_{1}

Japanese households experienced both the introduction of price consumption tax and two tax hikes from 1989 to 2014. According to a Bank of Japan report, the consumption tax hike (from 5% to 8%) in April 2014 was expected to increase the consumer price index by about two percentage points. In this study, we measure the impact of the consumption tax hike on the cost-of-living index, using the panel cointegrated demand system. We find that two consumption tax hikes in both 1997 and 2014 effectively raised the cost-of-living index. That is, it seems that the timing of two tax hikes was appropriate for Japanese households.

Japanese households experienced both the introduction of price consumption tax and two tax increases from 1989 to 2014. In 2012, the Japanese Prime Minister Shinzo Abe announced the “consumption tax increase” as one of his policy objectives under “Abenomics”^{1}, and it was enforced with an increase from 5% to 8% in April 2014^{2}. This policy objective is assumed as one of the key objectives leading to the future recovery of the Japanese economy.

It is wellknown that a consumption tax is a tax that a burden does not center on any particular person; it is collectively borne by the whole nation including the elderly. Therefore, the consumption tax is considered appropriate for social security resources in the aging Japanese society. In Japan, the consumption tax of 3% was first introduced in 1989, and which was next raised to 5% in 1997 and it was further raised to 8% in 2014. The Bank of Japan (BOJ) computed the direct effects of the tax increase with respect to the consumer price index (CPI). It showed that the consumption tax hike (from 5% to 8%) in April 2014 would increase the CPI by about two percentage points. Of course, as with other countries, the Japanese CPI itself includes the consumption tax in prices. In addition, it is difficult to measure the accurate influence of tax directly in CPI. Therefore, our estimation results also include the impacts of price rises by tax increase.

In this study, we measure how the introduction of tax increase will raise or lower the cost-of-living index. We believe the reasons for focusing on the movement of the cost-of-living index are as follows: the price trend in Japan has always been a topic of interest, and it is advocated as the main policy for eliminating deflation. As the consumption tax increase is considered the basic for future policy, it is important to measure and predict its degree of influence on house hold bud get and prices. Estimating the cost-of-living index could reveal the argument for a cost-of-living index is for the efficacy of inflation targeting policy, based on demand analysis.

To capture the influence of these price changes by consumption tax, we measure the cost-of-living index according to price changes and household budgets, and their fluctuations, using panel data for 26 years from 1989 to 2014. In our analysis, we use semi-macro panel data from 47 prefectural capitals. The panel data includes both time-series and cross-sectional dimensions and can measure the cost-of-living index for each prefectural capital.

The constitution of this paper is organized as follows. In Section 2, we introduce the almost ideal demand system (AIDS) model developed by Deaton and Muellbauer [

Area | Country | Consumption tax (%) | Area | Country | Consumption tax (%) |
---|---|---|---|---|---|

EU | Denmark | 25 | Asia | Korea | 10 |

France | 20 | Taiwan | 5 | ||

Germany | 19 | New Zealand | 15 | ||

Netherlands | 21 | Japan | 8 | ||

Sweden | 25 | China | 17 | ||

Norway | 25 | Singapore | 7 |

Note: The consumption tax indicates the value in 2015 and the source of reference is showed by national tax agency in Japan.

explain the data sources used in this study, and in Section 4, we report the estimation results, including the expenditure and price elasticity calculations. In Section 5, we calculate the cost-of-living index in Japan, including the effects of consumption tax increase during 1989 to 2014. Finally, in Section 6, we conclude the paper.

For this analysis, we use the AIDS model by Deaton and Muellbauer [

ln C ( u , p ) = α ( p ) + u β ( p ) (1)

where α ( p ) and β ( p ) are functions of prices as follows.

α ( p ) = a 0 + ∑ i = 1 n a i ln p i + 1 2 ∑ i = 1 n ∑ j = 1 n b i j * ln p i ln p j β ( p ) = b 0 ∏ i p i b i .

The cost function in (1) is homogeneous in p. Next, the i -th budget share can be derived from ∂ ln C / ∂ p = W and expressed by

w i k t = a i + ∑ j = 1 n b i j ln p j k t + c i ln ( x k t P k t ) + u i k t , i , j = 1 , ⋯ , n , k = 1 , ⋯ , K , t = 1 , ⋯ , T , (2)

where n is the number of commodities in the system, w i k t denotes the i -th budget share at the individual k in period , ln p j k t is the log price of com-

modity i at the individual k in period t , and ln ( x k t P k t ) is the log real expen-

diture with ln P k t of the aggregate price index. Originally, ln P k t is given by

ln P k t = a 0 + ∑ i = 1 n a i ln p i k t + 1 2 ∑ j = 1 n ∑ i = 1 n b i j ln p i k t ln p j k t . (3)

In this analysis, we use ln P k t linearly approximated by Stone’s [^{3} in substitution for (2):

ln P k t = ∑ i = 1 n w i k t ln p i k t (4)

That is, we estimate the linearly approximated model in this study.

Furthermore, in our panel data, the error term u i k t in (2) can be written as

u i k t = θ i k + μ i t + e i k t , (5)

where θ i k denotes an individual fixed effect and μ i t denotes a time fixed effect. Further, e i k t is usually assumed to have strong exogeneity and

E ( e | θ , μ , ln p , ln ( x / P ) ) = 0 .

The AIDS model requires satisfying the adding-up, homogeneity, and symmetry conditions in the parameters. The adding-up condition, which is automatically satisfied by the use of the n − 1 equations in the estimation, is

∑ i = 1 n a i = 1 and ∑ i = 1 n b i j = ∑ i = 1 n c i = 0.

The homogeneity restriction for price parameters is

∑ j = 1 n b i j = 0 , (6)

and the symmetry restriction is

b i j = b j i . (7)

Both these restrictions are imposed on price parameters in the estimation.

The expenditure elasticity of commodity i in individual k with respect to log real expenditure is given by

η i k = 1 + c i w i k . (8)

Marshallian price elasticity with respect to log prices^{4} is given by

λ i j k = − δ i j + b i j − c i w j k w i k , (9)

where δ i j represents the Kronecker delta and is 1 when i = j and is 0 otherwise.

We consider the evaluation of cost to a price change. The cost-of-living index is defined as the ratio of the minimum expenditure required to attain the base preference at prices p 0 to that required at prices p 1 . We measure the consumer surplus for a price change from p 1 to p 0 by the cost function of (1) as follows:

ln C ( u , p 1 ) − ln C ( u , p 0 ) = ln α ( p 1 ) α ( p 0 ) + u 0 ln β ( p 1 ) β ( p 0 ) , (10)

where u 0 is the base utility level and equals

ln [ x 0 / α ( p 0 ) ] / ln [ β ( p 0 ) / α ( p 0 ) ] . This is called the cost-of-living index. Equation (10) is specific to the PIGLOG model and shows how the price index varies with the households’ standard of living. The first term is expressed by the price index change for a price change from p 1 to p 0 . In addition, the second term is expressed by the base utility level and a price change from p 1 to p 0 . There is available literature for the cost-of-living index, such as that provided by Deaton and Muellbauer [

In this analysis, we use semi-macro panel data because the nonstationary problem is obvious in a time-series dimension and price effects can be accurately estimated using panel data. The household survey data include the panel data for workers’ households in 47 prefectural capitals. The source of data is the Family Income and Expenditure Survey (“Kakei Chosa” in Japanese) by the Japanese Statistics Bureau from 1989 to 2014. We classified the data into 10 goods: food, housing, fuel, furniture, clothing, medicine, transportation, education, recrea- tion, and miscellaneous. Price series data are obtained from the CPI and are calculated using 2010 as the base year^{5}. As described in section 1, we know that it is difficult to eliminate the influence of tax directly in CPI.

of own-budget shares. In contrast, the log prices for fuel, medicine, education, and miscellaneous display an upward trend. In particular, the increase for education is noteworthy, and this could be related to improvements in the level of education in Japan over the past 20 years.

When we observe the long-term trend of these prices, the change that shows the influence of tax increase appears in 2014. In addition, when we specifically focus on the prices for fuel, clothing, medicine, and recreation, the price change due to the increased tax can be observed in 1997 after its introduction. As can be seen in (10), the price of movement is important when we consider the effect of the tax increase on the cost-of-living index.

We know that budget shares lie between 0 and 1, and, therefore, cannot remain absolutely non stationary. Nevertheless, budget shares can closely approximate a nonstationary process. In fact, many previous studies on time-series have shown that budget shares have a nonstationary process, integrated with order one in unit root tests [

First,

Next, we confirm the existence of a long-term relationship between budget shares, log relative prices, and log real expenditure in panel data. We examine the panel cointegration test according to the economic relationship. Stationary residuals for equations would require these models to be cointegrated for each commodity i .

Variables | Test statistics | P-value | Variables | Test statistics | P-value | Variables | Test statistics | P-value |
---|---|---|---|---|---|---|---|---|

211.482 | 0.0000 | 295.928 | 0.0000 | 288.696 | 0.0000 | |||

326.555 | 0.0000 | 199.748 | 0.0000 | 154.699 | 0.0001 | |||

238.643 | 0.0000 | 24.0985 | 1.0000 | 127.924 | 0.0115 | |||

354.030 | 0.0000 | 158.952 | 0.0000 | 142.172 | 0.0010 | |||

231.410 | 0.0000 | 255.060 | 0.0000 | 211.546 | 0.0000 | |||

297.395 | 0.0000 | 121.658 | 0.0290 | 148.9.3 | 0.0003 | |||

513.374 | 0.0000 | 153.968 | 0.0001 | 124.486 | 0.0193 | |||

281.999 | 0.0000 | 465.842 | 0.0000 | 143.920 | 0.0007 | |||

297.726 | 0.0000 | 265.695 | 0.0000 | 172.324 | 0.0000 | |||

369.518 | 0.0000 | 110.717 | 0.1148 | 288.696 | 0.0000 | |||

385.942 | 0.0000 |

Notes: The number of subscript in variables is corresponding to commodity: 1) food, 2) housing, 3) fuel, 4) furniture, 5) clothing, 6) medicine, 7) transport, 8) education, 9) recreation, and 10.miscellaneous. ln P 6 , ln P 7 , ln P 8 , ln P 3 / P 10 ,and ln P 8 / P 10 assume no trend in the ADF regressions. In addition, ln P 2 / P 10 and ln P 6 / P 10 assume no trend and no intercept in the ADF regressions. Other variables assume a trend and individual effects in the regressions.

Residuals | Test statistics | P-value |
---|---|---|

−12.8094 | 0.0000 | |

−6.6365 | 0.0000 | |

−8.2214 | 0.0000 | |

−16.7947 | 0.0000 | |

−8.8651 | 0.0000 | |

−11.4385 | 0.0000 | |

−9.2008 | 0.0000 | |

−8.3629 | 0.0000 | |

−10.0427 | 0.0000 |

Notes: The number of subscript in each residual is corresponding to commodity: 1) food, 2) housing, 3) fuel, 4) furniture, 5) clothing, 6) medicine, 7) transport, 8) education, 9) recreation. We assume no deterministic trend in all regressions.

real expenditure that achieves stationarity. Even if we reveal long-term relationships in different sub-combination, it is not economically meaningful for our analysis. Therefore, we perform a residual-based cointegration test in each bu- dget share equation.

When we estimate a single cointegration equation where there is at most one cointegration relationship among I ( 1 ) variables, we may use the panel fully-modified estimator. However, we need to estimate the r = n − 1 cointegration relationships in a demand system framework. In addition, because the Slutsky symmetry in a demand system has cross-equation restrictions, we are also required to estimate the number of equations, simultaneously imposing these restrictions on cointegration vectors^{6}. Therefore, we use the panel triangular error correction model (TECM) in our estimation, and apply the TECM suggested by Philips [

y 1 t = B y 2 t + ε 1 t , (11)

Δ y 2 t = ε 2 t , (12)

where y 1 t is n 1 × 1 vector of left-hand side variables of the n − 1 cointegration system and y 2 t is n 2 × 1 vector of right-hand side variables. In addition, ε 1 t is n 1 × 1 subvector, and ε 2 t is n 2 × 1 subvector. The cointegration parameters B is the n 1 × n 2 matrix, and then y 1 t = B y 2 t represents the linear long-run relationship of demand system, and ε 1 t represents the short-run deviations from long-run equilibrium in (11), i.e. a measure of the difference between observed budget shares w ^ i k t and theoretical shares w i k t . Philips [^{7}:

y 1 t = B y 2 t + ∑ l = 0 L C l Δ y 2 t − l + v t . (13)

In addition, Philips [

H 0 : R 1 B r 2 = r ,

where R 1 is q × n 1 matrix, r 1 is n 2 × 1 vector, and r 2 is q × 1 vector. He also showed that the Wald test is valid for testing the hypothesis on the elements B. The form of Wald test statistics is given by

W = ( R 1 B r 1 − r 2 ) ′ ( R 1 Σ ˜ R ′ 1 ) − 1 ( R 1 B r 1 − r 2 ) r ′ 2 ( X ′ M X ) − 1 r 2 , (14)

where Σ ˜ = n − 1 V ˜ ′ V ˜ . But when there is serial correlation in equation error, it can be replaced by the variance-covariance matrix of footnote 7.

Specifications | Null hypothesis | df | Test statistics | P-value |
---|---|---|---|---|

Price effect | 81 | 6639.539 | 0.000 | |

Expenditure effect | 9 | 885.459 | 0.000 | |

90 | 780.091 | 0.000 | ||

Homogeneity test | 9 | 8.027 | 0.531 | |

Symmetry test (including homogeneity) | 45 | 35.593 | 0.841 |

Commodity | Marshallian price elasticity | Expenditure elasticity | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|

food | housing | fuel | furniture | clothing | medicine | transport | education | recreation | miscellaneous | ||

1. food | −0.0202 (0.0020) | −0.0676 (0.0003) | −0.0710 (0.0002) | −0.0578 (0.0002) | −0.0593 (0.0002) | 0.0156 (0.0001) | −0.0588 (0.0003) | −0.0187 (0.0002) | 0.0451 (0.0002) | −0.2563 (0.0006) | 0.5490 (0.0010) |

2. housing | −0.1498 (0.0013) | 0.2800 (0.0112) | 0.1262 (0.0012) | −0.0339 (0.0005) | 0.0083 (0.0004) | −0.0899 (0.0009) | −0.1003 (0.0013) | −0.1327 (0.0014) | −0.0838 (0.0008) | 0.1599 (0.0021) | 0.0159 (0.0090) |

3. fuel | −0.2341 (0.0013) | 0.1015 (0.0006) | −0.7569 (0.0011) | −0.0901 (0.0004) | −0.2031 (0.0010) | −0.1479 (0.0009) | −0.3802 (0.0023) | −0.1507 (0.0009) | −0.3951 (0.0021) | −0.0288 (0.0005) | 0.4080 (0.0032) |

4. furniture | −0.3981 (0.0032) | −0.1048 (0.0008) | −0.1659 (0.0013) | −0.3533 (0.0052) | −0.3083 (0.0025) | −0.0323 (0.0003) | 0.2627 (0.0022) | −0.0111 (0.0001) | −0.3096 (0.0025)) | −0.1760 (0.0014) | 0.9055 (0.0008) |

5. clothing | −0.3720 (0.0024) | −0.0571 (0.0004) | −0.2752 (0.0018) | −0.2462 (0.0015) | 0.1990 (0.0077) | 0.0501 (0.0003) | −0.2222 (0.0015) | −0.0039 (0.0001) | 0.1112 (0.0007) | −0.4194 (0.0027) | 1.1012 (0.0006) |

6. medicine | 0.0643 (0.0005) | -0.2158 (0.0014) | -0.3147 (0.0020) | -0.0367 (0.0002) | 0.1063 (0.0007) | -0.1759 (0.0051) | -0.6663 (0.0043) | 0.3180 (0.0020) | 0.0529 (0.0004) | 0.1075 (0.0008) | 0.7605 (0.0015) |

7. transport | −0.3891 (0.0023) | −0.1177 (0.0007) | −0.2273 (0.0012) | 0.0694 (0.0003) | −0.0435 (0.0003) | −0.1717 (0.0010) | −0.8737 (0.0009) | −0.3229 (0.0018) | −0.2961 (0.0017) | −1.5343 (0.0087) | 1.2306 (0.0013) |

8. education | −0.1532 (0.0012) | −0.2026 (0.0016) | −0.2066 (0.0017) | −0.0062 (0.0001) | 0.0101 (0.0001) | 0.1878 (0.0015) | −0.7685 (0.0062) | 0.4611 (0.0116) | 0.0376 (0.0003) | −0.2063 (0.0016) | 0.8470 (0.0012) |

9. recreation | 0.0301 (0.0041) | −0.1094 (0.0004) | −0.2628 (0.0011) | -0.1258 (0.0005) | 0.0536 (0.0002) | 0.0041 (0.0001) | −0.3370 (0.0014) | 0.0040 (0.0001) | 0.0301 (0.0041) | −0.3402 (0.0014) | 1.1137 (0.0004) |

10.miscellaneous | −0.4728 (0.0019) | −0.0621 (0.0005) | −0.0832 (0.0005) | −0.0532 (0.0003) | −0.1199 (0.0005) | −0.0147 (0.0001) | −0.2586 (0.0012) | −0.0856 (0.0005) | −0.2071 (0.0008) | −1.3550 (0.0007) | 1.6650 (0.0025) |

Note: The value in parenthesis is the standard error of elasticity.

ships cannot be satisfied. Our test result supports the null hypothesis statistically, and we find that the theoretical restrictions on the economics hold in the long-run vectors. The Slutsky symmetry is not violated in the panel cointegration relationships.

First,

Year | tax in | cost-of-living index | increase rate |
---|---|---|---|

1997 | 5% | 0.2296 (0.0072) | 19.7% |

2014 | 8% | 0.2518 (0.0111) | 18.3% |

Note: The values in parentheses are standard errors.

with an increase rate of 19.7%. In other words, the consumption tax hike in 1997 was substantial to raise the cost-of-living index and influenced for household budgets. However, the rise of the cost-of-living index did not last long. After 1999, the index movement dropped by continued price deflation, but temporarily recovered in 2008. During this time, the Japanese economy had been facing a deflation problem, but the cost-of-living index itself was at a higher level than in the 1990s, and had stopped declining after that. From 2012, under the “Abenomics,” the movement of the cost-of-living index has been recovering gradually. Furthermore, the index recorded the past highest level of 0.2518 due to the consumption tax hike in 2014 to 8%, with an increase rate of 18.3%. As well as in 1997, the consumption tax hike in 2014 largely pushed up the cost-of-living index. In addition to the tax increase, it would have also been affected by the improvement of utility level with the price rise. That is, two consumption tax increases in 1997 and 2014 was effective in raising the cost-of-living index and the impact on household budgets was large. As a result, we can observe the increase in consumer surplus for 26 years because of the changing consumption expenditures and prices, added to tax increase.

Next,

creasing, but there is no remarkable increase as compared to other local cities. In 2014, the cost-of-living index of Fukushima is 0.5078, which is the highest value among 47 prefectural capitals. Conversely, Kobe is 0.1207, which is the lowest value. Compared to 1997, the difference in the cost-of-living index among47 prefectural capitals is expanding in 2014. Looking at an increase rate, Kobe has the highest increase rate of 35.7% among 47 prefectural capitals. Further, Tokyo, Chiba, Kanazawa, and Fukuoka also have a high increase rate. Most of these are larger prefectural capitals located in urban areas, defined as government-or- dinance-designated cities and showing the large impact of tax increase in 2014. As a difference in the effect of the tax increase between 1997 and 2014, in many cases, in 1997the impact of tax increases was higher in local prefectural capitals than in larger prefectural capitals, but in 2014 the impact on larger prefectural capitals was higher. This difference between 1997 and 2014 is due to the differences in the reaction to price changes and differences in utility level. However, a reference about certain causes should be avoided in this study. In order to raise the effect of the tax increase on the cost-of-living index to each prefectural capital, appropriate policies of local governments need to be designed.

In this study, we measured the changes in the cost-of-living index experienced due to two tax increases in Japan. The impact of the first tax increase in 1997 may be high, and therefore the rise in the cost-of-living index by the tax increase is also large, although the rise in prices was not remarkable. However, the rise of the cost-of-living index did not last long; in the last few years, it has begun to decrease. That is, the impact of the 1997 tax increase for households was lasted only for a few years. On the other hand, the impact of the second tax increase in 2014 has been also high as well as that of 1997 and the cost-of-living index has reached the highest level ever. The consumption tax increase in 2014 was effective in raising the cost-of-living index, affected by price rise and the improvement of utility level with the price rise. Therefore, we expect that this effect is not temporary unlike the case in 1997. In addition, this result indicates that Japanese households are recovering from the stagnation.

However, comparing the cost-of-living indexes among 47 prefectural capitals, we found that the three major metropolitan areas such as Tokyo, Nagoya, and Osaka have a lower cost-of-living index than other local prefectural capitals. Further, the cost-of-living indexes of these three major cities have not increased much in the tax increase of 1997, but in the case of 2014 it had a high effect. On the other hand, the local prefectural capitals obtained the effect of increasing the cost-of-living index at the 1997 tax increase, but when in 2014 it was not able to obtain a noticeable effect as before. This sluggishness in the cost-of-living index suggests that some measures for prices and household budgets need to be implemented in each prefectural capital. It will bring about improvement of the cost-of-living index and have further effect on future tax increases.

The long-term stagnation in prices was problematic for the Japanese economy, but we found that the cost-of-living index increased over the 26 years studied here. Indeed, the tax increase has improved the cost-of-living index from the previous stagnant state, and it seems that the timing of two tax increases in both 1997 and 2014 was appropriate. In particular, the effect on the tax increase in 2014 is expected to the future recovery for household budgets. As a result, we had different effects between 1997 and 2014 in this study. It would be desirable to conduct a detailed analysis on the cause of this difference in the future. By clarifying this cause, it seems that it will become possible to increase the effect on future consumption tax increase. In addition, to measure the effects of the consumption tax increase more accurately, it is desirable to be able to remove the direct influence of consumption tax on prices. This task is difficult at the present study and should be required for future research.

Ogura, M. (2017) Measuring the Impact of Consumption Tax on the Cost-of-Living Index from Japanese Household Survey. Modern Economy, 8, 430-444. https://doi.org/10.4236/me.2017.83032