Evaluating welfare and economic effects of raised fertility
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This document discusses the economic implications of declining fertility rates and explores the potential effectiveness of natalist policies aimed at encouraging higher birth rates. It presents a large overlapping generations (OLG) model that incorporates family structures to analyze various fertility paths and their macroeconomic effects. The findings suggest that the costs and benefits of fertility changes have time and beneficiary mismatches, posing challenges for policymakers in designing effective family support initiatives.
![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](https://image.slidesharecdn.com/mtmfertility-180402113145/85/Evaluating-welfare-and-economic-effects-of-raised-fertility-41-320.jpg)
![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](https://image.slidesharecdn.com/mtmfertility-180402113145/85/Evaluating-welfare-and-economic-effects-of-raised-fertility-42-320.jpg)
![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](https://image.slidesharecdn.com/mtmfertility-180402113145/85/Evaluating-welfare-and-economic-effects-of-raised-fertility-43-320.jpg)
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Evaluating welfare and economic effects of raised fertility
- 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! w: grape.org.pl t: grape org f: grape.org e: [email protected]