Parallel Session IARIW 2014

1. Presentation and Discussion
Labour Supply Effects of Wealth
Shocks: Evidence for Germany
Nico Pestel and Karina Doorley
Brian Bucks
U.S. Consumer Financial Protection Bureau
The views expressed are those of the author and do not necessarily reflect those of the
Consumer Financial Protection Bureau or the United States

2. Overview
Question
How do (positive) wealth shocks affect labor supply, labor income,
and entrepreneurship?
Key data
German Socio-Economic Panel data include:
1. Individuals’ expectations of windfalls (inheritances, gifts, etc.)
in 2001
2. Annual data on household receipt of windfall in prior year
3. Annual data on individuals’ labor market outcomes

3. ~3% of households report a windfall each year
Yes: Expect windfall (15%)
(Certain or probable)
No: Don’t expect
windfall (62%)
Don’t
know (22%)
0.08
0.06
0.04
0.02
2001
2004
2007
2010
2001
2004
2007
2010
2001
2004
2007
2010
0
Pr(Windfallincome>0)
Share of households with windfall income, by 2001 expectation

4. Median windfall is 7,500–15,000 Euro
Yes: Expect windfall
(Certain or probable)
No: Don’t
expect
Don’t
know
Median windfall (if received in prior year), by 2001 expectation
20
15
10
5
2001
2004
2007
2010
2001
2004
2007
2010
2001
2004
2007
2010
0
Medianwindfallamount

5. Hypotheses about the effects on
labor market outcomes
Theory
 With no imperfections, labor mkt outcomes shouldn’t change
 With credit constraints or imperfect anticipation, a recipient may:
 Reduce wage/salary (W/S) hours or retire completely
 Invest in a new or existing business (SE)
 Consume more & keep working just as much as before
Four hypotheses
1. Hours worked inW/S sector should not rise
2. Income from SE should not fall
3. Business capital (proxied by # of employees) should not fall
4. Some (who lacked start-up capital) may switch fromW/S to SE

6. Empirical approach
Sample
Heads and spouses in households that reported a windfall between
2001 and 2011
Approach
Regress labor market outcome in each panel year separately by
gender and 2001 windfall expectation on:
 age, family type, education, dummy for East Germany
 Individual fixed effect
 (Windfall/100K)*{t=-5, t=-4, …, t=-2, t=0, t=1, …, t=5}
 Note:Windfall in t=0; baseline labor market outcome is t=-1

7. Hypothesis 1: Hours in wage/salary sector
should not rise
 Results for women may depend on choice of t=-1 as baseline,
since signs on most all coefficients are the same pre- and post
 Results for wage/salary income broadly similar
 Exception: Men who did not expect a windfall have significantly
higher wage/salary income before receipt (t=-4, -3, -2)
Change after windfall receipt
Men Women
Expected Unexpected Expected Unexpected
Wage/salary hours No clear ∆ No clear ∆ Lower Higher

8. Hypothesis 2: Income from self-employment
should not fall
 Interpreting sequence of windfall*year coefficients is difficult.
My read: Post-receipt patterns for men are similar regardless of
expectation
 Results for SE hours similar: no clear pattern for men but are
often greater (but insignificant) SE hours for women after receipt
Men Women
Expected Unexpected Expected Unexpected
Self-employment income No clear ∆ Higher No clear ∆ Higher
Change after windfall receipt

9. Hypotheses 3 & 4: Odds of any self-employment &
self-employment with staff should not fall
Men Women
Expected Unexpected Expected Unexpected
Self-employed No clear ∆ No clear ∆ No clear ∆ No clear ∆
SE with any employees “ “ “ Higher
SE with 10+ employees “ “ “ Higher
Change after windfall receipt
• Point of comparison: Lack of effect onW/S  SE switches
contrasts with Schäfer et al. (GSOEP but different approach)

10. A summary assessment
 Some evidence credit constraints or imperfect anticipation lead to
shifts in women’s labor mkt outcomes, especially business expansion
 Paper expands our understanding of relationship between windfalls
and labor market outcomes in at least two ways:
1. Expands focus from the effects windfalls on sector choice (W/S
versus SE) to broader range of labor market outcomes
2. Little prior evidence from Germany
 Aging population with substantial aggregate assets
 Declines in entrepreneurship in last decade and policy response

11. Household decision-making
 Windfall expectations & labor market outcomes at individual
level, windfall receipt at household level
 How often do spousal expectations differ?
 But it seems possible that the one who expects a windfall is
necessarily the one to use it
 Sensitivity test: Classify spouses identically (e.g., if one
anticipates a windfall, treat both as anticipating)
 Could even combine their income

12. Building on the current models
 Results are often hard to interpret: coefficients often insignificant
though large, and signs often same for adjacent years
 More structure may yield more precision. For example: linear
trends pre and post with unknown breakpoint
 Other covariates to add
 Prior literature suggests nonlinearity in windfall amount
 Control for income, wealth, and “ability” (Schäfer et al 2010)
 GSOEP labor force expectations questions

13. Other counterfactuals and models
 Current counterfactual: Outcomes should be like those in t=-1
absent the windfall
 But labor outcomes aren’t always that stable
 Two other counterfactuals to consider
1. Compare those who expected a windfall to those who did not in a
single model
 Reduces concern about selection bias of who receives an inheritance
2. Match windfall recipients (just those who did not expect it?) and
windfall non-recipients on pre- and stable characteristics
 Addresses concerns that outcomes vary year-to-year for many reasons