JRC in Ispra In the context of the second demographic transition, many countries consider rising fertility through pro-family polices as a potentially viable solution to the fiscal pressure stemming from longevity. However, an increased number of births implies private and immediate costs, whereas the gains are not likely to surface until later and appear via internalizing the public benefits of younger and larger population. Hence, quantification of the net effects remains a challenge. We propose using an overlapping generations model with a rich family structure to quantify the effects of increased birth rates. We analyze the overall macroeconomic and welfare effects as well as the distribution of these effects across cohorts and study the sensitivity of the final effects to the assumed target value and path of increased fertility. We find that fiscal effects are positive but, even in the case of relatively large fertility increase, they are small. The sign and the size of both welfare and fiscal effects depend substantially on the patterns of increased fertility: if increased fertility occurs via lower childlessness, the fiscal effects are smaller and welfare effects are more likely to be negative than in the case of the intensive margin adjustments.

1. Motivation Model Calibration Demographic scenarios Results Summary
Evaluating welfare and economic effects of raised fertility
Joanna Tyrowicz
with Krzysztof Makarski and Magda Malec
JRC in Ispra
March 2018

2. Motivation Model Calibration Demographic scenarios Results Summary
Motivation
Policy call for costly natalist policies and instruments – are they worth it?
substantial decline in population due to lowering fertility and longevity in most of
advanced and middle income economies
declining population and multiple long-term implications
=⇒ social security, pension system and health care expenditures
mixed empirical literature on previous policy interventions – can we explain why?
=⇒ negligible effects, ”too soon to tell”, methodological issues

3. Motivation Model Calibration Demographic scenarios Results Summary
Motivation
Policy call for costly natalist policies and instruments – are they worth it?
substantial decline in population due to lowering fertility and longevity in most of
advanced and middle income economies
declining population and multiple long-term implications
=⇒ social security, pension system and health care expenditures
mixed empirical literature on previous policy interventions – can we explain why?
=⇒ negligible effects, ”too soon to tell”, methodological issues

4. Motivation Model Calibration Demographic scenarios Results Summary
Motivation
Policy call for costly natalist policies and instruments – are they worth it?
substantial decline in population due to lowering fertility and longevity in most of
advanced and middle income economies
declining population and multiple long-term implications
=⇒ social security, pension system and health care expenditures
mixed empirical literature on previous policy interventions – can we explain why?
=⇒ negligible effects, ”too soon to tell”, methodological issues

5. Motivation Model Calibration Demographic scenarios Results Summary
Motivation
Policy call for costly natalist policies and instruments – are they worth it?
substantial decline in population due to lowering fertility and longevity in most of
advanced and middle income economies
declining population and multiple long-term implications
=⇒ social security, pension system and health care expenditures
mixed empirical literature on previous policy interventions – can we explain why?
=⇒ negligible effects, ”too soon to tell”, methodological issues

6. Motivation Model Calibration Demographic scenarios Results Summary
Motivation
Policy call for costly natalist policies and instruments – are they worth it?
substantial decline in population due to lowering fertility and longevity in most of
advanced and middle income economies
declining population and multiple long-term implications
=⇒ social security, pension system and health care expenditures
mixed empirical literature on previous policy interventions – can we explain why?
=⇒ negligible effects, ”too soon to tell”, methodological issues

7. Motivation Model Calibration Demographic scenarios Results Summary
What do we do?
We develop large OLG model with family structure and types of agents in a
household
Our model Keuschnigg et al.
GE + +
Frictions in labor market - +
Social assistance - +
Family structure + -
Alternative fertility paths + -
Macroeconomic effects + +
Welfare analysis + -
Main novelty
We can analyze various paths of fertility ⇒ sensitivity of macro to demographics

8. Motivation Model Calibration Demographic scenarios Results Summary
What do we do?
We develop large OLG model with family structure and types of agents in a
household
Our model Keuschnigg et al.
GE + +
Frictions in labor market - +
Social assistance - +
Family structure + -
Alternative fertility paths + -
Macroeconomic effects + +
Welfare analysis + -
Main novelty
We can analyze various paths of fertility ⇒ sensitivity of macro to demographics

9. Motivation Model Calibration Demographic scenarios Results Summary
What do we do?
We develop large OLG model with family structure and types of agents in a
household
Our model Keuschnigg et al.
GE + +
Frictions in labor market - +
Social assistance - +
Family structure + -
Alternative fertility paths + -
Macroeconomic effects + +
Welfare analysis + -
Main novelty
We can analyze various paths of fertility ⇒ sensitivity of macro to demographics

10. Motivation Model Calibration Demographic scenarios Results Summary
What do we do?
We develop large OLG model with family structure and types of agents in a
household
Our model Keuschnigg et al.
GE + +
Frictions in labor market - +
Social assistance - +
Family structure + -
Alternative fertility paths + -
Macroeconomic effects + +
Welfare analysis + -
Main novelty
We can analyze various paths of fertility ⇒ sensitivity of macro to demographics

11. Motivation Model Calibration Demographic scenarios Results Summary
What do we do?
We develop large OLG model with family structure and types of agents in a
household
Our model Keuschnigg et al.
GE + +
Frictions in labor market - +
Social assistance - +
Family structure + -
Alternative fertility paths + -
Macroeconomic effects + +
Welfare analysis + -
Main novelty
We can analyze various paths of fertility ⇒ sensitivity of macro to demographics

12. Motivation Model Calibration Demographic scenarios Results Summary
What do we do?
We develop large OLG model with family structure and types of agents in a
household
Our model Keuschnigg et al.
GE + +
Frictions in labor market - +
Social assistance - +
Family structure + -
Alternative fertility paths + -
Macroeconomic effects + +
Welfare analysis + -
Main novelty
We can analyze various paths of fertility ⇒ sensitivity of macro to demographics

13. Motivation Model Calibration Demographic scenarios Results Summary
What do we do?
We develop large OLG model with family structure and types of agents in a
household
Our model Keuschnigg et al.
GE + +
Frictions in labor market - +
Social assistance - +
Family structure + -
Alternative fertility paths + -
Macroeconomic effects + +
Welfare analysis + -
Main novelty
We can analyze various paths of fertility ⇒ sensitivity of macro to demographics

14. Motivation Model Calibration Demographic scenarios Results Summary
What do we do?
We develop large OLG model with family structure and types of agents in a
household
Our model Keuschnigg et al.
GE + +
Frictions in labor market - +
Social assistance - +
Family structure + -
Alternative fertility paths + -
Macroeconomic effects + +
Welfare analysis + -
Main novelty
We can analyze various paths of fertility ⇒ sensitivity of macro to demographics

15. Motivation Model Calibration Demographic scenarios Results Summary
Literature review
empirical evaluation with negative effects
Olivetti and Petrongolo (2017), Baizan et al. (2016), Rossin-Slater (2018)
empirical evaluation with positive effects
Drago et al. (2011), Milligan (2005), Brewer et al. (2012), Frejka and Zakharov (2013), Garganta et al. (2017),
Lalive and Zweimueller (2009), Rindfuss et al. (2010), Havnes and Mogstad (2011), Bauernschuster et al. (2015),
Del Boca et al. (2009)
OLG framework with more explicit fertility
Fehr et al. (2017), Georges and Seekin (2016), Mamota (2016), Hock and Weil (2012)
fertility may be endogenous
Liao (2011), Ludwig et al. (2012), Hock and Weil (2012)

16. Motivation Model Calibration Demographic scenarios Results Summary
Literature review
empirical evaluation with negative effects
Olivetti and Petrongolo (2017), Baizan et al. (2016), Rossin-Slater (2018)
empirical evaluation with positive effects
Drago et al. (2011), Milligan (2005), Brewer et al. (2012), Frejka and Zakharov (2013), Garganta et al. (2017),
Lalive and Zweimueller (2009), Rindfuss et al. (2010), Havnes and Mogstad (2011), Bauernschuster et al. (2015),
Del Boca et al. (2009)
OLG framework with more explicit fertility
Fehr et al. (2017), Georges and Seekin (2016), Mamota (2016), Hock and Weil (2012)
fertility may be endogenous
Liao (2011), Ludwig et al. (2012), Hock and Weil (2012)

17. Motivation Model Calibration Demographic scenarios Results Summary
Literature review
empirical evaluation with negative effects
Olivetti and Petrongolo (2017), Baizan et al. (2016), Rossin-Slater (2018)
empirical evaluation with positive effects
Drago et al. (2011), Milligan (2005), Brewer et al. (2012), Frejka and Zakharov (2013), Garganta et al. (2017),
Lalive and Zweimueller (2009), Rindfuss et al. (2010), Havnes and Mogstad (2011), Bauernschuster et al. (2015),
Del Boca et al. (2009)
OLG framework with more explicit fertility
Fehr et al. (2017), Georges and Seekin (2016), Mamota (2016), Hock and Weil (2012)
fertility may be endogenous
Liao (2011), Ludwig et al. (2012), Hock and Weil (2012)

18. Motivation Model Calibration Demographic scenarios Results Summary
Literature review
empirical evaluation with negative effects
Olivetti and Petrongolo (2017), Baizan et al. (2016), Rossin-Slater (2018)
empirical evaluation with positive effects
Drago et al. (2011), Milligan (2005), Brewer et al. (2012), Frejka and Zakharov (2013), Garganta et al. (2017),
Lalive and Zweimueller (2009), Rindfuss et al. (2010), Havnes and Mogstad (2011), Bauernschuster et al. (2015),
Del Boca et al. (2009)
OLG framework with more explicit fertility
Fehr et al. (2017), Georges and Seekin (2016), Mamota (2016), Hock and Weil (2012)
fertility may be endogenous
Liao (2011), Ludwig et al. (2012), Hock and Weil (2012)

19. Motivation Model Calibration Demographic scenarios Results Summary
Literature review
empirical evaluation with negative effects
Olivetti and Petrongolo (2017), Baizan et al. (2016), Rossin-Slater (2018)
empirical evaluation with positive effects
Drago et al. (2011), Milligan (2005), Brewer et al. (2012), Frejka and Zakharov (2013), Garganta et al. (2017),
Lalive and Zweimueller (2009), Rindfuss et al. (2010), Havnes and Mogstad (2011), Bauernschuster et al. (2015),
Del Boca et al. (2009)
OLG framework with more explicit fertility
Fehr et al. (2017), Georges and Seekin (2016), Mamota (2016), Hock and Weil (2012)
fertility may be endogenous
Liao (2011), Ludwig et al. (2012), Hock and Weil (2012)

20. Motivation Model Calibration Demographic scenarios Results Summary
Our questions
1 What are welfare effects of fertility changes?
⇒ costs are immediate and private, gains are delayed and public
2 What are macroeconomic effects of fertility changes?
⇒ assuming we know how to achieve given rise in fertility, how much to spend
3 Does it matter what kind of policy we do?
⇒ intensive (families with kids have more) vs extensive (more families has kids)
Potential effects
time mismatch: immediate costs and delayed benefits
beneficiary mismatch: private costs and public gains
general equilibrium effects: people adjust to expected fertility

21. Motivation Model Calibration Demographic scenarios Results Summary
Our questions
1 What are welfare effects of fertility changes?
⇒ costs are immediate and private, gains are delayed and public
2 What are macroeconomic effects of fertility changes?
⇒ assuming we know how to achieve given rise in fertility, how much to spend
3 Does it matter what kind of policy we do?
⇒ intensive (families with kids have more) vs extensive (more families has kids)
Potential effects
time mismatch: immediate costs and delayed benefits
beneficiary mismatch: private costs and public gains
general equilibrium effects: people adjust to expected fertility

22. Motivation Model Calibration Demographic scenarios Results Summary
Our questions
1 What are welfare effects of fertility changes?
⇒ costs are immediate and private, gains are delayed and public
2 What are macroeconomic effects of fertility changes?
⇒ assuming we know how to achieve given rise in fertility, how much to spend
3 Does it matter what kind of policy we do?
⇒ intensive (families with kids have more) vs extensive (more families has kids)
Potential effects
time mismatch: immediate costs and delayed benefits
beneficiary mismatch: private costs and public gains
general equilibrium effects: people adjust to expected fertility

23. Motivation Model Calibration Demographic scenarios Results Summary
Our questions
1 What are welfare effects of fertility changes?
⇒ costs are immediate and private, gains are delayed and public
2 What are macroeconomic effects of fertility changes?
⇒ assuming we know how to achieve given rise in fertility, how much to spend
3 Does it matter what kind of policy we do?
⇒ intensive (families with kids have more) vs extensive (more families has kids)
Potential effects
time mismatch: immediate costs and delayed benefits
beneficiary mismatch: private costs and public gains
general equilibrium effects: people adjust to expected fertility

24. Motivation Model Calibration Demographic scenarios Results Summary
Our questions
1 What are welfare effects of fertility changes?
⇒ costs are immediate and private, gains are delayed and public
2 What are macroeconomic effects of fertility changes?
⇒ assuming we know how to achieve given rise in fertility, how much to spend
3 Does it matter what kind of policy we do?
⇒ intensive (families with kids have more) vs extensive (more families has kids)
Potential effects
time mismatch: immediate costs and delayed benefits
beneficiary mismatch: private costs and public gains
general equilibrium effects: people adjust to expected fertility

25. Motivation Model Calibration Demographic scenarios Results Summary
Our questions
1 What are welfare effects of fertility changes?
⇒ costs are immediate and private, gains are delayed and public
2 What are macroeconomic effects of fertility changes?
⇒ assuming we know how to achieve given rise in fertility, how much to spend
3 Does it matter what kind of policy we do?
⇒ intensive (families with kids have more) vs extensive (more families has kids)
Potential effects
time mismatch: immediate costs and delayed benefits
beneficiary mismatch: private costs and public gains
general equilibrium effects: people adjust to expected fertility

26. Motivation Model Calibration Demographic scenarios Results Summary
Our questions
1 What are welfare effects of fertility changes?
⇒ costs are immediate and private, gains are delayed and public
2 What are macroeconomic effects of fertility changes?
⇒ assuming we know how to achieve given rise in fertility, how much to spend
3 Does it matter what kind of policy we do?
⇒ intensive (families with kids have more) vs extensive (more families has kids)
Potential effects
time mismatch: immediate costs and delayed benefits
beneficiary mismatch: private costs and public gains
general equilibrium effects: people adjust to expected fertility

27. Motivation Model Calibration Demographic scenarios Results Summary
Our questions
1 What are welfare effects of fertility changes?
⇒ costs are immediate and private, gains are delayed and public
2 What are macroeconomic effects of fertility changes?
⇒ assuming we know how to achieve given rise in fertility, how much to spend
3 Does it matter what kind of policy we do?
⇒ intensive (families with kids have more) vs extensive (more families has kids)
Potential effects
time mismatch: immediate costs and delayed benefits
beneficiary mismatch: private costs and public gains
general equilibrium effects: people adjust to expected fertility

28. Motivation Model Calibration Demographic scenarios Results Summary
Our questions
1 What are welfare effects of fertility changes?
⇒ costs are immediate and private, gains are delayed and public
2 What are macroeconomic effects of fertility changes?
⇒ assuming we know how to achieve given rise in fertility, how much to spend
3 Does it matter what kind of policy we do?
⇒ intensive (families with kids have more) vs extensive (more families has kids)
Potential effects
time mismatch: immediate costs and delayed benefits
beneficiary mismatch: private costs and public gains
general equilibrium effects: people adjust to expected fertility

29. Motivation Model Calibration Demographic scenarios Results Summary
Our questions
1 What are welfare effects of fertility changes?
⇒ costs are immediate and private, gains are delayed and public
2 What are macroeconomic effects of fertility changes?
⇒ assuming we know how to achieve given rise in fertility, how much to spend
3 Does it matter what kind of policy we do?
⇒ intensive (families with kids have more) vs extensive (more families has kids)
Potential effects
time mismatch: immediate costs and delayed benefits
beneficiary mismatch: private costs and public gains
general equilibrium effects: people adjust to expected fertility

30. Motivation Model Calibration Demographic scenarios Results Summary
Our questions
1 What are welfare effects of fertility changes?
⇒ costs are immediate and private, gains are delayed and public
2 What are macroeconomic effects of fertility changes?
⇒ assuming we know how to achieve given rise in fertility, how much to spend
3 Does it matter what kind of policy we do?
⇒ intensive (families with kids have more) vs extensive (more families has kids)
Potential effects
time mismatch: immediate costs and delayed benefits
beneficiary mismatch: private costs and public gains
general equilibrium effects: people adjust to expected fertility

31. Motivation Model Calibration Demographic scenarios Results Summary
1 Motivation
2 Model
3 Calibration
Economics
Demographics
4 Demographic scenarios
5 Results
Effects on the economy
Effects on welfare
Some more sensitivity analysis
6 Summary

32. Motivation Model Calibration Demographic scenarios Results Summary
Producers – very standard
Perfectly competitive representative firm
Standard Cobb-Douglas production function
Yt = Kα
t (ztLt)1−α
,
Profit maximization implies
wt = (1 − α)Kα
t zt(ztLt)−α
rt = αKα−1
(ztLt)1−α
− d
where d is the capital depreciation rate and zt is technological progress

33. Motivation Model Calibration Demographic scenarios Results Summary
Consumers
live up to j = 1, 2, ..., J years (J = 100)
face time and age specific mortality
labor supply l endogenous until retirement age ¯J = 65
until adult j < 21 they live in the household of birth
reaching adulthood j = 21 they form their own household
and observe the realization of the fertility

34. Motivation Model Calibration Demographic scenarios Results Summary
Consumers
live up to j = 1, 2, ..., J years (J = 100)
face time and age specific mortality
labor supply l endogenous until retirement age ¯J = 65
until adult j < 21 they live in the household of birth
reaching adulthood j = 21 they form their own household
and observe the realization of the fertility

35. Motivation Model Calibration Demographic scenarios Results Summary
Consumers
live up to j = 1, 2, ..., J years (J = 100)
face time and age specific mortality
labor supply l endogenous until retirement age ¯J = 65
until adult j < 21 they live in the household of birth
reaching adulthood j = 21 they form their own household
and observe the realization of the fertility

36. Motivation Model Calibration Demographic scenarios Results Summary
Consumers
live up to j = 1, 2, ..., J years (J = 100)
face time and age specific mortality
labor supply l endogenous until retirement age ¯J = 65
until adult j < 21 they live in the household of birth
reaching adulthood j = 21 they form their own household
and observe the realization of the fertility

37. Motivation Model Calibration Demographic scenarios Results Summary
Consumers
live up to j = 1, 2, ..., J years (J = 100)
face time and age specific mortality
labor supply l endogenous until retirement age ¯J = 65
until adult j < 21 they live in the household of birth
reaching adulthood j = 21 they form their own household
and observe the realization of the fertility

38. Motivation Model Calibration Demographic scenarios Results Summary
Consumers
live up to j = 1, 2, ..., J years (J = 100)
face time and age specific mortality
labor supply l endogenous until retirement age ¯J = 65
until adult j < 21 they live in the household of birth
reaching adulthood j = 21 they form their own household
and observe the realization of the fertility

39. Motivation Model Calibration Demographic scenarios Results Summary
Consumers
live up to j = 1, 2, ..., J years (J = 100)
face time and age specific mortality
labor supply l endogenous until retirement age ¯J = 65
until adult j < 21 they live in the household of birth
reaching adulthood j = 21 they form their own household
and observe the realization of the fertility

40. Motivation Model Calibration Demographic scenarios Results Summary
Consumers
live up to j = 1, 2, ..., J years (J = 100)
face time and age specific mortality
labor supply l endogenous until retirement age ¯J = 65
until adult j < 21 they live in the household of birth
reaching adulthood j = 21 they form their own household
and observe the realization of the fertility

41. Motivation Model Calibration Demographic scenarios Results Summary
Households
consist of men and women (the latter denoted by *)
differ by the number of children κ = 0, 1, 2, 3+
collective decision making within households
optimize lifetime utility derived from leisure and consumption
J
j=21
βj−21
πj,t+j−21[uj (˜cκ,j,t+j−21, lκ,j,t+j−21)
+ u∗
j ˜cκ,j,t+j−21, l∗
κ,j,t+j−21 ]
with individual consumption as follows
˜cκ,j,t =
1
(2 + ϑκ)
cκ,j,t = Ξκcκ,j,t
ϑ child consumption scaling factor,
consumption scaling factor, Ξκ scale effect

42. Motivation Model Calibration Demographic scenarios Results Summary
Households
consist of men and women (the latter denoted by *)
differ by the number of children κ = 0, 1, 2, 3+
collective decision making within households
optimize lifetime utility derived from leisure and consumption
J
j=21
βj−21
πj,t+j−21[uj (˜cκ,j,t+j−21, lκ,j,t+j−21)
+ u∗
j ˜cκ,j,t+j−21, l∗
κ,j,t+j−21 ]
with individual consumption as follows
˜cκ,j,t =
1
(2 + ϑκ)
cκ,j,t = Ξκcκ,j,t
ϑ child consumption scaling factor,
consumption scaling factor, Ξκ scale effect

43. Motivation Model Calibration Demographic scenarios Results Summary
Households
consist of men and women (the latter denoted by *)
differ by the number of children κ = 0, 1, 2, 3+
collective decision making within households
optimize lifetime utility derived from leisure and consumption
J
j=21
βj−21
πj,t+j−21[uj (˜cκ,j,t+j−21, lκ,j,t+j−21)
+ u∗
j ˜cκ,j,t+j−21, l∗
κ,j,t+j−21 ]
with individual consumption as follows
˜cκ,j,t =
1
(2 + ϑκ)
cκ,j,t = Ξκcκ,j,t
ϑ child consumption scaling factor,
consumption scaling factor, Ξκ scale effect

44. Motivation Model Calibration Demographic scenarios Results Summary
Households II
during child rearing “female” labor supply is reduced following ϕκ
households maximize utility:
men in age j < ¯J : uj (˜cκ,j,t, lκ,j,t) = log ˜cκ,j,t + φ log(1 − lκ,j,t)
women in age j < 41 : u
∗
j (˜cκ,j,t, l
∗
κ,j,t) = log ˜cκ,j,t + φ log(1 − l
∗
κ,j,t − ϕ(κ))
women in age 41 ≤ j < ¯J : u
∗
j (˜cκ,j,t, l
∗
κ,j,t) = log ˜cκ,j,t + φ log(1 − l
∗
κ,j,t)
men in age j ≥ ¯J : uj (˜cκ,j,t, lκ,j,t) = log ˜cκ,j,t
women in age j ≥ ¯J : u
∗
j (˜cκ,j,t, l
∗
κ,j,t) = log ˜cκ,j,t
subjected to:
(1 + τc)cκ,j,t + ˜sκ,j+1,t+1 = (1 − τ − τl)wj,tlκ,j,t + (1 − τ − τl)wj,tl
∗
κ,j,t
+ (1 + rt(1 − τk)) ˜sκ,j,t
+(1 − τl)bκ,j,t + (1 − τl)b
∗
κ,j,t
+beqκ,j,t + Υt (1)

45. Motivation Model Calibration Demographic scenarios Results Summary
Households II
during child rearing “female” labor supply is reduced following ϕκ
households maximize utility:
men in age j < ¯J : uj (˜cκ,j,t, lκ,j,t) = log ˜cκ,j,t + φ log(1 − lκ,j,t)
women in age j < 41 : u
∗
j (˜cκ,j,t, l
∗
κ,j,t) = log ˜cκ,j,t + φ log(1 − l
∗
κ,j,t − ϕ(κ))
women in age 41 ≤ j < ¯J : u
∗
j (˜cκ,j,t, l
∗
κ,j,t) = log ˜cκ,j,t + φ log(1 − l
∗
κ,j,t)
men in age j ≥ ¯J : uj (˜cκ,j,t, lκ,j,t) = log ˜cκ,j,t
women in age j ≥ ¯J : u
∗
j (˜cκ,j,t, l
∗
κ,j,t) = log ˜cκ,j,t
subjected to:
(1 + τc)cκ,j,t + ˜sκ,j+1,t+1 = (1 − τ − τl)wj,tlκ,j,t + (1 − τ − τl)wj,tl
∗
κ,j,t
+ (1 + rt(1 − τk)) ˜sκ,j,t
+(1 − τl)bκ,j,t + (1 − τl)b
∗
κ,j,t
+beqκ,j,t + Υt (1)

46. Motivation Model Calibration Demographic scenarios Results Summary
Households II
during child rearing “female” labor supply is reduced following ϕκ
households maximize utility:
men in age j < ¯J : uj (˜cκ,j,t, lκ,j,t) = log ˜cκ,j,t + φ log(1 − lκ,j,t)
women in age j < 41 : u
∗
j (˜cκ,j,t, l
∗
κ,j,t) = log ˜cκ,j,t + φ log(1 − l
∗
κ,j,t − ϕ(κ))
women in age 41 ≤ j < ¯J : u
∗
j (˜cκ,j,t, l
∗
κ,j,t) = log ˜cκ,j,t + φ log(1 − l
∗
κ,j,t)
men in age j ≥ ¯J : uj (˜cκ,j,t, lκ,j,t) = log ˜cκ,j,t
women in age j ≥ ¯J : u
∗
j (˜cκ,j,t, l
∗
κ,j,t) = log ˜cκ,j,t
subjected to:
(1 + τc)cκ,j,t + ˜sκ,j+1,t+1 = (1 − τ − τl)wj,tlκ,j,t + (1 − τ − τl)wj,tl
∗
κ,j,t
+ (1 + rt(1 − τk)) ˜sκ,j,t
+(1 − τl)bκ,j,t + (1 − τl)b
∗
κ,j,t
+beqκ,j,t + Υt (1)

47. Motivation Model Calibration Demographic scenarios Results Summary
Government
collects taxes
Tt = τl(1 − τ)wtLt + τlBt + τcCt + τkrtSt + Υt
where Lt, Ct, St, Bt denote labor, consumption, savings and benefits
finances spending on public goods and service Gt = gtYt,
and services debt ∆Dt = (1 + rt)Dt−1 − Dt
Tt = Gt + ∆Dt
PAYG defined contribution pension system with mandatory τ
bκ, ¯J,t =
¯Jt−1
s=1
Πs
ι=1(1 + rI
t−j+ι−1) τwt−j+s−1lκ,s,t−j+s−1
J
s= ¯J
πs,t
pensions indexed annually with the rate of payroll growth

48. Motivation Model Calibration Demographic scenarios Results Summary
Government
collects taxes
Tt = τl(1 − τ)wtLt + τlBt + τcCt + τkrtSt + Υt
where Lt, Ct, St, Bt denote labor, consumption, savings and benefits
finances spending on public goods and service Gt = gtYt,
and services debt ∆Dt = (1 + rt)Dt−1 − Dt
Tt = Gt + ∆Dt
PAYG defined contribution pension system with mandatory τ
bκ, ¯J,t =
¯Jt−1
s=1
Πs
ι=1(1 + rI
t−j+ι−1) τwt−j+s−1lκ,s,t−j+s−1
J
s= ¯J
πs,t
pensions indexed annually with the rate of payroll growth

49. Motivation Model Calibration Demographic scenarios Results Summary
Government
collects taxes
Tt = τl(1 − τ)wtLt + τlBt + τcCt + τkrtSt + Υt
where Lt, Ct, St, Bt denote labor, consumption, savings and benefits
finances spending on public goods and service Gt = gtYt,
and services debt ∆Dt = (1 + rt)Dt−1 − Dt
Tt = Gt + ∆Dt
PAYG defined contribution pension system with mandatory τ
bκ, ¯J,t =
¯Jt−1
s=1
Πs
ι=1(1 + rI
t−j+ι−1) τwt−j+s−1lκ,s,t−j+s−1
J
s= ¯J
πs,t
pensions indexed annually with the rate of payroll growth

50. Motivation Model Calibration Demographic scenarios Results Summary
1 Motivation
2 Model
3 Calibration
Economics
Demographics
4 Demographic scenarios
5 Results
Effects on the economy
Effects on welfare
Some more sensitivity analysis
6 Summary

51. Motivation Model Calibration Demographic scenarios Results Summary
Calibration to replicate 2014 Polish economy
Discounting rate (δ) matches interest rate of 6.5%
Depreciation rate (d) matches investment rate of 21%
Contribution rate (τ) matches benefits to GDP ratio of 7%
Labor income tax (τl) matches revenues to GDP ratio of 4.5%
Consumption tax (τc) matches revenues to GDP ratio of 11%
Capital tax (τk) de iure = de facto
Technological progress according to EC AWG projections, growth at 1.4%
Note: averages for 2000-2010 (investment rate) and 2005-2014

52. Motivation Model Calibration Demographic scenarios Results Summary
Calibration to replicate 2014 Polish economy
Discounting rate (δ) matches interest rate of 6.5%
Depreciation rate (d) matches investment rate of 21%
Contribution rate (τ) matches benefits to GDP ratio of 7%
Labor income tax (τl) matches revenues to GDP ratio of 4.5%
Consumption tax (τc) matches revenues to GDP ratio of 11%
Capital tax (τk) de iure = de facto
Technological progress according to EC AWG projections, growth at 1.4%
Note: averages for 2000-2010 (investment rate) and 2005-2014

53. Motivation Model Calibration Demographic scenarios Results Summary
Calibration to replicate 2014 Polish economy
Discounting rate (δ) matches interest rate of 6.5%
Depreciation rate (d) matches investment rate of 21%
Contribution rate (τ) matches benefits to GDP ratio of 7%
Labor income tax (τl) matches revenues to GDP ratio of 4.5%
Consumption tax (τc) matches revenues to GDP ratio of 11%
Capital tax (τk) de iure = de facto
Technological progress according to EC AWG projections, growth at 1.4%
Note: averages for 2000-2010 (investment rate) and 2005-2014

54. Motivation Model Calibration Demographic scenarios Results Summary
Calibration to replicate 2014 Polish economy
Discounting rate (δ) matches interest rate of 6.5%
Depreciation rate (d) matches investment rate of 21%
Contribution rate (τ) matches benefits to GDP ratio of 7%
Labor income tax (τl) matches revenues to GDP ratio of 4.5%
Consumption tax (τc) matches revenues to GDP ratio of 11%
Capital tax (τk) de iure = de facto
Technological progress according to EC AWG projections, growth at 1.4%
Note: averages for 2000-2010 (investment rate) and 2005-2014

55. Motivation Model Calibration Demographic scenarios Results Summary
Calibration to replicate 2014 Polish economy
Discounting rate (δ) matches interest rate of 6.5%
Depreciation rate (d) matches investment rate of 21%
Contribution rate (τ) matches benefits to GDP ratio of 7%
Labor income tax (τl) matches revenues to GDP ratio of 4.5%
Consumption tax (τc) matches revenues to GDP ratio of 11%
Capital tax (τk) de iure = de facto
Technological progress according to EC AWG projections, growth at 1.4%
Note: averages for 2000-2010 (investment rate) and 2005-2014

56. Motivation Model Calibration Demographic scenarios Results Summary
Preferences
Preference for leisure (φ) matches participation rate of 56.8%
Female child rearing time (ϕκ) according to Time Use Survey 2013, approx.
0.231 for κ = 1, 0.236 for κ = 2 and 0.257 for κ = 3
Consumption scaling factor ( ) and child consumption scaling factor (ϑκ)
matches OECD equivalence scale

57. Motivation Model Calibration Demographic scenarios Results Summary
Preferences
Preference for leisure (φ) matches participation rate of 56.8%
Female child rearing time (ϕκ) according to Time Use Survey 2013, approx.
0.231 for κ = 1, 0.236 for κ = 2 and 0.257 for κ = 3
Consumption scaling factor ( ) and child consumption scaling factor (ϑκ)
matches OECD equivalence scale

58. Motivation Model Calibration Demographic scenarios Results Summary
Preferences
Preference for leisure (φ) matches participation rate of 56.8%
Female child rearing time (ϕκ) according to Time Use Survey 2013, approx.
0.231 for κ = 1, 0.236 for κ = 2 and 0.257 for κ = 3
Consumption scaling factor ( ) and child consumption scaling factor (ϑκ)
matches OECD equivalence scale

59. Motivation Model Calibration Demographic scenarios Results Summary
1 Motivation
2 Model
3 Calibration
Economics
Demographics
4 Demographic scenarios
5 Results
Effects on the economy
Effects on welfare
Some more sensitivity analysis
6 Summary

60. Motivation Model Calibration Demographic scenarios Results Summary
Demographic assumptions
no mortality until children are raised (j < 41)
historical data on fertility and mortality 1964-2014
AWG projections until 2080, and stable afterwards
completed fertility data from household structure for 2006-2014

61. Motivation Model Calibration Demographic scenarios Results Summary
Data match
completed fertility

62. Motivation Model Calibration Demographic scenarios Results Summary
Data match
mortality

63. Motivation Model Calibration Demographic scenarios Results Summary
Data match – calibrating to path
Data Model
Completed fertility 1.38-1.52 1.44
Share of cohorts at j < 21 0.23 0.23
Share of cohorts at 20 < j < 41 0.31 0.30
Share of cohorts at j ≥ ¯J 0.18 0.19
Life expectancy at j = 1 73.47 73.83
Life expectancy at j = ¯J 15.41 15.42
Shares of childless women 0.36 0.35
s1 : s2 : s3+ 0.16 : 0.28 : 0.2 0.16 : 0.29 : 0.2
Note: Completed fertility measured as realized fertility for women aged 45 years, data averaged over 2006-
2014. Shares of age groups based on population structure data, averaged over 2006-2014. Data from
Eurostat.

64. Motivation Model Calibration Demographic scenarios Results Summary
Data match – calibrating to path
Data Model
Completed fertility 1.38-1.52 1.44
Share of cohorts at j < 21 0.23 0.23
Share of cohorts at 20 < j < 41 0.31 0.30
Share of cohorts at j ≥ ¯J 0.18 0.19
Life expectancy at j = 1 73.47 73.83
Life expectancy at j = ¯J 15.41 15.42
Shares of childless women 0.36 0.35
s1 : s2 : s3+ 0.16 : 0.28 : 0.2 0.16 : 0.29 : 0.2
Note: Completed fertility measured as realized fertility for women aged 45 years, data averaged over 2006-
2014. Shares of age groups based on population structure data, averaged over 2006-2014. Data from
Eurostat.

65. Motivation Model Calibration Demographic scenarios Results Summary
1 Motivation
2 Model
3 Calibration
Economics
Demographics
4 Demographic scenarios
5 Results
Effects on the economy
Effects on welfare
Some more sensitivity analysis
6 Summary

66. Motivation Model Calibration Demographic scenarios Results Summary
Fertility scenarios
What is baseline?
status quo demographic projection
unchanged fertility 1.44 (data averaged for 2006-2014)
data on household structure
What is fertility change scenario?
1.44 −→some higher level
How many combinations of household structure can generate a given fertility
increase path? Countless.
Does it matter? Yes.

67. Motivation Model Calibration Demographic scenarios Results Summary
Fertility scenarios
What is baseline?
status quo demographic projection
unchanged fertility 1.44 (data averaged for 2006-2014)
data on household structure
What is fertility change scenario?
1.44 −→some higher level
How many combinations of household structure can generate a given fertility
increase path? Countless.
Does it matter? Yes.

68. Motivation Model Calibration Demographic scenarios Results Summary
Fertility scenarios
What is baseline?
status quo demographic projection
unchanged fertility 1.44 (data averaged for 2006-2014)
data on household structure
What is fertility change scenario?
1.44 −→some higher level
How many combinations of household structure can generate a given fertility
increase path? Countless.
Does it matter? Yes.

69. Motivation Model Calibration Demographic scenarios Results Summary
Fertility scenarios
What is baseline?
status quo demographic projection
unchanged fertility 1.44 (data averaged for 2006-2014)
data on household structure
What is fertility change scenario?
1.44 −→some higher level
How many combinations of household structure can generate a given fertility
increase path? Countless.
Does it matter? Yes.

70. Motivation Model Calibration Demographic scenarios Results Summary
Fertility scenarios
What is baseline?
status quo demographic projection
unchanged fertility 1.44 (data averaged for 2006-2014)
data on household structure
What is fertility change scenario?
1.44 −→some higher level
How many combinations of household structure can generate a given fertility
increase path? Countless.
Does it matter? Yes.

71. Motivation Model Calibration Demographic scenarios Results Summary
Fertility scenarios
What is baseline?
status quo demographic projection
unchanged fertility 1.44 (data averaged for 2006-2014)
data on household structure
What is fertility change scenario?
1.44 −→some higher level
How many combinations of household structure can generate a given fertility
increase path? Countless.
Does it matter? Yes.

72. Motivation Model Calibration Demographic scenarios Results Summary
Generating fertility scenarios
1 Pick target (completed) fertility rate
target = reach that rate in 35 years, gradually
We test target fertilities of 1.5, 1.85 and 2.1
2 For intensive scenarios (families have more kids)
1 Keep childless ratio constant
2 Randomly generate N=100 paths yielding given fertility, from compositions of 1,
2 and 3+ kids per household
3 Number of paths can be any
3 For extensive scenarios (more families has kids)
1 Keep ratio of 1, 2 and 3+ families constant
2 Randomly simulate N=100 paths yielding given fertility, from varying the share of
households without kids and adjusting remaining shares (with a fixed ratio)

73. Motivation Model Calibration Demographic scenarios Results Summary
Generating fertility scenarios
1 Pick target (completed) fertility rate
target = reach that rate in 35 years, gradually
We test target fertilities of 1.5, 1.85 and 2.1
2 For intensive scenarios (families have more kids)
1 Keep childless ratio constant
2 Randomly generate N=100 paths yielding given fertility, from compositions of 1,
2 and 3+ kids per household
3 Number of paths can be any
3 For extensive scenarios (more families has kids)
1 Keep ratio of 1, 2 and 3+ families constant
2 Randomly simulate N=100 paths yielding given fertility, from varying the share of
households without kids and adjusting remaining shares (with a fixed ratio)

74. Motivation Model Calibration Demographic scenarios Results Summary
Generating fertility scenarios
1 Pick target (completed) fertility rate
target = reach that rate in 35 years, gradually
We test target fertilities of 1.5, 1.85 and 2.1
2 For intensive scenarios (families have more kids)
1 Keep childless ratio constant
2 Randomly generate N=100 paths yielding given fertility, from compositions of 1,
2 and 3+ kids per household
3 Number of paths can be any
3 For extensive scenarios (more families has kids)
1 Keep ratio of 1, 2 and 3+ families constant
2 Randomly simulate N=100 paths yielding given fertility, from varying the share of
households without kids and adjusting remaining shares (with a fixed ratio)

75. Motivation Model Calibration Demographic scenarios Results Summary
Generating fertility scenarios
1 Pick target (completed) fertility rate
target = reach that rate in 35 years, gradually
We test target fertilities of 1.5, 1.85 and 2.1
2 For intensive scenarios (families have more kids)
1 Keep childless ratio constant
2 Randomly generate N=100 paths yielding given fertility, from compositions of 1,
2 and 3+ kids per household
3 Number of paths can be any
3 For extensive scenarios (more families has kids)
1 Keep ratio of 1, 2 and 3+ families constant
2 Randomly simulate N=100 paths yielding given fertility, from varying the share of
households without kids and adjusting remaining shares (with a fixed ratio)

76. Motivation Model Calibration Demographic scenarios Results Summary
Generating fertility scenarios
1 Pick target (completed) fertility rate
target = reach that rate in 35 years, gradually
We test target fertilities of 1.5, 1.85 and 2.1
2 For intensive scenarios (families have more kids)
1 Keep childless ratio constant
2 Randomly generate N=100 paths yielding given fertility, from compositions of 1,
2 and 3+ kids per household
3 Number of paths can be any
3 For extensive scenarios (more families has kids)
1 Keep ratio of 1, 2 and 3+ families constant
2 Randomly simulate N=100 paths yielding given fertility, from varying the share of
households without kids and adjusting remaining shares (with a fixed ratio)

77. Motivation Model Calibration Demographic scenarios Results Summary
Generating fertility scenarios
1 Pick target (completed) fertility rate
target = reach that rate in 35 years, gradually
We test target fertilities of 1.5, 1.85 and 2.1
2 For intensive scenarios (families have more kids)
1 Keep childless ratio constant
2 Randomly generate N=100 paths yielding given fertility, from compositions of 1,
2 and 3+ kids per household
3 Number of paths can be any
3 For extensive scenarios (more families has kids)
1 Keep ratio of 1, 2 and 3+ families constant
2 Randomly simulate N=100 paths yielding given fertility, from varying the share of
households without kids and adjusting remaining shares (with a fixed ratio)

78. Motivation Model Calibration Demographic scenarios Results Summary
Generating fertility scenarios
1 Pick target (completed) fertility rate
target = reach that rate in 35 years, gradually
We test target fertilities of 1.5, 1.85 and 2.1
2 For intensive scenarios (families have more kids)
1 Keep childless ratio constant
2 Randomly generate N=100 paths yielding given fertility, from compositions of 1,
2 and 3+ kids per household
3 Number of paths can be any
3 For extensive scenarios (more families has kids)
1 Keep ratio of 1, 2 and 3+ families constant
2 Randomly simulate N=100 paths yielding given fertility, from varying the share of
households without kids and adjusting remaining shares (with a fixed ratio)

79. Motivation Model Calibration Demographic scenarios Results Summary
Generating fertility scenarios
1 Pick target (completed) fertility rate
target = reach that rate in 35 years, gradually
We test target fertilities of 1.5, 1.85 and 2.1
2 For intensive scenarios (families have more kids)
1 Keep childless ratio constant
2 Randomly generate N=100 paths yielding given fertility, from compositions of 1,
2 and 3+ kids per household
3 Number of paths can be any
3 For extensive scenarios (more families has kids)
1 Keep ratio of 1, 2 and 3+ families constant
2 Randomly simulate N=100 paths yielding given fertility, from varying the share of
households without kids and adjusting remaining shares (with a fixed ratio)

80. Motivation Model Calibration Demographic scenarios Results Summary
Generating fertility scenarios
1 Pick target (completed) fertility rate
target = reach that rate in 35 years, gradually
We test target fertilities of 1.5, 1.85 and 2.1
2 For intensive scenarios (families have more kids)
1 Keep childless ratio constant
2 Randomly generate N=100 paths yielding given fertility, from compositions of 1,
2 and 3+ kids per household
3 Number of paths can be any
3 For extensive scenarios (more families has kids)
1 Keep ratio of 1, 2 and 3+ families constant
2 Randomly simulate N=100 paths yielding given fertility, from varying the share of
households without kids and adjusting remaining shares (with a fixed ratio)

81. Motivation Model Calibration Demographic scenarios Results Summary
1 Motivation
2 Model
3 Calibration
Economics
Demographics
4 Demographic scenarios
5 Results
Effects on the economy
Effects on welfare
Some more sensitivity analysis
6 Summary

82. Motivation Model Calibration Demographic scenarios Results Summary
Measuring macroeconomic effects
net surplus of the government budget
difference between government spending in baseline (GB
t ) and reform (GR
t )
discounted for the moment of fertility change and expressed in terms of GDP
per capita
it acounts for GE effects (i.e. publc gains from fertility increase)

83. Motivation Model Calibration Demographic scenarios Results Summary
Measuring macroeconomic effects
net surplus of the government budget
difference between government spending in baseline (GB
t ) and reform (GR
t )
discounted for the moment of fertility change and expressed in terms of GDP
per capita
it acounts for GE effects (i.e. publc gains from fertility increase)

84. Motivation Model Calibration Demographic scenarios Results Summary
Measuring fiscal effects
fiscally beneficial
(−) labor market (women)
(−) higher government expenditures
(+) labor market (men)
(+) higher consumption tax base
(+) higher labor tax base
effects are small

85. Motivation Model Calibration Demographic scenarios Results Summary
Measuring fiscal effects
fiscally beneficial
(−) labor market (women)
(−) higher government expenditures
(+) labor market (men)
(+) higher consumption tax base
(+) higher labor tax base
effects are small

86. Motivation Model Calibration Demographic scenarios Results Summary
Measuring fiscal effects
fiscally beneficial
(−) labor market (women)
(−) higher government expenditures
(+) labor market (men)
(+) higher consumption tax base
(+) higher labor tax base
effects are small

87. Motivation Model Calibration Demographic scenarios Results Summary
Measuring fiscal effects
fiscally beneficial
(−) labor market (women)
(−) higher government expenditures
(+) labor market (men)
(+) higher consumption tax base
(+) higher labor tax base
effects are small

88. Motivation Model Calibration Demographic scenarios Results Summary
Measuring fiscal effects
fiscally beneficial
(−) labor market (women)
(−) higher government expenditures
(+) labor market (men)
(+) higher consumption tax base
(+) higher labor tax base
effects are small

89. Motivation Model Calibration Demographic scenarios Results Summary
Measuring fiscal effects
fiscally beneficial
(−) labor market (women)
(−) higher government expenditures
(+) labor market (men)
(+) higher consumption tax base
(+) higher labor tax base
effects are small

90. Motivation Model Calibration Demographic scenarios Results Summary
Measuring fiscal effects
How much can we spend every year, assuming increase of fertility?
target fertility 1.50 target fertility1.85

91. Motivation Model Calibration Demographic scenarios Results Summary
Measuring fiscal effects
Distribution of the fiscal effects over time
target fertility 1.50 target fertility1.85

92. Motivation Model Calibration Demographic scenarios Results Summary
1 Motivation
2 Model
3 Calibration
Economics
Demographics
4 Demographic scenarios
5 Results
Effects on the economy
Effects on welfare
Some more sensitivity analysis
6 Summary

93. Motivation Model Calibration Demographic scenarios Results Summary
Measuring welfare effects
Do I prefer to live in the world with increased fertility?
(discounted expected utility in the form of consumption equivalent)
raised fertility is not increasing
individually measured utility
negative effect:
wages ↓ > pension benefits ↑
really small effects: individual
welfare is not particularly responsive
to the population dynamics

94. Motivation Model Calibration Demographic scenarios Results Summary
Measuring welfare effects
Do I prefer to live in the world with increased fertility?
(discounted expected utility in the form of consumption equivalent)
raised fertility is not increasing
individually measured utility
negative effect:
wages ↓ > pension benefits ↑
really small effects: individual
welfare is not particularly responsive
to the population dynamics

95. Motivation Model Calibration Demographic scenarios Results Summary
Measuring welfare effects
Do I prefer to live in the world with increased fertility?
(discounted expected utility in the form of consumption equivalent)
raised fertility is not increasing
individually measured utility
negative effect:
wages ↓ > pension benefits ↑
really small effects: individual
welfare is not particularly responsive
to the population dynamics

96. Motivation Model Calibration Demographic scenarios Results Summary
Measuring welfare effects
Do I prefer to live in the world with increased fertility?
(discounted expected utility in the form of consumption equivalent)
raised fertility is not increasing
individually measured utility
negative effect:
wages ↓ > pension benefits ↑
really small effects: individual
welfare is not particularly responsive
to the population dynamics

97. Motivation Model Calibration Demographic scenarios Results Summary
Measuring welfare effects
Does intensive and extensive margin matter?
target fertility 1.50 target fertility1.85

98. Motivation Model Calibration Demographic scenarios Results Summary
Fiscal effect
Fertility rate prior to the simulated increase
1.20 1.70

99. Motivation Model Calibration Demographic scenarios Results Summary
Welfare effect
Fertility rate prior to the simulated increase
1.20 1.70

100. Motivation Model Calibration Demographic scenarios Results Summary
Insights from our study
1 Fiscal: net surplus in government budget, but small
small, but universal fiscal gains
for central path 0.2% GDP
not in the long run (!)
2 Welfare: negative welfare effect
fertility↑ −→ welfare↓
intensive and extensive margin matters (change in sign), but extensive margin can
be unrealistic (no trend in data)
3 Methodology: mixed empirical results make sense
intensive vs exensive fertility margin tilts the sign of the outcomes
even with unidirectional labor supply effects
effects are small (false rejection of null hypothesis in empirical research)
labor supply of men dominates that of women
potential perverse incentives should drag even further towards zero
it is not likely that technological progress is a “replacement” for fertility

101. Motivation Model Calibration Demographic scenarios Results Summary
Insights from our study
1 Fiscal: net surplus in government budget, but small
small, but universal fiscal gains
for central path 0.2% GDP
not in the long run (!)
2 Welfare: negative welfare effect
fertility↑ −→ welfare↓
intensive and extensive margin matters (change in sign), but extensive margin can
be unrealistic (no trend in data)
3 Methodology: mixed empirical results make sense
intensive vs exensive fertility margin tilts the sign of the outcomes
even with unidirectional labor supply effects
effects are small (false rejection of null hypothesis in empirical research)
labor supply of men dominates that of women
potential perverse incentives should drag even further towards zero
it is not likely that technological progress is a “replacement” for fertility

102. Motivation Model Calibration Demographic scenarios Results Summary
Insights from our study
1 Fiscal: net surplus in government budget, but small
small, but universal fiscal gains
for central path 0.2% GDP
not in the long run (!)
2 Welfare: negative welfare effect
fertility↑ −→ welfare↓
intensive and extensive margin matters (change in sign), but extensive margin can
be unrealistic (no trend in data)
3 Methodology: mixed empirical results make sense
intensive vs exensive fertility margin tilts the sign of the outcomes
even with unidirectional labor supply effects
effects are small (false rejection of null hypothesis in empirical research)
labor supply of men dominates that of women
potential perverse incentives should drag even further towards zero
it is not likely that technological progress is a “replacement” for fertility

103. Motivation Model Calibration Demographic scenarios Results Summary
Insights from our study
1 Fiscal: net surplus in government budget, but small
small, but universal fiscal gains
for central path 0.2% GDP
not in the long run (!)
2 Welfare: negative welfare effect
fertility↑ −→ welfare↓
intensive and extensive margin matters (change in sign), but extensive margin can
be unrealistic (no trend in data)
3 Methodology: mixed empirical results make sense
intensive vs exensive fertility margin tilts the sign of the outcomes
even with unidirectional labor supply effects
effects are small (false rejection of null hypothesis in empirical research)
labor supply of men dominates that of women
potential perverse incentives should drag even further towards zero
it is not likely that technological progress is a “replacement” for fertility

104. Motivation Model Calibration Demographic scenarios Results Summary
Thank you for your attention!
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t: grape org
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