ETR and STR are entered.
Columns (6) through (10) report IV regression results. The estimated coefficients of ETR and STR become larger in absolute value in IV estimations. When both ETR and STR are entered in column (8), they are estimated to be significantly negative. Here, though the estimated coefficient of STR is larger than that of ETR in the absolute value, t-value of the estimated coefficient of ETR is larger than that of STR. In column (9), we check differential effect of ETR on economic growth of OECD and non-OECD countries. As reported in Lee and Gordon (2005), we find that lower corporate tax rates lead to higher economic growth in non-OECD countries, while this pattern is not observed in OECD countries.
In column (10) we added the standard deviation of ETR. Here, both ETR and the standard deviation of ETR are insignificant, contrary to the finding of the strong negative effect of standard deviation of ETR on industry growth. The reason for insignificant effect of standard deviation of ETR on economic growth might be a smaller number of observations and/or omitted variables, such as the share of corporate sector. Note, however, that we don’t find evidence that industrial policy through taxation promote economic growth either.
7. Concluding Remarks
This paper uses a firm-level data set of financial statements for a large sample of corporations around the world to estimate sector-specific ETR in corporate income taxation. We further investigate the effects of effective tax rates on growth of the favored sectors and of the economy as a whole. The key findings can be summarized as follows. First, industry and country with lower effective tax rates tend to grow faster. In the industry-level regression, we find that sectors with low tax rates show a higher growth of earnings, sales, and assets. Employment, however, is not found to be significantly associated with the tax rate.
Second, it is found that industrial policy through preferential treatment in corporate income taxation fails to achieve the desired effect. In the industry-level regressions, we find that standard deviation of ETR is significantly negatively correlated with sector growth. In the country-level regressions, we do not find evidence that countries with larger standard deviation of ETR grow faster.
Our results show that industrial policy through taxation hampers the growth of other sectors and fails to promote the economic growth, though it can promote the growth of the favored sector. This might reflect the lack of government capacity to pick industries appropriate for favors, or high-positive-spillover industries, or the proneness of the government to succumb to political pressures. In either case, industrial policy through corporate income taxation would not be an appropriate policy tool, if the desired outcome is to promote overall economic growth, not merely the growth of the preferred sectors or industries.
Third, we find that negative effect of industrial policy is stronger in OECD countries, where the developed markets make government intervention more costly. This implies that the room for government intervention becomes smaller as the economy and the market develops.
Fourth, we find a large drop in the effective corporate income tax rates as well as statutory tax rates. Interestingly, the effective tax rates did not drop as much as statutory tax rates in OECD, while the former dropped more than the latter in non-OECD. Among various industries, it is observed that on average, non-depository, service industries, and communication are less heavily taxed than construction, wholesale, depository, and utility.
Fifth, we observed a negative effect of initial GDP per capita on industry growth, implying that convergence is observed not only in the country-level data but also in the industry-level data.
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Summary Statistics for Industry-Level Regressions
|Variable |Notation |Obs. |Mean |Std. Dev. |Min |Max |
|growth rate |Ebtgr |1,411 |0.203 |0.407 |-0.642 |2.038 |
|for before-tax profits | | | | | | |
|growth rate |Eatgr |1,340 |0.223 |0.459 |-0.852 |6.295 |
|for after-tax profits | | | | | | |
|growth rate |NSgr |1,408 |0.149 |0.479 |-0.694 |10.236 |
|for net sales | | | | | | |
|growth rate |Assetgr |1,411 |0.144 |0.355 |-0.677 |3.881 |
|for total asset | | | | | | |
|growth rate |Empgr |901 |0.223 |0.579 |-0.890 |4.856 |
|for employment | | | | | | |
|effective tax rate |ETR_ijT0 |1,411 |0.277 |0.126 |0.000 |0.979 |
|estimated for each industry | | | | | | |
|st. deviation of effective tax rate|ETRsd_iT0 |1,411 |0.095 |0.042 |0.001 |0.296 |
|across industry | | | | | | |
|GDP per capita |GDPpcIni |1,411 |15,019 |10,370 |216 |47,281 |
|of the initial year | | | | | | |
ETR and Industry Growth, Baseline Regressions, OLS
| |(1) |(2) |(3) |(4) |(5) |(6) |(7) |
|dependent variable |Ebt |Ebt |Ebt |Eat |NS |Asset |Emp |
|ETR_ijT0 |-0.357 |-0.377 |-0.307 |-0.286 |-0.297 |-0.247 |-0.110 |
| |(0.088)*** |(0.090)*** |(0.090)*** |(0.105)*** |(0.106)*** |(0.076)*** |(0.167) |
|ETRsd_iT0 | |-0.521 |-0.629 |-0.669 |-0.048 |-0.404 |-0.016 |
| | |(0.262)** |(0.260)** |(0.303)** |(0.305) |(0.220)* |(0.462) |
|log of GDPpcIni | | |-0.051 |-0.051 |-0.079 |-0.071 |-0.067 |
| | | |(0.009)*** |(0.011)*** |(0.011)*** |(0.008)*** |(0.019)*** |
|Constant |0.332 |0.393 |0.855 |0.932 |1.009 |1.006 |1.265 |
| |(0.071)*** |(0.077)*** |(0.113)*** |(0.131)*** |(0.133)*** |(0.096)*** |(0.238)*** |
|Obs. |1,421 |1,411 |1,411 |1,340 |1,408 |1,411 |901 |
|Adjusted [pic] |0.014 |0.017 |0.037 |0.031 |0.045 |0.095 |0.078 |
Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
ETR and Industry Growth, IV, OECD vs. non-OECD (with OECD dummy)
| |(1) |(2) |(3) |(4) |(5) |(6) |
| |Ebt |Ebt |Eat |Eat |NS |NS |
|growth rate of GDP per capita |GDPpcgr |161 |0.028 |0.022 |-0.017 |0.099 |
|Effective tax rate estimated for |ETR_iT0 |161 |0.281 |0.118 |0.029 |0.986 |
|each country | | | | | | |
|st. deviation of effective tax |ETRsd_iT0 |152 |0.098 |0.047 |0.001 |0.296 |
|rate across industry | | | | | | |
|Statutory tax rate |STR |161 |0.324 |0.089 |0.000 |0.540 |
|Initial GDP per capita |GDPpcIni |161 |12,564 |10,913 |216 |37,992 |
|Average inflation rate using CPI |InfCPIAvg |161 |0.068 |0.115 |-0.013 |0.793 |
|(imports+exports) / GDP |TradeGDPAvg |161 |0.785 |0.587 |0.171 |4.062 |
ETR and Country-level Growth
|(1)
OLS |(2)
OLS |(3)
OLS |(4)
OLS |(5)
OLS |(6)
IV |(7)
IV |(8)
IV |(9)
IV |(10)
IV | |ETR_ijT0 |-0.050
(0.013)*** |-0.039
(0.013)*** | | |-0.032
(0.014)** |-0.044
(0.015)*** | |-0.031
(0.016)* |-0.013
(0.021) |-0.069
(0.018)*** | |ETR_ijT0 X CntOECD | | | | | | | | | |0.078
(0.035)** | |ETRsd_iT0 | | | | | | | | |0.010
(0.052) | | |STR | | |-0.050
(0.022)** |-0.050
(0.022)** |-0.028
(0.024) | |-0.080
(0.029)*** |-0.052
(0.031)* | | | |lnGDPpcIni | |-0.004
(0.001)*** | |-0.005
(0.001)*** |-0.005
(0.001)*** |-0.004
(0.001)*** |-0.006
(0.001)*** |-0.005
(0.001)*** |-0.006
(0.001)*** |-0.006
(0.002)*** | |InfCPIAvg | |-0.029
(0.015)* | |-0.024
(0.015) |-0.029
(0.015)* |-0.030
(0.015)* |-0.025
(0.015) |-0.030
(0.015)* |-0.033
(0.016)** |-0.032
(0.015)** | |TradeGDPAvg | |0.009
(0.003)*** | |0.010
(0.003)*** |0.009
(0.003)*** |0.009
(0.003)*** |0.009
(0.003)*** |0.008
(0.003)** |0.012
(0.003)*** |0.011
(0.003)*** | |OECD dummy | | | | | | | | | |-0.019
(0.011)* | |Constant |0.048
(0.006)*** |0.078
(0.013)*** |0.050
(0.009)*** |0.091
(0.017)*** |0.091
(0.017)*** |0.080
(0.013)*** |0.107
(0.020)*** |0.103
(0.020)*** |0.081
(0.014)*** |0.100
(0.018)*** | |Obs. |161 |161 |166 |161 |161 |161 |161 |161 |152 |161 | |Adjusted [pic] |0.082 |0.169 |0.020 |0.149 |0.171 | | | | | | |[pic] | | | | | |0.204 |0.176 |0.206 |0.191 |0.197 | |ETR_ijT0 for OECD
[p-value] | | | | | | | | | |0.009
[0.733] | |Standard errors in parentheses
+ significant at 10%; * significant at 5%; ** significant at 1%
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[1] In a neoclassical setup, taxes do not have impact on determining the long-term growth as long as productivity is not affected by taxes, although taxes can change output in the long run. However, even in the neoclassical model, tax changes can move short-term output growth along a transitional path to the new steady state. For example, as shown in Hall and Jorgenson (1967), low effective tax rates on new investment can derive faster short-run growth, due to an investment increase in response to lower tax rates. Furthermore, in the context of endogenous growth models, taxes can have long-term effects since they can influence on entrepreneurial activity, technology, and productivity growth.
[2] For the detailed explanation on the database, see Section 3.
[3] Our effective marginal tax rates do not reflect differences in depreciation allowance and interest payment because they are already deducted in the calculation of before-tax earnings. To reflect effects of depreciation allowance and corporate financial structure on effective tax rates, one needs to regress corporate income taxes on before-tax earnings plus depreciation and interest payments. We explore the effect of differences in depreciation and corporate financial structure on effective tax burden in a separate paper.
[4] shows that, on average, statutory tax rates also decreased by around 10%p from 37% in the late 1980s to 27% in the early 2000s.
[5] Of course, we use industry-level data in the calculation of ETR by industry, while we use country-level data in the calculation of over-time changes in ETR.
[6] The correlation coefficient between ETR and standard of ETR is 0.180 with p-value 0.027.
[7] The correlation between ETR and the weighted average of ETR in other countries is .7434 with p-value 0.000, while the equivalent correlation for standard deviation of ETR is .6412 with p-value 0.000.
[8] For the explanation on the pattern and possible reason for the discrepancy between ETR and STR, see section 3.
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