Abstract
This study aims to present a methodology and construct a model for estimating corporate tax revenue, which is one of the main revenues. There is a need for a new tax revenue estimation model to replace both the existing tax revenue elasticity values that have been adopted for estimating tax revenue and the method of directly estimating tax revenue without first analyzing the increase and decrease in the taxable amount. The author focuses on the estimation of corporate tax revenue. To do it, the author focuses on the taxable object, that is, corporate profits, and the corporation that records it (the profit-making corporation). In addition, it hypothesizes the factors that increase or decrease the declared income, which are GDP growth rate, inflation rate, unemployment rate, interest rate, and population, and hypothesizes how the influence of these macro-variables differs in each industry. The author estimates the declared profit for each industry. The author then estimates the corporate tax revenue by multiplying the declared income that the model estimated for each industry by the given tax rates. The result of this two-stage estimation method reveals that the accuracy of the estimation of this model is higher than the estimation values based on the conventional tax revenue elasticity. Furthermore, unlike the conventional tax revenue elasticity method, the author reveals that this model can accurately estimate (approximate) the actual increase in tax revenue in FY2020, when the economic growth was negative.
Keywords: Corporate income tax, Corporate profit, Elasticity, Tax revenue estimate
Introduction
When considering fiscal consolidation, indices of future tax revenue are an important reference. The long-term forecast of Japan government’s tax revenue is made by assuming the elasticity of tax revenue to be 1.1 under the exogenously given growth rate,1 which has been widely used as the standard for tax revenue estimation. Moreover, GDP elasticity values are the criteria for estimating major tax revenues, with 1.30 for corporate tax, 1.22 for income tax, and 1.00 for indirect tax (e.g., consumption and liquor taxes).2
However, the actual tax revenues do not always correlate with GDP. When the author measured the elasticity values of the tax revenue for the past 19 years, the variation was large and unstable over time.3 In addition, the elasticity of tax revenue was negative.4 From the perspective of individual tax items, there are cases where tax revenues do not necessarily have a strong link to changes in GDP. For example, tobacco and liquor taxes depend on tobacco and liquor consumption, respectively. However, tobacco consumption itself is not strongly influenced by economic conditions and is determined by the number of smokers and their autocorrelation (habits). Alcohol consumption depends on economic regulations, entertainment demand, autocorrelation (habits), population factors, and public health regulations, such as the government’s declaration of a state of emergency. Moreover, the most basic factor that determines tax revenue is the tax system itself, such as the tax rate. The elasticity of tax revenue obtained from the relationship between GDP and tax revenue in the past did not consider changes in tax factors, and if the tax rate table is revised, it can no longer explain the change in tax revenue that accompanies it.
Based on the conventional elasticity of tax revenue, during the 2020 COVID-19 economic crisis, it was expected that the actual tax revenue would decrease significantly, but contrary to expectations, it increased. Although the nominal GDP growth rate in 2020 was − 4.1% (real GDP growth rate was − 4.5%), tax revenue has increased.5 Thus, it is clear that tax revenues do not necessarily increase or decrease in parallel with GDP growth. With the existing assumptions and analysis methods, it is difficult to explain the reason for the increase in tax revenue, especially when the economic situation deteriorates.
Therefore, in this study, we propose a methodology for estimating tax revenue and consumption tax by advocating that they should be calculated mechanically based on the tax systems. Thus, we first focus on investigating the determinants of the taxable object, that is, corporate profit (declared income amount), which is the basis of the tax revenue. The author believes that tax revenue cannot be estimated correctly without analyzing the size of the taxable object—corporate profit.
In this study, we focus on profit-making corporations. This is because even if an economic crisis occurs and the resulting decline in production is concentrated on loss-making corporations, which account for 62% of the number of corporations,6 the amount of corporate tax, corporate inhabitant tax, corporate enterprise tax, and other taxes cannot be less than 0 (or lower than the limit). Therefore, the effect of the decrease in tax revenue will be limited even if the deficit of loss-making corporations expands.7 However, even in a situation where economic growth is negative, if the profit of a profit-making corporation, which accounts for only 38% of the number of corporations,8 increases, its impact on the economic growth of a country might be relatively small due to its scale. However, focusing on a tax system that is based on the amount of profit per corporation will help improve the fiscal balance of a country. Currently, tax revenues are projected by aggregating profits and losses of both profitable and loss-making corporations. This approach offsets the surplus and deficit between businesses, thereby underestimating the target operating income. Therefore, it is ideal to focus on the declared income of profit-making corporations. Using this approach, we determine the target of the analysis and construct a model to explain corporate profits, forming the basis of determining corporate tax by configuring “price,” “unemployment,” “interest rate,” “population,” and “GDP,” which has been an index of conventional tax revenue elasticity, and analyzing their effects on the amount of corporate profits.
Furthermore, this study attempts to explain the problem that the influence of these macroexogenous variables could differ depending upon the industry. Thus, although in some industries, such as the restaurant and travel industries, where production restrictions were imposed under the state of emergency as measures to prevent the spread of COVID-19, profits declined. However, some industries produced high-tech equipment, such as PC equipment and game software, to increase profits. Therefore, in this study, to accurately predict the future tax revenue of the national government, we calculate corporate profit (declared income amount) and the average profit based on the actual situation of each industry and business operator. Then, we multiply the average corporate profits and the tax rate for each and estimate the gross amount of corporate tax revenue. In estimating the tax amount, the reduced tax rate is applied to declared profits that are less than 8 million yen for small- and medium-sized enterprises (SMEs).9 In this study, we compare the estimated and actual tax revenues obtained using this formula to confirm the accuracy of the model. Furthermore, we use this model to estimate corporate tax return10 by industry in FY2020, which had not been reported at the time of this research, and based on the estimated value, we estimate the corporate tax revenue. We determine whether the corporate tax revenue in FY2020, when GDP growth was negative, increases and compares it with the projected corporate tax revenue.
In summary, there are two primary aims of this study. First, this study aims to estimate declared incomes by industry, examining the macroeconomic variables that affect corporate tax returns (corporate declared profit). In addition to the nominal GDP growth rate, which is the only macroeconomic variable that has been applied in the existing literature, consumer purchase index (CPI), unemployment rate, interest rate, and population are applied in this study to determine whether the influence of the macroeconomic variables differs among the industries. Second, this study aims to estimate corporate tax revenue by multiplying corporate declared profit by the given tax rates, including the reduced tax rate for SMEs, and compares the results with the actual tax revenue. This study systematically reviews the data about corporate declared income by industry, aiming to clarify the role of this two-stage estimation method and the rationality. The aim of this study is not necessarily to present a comprehensive review of estimating total tax revenue but focuses on the method of determining corporate tax revenue. A full discussion of investigating total tax revenue lies beyond the scope of this study. Although most studies about estimating tax revenue only focused on the overall tax revenue, the analysis of individual tax items has been neglected. The impact of macroeconomic conditions on tax revenue has been poorly analyzed; thus, the revision of the elasticity of tax revenue and the improvement of the estimation method have not progressed enough. We believe that this research will make an important contribution by presenting a method to estimate corporate tax revenue that considers the financial base as one of the major revenues. Furthermore, this study will serve as a significant reference for policymakers when they are considering the economic measures of tax systems.
Previous research
Most studies about estimating tax revenue only focused on the total amount of revenue. Despite the importance of forecasting tax revenue, only a few existing studies have predicted tax revenue by examining the determinants of tax revenue for each tax item. Ichikawa and Hayashi [13] estimated the source of tax revenue, and Ichikawa and Hayashi [12], Creedy and Gemmell [5, 6], Giles and Hall [8], Hutton and Lambert [11], Johnson and Lambert [15], and Hayashi [10] comprehensively estimated individual tax revenue functions. In their studies, tax revenue function was estimated after examining the actual tax system in detail. However, their studies are based on the tax structure at that time and cannot be used for future estimation. Further, Kitaura and Nagashima [16]11 analyzed tax revenue trends, focusing on the fact that tax revenue elasticity is greatly affected by factors other than flow-based added value. Moreover, Girouard and André [9] used taxing wages12 to estimate the elasticity of household tax revenue, employing the average marginal tax rate. However, their studies did not comprehensively analyze government tax revenue. Nishizaki and Nakagawa [27] decomposed the tax elasticity formula into several elasticity values and estimated the OLS using time-series data, obtaining 1.15 as the estimation result. However, these studies ignored the effects of revisions of the tax system, such as reductions in corporate and income taxes when there is an increase in consumption tax and increases in indirect taxes, which is considered to be the cause of high elasticity and cannot explain the relationship between unstable fluctuations in tax revenue and recent changes in GDP. Simulation tax revenue forecasting has the advantage of being able to easily measure the impact of tax reforms. However, in their study, other than income tax and personal residence tax, tax items were not analyzed by focusing on the characteristics of individual tax items. Moreover, their analyses were only focused on revenue directly but did not consider the taxable object. Such analyses are unsatisfactory, because tax is levied on the subject of taxation, which differs for each tax item, and the increase or decrease is determined by changes in the macroeconomic situation and the flow of production, consumption, and savings over time. Moreover, there are complex factors that cannot be determined by considering changes in GDP alone. Thus, it is important to estimate tax revenue based on the tax rate after analyzing what increases or decreases the taxable amounts.
This thesis, which focuses on the characteristics of a specific tax item, i.e., corporate tax, conducts an in-depth two-stage analysis of the effects of macroeconomic variables on the corporate profit of each industry and a tax reform-responded method that replaces the tax revenue elasticity method, which has been adopted to estimate tax revenue. Moreover, instead of directly estimating tax revenue, we first investigate the cause of the increase or decrease in taxable variables (corporate declared income). Then, we calculate tax revenue based on each indigenous tax system, such as multiplying each of the results by given tax rates. The two-step estimation method proposed in this research is ideal for estimating tax revenue and is different from most of the conventional tax revenue estimation methods. After reviewing the financial results for FY2020,13 we find that tax revenues may increase even when economic growth is negative, so we hypothesize that the impact of changes in the macroeconomic conditions,14 such as price, unemployment, interest rate, population, and GDP, on corporate profits differs by industry and investigate how “price,” “unemployment,” “interest rate,” and “population” affect corporate profits and clarify how corporate tax revenues are determined.
Hypotheses
Based on the idea that the meaning of macro-variables differs from industry to industry and the influence of each industry also differs, in this chapter, we address the following research questions: “How do prices, unemployment, interest rate, population, and GDP growth rate influence corporate profits both negatively and positively?.” We discuss each of the factors as follows.
Prices
In industries that are relatively small in scale, such as agriculture, textiles, wholesale, and retail, or industries that handle daily necessities, whose price elasticities of demand are small, consumption does not decrease in response to an increase (decrease) in consumer prices. They benefit, because it is difficult to increase consumption. However, in the culinary and accommodation industries, which correspond to the demand for luxury goods, consumption decreases (increases) when prices increase (decrease), leading to a decrease (increase) in their profits. Moreover, rising prices may increase the prices of intermediate inputs in the manufacturing industry and the prices of materials in the real-estate industry. Therefore, when the increase cannot be passed on to final products, it puts pressure on profit.
In addition, an increase (decrease) in prices decreases (increases) the real interest rate, which may reduce (increase) savings. Thus, it is expected that financial institutions’ investment capacity, such as lending, will decrease (increase), leading to a decrease (increase) in profit. However, this analysis holds under a situation in which the financial institution, such as a bank, has a capital of over 100 million yen. When the capital of the financial institution is less than 100 million yen, the business nature will be different, so this function shall not fully apply.
Unemployment
As labor share tends to decrease during a boom and increase during a recession15 and unemployment is a sign of a decrease in production, it is expected that the labor supply capacity will increase. Thus, for a labor-intensive industry, a high unemployment rate could be a factor in increasing production efficiency. For example, the textile, wholesale, retail, service, and medical industries are labor-intensive, and in these industries, production opportunities could be obtained under favorable conditions.
However, unemployment is nothing but a sign of a decline in production, so the following impact could be expected. During an economic recession, the competitive conditions between large companies that are able to expand their market share by capital strength and achieve high-profit levels by high price bargaining power and SMEs that do not have such ability would differ.
Interest rate
Interest rates represent the productivity of capital associated with an investment in new production opportunities and business development and are indicators of the degree of demand for funds. Therefore, an increase (decrease) in interest rates is a numerical value that indicates the strong production activities (slumping production activities) in many industries and the financial industry. However, interest rate is the funding cost (interest expense) and can be deducted before arriving at the taxable income. An increase (decrease) in interest rate puts pressure (securing) on the declared profit of a company, eventually becoming a factor that decreases (increases) corporate tax revenue. Therefore, for industries that are capital-intensive but have low price elasticity or industries that cannot improve capital productivity commensurate with rising interest rates, such as steel and construction, which do not seem to be dependent on innovations as other industries do, rising interest rates put pressure on their corporate profits and put them in a difficult situation.
Population
Population density is closely related to industrial agglomeration, and for business owners, population density and industrial agglomeration in the area that they operate are important conditions that influence management in terms of business recognition, penetration, and transactions.16 According to Clark [7] and Newling [26],17 population growth makes people move to new settlements, thereby decreasing the population density. Moreover, Suzuki [29]18 and Adams [1]19 demonstrated that there is a positive correlation between the number of years of municipal administration and the density of cities, suggesting that short years of municipal administration are disadvantageous for wholesalers and retailers who require high population density. Thus, an increase (decrease) in population also causes people to move to suburbs (to return to the old city center), that is, they move to a new city that has been developed as a large living space. In summary, population density decreases (increases) when the local population concentration is alleviated, such as the “donut phenomenon” in urban areas (the population concentration increases, such as a “reverse donut phenomenon”). We believe that this phenomenon will lead to a decline in industrial agglomeration, such as wholesale and retail, and reduce profits in many industries that depend on it. Therefore, in Japan, where the population is declining, a return to the old town—a reverse donut phenomenon—will occur, thereby increasing the population density. As a result, industries, such as wholesale and retail, which are highly dependent on industrial agglomeration, can achieve cost-effective production, and a decrease in the population can increase the corporate profits of these industries.
The increase (decrease) in population would not be disadvantageous (advantageous) for industries that are less dependent on the concentration of industries and population density, such as agriculture, finance, cooking/accommodation, and medical care. However, it may be a factor in increasing (decreasing) the profits of other industries, such as increasing (decreasing) the demand for agricultural products. In addition, according to Morita et al. [24], when the population increases, the elderly population decreases. In such a case, the medical care of the elderly reduces profits. Moreover, it increases the profits of accommodation-related industries, because the number of young people who travel a lot will increase. If we apply the study of Morita et al. [24] to Japan, where the population is declining, we can expect the profit of the medical industry to increase and that of the accommodation industry to decrease.
GDP growth rate
As the nominal GDP growth rate is an index that reveals the state of a country’s economy, there is a basis for its fluctuation to be used as a variable for estimating tax revenues, including corporate tax. However, by definition, GDP is the total amount of employee income, operating surplus, depreciation, and tax, so the taxable subject portion is limited, including the tax revenue itself. Furthermore, according to the definition of the System of National Accounts (SNA),20 operating surplus includes the contribution of the corporate sector to the added value generated from production activities, including profits from owning a house (attributed rent).21 In addition, although GDP is not irrelevant to corporate profits and tax, it is derived from a company’s production activities or its after-tax profit. As mentioned above, the scope of taxation is limited, and tax revenue itself is included. Hence, there is a limit to measuring GDP with corporate profits and tax revenue, and we expect that a clear influence of GDP on corporate profits and tax revenue would not be fully observed (although it may have a certain influence as a balance adjustment variable).
Quantitative analyses and methodology
This chapter presents the data analyzed as well as the estimation methods and models that clarify what determines taxable corporate profits by industry. Finally, we estimate the corporate tax amount based on the estimated corporate income.
Process and methods of analyses (two-stage estimation)
First, in estimating corporate tax revenue, we limit the data to be analyzed to the number of profit-making enterprises and declared income. Moreover, we divide the taxable corporate income into those of large enterprises and SMEs and further categorize them by industry. The reason why we do not directly estimate corporate tax revenue but estimate corporate profit before is that corporate tax is levied on the declared profit of a corporation, and the reason for analyzing it separately for large enterprises and SMEs is that a reduced tax rate is applied to SMEs. Moreover, we assume that the level of the profit from and the impact of macroeconomic variables are different in each industry, so we divide the data into industries and conduct the estimation at the industry level. Using this procedure, we investigate the cause of the increase or decrease in the taxable object or the basis of the tax, that is, corporate profits, and build models with the dependent variable, which is the average profit of each large enterprise and SME. In this study, we consider all industries in the company sample survey (National Tax Agency). Then, we hypothesize that the profit per corporation (declared income) can be explained by GDP growth rate, CPI, unemployment rate, interest rate, and population and construct the models. However, the importance and impact of the variables differ depending upon the nature of the industry. Then, we multiply the industry-classified average profit per enterprise estimated in this model by the prescribed statutory tax rates and project the corporate tax revenue.
Variables and statistics
Table 1 presents the lists of the symbols, the explanations of both the dependent and explanatory variables, and the names of industries considered in this research. The explanation and data sources are presented in the footnote.22 The data used to estimate the variables are from 2013 to 2019.
Table 1.
Dependent variables, explanatory variables, and industries
| Variable | Meaning | Unit | |
|---|---|---|---|
| Dependent var. | Profit | Declared income amount of enterprise | Million JPY |
| Profit per enterprise | Average declared income amount per enterprise | Million JPY | |
| Explanatory variables | GDP growth rate | Nominal GDP growth rate | Percentage |
| CPI increase rate | Consumer price index | Real number | |
| Unemployment rate | Unemployment rate | Percentage | |
| Interest rate | Long term interest rate | Percentage | |
| Population | Gender total population in Japan | Ten thousand | |
| Industries | Agriculture | Agriculture, Forestry, and Fisheries | – |
| Mining | Mining industry | – | |
| Construction | Construction industry | – | |
| Textile | Fiber industry | – | |
| Chemical | Chemical industry | – | |
| Steel | Steel industry | – | |
| Machine | Machinery industry | – | |
| Food | Food industry | – | |
| Publication | Publication industry | – | |
| Other manufacturing | Other manufacturing industry | – | |
| Wholesale | Wholesale industry | – | |
| Retail | Retail industry | – | |
| Cooking/accommodation | Cooking/accommodation industry | – | |
| Finance | Financial industry | – | |
| Real estate | Real estate industry | – | |
| Transportation/communication | Transportation/communication industry | – | |
| Service | Service industry | – | |
| Medical | Medical industry | – |
We adopt a multivariate regression analysis and conduct a robustness test for each industry of both capital classes, i.e., large enterprises and SMEs. We reveal the impact of GDP growth rate, CPI, unemployment, interest rates, and population on the income of an industry or a company using a quantitative method. The results of the explanatory variables used to verify this model are presented in Appendix 2.
First, the descriptive statistics of the dependent variable (profit) and explanatory variables (GDP, prices, unemployment rate, interest rates, and population) of large companies whose capital is over 100 million JPY are presented in Table 2. Table 3 describes SMEs whose capital is 100 million JPY or less. Then, regarding the profit per enterprise (average profit) by industry, we illustrate large companies in Fig. 1 and illustrate the average profit of SMEs in Fig. 2.
Table 2.
Descriptive statistics (large entities)
| Variable | Obs | Mean | Std. Dev | Min | Max |
|---|---|---|---|---|---|
| Agriculture | 7 | 359.28 | 102.53 | 182.04 | 524.64 |
| Mining | 7 | 7539.43 | 2922.33 | 4887.88 | 12,204.00 |
| Construction | 7 | 1522.81 | 421.89 | 961.26 | 1944.29 |
| Textile | 7 | 931.93 | 140.27 | 712.95 | 1161.93 |
| Chemical | 7 | 2342.65 | 184.23 | 2030.50 | 2519.21 |
| Steel | 7 | 935.13 | 52.26 | 893.95 | 1016.95 |
| Machine | 7 | 1942.46 | 132.48 | 1738.36 | 2076.37 |
| Food | 7 | 1459.45 | 225.12 | 1208.81 | 1726.45 |
| Publication | 7 | 904.45 | 423.77 | 560.57 | 1809.09 |
| Other manufacturing | 7 | 1836.84 | 474.37 | 1182.51 | 2504.04 |
| Wholesale | 7 | 856.38 | 105.33 | 743.22 | 1042.43 |
| Retail | 7 | 1624.06 | 176.71 | 1490.39 | 1989.43 |
| Cooking/accommodation | 7 | 646.34 | 187.07 | 422.54 | 929.57 |
| Finance | 7 | 4036.12 | 562.82 | 3057.76 | 4641.81 |
| Real estate | 7 | 976.22 | 331.54 | 648.65 | 1473.16 |
| Transportation/communication | 7 | 2639.22 | 362.60 | 1955.52 | 2979.61 |
| Service | 7 | 790.38 | 90.21 | 724.07 | 984.71 |
| Medical | 7 | 101.11 | 4.76 | 94.21 | 110.23 |
Table 3.
Descriptive statistics (SMEs)
| Variable | Obs | Mean | Std. dev | Min | Max |
|---|---|---|---|---|---|
| Agriculture | 7 | 14.01 | 1.79 | 11.38 | 16.30 |
| Mining | 7 | 35.12 | 7.56 | 26.85 | 45.86 |
| Construction | 7 | 13.02 | 2.05 | 9.96 | 15.57 |
| Textile | 7 | 17.72 | 1.17 | 15.63 | 18.79 |
| Chemical | 7 | 47.91 | 4.92 | 42.58 | 53.16 |
| Steel | 7 | 30.36 | 3.37 | 26.73 | 35.10 |
| Machine | 7 | 34.95 | 4.01 | 31.09 | 40.69 |
| Food | 7 | 29.79 | 2.39 | 26.31 | 32.66 |
| Publication | 7 | 20.30 | 1.88 | 18.20 | 22.62 |
| Other manufacturing | 7 | 24.74 | 3.37 | 20.64 | 29.45 |
| Wholesale | 7 | 27.89 | 2.49 | 25.48 | 30.88 |
| Retail | 7 | 15.47 | 1.56 | 13.70 | 17.53 |
| Cooking/accommodation | 7 | 13.52 | 1.21 | 12.04 | 15.35 |
| Finance | 7 | 18.28 | 2.62 | 14.90 | 20.86 |
| Real estate | 7 | 14.00 | 1.73 | 11.70 | 15.66 |
| Transportation/communication | 7 | 21.09 | 2.43 | 16.80 | 23.66 |
| Service | 7 | 16.63 | 1.95 | 15.17 | 20.48 |
| Medical | 7 | 27.16 | 1.49 | 25.61 | 29.34 |
Apart from the number of observations (Obs.) and standard deviation (Std.dev.), all other units are in million JPY
Fig. 1.
Average profit per corporation and average profit by industry of large enterprises. Average profit per corporation (line graph) is calculated by dividing the total amount of declared profits by the number of enterprises (bar graph)
Fig. 2.
Average profit per corporation and average profit by industry of SMEs. The average profit per corporation (line graph) is calculated by dividing the total amount of declared profits by the number of enterprises (bar graph)
Apart from the number of observations (Obs.) and standard deviation (Std. dev.), all other units are in million JPY.
From the descriptive statistics, first, there seems to be a large difference in the corporate profits of SMEs and large enterprises. However, as the number of SMEs is overwhelmingly larger than that of large enterprises, even if the profit per enterprise is small, the final calculated tax revenue of SMEs will by no means be small. This size is directly linked to the tax amount, which will be described later. Then, whether the reduced tax rate is applied or not depends on whether the capital of the corporation exceeds 100 million yen. Moreover, whether the business entity makes a profit or not depends on the competitive state of the market, and as the impacts of macroeconomic variables vary from industry to industry, the total profit does not sufficiently reflect the competitive conditions of markets and the production characteristics of each industry. Unless the coefficient corresponding to the tax system is used, the total amount of the tax revenue cannot be estimated correctly. Therefore, we divide the corporate statistics into large enterprises and SMEs and estimate the effects of GDP growth rate, CPI, unemployment rate, interest rates, and population on the declared profit per entity for each industry using a quantitative method, which is described in the next subheading.
Quantitative analyses and results
We construct the following model to estimate the impact of each variable on the declared profits of large enterprises by industry. We examine it for each industry and obtain the following results, which describe how each macroeconomic variable affects the declared profits of each industry
Using the same approach, we also construct the following model to estimate how the macroeconomic variables affect the declared profits of SMEs by industry and obtain the following results:
We interpret the results of the quantitative analysis as follows. First, presenting the result of the elastic value of each variable on the declared income of a company by industry, Tables 4 and 5 demonstrate that GDP, prices, and population have a positive causal relationship with the income of a company. Moreover, unemployment and interest rates have a negative causal relationship with the income of a company, although the influence of the latter differs depending on the industry.
Table 4.
Estimated results by robustness test to explain expected profits of large enterprises by industry
| Variables | GDP growth rate (β1) | CPI increase rate (β2) | Unemployment rate (β3) | Interest rate (β4) | Population (β5) | Constant term | R-squared | Number of obs | |
|---|---|---|---|---|---|---|---|---|---|
| Agriculture | 71.95*** | 104.03** | – 413.50*** | – 412.62** | 4.23** | – 52,099.84** | 0.90 | 7 | |
| t-value | 3.15 | 2.06 | – 3.81 | – 2.26 | 2.36 | – 2.33 | |||
| Mining | – 817.95 | 1174.62 | 8478.39*** | 6403.88 | – 102.89** | 1,285,947.00** | 0.93 | 7 | |
| t-value | – 1.47 | 0.95 | 3.20 | 1.44 | – 2.35 | 2.35 | |||
| Construction | 133.39*** | 105.22*** | – 789.44*** | – 323.84*** | 0.09 | 2641.82 | 1.00 | 7 | |
| t-value | 11.16 | 3.97 | – 13.90 | – 3.39 | 0.10 | 0.23 | |||
| Textile | 86.64*** | 38.99*** | 577.55*** | – 78.88 | – 10.22*** | 128,761.90*** | 1.00 | 7 | |
| t-value | 12.94 | 2.63 | 18.14 | – 1.47 | – 19.47 | 19.61 | |||
| Chemical | 66.52*** | – 23.58 | – 266.94** | 129.11 | – 1.53 | 22,477.65 0 | 0.97 | 7 | |
| t-value | 2.78 | – 0.44 | – 2.35 | 0.68 | – 0.81 | 0.96 | |||
| Steel | – 11.54*** | 45.91*** | 219.48*** | – 72.74*** | – 2.68*** | 34,273.48*** | 1.00 | 7 | |
| t-value | – 7.93 | 14.23 | 31.71 | – 6.25 | – 23.50 | 24.02 | |||
| Machine | 131.00*** | 193.97*** | – 1.01 | 7.76 | – 2.25*** | 30,167.14*** | 0.99 | 7 | |
| t-value | 18.19 | 12.15 | – 0.03 | 0.13 | – 3.98 | 4.27 | |||
| Food | – 6.11 | – 40.69 | – 598.89*** | 206.71 | 1.98 | – 21,882.87 0 | 0.94 | 7 | |
| t-value | – 0.16 | – 0.48 | – 3.29 | 0.68 | 0.66 | – 0.58 | |||
| Publication | – 94.32*** | – 101.61*** | 2519.46*** | 91.51*** | – 34.57*** | 432,063.80*** | 1.00 | 7 | |
| t-value | – 65.59 | – 31.87 | 368.49 | 7.96 | 306.70 | 306.44 | |||
| Other manufacturing | 0.07*** | – 805.36*** | – 3076.80*** | 2071.70*** | 7.84*** | – 88,622.55*** | 1.00 | 7 | |
| t-value | 18.22 | – 16.52 | – 16.34 | 22.02 | 5.26 | – 4.83 | |||
| Wholesale | 22.33* | 17.93 | 304.94*** | – 45.34 | – 5.83*** | 73,935.66*** | 0.97 | 7 | |
| t-value | 1.85 | 0.67 | 5.30 | – 0.47 | – 6.15 | 6.23 | |||
| Retail | 88.40*** | 152.63*** | 1992.78*** | – 518.96*** | – 24.30*** | 303,750.90*** | 0.98 | 7 | |
| t-value | 4.77 | 3.72 | 22.64 | – 3.51 | – 16.74 | 16.73 | |||
| Cooking/accommodation | – 115.96*** | – 149.07*** | – 792.03*** | 857.83*** | 3.95*** | – 47,054.69*** | 1.00 | 7 | |
| t-value | – 57.09 | – 33.11 | – 82.02 | 52.86 | 24.84 | – 23.63 | |||
| Finance | – 189.33 | – 370.89 | – 5464.79*** | 901.55 | 66.88*** | – 827,732.20*** | 0.79 | 7 | |
| t-value | – 1.01 | – 0.89 | – 6.14 | 0.60 | 4.56 | – 4.51 | |||
| Real estate | – 144.03*** | – 169.10** | – 1183.33*** | 1396.80*** | 3.50 | – 39,837.39 | 0.98 | 7 | |
| t-value | – 4.56 | – 2.41 | – 7.87 | 5.53 | 1.41 | – 1.29 | |||
| Transportation/communication | 140.38 | 132.15 | 8.21 | – 1984.68*** | 2.38 | – 27,442.59 | 0.87 | 7 | |
| t-value | 1.50 | 0.64 | 0.02 | – 2.65 | 0.32 | – 0.30 | |||
| Service | 3.00 | 15.37 | 693.55*** | – 274.77*** | – 8.28*** | 103,731.90*** | 0.99 | 7 | |
| t-value | 0.43 | 1.00 | 21.00 | – 4.95 | – 15.20 | 15.23 | |||
| Medical | – 0.63 | 4.56 | 7.67 | – 6.62 | – 0.06 | 891.70 | 0.85 | 7 | |
| t-value | – 0.48 | 1.56 | 1.22 | – 0.63 | – 0.62 | 0.69 | |||
The degree of significance is expressed as follows: ***denotes p < 0.01 (t > 2.57); **denotes p < 0.05 (t > 1.96); *denotes p < 0.10 (t > 1.64)
Table 5.
Estimated results by robustness test to explain the expected profit of SMEs by industry
| Variables | GDP growth rate (β1) | CPI increase rate (β2) | Unemployment rate (β3) | Interest rate (β4) | Population (β5) | Constant term | R-squared | Number of obs |
|---|---|---|---|---|---|---|---|---|
| Agriculture | 0.9786273*** | 0.6294698** | – 3.256849*** | – 10.32101*** | 0.0560409*** | – 686.6923 | 0.99 | 7 |
| t-value | 6.93 | 2.01 | – 4.85 | – 9.15 | 5.06 | 138.4552 | ||
| Mining | 0.5650218*** | 0.1775952 | – 6.658873*** | 27.5603*** | – 0.2103944*** | 2719.274*** | 1.00 | 7 |
| t-value | 2.84 | 0.4 | – 7.04 | 17.33 | – 13.49 | 13.93 | ||
| Construction | 0.273443*** | 0.2518545*** | – 0.9272907*** | – 2.871225*** | – 0.0210552*** | 283.076*** | 1.00 | 7 |
| t-value | 298.02 | 123.81 | – 212.56 | – 391.6 | – 292.76 | 314.66 | ||
| Textile | 0.2980925 | 0.2803954 | – 3.734248 | – 0.6463604 | 0.0270768 | – 314.8885 | 0.49 | 7 |
| t-value | 0.5 | 0.321 | – 1.31 | – 0.14 | 0.58 | – 0.54 | ||
| Chemical | 1.690615 | 0.6489236 | – 6.650912 | 9.270733 | – 0.0857322 | 1152.187 | 0.91 | 7 |
| t-value | 1.57 | 0.27 | – 1.3 | 1.08 | – 1.01 | 1.09 | ||
| Steel | 2.611928*** | 2.93748** | – 8.043684*** | 0.8550592 | – 0.0186375 | 286.3337 | 0.95 | 7 |
| t-value | 4.83 | 2.45 | – 3.13 | 0.2 | – 0.44 | 0.54 | ||
| Machine | 1.410189** | 1.113856 | – 17.03682*** | 13.08942*** | 0.0408066 | – 435.6962 | 0.96 | 7 |
| t-value | 2.4 | 0.86 | – 6.11 | 2.79 | 0.89 | – 0.76 | ||
| Food | 1.189009*** | 0.1787125 | – 8.64796*** | 4.376037*** | 0.0218088 | – 222.7006 | 0.99 | 7 |
| t-value | 7.03 | 0.48 | – 10.75 | 3.24 | 1.64 | – 1.34 | ||
| Publication | 0.8604829*** | 0.8186656** | – 0.0782041 | – 2.331936** | – 0.0419309*** | 551.5657*** | 0.99 | 7 |
| t-value | 5.94 | 2.55 | – 0.11 | – 2.02 | – 3.69 | 3.89 | ||
| Other manufacturing | – 0.5350599 | – 1.794631** | – 3.906403** | 18.77726*** | – 0.0854475*** | 1119.345*** | 0.97 | 7 |
| t-value | – 1.32 | – 2 | – 2.03 | 5.81 | – 2.7 | 2.82 | ||
| Wholesale | 0.8059325*** | 0.6945159*** | – 3.522211*** | 2.804835*** | – 0.03789*** | 517.5563*** | 1.00 | 7 |
| t-value | 8.03 | 3.12 | – 7.38 | 3.5 | – 4.82 | 5.26 | ||
| Retail | 0.0620356 | – 0.2413204 | – 2.021612 | 4.394121 | – 0.0265405 | 357.7152 | 0.88 | 7 |
| t-value | 0.16 | – 0.28 | – 1.11 | 1.44 | – 0.89 | 0.95 | ||
| Cooking/accommodation | – 0.5134966*** | – 1.281607*** | – 12.22787*** | 4.790938*** | 0.1218917*** | – 1495.358*** | 1.00 | 7 |
| t-value | – 24.08 | – 27.11 | – 120.59 | 28.11 | 72.92 | – 71.51 | ||
| Finance | 0.3540839 | – 0.1904392 | – 6.521624 | – 0.6952505 | 0.0341966 | – 395.8536 | 0.74 | 7 |
| t-value | 0.37 | – 0.09 | – 1.42 | – 0.09 | 0.45 | – 0.42 | ||
| Real estate | 0.2871876* | 0.0652445 | – 3.347202*** | – 1.750436 | 0.0105935 | – 110.242 | 0.98 | 7 |
| t-value | 1.81 | 0.19 | – 4.43 | – 1.38 | 0.85 | – 0.71 | ||
| Transportation/communication | 0.1081276 | – 0.0532771 | – 1.574457 | – 4.412934 | – 0.0051352 | 91.80147 | 0.93 | 7 |
| t-value | 0.24 | – 0.05 | – 0.72 | – 1.21 | – 0.14 | 0.2 | ||
| Service | – 1.317465*** | – 1.771301*** | – 15.08007*** | 16.0331*** | 0.0953544*** | – 1147.796*** | 0.97 | 7 |
| t-value | – 5.73 | – 3.47 | – 13.79 | 8.72 | 5.29 | – 5.09 | ||
| Medical | – 0.3468283 | – 0.514374 | 8.066557*** | 6.684266*** | – 0.1247993*** | 1585.716*** | 0.93 | 7 |
| t-value | – 1.23 | – 0.83 | 6.04 | 2.98 | – 5.66 | 5.75 |
The degree of significance is expressed as follows: ***denotes p < 0.01 (t > 2.57); **denotes p < 0.05 (t > 1.96); *denotes p < 0.10 (t > 1.64)
The industries that are greatly affected by changes in GDP are chemicals, machinery, and textiles, but the impact on retail and construction is small. Although the elasticity value is small in the cooking and service industries, negative causal relationships are observed. The impact of changes in GDP is not uniform in some industries.
Prices are significantly affected by machinery, wholesale, retail, textile, and agriculture, whereas the effect of steel is small, and construction, publication, cooking, and accommodation have a negative causal relationship with prices. In industries that produce necessities, the price elasticity of demand is small and benefits from rising prices. However, in other industries, it is not necessarily a factor that increases sales and profits.
Second, unemployment rate has a negative impact on corporate income in all industries. Among them, we find that its impact on construction, steel, machinery, cooking, finance, and real estate is large, and its impact on agriculture, retail, services, and medical care is relatively small.
Third, interest rates boost income in a wide range of industries, including mining, steel, machinery, other manufacturing, retail, cooking and accommodation, real estate, services, and medical. This may be a sign of their high demand for money and increasing productivity (of capital), suggesting an increase in income. However, interest rates appear to have a small but negative impact on agriculture, construction, and transportation. Compared with other industries, in the agriculture and transportation industries, technological innovation commensurate with the rising interest rates in the market is less likely to occur, and because of their nature, demand may be rigid. Thus, unless there is no increase in investment that is commensurate with rising interest rates, the burden of interest expense will increase relative to the income originally earned.
Fourth, population is a factor that increases the income of agriculture, construction, steel, machinery, food, cooking and accommodation, finance, real estate, transportation, services, and publishing industries. As the number of people increases, the number of potential consumers in the market also increases. Therefore, it is inferred that as production increases, income also increases. It also increases labor supply, which is advantageous for companies as it helps them to determine the labor share to maximize profits. However, when the population increases (decreases), it has a negative (positive) effect on medical care, which is often used by the elderly, whose population decrease (increase). It also has a negative effect on wholesalers and retailers, because both of them require a high population density.
Next, from the quantitative analysis (Tables 4 and 5), at first glance, the changes in each explanatory variable reveal that SMEs seem to have a small effect (coefficient) on corporate profits, but SMEs are overwhelmingly superior to large enterprises in number. Therefore, when looking at the impact on the final taxable amount, which will be confirmed later, the impact of macro-variables on the profit each of SME cannot be ignored. Moreover, the magnitudes of each coefficient are directly linked to the tax amount. This is because corporate tax is calculated based on the income level of each company. In addition, for SMEs, a reduced tax rate is applied to a declared income of 8 million yen or less. Therefore, it is not enough to look at the amount of income of the entire industry, but estimating the declared income of each large company and SME is the appropriate basis for estimating this tax revenue. Based on this estimation of taxable income, the correct tax revenue can be estimated using the tax system of each year. Thus, the model proposed in this study and its verification results are valuable contributions to the literature and have policy significance.
Estimates of corporate tax revenues
We use the corporate tax revenue estimated by multiplying the industry-classified average profit per enterprise23 obtained from the model (Tables 4, 5) by the prescribed statutory tax rate24 to derive the results for both large enterprises and SMEs presented in Table 6.
Table 6.
Estimated values by industry
| Year | Estimated values of corporate tax (large enterprises) | Average amount of corporate profits (large enterprises) | Number of enterprises (large enterprises) | Estimated values of corporate tax (SMEs) | Average amount of corporate profits (SMEs) | Number of enterprises (SMEs) |
|---|---|---|---|---|---|---|
| 2013 | 6641.47 | 1272.10 | 20,474 | 2833.81 | 16.97 | 812,683 |
| 2014 | 6985.08 | 1351.78 | 20,264 | 2958.51 | 16.71 | 864,476 |
| 2015 | 7023.66 | 1422.58 | 20,658 | 3300.22 | 17.86 | 927,985 |
| 2016 | 7288.56 | 1514.60 | 20,565 | 3273.08 | 17.60 | 949,716 |
| 2017 | 7853.12 | 1581.24 | 21,224 | 3832.39 | 19.51 | 984,259 |
| 2018 | 7881.94 | 1645.06 | 20,652 | 4239.75 | 20.92 | 1,009,883 |
| 2019 | 7079.19 | 1721.61 | 17,724 | 4126.97 | 20.01 | 1,035,380 |
| 2020 | 10,898.95 | 2650.54 | 17,724 | 2219.09 | 12.07 | 1,035,380 |
As at February 2022, “The Corporation Sample Survey” (National Tax Agency) for the estimated year (FY2020) had not yet been published. Therefore, using this model (Tables 4, 5), we estimate the underlined declared data (declared profit amount) for FY2020 based on the macroeconomic conditions [explanatory variables (Appendix 2)]. Due to the same reason, the number of profit-making corporations cannot be determined, so the industrial weight cannot be obtained. Therefore, to determine the corporate tax revenue in FY2020, the author places the number of corporations of both large enterprises and SMEs in the previous fiscal year horizontally and calculates industry-classified weights and a prorate estimation of the average profit of each industry.25
In Fig. 3 and Table 7, we present the provisional estimates of the corporate tax revenues of large enterprises and SMEs and compare them with the actual tax paid.
Fig. 3.
Estimated and actual values of the corporate tax revenue
Table 7.
Estimated values and actual values
| Year | Total estimated values of corporate tax revenue | Actual values of corporate tax revenue | Residual (%) |
|---|---|---|---|
| 2013 | 9475.28 | 10,493.72 | − 9.71 |
| 2014 | 9943.59 | 11,031.61 | − 9.86 |
| 2015 | 10,323.87 | 10,827.40 | − 4.65 |
| 2016 | 10,561.64 | 10,328.90 | 2.25 |
| 2017 | 11,685.51 | 11,995.30 | − 2.58 |
| 2018 | 12,121.68 | 12,318.03 | − 1.59 |
| 2019 | 11,206.16 | 10,797.11 | 3.79 |
| 2020 | 13,118.04 | 11,234.63 | 16.76 |
The estimation results reveal that the values are fairly equal to the actual values. Our model focuses on estimating the size of the taxable object to calculate corporate tax revenue, and we successfully estimate the corporate tax revenue with high accuracy by multiplying the predetermined tax rates with the corporate profit estimated using the model, which has been verified with the hypotheses that in addition to GDP growth rate, corporate profit is influenced by inflation, interest rates, unemployment rate, and population movements. By focusing on the fact that the reaction to changes in macro-variables differs for each industry, narrowing down the analysis target to profit-making corporations, and finding how the macroeconomic variables affect changes in profit, we demonstrate that tax revenue is positively or negatively affected by changes in macroeconomic conditions, which cannot be described by the conventional method—tax revenue elasticity. This makes it possible to a model composed of these multidimensional macro-variables to estimate the corporate tax revenue for each industry, providing important implications in examining economic policies that are consistent with the issues of fiscal consolidation.
Another the remarkable thing about the results of this analysis is the estimation of tax revenue in 2020, when GDP grew negatively. Based on the conventional elasticity of tax revenue method, it is expected that tax revenue would drop significantly.26 Contrary to the government’s estimation, tax revenue increased.27 However, our model draws a figure of the tax revenue that had fallen in FY2019, which then increased in FY2020. This is nothing but the appearance of corporate profit reflected as the expected tax revenue through a given tax rate as a result of estimating corporate profit through the programed correspondence to the changes in the aforementioned macro-variables that make up the model. This suggests that correctly estimating tax revenue—that is, to correctly estimate the corporate income that is subject to taxation—is essential.
Furthermore, the data used to construct the model is up to 2019, before the COVID-19 shock occurred.
The estimated value for FY2020 is slightly higher than the actual value. As of 2020, a serious economic crisis occurred because of the COVID-19 shock, so the government introduced special tax measures for enterprises, including “tax payment deferment” and “extension of the filing deadline.” Due to such external policy factors, the tax revenue originally expected in FY2020 will flow to the tax revenue after FY2020. Therefore, the actual value would have become lower than the estimated value. However, because the policy authorities estimated the impact of these policy measures on tax revenue in advance, the impact on tax revenue in the later years can be supplemented appropriately by creating a separate amendment and applying it to the model, so that they can estimate tax revenues more accurately.
Conclusion
In this study, we investigate the effects of each variable on corporate income by industry. In addition to GDP, which has been used to estimate tax revenue, we add prices, unemployment rate, interest rates, and population as new explanatory variables and analyze the extent that they affect the profits of companies (declared income amount), which is the basis of tax revenue estimation. After analyzing the data about SMEs and large enterprises and industries, we find that GDP, prices, and population have a positive causal relationship with corporate income in all industries. However, unemployment and interest rates have a negative causal relationship, but their influence differs depending on the industry. From the results of the analyses, we find the following about how corporate income by industry is affected by each macro-variable.
Industries that are significantly affected by changes in GDP include chemicals, machinery, and fiber, but the impact on retail, construction, cooking, and service industries is small, indicating that their elasticity values are not uniform. Prices significantly affect the machinery and agriculture industries, whereas it has a small effect on the steel industry. In the construction, retail, cooking, and services industries, prices have a negative causal relationship with corporate income. In industries that produce necessities, the price elasticity of demand is small and may benefit from rising prices, but in other industries, it may not necessarily be a factor in increasing sales and profits. We find that unemployment rate has a negative impact on income in all industries. The results indicate that its impact on the profits of the construction, steel, machinery, cooking, finance, and real-estate industries is large, and its impact on the profits of the agriculture, retail, services, and medical care industries is relatively small. Regarding interest rates, it seems that it increases income in a wide range of industries, such as steel, machinery, retail, cooking, real estate, services, and medical care. It may reveal the widely increasing demand for funds and the increase in productivity (of capital), suggesting that it is a signal that income will increase. However, interest rates appear to have a small but negative impact on agriculture, construction, and transportation. Compared with other industries, in the agriculture and transportation industries, the commensuration of technological progress with rising interest rates in the market is slow, and by their nature, demand may be rigid, so if there is neither an expansion of production nor an increase in investment that is commensurate with increasing interest rates, the burden of interest expense increases. In the construction industry, assets’ value declines with rising interest rates. We find that population is a factor in increasing the income of the agriculture, construction, steel, machinery, food, cooking, finance, real estate, services, and publishing industries. Because an increase in population increases potential consumers in the market, it increases production and income. Moreover, increasing labor supply is beneficial to businesses in determining the labor share to maximize profits. However, we also described that in the phase of population decreases, medical care used by the elderly population increases and that the publishing industry—which is considered to be highly dependent on economies of scale—and wholesalers as well as retailers would benefit as they both require a high population density. These findings are consistent with previous studies that show that population increase leads to both a decrease in the elderly population and changes in population flow from the city center to the suburbs, leveling off (making even) the concentration of population density. Moreover, as an important finding, estimating the effects of the explanatory variables (GDP, prices, unemployment rate, interest rate, and population) on the profit of each corporation, we clarify their impact on tax revenue by incorporating (considering) the tax system into the calculation as an extrinsic factor.
As discussed above, based on the idea that the profit per corporation (declared income) is the basis for calculating corporate tax revenue, the effect (elastic value) of each variable on the profit of each corporation in each industry is presented. Then, it is demonstrated that corporate tax revenue can be estimated with high accuracy by multiplying the estimated profit of the company by a predetermined tax rate. To provide a method for estimating corporate tax revenue, we first focus on the declared income of the corporation subject to taxation and analyze the factors that cause it to increase or decrease; thus, we make it possible to estimate tax revenue with high accuracy. It is an important contribution to the estimation of corporate tax and has important policy significance. Proposing a more reliable tax revenue estimation method to replace the tax revenue elasticity method, which is solely based on GDP, has an important implication for estimating tax revenue, which is the basis of the government’s fiscal policy, advocating fiscal.28 Furthermore, the findings obtained in this study reveal the impact of changes in economic conditions on a company’s profit level by industry. Therefore, because this is an analysis of the causes of changes in taxable objects, it is suggested that the importance of this model will not change even if the tax system is revised. It is also a significant contribution in the sense that our findings can be applied to countries with different tax systems from those of Japan, such as OECD member countries that calculate the elasticity of tax revenue based on GDP.
Appendix 1: Revenue elasticity
| Fiscal year | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | Average | Std. dev. |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nominal GDP growth rate (%) | – 0.75 | 0.53 | 0.65 | 0.85 | 0.59 | 0.23 | – 4.14 | – 3.64 | 1.51 | – 0.96 | – 0.12 | 2.66 | 2.09 | 3.31 | 0.76 | 1.99 | 0.11 | 0.33 | – 3.94 | 0.11 | 2.03 |
| Revenue elasticity | |||||||||||||||||||||
| Revenue of central gov. | 11.48 | – 2.39 | 8.22 | 9.02 | 0.01 | 17.45 | 3.19 | 3.43 | 4.71 | – 3.39 | – 20.80 | 2.59 | 7.14 | 1.30 | – 1.92 | 2.99 | 25.49 | – 17.60 | – 1.04 | 2.63 | 10.21 |
| Income tax | 22.50 | – 11.51 | 8.38 | 7.38 | – 16.66 | 63.32 | 1.64 | 3.79 | 0.36 | – 3.96 | – 31.07 | 4.14 | 3.87 | 1.83 | – 1.46 | 3.61 | 51.55 | – 20.35 | – 0.03 | 4.60 | 21.48 |
| Corporate tax | 9.58 | 11.80 | 20.26 | 18.92 | 21.00 | – 5.11 | 7.75 | 10.02 | 27.22 | – 4.47 | – 35.29 | 2.84 | 2.45 | – 0.56 | – 6.09 | 8.07 | 25.70 | – 68.50 | – 1.04 | 2.34 | 21.76 |
| Consumption tax | – 0.61 | – 1.91 | 4.15 | 7.23 | – 1.92 | – 8.04 | 0.71 | 0.44 | 1.53 | – 1.68 | – 12.40 | 1.74 | 22.94 | 2.63 | – 1.50 | 0.83 | 9.11 | 21.08 | – 3.65 | 2.14 | 8.25 |
Note 1. Revenue elasticities are calculated as percentage change in tax revenue divided by percentage change in nominal GDP growth rate.
Note 2. National tax is the total of corporate tax, income tax, other direct tax (e.g., inheritance tax and land price tax), liquor tax, tobacco tax, consumption tax, and other indirect taxes (e.g., volatile oil tax, oil gas tax, aircraft fuel tax, petroleum coal tax, and power source development), promotion tax, automobile weight tax, customs tax, tongue tax, international tourist tax, and total stamp income.
Source: Financial Results Summary 2016-2020, Ministry of Finance, Japan
Appendix 2: Actual values of the macroeconomic variables
| Macroeconomic var | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 |
|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 2.73% | – 0.35% | 1.74% | 0.75% | 1.79% | 0.19% | – 0.66% | – 4.53% |
| CPI increase rate | 0.90% | 2.90% | 0.20% | – 0.10% | 0.70% | 0.70% | 0.50% | 0.00% |
| Unemployment rate | 3.89% | 3.55% | 3.30% | 3.03% | 2.72% | 2.43% | 2.35% | 2.89% |
| Interest rate | 0.68% | 0.45% | 0.26% | – 0.06% | 0.04% | 0.04% | – 0.11% | 0.03% |
| Population (10 thou) | 12,753.51 | 12,734.32 | 12,709.47 | 12,693.70 | 12,671.10 | 12,644.00 | 12,616.40 | 12,321.43 |
The table above shows the transition of the explanatory variables from 2013 to 2020.
Appendix 3. Results of the expected profits of large entities by industry
See Tables 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22 and 23.
Table 8.
Estimated results by robustness test to explain the expected profit of large company by industry for FY2013
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 2.0 | – 22.4 | 3.6 | 2.4 | 1.8 | – 0.3 | 3.6 | – 0.2 | – 2.6 |
| CPI increase rate | 93.6 | 1057.2 | 94.7 | 35.1 | – 21.2 | 41.3 | 174.6 | – 36.6 | – 91.5 |
| Unemployment rate | – 1610.3 | 33,016.7 | – 3074.2 | 2249.1 | – 1039.5 | 854.7 | – 3.9 | – 2332.2 | 9811.3 |
| Interest rate | – 280.1 | 4346.6 | – 219.8 | – 53.5 | 87.6 | – 49.4 | 5.3 | 140.3 | 62.1 |
| Population | 53,913.7 | – 1,312,234.6 | 1137.6 | – 130,341.4 | – 19,487.2 | – 34,192.6 | – 28,647.7 | 25,291.4 | – 440,895.8 |
| Constant term | – 52,099.8 | 1,285,947.0 | 2641.8 | 128,761.9 | 22,477.7 | 34,273.5 | 30,167.1 | – 21,882.9 | 432,063.8 |
| Variables | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 0.0 | 0.6 | 2.4 | – 3.2 | – 5.2 | – 3.9 | 3.8 | 0.1 | – 0.0 |
| CPI increase rate | – 724.8 | 16.1 | 137.4 | – 134.2 | – 333.8 | – 152.2 | 118.9 | 13.8 | 4.1 |
| Unemployment rate | – 11,981.7 | 1187.5 | 7760.3 | – 3084.3 | – 21,281.1 | – 4608.1 | 32.0 | 2700.8 | 29.9 |
| Interest rate | 1406.2 | – 30.8 | – 352.2 | 582.3 | 611.9 | 948.1 | – 1347.1 | – 186.5 | – 4.5 |
| Population | 99,943.8 | – 74,409.5 | – 309,862.5 | 50,433.3 | 853,007.2 | 44,648.0 | 30,341.0 | – 105,548.2 | – 820.0 |
| Constant term | – 88,622.6 | 73,935.7 | 303,750.9 | – 47,054.7 | – 827,732.2 | – 39,837.4 | – 27,442.6 | 103,731.9 | 891.7 |
Table 9.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication |
|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 19.08 | 12,110.46 | 583.67 | 653.49 | 2019.19 | 927.21 | 1698.97 | 1,179.81 | 947.43 |
| Weighted value of profits for each industry | 0.08 | 43.61 | 29.26 | 3.19 | 123.12 | 28.95 | 128.71 | 34.43 | 11.72 |
| Weighted number of enterprises | 0.00 | 0.00 | 0.05 | 0.00 | 0.06 | 0.03 | 0.08 | 0.03 | 0.01 |
| Settlement value of corporate profits | 182.04 | 10,672.79 | 961.26 | 897.25 | 2232.51 | 893.95 | 2063.52 | 1216.51 | 690.25 |
| Statistics | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 20.91 | 699.64 | 1436.30 | 739.16 | 4266.91 | 994.41 | 1706.03 | 711.89 | 101.12 | |
| Weighted value of profits for each industry | 0.63 | 95.53 | 77.71 | 14.15 | 304.24 | 78.16 | 141.20 | 153.56 | 3.87 | 1272.1 |
| Weighted number of enterprises | 0.03 | 0.14 | 0.05 | 0.02 | 0.07 | 0.08 | 0.08 | 0.22 | 0.04 | 1.0 |
| Settlement value of corporate profits | 1628.39 | 743.22 | 1701.25 | 422.54 | 3494.14 | 648.65 | 1955.52 | 729.66 | 101.26 | 1322.3 |
Table 10.
Estimated results by Robustness test to explain the expected profit of large company by industry for FY2014
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | – 0.3 | 2.9 | – 0.5 | – 0.3 | – 0.2 | 0.0 | – 0.5 | 0.0 | 0.3 |
| CPI increase rate | 301.7 | 3,406.4 | 305.1 | 113.1 | – 68.4 | 133.2 | 562.5 | – 118.0 | – 294.7 |
| Unemployment rate | – 1468.7 | 30,113.3 | – 2803.9 | 2051.3 | – 948.1 | 779.5 | – 3.6 | – 2127.1 | 8948.5 |
| Interest rate | – 185.0 | 2871.6 | – 145.2 | – 35.4 | 57.9 | – 32.6 | 3.5 | 92.7 | 41.0 |
| Population | 53,832.5 | – 1,310,259.4 | 1,135.8 | – 130,145.2 | – 19,457.8 | – 34,141.1 | – 28,604.5 | 25,253.3 | – 440,232.1 |
| Constant term | – 52,099.8 | 1,285,947.0 | 2,641.8 | 128,761.9 | 22,477.7 | 34,273.5 | 30,167.1 | – 21,882.9 | 432,063.8 |
| Variables | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | – 0.0 | – 0.1 | – 0.3 | 0.4 | 0.7 | 0.5 | – 0.5 | – 0.0 | 0.0 |
| CPI increase rate | – 2335.5 | 52.0 | 442.6 | – 432.3 | – 1075.6 | – 490.4 | 383.2 | 44.6 | 13.2 |
| Unemployment rate | – 10,928.1 | 1083.1 | 7077.9 | – 2813.1 | – 19,409.7 | – 4202.9 | 29.1 | 2463.3 | 27.2 |
| Interest rate | 929.0 | – 20.3 | – 232.7 | 384.7 | 404.3 | 626.3 | – 890.0 | – 123.2 | – 3.0 |
| Population | 99,793.4 | – 74,297.5 | – 309,396.0 | 50,357.4 | 851,723.2 | 44,580.8 | 30,295.3 | – 105,389.4 | – 818.8 |
| Constant term | – 88,622.6 | 73,935.7 | 303,750.9 | – 47,054.7 | – 827,732.2 | – 39,837.4 | – 27,442.6 | 103,731.9 | 891.7 |
Table 11.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 380.41 | 12,081.85 | 1133.21 | 745.39 | 2060.98 | 1012.46 | 2124.55 | 1218.01 | 526.93 | 0.00 | 752.83 | 1642.37 | 442.32 | 3910.69 | 676.95 | 2374.64 | 727.21 | 110.40 | |
| Weighted value of profits for each industry | 1.51 | 42.80 | 60.48 | 3.32 | 120.26 | 32.37 | 170.75 | 32.42 | 6.23 | 0.00 | 103.75 | 83.49 | 8.13 | 266.19 | 54.70 | 202.33 | 159.33 | 3.71 | 1351.8 |
| Weighted number of enterprises | 0.00 | 0.00 | 0.05 | 0.00 | 0.06 | 0.03 | 0.08 | 0.03 | 0.01 | 0.03 | 0.14 | 0.05 | 0.02 | 0.07 | 0.08 | 0.09 | 0.22 | 0.03 | 1.0 |
| Settlement value of corporate profits | 361.89 | 12,204.00 | 1082.79 | 712.95 | 2030.49 | 1016.95 | 2076.37 | 1208.81 | 560.57 | 1716.93 | 748.57 | 1605.75 | 483.68 | 4032.81 | 718.18 | 2353.12 | 724.07 | 110.23 | 1400.7 |
Table 12.
Estimated results by Robustness test to explain the expected profit of large company by industry for FY2015
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 1.3 | – 14.2 | 2.3 | 1.5 | 1.2 | – 0.2 | 2.3 | – 0.1 | – 1.6 |
| CPI increase rate | 20.8 | 234.9 | 21.0 | 7.8 | – 4.7 | 9.2 | 38.8 | – 8.1 | – 20.3 |
| Unemployment rate | – 1365.4 | 27,995.3 | – 2606.7 | 1907.0 | – 881.4 | 724.7 | – 3.3 | – 1977.5 | 8319.2 |
| Interest rate | – 106.7 | 1656.5 | – 83.8 | – 20.4 | 33.4 | – 18.8 | 2.0 | 53.5 | 23.7 |
| Population | 53,727.5 | – 1,307,703.3 | 1133.6 | – 129,891.3 | – 19,419.9 | – 34,074.5 | – 28,548.7 | 25,204.0 | – 439,373.3 |
| Constant term | – 52,099.8 | 1,285,947.0 | 2641.8 | 128,761.9 | 22,477.7 | 34,273.5 | 30,167.1 | – 21,882.9 | 432,063.8 |
| Variables | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 0.0 | 0.4 | 1.5 | – 2.0 | – 3.3 | – 2.5 | 2.4 | 0.1 | – 0.0 |
| CPI increase rate | – 161.1 | 3.6 | 30.5 | – 29.8 | – 74.2 | – 33.8 | 26.4 | 3.1 | 0.9 |
| Unemployment rate | – 10,159.5 | 1006.9 | 6580.1 | – 2615.3 | – 18,044.5 | – 3907.3 | 27.1 | 2290.1 | 25.3 |
| Interest rate | 535.9 | – 11.7 | – 134.2 | 221.9 | 233.2 | 361.3 | – 513.4 | – 71.1 | – 1.7 |
| Population | 99,598.7 | – 74,152.5 | – 308,792.4 | 50,259.1 | 850,061.6 | 44,493.8 | 30,236.2 | – 105,183.8 | – 817.2 |
| Constant term | – 88,622.6 | 73,935.7 | 303,750.9 | – 47,054.7 | – 827,732.2 | – 39,837.4 | – 27,442.6 | 103,731.9 | 891.7 |
Table 13.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 177.60 | 8116.24 | 1108.35 | 766.51 | 2206.19 | 913.83 | 1658.16 | 1388.88 | 1011.40 | 1191.49 | 782.27 | 1436.36 | 779.24 | 4440.64 | 1074.09 | 2336.23 | 770.25 | 99.01 | |
| Weighted value of profits for each industry | 0.65 | 29.34 | 63.75 | 3.35 | 126.17 | 26.69 | 121.46 | 40.17 | 11.35 | 35.69 | 106.85 | 75.27 | 16.54 | 299.42 | 92.90 | 200.97 | 168.87 | 3.13 | 1422.6 |
| Weighted number of enterprises | 0.00 | 0.00 | 0.06 | 0.00 | 0.06 | 0.03 | 0.07 | 0.03 | 0.01 | 0.03 | 0.14 | 0.05 | 0.02 | 0.07 | 0.09 | 0.09 | 0.22 | 0.03 | 1.0 |
| Settlement value of corporate profits | 365.67 | 5138.71 | 1304.28 | 896.75 | 2253.27 | 898.06 | 1863.35 | 1270.63 | 853.16 | 1182.51 | 854.77 | 1536.33 | 585.38 | 4641.81 | 737.16 | 2841.74 | 755.83 | 94.21 | 1480.8 |
Table 14.
Estimated results by robustness test to explain the expected profit of large company by industry for FY2016
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 0.5 | – 6.1 | 1.0 | 0.7 | 0.5 | – 0.1 | 1.0 | – 0.0 | – 0.7 |
| CPI increase rate | – 10.4 | – 117.5 | – 10.5 | – 3.9 | 2.4 | – 4.6 | – 19.4 | 4.1 | 10.2 |
| Unemployment rate | – 1253.8 | 25,707.1 | – 2393.6 | 1751.2 | – 809.4 | 665.5 | – 3.1 | – 1815.9 | 7639.2 |
| Interest rate | 25.5 | – 396.5 | 20.1 | 4.9 | – 8.0 | 4.5 | – 0.5 | – 12.8 | – 5.7 |
| Population | 53,660.8 | – 1,306,080.2 | 1,132.2 | – 129,730.1 | – 19,395.8 | – 34,032.2 | – 28,513.3 | 25,172.7 | – 438,827.9 |
| Constant term | – 52,099.8 | 1,285,947.0 | 2,641.8 | 128,761.9 | 22,477.7 | 34,273.5 | 30,167.1 | – 21,882.9 | 432,063.8 |
| Variables | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 0.0 | 0.2 | 0.7 | – 0.9 | – 1.4 | – 1.1 | 1.1 | 0.0 | – 0.0 |
| CPI increase rate | 80.5 | – 1.8 | – 15.3 | 14.9 | 37.1 | 16.9 | – 13.2 | – 1.5 | – 0.5 |
| Unemployment rate | – 9329.1 | 924.6 | 6042.3 | – 2401.5 | – 16,569.7 | – 3587.9 | 24.9 | 2102.9 | 23.3 |
| Interest rate | – 128.3 | 2.8 | 32.1 | – 53.1 | – 55.8 | – 86.5 | 122.9 | 17.0 | 0.4 |
| Population | 99,475.1 | – 74,060.5 | – 308,409.2 | 50,196.7 | 849,006.6 | 44,438.6 | 30,198.7 | – 105,053.2 | – 816.2 |
| Constant term | – 88,622.6 | 73,935.7 | 303,750.9 | – 47,054.7 | – 827,732.2 | – 39,837.4 | – 27,442.6 | 103,731.9 | 891.7 |
Table 15.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 322.87 | 5053.84 | 1390.93 | 784.59 | 2267.36 | 906.55 | 1631.89 | 1465.22 | 878.84 | 1475.70 | 800.94 | 1401.51 | 701.47 | 4684.56 | 942.59 | 2891.71 | 797.07 | 98.71 | |
| Weighted value of profits for each industry | 1.19 | 16.21 | 81.22 | 3.63 | 128.23 | 27.79 | 118.23 | 41.95 | 10.20 | 42.67 | 111.71 | 72.52 | 13.70 | 331.66 | 84.83 | 251.92 | 174.75 | 2.20 | 1514.6 |
| Weighted number of enterprises | 0.00 | 0.00 | 0.06 | 0.00 | 0.06 | 0.03 | 0.07 | 0.03 | 0.01 | 0.03 | 0.14 | 0.05 | 0.02 | 0.07 | 0.09 | 0.09 | 0.22 | 0.02 | 1.0 |
| Settlement value of corporate profits | 347.86 | 5142.88 | 1505.10 | 857.40 | 2346.78 | 896.14 | 1738.36 | 1508.54 | 806.94 | 1515.36 | 802.41 | 1490.39 | 612.68 | 4309.63 | 875.01 | 2878.91 | 807.98 | 99.89 | 1508.6 |
Table 16.
Estimated results by Robustness test to explain the expected profit of large company by industry for FY2017
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 1.3 | – 14.7 | 2.4 | 1.6 | 1.2 | – 0.2 | 2.4 | – 0.1 | – 1.7 |
| CPI increase rate | 72.8 | 822.2 | 73.7 | 27.3 | – 16.5 | 32.1 | 135.8 | – 28.5 | – 71.1 |
| Unemployment rate | – 1123.6 | 23,038.7 | – 2145.2 | 1569.4 | – 725.4 | 596.4 | – 2.7 | – 1627.4 | 6846.2 |
| Interest rate | – 17.5 | 272.2 | – 13.8 | – 3.4 | 5.5 | – 3.1 | 0.3 | 8.8 | 3.9 |
| Population | 53,565.3 | – 1,303,754.8 | 1130.2 | – 129,499.1 | – 19,361.2 | – 33,971.6 | – 28,462.5 | 25,127.9 | – 438,046.6 |
| Constant term | – 52,099.8 | 1,285,947.0 | 2641.8 | 128,761.9 | 22,477.7 | 34,273.5 | 30,167.1 | – 21,882.9 | 432,063.8 |
| Variables | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 0.0 | 0.4 | 1.6 | – 2.1 | – 3.4 | – 2.6 | 2.5 | 0.1 | – 0.0 |
| CPI increase rate | – 563.7 | 12.5 | 106.8 | – 104.4 | – 259.6 | – 118.4 | 92.5 | 10.8 | 3.2 |
| Unemployment rate | – 8360.7 | 828.6 | 5415.1 | – 2152.2 | – 14,849.7 | – 3215.5 | 22.3 | 1884.6 | 20.8 |
| Interest rate | 88.0 | – 1.9 | – 22.1 | 36.5 | 38.3 | 59.4 | – 84.3 | – 11.7 | – 0.3 |
| Population | 99,298.0 | – 73,928.6 | – 307,860.1 | 50,107.4 | 847,495.0 | 44,359.5 | 30,144.9 | – 104,866.2 | – 814.7 |
| Constant term | – 88,622.6 | 73,935.7 | 303,750.9 | – 47,054.7 | – 827,732.2 | – 39,837.4 | – 27,442.6 | 103,731.9 | 891.7 |
Table 17.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 398.37 | 6310.65 | 1689.13 | 857.65 | 2,381.22 | 927.08 | 1840.32 | 1597.86 | 794.47 | 1838.99 | 846.66 | 1392.26 | 830.48 | 4688.35 | 1244.97 | 2735.31 | 749.47 | 100.70 | |
| Weighted value of profits for each industry | 1.40 | 19.61 | 80.90 | 3.57 | 134.95 | 28.98 | 137.18 | 45.28 | 9.37 | 55.72 | 118.64 | 73.24 | 16.09 | 333.19 | 118.46 | 234.65 | 167.94 | 2.07 | 1581.2 |
| Weighted number of enterprises | 0.00 | 0.00 | 0.05 | 0.00 | 0.06 | 0.03 | 0.07 | 0.03 | 0.01 | 0.03 | 0.14 | 0.05 | 0.02 | 0.07 | 0.10 | 0.09 | 0.22 | 0.02 | 1.0 |
| Settlement value of corporate profits | 524.64 | 4,887.88 | 1,925.53 | 1,011.14 | 2,499.87 | 906.61 | 2,072.31 | 1588.67 | 627.68 | 1917.85 | 885.64 | 1549.35 | 625.33 | 4345.59 | 991.60 | 2979.61 | 755.05 | 99.65 | 1585.7 |
Table 18.
Estimated results by robustness test to explain the expected profit of large company by industry for FY2018
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 0.1 | – 1.5 | 0.3 | 0.2 | 0.1 | – 0.0 | 0.2 | – 0.0 | – 0.2 |
| CPI increase rate | 72.8 | 822.2 | 73.7 | 27.3 | – 16.5 | 32.1 | 135.8 | – 28.5 | – 71.1 |
| Unemployment rate | – 1004.0 | 20,584.9 | – 1916.7 | 1402.2 | – 648.1 | 532.9 | – 2.4 | – 1454.0 | 6117.0 |
| Interest rate | – 17.6 | 273.2 | – 13.8 | – 3.4 | 5.5 | – 3.1 | 0.3 | 8.8 | 3.9 |
| Population | 53,450.7 | – 1,300,966.4 | 1127.8 | – 129,222.2 | – 19,319.8 | – 33,899.0 | – 28,401.7 | 25,074.2 | – 437,109.8 |
| Constant term | – 52,099.8 | 1,285,947.0 | 2641.8 | 128,761.9 | 22,477.7 | 34,273.5 | 30,167.1 | – 21,882.9 | 432,063.8 |
| Variables | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 0.0 | 0.0 | 0.2 | – 0.2 | – 0.4 | – 0.3 | 0.3 | 0.0 | – 0.0 |
| CPI increase rate | – 563.7 | 12.5 | 106.8 | – 104.4 | – 259.6 | – 118.4 | 92.5 | 10.8 | 3.2 |
| Unemployment rate | – 7470.2 | 740.4 | 4838.3 | – 1923.0 | – 13,268.1 | – 2873.0 | 19.9 | 1683.9 | 18.6 |
| Interest rate | 88.4 | – 1.9 | – 22.1 | 36.6 | 38.5 | 59.6 | – 84.7 | – 11.7 | – 0.3 |
| Population | 99,085.6 | – 73,770.5 | – 307,201.7 | 50,000.2 | 845,682.4 | 44,264.6 | 30,080.5 | – 104,641.9 | – 813.0 |
| Constant term | – 88,622.6 | 73,935.7 | 303,750.9 | – 47,054.7 | – 827,732.2 | – 39,837.4 | – 27,442.6 | 103,731.9 | 891.7 |
Table 19.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 402.26 | 6659.34 | 1913.00 | 966.05 | 2498.84 | 936.39 | 1899.39 | 1717.58 | 1003.66 | 2517.47 | 916.15 | 1472.42 | 954.55 | 4460.64 | 1495.13 | 2665.88 | 772.92 | 100.23 | |
| Weighted value of profits for each industry | 1.36 | 20.33 | 97.04 | 3.95 | 140.72 | 29.56 | 146.40 | 42.84 | 11.88 | 63.61 | 131.24 | 78.42 | 14.12 | 325.79 | 114.26 | 237.15 | 184.53 | 1.86 | 1645.1 |
| Weighted number of enterprises | 0.00 | 0.00 | 0.05 | 0.00 | 0.06 | 0.03 | 0.08 | 0.02 | 0.01 | 0.03 | 0.14 | 0.05 | 0.01 | 0.07 | 0.08 | 0.09 | 0.24 | 0.02 | 1.0 |
| Settlement value of corporate profits | 411.38 | 6635.00 | 1944.29 | 986.08 | 2519.21 | 933.57 | 1928.92 | 1726.45 | 983.46 | 2504.04 | 917.65 | 1495.92 | 929.57 | 4371.08 | 1473.16 | 2670.61 | 775.38 | 100.45 | 1629.2 |
Table 20.
Estimated results by robustness test to explain the expected profit of large company by industry for FY2019
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | – 0.5 | 5.4 | – 0.9 | – 0.6 | – 0.4 | 0.1 | – 0.9 | 0.0 | 0.6 |
| CPI increase rate | 52.0 | 587.3 | 52.6 | 19.5 | – 11.8 | 23.0 | 97.0 | – 20.3 | – 50.8 |
| Unemployment rate | – 970.5 | 19,899.5 | – 1,852.9 | 1,355.6 | – 626.5 | 515.1 | – 2.4 | – 1405.6 | 5913.4 |
| Interest rate | 46.9 | – 727.9 | 36.8 | 9.0 | – 14.7 | 8.3 | – 0.9 | – 23.5 | – 10.4 |
| Population | 53,334.0 | – 1,298,126.6 | 1125.3 | – 128,940.1 | – 19,277.7 | – 33,825.0 | – 28,339.7 | 25,019.4 | – 436,155.6 |
| Constant term | – 52,099.8 | 1,285,947.0 | 2641.8 | 128,761.9 | 22,477.7 | 34,273.5 | 30,167.1 | – 21,882.9 | 432,063.8 |
| Variables | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | – 0.0 | – 0.1 | – 0.6 | 0.8 | 1.2 | 0.9 | – 0.9 | – 0.0 | 0.0 |
| CPI increase rate | – 402.7 | 9.0 | 76.3 | – 74.5 | – 185.4 | – 84.6 | 66.1 | 7.7 | 2.3 |
| Unemployment rate | – 7221.5 | 715.7 | 4677.2 | – 1859.0 | – 12,826.3 | – 2777.4 | 19.3 | 1627.8 | 18.0 |
| Interest rate | – 235.5 | 5.2 | 59.0 | – 97.5 | – 102.5 | – 158.8 | 225.6 | 31.2 | 0.8 |
| Population | 98,869.3 | – 73,609.5 | – 306,531.1 | 49,891.1 | 843,836.4 | 44,168.0 | 30,014.8 | – 104,413.5 | – 811.2 |
| Constant term | – 88,622.6 | 73,935.7 | 303,750.9 | – 47,054.7 | – 827,732.2 | – 39,837.4 | – 27,442.6 | 103,731.9 | 891.7 |
Table 21.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 362.11 | 7584.61 | 2002.82 | 1205.24 | 2546.56 | 994.93 | 1920.35 | 1687.14 | 1760.95 | 2387.11 | 1055.85 | 2031.76 | 806.12 | 2991.25 | 1310.83 | 2882.21 | 985.13 | 101.52 | |
| Weighted value of profits for each industry | 1.47 | 26.98 | 116.84 | 4.55 | 164.18 | 28.42 | 134.52 | 44.23 | 17.64 | 65.45 | 138.22 | 87.06 | 11.29 | 256.03 | 115.21 | 291.66 | 215.87 | 1.97 | 1721.6 |
| Weighted number of enterprises | 0.00 | 0.00 | 0.06 | 0.00 | 0.06 | 0.03 | 0.07 | 0.03 | 0.01 | 0.03 | 0.13 | 0.04 | 0.01 | 0.09 | 0.09 | 0.10 | 0.22 | 0.02 | 1.0 |
| Settlement value of corporate profits | 321.53 | 8094.74 | 1936.43 | 1161.93 | 2516.38 | 1000.60 | 1854.41 | 1696.51 | 1809.09 | 2392.81 | 1042.43 | 1989.43 | 865.21 | 3057.76 | 1389.79 | 2795.01 | 984.71 | 102.05 | 1700.1 |
Table 22.
Estimated results by robustness test to explain the expected profit of large company by industry for FY2020
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | – 3.3 | 37.1 | – 6.0 | – 3.9 | – 3.0 | 0.5 | – 5.9 | 0.3 | 4.3 |
| CPI increase rate | – 0.2 | – 2.3 | – 0.2 | – 0.1 | 0.0 | – 0.1 | – 0.4 | 0.1 | 0.2 |
| Unemployment rate | – 1196.6 | 24,534.3 | – 2284.4 | 1671.3 | – 772.5 | 635.1 | – 2.9 | – 1733.0 | 7290.7 |
| Interest rate | – 12.8 | 199.1 | – 10.1 | – 2.5 | 4.0 | – 2.3 | 0.2 | 6.4 | 2.8 |
| Population | 52,087.1 | – 1,267,776.2 | 1099.0 | – 125,925.5 | – 18,826.9 | – 33,034.1 | – 27,677.1 | 24,434.5 | – 425,958.2 |
| Constant term | – 52,099.8 | 1,285,947.0 | 2641.8 | 128,761.9 | 22,477.7 | 34,273.5 | 30,167.1 | – 21,882.9 | 432,063.8 |
| Variables | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | – 0.0 | – 1.0 | – 4.0 | 5.3 | 8.6 | 6.5 | – 6.4 | – 0.1 | 0.0 |
| CPI increase rate | 1.6 | – 0.0 | – 0.3 | 0.3 | 0.7 | 0.3 | – 0.3 | – 0.0 | – 0.0 |
| Unemployment rate | – 8903.5 | 882.4 | 5766.6 | – 2291.9 | – 15,813.7 | – 3424.3 | 23.7 | 2006.9 | 22.2 |
| Interest rate | 64.4 | – 1.4 | – 16.1 | 26.7 | 28.0 | 43.4 | – 61.7 | – 8.5 | – 0.2 |
| Population | 96,557.7 | – 71,888.5 | – 299,364.3 | 48,724.6 | 824,107.4 | 43,135.3 | 29,313.0 | – 101,972.3 | – 792.3 |
| Constant term | – 88,622.6 | 73,935.7 | 303,750.9 | – 47,054.7 | – 827,732.2 | – 39,837.4 | – 27,442.6 | 103,731.9 | 891.7 |
Table 23.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 0.00 | 42,938.92 | 1440.08 | 4501.25 | 2879.30 | 1872.61 | 2481.06 | 825.38 | 13,403.56 | 0.00 | 2927.12 | 10,132.72 | 0.00 | 0.00 | 0.00 | 1825.88 | 3757.87 | 121.45 | |
| Weighted value of profits for each industry | 0.00 | 152.75 | 84.01 | 17.00 | 185.63 | 53.50 | 173.80 | 21.64 | 134.24 | 0.00 | 383.19 | 434.21 | 0.00 | 0.00 | 0.00 | 184.76 | 823.47 | 2.35 | 2650.5 |
| Weighted number of enterprises | 0.00 | 0.00 | 0.06 | 0.00 | 0.06 | 0.03 | 0.07 | 0.03 | 0.01 | 0.03 | 0.13 | 0.04 | 0.01 | 0.09 | 0.09 | 0.10 | 0.22 | 0.02 | 1.0 |
| Settlement value of corporate profits | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― |
This table describes the results of multiplying the estimated results of corporate income in Table 4 by the actual values of macroeconomic variables in Appendix 2.
Appendix 4: Results of the expected profits of SMEs by industry
See Tables 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38 and 39.
Table 24.
Estimated results by robustness test to explain the expected profits of SMEs by industry for FY2013
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | – 0.0 | 0.0 | 0.0 | – 0.0 | 0.0 | 0.0 | 0.0 | – 0.0 | – 0.0 |
| CPI increase rate | 0.6 | 0.2 | 0.2 | 0.3 | 0.6 | 2.6 | 1.0 | 0.2 | 0.7 | – 1.6 | 0.6 | – 0.2 | – 1.2 | – 0.2 | 0.1 | – 0.0 | – 1.6 | – 0.5 |
| Unemployment rate | – 12.7 | – 25.9 | – 3.6 | – 14.5 | – 25.9 | – 31.3 | – 66.3 | – 33.7 | – 0.3 | – 15.2 | – 13.7 | – 7.9 | – 47.6 | – 25.4 | – 13.0 | – 6.1 | – 58.7 | 31.4 |
| Interest rate | – 7.0 | 18.7 | – 1.9 | – 0.4 | 6.3 | 0.6 | 8.9 | 3.0 | – 1.6 | 12.7 | 1.9 | 3.0 | 3.3 | – 0.5 | – 1.2 | – 3.0 | 10.9 | 4.5 |
| Population | 714.7 | – 2683.3 | – 268.5 | 345.3 | – 1093.4 | – 237.7 | 520.4 | 278.1 | – 534.8 | – 1089.8 | – 483.2 | – 338.5 | 1554.5 | 436.1 | 135.1 | – 65.5 | 1216.1 | – 1591.6 |
| Constant term | – 686.7 | 2719.3 | 283.1 | – 314.9 | 1152.2 | 286.3 | – 435.7 | – 222.7 | 551.6 | 1119.3 | 517.6 | 357.7 | – 1495.4 | – 395.9 | – 110.2 | 91.8 | – 1147.8 | 1585.7 |
Table 25.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 8.93 | 28.96 | 9.22 | 15.72 | 39.82 | 20.61 | 28.31 | 24.92 | 15.67 | 25.49 | 23.16 | 14.12 | 13.66 | 14.24 | 10.71 | 17.14 | 18.83 | 29.56 | |
| Weighted value of profits for each industry | 0.08 | 0.04 | 1.53 | 0.04 | 0.49 | 0.39 | 0.80 | 0.32 | 0.13 | 0.69 | 2.20 | 1.47 | 0.40 | 0.24 | 1.29 | 0.53 | 5.14 | 1.17 | 17.0 |
| Weighted number of enterprises | 0.01 | 0.00 | 0.17 | 0.00 | 0.01 | 0.02 | 0.03 | 0.01 | 0.01 | 0.03 | 0.10 | 0.10 | 0.03 | 0.02 | 0.12 | 0.03 | 0.27 | 0.04 | 1.0 |
| Settlement value of corporate profits | 11.38 | 30.21 | 9.96 | 17.35 | 42.90 | 28.43 | 32.94 | 28.38 | 18.20 | 24.61 | 25.48 | 14.82 | 12.24 | 16.55 | 11.70 | 16.80 | 15.59 | 29.02 | 17.1 |
Table 26.
Estimated results by robustness test to explain the expected profits of SMEs by industry for FY2014
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | 0.0 | – 0.0 | – 0.0 | 0.0 | – 0.0 | – 0.0 | – 0.0 | 0.0 | 0.0 |
| CPI increase rate | 1.8 | 0.5 | 0.7 | 0.8 | 1.9 | 8.5 | 3.2 | 0.5 | 2.4 | – 5.2 | 2.0 | – 0.7 | – 3.7 | – 0.6 | 0.2 | – 0.2 | – 5.1 | – 1.5 |
| Unemployment rate | – 11.6 | – 23.7 | – 3.3 | – 13.3 | – 23.6 | – 28.6 | – 60.5 | – 30.7 | – 0.3 | – 13.9 | – 12.5 | – 7.2 | – 43.4 | – 23.2 | – 11.9 | – 5.6 | – 53.6 | 28.7 |
| Interest rate | – 4.6 | 12.4 | – 1.3 | – 0.3 | 4.2 | 0.4 | 5.9 | 2.0 | – 1.0 | 8.4 | 1.3 | 2.0 | 2.1 | – 0.3 | – 0.8 | – 2.0 | 7.2 | 3.0 |
| Population | 713.6 | – 2679.2 | – 268.1 | 344.8 | – 1091.7 | – 237.3 | 519.6 | 277.7 | – 534.0 | – 1088.1 | – 482.5 | – 338.0 | 1552.2 | 435.5 | 134.9 | – 65.4 | 1214.3 | – 1589.2 |
| Constant term | – 686.7 | 2719.3 | 283.1 | – 314.9 | 1152.2 | 286.3 | – 435.7 | – 222.7 | 551.6 | 1119.3 | 517.6 | 357.7 | – 1495.4 | – 395.9 | – 110.2 | 91.8 | – 1147.8 | 1585.7 |
Table 27.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 12.58 | 29.27 | 11.10 | 17.18 | 42.86 | 29.32 | 32.53 | 26.78 | 18.65 | 20.57 | 25.81 | 13.83 | 11.85 | 15.59 | 12.17 | 18.68 | 14.97 | 26.64 | |
| Weighted value of profits for each industry | 0.12 | 0.04 | 2.00 | 0.05 | 0.51 | 0.58 | 0.97 | 0.32 | 0.15 | 0.56 | 2.37 | 1.36 | 0.35 | 0.26 | 1.45 | 0.60 | 4.03 | 0.99 | 16.7 |
| Weighted number of enterprises | 0.01 | 0.00 | 0.18 | 0.00 | 0.01 | 0.02 | 0.03 | 0.01 | 0.01 | 0.03 | 0.09 | 0.10 | 0.03 | 0.02 | 0.12 | 0.03 | 0.27 | 0.04 | 1.0 |
| Settlement value of corporate profits | 12.27 | 29.13 | 11.01 | 16.89 | 42.58 | 28.24 | 31.86 | 26.31 | 18.31 | 20.64 | 25.50 | 13.70 | 12.04 | 15.18 | 12.03 | 18.78 | 15.37 | 26.68 | 16.8 |
Table 28.
Estimated results by robustness test to explain the expected profits of SMEs by industry for FY2015
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | – 0.0 | 0.0 | 0.0 | – 0.0 | 0.0 | 0.0 | 0.0 | – 0.0 | – 0.0 |
| CPI increase rate | 0.1 | 0.0 | 0.1 | 0.1 | 0.1 | 0.6 | 0.2 | 0.0 | 0.2 | – 0.4 | 0.1 | – 0.0 | – 0.3 | – 0.0 | 0.0 | – 0.0 | – 0.4 | – 0.1 |
| Unemployment rate | – 10.8 | – 22.0 | – 3.1 | – 12.3 | – 22.0 | – 26.6 | – 56.3 | – 28.6 | – 0.3 | – 12.9 | – 11.6 | – 6.7 | – 40.4 | – 21.5 | – 11.1 | – 5.2 | – 49.8 | 26.6 |
| Interest rate | – 2.7 | 7.1 | – 0.7 | – 0.2 | 2.4 | 0.2 | 3.4 | 1.1 | – 0.6 | 4.9 | 0.7 | 1.1 | 1.2 | – 0.2 | – 0.5 | – 1.1 | 4.1 | 1.7 |
| Population | 712.3 | – 2674.0 | – 267.6 | 344.1 | – 1089.6 | – 236.9 | 518.6 | 277.2 | – 532.9 | – 1086.0 | – 481.6 | – 337.3 | 1549.2 | 434.6 | 134.6 | – 65.3 | 1211.9 | – 1586.1 |
| Constant term | – 686.7 | 2719.3 | 283.1 | – 314.9 | 1152.2 | 286.3 | – 435.7 | – 222.7 | 551.6 | 1119.3 | 517.6 | 357.7 | – 1495.4 | – 395.9 | – 110.2 | 91.8 | – 1147.8 | 1585.7 |
Table 29.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 12.28 | 30.46 | 11.73 | 16.81 | 43.17 | 23.75 | 30.31 | 27.11 | 17.96 | 24.94 | 25.24 | 14.81 | 14.42 | 17.02 | 12.91 | 20.19 | 18.08 | 27.84 | |
| Weighted value of profits for each industry | 0.13 | 0.04 | 2.12 | 0.05 | 0.48 | 0.46 | 0.90 | 0.33 | 0.14 | 0.66 | 2.22 | 1.44 | 0.46 | 0.30 | 1.54 | 0.70 | 4.87 | 1.03 | 17.9 |
| Weighted number of enterprises | 0.01 | 0.00 | 0.18 | 0.00 | 0.01 | 0.02 | 0.03 | 0.01 | 0.01 | 0.03 | 0.09 | 0.10 | 0.03 | 0.02 | 0.12 | 0.03 | 0.27 | 0.04 | 1.0 |
| Settlement value of corporate profits | 14.36 | 31.99 | 12.20 | 15.63 | 49.12 | 26.73 | 31.09 | 28.68 | 19.04 | 22.88 | 26.35 | 13.84 | 13.60 | 14.90 | 12.96 | 21.66 | 15.17 | 26.45 | 17.1 |
Table 30.
Estimated results by robustness test to explain the expected profits of SMEs by industry for FY2016
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | – 0.0 | 0.0 | 0.0 | – 0.0 | 0.0 | 0.0 | 0.0 | – 0.0 | – 0.0 |
| CPI increase rate | – 0.1 | – 0.0 | – 0.0 | – 0.0 | – 0.1 | – 0.3 | – 0.1 | – 0.0 | – 0.1 | 0.2 | – 0.1 | 0.0 | 0.1 | 0.0 | – 0.0 | 0.0 | 0.2 | 0.1 |
| Unemployment rate | – 9.9 | – 20.2 | – 2.8 | – 11.3 | – 20.2 | – 24.4 | – 51.7 | – 26.2 | – 0.2 | – 11.8 | – 10.7 | – 6.1 | – 37.1 | – 19.8 | – 10.1 | – 4.8 | – 45.7 | 24.5 |
| Interest rate | 0.6 | – 1.7 | 0.2 | 0.0 | – 0.6 | – 0.1 | – 0.8 | – 0.3 | 0.1 | – 1.2 | – 0.2 | – 0.3 | – 0.3 | 0.0 | 0.1 | 0.3 | – 1.0 | – 0.4 |
| Population | 711.4 | – 2670.7 | – 267.3 | 343.7 | – 1088.3 | – 236.6 | 518.0 | 276.8 | – 532.3 | – 1084.6 | – 481.0 | – 336.9 | 1547.3 | 434.1 | 134.5 | – 65.2 | 1210.4 | – 1584.2 |
| Constant term | – 686.7 | 2719.3 | 283.1 | – 314.9 | 1152.2 | 286.3 | – 435.7 | – 222.7 | 551.6 | 1119.3 | 517.6 | 357.7 | – 1495.4 | – 395.9 | – 110.2 | 91.8 | – 1147.8 | 1585.7 |
Table 31.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 15.38 | 26.68 | 13.15 | 17.51 | 43.14 | 25.04 | 29.72 | 27.63 | 19.14 | 21.87 | 25.68 | 14.44 | 14.65 | 18.52 | 14.18 | 22.12 | 16.05 | 25.64 | |
| Weighted value of profits for each industry | 0.18 | 0.03 | 2.41 | 0.05 | 0.47 | 0.48 | 0.88 | 0.33 | 0.15 | 0.58 | 2.24 | 1.38 | 0.48 | 0.31 | 1.74 | 0.80 | 4.38 | 0.70 | 17.6 |
| Weighted number of enterprises | 0.01 | 0.00 | 0.18 | 0.00 | 0.01 | 0.02 | 0.03 | 0.01 | 0.01 | 0.03 | 0.09 | 0.10 | 0.03 | 0.02 | 0.12 | 0.04 | 0.27 | 0.03 | 1.0 |
| Settlement value of corporate profits | 15.93 | 26.85 | 13.35 | 18.48 | 43.04 | 27.66 | 31.51 | 28.73 | 19.96 | 21.98 | 26.40 | 14.97 | 14.24 | 19.99 | 14.60 | 21.63 | 15.36 | 25.74 | 17.7 |
Table 32.
Estimated results by robustness test to explain the expected profits of SMEs by industry for FY2017
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | – 0.0 | 0.0 | 0.0 | – 0.0 | 0.0 | 0.0 | 0.0 | – 0.0 | – 0.0 |
| CPI increase rate | 0.4 | 0.1 | 0.2 | 0.2 | 0.5 | 2.1 | 0.8 | 0.1 | 0.6 | – 1.3 | 0.5 | – 0.2 | – 0.9 | – 0.1 | 0.0 | – 0.0 | – 1.2 | – 0.4 |
| Unemployment rate | – 8.9 | – 18.1 | – 2.5 | – 10.1 | – 18.1 | – 21.9 | – 46.3 | – 23.5 | – 0.2 | – 10.6 | – 9.6 | – 5.5 | – 33.2 | – 17.7 | – 9.1 | – 4.3 | – 41.0 | 21.9 |
| Interest rate | – 0.4 | 1.2 | – 0.1 | – 0.0 | 0.4 | 0.0 | 0.6 | 0.2 | – 0.1 | 0.8 | 0.1 | 0.2 | 0.2 | – 0.0 | – 0.1 | – 0.2 | 0.7 | 0.3 |
| Population | 710.1 | – 2665.9 | – 266.8 | 343.1 | – 1086.3 | – 236.2 | 517.1 | 276.3 | – 531.3 | – 1082.7 | – 480.1 | – 336.3 | 1544.5 | 433.3 | 134.2 | – 65.1 | 1208.2 | – 1581.3 |
| Constant term | – 686.7 | 2719.3 | 283.1 | – 314.9 | 1152.2 | 286.3 | – 435.7 | – 222.7 | 551.6 | 1119.3 | 517.6 | 357.7 | – 1495.4 | – 395.9 | – 110.2 | 91.8 | – 1147.8 | 1585.7 |
Table 33.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 14.58 | 36.56 | 13.82 | 18.23 | 48.67 | 30.46 | 36.43 | 30.47 | 20.53 | 25.55 | 28.50 | 15.94 | 15.21 | 19.58 | 14.87 | 22.23 | 18.89 | 26.21 | |
| Weighted value of profits for each industry | 0.18 | 0.04 | 2.52 | 0.05 | 0.53 | 0.58 | 1.05 | 0.36 | 0.15 | 0.68 | 2.49 | 1.48 | 0.50 | 0.31 | 1.87 | 0.78 | 5.24 | 0.70 | 19.5 |
| Weighted number of enterprises | 0.01 | 0.00 | 0.18 | 0.00 | 0.01 | 0.02 | 0.03 | 0.01 | 0.01 | 0.03 | 0.09 | 0.09 | 0.03 | 0.02 | 0.13 | 0.04 | 0.28 | 0.03 | 1.0 |
| Settlement value of corporate profits | 16.30 | 37.55 | 14.31 | 18.78 | 51.62 | 35.10 | 38.95 | 32.58 | 22.06 | 24.62 | 29.93 | 16.07 | 14.31 | 20.24 | 15.38 | 22.40 | 16.57 | 25.61 | 19.3 |
Table 34.
Estimated results by robustness test to explain the expected profits of SMEs by industry for FY2018
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | – 0.0 | 0.0 | 0.0 | – 0.0 | 0.0 | 0.0 | 0.0 | – 0.0 | – 0.0 |
| CPI increase rate | 0.4 | 0.1 | 0.2 | 0.2 | 0.5 | 2.1 | 0.8 | 0.1 | 0.6 | – 1.3 | 0.5 | – 0.2 | – 0.9 | – 0.1 | 0.0 | – 0.0 | – 1.2 | – 0.4 |
| Unemployment rate | – 7.9 | – 16.2 | – 2.3 | – 9.1 | – 16.1 | – 19.5 | – 41.4 | – 21.0 | – 0.2 | – 9.5 | – 8.6 | – 4.9 | – 29.7 | – 15.8 | – 8.1 | – 3.8 | – 36.6 | 19.6 |
| Interest rate | – 0.4 | 1.2 | – 0.1 | – 0.0 | 0.4 | 0.0 | 0.6 | 0.2 | – 0.1 | 0.8 | 0.1 | 0.2 | 0.2 | – 0.0 | – 0.1 | – 0.2 | 0.7 | 0.3 |
| Population | 708.6 | – 2660.2 | – 266.2 | 342.4 | – 1084.0 | – 235.7 | 516.0 | 275.8 | – 530.2 | – 1,080.4 | – 479.1 | – 335.6 | 1541.2 | 432.4 | 133.9 | – 64.9 | 1205.7 | – 1578.0 |
| Constant term | – 686.7 | 2719.3 | 283.1 | – 314.9 | 1152.2 | 286.3 | – 435.7 | – 222.7 | 551.6 | 1,119.3 | 517.6 | 357.7 | – 1495.4 | – 395.9 | – 110.2 | 91.8 | – 1147.8 | 1585.7 |
Table 35.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 13.98 | 44.18 | 14.66 | 18.57 | 52.89 | 33.25 | 40.24 | 32.37 | 21.68 | 29.01 | 30.53 | 17.25 | 15.46 | 20.53 | 15.55 | 22.82 | 20.69 | 27.26 | |
| Weighted value of profits for each industry | 0.16 | 0.05 | 2.71 | 0.05 | 0.57 | 0.65 | 1.20 | 0.36 | 0.15 | 0.76 | 2.61 | 1.52 | 0.49 | 0.32 | 2.02 | 0.79 | 5.80 | 0.69 | 20.9 |
| Weighted number of enterprises | 0.01 | 0.00 | 0.18 | 0.00 | 0.01 | 0.02 | 0.03 | 0.01 | 0.01 | 0.03 | 0.09 | 0.09 | 0.03 | 0.02 | 0.13 | 0.03 | 0.28 | 0.03 | 1.0 |
| Settlement value of corporate profits | 14.15 | 44.25 | 14.71 | 18.79 | 52.96 | 33.94 | 40.69 | 32.66 | 21.90 | 29.19 | 30.73 | 17.36 | 15.35 | 20.86 | 15.65 | 22.73 | 20.48 | 27.26 | 20.8 |
Table 36.
Estimated results by robustness test to explain the expected profits of SMEs by industry for FY2019
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | 0.0 | – 0.0 | – 0.0 | 0.0 | – 0.0 | – 0.0 | – 0.0 | 0.0 | 0.0 |
| CPI increase rate | 0.3 | 0.1 | 0.1 | 0.1 | 0.3 | 1.5 | 0.6 | 0.1 | 0.4 | – 0.9 | 0.3 | – 0.1 | – 0.6 | – 0.1 | 0.0 | – 0.0 | – 0.9 | – 0.3 |
| Unemployment rate | – 7.6 | – 15.6 | – 2.2 | – 8.8 | – 15.6 | – 18.9 | – 40.0 | – 20.3 | – 0.2 | – 9.2 | – 8.3 | – 4.7 | – 28.7 | – 15.3 | – 7.9 | – 3.7 | – 35.4 | 18.9 |
| Interest rate | 1.2 | – 3.1 | 0.3 | 0.1 | – 1.1 | – 0.1 | – 1.5 | – 0.5 | 0.3 | – 2.1 | – 0.3 | – 0.5 | – 0.5 | 0.1 | 0.2 | 0.5 | – 1.8 | – 0.8 |
| Population | 707.0 | – 2654.4 | – 265.6 | 341.6 | – 1081.6 | – 235.1 | 514.8 | 275.1 | – 529.0 | – 1,078.0 | – 478.0 | – 334.8 | 1537.8 | 431.4 | 133.7 | – 64.8 | 1203.0 | – 1574.5 |
| Constant term | – 686.7 | 2719.3 | 283.1 | – 314.9 | 1152.2 | 286.3 | – 435.7 | – 222.7 | 551.6 | 1,119.3 | 517.6 | 357.7 | – 1495.4 | – 395.9 | – 110.2 | 91.8 | – 1147.8 | 1585.7 |
Table 37.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | 14.18 | 46.18 | 15.71 | 18.17 | 54.20 | 33.67 | 38.21 | 31.73 | 23.03 | 29.11 | 31.28 | 17.50 | 12.59 | 20.26 | 15.78 | 23.79 | 17.14 | 29.12 | |
| Weighted value of profits for each industry | 0.15 | 0.05 | 2.99 | 0.04 | 0.56 | 0.62 | 1.05 | 0.34 | 0.16 | 0.73 | 2.59 | 1.53 | 0.40 | 0.32 | 2.10 | 0.81 | 4.83 | 0.73 | 20.0 |
| Weighted number of enterprises | 0.01 | 0.00 | 0.19 | 0.00 | 0.01 | 0.02 | 0.03 | 0.01 | 0.01 | 0.02 | 0.08 | 0.09 | 0.03 | 0.02 | 0.13 | 0.03 | 0.28 | 0.03 | 1.0 |
| Settlement value of corporate profits | 13.65 | 45.86 | 15.57 | 18.11 | 53.16 | 32.42 | 37.58 | 31.15 | 22.62 | 29.45 | 30.88 | 17.53 | 12.86 | 20.23 | 15.66 | 23.66 | 17.85 | 29.34 | 20.0 |
Table 38.
Estimated results by robustness test to explain the expected profits of SMEs by industry for FY2020
| Variables | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GDP growth rate | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.1 | – 0.1 | – 0.1 | – 0.1 | – 0.0 | 0.0 | – 0.0 | – 0.0 | 0.0 | – 0.0 | – 0.0 | – 0.0 | 0.1 | 0.0 |
| CPI increase rate | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | – 0.0 | 0.0 | – 0.0 | 0.0 | 0.0 | 0.0 | – 0.0 | 0.0 | 0.0 | 0.0 |
| Unemployment rate | – 9.4 | – 19.3 | – 2.7 | – 10.8 | – 19.2 | – 23.3 | – 49.3 | – 25.0 | – 0.2 | – 11.3 | – 10.2 | – 5.9 | – 35.4 | – 18.9 | – 9.7 | – 4.6 | – 43.6 | 23.3 |
| Interest rate | – 0.3 | 0.9 | – 0.1 | – 0.0 | 0.3 | 0.0 | 0.4 | 0.1 | – 0.1 | 0.6 | 0.1 | 0.1 | 0.1 | – 0.0 | – 0.1 | – 0.1 | 0.5 | 0.2 |
| Population | 690.5 | – 2592.4 | – 259.4 | 333.6 | – 1056.3 | – 229.6 | 502.8 | 268.7 | – 516.6 | – 1052.8 | – 466.9 | – 327.0 | 1501.9 | 421.4 | 130.5 | – 63.3 | 1174.9 | – 1537.7 |
| Constant term | – 686.7 | 2719.3 | 283.1 | – 314.9 | 1152.2 | 286.3 | – 435.7 | – 222.7 | 551.6 | 1119.3 | 517.6 | 357.7 | – 1495.4 | – 395.9 | – 110.2 | 91.8 | – 1147.8 | 1585.7 |
Table 39.
Proportional calculation from the indicated results on the above table
| Statistics | Agriculture | Mining | Construction | Textile | Chemical | Steel | Machine | Food | Publication | Other manufacturing | Wholesale | Retail | Cooking/accommodation | Finance | Real estate | Transportation/communication | Service | Medical | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimated corporate profits | – 5.98 | 108.48 | 20.86 | 7.90 | 76.81 | 33.32 | 18.14 | 21.07 | 34.58 | 55.82 | 40.55 | 24.98 | – 28.69 | 6.59 | 10.53 | 23.83 | – 15.97 | 71.58 | |
| Weighted value of profits for each industry | – 0.06 | 0.12 | 3.96 | 0.02 | 0.79 | 0.62 | 0.50 | 0.23 | 0.24 | 1.39 | 3.36 | 2.18 | – 0.90 | 0.10 | 1.40 | 0.81 | – 4.50 | 1.80 | 12.1 |
| Weighted number of enterprises | 0.01 | 0.00 | 0.19 | 0.00 | 0.01 | 0.02 | 0.03 | 0.01 | 0.01 | 0.02 | 0.08 | 0.09 | 0.03 | 0.02 | 0.13 | 0.03 | 0.28 | 0.03 | 1.0 |
| Settlement value of corporate profits | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― | ― |
This table describes the results of multiplying the estimated results of corporate income in Table 5 by the actual values of macroeconomic variables in Appendix 2.
For simplification, industries whose amount of profit is negative are set to 0, as there is no declared profit that can be taxed; as this indicates the average values of the profit of the entire industry, some enterprises could have made profits. This leads to the error observed between the actual and estimated values, which is the limitation of this model.
As at February 2022, “The Corporation Sample Survey” (National Tax Agency) for the estimated year (FY2020) had not yet been published. Therefore, we estimate the data (declared profit amount) for FY2020 using this model (Table 3), which is based on the macroeconomic conditions for FY2020 [explanatory variables (Appendix 2)]. Due to the same reason, the number of profit-making corporations cannot be determined, so the industrial weight cannot be obtained. Therefore, to determine the corporate tax revenue in FY2020, the author places the number of corporations of both large enterprises and SMEs in the previous fiscal year horizontally and calculates industry-classified weights and a prorate estimation of the average profit of each industry.29
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Footnotes
In the “Estimation of Impact of FY2022 budget on Expenditures and Revenues in the later years” (Ministry of Finance, 2022) [22], the tax revenues for the later years are estimated by multiplying the nominal economic growth rate by the elasticity of tax revenue, which is 1.1 (revenue growth rate = GDP growth rate × 1.1).
Cabinet office (2022, Jan 12) Estimate of the structural balance [Note 2–2-2(1)] retrieved from https://www5.cao.go.jp/j-j/wp/wp-je00/wp-je00fu-2-2-2(1)fc.html.
Please refer to Appendix 1, which indicates that the standard deviation of the tax revenue of the central government is 7.39, and that of the local government is 10.35.
The tax revenues had negative tax revenue elasticities in 2003, 2011, 2012, 2016, 2019, and 2020 fiscal years for national tax and in 2003, 2010, and 2012 fiscal years for local tax. Please refer to Appendix 1.
The income tax was 19,570.8 billion yen (2019) to 19,578.2 billion yen (2020); corporate tax increased from 10,797.7 billion yen (2019) to 11,235.1 billion yen (2020), and consumption tax increased from 23,148.3 billion yen (2019) to 26,395.1 billion yen (2020).
Financial Statements Statistics of Corporations by Industry FY2019, Ministry of Finance Japan [17, 19, 21].
Also, here, for the sake of simplification of the model, we once assumed a high autocorrelation regarding the rate of return, but it should be noted that this needs to be analyzed from the individual data.
In this paper, corporations with capital of 100 million yen or less to which the reduced tax rate is applied are defined as small- and medium-sized enterprises, and corporations with capital of more than 100 million yen are defined as large enterprises.
The financial result of corporate profits tax return (The Corporation Sample Survey) in FY2020 is scheduled to be announced in June 2022, according to the annual schedule by the National Tax Agency.
An example is the variation of asset prices. In particular, since the 1990s, asset prices fluctuate greatly in Japan, and this would have eroded tax-based corporate income in the form of an extraordinary loss and loss carryforwards, reducing the tax revenue significantly.
OECD (1999–2021).
According to the Ministry of Finance [19], compared with that of the previous year (10.797 trillion yen), corporate tax revenue increased significantly in FY2020 (11.235 trillion yen).
We construct a model to explain corporate profit, which is the basis of determining corporate tax, by configuring “price,” “unemployment,” “interest rate,” “population,” and “GDP,” which has been an index of conventional tax revenue elasticity, and analyze their effects on corporate profits.
Cabinet office [2].
According to the Ministry of Land, Infrastructure, Transport, and Tourism (2021) [23], the concentration of population and industry can reduce transportation and transaction costs, and when related companies complement each other, it is possible to build an efficient division of labor and accumulate management resources, such as human resources and know-how. The agglomeration of industry and population has attracted attention for its effect of increasing labor productivity in the region, and the government’s efforts to realize a compact city are underway.
According to Clark [7] and Newling [26], the density gradient, which indicates the degree of urban concentration and suburbanization, decreases with population growth, and the urban population density slopes from the center to the outer edge, i.e., decreases in the central part of the city and rises in the outer edge. As we are focusing on the fact that it tends to decrease at the central part and increase at the outer edge, it is possible that population increase will lead to a decrease in population density. In addition, only a few analyses deal with the population density of small spatial areas and follow the transition. Moreover, although the population movement between the central part of the city and the suburbs in the present age is remarkable, to analyze the corporate finance that pursue profit to move the business base, this is a useful previous study.
Suzuki [29].
Adams [1] pointed out that there is a positive correlation between the city center density and the number of years of the city system, that is, the older the city, the higher the city center density, and there is a positive correlation between the density gradient and the age of the city.
Cabinet Office, The Government of Japan [4] SNA.
If an individual lives in his/her own house (owned house), although the individual will not receive the rent, it is assumed that the same services as those of a normal rented house will be produced and consumed, and its value will be evaluated at a general market price. Attributive rent is not subject to either national, corporate, or local tax.
The data on GDP growth rate, CPI, and unemployment rate are from the Cabinet Office, Government of Japan [4]. The data on interest rate are from the Japan Securities Dealers Association [14]. The data on population are from the Statistics Bureau of Japan [28]. The data on profit (declared income) and the data on the number of enterprise are from the National Tax Agency [25].
Please refer to Tables 8 and 9 in Appendix 3 for the estimation results of the corporate profits of both large enterprises and SMEs by industry.
The corporate tax rate levied on the declared income is 25.5% in 2013 and 2014, 23.9% in 2015, 23.4% in 2016 and 2017, and 23.2% in 2018 and 2019. We estimate the corporate tax revenue by multiplying the corporate profits estimated in the model with these rates. Regarding the corporate tax levied on SMEs whose capital amount is 100 million yen or less, we multiply the first 8 million yen of declared profit by the reduced tax rate (15%) and multiply the declared profit in excess of 8 million yen by the above tax rate. As for medical and public interest corporations, the number of corporations and the amount of profits are small, so the impact on the estimation is also small. Thus, we omit specially set tax rates that apply to their declared incomes.
Comparing the “Economic and Fiscal Projections for Medium to Long Term Analysis (Jul. 31, 2020)” (Cabinet Office) published in July 2020 with the estimate (Jan. 17, 2020) published in January 2020, just before the occurrence of the COVID-19 shock, we find that whereas the January 2020 projection estimated the tax revenue of FY2020 to be 63.5 trillion JPY (General Account of the Central Government), the July 2020 projection drastically reduced the estimated value of FY2020 to 56.1 trillion JPY.
According to Summary of Financial Results, Ministry of Finance Japan (2020), Explanation of Financial Results, Ministry of Finance Japan (2020), Summary of Financial Results, Ministry of Finance Japan (2021), and Explanation of Financial Results, the Ministry of Finance (2021) [18–21], the national tax revenue in 2020 was 60.8 trillion JPY, which was higher than the previous year’s 58.4 trillion yen. Among the national taxes, corporate tax increased from 10.8 trillion JPY (FY2019) to 11.2 trillion JPY (FY2020), the second largest increase after the consumption tax, which increased in the latter half of FY2019. This effect also has a direct impact on local tax revenues. According to the Ministry of Finance [3, 19, 20], the corporate tax amount for the statutory rate (33.1%) in local allocation tax grants increased from 3.57 trillion JPY (FY2019) to 3.78 trillion JPY (FY2020), which helps increase local revenues.
The Japan’s government makes and provides a financial estimate that is a compass of fiscal consolidation, that is, “Economic and Fiscal Projections for Medium to Long Term Analysis” (footnote 26) with the aim of contributing to deliberation at the Council on Economic and Fiscal Policy. It evaluates the progress of economic revitalization and fiscal consolidation.
Please refer to Appendix 3 for the estimation results of corporate profits by industry.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.