japan economic analysis issue no. 13

ANALYST CERTIFICATIONS AND IMPORTANT DISCLOSURES ARE IN THE DISCLOSURE APPENDIX. FOR OTHER IMPORTANT DISCLOSURES, PLEASE REFER TO https://firesearchdisclosure.credit-suisse.com.

Japan Economic Analysis Issue No. 13

A new era for personal consumption in Japan: young consumers will likely lead the recovery • The Japanese economy has been in recovery mode since hitting bottom in 1Q

2009. Foreign demand and industrial production once again provided the initial impetus, but private consumption has also staged a solid recovery, as evidenced by the significant increase in the ratio of household spending to GDP. This phenomenon is worthy of attention given that it is quite rare for the consumption-to-GDP ratio to keep rising when the economy is in expansion mode.

• Pessimists continue to outnumber optimists regarding the outlook for Japanese consumer spending, however, with commonly cited negatives including a gradually shrinking population and a downtrend in the average wage reflecting both an aging workforce and weak productivity gains in the services sectors. Such pessimists would characterize the recent recovery in household consumption as nothing more than a temporary blip attributable to the government's recent slew of income support measures.

• Demographic and structural downward pressure on average wages does of course make it difficult to envisage a scenario under which consumer spending might maintain a sustainable upward trajectory, but it would be premature to consider such an outcome entirely impossible, in our view. Even without a significant increase in demand for labor under the trend growth, a gradual decline in the labor force could bring about a significant reduction in the unemployment rate, thereby creating the potential for a decent increase in per capita wages and hence an increase in macro-level employee compensation. It is particularly important to recognize that such a scenario appears feasible even after allowing for the fact that the proportion of elderly workers in the workforce, the wage level of whom is much lower than that of mid-aged workers, is rising on a sustained basis.

• Spending by younger consumers is likely to take on increasing importance if Japan does indeed manage to achieve a sustainable recovery in macro-level personal consumption. Younger workers are likely to enjoy relatively high wage and income growth as a consequence of increasing competition for their limited supply of labor (estimates for the unemployment rate and wage growth for Q4/2011 are slightly higher than 3% and somewhat lower than +1% for workers overall, respectively, and slightly lower than 5% and somewhat higher than +3% for young workers). It is also important to note that the marginal propensity to consume is higher for young consumers than for older consumers. It is thus expected that younger consumers are likely to account for an increasing share of overall consumption. We see a strong likelihood of a moderate recovery in macro-level personal consumption being led by younger consumers over the next few years.

14 May 2010Economics Research

http://www.credit-suisse.com/researchandanalytics

Contributors

Hiromichi Shirakawa + 81 3 4550 7117

[email protected]

Takashi Shiono +81 3 4550 7189

[email protected]

14 May 2010

Japan Economic Analysis Issue No. 13 2

• Elasticity analysis indicates that, compared with older cohorts, younger consumers tend to spend more on household utensils (non-durable items), recreational durables, personal care services, and personal effects such as bags. In the meantime, we have found that younger consumers account for a relatively high share of total spending on housing (rents), transportation & communication (passenger cars and telecommunications), clothing & footwear, and furniture & household utensils. These analyses suggest that spending on household utensils could grow the most in the event of stronger spending by younger consumers.

• Finally, the two major risk factors to our scenario in which we foresee a strong recovery in young consumers’ spending leading to a moderate overall consumption recovery are a possible weakening of consumption by mid-aged consumers and early tax hikes.

Overview

Japan has experienced two successive "lost decades" since the collapse of the massive domestic asset bubble at the start of the 1990s. The first "lost decade" was triggered by deflation-driven concerns over the stability of the domestic financial system, and was then exacerbated by the Asian financial crisis, while the second was largely a reflection of a failure to implement appropriate macroeconomic policies, in our view. The dangers inherent in Japan's excessive reliance on foreign demand became all too apparent in 2001–2 following the bursting of the global IT bubble, but policymakers nevertheless maintained their focus on stimulating foreign demand via a monetary easing aimed at devaluing the yen, and ultimately brought about the biggest slump in economic activity of the postwar era between early 2008 and early 2009.

We believe there is a clear need for policymakers to find some means of shifting the Japanese economy into a trajectory of stable growth driven by domestic demand. However, with longer-term fiscal sustainability now a very real concern—and the general government's annual fiscal deficit hanging around 10% of GDP—there appears to be very little room for expansionary fiscal policy.

Is it possible for Japan to rely on domestic demand to drive a sustainable economic recovery in the absence of strong fiscal support? Pessimists would surely answer this question in a negative tone, pointing to adverse demographic factors—such as low fertility rates and an aging and shrinking population—and their negative ramifications for consumer spending (the obvious biggest component of domestic demand).

These demographic trends do indeed pose a major threat to the Japanese economy and the government's ongoing quest for fiscal sustainability. However, it is by no means impossible to envisage a scenario under which consumer spending might maintain a gradual upward trajectory despite these numerous headwinds in the medium run. The contraction in the workforce as the population ages is likely to bring about a steady improvement in supply/demand conditions throughout the labor market, with our previous analysis having already shown that the "labor input gap"—that is, the excess of labor supply over demand—should be considerably easier to close than the "capital input gap". This opens up the possibility of positive wage growth for the current working generation despite downward pressure from both population shrinkage and life-cycle factors (whereby an average worker's income peaks at around age 50), in which case Japan could still see a gradual increase in household income at the macroeconomic level.

The spending behavior of younger consumers may be particularly important in this regard given that younger workers are likely to enjoy certain competitive advantages in an aging society. We expect most Japanese firms to try to keep average worker age as constant and stable as possible with a view to maintaining productivity levels and capacity to

14 May 2010


innovate, in which case a natural increase in the proportion of older workers is likely to be counterbalanced by maintaining younger age cohorts. New university graduates are still finding it very difficult to secure jobs and unemployment rates for younger workers remain at rather high levels, but we expect to see a marked improvement in labor market conditions for young workers over the next few years as the corporate sector seeks to compensate for an increase in the number of older workers, with recent data suggesting that such a shift in hiring stances may already be under way.

We are somewhat optimistic regarding the outlook for spending by younger Japanese consumers for two main reasons: (1) younger age groups have a much higher marginal propensity to consume than their older counterparts, which means that any increase in wage and income levels in line with improved labor market conditions is significantly more likely to be spent than saved; and (2) younger consumers tend to spend their money on a relatively broad range of goods and services, whereas "mature" consumers in older age groups—who are typically saddled with mortgage repayments and tuition bills—tend to focus most of their spare resources on durable goods.

We therefore see a good chance of spending by younger Japanese consumers driving a recovery in macro-level private consumption over the next few years. Moreover, with excessive production capacity likely to continue acting as a brake on corporate capex, we expect consumer spending to account for an even greater share of domestic demand than is currently the case.

There are of course some risks to this scenario, however. One possibility is that the middle-aged cohort's propensity to consume might decline. Spending by people in their 30s to 50s has lagged somewhat behind the overall recovery in consumption over the past year or so, which may be a reflection of structural factors such as a relatively low marginal propensity to consume and limited prospects of higher income levels (due to life-cycle considerations). There also appears to be relatively little chance of a significant improvement in labor market conditions given that many firms are likely to prefer younger workers as a means of countering overall workforce aging. Doubts over the sustainability of Japan's social security system may also be making middle-aged households somewhat reluctant to loosen their purse strings.

Tax hikes could also throw a spanner into the works. Middle-aged consumer spending might conceivably be boosted to some extent if a consumption tax hike were to help restore their confidence in the social security system in the future, but the highly regressive nature of the consumption tax means that any increase in the tax rate would have its greatest impact on younger (lower-income) consumers. We would therefore recommend that any efforts to improve Japan's fiscal health center around asset taxes or other levies aimed primarily at high-income households for the time being.

1. Consumer spending as a component of GDP

Recent movement in consumer spending

The Japanese economy has been in recovery mode since hitting bottom in 1Q 2009, with consumer spending making an unusually strong contribution. Importantly, consumer spending has been buoyant, and our latest estimates suggest that real personal consumption had grown by around 4.0% over the four quarters up to 1Q 2010.

Indeed, the January–March quarter saw a one-off increase in household appliance sales ahead of a scaling-back of the "eco points" purchase incentive scheme. That factor alone was not enough to explain the jump in overall consumption, however, with retail sales figures also pointing to greater demand for foods, beverages, and clothing, and Family Income and Expenditure Survey data showing higher spending on household utensils, communication, travel, and other recreational services.

14 May 2010


An increase in spending by younger consumers appears to have been a major factor behind the breadth of this recovery. As we shall discuss below, data grouped by household-head age show that the under-30 cohort recorded the biggest increase in consumption over the past year or so, with purchases by this age group extending beyond household appliances and entertainment-related items—the direct beneficiaries of government stimulus measures—to encompass foods, household utensils, communication charges, recreational services, personal effects such as bags, and personal care services.

Our estimates indicate that nominal private consumption has increased by around 2% since 1Q 2009, during which time nominal GDP has grown by only 0.6-0.7% as a consequence of the big slump in fixed capital formation throughout 3Q 2009. This means that the ratio of household spending (excluding imputed rents) to GDP has risen from just around 48% to close to 49% over the past year (Exhibit 1). It is extremely rare for this ratio to increase once an economic recovery is under way (with real GDP having bottomed out in 1Q 2009), and it is in fact now at its highest level since 1983. This cannot be explained in terms of a sharp increase in employee compensation over the past year; indeed, the ratio of employee compensation to GDP has followed a gradual downtrend similar to what has been observed during previous recovery phases (Exhibit 2).

Japan therefore appears to be in the midst of a highly unusual upswing in consumer spending. Recent consumption growth must be at least partially attributed to policy stimulus measures such as cash handouts and the aforementioned "eco points" purchase incentive scheme, but clearly other factors are also at play. In the remainder of this report we consider the sustainability of Japan's ongoing consumer spending recovery in terms of the outlook for employment, wages, and household incomes, with a particular focus on younger consumers.

Exhibit 1: Household consumption to GDP ratios Exhibit 2:Household consumption and employee compensation to GDP ratios

0.44

0.45

0.46

0.47

0.48

0.49

0.50

Q18

2

Q18

4

Q18

6

Q18

8

Q19

0

Q19

2

Q19

4

Q19

6

Q19

8

Q10

0

Q10

2

Q10

4

Q10

6

Q10

8

Q11

0

Nominal household consumption

Real household consumption

0.44

0.46

0.48

0.50

0.52

0.54

0.56

Q18

2

Q18

4Q

186

Q18

8Q

190

Q19

2

Q19

4Q

196

Q19

8

Q10

0Q

102

Q10

4Q

106

Q10

8

Q11

0

Nominal household consumptionNominal employees' compensation

Note: Household consumption excludes imputed rent, data for Q1/2010 are Credit Suisse estimates, shadows indicate recessions. Source: Cabinet Office, Credit Suisse

Note: Household consumption excludes imputed rent, data for Q1/2010 are Credit Suisse estimates, shadows indicate recessions. Source: Cabinet Office, Credit Suisse

14 May 2010


2. Outlook for employment, wages, incomes and consumption

GDP and demand for labor

What will macroeconomic demand for labor look like if the Japanese economy matches our GDP forecasts for the next two years before returning to its trend growth rate?

Demand for labor is not expected to trend lower provided that the real GDP growth rate remains in positive territory. Our estimates put the trend real GDP growth rate at around +0.8%qoq 1 annualized, but we are currently forecasting average growth of +1.2%qoq annualized through Q1/2010 to Q1/2012. Our log-linear regression model describing the relationship between real GDP and the employed population2 indicates that this growth path—+1.2% growth through Q1/2010 to Q1/2012 followed by trend growth of +0.8%—would be consistent with a 0.4% annualized increase in macro-level demand for labor (the employed population) over the four-year period from 2010 through 2013 (Exhibit 3).

Demand for labor might of course increase to a somewhat lesser extent if (for example) firms continue their productivity-boosting efforts in a bid to achieve wider profit margins, and a downward shift could even be on the cards if supply/demand mismatches leave firms unable to fill positions with suitably qualified workers (in which case additional capital might be substituted for labor). We have yet to see any evidence of a structural change, however, with demand for labor remaining closely correlated with GDP.

Impact of aging on demand for younger workers

Demand for labor is unlikely to increase uniformly across all age cohorts, however, in our view. Indeed, we expect to see a relatively big increase in demand for younger workers in view of the various incentives for firms to keep average worker age as steady as possible.

Panel data analysis shows that industries with a relatively high proportion of older workers have a greater tendency to increase their hiring from younger cohorts. More specifically, a linear regression taking the proportions of 40+, 50+, or 60+ workers as independent (explanatory) variables and the growth rate for the number of under-30 workers as a dependent (explained) variable yields statistically significant positive coefficients for all three explanatory variables (Exhibit 4). This suggests the existence of “compensation effects” between younger workers and older workers.

Time series data analysis also shows that changes in the proportion of younger workers can be explained in terms of changes in the proportion of older workers (Exhibit 5). The regression analysis taking the changes in the proportion of 60+ workers and real GDP growth rate as independent variables, and the changes in the proportion of under-30

1 Trend growth rate is estimated as a quarter-to-quarter changing rate of HP filtered (frequency = 1600) GDP level. 2 ln (employed population) = 0.1478 * ln (real GDP ) + 6.8077、R2 = 0.8946

Exhibit 3: Real GDP and employment

470480490500510520530540550560570580

03/0

3

04/0

3

05/0

3

06/0

3

07/0

3

08/0

3

09/0

3

10/0

3

11/0

3

12/0

3

13/0

3

61.5

62.0

62.5

63.0

63.5

64.0

64.5Real GDP(trn yen, lhs)Employed population (million, rhs)

→ forecasts

Source: Cabinet Office, MIC, Credit Suisse

14 May 2010


workers as the dependent variable, shows a statistically significant positive relationship between them. A 1% increase in the proportion of 60+ workers is estimated to push up the proportion of under-30 workers by +0.47pp after four quarters.

Exhibit 4: Results of regression analysis on demand for young workers (1)

Exhibit 5: Results of regression analysis on demand for young workers (2)

Independent variables

Dependent variable: growth of employment for aged under 30 at time t

0.20 Proportion of workers aged 40 and above (t-1) (5.21)



0.01 Average age of workers (t-1) (4.30)

Adj.R2 0.44 0.43 0.44 0.43

Dependent variable: Share of young （aged 29 and under） workers (yoy)

Independent variables coefficients t-

valuesIntercept -0.004 -7.565

Real GDP yoy (1-quarter lag) 0.065 5.593 Share of elder （aged 60 and above） workers

(yoy, 4 quarters lag) 0.472 3.744

Note: JIP panel data for 31 industries for 1971-2006 are used, a fixed-effect linear panel regression model is applied, and numbers in parentheses are t-values.

Source: JIP database, Credit Suisse

Note: Labor Force Survey data for Jan 1981-Mar 2009 are used. Source: MIC, Cabinet Office, Credit Suisse

Assuming that demand for younger workers will depend in considerable part on demand for older workers, we next need to ask how demand for older workers is likely to be determined.

Businesses may be reluctant to take on older workers if doing so would increase the average age of their workforces and have negative ramifications for productivity and innovative capacity, but it is also possible that many firms will be willing to extend employment contracts beyond the age of 60—at significantly lower pay levels—and thereby continue to take advantage of accumulated experience and know-how. We therefore expect demand for older workers to increase sufficiently to absorb any (gradual) increase in supply, which would enable us to estimate the size of the older workforce assuming a constant unemployment rate and trend growth in the labor participation rate.

Determining the path of the older workforce in this fashion will then enable us to determine the path of the younger workforce, after which the path of the middle-aged workforce can be determined as the residual relative to the overall workforce growth rate.

Based on these assumptions, we estimate that the number of 60+ workers will increase at an average pace of +2.9% over the four-year period from 2010 through 2013 (with 1.9%pp of this increase attributable to demographic factors), resulting in a -0.6% decrease in workers aged 20–29 (with demographics making a -1.7%pp contribution) and a 0.03% increase in workers aged 30–59 (demographics: -

Exhibit 6: Growth rates in the number of employed by age group

-3.0%

-1.3%

-4.5%

4.5%

-1.0%

0.4%0.6%

-1.2%-0.6%

-1.6%

2.9%2.8%

-5.0%-4.0%-3.0%-2.0%-1.0%0.0%1.0%2.0%3.0%4.0%5.0%

-29 30-39 40-49 50-59 60- Overall

2008-09 average2010-13 average (estimates)

Source: MIC, MHLW, Credit Suisse

14 May 2010


0.9pp). As mentioned earlier, our model points to a 0.4% annualized increase in macro-level demand for labor over the period in question (Exhibit 6).

Estimating paths for each age cohort in this manner will enable us to examine how per capita wages might be affected by changes in the relative weight of each workforce (see below). Intuition suggests that because older workers tend to be paid less than their middle-aged counterparts, any increase in the relative proportion of the older age group within the overall workforce is likely to generate a certain amount of downward pressure on the macro-level per capita wage (which can of course be viewed as a weighted average across age cohorts).

Future size of the civilian labor force

Although we have now found some means of projecting the future trajectory of demand for labor (the employed population), we must still make some sort of assumption regarding the future supply of labor (labor participation rates and hence the size of the civilian labor force) in order to estimate unemployment rates and wage levels in the future (a key determinant of personal income levels). While the labor force does appear likely to shrink in the longer term as a consequence of the aforementioned adverse demographic trends, changes in the labor participation rate are likely to have a somewhat greater impact over a medium-term (several-year) horizon, and may of course differ significantly across age cohorts.

The results of estimated labor participation rates by age group are summarized in Exhibit 7, which shows that the overall participation rate is projected to fall slightly from 60.0% to 59.6%. This is due to a dynamic of demographics as the share of 60+ workers, whose participation rate is substantially lower, rises. Let us briefly explain our assumptions for labor participation rates by age group.

First, as we pointed out above, we assume the labor participation rate for the older (60+) cohort will continue to rise at its trend growth rate (Exhibit 8).

For the young (under-30) cohort we have posited a feedback mechanism whereby the labor participation rate fluctuates in response to changes in wage growth. In the absence of a significant increase in the supply of young workers, increased demand is likely to lead to increasingly tight labor market conditions, thereby driving wage levels higher and encouraging a greater number of young people to participate in the labor force. This hypothesis is backed up by the solid performance of a simple regression explaining the year-on-year change in the under-30 labor participation rate in terms of the year-on-year change in scheduled pay for the previous period3 (Exhibit 9).

3 Δlabor participation rate for aged 29 and under = 0.325*Δscheduled pay（2-quarter lag） -0.00043、R2 = 0.54

Exhibit 7: Labor participation rates by age group

85.1%

59.3%

81.3% 80.2%

29.7%

60.0%59.0%

82.5% 85.4%81.0%

30.5%

59.6%

0%10%20%30%40%50%60%70%80%90%

-29 30-39 40-49 50-59 60- Overall


Source: MIC, MHLW, Credit Suisse

14 May 2010


Exhibit 8: Labor participation rate for those aged 60 and above

Exhibit 9: Labor participation rate for those aged 29 and under

25%

26%

27%

28%

29%

30%

31%

32%

00/0

3

01/0

3

02/0

3

03/0

3

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3

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3

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3

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3

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3

→ forecasts

58%

59%

59%

60%

60%

61%

61%

62%

00/0

3

01/0

3

02/0

3

03/0

3

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3

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3

08/0

3

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3

10/0

3

11/0

3

12/0

3

13/0

3

→ forecasts

Source: MIC, MHLW, Credit Suisse estimates Source: MIC, MHLW, Credit Suisse estimates

We also expect the labor participation rate for the middle-aged (30–59) cohort to continue rising (albeit only gradually) for as long as the economy remains in expansion mode. However, for the purposes of our analysis—given that the correlation between wage levels and the labor participation rate for this cohort is not significant—we have simply assumed that the middle-aged participation rate will behave as it did during Japan's previous economic upswing (Exhibit 10). It is worth noting, however, that current pace of trend GDP growth is slower than that in the previous upswing, which may result in smaller increases in the labor participation rates. In that sense, there exists a risk of over-estimating the middle-aged (30-59) labor participation rates and hence the unemployment rates.

Macro-level unemployment, wages and employee compensation

Based on these assumptions, our projected trajectories for the employed population (labor demand) and the size of the labor force (labor supply) imply that the unemployment rate will fall from an average of 5.1% in 2009 to 3.7% at the Q4/2010 and 3.2% at Q4/2011 (Exhibits 11 and 12). As such, if the level of real GDP recovers to some extent, backed by rebounding exports and industrial production, while a double dip recession is avoided, the overall unemployment rate could be lower than 4% at the end of 2010, even with the widespread rises in the labor participation rates assumed.

Exhibit 10: Labor participation rates for those aged between 30 and 59

78%

79%

80%

81%

82%

83%

84%

85%

86%00

/03

01/0

6

02/0

9

03/1

2

05/0

3

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6

07/0

9

08/1

2

10/0

3

11/0

6

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9

13/1

2

30-39 40-49 50-59

→ forecasts

Source: MIC, MHLW, Credit Suisse estimates

14 May 2010


Exhibit 11: The labor force and the employed population (2010/Q1 = 100)

Exhibit 12: The unemployment rate (UR)

99.0

99.5

100.0

100.5

101.0

101.5

102.0

102.5

07/0

3

07/0

9

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3

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9

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3

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9

Labor forceEmployed population

→ forecasts

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

5.5%

6.0%

07/0

3

07/0

9

08/0

3

08/0

9

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3

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3

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9

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3

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9

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9

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3

13/0

9

→ forecasts

Source: MIC, MHLW, Credit Suisse estimates Source: MIC, MHLW, Credit Suisse estimates

What would be the reaction of wages to such a decline in the unemployment rate?

Japan's financial crisis of the late 1990s appears to have affected wage-setting behavior in a manner that caused a simple Phillips curve to flatten and shift somewhat to the right (Exhibit 13). The post-1990s Phillips curve implies that big year-on-year increases in the macro-level per capita wage (scheduled pay) are unlikely even if the unemployment rate should fall below 4%, and we expect to start seeing only moderate increases in wages sometime around the final quarter of this year (a 3.7% unemployment rate is expected).

Even if we allow for an increase in the middle-aged participation rate in line with a cyclical economic recovery and a moderate increase in the under-30 participation rate (as shown in Exhibit 9) as tighter labor market conditions push wage levels higher, we still see room for the macro-level unemployment rate to drop well below 4% before the end of 2011, thereby helping per capita wage (scheduled pay) growth to remain in positive territory.

The macro-level paths of the employed population and the per capita wage (scheduled pay) should give us a broad idea of the outlook for household income levels, although it is still necessary to make allowance for how the per capita wage might be affected by changes in the age composition of the employed population. We expect the relative proportion of older workers to increase at the expense of middle-aged workers, but the life cycle of the typical Japanese worker is such that older workers tend to be paid less than their middle-aged counterparts (Exhibit 14).

That said, our estimates indicate that per capita scheduled pay is actually unlikely to be affected (increasing on average by +0.02% per annum over the estimated horizon) by changes in the age composition of the employed population from 2010 through 2013. This

Exhibit 13: Wage-UR curves for entire workers (1981-1997 and 1998-2009)

y = 154.84x2 - 10.616x + 0.1945R2 = 0.864

y = -0.7523x + 0.0298R2 = 0.399

-3.0%-2.0%

-1.0%0.0%1.0%

2.0%3.0%4.0%

5.0%6.0%

1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0% 5.5% 6.0%

Note: Unemployment rate on X-axis, year-on-year changes in contracted earnings on Y-axisSource: MHLW, MIC, Credit Suisse

14 May 2010


is mainly due to "the second baby boomer generation" continuing to enjoy wage level increases (Exhibit 15).

Exhibit 14: Contracted earnings of workers by age group (million yen, average for 1993-2009)

Exhibit 15: Shares in the number of employed people by age group

1.0

1.5

2.0

2.5

3.0

3.5

4.0

15-29 30-39 40-49 50-59 60-

(million yen)

18.1%

22.6%

21.3%

18.6%

17.0%

21.0%

19.7%

22.5%21.8%

17.4%

15%16%17%18%19%20%21%22%23%24%

-29 30-39 40-49 50-59 60-


Source: MHLW, Credit Suisse Source: MHLW, MIC, Credit Suisse

Assuming that unscheduled pay and special pay levels remain relatively steady, macro-level household income is likely to start showing positive growth even as the 60+ cohort increase their share of the overall working population (Exhibit 16). We are anticipating a 0.3% increase in household income for 2010 followed by +1.3% growth in 2011, and we would obviously expect this to have positive ramifications for consumer spending at the macroeconomic level, if there are no significant changes in tax policy and the propensity to consume/save. As we discuss below, we believe that younger consumers will probably experience the biggest improvements in employment prospects and income levels over the next few years, and are therefore likely to be the main driver of macro-level private consumption growth in the near to medium term.

3. Employment prospects for the under-30 cohort

Employment, wages, and the Phillips curve for younger workers

For the reasons outlined above, demand for workers in their 20s appears likely to increase steadily in line with a demographics-driven increase in the proportion of elderly workers, and with the supply of younger labor shrinking at a particularly rapid pace, we expect supply/demand conditions within the labor market to tighten to a much greater extent than for older cohorts (Exhibit 17). Even allowing for an increase in the under-30 labor participation rate as a result of the aforementioned feedback mechanism whereby an improved supply/demand balance (lower unemployment rate) pushes wages up to more

Exhibit 16: Annual changes in workers’ income and breakdowns

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

07 08 09 10 11 12 13

Change in the employed populationEstimated w age grow thEstimated impacts of demographics on w agesChange in total income of employed population

Source: MHLW, MIC, Cabinet Office, Credit Suisse

14 May 2010


attractive levels, we still expect the under-30 unemployment rate to fall from an average of 8.1% in 2009 to 5.8% at Q4/2010 and 4.7% at Q4/2011 (on a three-quarter moving average basis), which would mark the lowest level since Q2/1994 (Exhibit 18). Importantly, the employed population is not expected to increase and the decline in unemployment is mainly owing to a faster pace of shrinkage in the labor force in our simulation. We need to stress that “compensation effects” from the ageing demographics do not mean an increase in the employed population for young workers in absolute terms.

Exhibit 17: Change in the labor force and the number of employed people aged 29 and under (2010/Q1 = 100)

Exhibit 18: The unemployment rate for those aged 29 and under

95

97

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103

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111

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Labor forceEmployed population

→ forecasts

2.5%

3.5%

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9.5%

07/0

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10/0

3

10/0

9

11/0

3

11/0

9

12/0

3

12/0

9

13/0

3

13/0

9

Unemploy ment rate（excluding compensation ef f ects)Unemploy ment rate

→ forecasts

Source: MHLW, MIC, Credit Suisse estimates Source: MHLW, MIC, Credit Suisse estimates

How would this decline in the unemployment rate be likely to affect the wages of younger workers? As with the macro-level Phillips curve, the under-30 Phillips curve appears to have experienced a structural shift sometime around the late 1990s (Exhibit 19). However, whereas the macro-level Phillips curve flattened and shifted downward, the under-30 Phillips curve seems to have steepened and shifted somewhat upward, thereby implying that wages of younger workers are likely to face somewhat greater upward pressure (in response to an improving unemployment rate) than was previously the case.

This change in the under-30 Phillips curve appears to be a reflection of supply-side circumstances that have translated into an increase in the number of long-term unemployed. Specifically, some 57.4% of unemployed persons in the 15–24 age bracket attribute their unemployment to problems finding suitable salary levels, leave arrangements, work hours, and/or job responsibilities (average for 2009; see Exhibit 20), which suggests that many younger job-seekers may be holding out in the hope of securing the best possible employment conditions.

14 May 2010


Exhibit 19: Wage-UR curve for those aged 29 and under (1981-1997 and 1998-2009)

Exhibit 20: Unemployment by age group based on job seekers’ reasons (2009 average)

3.5%（Est）

y = 2E-06x-3.0645

R2 = 0.8272

y = 24.249x2 - 4.2293x + 0.1804R2 = 0.392

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0%

0%

10%

20%

30%

40%

50%

60%

70%

Ove

rall

15-2

4

25-3

4

35-4

4

45-5

4

55-6

4

65-

Note: Unemployment rate on X-axis, year-on-year changes in contracted earnings on Y-axisSource: MHLW, MIC, Credit Suisse

Note: Job seekers’ reasons include “mismatch in wage", "mismatch in hours worked or leave eligibility" and "mismatch in type of job; job offers’ reasons include “mismatch in worker’s age", "mismatch in required skills" and "cannot provide jobs".

Source: MIC, Credit Suisse

In other words, today's younger generation is perhaps less willing to compromise than its predecessors, as a result of which the supply of labor may be somewhat slower to increase than might previously have been the case.

The newer (post-1990s) Phillips curve for the under-30 cohort implies that scheduled pay for young workers is likely to start rising (year on year) once the unemployment rate drops below 7%, with growth set to exceed +2%yoy if the 6% level is breached and to move beyond +3%yoy if the unemployment rate should fall below 5%. Given that we expect the under-30 unemployment rate to fall (slightly) below 5% by the end of next year, it seems quite possible that scheduled pay growth for this cohort for 2011 could be somewhere in the order of +3%yoy, whereas macro-level per capita scheduled pay is not expected to rise by much more than around 0.3-0.4%yoy.

Compensation effects associated with an increase in the share of older workers

We therefore expect Japan's younger (under-30) population to see a relatively steep decline in their unemployment rate and a relatively big increase in wages over the next few years. We expect an increase in the proportion of elderly (60+) workers within the working population to subtract around 3.8pp from the under-30 unemployment rate between 2010 and 2013, an estimate that we have obtained by calculating the baseline path of the under-30 working population under the assumption that an increase in the relative size of the 60+ population actually has no impact whatsoever on the relative size of the under-30 working population (which, along with the 30–59 working population, can therefore be derived—using the relevant cohort weights, assumed to remain constant—from the residual obtained by subtracting the 60+ working population from the total working population). This baseline scenario would see the under-30 unemployment rate fall only as far as 6.5% by the end of 2011 and settle at a relatively high 5.9% in 2013, with wage growth effectively doing nothing to boost the under-30 workforce participation rate (Exhibit 18).

4. How do younger consumers spend their money? Growth in macro-level household income—obtained by multiplying the total number of employed persons by per capita scheduled pay—appears unlikely to significantly exceed +1% in 2011 (allowing for the impact of structural changes in the age composition of the work force), but a relatively big improvement in the supply/demand balance for younger workers is likely to see their income rise by around 3% despite the continued decline in the

14 May 2010


employed population of the age cohort, thereby leaving the younger generation with considerably greater leeway to ramp up their consumer spending.

With this outlook in mind, let us now consider the recent spending behavior of younger consumers along with some slightly longer-term structural characteristics.

Younger consumers and their share of total private consumption

We begin by using the 2005 Population Census, the Family Income and Expenditure Survey, and GDP data to estimate that the under-30 cohort accounts for around 8% of macro-level consumer spending. Speci-fically, we have multiplied per household spending figures from the Family Income and Expenditure Survey (for both single-person and two-or-more-person households) for each age cohort by the relevant numbers of households shown by the 2005 Population Census, and have then adjusted spending amounts for each age cohort so as to equate the aggregate spending total with macro-level household spending (the private consumption component of GDP, excluding imputed rents). The resulting shares of total consumer spending for each age cohort are as shown in Exhibit 21.

Recent spending patterns for younger consumers

Let us now use data from the Family Income and Expenditure Survey to examine the recent spending behavior of younger consumers.

We begin by noting that real consumer spending rose by 16.7% for the 20–24 age bracket and 10.4% for the 25–29 cohort between 1Q 2009 and 1Q 2010 (cohorts based on age of household head; see Exhibits 22 and 23), while real consumption (adjusted to GDP base4) rose by 12.1% and 12.2%—bigger increases than for any other age bracket—for the 20–24 and 25–29 cohorts respectively. The 25–29 age bracket also showed particularly strong growth over the four quarters commencing 1Q 2003, which suggests that spending by younger consumers tends to pick up relatively quickly during the initial stages of recoveries in employment levels and broader consumer spending. This in turns suggests that younger consumers have a relatively high marginal propensity to consume.

4 To adjust MIC’s consumption expenditure to the GDP base, the following items are excluded: school meals, housing rent, housing refurbishment, health services, car purchases, auto insurance, tuition fees, spending on religion, accident insurance, and social expenses.

Exhibit 21: Shares of total domestic consumers’ market by age group (CS estimates, 2005 average)

3% 5%7%

8%

8%

10%

12%14%

11%

22%

-2425-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-

Source: MIC, Cabinet Office, Credit Suisse

14 May 2010


Exhibit 22: Consumption spending growth by age group (households with two persons or more) (1) Changes from 09Q1 to 10Q1 (the current cycle)

Average （Age group） -24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-

Real consumption 2.0% 16.7% 10.4% -0.4% 3.8% 0.5% -4.2% 2.7% 2.5% 0.1% 4.0% Real consumption (adjusted to GDP) 2.3% 12.1% 12.2% 1.1% 0.3% 2.6% 0.8% 4.4% 1.1% 2.7% 4.5%

Changes from 03Q2 to 04Q2 (the previous cycle)

Average （Age group） -24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-

Real consumption 1.4% -3.9% 18.3% 5.1% -1.5% -0.5% 0.1% 0.6% 6.7% -0.8% 0.9% Real consumption (adjusted to GDP) 1.1% 2.0% 13.4% 6.8% 2.0% -0.2% 1.1% 3.9% 7.1% -1.3% -0.5%


Exhibit 23: Consumption spending growth by age group (households with two persons or more) (2)

Changes from 09Q1 to 10Q1 (the current cycle) Average （Age class） -29 30-39 40-49 50-59 60-

Real consumption 2.0% 10.2% 2.1% -1.6% 2.5% 3.0% Real consumption (adjusted to GDP) 2.3% 13.7% 0.5% 2.0% 2.6% 4.1%


Average （Age class） -29 30-39 40-49 50-59 60-

Real consumption 1.4% 16.0% 1.4% -0.1% 3.2% 0.2% Real consumption (adjusted to GDP) 1.1% 12.2% 4.2% 0.5% 5.1% -0.8%


We also note that under-30 consumers have increased their spending on a relatively broad range of goods and services, whereas older consumers have focused most of their additional spending on durable items (Exhibit 24). The "eco points" purchase incentive scheme and other temporary stimulus policies appear to have been the main factor behind higher spending on durable consumer goods by older consumers over the past year or so, but increased spending by younger consumers is in large part a reflection of cyclical improvements in income levels. A similar difference between the spending behavior of younger and older consumers was also observed during Japan's previous economic upswing.

14 May 2010


Exhibit 24: Consumption spending growth by age group (households with two persons or more) (3) Changes from 09Q1 to 10Q1 (the current cycle)

Consumption Expenditure Food Housing Utilities Household

durables Household

utensils Clothing Health care

Car purchase

Transportation and Communication Education Recreational

durables Recreational

goods Other

expenditure -29 10.2% 5.6% 28.1% 11.8% 47.4% 19.9% -3.1% 5.8% -55.2% 20.7% 18.3% 63.9% 57.7% 13.4%

30-39 2.1% 1.7% 12.2% 0.8% 53.7% 0.2% 5.8% -13.1% 111.5% 7.3% -1.4% -23.1% 24.3% -7.9% 40-49 -1.6% 0.6% -20.1% 0.5% 18.8% 7.6% 1.9% -11.0% -29.7% 4.3% -1.8% 21.4% 23.2% -1.2% 50-59 2.5% -1.5% 15.9% 0.4% 34.5% 17.6% 3.7% -5.2% 51.3% 6.0% 6.9% 22.7% 21.8% 1.4% 60- 3.0% 3.1% 25.3% 0.8% 24.3% 13.7% 2.3% 2.7% 41.3% 6.1% -59.2% 43.0% 30.4% -2.7%

Changes from 03Q2 to 04Q2 (the previous cycle) Consumption

Expenditure Food Housing Utilities Household durables

Household utensils Clothing Health

care Car

purchase Transportation and

Communication Education Recreational durables

Recreational goods

Other expenditure

-29 16.0% 1.3% 1.6% 6.7% 141.6% 16.0% 3.1% 4.1% 778.4% 23.7% 26.5% 0.2% 38.1% 14.9% 30-39 1.4% -0.1% -1.7% 3.6% 11.4% 14.4% -3.6% -16.7% -36.7% 6.0% 11.5% 9.3% 22.8% 5.5% 40-49 -0.1% -4.6% -14.9% -0.7% 37.6% 0.7% -4.7% 3.2% 8.4% 3.3% 7.1% 45.8% 20.7% -1.2% 50-59 3.2% 1.4% -12.9% 3.6% 20.0% 8.4% 5.6% 13.9% -7.8% 4.9% -10.3% 7.6% 22.3% 4.2% 60- 0.2% -3.2% 4.5% -1.7% -8.3% 1.9% -6.7% -4.5% 48.6% 5.9% -35.6% -1.9% 20.7% -2.4%


The data grouped by income quintile show relatively strong recoveries in the consumption expenditure of the first and second quintile households in the current economic upswing (Exhibits 25 and 26), which is also consistent with the aforementioned strong recovery in consumption by younger households.

Exhibit 25: Consumption spending growth by income group (households with two persons or more) (1)

Changes from 09Q1 to 10Q1 (the current cycle) （Income group）

Average 1st quintile 2nd quintile 3rd quintile 4th quintile 5th quintile

Real Consumption 2.0% 5.9% 3.4% 0.7% 1.5% 1.0% Real Consumption (adjusted to GDP base) 2.3% 6.7% 3.8% -0.1% 4.0% 1.7%


（Income group）

Average 1st quintile 2nd quintile 3rd quintile 4th quintile 5th quintile

Real Consumption 1.4% -1.4% 0.4% -2.0% 0.9% 5.7% Real Consumption (adjusted to GDP base) 1.1% 0.1% -0.3% -0.7% 3.8% 4.7%


Exhibit 26: Consumption spending growth by income group (households with two persons or more) (2) Changes from 09Q1 to 10Q1 (the current cycle)

Consumption Expenditure

Food Housing Utilities Household durables

Household utensils

Clothing Health careCar

purchase Transportation and

Communication Education

Recreational durables

Recreational goods

Other expenditure

1st quintile 5.9% 3.1% 16.9% -0.3% -13.5% 15.3% 3.1% -2.4% -36.0% 5.7% 9.2% 69.1% 6.2% 3.1% 2nd quintile 3.4% 4.4% 1.4% 1.4% -8.2% 0.5% -4.8% 7.3% 41.9% 3.1% -24.3% 50.8% 4.4% -6.2% 3rd quintile 0.7% 1.5% -1.3% 1.8% -9.4% -6.7% -4.0% 5.8% 82.8% -2.8% 15.2% 38.8% 0.9% -7.6% 4th quintile 1.5% 1.3% -16.1% 0.7% 54.9% 10.8% -3.2% -14.6% 25.7% 5.8% -2.6% 29.0% -4.3% -8.2% 5th quintile 1.0% -2.7% 17.1% -4.5% 96.4% 11.6% 8.8% -2.0% -15.5% -6.3% -14.6% 7.3% -1.8% -0.5%


14 May 2010


Analysis of marginal propensity to consume

It is sometimes argued that lower-income households are more likely to save any increase in income than spend it due to their insufficient savings levels, but there is a considerable body of evidence suggesting that low-income households with relatively few financial assets—most of which tend to fall into the younger age cohorts—may actually have the highest marginal propensity to consume. As noted above, the tendency for younger consumers to increase their spending relatively sharply during the initial stages of a broader consumption rally suggests that they are likely to have a relatively high marginal propensity to consume.

Data for 1Q 2009 through 1Q 2010 do indeed show a marginal propensity to consume of above 1 for the 20–29 cohort, with the increase in nominal consumer spending over the period in question actually having exceeded the increase in disposable income for this age group (Exhibit 27). The same tendency is also observed in the previous cycle (2003-04).

Exhibit 27: Marginal propensities to consume by age group (net changes in consumption expenditure vs. net changes in disposable income)

2010/Q1 - 2009/Q1 (the current cycle) (yen) -29 30-39 40-49 50-59 60-

(a) Net change in consumption 17,324 -603 -9,883 -11,976 1,272 (b) Net change in income 16,334 -6,178 10,167 -17,937 -1,617

(a) / (b) 1.06 0.10 -0.97 0.67 -0.79

2004/Q2 - 2003/Q2 (the previous cycle) (yen) -29 30-39 40-49 50-59 60-

(a) Net change in consumption 35,901 2,015 5,388 6,837 14,552 (b) Net change in income 14,175 -4,995 16,644 608 -19,915

(a) / (b) 2.53 -0.40 0.32 11.24 -0.73 Source: MIC, Credit Suisse

It is worth noting that the calculated marginal propensity to consume for younger consumers was higher in the previous cycle than it is in the current cycle. This might be interpreted as a reduced marginal propensity to consume. However, we tend to think a very high marginal propensity to consume by young households in 2003-2004 was somewhat biased by so-called “baby boomer generation effects”, in other words, because of influences from the baby boomer generation.

Let us now consider this point from a somewhat more academic perspective by introducing the concept of "disposable cash", which is defined as the sum of disposable income for the current period and existing savings as of the end of the previous period. Recent studies using micro panel data tracking individual households across time have shown that low-income, asset-poor households tend to have a higher marginal propensity to consume (Carroll & Summers [1991], Souleles [1999]), a phenomenon that had not previously been evident from time-series analysis of macro-level household averages for consumption and savings levels. This discovery led to the development of a new model—which makes allowance for differences in consumption behavior attributable to differences in asset levels—that has been widely used in subsequent research (please refer to Appendix 1 for further details).

It is of course important to confirm whether the same phenomenon applies to Japanese households. We have constructed a set of quarterly panel data by using savings figures for various age cohorts from the Family Income and Expenditure Survey (for households of two or more persons; please refer to Appendix 2 for further details). Note that we have excluded data on 60+ workers' households due to our concerns over sampling bias.

14 May 2010


Exhibit 28 is based on this newly constructed panel data set and plots consumer spending for a given quarter (vertical axis) versus disposable cash for that quarter. As one might expect in view of the aforementioned overseas studies, we find that consumer spending is an increasing function of disposable cash.

Exhibit 28: “Disposable cash” vs. consumption: a consumption function

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

0 5 10 15 20 25 30 35 40 45 50(Disposable cash, mln yen)

（C

onsu

mpt

ion、

mln

yen

）

C=10.8*X^0.187R2=0.811

Note: Disposable cash = household disposable income + outstanding household savings, quarterly data are used Source: MIC, Credit Suisse

Exhibit 28 also shows the curve obtained by fitting a log-linear model. The power parameter (in level terms) of 0.187 is indeed smaller than 1, indicating that consumer spending is a concave function of disposable cash. This in turn implies that the marginal propensity to consume (first derivative) is a decreasing function of disposable cash, meaning that a one-unit increase in disposable cash translates into a smaller increase in consumption at higher levels of disposable cash.

Our panel data therefore confirm that low-income, low-savings Japanese households tend to have a relatively high marginal propensity to consume. Such households tend to have a relatively high income elasticity of consumption, meaning that a 1% income shock tends to translate into a relatively large change in consumer spending. To demonstrate this point we use panel data tracking households grouped by age bracket and savings level (in quintiles) across time, and calculate the change in consumer spending—for both the initial quarter and for the entire five-year (cumulative 20 quarters) periods—attributable to a 1% increase beyond the initial disposable income level5. It can be seen from Exhibits 29 and30—which plot the simulation results for the income elasticity of consumption (vertical axis) versus initial disposable cash (horizontal axis)—that a household with lower initial disposable cash does indeed tend to have a higher income elasticity of consumption.

5 Please refer to appendix1 for the details of the simulation model.

14 May 2010


Exhibit 29: Estimated income elasticity of consumption (simultaneous impacts at t = 1)

Exhibit 30: Estimated income elasticity of consumption (cumulative impacts for 5 years until time t = 20)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.0 10.0 20.0 30.0 40.0 50.0Disposable cash （mln yen）

Income elasticity of consumption

0.000.100.200.300.400.500.600.700.800.901.00

0.0 10.0 20.0 30.0 40.0 50.0Disposable cash （mln yen）

Income elasticity of consumption

Source: MIC, Credit Suisse Source: MIC, Credit Suisse

Exhibit 31 shows the results of a similar analysis based on averages for each age cohort. A temporary 1% increase in income is found to boost spending by consumers aged 20–29 by around three times more than spending by consumers in the 50–59 bracket.

Exhibit 31: Income elasticity of consumption by age group -29 30-39 40-49 50-59

Simultaneous impacts Income change （%） 1.00% 1.00% 1.00% 1.00% Consumption change （%） 0.04% 0.03% 0.02% 0.02% Elasticity 0.045 0.028 0.022 0.016 5years cumulative impacts Income change （%） 0.05% 0.05% 0.05% 0.05% Consumption change （%） 0.03% 0.02% 0.01% 0.01% Elasticity 0.529 0.376 0.299 0.241

Note: Initial values of disposable cash by age group used in the simulation are sample averages from 2002 to 2009 (aged 29 and under = ¥3.24 million, aged 30-39 = ¥7.07m, aged 40-49 = ¥11.65m, aged 50-59 = ¥16.52m). Source: MIC, Credit Suisse

Has the marginal propensity to consume for (young) low-income, low-savings households been rising or falling in recent years? Our estimates point to power parameters of 0.186 and 0.188 for data samples for 2002–2007 and 2004–2009 respectively, which suggests that there has in fact been very little change over the past few years. We do, however, find that the income elasticity of consumption has increased slightly for the under-30 cohort, from 0.0145 to 0.046 for the initial impact and from 0.527 to 0.538 for the cumulative five-year impact. In other words, our analysis indicates that the younger generation's marginal propensity to consume has held steady over the past few years.

14 May 2010


Characteristics of spending by younger consumers

Let us now conclude this section by looking at some key characteristics of spending by younger consumers.

First, the replacement cycle for durable goods tends to be relatively short (Exhibit 32). This suggests that durable goods consumption by young consumers would be less affected by a temporary policy boost to durable goods purchasers.

Exhibit 32: Average duration of usage for major durable consumer goods by age group (2008-2009)

Refrige-

rator Washing machine

Vacuum cleaner

Air-conditioner

TV set Video

camera Digital

camera PC

DVD players

Mobile phone

New car

Households with two and more 10.6 8.8 7.2 11.0 9.5 6.3 4.1 5.4 5.7 3.3 7.9 Male -29 4.3 4.3 3.4 5.2 5.2 5.5 6.7 2.2 1.7 2.1 5.1

30-59 10.1 8.4 7.0 10.5 8.9 6.8 3.9 5.3 5.2 3.1 7.4 60- 11.0 9.2 7.6 11.7 9.9 5.3 4.4 5.6 7.0 3.7 8.5

Female -29 n.a. n.a. n.a. n.a. n.a. n.a. 3.0 n.a. n.a. 2.0 n.a. 30-59 6.5 6.2 5.3 9.2 8.5 7.6 3.2 5.6 3.3 3.1 5.9

60- 11.6 10.4 6.1 10.7 9.5 5.5 5.7 5.5 4.4 3.3 8.4 One-person households 10.6 9.4 9.3 10.4 9.5 5.8 3.5 4.7 5.1 3.4 7.1

Male -29 3.3 2.4 3.0 2.5 4.9 1.0 3.0 3.7 3.7 2.3 3.2 30-59 10.2 7.9 9.6 8.4 7.9 n.a. 3.8 4.4 4.1 3.2 7.0

60- 12.0 10.3 9.0 10.6 10.9 7.5 2.9 5.0 5.7 4.2 8.0 Female -29 6.8 n.a. 6.0 n.a. 5.9 n.a. 2.9 5.4 4.1 2.3 n.a.

30-59 9.9 7.9 9.1 12.3 9.2 n.a. 4.6 5.9 5.1 3.1 7.8 60- 11.9 10.9 10.3 11.2 10.6 n.a. 3.2 4.8 11.0 4.0 8.0

Source: Cabinet Office, Credit Suisse

Second, items with relatively high elasticities to the overall consumption changes include household durables, furniture & household utensils (non-durable items), recreational durables, and miscellaneous spending on personal care services, personal effects, and social expenses (Exhibit 33). Note that we have excluded housing, medical care, motor vehicles, and education from our analysis of consumer spending due to the potential for significant fluctuations from one month to the next (as a result of which we are effectively dealing with the private consumption component of GDP as measured by the Family Income and Expenditure Survey).

Third, data from the Family Income and Expenditure Survey for households of two or more persons6 show that under-30 households accounted for some 8.1% of all spending on housing from 2007 through 1Q 2010, 4.3% of all transportation & communication expenditures, 3.6% of all spending on clothing & footwear, and 3.2% of all spending on furniture & household utensils, with each of these shares exceeding (or matching) the under-30 cohort's 3.2% share of total consumer spending by households of two or more persons (Exhibit 34). Looking at a more detailed breakdown for each category, we find relatively high shares of macro-level consumption for rents (13.2%), motor vehicle purchases (5.6%), communication (4.7%), general furniture (6.1%), domestic utensils (4.3%), clothing (4.4%), dairy products (4.1%), eating out (4.5%), personal care goods (3.5%), personal effects (3.6%), and cigarettes (5.6%).

6 Consumption share of each age cohort by item is calculated from the product of itemized consumption per household and the number of households for each age cohort. Data for the number of households is from the census surveys for 2000 and 2005, those for 2006 and 2009 are estimates from the National Institute of Population and Social Security research. For 2001-2004, available annual data on total household numbers are distributed to each age cohort according to sample distribution by age in the Family Income and Expenditure Survey.

14 May 2010


Exhibit 33: Itemized consumption elasticity to total consumption expenditure by age group

Food Utilities Household durables

Household utensils Clothing

Transportation and

Communication Recreational

durables Recreational

goods Other

expenditure

-29 0.27 0.07 1.05 1.66 0.49 0.39 2.40 0.97 2.65 30-39 0.29 0.46 3.07 1.00 0.81 0.45 1.05 1.06 2.39 40-49 0.13 0.08 1.76 1.15 0.68 0.96 -0.02 1.06 2.39 50-59 0.14 0.33 4.63 0.56 0.41 0.96 0.54 1.26 1.79 60- 0.14 0.06 1.68 0.83 0.98 1.00 0.38 0.81 2.21

Note: Data used are from January 2000 to March 2010. Source: MIC, Credit Suisse

Exhibit 34: Shares of total consumption by item and age group (average between 2007 and March 2010) -29 30-39 40-49 50-59 60- Total consumption 3.2% 14.1% 20.1% 24.6% 38.0% Food 2.7% 13.6% 19.6% 23.1% 41.1% Housing 8.1% 22.0% 16.8% 17.8% 35.4% Utilities 2.8% 13.2% 18.8% 23.2% 42.0% Household related expenditure 3.1% 13.5% 17.9% 23.3% 42.1% Clothing 3.6% 16.0% 22.7% 25.1% 32.6% Health care 3.2% 12.5% 15.0% 19.5% 49.8% Transportation and Communication 4.3% 17.1% 21.7% 26.2% 30.9% Education 1.8% 15.6% 46.0% 32.5% 4.2% Recreational expenditure 2.7% 14.8% 21.5% 21.0% 39.9% Other expenditure 2.4% 10.6% 16.3% 28.7% 42.0%


5. Potential risk factors

(1) An increase in the older worker labor participation rate

As should be clear from the above discussion, our central scenario is for an improvement in the employment prospects of younger consumers—reflecting efforts by the corporate sector to counterbalance an increase in the proportion of elderly workers—to translate into increases in wage and income levels that should be sufficient to provide a boost to overall (macro-level) consumer spending.

We believe the biggest risk to this scenario is the potential for a slump in spending by middle-aged consumers.

For the purposes of this report, we have assumed that an increase in the number of older workers will be accompanied by an increase in the number of young workers, which implies that unless there is a sufficient increase in the size the total working population, middle-aged workers are likely to face relatively weak demand for their labor services.

In fact, our simulation results show that proportion of middle-aged workers is expected to shrink, except for those aged 40-49 as younger baby-boomers move into that bracket (Exhibit 15). Also, the margin of decline in the unemployment rates for middle-aged workers would be more limited than that of young workers.

In other words, middle-aged households could very well end up losing out to young households as a consequence of demographics-driven changes in the labor market, thereby leading middle-aged consumers to cut back their spending even more than has been apparent in recent data.

14 May 2010


Perhaps the greatest threat in this regard is the possibility of an upward shift in the participation rate of older workers. Our forecasts in this report are based on the assumption that the participation rate of older workers will continue to rise at its trend growth rate, but sharper increases could be on the cards if so-called baby-boomers opt not to retire at 65, and there is also a possibility that the government will hike the average public pension age to 70 as part of its efforts to reduce the fiscal deficit. If the 60+ cohort's share of the total working population does indeed increase by more than we are currently anticipating, then we would expect this to create further demand for the under-30 cohort at the expense of middle-aged workers, who might end up facing an increase in unemployment and a decline in wages (which could possibly even extend to the macroeconomic level).

(2) Premature tax hikes

Tax hikes could also throw a spanner into the works. Middle-aged consumer spending might conceivably be boosted to some extent if a consumption tax hike were to help restore confidence in the social security system, but the highly regressive nature of the consumption tax means that any increase would have its greatest impact on younger (lower-income) consumers, who would also face a disproportionately high burden in the event that income deductions are abolished. We would therefore recommend that any efforts to improve Japan's fiscal health center around asset taxes or other levies aimed primarily at high-income households for the time being.

Exhibit 35: Distribution of sample households by survey (2005) Household survey (Family Income and Expenditure Survey) Population census

Households with two persons or more Sample size Shares Sample size Shares

Total 7,825 100% 34,605,447 100% -24 29 0% 289,601 1%

25-29 157 2% 1,461,536 4% 30-34 454 6% 2,522,879 7% 35-39 703 9% 2,846,395 8% 40-44 742 9% 2,992,975 9% 45-49 739 9% 3,064,530 9% 50-54 727 9% 3,665,708 11% 55-59 834 11% 4,498,766 13% 60-64 897 11% 3,858,864 11%

65- 2,544 33% 9,680,795 28% One-person households

Sample size Shares Sample size Shares Total 714 100% 14,457,083 100.0% -34 111 16% 5,148,306 35.6%

35-59 163 23% 4,504,466 31.2% 60- 439 62% 4,804,311 33.2%

Consumer confidence survey Population census Households with two persons or more

Sample size Shares Sample size Shares Total 3,361 100% 34,605,447 100% -29 51 2% 1,474,535 4%

30-59 1,523 45% 19,591,253 57% 60- 1,788 53% 13,539,659 39%

One-person households Sample size Shares Sample size Shares

Total 1,676 100% 14,457,083 100% -29 174 10% 3,797,106 26%

30-59 495 30% 5,855,666 41% 60- 1,007 60% 4,804,311 33%

Source: MIC, Cabinet Office, Credit Suisse

14 May 2010


Appendix 1: A new model of household spending Our analysis in Section 4 of this report confirmed that panel data for Japanese households show a higher marginal propensity to consume among households with lower incomes and lower savings. In this Appendix, we introduce a relatively recently developed model capable of explaining this phenomenon and consider its place within the broader historical context of economic theory and its treatment of consumer spending.

Essence of the "buffer-stock saving model"

Refined in Carroll [2009], the so-called "buffer-stock saving model" was first used by Carroll [1996] and Carroll & Kimball [1996] to explain why panel data show a higher marginal propensity to consume among low-income, low-savings households, and has since been widely adopted in the literature. The model involves households setting aside "precautionary savings" by way of a buffer against unhedgeable income risk (the possibility of wage cuts or retrenchment), meaning that life-cycle factors—preparations for a post-retirement decline in income—are no longer considered the sole determinant of savings behavior.

Low-income, low-savings households face a particularly strong need to prepare for the unexpected, but may become somewhat less cautious in response to an increase in income, resulting in a relatively large increase in consumption. Older households with ample savings already have relatively high levels of consumption, however, and are more likely to use any increase in income to support even higher consumption levels throughout their remaining lifetime (rather than spending it immediately).

In other words, the buffer-stock saving model provides a framework for explaining why low-income, low-savings households tend to have a relatively high marginal propensity to consume, as a corollary of which consumer spending can be shown to be a concave function of disposable income and cash (defined as the sum of disposable income for the current period and existing savings as of the end of the previous period). This means that while consumer spending is an increasing function of disposable income and cash, the function in question has a decreasing gradient.

Equations and simulation

The equations describing the buffer-stock saving model are presented below for reference purposes.

( )

( )( )

( )factor) variant income ofdeviation (standardparameter risk Income ,aversionrisk relative of Degree

,factor variant Income ,income Base ,income Disposable ,nconsumptio from Utility u

,factorInterest ,nConsumptio ,Savings ,Cash Disposable ,factordiscount Time

,

22

1

111

11

,2/Νlog

1u

,uEmax

σρ

β

σσρ

β

ρ

　

　　

　　　　

～

ssPsYC

RsCsAsM

where

tt

sss

sss

sss

s

T

ts

tstC

CC

PYYRAM

CMA

C

Θ

−Θ

−=

Θ=+=

−=

⎥⎦

⎤⎢⎣

⎡

−

+++

++

=

−∑

14 May 2010


Solving this dynamic optimization system yields a function for consumer spending with disposable income and cash as the explanatory variable.

( )sst MfC =

Carroll & Kimball [1996] showed that this consumption function is concave, a fact that we used in Section 4 of this report in fitting an index function to our panel data. Our analysis of the income elasticity of consumption also relied on the dynamics of the buffer-stock saving model in simulating the path of consumer spending over a 20-quarter horizon following a one-off 1% increase in disposable income.

The shape of this function may change over time, however, in response to (for example) changes in government policy relating to income risk or changes in households' expectations regarding future income levels. We were able to rule out the existence of a structural break by using sub-samples of our panel data to re-estimate the power parameter, but it is nevertheless important to remain aware of how the shape of the consumption function might be affected by a change in household sentiment at some point in the future. Exhibit 36 uses a numeric solution of the buffer-stock saving model7 to simulate how the shape of the consumption function might react to: (1) a perceived improvement in job security; and (2) an improvement in the outlook for household incomes.

(1) would see the consumption function shift upwards, with the size of this shift greater for lower levels of disposable income and cash. This can be understood in terms of a diminished incentive to set aside precautionary savings, which (as discussed above) would translate into a relatively big increase in spending by low-income, low-savings households. (2) would result in a steepening of the consumption function, reflecting an increase in the (current) propensity to consume consistent with the prospect of higher future income levels8.

Historical background to the buffer-stock saving model

The buffer-stock saving model arose out of efforts to refute or improve the Permanent Income Hypothesis (life-cycle savings model) presented in Friedman [1957]. Under the standard formulation of this model, a given individual's consumption is assumed to be equal to his "permanent income", which is defined as his current wealth plus his expected future income, divided by his expected remaining lifespan (Flavin [1981]). Temporary shocks to income have very little impact on consumer spending under this framework given that they are likely to leave "lifetime income" virtually unchanged. The PIH also leads

7 The parameters of income risk, relative risk aversion, interest factor, and time discount factor are quoted from Carroll [2009b]. Base values of age-scheduled pay curve are 2003-09 average in Basic Survey on Wage Structure. Numerical solution method used here is Parametric Value Function Iteration Method (please refer to Carroll[2009b] for details). 8 In case (1), the value of the income risk parameter is increased from 0.1 in the base case to 0.5. In case (2), the peak of the age-scheduled pay curve is hiked 15% from the base case.

Exhibit 36: Simulated changes in the consumption function

0

100

200

300

400

500

600

0 500 1,000 1,500 2,000 2,500 3,000Disposable cash（10,000yen）

Con

sum

ptio

n （10

,000

yen）

Base case(1) Higher job security case(2) Higher income grow th case

Note: Average for all age groups is used for consumption levels. Source: MIC, MHLW, Credit Suisse

14 May 2010


to the so-called "Barro-Ricardo Equivalence Proposition" 9 , which states that tax cuts financed through issuance of government bonds—to be repaid via higher taxes at a later date—will have no impact on lifetime income and hence do nothing to stimulate consumer spending.

Empirical analysis using a number of different consumption data sources has revealed two phenomena which appear to be at odds with the PIF, however, namely: (1) an excessive reaction to "income" shocks that had already been anticipated by households; and (2) excessive "smoothing", such that fluctuations in consumer spending tend to be somewhat smaller than any fluctuations in permanent income (Ishihara [2001] pp. 11–12, Shintani [1996]). Numerous counterhypotheses have been posited by way of explanation—incorporating such factors as "continuous" spending on durable goods, liquidity constraints, and the formation of consumption habits—but the buffer-stock saving model presented in Carroll [1996] was the first framework capable of simultaneously explaining both of these phenomena without contradiction, and is therefore considered the most "successful" model of consumer spending behavior currently in existence.

9 To validate the Ricardo equivalence proposition, an additional assumption is needed with PIH, such that households who received tax breaks are alive at the timing of debt redemption. Meanwhile, for the Barro-equivalence proposition, “altruism“ is required with PIH, which means that households who received tax breaks consider the postponed cost on next generations as being negative for their utility. For both propositions, PIH is necessary. Therefore, in the buffer stock saving model where PIH does not hold, tax breaks can stimulate current consumption.

14 May 2010


Appendix 2: Constructing household spending data sets grouped by age and savings level In Section 4 of this report we used data from the Family Income and Expenditure Survey tracking households of two or more persons grouped by age bracket and savings level (in quintiles) to examine differences in the income elasticity of consumption across age groups. This Appendix explains how we constructed our data set.

Quarterly data on consumption, income, and savings grouped by age (in ten-year brackets) and savings level (in quintiles) are only available for 4Q 2005 through 3Q 2009. We opted to exclude workers' households in the 60–69 and 70+ cohorts due to the potential for sampling bias, leaving us with a sample size of 320 observations (four age groups × five income quintiles × 16 quarters).

However, older aggregate data grouped by age only and data grouped by savings level only were still useful to our analysis, with the so-called RAS (row-and-column sum) method—developed for the purpose of constructing extended input/output tables—enabling us to use two (single-dimension) vectors of data for a given period to estimate a (two-dimensional) cross-category matrix for the same period. (Please refer to Takagawa & Okada [2004] for a more detailed explanation of the RAS method.) For the "initial" elements of the cross-category matrix at each period, which are necessary to execute the RAS method, we used backward estimates from 4Q 2005, by discounting at the growth rates for each age group).

14 May 2010


References • Abe, N., and Yamada, T., (2004) “Life-Cycle Model and Consumption: Structural

Estimation of Precautionary and Life-Cycle Motives”, Hi-Stat Discussion Paper Series, 37, originally in Japanese.

• Carroll, R., (1996) "Buffer-Stock Saving and the Life Cycle/Permanent Income Hypothesis", Quarterly Journal of Economics, 112, pp.1-55.

• Carroll, R., (2009a) "A Tractable Model of Buffer Stock Saving", NBER Working Paper Series, 15265.

• Carroll, R., (2009b) "Lecture Notes On Solution Methods for Microeconomic Dynamic Stochastic Optimization Problems", Mimeo: http://www.econ.jhu.edu/people/ccarroll

• Carroll, R., and Kimball, M., (1996) "On the Concavity of the Consumption Function" Econometrica, 64, pp.981-992.

• Carroll, R., and Summers, L. H., (1989) "Consumption Growth Parallels Income Growth: Some New Evidence", NBER Working Paper Series, 3090.

• Flavin, A., (1981) "The Adjustment of Consumption to Changing Expectations About Future Income", Journal of Political Economy, 89, pp.974-1009.

• Friedman, M., (1957) A Theory of The Consumption Function, Princeton University Press.

• Ishihara, H., (2001) “Live-Cycle/Permanent Income Hypothesis and Precautionary Saving”, ESRI Discussion Paper Series, 2, originally in Japanese.

• Shintani, M., (1996) "Excess Smoothness of Consumption in Japan", The Japanese Economic Review, 47, pp.271-285.

• Souleles, N., S., (1999) "The Response of Household Consumption to Income Tax Refunds" The American Economic Review, 89, 4, pp.947-958.

• Takagawa, I., and Okada, T., (2006) “Interrelation among Asia Pacific Region from the Analysis of International Input and Output Matrix”, Bank of Japan Working Paper Series, 04-J-6, originally in Japanese.

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