Silviase/jgov_v1.4_full_ann huggingface dataset:
早期に審査を行う早期審査制度を実施しているところ、平成23年8月からは、東日本大震災により被災した企業等が知的財産を活用し、復興していくことを支援するため、被災者や被災地の事業所等からの特許出願を早期に審査する「震災復興支援早期審査」を実施している。

また、平成24年11月からは、グローバル企業の研究開発拠点等の我が国への呼び込みを推進するために施行された「特定多国籍企業による研究開発事業等の促進に関する特別措置法（アジア拠点化推進法）」（平成24年法律第55号）に基づく認定を受けた研究開発事業の成果に係る発明についても、試行的に早期審査の対象に加えた。

## c）事業戦略対応まとめ審査の実施

近年、企業活動のグローバル化や事業形態の多様化に伴い、企業の知的財産戦略も事業を起点としたものに移りつつある。特許庁では、当該知的財産戦略に基づいた出願に対応するための審査体制について検討を進め、事業で活用される知的財産の包括的な取得を支援するために、国内外の事業に結びつく複数の知的財産（特許・意匠・商標）を対象として、分野横断的に事業展開の時期に合わせて審査・権利化を行う「事業戦略対応まとめ審査」を、平成25年4月から開始した。

## d）開放特許に関する情報やリサーチツール特許に関する情報提供

知的財産の円滑な活用を促進するため、工業所有権情報・研修館を通じて、開放特許に関する情報やリサーチツール特許に関する情報をデータベース化して提供している。

## e）技術動向調査の実施・公表

特許情報を研究開発に活用する等、研究開発戦略と知的財産戦略との連携が求められている。このため、特許庁では、研究開発戦略に資するよう、国の研究開発プロジェクトに関連するテーマを中心に、「研究開発動向」「市場動向」等を踏まえて、「特許出願動向」を総合的に分析した技術動向の調査を行い、その結果を公表している。

## f）人材育成·確保

知的財産に関する知識の普及のため、工業所有権情報・研修館を通じて、知的財産を踏まえた実践的な人材育成を行う高等学校・高等専門学校に対する支援を行っている。

## （ii）科学技術振興機構

優れた研究成果の発掘、特許化の支援から、企業化開発に至るまでの一貫した取組を進めている。具体的には、「知財活用支援事業」において、特許群化も視野に入れた大学等における研究成果の戦略的な海外特許取得の支援、実用化を念頭に特許の強化・データの追加取得等を希望する研究者に対する試験研究費の提供、大学等の特許情報のインターネットでの無料提供（J－S T O R E）、大学等が保有する特許の研究段階における利用の開放による特許が制約とならない研究環境の提供（科学技術コモンズ）を実施するなど、総合的に知的財産の活用を支援している。
ESRI Discussion Paper Series No.264

Food Consumption Expenditure and Habit Formation : Evidence from Japanese Household Panel Data

By

Koichiro Iwamoto

February 2011

Economic and Social Research Institute

Cabinet Office

Tokyo, Japan
# Food Consumption Expenditure and Habit Formation: Evidence from Japanese Household Panel Data

By Koichiro Iwamoto $ ^{†} $

## Abstract

Because habit formation among consumers preference has strong influence on policy effects, it is crucially important for public policy management. In this article, I estimate the Euler equation allowing habit formation based on Dynan (2000), using data from the Japanese Panel Survey of Consumers (JPSC) for the period 2000-2004, and compare my estimation results with those of previous studies. Like Dynan (2000) and Guariglia and Rossi (2002), my estimation results yield no evidence of habit formation. And as Guariglia and Rossi (2002), my results are consistent with the durability of consumption.

JEL Classification Codes: D12

Key Words: household consumption, habit formation, panel data
### I. Introduction

Since the 1980s, Hall (1978)'s life-cycle/permanent-income hypothesis (LCPIH) has become a standard hypothesis of household consumption and savings behavior. But even today, some "puzzles"—inconsistencies between predictions of the LCPIH and observed household data—persist. $ ^{1} $ "Excess smoothness" is one of those puzzles. $ ^{2} $ This excess smoothness is the phenomenon that real observed consumption is too smooth relative to the implications of the LCPIH. Since Deaton (1987), many empirical studies, for example Campbell and Deaton (1989), Carroll and Weil (1994), Shintani (1996), have showed the existence of excess smoothness.

In some works, the consumption habit was thought as a key to the excess smoothness puzzle. The consumption habit refers to the idea of accepting time non-separable preference in a consumer's utility function; this concept is called "habit formation" in the context of economics. $ ^{3} $ Since the late 1980s, particularly in Europe and the United States, consistency between the implications of a household consumption behavior model with habit formation and real consumption data has been tested actively. It's easy to assume that the existence or nonexistence (and effectiveness level) of the excess smoothness based on habit formation should have strong influence to policy effects, for example at the case of commodity tax rate change or benefits for consumption stimulating. So, an empirical analysis for consumption habits has been considered a very important issue, not only for economists but also for public policy makers. And recently, habit formation has come to play an important role in the macroeconomic model grounded in micro-foundations like DSGE, and has attracted new attention among many researchers. Therefore, in the real economy, more rigorous tests for habit formation, especially at the micro-level, are needed.

As for Japanese household consumption, there are some analyses involving habit formation (e.g. Maki (1983), Braun et al. (1993), Iwata and Shimotsu (1996), Pagano (2004)), and their results basically showed that habit formation played an important role in Japanese household consumption. But most of them were analyses of macrodata; analyses using micro-data were very few. One reason for that would be a delay in building a panel data set. But, fortunately, today we can use the data of the Japanese
Panel Survey of Consumers (JPSC), which is a continuing panel investigation for Japanese households from 1993 to today. In this article, by the household food expenditure data from the JPSC and Dynan (2000)'s consumption Euler equation allowing habit formation, I try to analyze empirically the relationship between Japanese household consumption and habit formation. From my estimation, I see two key implications: 1) like Dynan (2000), my results yield no evidence of habit formation in Japanese household consumption, and 2) my results are consistent with the durability of consumption as found by Guariglia and Rossi (2002).

The remainder of this article proceeds as follows: Section II describes a small literature survey and theoretical model; Section III discusses data; Section IV presents the results of my estimation; and Section V concludes.

### I I. Theoretical Framework

From the late 1980s to the early 1990s, the role of habit formation in the household consumption had been tested actively by macro time-series data. But, no test could reach a decisive conclusion about that role. Some researchers suspected the influence of the bias with data aggregation (e.g. Goodfriend (1992), Attanasio and Weber (1993), Cherry and List (2002), Garrett (2002), Blundell and Stoker (2005)). Perhaps most researchers would deny the perfect homogeneity among households. Actually, many works implied that the analysis ignoring heterogeneity among households might produce a biased result (e.g. Constantinides and Duffie (1996), Pesaran (2003), Jones and Labeaga (2003)).

Of course, micro-data would be more suitable for the heterogeneity adjusted analysis than would macro-data. Since the early 2000s, the starting time for using panel data, micro-data analysis about habit formation began to be reported. Dynan (2000) and Guariglia and Rossi (2002) were pioneers and representative of this trend. $ ^{4} $ One of my objectives in this article is to compare my estimation results with those of these two previous studies. Dynan (2000) used food expenditure data from the Panel Study of Income Dynamics (PSID), which was a panel survey of American households, and estimated the Euler equation including habit formation by the method of GMM. But her estimation yielded no evidence for habit formation in American household consumption $ ^{5} $ . Guariglia and Rossi (2002) estimated the Euler equation accepting habit formation and
precautionary motive by food expenditure data of the British Household Panel Survey (BHPS), which was a panel survey of British households and had a similar survey design to that of the PSID. They concluded that past consumption changes and labor income risk played important roles in current consumption changes. But their results were consistent with durability of consumption, not habit formation. In addition, there are many studies for testing habit formation by micro data, and some of their results are consistent with habit formation (e.g. Alessie and Teppa (2002), Carrasco et al. (2005), Browning and Collado (2007) $ ^{6} $ ), unlike Dynan (2000) or Guariglia and Rossi (2002). Actually, a final conclusion or consensus on this issue has yet to be drawn. The test for habit formation has been ongoing.

The targets of these studies presented here are Western households. But, there are few micro-data analyses about Japanese households' habit formation. $ ^{7} $ Perhaps, the accumulation of micro-level empirical analyses is not yet sufficient. So, in this article, I try to analyze the role of habit formation in Japanese household consumption by Japanese household panel data. For my estimation, I use Dynan (2000)'s Euler equation allowing habit formation. Dynan (2000) thinks Household i's consumption at time t, $ C_{it} $ should look like the following:

$$ C_{it}=CE_{it}-a CE_{it-1} $$

where $ CE_{it} $ represents this household's consumption expenditure, parameter a is “habit formation parameter,” which measures the strength of habit formation. And the consumer's utility function is settled as follows, in isoelastic form:

$$ u\big(C_{it};T_{it}\big)=T_{it}\frac{C_{it}^{1-\rho}}{1-\rho} $$

where $ T_{it} $ corresponds to taste shifters of utility function, and $ -\rho $ is the elasticity of marginal utility of consumption. This framework shows that a part of past consumption expenditure has become a habit and is expended preferentially at the current time (in other words, “consumption is committed”), but has no influence on current utility. From a utility maximization problem of the household whose utility function is like formulation (2), Dynan (2000) derives the loglinearized Euler equation for her estimation $ ^{8} $ . Her estimation model is as follows:
$$ \Delta\ln C E_{i t}=a_{0}+a_{1}\Delta\ln C E_{i t-1}+a_{2}\Delta\ln T_{i t}+e_{i t} $$

where $ e_{it} $ represents a stochastic error term with mean zero and constant variance, and $ a_{0} \sim a_{2} $ are estimating parameters. Especially, $ a_{1} $ is the habit formation parameter, and equals parameter a in formulation (1). If the estimated $ a_{1} $ is positive and less than one, that result would be consistent with habit formation.

### I II. Data

In this article, as mentioned above, I estimate formulation (3) using data from the JPSC. Originally, the JPSC published an annual survey of 1,500 married and unmarried women about their social and family lives. But, because the JPSC contains information about the whole family, the data of the JPSC is often used to analyze Japanese households. Since 1998, the JPSC has started to research household expenditures by item category for September every year. Categories correspond approximately to the ten major classifications of the Family Income and Expenditure Survey (FIES). $ ^{9} $ Of course, food expenditure is one of the JPSC's expenditure items, and, therefore, I can compare my estimation result using food data directly with the results of Dynan (2000) or Guariglia and Rossi (2002).

Though I use JPSC data from 1998 to 2004, for the purpose of omitting outliers I eliminate observations and households as follows: monthly food expenditure is zero or more than 170,000 yen, and last annual household income (before tax and public dues) is less than one million yen or more than 22.8 million yen. $ ^{10} $ I also omitted the unmarried sample, except single-person households because the JPSC has not researched cohabitating family members' expenditure for the unmarried sample. The JPSC doesn't have information for whole family expenditure of the unmarried sample (except women living solo). These omissions bring the sample size of my data set to 7,107 observations.
Table 1. Basic Statistics of My Data Set

numerical variables	obs	mean	Std.Dev.	min	max
food expenditure (nominal, thousand yen)	7,107	62.5	30.22	1.0	170.0
age of wives or live-alone women	7,107	35.0	4.83	25.0	45.0
number of family members	7,107	4.0	1.61	1.0	11.0
family annual income (not exclude tax and public charge, nominal, ten thousand yen)	7,107	667.4	322.42	100.0	2,271.0

(about marriage sample)
food expenditure (nominal, thousand yen)	6,640	64.6	29.89	2.0	170.0
age of wives or live-alone women	6,640	35.2	4.74	25.0	45.0
number of family members	6,640	4.3	1.45	1.0	11.0
family annual income (not exclude tax and public charge, nominal, ten thousand yen)	6,640	688.9	320.06	100.0	2,271.0

dummy variables	obs	Freq.	Percent
husbands at work (at work = 1)	7,107	1: 6,235	87.7
0: 872	12.3
wives or live-alone women at work (at work = 1)	7,107	1: 1,645	23.2
0: 5,462	76.9
other family members at work (at work = 1)	7,107	1: 1,645	23.2
0: 5,462	76.9
(about marriage sample)
husbands at work (at work = 1)	6,640	1: 6,235	93.9
0: 405	6.1
wives or live-alone women at work (at work = 1)	6,640	1: 3,541	53.3
0: 3,099	46.7
other family members at work (at work = 1)	6,640	1: 1,640	24.7
0: 5,000	75.3

Table 1 presents basic statistics of selected variables for my estimation. Food expenditure is for the whole family and in September. Family annual income is the sum of annual pretax earnings of husband, wife, and other family members. Of course, for a household in the unmarried sample group, the investigated woman's income equals family income. In my estimation, these food expenditures and family income are deflated by the CPI. As taste shifters, I chose investigated woman's age $ ^{11} $ and family size in accordance with Dynan (2000). And I define three dummy variables: Dummy for husband working, Dummy for investigated woman working, Dummy for other family member working. These dummies take the value 1 if husband, wife (investigated woman) or other family member is either employed or self-employed.
### I V. Results of Estimation

In this section, I estimate formulation (3) and compare my results with previous works. Formulation (3) has the lagged dependent variable as one of the independent variables. It means my estimation is the dynamic panel estimation. So I chose the system GMM, not the fixed effects model or random effects model, as my estimation method.

In all estimations, the dependent variable is the log difference in real food expenditure and the independent variables are the lagged change in food expenditure, the investigated woman's age and that square, the log difference in household size, year dummies, and a constant term.

Table 2. Estimation Results

Estimation results by System GMM :	Respectively, *, **, *** indicate significance at the 10,5,1% levels.
(i)	(ii)	(iii)
$ \Delta \log(C(-1)) $	-0.3162	***	-0.2356	**	-0.3046
(0.033)	(0.099)	(0.027)
age	2.203	**	-0.908	0.371
(1.062)	(0.862)	(0.408)
age $ ^{2} $ /1000	-29.104	**	11.932	-4.794
(14.150)	(12.390)	(5.814)
$ \Delta \log(\text{family size}) $	2.581	**	0.189	0.933
(1.180)	(0.227)	(0.302)
constant	-40.751	**	17.484	-6.474
(19.737)	(15.046)	(6.961)
joint significance of year dummies (p-value)	0.142	0.407	0.003
Arellano-Bond test for AR(2) in first differences (p-value)	0.065	0.650	0.010
Hansen test of overidentifying restrictions (p-value)	0.062	0.714	0.080
Wald test (p-value)	0.00	0.01	0.00
number of obs.	3,910	242	4,152
95% confidence interval for HF parameter	(-0.3816, -0.2507)	(-0.4298, -0.0415)	(-0.3582, -0.2509)

notes: $ \Delta $ represents the first difference. Standard errors are in parentheses. Instruments in all columns: $ \Delta \log(C(-2)) $ and $ \Delta $ of husband at work, wife at work, other family members at work, lagged real family income, and lagged family size for the difference estimation; $ \Delta \log(C(-1)) $ , husband at work, wife at work, other family members at work, lagged real family income, lagged family size, and constant term for the level estimation.

Table 2 reports the results of my estimation: Column (i) refers to married women (= family) sub-sample estimation, Column (ii) refers to unmarried and live-alone women (= single household) sub-sample estimation, and Column (iii) refers to the full-sample (= i
+ ii) estimation. At the estimation of (i) ~ (iii), I use the same set of instrument variables (see Table 2's note).

All p-values for the Hansen test of overidentifying restriction are over 0.05, providing no evidence of a significant correlation between instruments and error term at the 5% level. The (i) and (ii)'s p-values for the Arellano-Bond test of second order serial correlation of the residual are over 0.05, providing no signs of the serial correlation at the 5% level. But the (iii)'s p-value for the Arellano-Bond test is 0.010. It means that, in (iii), there is no sign of the second order serial correlation of the 1% level, nor that 5% level. And for the Wald test, whose null hypothesis is “all estimated parameters equal zero”, all p-values are over 0.05, indicating that I can reject the null hypothesis at the 5% level. From these facts, I conclude that my specification is essentially correct.

In the all of (i) ~ (iii), the estimated coefficient $ a_{1} $ is negatively significant at the 5% level. $ ^{12} $ The upper bound for 95% confidence interval is negative, too. These facts suggest that the lagged change in food expenditure has a negative and statistically significant effect on the current change. This result is consistent with the durability of consumption, not habit formation.

In Column (i), the family sub-sample, aging has a positive and significant effect. This result would be valid because the highest age of wife is 45 years old (in Table 1) and it means that all families would be in the younger stage of the family life-cycle. And the change in family size has a positive and significant effect on the change in food expenditure. This result would also be valid and commonsensical. But in Column (ii), the single-household sub-sample, aging and family size have no significant effects. Considering that this sample has only single-households, the result on family size wouldn't be invalid. But it's rather unexpected that aging has no significant effect. And surprisingly, aging also has no effect in Column (iii), the full sample estimation. However, the effect of family size is significantly positive in Column (iii). It would be a valid result.
Table 3. Comparison with the Results of Previous Works: Parameters of the Lagged Change on Food Expenditure

COEF	p-value	95% confidence interval	Method
Table 2	(i)	-0.316	0.00	-0.382	-0.251	System GMM
(ii)	-0.236	0.02	-0.430	-0.042	System GMM
(iii)	-0.305	0.00	-0.358	-0.251	System GMM
Dynan (2000), Table 2	(1)	-0.039	0.57	-0.180	0.100	GMM
(2)	-0.046	0.51	-0.190	0.090	GMM
(3)	-0.038	0.63	-0.190	0.120	GMM
Guariglia and Rossi (2002), Table 2	(1)	-0.382	0.00	-0.418	-0.346	OLS
(2)	-0.470	0.00	-0.504	-0.436	Fixed Effects
(3)	-0.262	0.00	-0.305	-0.219	GMM
(4)	-0.272	0.00	-0.314	-0.230	System GMM

note: The (2) of Dynan (2000): Race and sex are added to (1) as independent variables.

The (3) of Dynan (2000): The real after-tax interest rate is added to (2).

Table 3 presents the results of comparing my estimated parameter of the lagged change on food expenditure, $ a_{1} $ in formulation (3), with those of earlier works. As with those earlier works, I chose Dynan (2000) and Guariglia and Rossi (2002), which are representative studies that estimate the Euler equation by the panel data of food expenditure. This table shows the following two facts: the results of Dynan (2000), Guariglia and Rossi (2002), and mine yield no evidence for habit formation, and the results of Guariglia and Rossi (2002) and mine are very similar and consistent with the durability of consumption.

### V. Conclusion

In this article, I estimate the Euler equation allowing habit formation by Japanese household panel data, and compare that estimation results with previous studies' ones. But my estimation results yield no evidence of habit formation like Dynan (2000) and Guariglia and Rossi (2002), and are consistent with the durability of consumption, not habit formation, like Guariglia and Rossi (2002). These facts suggest that habit formation plays no role in Japanese household consumption, don't they?

As above mentioned, there are several studies that are consistent with habit formation, like Alessie and Teppa (2002), Carrasco et al. (2005), and Browning and Collado (2007). Today, among a lot of empirical analyses using micro-data to study the role of habit formation on consumption, there are both results, accepted and rejected.

For the inconsistent result with habit formation of previous works, the influences of some factors that bias the results have been suspected (see Iwamoto (2010)). Nowadays, the three main candidates of those factors are as follows: 1) measurement errors contained in data, 2) durability of consumption goods and research intervals, and 3) heterogeneity among households. But, of course, it would be difficult to analyze
household consumption by controlling all these factors. Many researchers have been trying to address these difficulties. It is, in fact, too early to draw final conclusions.

The degree of strength of habit formation has strong influence on policy effects, as I noted at the introduction. This issue is crucially important for public policy management, and should continue to be analyzed empirically. I also shall try an expansive analysis for the relationship between habit formation and Japanese household consumption.
Appendix Results of Estimation by the JPSC Expenditure Category Data

### A1. Habit Formation and Durability of Consumption Goods

In the main article, I estimate the Euler equation allowing for habit formation, which is based on Dynan (2000), by the household food expenditure data (1998~2004) of the JPSC. Because one of my goals in this article is to compare my estimation results with those of important previous studies, e.g. Dynan (2000) or Guariglia and Rossi (2002), my analysis is concentrated on food expenditure. Constraints on data are Dynan (2000) or Guariglia and Rossi (2002)'s major reasons for using food expenditure data almost exclusively. They use only food data by assuming separability for the utility of food consumption. And perhaps, they would think that using food data was an advantage, because food is by definition not durable. According to Ferson and Constantinides (1991), durability of consumption goods cancels out the effect of habit formation. If durability were stronger than habit, the results of habit formation test would be consistent with durability. For the strict analysis of habit formation, the effects of durability should be removed from the result.

In the estimation of Dynan (2000), Guariglia and Rossi (2002), and in my own estimation, are estimated parameters of lagged consumption change influenced by durability? Are the effects of habit formation canceled out? To identify the effects of durability or habit formation directly is difficult because both effects are mixed in estimated parameters. But, comparing estimation results by data of expenditures, whose durability differ from one another, we could get some hints to answers for these questions.

Unfortunately, for the results of Dynan (2000) and Guariglia and Rossi (2002) —both are representative earlier studies— it's very hard to compare estimation results by multi- consumption goods because of limitations of data $ ^{13} $ . However, for this article, it's not so difficult for this comparison, because the JPSC surveys household expenditure by category of expense items, whose classification is based on the FIES or National Survey of Family Income and Expenditure (NSFIE). $ ^{14} $ Table A1 shows a list of surveyed