1. Introduction

1. Analyzing Care Policies for South Korea: Potential Simulations with a CGE ModelHans LofgrenBinderiya Byambasuren Glen Kwende Presentation at the Annual Meeting of the project “Care Work and the Economy – Advancing Policy Solutions with Gender-Aware Macro-economic models” (CWE-GAM), held in Glasgow, June 30 – July 2, 2019.

2. 1. Introduction • Group III progress to date: • Draft SAM • Development and testing of GEM-Care (a dynamic CGE model for Korea) • Review of relevant CGE literature • The next task: • Use model and database to address questions related to care policies in Korea, adjusting model and database as needed. • Purpose of this session: Brainstorming on how to proceed, drawing on the insights of a group of experts! • How we will proceed: • General remarks about simulation analysis using CGE models • Summary of relevant child and elderly policies • Preliminary thoughts about simulations and what they may tell us.

3. 2. Simulation analysis with CGE models: General remarks • Model structure and disaggregation (both constrained by database) determine what kinds of issues the simulations can address. • Dynamic models simulate the economy over time, for each year finding an equilibrium solution. • Simulation 1: A base(line) or business-as-usual scenario, showing how the economy would evolve if initial policies are left intact – a bench mark for comparisons • Subsequent simulations: Starting in year 2 or later, parameters reflecting policies (or other features of the economy) or changed relative to the base scenario.

4. 2. Simulation analysis with CGE models: General remarks (cont.) • Given that our SAM is for 2014, the model will be simulated from 2014 and forwards. • Given that we are in 2019, cannot change the past, and want to be policy relevant, better to introduce policy shocks starting from 2020. • For period up to and including 2019, keep all scenarios equal to base, and define them to stay close to the observed evolution of the economy.

5. 3. Policy summary – child care • Korea has a universal child care program that covers all children up to 7 years old. • The central benefit: • 200,000 won (in 2014; ≈$175) per month to pay for public or private child care expenses. • Sufficient to pay for public care but places only available for 10% of children. • On average, out-of-pocket household pay is also around 200,000 won. • Households prefer public care. For each child, more (and better paid) staff per child, and also higher subsidy • Public vs. private staff monthly wages: $2,100 vs. $1,630. Also better working conditions and job security for public staff. • Total cost of central benefit: ≈0.6% of GDP (2014 data)

6. 3. Policy summary – child care (cont.) • Other benefits: • Prenatal expenses: 500,000 won • Pension credit: one year per child • Post-birth care services: Voucher • Paid parental leave: 12 months per parent per child (to be taken before child reaches 12 years). • Irregular workers and self- employed are not covered • Among leave takers, only 10% are men • In spite of this, the total fertility rate is stubbornly low, perhaps declining:

7. Total fertility rate

8. 3. Policy summary – elderly care • LTCI (Long-Term Care Insurance) is the main policy tool. • Three types of benefits: Home-based services, aged care facilities, and cash benefits. • Rapid expansion: • In 2008, ≈150,000 beneficiaries (2.9% of a population of 5.0 million 65+) • In 2014, ≈ 400,000 beneficiaries (6.2% of population of 6.3 million 65+) • In 2018, ≈ 600,000 beneficiaries (8.3% of population of 7.4 million of 65+) • Cost in 2014: 3.5 trillion won, ≈0.25% of GDP • Issues facing elderly care: • Low wages for workers in sector • No allowances for care of elderly family members

9. Contrasting demographic challenges for child and elderly care

10. 5a. Potential simulations – child care • The government goal: parents should have access to universal free child care. • Simulation 1. Expand public care spots so that there is enough places to cover every child ( expanding sector that pays higher wages to care workers) • Simulation 2. Expand subsidy so that full cost of child care is covered (irrespective of whether child receives private or public care). • Simulation 3. Raise wages of private care workers to public level. • Simulation 4. Wage to household members caring for own children. • All simulations may be done with • alternative sources of revenue to meet additional financing needs; and • alternative assumptions regarding labor market flexibility

11. 5b. Potential simulations – elderly care • Base simulation provides information about the additional fiscal burden of continuing program with projected (and significant) increase of beneficiaries up to 2030. • Simulation 1. No increase in share of aged 65+ benefitting from the LTCI. • Simulation 2. Accelerated increase in share of aged 65+ benefitting from the LTCI. • Simulation 3. Raise wages of private care workers to public level (regulation). • Simulation 4. Wage to household members caring for own elderly. • All simulations may be done with • alternative sources of revenue to meet additional financing needs; and • alternative assumptions regarding labor market flexibility

12. 5c. Potential simulations – other • Fertility rate (thought experiment): Very long run impact of a gradual increase in the total fertility rate to 2.1.

13. 6. Simulation results show from simulations • For households (by type): • Market incomes: labor by type [gender, etc.] and other • Time use: by gender, education. More/less household care work? • Consumption (by product) • For labor market: • Incomes and employment by sector and labor type • Wages by labor type • For government budget: • Spending (split into child care, elderly care, and other) • Taxation (split into different tax types) • For production and trade: • Value-added (by sector; both GDP and household sphere) • Exports and imports by sector

14. 7. Insights from simulations • Realistic assessments of benefits/cost of reforms under alternative assumptions about labor market responses and financing. • Motivation for changes that address care challenges and reduce gender inequalities while improving prospects for higher fertility.